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@@ -4479,3 +4479,4 @@ tool_server/.venv/lib/python3.12/site-packages/torch/_inductor/fx_passes/__pycac
 tool_server/.venv/lib/python3.12/site-packages/torch/_inductor/kernel/__pycache__/flex_attention.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
 tool_server/.venv/lib/python3.12/site-packages/torch/_refs/__pycache__/__init__.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
 tool_server/.venv/lib/python3.12/site-packages/torch/_subclasses/__pycache__/fake_tensor.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
+tool_server/.venv/lib/python3.12/site-packages/torch/backends/_nnapi/__pycache__/serializer.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text

tool_server/.venv/lib/python3.12/site-packages/torch/autograd/_functions/__init__.py

@@ -0,0 +1 @@
+from .tensor import *  # noqa: F403

tool_server/.venv/lib/python3.12/site-packages/torch/autograd/_functions/tensor.py

@@ -0,0 +1,65 @@
+# mypy: allow-untyped-defs
+import operator
+from functools import reduce
+from typing_extensions import deprecated
+
+import torch
+import torch._utils
+from torch.autograd.function import Function
+
+
+class Type(Function):
+    @staticmethod
+    @deprecated(
+        "`torch.autograd._functions.Type` is deprecated as of PyTorch 2.1, "
+        "please use `torch.tensor.to(dtype=dtype)` instead.",
+        category=FutureWarning,
+    )
+    def forward(ctx, i, dest_type):
+        ctx.input_type = type(i)
+        ctx.input_device = -1 if not i.is_cuda else i.get_device()
+        return i.type(dest_type)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.input_device == -1:
+            return grad_output.type(ctx.input_type), None
+        else:
+            with torch.cuda.device(ctx.input_device):
+                return grad_output.type(ctx.input_type), None
+
+
+# TODO: deprecate this
+class Resize(Function):
+    @staticmethod
+    def forward(ctx, tensor, sizes):
+        ctx.sizes = sizes
+        ctx.numel = reduce(operator.mul, sizes, 1)
+        if tensor.numel() != ctx.numel:
+            raise RuntimeError(
+                (
+                    "requested resize to {} ({} elements in total), "
+                    "but the given tensor has a size of {} ({} elements). "
+                    "autograd's resize can only change the shape of a given "
+                    "tensor, while preserving the number of elements. "
+                ).format(
+                    "x".join(map(str, sizes)),
+                    ctx.numel,
+                    "x".join(map(str, tensor.size())),
+                    tensor.numel(),
+                )
+            )
+        ctx.input_sizes = tensor.size()
+        if tensor.is_quantized:
+            tensor.copy_(tensor)
+            return tensor.contiguous().view(*sizes)
+        if tensor.is_contiguous():
+            result = tensor.new(tensor).contiguous().view(*sizes)
+            return result
+        else:
+            return tensor.contiguous().view(*sizes)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        assert grad_output.numel() == ctx.numel
+        return grad_output.contiguous().view(ctx.input_sizes), None

tool_server/.venv/lib/python3.12/site-packages/torch/autograd/_functions/utils.py

@@ -0,0 +1,63 @@
+# mypy: allow-untyped-defs
+import operator
+from functools import reduce
+
+
+def maybe_view(tensor, size, check_same_size=True):
+    if check_same_size and tensor.size() == size:
+        return tensor
+    return tensor.contiguous().view(size)
+
+
+def maybe_unexpand(tensor, old_size, check_same_size=True):
+    if check_same_size and tensor.size() == old_size:
+        return tensor
+    num_unsqueezed = tensor.dim() - len(old_size)
+    expanded_dims = [
+        dim
+        for dim, (expanded, original) in enumerate(
+            zip(tensor.size()[num_unsqueezed:], old_size)
+        )
+        if expanded != original
+    ]
+
+    for _ in range(num_unsqueezed):
+        tensor = tensor.sum(0, keepdim=False)
+    for dim in expanded_dims:
+        tensor = tensor.sum(dim, keepdim=True)
+    return tensor
+
+
+# Check whether the op enable broadcasting, and whether it is supported by ONNX.
+# If dims1 and dims2 are different, then broadcast is True.
+# We always assume the combination of dims1 and dims2 is broadcastable.
+# The following types of broadcasting are supported in ONNX:
+#     1) Only one element in dims2, such as dims2 = [1, 1]
+#     2) dims2 is suffix of dims1, such as dims1 = [2, 3, 4], and dims2 = [3, 4]
+# Details can be found here: https://github.com/onnx/onnx/blob/master/docs/Operators.md#Gemm
+def check_onnx_broadcast(dims1, dims2):
+    broadcast = False
+    supported = True
+    len1 = len(dims1)
+    len2 = len(dims2)
+
+    numel2 = reduce(operator.mul, dims2)
+    if len1 < len2:
+        broadcast = True
+        if numel2 != 1:
+            supported = False
+    elif len1 > len2:
+        broadcast = True
+        if numel2 != 1 and dims1[len1 - len2 :] != dims2:
+            supported = False
+    else:
+        if dims1 != dims2:
+            broadcast = True
+            if numel2 != 1:
+                supported = False
+
+    if not supported:
+        raise ValueError(
+            f"Numpy style broadcasting is not supported in ONNX. Input dims are: {dims1}, {dims2}"
+        )
+    return broadcast

tool_server/.venv/lib/python3.12/site-packages/torch/backends/_coreml/preprocess.py

@@ -0,0 +1,147 @@
+# mypy: allow-untyped-defs
+import hashlib
+import json
+
+import coremltools as ct  # type: ignore[import]
+from coremltools.converters.mil.input_types import TensorType  # type: ignore[import]
+from coremltools.converters.mil.mil import types  # type: ignore[import]
+from coremltools.models.neural_network import quantization_utils  # type: ignore[import]
+
+import torch
+
+
+CT_METADATA_VERSION = "com.github.apple.coremltools.version"
+CT_METADATA_SOURCE = "com.github.apple.coremltools.source"
+
+
+class ScalarType:
+    Float = 0
+    Double = 1
+    Int = 2
+    Long = 3
+    Undefined = 4
+
+
+# Supported Tensor types in coremltools:
+# https://github.com/apple/coremltools/blob/main/coremltools/converters/mil/frontend/torch/converter.py#L28
+torch_to_mil_types = {
+    ScalarType.Float: types.fp32,
+    ScalarType.Double: types.fp64,
+    ScalarType.Int: types.int32,
+    ScalarType.Long: types.int64,
+}
+
+
+class CoreMLComputeUnit:
+    CPU = "cpuOnly"
+    CPUAndGPU = "cpuAndGPU"
+    ALL = "all"
+
+
+class CoreMLQuantizationMode:
+    LINEAR = "linear"
+    LINEAR_SYMMETRIC = "linear_symmetric"
+    NONE = "none"
+
+
+def TensorSpec(shape, dtype=ScalarType.Float):
+    return (shape, dtype)
+
+
+def CompileSpec(
+    inputs,
+    outputs,
+    backend=CoreMLComputeUnit.CPU,
+    allow_low_precision=True,
+    quantization_mode=CoreMLQuantizationMode.NONE,
+    mlmodel_export_path=None,
+):
+    return (
+        inputs,
+        outputs,
+        backend,
+        allow_low_precision,
+        quantization_mode,
+        mlmodel_export_path,
+    )
+
+
+def _check_enumerated_shape(shape):
+    for s in shape:
+        if not isinstance(s, (list, tuple)):
+            return False
+    return True
+
+
+def _convert_to_mil_type(shape, dtype, name: str):
+    mil_shape = shape
+    if _check_enumerated_shape(shape):
+        mil_shape = ct.EnumeratedShapes(shape)
+    ml_type = TensorType(shape=mil_shape, dtype=torch_to_mil_types[dtype])
+    ml_type.name = name
+    return ml_type
+
+
+def preprocess(script_module: torch._C.ScriptObject, compile_spec: dict[str, tuple]):
+    spec = compile_spec["forward"]
+    (
+        input_specs,
+        output_specs,
+        backend,
+        allow_low_precision,
+        quantization_mode,
+        mlmodel_export_path,
+    ) = spec
+    mil_inputs = []
+    inputs = []
+    for index, input in enumerate(input_specs):
+        shape, dtype = input
+        name = "input_" + str(index)
+        inputs.append([name, str(dtype), str(shape)])
+        ml_type = _convert_to_mil_type(shape, dtype, name)
+        mil_inputs.append(ml_type)
+    model = torch.jit.RecursiveScriptModule._construct(script_module, lambda x: None)
+    mlmodel = ct.convert(model, inputs=mil_inputs)
+
+    if quantization_mode != CoreMLQuantizationMode.NONE:
+        quant_model_spec = quantization_utils.quantize_weights(
+            mlmodel, nbits=8, quantization_mode=quantization_mode
+        )
+        mlmodel = ct.models.MLModel(quant_model_spec)
+
+    spec = mlmodel.get_spec()
+    assert len(spec.description.output) == len(output_specs)  # type: ignore[attr-defined]
+    outputs = []
+    for index, output in enumerate(output_specs):
+        shape, dtype = output
+        name = spec.description.output[index].name  # type: ignore[attr-defined]
+        outputs.append([name, str(dtype), str(shape)])
+    mlmodel = ct.models.model.MLModel(spec)
+    print(mlmodel)
+
+    if mlmodel_export_path is not None:
+        print(f"Saving CoreML .mlmodel file to {mlmodel_export_path}")
+        mlmodel.save(mlmodel_export_path)
+
+    config = {
+        "spec_ver": str(spec.specificationVersion),  # type: ignore[attr-defined]
+        "backend": backend,
+        "allow_low_precision": str(allow_low_precision),
+    }
+    metadata = {
+        "coremltool_ver": mlmodel.user_defined_metadata[CT_METADATA_VERSION],
+        "torch_ver": mlmodel.user_defined_metadata[CT_METADATA_SOURCE],
+    }
+    coreml_compile_spec = {
+        "inputs": inputs,
+        "outputs": outputs,
+        "config": config,
+        "metadata": metadata,
+    }
+    mlmodel = spec.SerializeToString()  # type: ignore[attr-defined]
+
+    return {
+        "model": mlmodel,
+        "hash": str(hashlib.sha256(mlmodel).hexdigest()),
+        "extra": json.dumps(coreml_compile_spec),
+    }

tool_server/.venv/lib/python3.12/site-packages/torch/backends/_nnapi/__pycache__/serializer.cpython-312.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e82ab73d8d43f99ff8dbdad38c66f55d2f3f1ed0c3abb6e4d462f0acaaa660a
+size 107979

tool_server/.venv/lib/python3.12/site-packages/torch/backends/_nnapi/prepare.py

@@ -0,0 +1,199 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+from torch.backends._nnapi.serializer import _NnapiSerializer
+
+
+ANEURALNETWORKS_PREFER_LOW_POWER = 0
+ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1
+ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2
+
+
+class NnapiModule(torch.nn.Module):
+    """Torch Module that wraps an NNAPI Compilation.
+
+    This module handles preparing the weights, initializing the
+    NNAPI TorchBind object, and adjusting the memory formats
+    of all inputs and outputs.
+    """
+
+    # _nnapi.Compilation is defined
+    comp: Optional[torch.classes._nnapi.Compilation]  # type: ignore[name-defined]
+    weights: list[torch.Tensor]
+    out_templates: list[torch.Tensor]
+
+    def __init__(
+        self,
+        shape_compute_module: torch.nn.Module,
+        ser_model: torch.Tensor,
+        weights: list[torch.Tensor],
+        inp_mem_fmts: list[int],
+        out_mem_fmts: list[int],
+        compilation_preference: int,
+        relax_f32_to_f16: bool,
+    ):
+        super().__init__()
+        self.shape_compute_module = shape_compute_module
+        self.ser_model = ser_model
+        self.weights = weights
+        self.inp_mem_fmts = inp_mem_fmts
+        self.out_mem_fmts = out_mem_fmts
+        self.out_templates = []
+        self.comp = None
+        self.compilation_preference = compilation_preference
+        self.relax_f32_to_f16 = relax_f32_to_f16
+
+    @torch.jit.export
+    def init(self, args: list[torch.Tensor]):
+        assert self.comp is None
+        self.out_templates = self.shape_compute_module.prepare(self.ser_model, args)  # type: ignore[operator]
+        self.weights = [w.contiguous() for w in self.weights]
+        comp = torch.classes._nnapi.Compilation()
+        comp.init2(
+            self.ser_model,
+            self.weights,
+            self.compilation_preference,
+            self.relax_f32_to_f16,
+        )
+
+        self.comp = comp
+
+    def forward(self, args: list[torch.Tensor]) -> list[torch.Tensor]:
+        if self.comp is None:
+            self.init(args)
+        comp = self.comp
+        assert comp is not None
+        outs = [torch.empty_like(out) for out in self.out_templates]
+
+        assert len(args) == len(self.inp_mem_fmts)
+        fixed_args = []
+        for idx in range(len(args)):
+            fmt = self.inp_mem_fmts[idx]
+            # These constants match the values in DimOrder in serializer.py
+            # TODO: See if it's possible to use those directly.
+            if fmt == 0:
+                fixed_args.append(args[idx].contiguous())
+            elif fmt == 1:
+                fixed_args.append(args[idx].permute(0, 2, 3, 1).contiguous())
+            else:
+                raise ValueError("Invalid mem_fmt")
+        comp.run(fixed_args, outs)
+        assert len(outs) == len(self.out_mem_fmts)
+        for idx in range(len(self.out_templates)):
+            fmt = self.out_mem_fmts[idx]
+            # These constants match the values in DimOrder in serializer.py
+            # TODO: See if it's possible to use those directly.
+            if fmt in (0, 2):
+                pass
+            elif fmt == 1:
+                outs[idx] = outs[idx].permute(0, 3, 1, 2)
+            else:
+                raise ValueError("Invalid mem_fmt")
+        return outs
+
+
+def convert_model_to_nnapi(
+    model,
+    inputs,
+    serializer=None,
+    return_shapes=None,
+    use_int16_for_qint16=False,
+    compilation_preference=ANEURALNETWORKS_PREFER_SUSTAINED_SPEED,
+    relax_f32_to_f16=False,
+):
+    (
+        shape_compute_module,
+        ser_model_tensor,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        retval_count,
+    ) = process_for_nnapi(
+        model, inputs, serializer, return_shapes, use_int16_for_qint16
+    )
+
+    nnapi_model = NnapiModule(
+        shape_compute_module,
+        ser_model_tensor,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        compilation_preference,
+        relax_f32_to_f16,
+    )
+
+    class NnapiInterfaceWrapper(torch.nn.Module):
+        """NNAPI list-ifying and de-list-ifying wrapper.
+
+        NNAPI always expects a list of inputs and provides a list of outputs.
+        This module allows us to accept inputs as separate arguments.
+        It returns results as either a single tensor or tuple,
+        matching the original module.
+        """
+
+        def __init__(self, mod):
+            super().__init__()
+            self.mod = mod
+
+    wrapper_model_py = NnapiInterfaceWrapper(nnapi_model)
+    wrapper_model = torch.jit.script(wrapper_model_py)
+    # TODO: Maybe make these names match the original.
+    arg_list = ", ".join(f"arg_{idx}" for idx in range(len(inputs)))
+    if retval_count < 0:
+        ret_expr = "retvals[0]"
+    else:
+        ret_expr = "".join(f"retvals[{idx}], " for idx in range(retval_count))
+    wrapper_model.define(
+        f"def forward(self, {arg_list}):\n"
+        f"    retvals = self.mod([{arg_list}])\n"
+        f"    return {ret_expr}\n"
+    )
+    return wrapper_model
+
+
+def process_for_nnapi(
+    model, inputs, serializer=None, return_shapes=None, use_int16_for_qint16=False
+):
+    model = torch.jit.freeze(model)
+
+    if isinstance(inputs, torch.Tensor):
+        inputs = [inputs]
+
+    serializer = serializer or _NnapiSerializer(
+        config=None, use_int16_for_qint16=use_int16_for_qint16
+    )
+    (
+        ser_model,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        shape_compute_lines,
+        retval_count,
+    ) = serializer.serialize_model(model, inputs, return_shapes)
+    ser_model_tensor = torch.tensor(ser_model, dtype=torch.int32)
+
+    # We have to create a new class here every time this function is called
+    # because module.define adds a method to the *class*, not the instance.
+    class ShapeComputeModule(torch.nn.Module):
+        """Code-gen-ed module for tensor shape computation.
+
+        module.prepare will mutate ser_model according to the computed operand
+        shapes, based on the shapes of args.  Returns a list of output templates.
+        """
+
+    shape_compute_module = torch.jit.script(ShapeComputeModule())
+    real_shape_compute_lines = [
+        "def prepare(self, ser_model: torch.Tensor, args: List[torch.Tensor]) -> List[torch.Tensor]:\n",
+    ] + [f"    {line}\n" for line in shape_compute_lines]
+    shape_compute_module.define("".join(real_shape_compute_lines))
+
+    return (
+        shape_compute_module,
+        ser_model_tensor,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        retval_count,
+    )

tool_server/.venv/lib/python3.12/site-packages/torch/backends/_nnapi/serializer.py

@@ -0,0 +1,2228 @@
+# mypy: allow-untyped-defs
+import array
+import enum
+import functools
+import logging
+import operator
+import struct
+import sys
+from typing import NamedTuple, Optional
+
+import torch
+
+
+# TODO: Add type annotations
+# TODO: Check tensor types for ops
+
+
+LOG = logging.getLogger("nnapi_serialize")
+
+
+class NNAPI_OperandCode:
+    FLOAT32 = 0
+    INT32 = 1
+    UINT32 = 2
+    TENSOR_FLOAT32 = 3
+    TENSOR_INT32 = 4
+    TENSOR_QUANT8_ASYMM = 5
+    BOOL = 6
+    TENSOR_QUANT16_SYMM = 7
+    TENSOR_FLOAT16 = 8
+    TENSOR_BOOL8 = 9
+    FLOAT16 = 10
+    TENSOR_QUANT8_SYMM_PER_CHANNEL = 11
+    TENSOR_QUANT16_ASYMM = 12
+
+
+class NNAPI_OperationCode:
+    ADD = 0
+    AVERAGE_POOL_2D = 1
+    CONCATENATION = 2
+    CONV_2D = 3
+    DEPTHWISE_CONV_2D = 4
+    DEPTH_TO_SPACE = 5
+    DEQUANTIZE = 6
+    EMBEDDING_LOOKUP = 7
+    FLOOR = 8
+    FULLY_CONNECTED = 9
+    HASHTABLE_LOOKUP = 10
+    L2_NORMALIZATION = 11
+    L2_POOL_2D = 12
+    LOCAL_RESPONSE_NORMALIZATION = 13
+    LOGISTIC = 14
+    LSH_PROJECTION = 15
+    LSTM = 16
+    MAX_POOL_2D = 17
+    MUL = 18
+    RELU = 19
+    RELU1 = 20
+    RELU6 = 21
+    RESHAPE = 22
+    RESIZE_BILINEAR = 23
+    RNN = 24
+    SOFTMAX = 25
+    SPACE_TO_DEPTH = 26
+    SVDF = 27
+    TANH = 28
+    BATCH_TO_SPACE_ND = 29
+    DIV = 30
+    MEAN = 31
+    PAD = 32
+    SPACE_TO_BATCH_ND = 33
+    SQUEEZE = 34
+    STRIDED_SLICE = 35
+    SUB = 36
+    TRANSPOSE = 37
+    ABS = 38
+    ARGMAX = 39
+    ARGMIN = 40
+    AXIS_ALIGNED_BBOX_TRANSFORM = 41
+    BIDIRECTIONAL_SEQUENCE_LSTM = 42
+    BIDIRECTIONAL_SEQUENCE_RNN = 43
+    BOX_WITH_NMS_LIMIT = 44
+    CAST = 45
+    CHANNEL_SHUFFLE = 46
+    DETECTION_POSTPROCESSING = 47
+    EQUAL = 48
+    EXP = 49
+    EXPAND_DIMS = 50
+    GATHER = 51
+    GENERATE_PROPOSALS = 52
+    GREATER = 53
+    GREATER_EQUAL = 54
+    GROUPED_CONV_2D = 55
+    HEATMAP_MAX_KEYPOINT = 56
+    INSTANCE_NORMALIZATION = 57
+    LESS = 58
+    LESS_EQUAL = 59
+    LOG = 60
+    LOGICAL_AND = 61
+    LOGICAL_NOT = 62
+    LOGICAL_OR = 63
+    LOG_SOFTMAX = 64
+    MAXIMUM = 65
+    MINIMUM = 66
+    NEG = 67
+    NOT_EQUAL = 68
+    PAD_V2 = 69
+    POW = 70
+    PRELU = 71
+    QUANTIZE = 72
+    QUANTIZED_16BIT_LSTM = 73
+    RANDOM_MULTINOMIAL = 74
+    REDUCE_ALL = 75
+    REDUCE_ANY = 76
+    REDUCE_MAX = 77
+    REDUCE_MIN = 78
+    REDUCE_PROD = 79
+    REDUCE_SUM = 80
+    ROI_ALIGN = 81
+    ROI_POOLING = 82
+    RSQRT = 83
+    SELECT = 84
+    SIN = 85
+    SLICE = 86
+    SPLIT = 87
+    SQRT = 88
+    TILE = 89
+    TOPK_V2 = 90
+    TRANSPOSE_CONV_2D = 91
+    UNIDIRECTIONAL_SEQUENCE_LSTM = 92
+    UNIDIRECTIONAL_SEQUENCE_RNN = 93
+    RESIZE_NEAREST_NEIGHBOR = 94
+
+
+class NNAPI_FuseCode:
+    FUSED_NONE = 0
+    FUSED_RELU = 1
+    FUSED_RELU1 = 2
+    FUSED_RELU6 = 3
+
+
+class OperandValueSourceType:
+    IMMEDIATE = 0
+    NUMBERED_BUFFER = 2
+    NUMBERED_MEMORY = 3
+
+
+# Scalar types that appear explicitly in models.
+# These must be kept in sync with
+# AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS.
+# TODO: Expose these directly to Python to avoid maintaining this list.
+class TorchScalarTypes(enum.Enum):
+    QUINT8 = 13
+
+
+def approx_equal(lhs, rhs, tolerance=1e-6):
+    return abs(lhs - rhs) <= tolerance * min(lhs, rhs)
+
+
+def tensor_size(op_type, dims):
+    ITEM_SIZES = {
+        NNAPI_OperandCode.TENSOR_FLOAT32: 4,
+        NNAPI_OperandCode.TENSOR_INT32: 4,
+        NNAPI_OperandCode.TENSOR_QUANT8_ASYMM: 1,
+        NNAPI_OperandCode.TENSOR_QUANT16_SYMM: 2,
+        NNAPI_OperandCode.TENSOR_QUANT16_ASYMM: 2,
+    }
+    size = ITEM_SIZES[op_type]
+    for d in dims:
+        size *= d
+    return size
+
+
+def change_element(tup, index, value):
+    ls = list(tup)
+    ls[index] = value
+    return tuple(ls)
+
+
+class ConvPoolArgs2d(NamedTuple):
+    """Configuration arguments for a convolution."""
+
+    kernel_h: int
+    kernel_w: int
+    stride_h: int
+    stride_w: int
+    pad_t: int
+    pad_b: int
+    pad_l: int
+    pad_r: int
+    dilation_h: int
+    dilation_w: int
+    group: int
+
+
+class DimOrder(enum.Enum):
+    PRESUMED_CONTIGUOUS = 0
+    CHANNELS_LAST = 1
+    SCALAR_OR_VECTOR = 2
+    UNKNOWN_CONSTANT = 999
+
+
+class Operand(NamedTuple):
+    """Represenation of an NNAPI operand."""
+
+    # NNAPI operand type.  One of NNAPI_OperandCode.
+    # TODO: Make this an enum.
+    op_type: int
+
+    # This is always the PyTorch shape, which is NCHW for feature maps.
+    # The actual NNAPI operand might have a transposed shape.
+    # we use 0 for load time dynamic shapes & -1 for runtime dynamic shapes
+    shape: tuple[int, ...]
+
+    # Specifies how the shape of the operand that we define in NNAPI
+    # relates to the shape we track above.
+    # - PRESUMED_CONTIGUOUS: physical NNAPI operand will exactly match
+    #   the shape of the PyTorch tensor.
+    # - CHANNELS_LAST: The PyTorch tensor is expected to be NCHW, and
+    #   the NNAPI operand will be represented explicitly as NHWC.
+    dim_order: DimOrder
+
+    # Quantization params
+    scale: float
+    zero_point: int
+
+    def use_nchw(self):
+        if self.dim_order is DimOrder.PRESUMED_CONTIGUOUS:
+            return True
+        if self.dim_order is DimOrder.CHANNELS_LAST:
+            return False
+        raise Exception("Unknown dim order")  # noqa: TRY002
+
+
+def broadcast_shapes(shape1, shape2):
+    assert len(shape1) > 0
+    assert len(shape2) > 0
+    s1 = list(shape1)
+    s2 = list(shape2)
+    # TODO: Support non-equal-rank broadcast where semantics match.
+    # This can be tricky for NHWC tensors because dimension orders
+    # don't match between PT and NNAPI, even though semantics match.
+    if len(s1) > len(s2):
+        # s2 = [1] * (len(s1) - len(s2)) + s2
+        raise Exception(  # noqa: TRY002
+            "Non-equal-rank broadcast is not supported yet."
+        )  # noqa: TRY002
+    if len(s2) > len(s1):
+        # s3 = [1] * (len(s2) - len(s1)) + s1
+        raise Exception(  # noqa: TRY002
+            "Non-equal-rank broadcast is not supported yet."
+        )  # noqa: TRY002
+    ret = []
+    for d1, d2 in zip(s1, s2):
+        if d1 == 1:
+            ret.append(d2)
+        elif d2 == 1:
+            ret.append(d1)
+        elif d1 == d2:
+            ret.append(d1)
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Cannot broadcast shapes: {shape1} and {shape2}"
+            )  # noqa: TRY002
+    return tuple(ret)
+
+
+def get_conv_pool_shape(image_shape, args, out_ch, transpose):
+    batch, _in_c, in_h, in_w = image_shape
+
+    # TODO: Handle dilation
+    if args.dilation_h != 1 or args.dilation_w != 1:
+        raise Exception("Dilation not supported yet.")  # noqa: TRY002
+
+    if transpose:
+        out_h = (in_h - 1) * args.stride_h + args.kernel_h - args.pad_t - args.pad_b
+        out_w = (in_w - 1) * args.stride_w + args.kernel_w - args.pad_l - args.pad_l
+    else:
+        out_h = (in_h - args.kernel_h + args.pad_t + args.pad_b) // args.stride_h + 1
+        out_w = (in_w - args.kernel_w + args.pad_l + args.pad_r) // args.stride_w + 1
+
+    # Handle variable-sized tensors.
+    if in_h == 0:
+        out_h = 0
+    if in_w == 0:
+        out_w = 0
+
+    out_shape = (batch, out_ch, out_h, out_w)
+    return out_shape
+
+
+def fix_shape(shape, dim_order):
+    # Return the actual shape that an operand should have in NNAPI,
+    # given a PyTorch shape and dimension order.  This is where we
+    # convert from PyTorch's "always NCHW" shape to explicit NHWC.
+    if dim_order is DimOrder.PRESUMED_CONTIGUOUS:
+        return shape
+    if dim_order is DimOrder.CHANNELS_LAST:
+        return tuple([shape[0]] + list(shape[2:]) + [shape[1]])
+    if dim_order is DimOrder.SCALAR_OR_VECTOR:
+        assert len(shape) == 0 or len(shape) == 1
+        return shape
+    if dim_order is DimOrder.UNKNOWN_CONSTANT:
+        # XXX think this through
+        return shape
+    raise Exception(f"Bad dim_order: {dim_order!r}.")  # noqa: TRY002
+
+
+def reverse_map_dim(dim_order, d):
+    # Return the original PyTorch dimension position for a given dimension.
+    # d should be the dimension that NNAPI will see.
+    # reverse_map_dim(PRESUMED_CONTIGUOUS, x) == x
+    # reverse_map_dim(CHANNELS_LAST, 3) == 1
+    if dim_order in (DimOrder.PRESUMED_CONTIGUOUS, DimOrder.SCALAR_OR_VECTOR):
+        return d
+    assert dim_order is DimOrder.CHANNELS_LAST
+    return [0, 2, 3, 1][d]
+
+
+def flex_name(op_id, dim):
+    # Return the local variable name for the computed flexible size
+    # for a given op and dimension.
+    return f"s_{op_id}_{dim}"
+
+
+class _NnapiSerializer:
+    def __init__(self, config, use_int16_for_qint16=False):
+        self.operands = []
+        self.values = []
+        self.operations = []
+        self.value_data = []
+        self.operation_args = []
+        self.inputs = []
+        self.outputs = []
+        self.flexible_shape_computation_lines = []
+
+        self.modules = {}
+        self.constants = {}
+        self.tensor_sequences = {}
+        self.jitval_operand_map = {}
+        self.cached_immediates = {}
+        self.used_weights = []
+        self.weight_offset = 0
+        self.use_int16_for_qint16 = use_int16_for_qint16
+
+        if config is None:
+            config = {}
+
+    def get_next_operand_id(self):
+        return len(self.operands)
+
+    # Add a tensor operand corresponding to a JIT Value.
+    # Returns the NNAPI operand ID.  Can be looked up later with
+    # get_tensor_operand_by_jitval.
+    def add_tensor_operand(self, jitval, oper):
+        assert isinstance(oper, Operand)
+        if jitval in self.jitval_operand_map:
+            raise Exception(f"Duplicate tensor: {jitval!r}")  # noqa: TRY002
+
+        operand_id = self.get_next_operand_id()
+        self.operands.append(oper)
+        self.jitval_operand_map[jitval] = operand_id
+        return operand_id
+
+    # Add a tensor operand that does not correspond to a JIT Value.
+    # Useful for cases where multiple NNAPI operands are required
+    # to implement one JIT IR node.  Returns the NNAPI operand ID.
+    def add_anonymous_tensor_operand(self, oper):
+        assert isinstance(oper, Operand)
+        operand_id = self.get_next_operand_id()
+        self.operands.append(oper)
+        return operand_id
+
+    def torch_tensor_to_operand(self, tensor, dim_order):
+        dtype = str(tensor.dtype).replace("torch.", "")
+        scale = 0.0
+        zero_point = 0
+        if dtype == "float32":
+            op_type = NNAPI_OperandCode.TENSOR_FLOAT32
+        elif dtype == "int32":
+            op_type = NNAPI_OperandCode.TENSOR_INT32
+        elif dtype == "quint8":
+            op_type = NNAPI_OperandCode.TENSOR_QUANT8_ASYMM
+            scale = tensor.q_scale()
+            zero_point = tensor.q_zero_point()
+        elif dtype == "qint32":
+            op_type = NNAPI_OperandCode.TENSOR_INT32
+            scale = tensor.q_scale()
+            zero_point = tensor.q_zero_point()
+            assert zero_point == 0
+        elif dtype == "int16":
+            if self.use_int16_for_qint16:
+                nnapi_dtype = getattr(tensor, "nnapi_dtype", None)
+                op_codes = (
+                    NNAPI_OperandCode.TENSOR_QUANT16_SYMM,
+                    NNAPI_OperandCode.TENSOR_QUANT16_ASYMM,
+                )
+                if nnapi_dtype in op_codes:
+                    op_type = nnapi_dtype
+                    scale = tensor.nnapi_scale
+                    zero_point = tensor.nnapi_zero_point
+                else:
+                    raise Exception(  # noqa: TRY002
+                        f"`nnapi_type` needs to be one of {op_codes} for `int16`"
+                    )
+            else:
+                raise Exception(  # noqa: TRY002
+                    "`int16` isn't supported. If you're trying to represent NNAPI"
+                    " qint16 with Pytorch int16, set `use_int16_for_qint16 = True`"
+                )
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Can't handle input with dtype '{tensor.dtype}'"
+            )  # noqa: TRY002
+        return Operand(
+            shape=tuple(tensor.shape),
+            op_type=op_type,
+            dim_order=dim_order,
+            scale=scale,
+            zero_point=zero_point,
+        )
+
+    def add_tensor_operand_for_input(self, arg_idx, jitval, tensor):
+        dim_order = (
+            DimOrder.CHANNELS_LAST
+            if getattr(tensor, "nnapi_nhwc", False)
+            else DimOrder.PRESUMED_CONTIGUOUS
+        )
+        toper = self.torch_tensor_to_operand(tensor, dim_order)
+        operand_id = self.add_tensor_operand(jitval, toper)
+        self.inputs.append(operand_id)
+        for dim, size in enumerate(tensor.shape):
+            if size == 0:
+                self.compute_operand_shape(
+                    operand_id, dim, f"args[{arg_idx}].shape[{dim}]"
+                )
+        return operand_id
+
+    def add_tensor_operand_for_weight(
+        self, tensor, dim_order=DimOrder.UNKNOWN_CONSTANT
+    ):
+        toper = self.torch_tensor_to_operand(tensor, dim_order)
+        operand_id = len(self.operands)
+        self.operands.append(toper)
+        tsize = tensor_size(toper.op_type, toper.shape)
+        self.values.append((operand_id, OperandValueSourceType.NUMBERED_BUFFER))
+        buf_num = len(self.used_weights)
+        offset = 0
+        self.value_data.append(struct.pack("iii", buf_num, offset, tsize))
+        # For NHWC NNAPI op, lay out data in the same dim order by permuting torch tensor
+        if dim_order == DimOrder.CHANNELS_LAST:
+            tensor = tensor.permute(0, 2, 3, 1)
+        self.used_weights.append(tensor)
+        return operand_id
+
+    def add_immediate_operand(self, code, value, dims):
+        assert isinstance(dims, tuple)
+        cache_key = (code, value)
+        if cache_key not in self.cached_immediates:
+            operand_id = len(self.operands)
+            self.operands.append(Operand(code, dims, DimOrder.SCALAR_OR_VECTOR, 0.0, 0))
+            self.values.append((operand_id, OperandValueSourceType.IMMEDIATE))
+            self.value_data.append(value)
+            self.cached_immediates[cache_key] = operand_id
+        return self.cached_immediates[cache_key]
+
+    def add_immediate_int_scalar(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.INT32, struct.pack("i", value), ()
+        )
+
+    def add_immediate_float_scalar(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.FLOAT32, struct.pack("f", value), ()
+        )
+
+    def add_immediate_bool_scalar(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.BOOL, b"\x01" if value else b"\x00", ()
+        )
+
+    def add_immediate_int_vector(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.TENSOR_INT32,
+            array.array("i", value).tobytes(),
+            (len(value),),
+        )
+
+    def has_operand_for_jitval(self, jitval):
+        return jitval in self.jitval_operand_map
+
+    def get_tensor_operand_by_jitval(self, jitval):
+        operand_id = self.jitval_operand_map[jitval]
+        return (operand_id, self.operands[operand_id])
+
+    def get_tensor_operand_by_jitval_fixed_size(self, jitval):
+        op_id, oper = self.get_tensor_operand_by_jitval(jitval)
+        for s in oper.shape:
+            if s == 0:
+                # TODO: Improve this error message, possibly after converting
+                # many callsites to support flexible size.
+                raise Exception(  # noqa: TRY002
+                    "Flexible size is not supported for this operand."
+                )  # noqa: TRY002
+            if s < 0:
+                # runtime flex
+                LOG.warning("Operand %s has runtime flex shape", oper)
+        return op_id, oper
+
+    def get_tensor_operand_or_constant(
+        self, jitval, dim_order=DimOrder.PRESUMED_CONTIGUOUS
+    ):
+        operand_id = self.jitval_operand_map.get(jitval)
+        if operand_id is None:
+            _, value = self.get_constant_value(jitval, "TensorType")
+            operand_id = self.add_tensor_operand_for_weight(value, dim_order)
+        return (operand_id, self.operands[operand_id])
+
+    def get_tensor_operand_for_weight(self, jitval):
+        _, value = self.get_constant_value(jitval, "TensorType")
+        operand_id = self.add_tensor_operand_for_weight(value)
+        return (operand_id, self.operands[operand_id])
+
+    def add_operation(self, opcode, inputs, outputs):
+        self.operations.append((opcode, len(inputs), len(outputs)))
+        self.operation_args.extend(inputs + outputs)
+
+    def add_tensor_sequence(self, jitval, values):
+        assert jitval not in self.tensor_sequences
+        self.tensor_sequences[jitval] = values
+
+    def add_constant_value(self, jitval, ctype, value):
+        assert jitval not in self.constants
+        self.constants[jitval] = (ctype, value)
+
+    def get_constant_value(self, jitval, typekind=None):
+        record = self.constants.get(jitval)
+        if record is None:
+            raise Exception(  # noqa: TRY002
+                f"Could not find constant value for '{jitval!r}'."
+            )  # noqa: TRY002
+        ctype, _ = record
+        if typekind is not None and ctype.kind() != typekind:
+            raise Exception(  # noqa: TRY002
+                f"Expected constant value of type {typekind}, but got {ctype.kind()} for value '{jitval!r}'"
+            )
+        return record
+
+    def operand_to_template_torchscript(self, op_id, oper, shape=None):
+        """Return a TorchScript expression to build a template for a given operand."""
+        if shape is None:
+            shape = oper.shape
+        else:
+            assert len(shape) == len(oper.shape)
+
+        shape_parts = ["("]
+        for d, s in enumerate(shape):
+            if s > 0:
+                # Fixed shape dimension: just add the value.
+                shape_parts.append(str(s))
+            elif s == 0:
+                # Load time flexible shape dimension: it should have been computed in a variable.
+                shape_parts.append(flex_name(op_id, d))
+            elif s == -1:
+                # Runtime flexible shape
+                shape_parts.append("0")
+            else:
+                raise Exception(  # noqa: TRY002
+                    "Unknown dim value, dimensions should be >= -1"
+                )  # noqa: TRY002
+            shape_parts.append(",")
+        shape_parts.append(")")
+        shape_code = "".join(shape_parts)
+        if oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32:
+            return f"torch.zeros({shape_code}, dtype=torch.float32)"
+        elif oper.op_type == NNAPI_OperandCode.TENSOR_INT32:
+            return f"torch.zeros({shape_code}, dtype=torch.int32)"
+        elif oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM:
+            return (
+                f"torch.quantize_per_tensor("
+                f"torch.zeros(1), scale={oper.scale}, zero_point={oper.zero_point}, dtype=torch.quint8)"
+                f".expand({shape_code}).contiguous()"
+            )
+        elif oper.op_type in (
+            NNAPI_OperandCode.TENSOR_QUANT16_ASYMM,
+            NNAPI_OperandCode.TENSOR_QUANT16_SYMM,
+        ):
+            if self.use_int16_for_qint16:
+                return f"torch.zeros({shape_code}, dtype=torch.int16)"
+            else:
+                raise Exception(  # noqa: TRY002
+                    "`int16` isn't supported. If you're trying to represent NNAPI"
+                    " qint16 with Pytorch int16, set `use_int16_for_qint16 = True`"
+                )
+
+        raise Exception(  # noqa: TRY002
+            f"Unsupported output operand type: {oper.op_type}"
+        )  # noqa: TRY002
+
+    def forward_operand_shape(self, out_op_id, out_dim, in_op_id, in_dim):
+        self.compute_operand_shape(out_op_id, out_dim, flex_name(in_op_id, in_dim))
+
+    def compute_operand_shape(self, op_id, dim, expr):
+        self.flexible_shape_computation_lines.append(
+            f"{flex_name(op_id, dim)} = {expr}"
+        )
+
+    def transpose_to_nhwc(self, in_id, oper):
+        if oper.shape[2:] != (1, 1):
+            raise Exception(  # noqa: TRY002
+                "Automatic transpose only supported for H,W == 1,1"
+            )  # noqa: TRY002
+
+        out_oper = oper._replace(dim_order=DimOrder.CHANNELS_LAST)
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector([0, 2, 3, 1])
+
+        outputs = [None] * 1
+        outputs[0] = self.add_anonymous_tensor_operand(out_oper)
+
+        self.add_operation(NNAPI_OperationCode.TRANSPOSE, inputs, outputs)
+
+        return outputs[0], out_oper
+
+    # Transpose inputs as necessary to allow broadcasting.
+    def transpose_for_broadcast(self, in0_id, in0_oper, in1_id, in1_oper):
+        if in0_oper.dim_order == in1_oper.dim_order:
+            return in0_id, in0_oper, in1_id, in1_oper
+
+        # Assume NHWC is preferred if there is a mismatch.
+        orders = (in0_oper.dim_order, in1_oper.dim_order)
+        if orders == (DimOrder.PRESUMED_CONTIGUOUS, DimOrder.CHANNELS_LAST):
+            return self.transpose_to_nhwc(in0_id, in0_oper) + (in1_id, in1_oper)
+        if orders == (DimOrder.CHANNELS_LAST, DimOrder.PRESUMED_CONTIGUOUS):
+            return (in0_id, in0_oper) + self.transpose_to_nhwc(in1_id, in1_oper)
+
+        raise Exception(  # noqa: TRY002
+            f"Automatic transpose not supported for dim_orders: {in0_oper.dim_order!r}, {in1_oper.dim_order!r}"
+        )
+
+    def get_size_arg(self, jitval):
+        ctype, value = self.get_constant_value(jitval)
+        if ctype.kind() == "ListType":
+            assert ctype.getElementType().kind() == "IntType"
+            return value
+        raise Exception(  # noqa: TRY002
+            f"Can't handle size arg of type '{ctype!r}' for '{jitval!r}'"
+        )  # noqa: TRY002
+
+    def get_conv_pool_args_2d_from_pack(self, kernel_size, packed_config):
+        pc = [i.item() for i in packed_config]
+        assert pc[0] == 2
+        strides = [pc[1], pc[2]]
+        paddings = [pc[3], pc[4]]
+        dilations = [pc[5], pc[6]]
+        output_padding = [pc[7], pc[8]]
+        group_num = pc[9]
+
+        assert len(pc) == 11
+        assert output_padding == [0, 0]
+
+        return self.get_conv_pool_args_2d_common(
+            kernel_size, strides, paddings, dilations, group_num
+        )
+
+    def get_conv_pool_args_2d_from_jit(
+        self, kernel_size, stride, padding, dilation=None, group=None
+    ):
+        strides = self.get_size_arg(stride)
+        paddings = self.get_size_arg(padding)
+        if dilation is None:
+            dilations = [1, 1]
+        else:
+            dilations = self.get_size_arg(dilation)
+        if group is not None:
+            _, group_num = self.get_constant_value(group, "IntType")
+        else:
+            group_num = None
+        return self.get_conv_pool_args_2d_common(
+            kernel_size, strides, paddings, dilations, group_num
+        )
+
+    def get_conv_pool_args_2d_common(
+        self, kernel_size, strides, paddings, dilations, group_num
+    ):
+        kernels = list(kernel_size)
+
+        assert len(kernels) == 2
+        assert len(strides) == 2
+        assert len(paddings) == 2
+        assert len(dilations) == 2
+
+        # NNAPI uses 4 values for padding.
+        ph, pw = paddings
+        real_paddings = [ph, ph, pw, pw]
+
+        return ConvPoolArgs2d(
+            *(kernels + strides + real_paddings + dilations + [group_num])
+        )
+
+    def serialize_model(self, model, inputs, return_shapes=None):
+        self.add_immediate_bool_scalar(False)
+        self.add_immediate_bool_scalar(True)
+
+        inp_dim_orders = []
+        out_dim_orders = []
+
+        self_jitval = next(model.graph.inputs())
+        self.add_constant_value(self_jitval, self_jitval.type(), model)
+
+        for arg_idx, (input_value, input_tensor) in enumerate(
+            zip(list(model.graph.inputs())[1:], inputs)
+        ):
+            op_id = self.add_tensor_operand_for_input(
+                arg_idx, input_value, input_tensor
+            )
+            inp_dim_orders.append(self.operands[op_id].dim_order.value)
+
+        for idx, node in enumerate(model.graph.nodes()):
+            LOG.debug("Processing node #%d: %r", idx, node)
+            self.add_node(node)
+
+        retn = model.graph.return_node()
+        assert retn.inputsSize() == 1
+        assert retn.outputsSize() == 0
+        retn_input = retn.inputsAt(0)
+        template_return_lines = ["return ["]
+        if retn_input.type().kind() == "TensorType":
+            return_values = [retn_input]
+            retval_count = -1
+        elif retn_input.type().kind() == "TupleType":
+            return_values = self.tensor_sequences[retn_input]
+            retval_count = len(return_values)
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Unsupported return type: {retn_input.type()}"
+            )  # noqa: TRY002
+
+        if return_shapes is not None:
+            assert len(return_shapes) == len(return_values)
+        for i, v in enumerate(return_values):
+            op_id = self.jitval_operand_map[v]
+            self.outputs.append(op_id)
+            out_dim_orders.append(self.operands[op_id].dim_order.value)
+            shape = return_shapes[i] if return_shapes else None
+            template_return_lines.append(
+                self.operand_to_template_torchscript(op_id, self.operands[op_id], shape)
+                + ","
+            )
+        template_return_lines.append("]")
+
+        model = []
+
+        version = 1
+        header = struct.pack(
+            "iiiiii",
+            version,
+            len(self.operands),
+            len(self.values),
+            len(self.operations),
+            len(self.inputs),
+            len(self.outputs),
+        )
+        model.append(header)
+
+        serialized_values, serialized_value_data = self.serialize_values()
+
+        model.extend(
+            struct.pack("iifi", t, len(d), s, z) for (t, d, _m, s, z) in self.operands
+        )
+        model.extend(serialized_values)
+        model.extend(struct.pack("iii", *x) for x in self.operations)
+
+        # Compact the model so we can get its length so far.
+        model = [b"".join(model)]
+        model_offset = len(model[0])
+        # Model offset is the index into the model (in 32-bit words, not bytes)
+        # of the next dimension we're about to serialize.  If it's 0,
+        # generate code to mutate it before passing to NNAPI.
+        assert model_offset % 4 == 0
+        model_offset = int(model_offset / 4)
+
+        for op_id, (_, dims, dim_order, _, _) in enumerate(self.operands):
+            shape = fix_shape(dims, dim_order)
+            for d, s in enumerate(shape):
+                if s == 0:
+                    pt_d = reverse_map_dim(dim_order, d)
+                    self.flexible_shape_computation_lines.append(
+                        f"ser_model[{model_offset}] = {flex_name(op_id, pt_d)}"
+                    )
+                model_offset += 1
+
+            # convert runtime flex shape from -1 to 0
+            shape = tuple(d if d != -1 else 0 for d in shape)
+            model.append(self.serialize_ints(shape))
+
+        model.extend(serialized_value_data)
+        model.append(self.serialize_ints(self.operation_args))
+        model.append(self.serialize_ints(self.inputs))
+        model.append(self.serialize_ints(self.outputs))
+
+        self.flexible_shape_computation_lines.extend(template_return_lines)
+
+        return (
+            array.array("i", b"".join(model)),
+            self.used_weights,
+            inp_dim_orders,
+            out_dim_orders,
+            self.flexible_shape_computation_lines,
+            retval_count,
+        )
+
+    def serialize_values(self):
+        serialized_values = []
+        serialized_value_data = []
+        assert len(self.values) == len(self.value_data)
+        for (op_index, source_type), data in zip(self.values, self.value_data):
+            source_length = len(data)
+
+            # Pad with 0 bytes out to a multiple of 4 for alignment.
+            physical_length = ((source_length - 1) | 0x3) + 1
+            padded_data = data + (b"\0" * (physical_length - source_length))
+
+            serialized_values.append(
+                struct.pack("iii", op_index, source_type, source_length)
+            )
+            serialized_value_data.append(padded_data)
+
+        return serialized_values, serialized_value_data
+
+    @staticmethod
+    def serialize_ints(ints):
+        return array.array("i", ints).tobytes()
+
+    ADDER_MAP = {
+        "prim::GetAttr": lambda self, node: self.add_getattr(node),
+        "prim::Constant": lambda self, node: self.add_constant_node(node),
+        "prim::ListConstruct": lambda self, node: self.add_list_construct(node),
+        "prim::TupleConstruct": lambda self, node: self.add_tuple_construct(node),
+        "aten::unsqueeze": lambda self, node: self.add_unsqueeze(node),
+        "aten::to": lambda self, node: self.add_to(node),
+        "aten::detach": lambda self, node: self._identity(node),
+        "aten::reshape": lambda self, node: self.add_reshape(node),
+        "aten::flatten": lambda self, node: self.add_flatten(node),
+        "aten::slice": lambda self, node: self.add_slice(node),
+        "aten::size": lambda self, node: self.add_size(node),
+        "aten::cat": lambda self, node: self.add_cat(node),
+        "aten::mean": lambda self, node: self.add_mean(node),
+        "aten::quantize_per_tensor": lambda self, node: self.add_quantize(node),
+        "aten::dequantize": lambda self, node: self.add_dequantize(node),
+        "aten::add": lambda self, node: self.add_add_sub_op(
+            node, NNAPI_OperationCode.ADD, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::sub": lambda self, node: self.add_add_sub_op(
+            node, NNAPI_OperationCode.SUB, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::mul": lambda self, node: self.add_pointwise_simple_binary_broadcast_op(
+            node, NNAPI_OperationCode.MUL, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::div": lambda self, node: self.add_pointwise_simple_binary_broadcast_op(
+            node, NNAPI_OperationCode.DIV, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::relu": lambda self, node: self.add_pointwise_simple_unary_op(
+            node, NNAPI_OperationCode.RELU
+        ),
+        "aten::sigmoid": lambda self, node: self.add_pointwise_simple_unary_op(
+            node, NNAPI_OperationCode.LOGISTIC
+        ),
+        "aten::softmax": lambda self, node: self.add_softmax(node),
+        "aten::hardtanh": lambda self, node: self.add_hardtanh(node),
+        "aten::avg_pool2d": lambda self, node: self.add_avg_pool2d(node),
+        "aten::max_pool2d": lambda self, node: self.add_pool2d_node(
+            node, NNAPI_OperationCode.MAX_POOL_2D
+        ),
+        "aten::adaptive_avg_pool2d": lambda self, node: self.add_adaptive_avg_pool2d(
+            node
+        ),
+        "aten::upsample_nearest2d": lambda self, node: self.add_upsample_nearest2d(
+            node
+        ),
+        "aten::prelu": lambda self, node: self.add_prelu_op(node),
+        "aten::addmm": lambda self, node: self.add_addmm(node),
+        "aten::linear": lambda self, node: self.add_linear(node),
+        "aten::_convolution": lambda self, node: self.add_conv_underscore(node),
+        "aten::conv2d": lambda self, node: self.add_conv2d(node),
+        "aten::log_softmax": lambda self, node: self.add_log_softmax(node),
+        "quantized::linear": lambda self, node: self.add_qlinear(node),
+        "quantized::conv2d": lambda self, node: self.add_qconv2d(
+            node, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "quantized::conv2d_relu": lambda self, node: self.add_qconv2d(
+            node, NNAPI_FuseCode.FUSED_RELU
+        ),
+        "quantized::conv_transpose2d": lambda self, node: self.add_qconv2d(
+            node, NNAPI_FuseCode.FUSED_NONE, transpose=True
+        ),
+        "quantized::add": lambda self, node: self.add_qadd(
+            node, NNAPI_OperationCode.ADD, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "quantized::add_relu": lambda self, node: self.add_qadd(
+            node, NNAPI_OperationCode.ADD, NNAPI_FuseCode.FUSED_RELU
+        ),
+        "quantized::mul": lambda self, node: self.add_qadd(
+            node, NNAPI_OperationCode.MUL, NNAPI_FuseCode.FUSED_NONE
+        ),
+    }
+
+    def add_node(self, node):
+        adder = self.ADDER_MAP.get(node.kind())
+        if not adder:
+            raise Exception(  # noqa: TRY002
+                f"Unsupported node kind ({node.kind()!r}) in node {node!r}"
+            )  # noqa: TRY002
+        adder(self, node)
+
+    def _identity(self, node):
+        in_id, _in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+        jitval = node.outputsAt(0)
+        self.jitval_operand_map[jitval] = in_id
+
+    def add_getattr(self, node):
+        assert node.inputsSize() == 1
+        assert node.outputsSize() == 1
+        obj_ctype, obj = self.get_constant_value(node.inputsAt(0))
+        assert str(obj_ctype).startswith("__torch__.")
+        name = node.s("name")
+        value = getattr(obj, name)
+        output = node.outputsAt(0)
+        ctype = output.type()
+        self.add_constant_value(output, ctype, value)
+
+    def add_constant_node(self, node):
+        assert node.inputsSize() == 0
+        assert node.outputsSize() == 1
+        output = node.outputsAt(0)
+        ctype = output.type()
+        value = output.toIValue()
+        self.add_constant_value(output, ctype, value)
+
+    def add_list_construct(self, node):
+        assert node.outputsSize() == 1
+        output = node.outputsAt(0)
+        ctype = output.type()
+        const_vals: Optional[list] = []
+        tensors: Optional[list] = []
+        for inp in node.inputs():
+            if const_vals is not None and inp in self.constants:
+                _, val = self.get_constant_value(inp)
+                const_vals.append(val)
+            else:
+                const_vals = None
+            if tensors is not None and inp.type().kind() == "TensorType":
+                tensors.append(inp)
+            else:
+                tensors = None
+
+        if const_vals is not None:
+            # NOTE: Now that TorchScript supports list constants,
+            # this code path might not be used anymore.
+            self.add_constant_value(output, ctype, const_vals)
+        if tensors is not None:
+            self.add_tensor_sequence(output, tensors)
+        if const_vals is None and tensors is None:
+            raise Exception(  # noqa: TRY002
+                f"Unable to handle ListConstruct node.  Neither all constants nor all tensors. {node!r}"
+            )
+
+    def add_tuple_construct(self, node):
+        assert node.outputsSize() == 1
+        output = node.outputsAt(0)
+        values = list(node.inputs())
+        self.add_tensor_sequence(output, values)
+
+    def add_unsqueeze(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+
+        _, dim = self.get_constant_value(node.inputsAt(1), "IntType")
+        assert in_oper.dim_order == DimOrder.PRESUMED_CONTIGUOUS
+
+        real_dim = dim if dim >= 0 else dim + len(in_oper.shape) + 1
+        out_shape_list = list(in_oper.shape)
+        out_shape_list.insert(real_dim, 1)
+        out_shape = tuple(out_shape_list)
+        out_oper = in_oper._replace(shape=out_shape)
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_scalar(dim)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.EXPAND_DIMS, inputs, outputs)
+
+    def add_to(self, node):
+        # Handle to("cpu") / to("gpu") case
+        self._identity(node)
+
+    def add_reshape(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+
+        shape_ctype, shape = self.get_constant_value(node.inputsAt(1))
+        assert shape_ctype.kind() == "ListType"
+        assert shape_ctype.getElementType().kind() == "IntType"
+        is_trivial_reshape = len(shape) == 2 and shape[1] == -1
+
+        if in_oper.dim_order != DimOrder.PRESUMED_CONTIGUOUS and not is_trivial_reshape:
+            raise Exception(  # noqa: TRY002
+                "Currently, reshape is only supported on NHWC tensors if the target size is [X, -1]."
+            )
+
+        # Bit of a hack here.  Use a real tensor to infer the output shape.
+        out_shape = torch.zeros(1).expand(in_oper.shape).reshape(shape).shape
+        out_oper = in_oper._replace(
+            shape=out_shape, dim_order=DimOrder.PRESUMED_CONTIGUOUS
+        )
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(shape)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.RESHAPE, inputs, outputs)
+
+    def add_flatten(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+
+        _start_ctype, start_dim = self.get_constant_value(node.inputsAt(1), "IntType")
+        _end_ctype, end_dim = self.get_constant_value(node.inputsAt(2), "IntType")
+
+        # channels last with channels == 1 or (height & width both 1)
+        is_trivial_flatten = len(in_oper.shape) == 4 and (
+            in_oper.shape[1] == 1 or (in_oper.shape[2] == 1 and in_oper.shape[3] == 1)
+        )
+        if in_oper.dim_order != DimOrder.PRESUMED_CONTIGUOUS and not is_trivial_flatten:
+            raise Exception(  # noqa: TRY002
+                "Currently, flatten is not supported on NHWC tensors unless C=1 or H=W=1"
+            )
+
+        if start_dim < 0:
+            start_dim += len(in_oper.shape)
+        if end_dim < 0:
+            end_dim += len(in_oper.shape)
+
+        out_shape = (
+            in_oper.shape[:start_dim]
+            + (functools.reduce(operator.mul, in_oper.shape[start_dim : end_dim + 1]),)
+            + in_oper.shape[end_dim + 1 :]
+        )
+
+        if any(dim == 0 for dim in in_oper.shape[start_dim : end_dim + 1]):
+            raise Exception(  # noqa: TRY002
+                "Flattening flexible dims is not supported yet"
+            )  # noqa: TRY002
+        non_flattened_dims = in_oper.shape[:start_dim] + in_oper.shape[end_dim + 1 :]
+        if non_flattened_dims.count(0) > 1:
+            raise Exception("Only 1 dim can be flexible")  # noqa: TRY002
+
+        out_oper = in_oper._replace(
+            shape=out_shape, dim_order=DimOrder.PRESUMED_CONTIGUOUS
+        )
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        for idx, dim in enumerate(out_shape):
+            if dim == 0:
+                self.forward_operand_shape(out_id, idx, in_id, in_oper.shape.index(0))
+
+        inputs_1 = tuple(dim if dim != 0 else -1 for dim in out_shape)
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(inputs_1)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.RESHAPE, inputs, outputs)
+
+    def add_slice(self, node):
+        assert node.inputsSize() == 5
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+        _, dim_value = self.get_constant_value(node.inputsAt(1))
+        _, start_value = self.get_constant_value(node.inputsAt(2))
+        _, stop_value = self.get_constant_value(node.inputsAt(3))
+        _, step_value = self.get_constant_value(node.inputsAt(4))
+
+        if start_value is None:
+            start_value = 0
+        if stop_value is None:
+            stop_value = sys.maxsize
+
+        if start_value < 0:
+            start_value += in_oper.shape[dim_value]
+        elif start_value == sys.maxsize:
+            start_value = 0
+
+        if start_value == 0 and stop_value == sys.maxsize:
+            self._identity(node)
+            return
+
+        if in_oper.shape[dim_value] == 0:
+            raise Exception("Unable to slice with flexible shape")  # noqa: TRY002
+
+        if stop_value < 0:
+            stop_value += in_oper.shape[dim_value]
+        elif stop_value == sys.maxsize:
+            stop_value = in_oper.shape[dim_value]
+
+        if start_value >= stop_value:
+            raise Exception(  # noqa: TRY002
+                "Slice start value should be less than stop value"
+            )  # noqa: TRY002
+
+        out_len = (stop_value - start_value) // step_value
+        out_shape = tuple(
+            out_len if i == dim_value else dim for i, dim in enumerate(in_oper.shape)
+        )
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), in_oper._replace(shape=out_shape)
+        )
+
+        # flex inputs
+        end_mask = 0
+        for idx, dim in enumerate(out_shape):
+            if dim == 0:
+                self.forward_operand_shape(out_id, idx, in_id, idx)
+                end_mask |= 1 << idx
+
+        inputs = [None] * 7
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(
+            [start_value if i == dim_value else 0 for i in range(len(in_oper.shape))]
+        )
+        inputs[2] = self.add_immediate_int_vector(
+            [
+                stop_value if i == dim_value else dim
+                for i, dim in enumerate(in_oper.shape)
+            ]
+        )
+        inputs[3] = self.add_immediate_int_vector(
+            [step_value if i == dim_value else 1 for i in range(len(in_oper.shape))]
+        )
+        inputs[4] = self.add_immediate_int_scalar(0)  # begin mask
+        inputs[5] = self.add_immediate_int_scalar(end_mask)
+        inputs[6] = self.add_immediate_int_scalar(0)  # shrink axis mas
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.STRIDED_SLICE, inputs, outputs)
+
+    def add_size(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        _, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        _, value = self.constants[node.inputsAt(1)]
+        res = in_oper.shape[value]
+        output = node.outputsAt(0)
+        self.add_constant_value(output, output.type(), res)
+
+    def add_cat(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        tensors = self.tensor_sequences[node.inputsAt(0)]
+        _, dim = self.get_constant_value(node.inputsAt(1), "IntType")
+
+        assert len(tensors) > 0
+        in_ids = []
+        out_oper = None
+        out_dim_size = 0
+        for inp in tensors:
+            in_id, in_oper = self.get_tensor_operand_by_jitval(inp)
+            if out_oper is None:
+                out_shape = change_element(in_oper.shape, dim, -1)
+                out_oper = in_oper._replace(shape=out_shape)
+            assert in_oper.op_type == out_oper.op_type
+            assert in_oper.dim_order == out_oper.dim_order
+            assert change_element(in_oper.shape, dim, -1) == change_element(
+                out_oper.shape, dim, -1
+            )
+            # TODO: Possibly check scale and zero point.
+            in_ids.append(in_id)
+            # TODO: Possibly support variable-sized inputs.
+            out_dim_size += in_oper.shape[dim]
+
+        assert out_oper is not None
+        out_oper = out_oper._replace(
+            shape=change_element(out_oper.shape, dim, out_dim_size)
+        )
+
+        if in_oper.dim_order == DimOrder.CHANNELS_LAST:  # type: ignore[possibly-undefined]
+            assert len(out_oper.shape) == 4
+            nnapi_dim = [0, 3, 1, 2][dim]
+        else:
+            nnapi_dim = dim
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+        for idx, d in enumerate(out_oper.shape):
+            if d == 0:
+                if idx == dim:
+                    shape = " + ".join(flex_name(ip_id, dim) for ip_id in in_ids)
+                    self.compute_operand_shape(out_id, idx, shape)
+                else:
+                    self.forward_operand_shape(out_id, idx, in_ids[0], idx)
+
+        inputs = in_ids + [self.add_immediate_int_scalar(nnapi_dim)]
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.CONCATENATION, inputs, outputs)
+
+    def add_mean(self, node):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        dim_ctype, dim = self.get_constant_value(node.inputsAt(1))
+        assert dim_ctype.kind() == "ListType"
+        assert dim_ctype.getElementType().kind() == "IntType"
+        _, keep_dim = self.get_constant_value(node.inputsAt(2), "BoolType")
+        # Expect None for dtype
+        self.get_constant_value(node.inputsAt(3), "NoneType")
+
+        if in_oper.dim_order == DimOrder.CHANNELS_LAST:
+            assert len(in_oper.shape) == 4
+            nnapi_dim = [[0, 3, 1, 2][d] for d in dim]
+        else:
+            nnapi_dim = dim
+
+        collapsed_dims = set()
+        for d in dim:
+            if d < 0:
+                d += len(in_oper.shape)
+            collapsed_dims.add(d)
+
+        if in_oper.dim_order == DimOrder.CHANNELS_LAST and not keep_dim:
+            assert collapsed_dims.issuperset({2, 3})
+            out_dim_order = DimOrder.PRESUMED_CONTIGUOUS
+        else:
+            out_dim_order = in_oper.dim_order
+
+        out_shape = []
+        for i, s in enumerate(in_oper.shape):
+            if i not in collapsed_dims:
+                out_shape.append(s)
+            elif keep_dim:
+                out_shape.append(1)
+
+        out_oper = in_oper._replace(shape=out_shape, dim_order=out_dim_order)
+
+        inputs = [None] * 3
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(nnapi_dim)
+        inputs[2] = self.add_immediate_int_scalar(keep_dim)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.MEAN, inputs, outputs)
+
+    def add_quantize(self, node):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        if in_oper.dim_order != DimOrder.CHANNELS_LAST:
+            raise Exception(  # noqa: TRY002
+                "Most hardware backends prefer NHWC quantized tensors.  "
+                "Try setting `t.nnapi_nhwc = True` on your tensor inputs.  "
+            )
+        _, scale = self.get_constant_value(node.inputsAt(1), "FloatType")
+        _, zero_point = self.get_constant_value(node.inputsAt(2), "IntType")
+        _, scalar_type = self.get_constant_value(node.inputsAt(3), "IntType")
+        if scalar_type != TorchScalarTypes.QUINT8.value:
+            raise Exception(  # noqa: TRY002
+                "PyTorch NNAPI export only supports quantized tensors "
+                "with the quint8 dtype."
+            )
+        op_type = NNAPI_OperandCode.TENSOR_QUANT8_ASYMM
+
+        out_oper = in_oper._replace(
+            op_type=op_type,
+            scale=scale,
+            zero_point=zero_point,
+        )
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.QUANTIZE, inputs, outputs)
+
+    def add_dequantize(self, node):
+        assert node.inputsSize() == 1
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        out_oper = in_oper._replace(
+            op_type=NNAPI_OperandCode.TENSOR_FLOAT32,
+            scale=0.0,
+            zero_point=0,
+        )
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.DEQUANTIZE, inputs, outputs)
+
+    def add_pointwise_simple_unary_op(self, node, opcode):
+        assert node.inputsSize() == 1
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+
+        out_oper = in_oper
+        if opcode == NNAPI_OperationCode.LOGISTIC:
+            # NNAPI docs: For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the scale
+            # must be 1.f / 256 and the zeroPoint must be 0.
+            # https://fburl.com/h52stoog
+            if in_oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM:
+                out_oper = in_oper._replace(zero_point=0, scale=1.0 / 256)
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        for idx, dim in enumerate(in_oper.shape):
+            if dim == 0:
+                self.forward_operand_shape(out_id, idx, in_id, idx)
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def _do_add_binary(self, node, opcode, fuse_code, *, qparams=None):  # noqa: D401
+        """Helper for pointwise binary broadcast ops with superfluous extra args."""
+        assert node.outputsSize() == 1
+
+        assert node.inputsAt(0).type().kind() == "TensorType"
+        assert node.inputsAt(1).type().kind() == "TensorType"
+
+        if self.has_operand_for_jitval(node.inputsAt(0)):
+            in0_id, in0_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+            in1_id, in1_oper = self.get_tensor_operand_or_constant(
+                node.inputsAt(1), in0_oper.dim_order
+            )
+        elif self.has_operand_for_jitval(node.inputsAt(1)):
+            in1_id, in1_oper = self.get_tensor_operand_by_jitval(node.inputsAt(1))
+            in0_id, in0_oper = self.get_tensor_operand_or_constant(
+                node.inputsAt(0), in1_oper.dim_order
+            )
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Can't do a NNAPI binary op: {opcode} on two constants"
+            )  # noqa: TRY002
+
+        assert in0_oper.op_type == in1_oper.op_type
+        in0_id, in0_oper, in1_id, in1_oper = self.transpose_for_broadcast(
+            in0_id, in0_oper, in1_id, in1_oper
+        )
+        # NOTE: PyTorch and NNAPI have the same broadcast semantics.
+        out_shape = broadcast_shapes(in0_oper.shape, in1_oper.shape)
+        out_oper = in0_oper._replace(shape=out_shape)
+        if qparams is not None:
+            scale, zp = qparams
+            out_oper = out_oper._replace(scale=scale, zero_point=zp)
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+        for idx, (d0, d1) in enumerate(zip(in0_oper.shape, in1_oper.shape)):
+            if d0 == 1 and d1 == 0:
+                self.forward_operand_shape(out_id, idx, in1_id, idx)
+            elif d0 == 0 and d1 == 1:
+                self.forward_operand_shape(out_id, idx, in0_id, idx)
+            elif d0 == 0 and d1 == 0:
+                self.flexible_shape_computation_lines.append(
+                    f"assert {flex_name(in0_id, idx)} == {flex_name(in1_id, idx)}"
+                )
+                self.forward_operand_shape(out_id, idx, in0_id, idx)
+
+        inputs = [None] * 3
+        inputs[0] = in0_id
+        inputs[1] = in1_id
+        inputs[2] = self.add_immediate_int_scalar(fuse_code)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def add_pointwise_simple_binary_broadcast_op(self, node, opcode, fuse_code):
+        assert node.inputsSize() == 2
+        self._do_add_binary(node, opcode, fuse_code)
+
+    def add_add_sub_op(self, node, opcode, fuse_code):
+        assert node.inputsSize() == 3
+
+        _, alpha = self.get_constant_value(node.inputsAt(2), "IntType")
+        if alpha != 1:
+            raise Exception(  # noqa: TRY002
+                "NNAPI does not support add/sub with alpha."
+            )  # noqa: TRY002
+
+        self._do_add_binary(node, opcode, fuse_code)
+
+    def add_qadd(self, node, opcode, fuse_code):
+        assert node.inputsSize() == 4
+
+        _, scale = self.get_constant_value(node.inputsAt(2), "FloatType")
+        _, zero_point = self.get_constant_value(node.inputsAt(3), "IntType")
+
+        self._do_add_binary(node, opcode, fuse_code, qparams=(scale, zero_point))
+
+    def add_softmax(self, node):
+        assert node.inputsSize() == 3
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+
+        _, softmax_dim = self.get_constant_value(node.inputsAt(1), "IntType")
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), in_oper)
+        for dim, size in enumerate(in_oper.shape):
+            if size == 0:
+                self.forward_operand_shape(out_id, dim, in_id, dim)
+
+        inputs = [None] * 3
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_float_scalar(
+            1.0
+        )  # positive scaling factor of exponent, beta
+        inputs[2] = self.add_immediate_int_scalar(softmax_dim)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.SOFTMAX, inputs, outputs)
+
+    def add_hardtanh(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        _, min_val = self.get_constant_value(node.inputsAt(1), "FloatType")
+        _, max_val = self.get_constant_value(node.inputsAt(2), "FloatType")
+
+        op_map = {
+            (-1, 1): NNAPI_OperationCode.RELU1,
+            (0, 6): NNAPI_OperationCode.RELU6,  # noqa: E201
+        }
+
+        opcode = op_map.get((min_val, max_val))
+        if opcode is None:
+            raise Exception(  # noqa: TRY002
+                "NNAPI only supports hardtanh with args (-1, 1) or (0, 6)."
+            )  # noqa: TRY002
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), in_oper)
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def add_prelu_op(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        assert node.inputsAt(0).type().kind() == "TensorType"
+        assert node.inputsAt(1).type().kind() == "TensorType"
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+        w_id, w_oper = self.get_tensor_operand_for_weight(node.inputsAt(1))
+        assert len(w_oper.shape) == 1
+        assert w_oper.shape[0] > 0
+        if w_oper.shape[0] > 1:
+            if in_oper.use_nchw():
+                # TODO: Support this by adding trailing 1 dims.
+                raise Exception(  # noqa: TRY002
+                    "Per-channel PReLU only supports channels_last right now."
+                )
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), in_oper)
+        for dim, size in enumerate(in_oper.shape):
+            if size > 0:
+                pass
+            elif dim <= 1:
+                raise Exception(  # noqa: TRY002
+                    "PReLU requires fixed size for dim 0 and dim 1."
+                )  # noqa: TRY002
+            else:
+                self.forward_operand_shape(out_id, dim, in_id, dim)
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = w_id
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.PRELU, inputs, outputs)
+
+    def add_pool2d_node(self, node, opcode):
+        assert node.inputsSize() == 6
+        assert node.outputsSize() == 1
+        image, kernel, stride, padding, dilation, _ceil_mode = node.inputs()
+
+        stride = stride or kernel
+
+        # TODO: Validate ceil_mode semantics.
+
+        args = self.get_conv_pool_args_2d_from_jit(
+            self.get_size_arg(kernel), stride, padding, dilation
+        )
+        if args.dilation_h != 1 or args.dilation_w != 1:
+            raise Exception("NNAPI does not support dilated pooling.")  # noqa: TRY002
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval_fixed_size(image)
+        assert len(image_oper.shape) == 4
+
+        out_shape = get_conv_pool_shape(
+            image_oper.shape, args, image_oper.shape[1], False
+        )
+        use_nchw = image_oper.use_nchw()
+
+        inputs = [None] * 11
+        inputs[0] = image_id
+        inputs[1] = self.add_immediate_int_scalar(args.pad_l)
+        inputs[2] = self.add_immediate_int_scalar(args.pad_r)
+        inputs[3] = self.add_immediate_int_scalar(args.pad_t)
+        inputs[4] = self.add_immediate_int_scalar(args.pad_b)
+        inputs[5] = self.add_immediate_int_scalar(args.stride_w)
+        inputs[6] = self.add_immediate_int_scalar(args.stride_h)
+        inputs[7] = self.add_immediate_int_scalar(args.kernel_w)
+        inputs[8] = self.add_immediate_int_scalar(args.kernel_h)
+        inputs[9] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+        inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def add_avg_pool2d(self, node):
+        assert node.inputsSize() == 7
+        assert node.outputsSize() == 1
+        (
+            image,
+            kernel,
+            stride,
+            padding,
+            _ceil_mode,
+            count_include_pad,
+            divisor_override,
+        ) = node.inputs()
+
+        _, count_include_pad_value = self.get_constant_value(count_include_pad)
+        _, divisor_override_value = self.get_constant_value(divisor_override)
+        if not count_include_pad_value or divisor_override_value:
+            raise Exception(  # noqa: TRY002
+                "NNAPI doesn't support count_include_pad=False or divisor_override"
+            )
+
+        args = self.get_conv_pool_args_2d_from_jit(
+            self.get_size_arg(kernel), stride, padding
+        )
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval(image)
+        assert len(image_oper.shape) == 4
+
+        out_shape = get_conv_pool_shape(
+            image_oper.shape, args, image_oper.shape[1], False
+        )
+        use_nchw = image_oper.use_nchw()
+
+        inputs = [None] * 11
+        inputs[0] = image_id
+        inputs[1] = self.add_immediate_int_scalar(args.pad_l)
+        inputs[2] = self.add_immediate_int_scalar(args.pad_r)
+        inputs[3] = self.add_immediate_int_scalar(args.pad_t)
+        inputs[4] = self.add_immediate_int_scalar(args.pad_b)
+        inputs[5] = self.add_immediate_int_scalar(args.stride_w)
+        inputs[6] = self.add_immediate_int_scalar(args.stride_h)
+        inputs[7] = self.add_immediate_int_scalar(args.kernel_w)
+        inputs[8] = self.add_immediate_int_scalar(args.kernel_h)
+        inputs[9] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+        inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+        self._handle_conv_pool_flexible_input(out_id, image, args, False)
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.AVERAGE_POOL_2D, inputs, outputs)
+
+    def add_adaptive_avg_pool2d(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval_fixed_size(
+            node.inputsAt(0)
+        )
+        assert len(image_oper.shape) == 4
+
+        size_ctype, size_arg = self.get_constant_value(node.inputsAt(1))
+        assert size_ctype.kind() == "ListType"
+        assert size_ctype.getElementType().kind() == "IntType"
+        if size_arg != [1, 1]:
+            raise Exception(  # noqa: TRY002
+                "NNAPI only supports adaptive_avg_pool2d with output size (1, 1)."
+            )
+
+        out_shape = image_oper.shape[0:2] + tuple(size_arg)
+        use_nchw = image_oper.use_nchw()
+
+        inputs = [None] * 11
+        inputs[0] = image_id
+        inputs[1] = self.add_immediate_int_scalar(0)
+        inputs[2] = self.add_immediate_int_scalar(0)
+        inputs[3] = self.add_immediate_int_scalar(0)
+        inputs[4] = self.add_immediate_int_scalar(0)
+        inputs[5] = self.add_immediate_int_scalar(1)
+        inputs[6] = self.add_immediate_int_scalar(1)
+        inputs[7] = self.add_immediate_int_scalar(image_oper.shape[3])
+        inputs[8] = self.add_immediate_int_scalar(image_oper.shape[2])
+        inputs[9] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+        inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+
+        self.add_operation(NNAPI_OperationCode.AVERAGE_POOL_2D, inputs, outputs)
+
+    def add_upsample_nearest2d(self, node):
+        assert node.inputsSize() == 3 or node.inputsSize() == 4
+        assert node.outputsSize() == 1
+        if node.inputsSize() == 3:
+            image, size_jit, scale_jit = node.inputs()
+        else:
+            image, size_jit, scale_h_jit, scale_w_jit = node.inputs()
+        size_ctype, size_arg = self.get_constant_value(size_jit)
+
+        if node.inputsSize() == 3:
+            scale_ctype, scale_arg = self.get_constant_value(scale_jit)  # type: ignore[possibly-undefined]
+        else:
+            scale_h_ctype, scale_h_arg = self.get_constant_value(scale_h_jit)  # type: ignore[possibly-undefined]
+            scale_w_ctype, _scale_w_arg = self.get_constant_value(scale_w_jit)  # type: ignore[possibly-undefined]
+
+            # The only way for the 4-argument overload of upsample_nearest2d to
+            # have been added to the graph without error is if the scale_h and
+            # scale_w arguments are None
+            assert scale_h_ctype.kind() == "NoneType"
+            assert scale_w_ctype.kind() == "NoneType"
+
+            scale_ctype = scale_h_ctype
+            scale_arg = scale_h_arg
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval(image)
+        assert len(image_oper.shape) == 4
+
+        if size_ctype.kind() != "NoneType" and scale_ctype.kind() != "NoneType":
+            raise Exception("Size and scale cannot both be non-None.")  # noqa: TRY002
+        elif size_ctype.kind() != "NoneType":
+            assert size_ctype.kind() == "ListType"
+            assert size_ctype.getElementType().kind() == "IntType"
+            assert scale_ctype.kind() == "NoneType"
+            assert scale_arg is None
+            assert isinstance(size_arg, list)
+            assert size_arg
+            assert all(isinstance(val, int) for val in size_arg)
+            if len(size_arg) == 1:
+                size_arg = size_arg * 2
+            assert len(size_arg) == 2
+            out_h = size_arg[0]
+            out_w = size_arg[1]
+            arg_h = self.add_immediate_int_scalar(out_h)
+            arg_w = self.add_immediate_int_scalar(out_w)
+        elif scale_ctype.kind() != "NoneType":
+            assert scale_ctype.kind() == "ListType"
+            assert scale_ctype.getElementType().kind() == "FloatType"
+            assert size_ctype.kind() == "NoneType"
+            assert size_arg is None
+            assert isinstance(scale_arg, list)
+            assert scale_arg
+            assert all(isinstance(val, float) for val in scale_arg)
+            if len(scale_arg) == 1:
+                scale_arg = scale_arg * 2
+            assert len(scale_arg) == 2
+            out_h = int(scale_arg[0] * image_oper.shape[2])
+            out_w = int(scale_arg[1] * image_oper.shape[3])
+            arg_h = self.add_immediate_float_scalar(scale_arg[0])
+            arg_w = self.add_immediate_float_scalar(scale_arg[1])
+        else:
+            raise Exception("Size and scale cannot both be None.")  # noqa: TRY002
+
+        out_shape = (image_oper.shape[0], image_oper.shape[1], out_h, out_w)
+        use_nchw = image_oper.use_nchw()
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+
+        if image_oper.shape[0] == 0 or image_oper.shape[1] == 0:
+            raise Exception("Flexible batch or channels not supported")  # noqa: TRY002
+
+        # Handle variable input size
+        for dim in (2, 3):  # h, w indices
+            if image_oper.shape[dim] == 0:
+                if size_ctype.kind() != "NoneType":
+                    self.compute_operand_shape(out_id, dim, size_arg[dim - 2])
+                elif scale_ctype.kind() != "NoneType":
+                    self.compute_operand_shape(
+                        out_id,
+                        dim,
+                        f"int({scale_arg[dim - 2]} * {flex_name(image_id, dim)})",
+                    )
+                else:
+                    raise Exception(  # noqa: TRY002
+                        "Size and scale cannot both be None."
+                    )  # noqa: TRY002
+
+        inputs = [None] * 4
+        inputs[0] = image_id
+        inputs[1] = arg_w
+        inputs[2] = arg_h
+        inputs[3] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.RESIZE_NEAREST_NEIGHBOR, inputs, outputs)
+
+    def add_addmm(self, node):
+        assert node.inputsSize() == 5
+        assert node.outputsSize() == 1
+        jit_bias, jit_input, jit_weight, jit_beta, jit_alpha = node.inputs()
+
+        for jitval in (jit_beta, jit_alpha):
+            scale_ctype, scale_value = self.get_constant_value(jitval)
+            assert scale_ctype.kind() in ("IntType", "FloatType")
+            if scale_value != 1:
+                raise Exception(  # noqa: TRY002
+                    "NNAPI Fully-Connected does not support alpha and beta."
+                )
+
+        self.add_addmm_or_linear(node, True, jit_input, jit_weight, jit_bias)
+
+    def add_linear(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+        jit_input, jit_weight, jit_bias = node.inputs()
+
+        self.add_addmm_or_linear(node, False, jit_input, jit_weight, jit_bias)
+
+    def add_addmm_or_linear(
+        self, node, transpose_weight, jit_input, jit_weight, jit_bias
+    ):
+        input_id, input_oper = self.get_tensor_operand_by_jitval(jit_input)
+        bias_id, bias_oper = self.get_tensor_operand_for_weight(jit_bias)
+
+        assert len(input_oper.shape) == 2
+        assert len(bias_oper.shape) == 1
+
+        # TODO: Transform at load time to share weights with CPU model.
+        _, weight_tensor = self.get_constant_value(jit_weight, "TensorType")
+        assert len(weight_tensor.shape) == 2
+        if transpose_weight:
+            nnapi_weight_tensor = weight_tensor.t().contiguous()
+        else:
+            nnapi_weight_tensor = weight_tensor.contiguous()
+        weight_id = self.add_tensor_operand_for_weight(nnapi_weight_tensor)
+        weight_oper = self.operands[weight_id]
+
+        out_shape = (input_oper.shape[0], weight_oper.shape[0])
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), input_oper._replace(shape=out_shape)
+        )
+
+        if input_oper.shape[0] == 0:
+            self.forward_operand_shape(out_id, 0, input_id, 0)
+
+        inputs = [None] * 4
+        inputs[0] = input_id
+        inputs[1] = weight_id
+        inputs[2] = bias_id
+        inputs[3] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.FULLY_CONNECTED, inputs, outputs)
+
+    def add_qlinear(self, node):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+        (
+            jit_input,
+            jit_packed_weight,
+            jit_scale,
+            jit_zero_point,
+        ) = node.inputs()
+
+        input_id, input_oper = self.get_tensor_operand_by_jitval_fixed_size(jit_input)
+        # TODO: Support automatic reshape
+        assert len(input_oper.shape) == 2
+
+        _, out_scale = self.get_constant_value(jit_scale, "FloatType")
+        _, out_zero_point = self.get_constant_value(jit_zero_point, "IntType")
+        weight_ctype, packed_weight = self.get_constant_value(jit_packed_weight)
+        assert weight_ctype.name() == "LinearPackedParamsBase"
+        raw_weight, raw_bias = packed_weight.__getstate__()[0]
+        assert raw_bias is not None
+
+        assert len(raw_weight.shape) == 2
+        assert len(raw_bias.shape) == 1
+        assert raw_bias.shape[0] == raw_weight.shape[0]
+        assert raw_weight.shape[1] == input_oper.shape[1]
+
+        assert raw_weight.qscheme() == torch.per_tensor_affine
+        if raw_weight.dtype == torch.quint8:
+            unsigned_weight = raw_weight
+        else:
+            assert raw_weight.dtype == torch.qint8
+            unsigned_weight = torch._make_per_tensor_quantized_tensor(
+                (raw_weight.int_repr().int() + 128).to(torch.uint8),
+                scale=raw_weight.q_scale(),
+                zero_point=raw_weight.q_zero_point() + 128,
+            )
+        weight_scale = unsigned_weight.q_scale()
+        bias_scale = input_oper.scale * weight_scale
+        int_bias = torch.quantize_per_tensor(raw_bias, bias_scale, 0, torch.qint32)
+        bias_id = self.add_tensor_operand_for_weight(int_bias)
+
+        multiplier = input_oper.scale * weight_scale / out_scale
+        assert multiplier > 0
+        if multiplier >= 1:
+            raise Exception(  # noqa: TRY002
+                "Quantized convolution multiplier is greater than 1.  "
+                "This is supported by NNAPI, but not by most hardware backends.  "
+                "Try training a model without quantization-aware training.  "
+            )
+
+        # TODO: Transform at load time to share weights with CPU model.
+        nnapi_weight_tensor = unsigned_weight.contiguous()
+        weight_id = self.add_tensor_operand_for_weight(nnapi_weight_tensor)
+        weight_oper = self.operands[weight_id]
+
+        out_shape = (input_oper.shape[0], weight_oper.shape[0])
+        out_oper = input_oper._replace(
+            shape=out_shape,
+            scale=out_scale,
+            zero_point=out_zero_point,
+        )
+
+        inputs = [None] * 4
+        inputs[0] = input_id
+        inputs[1] = weight_id
+        inputs[2] = bias_id
+        inputs[3] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.FULLY_CONNECTED, inputs, outputs)
+
+    def get_optional_bias(self, jit_bias, weight_tensor, transpose=False):
+        ctype, _value = self.get_constant_value(jit_bias)
+        if ctype.kind() == "NoneType":
+            bias_idx = 1 if transpose else 0
+            nnapi_bias_tensor = torch.zeros(
+                weight_tensor.size()[bias_idx], dtype=weight_tensor.dtype
+            )
+            bias_id = self.add_tensor_operand_for_weight(nnapi_bias_tensor)
+            bias_oper = self.operands[bias_id]
+            return bias_id, bias_oper
+        else:
+            return self.get_tensor_operand_for_weight(jit_bias)
+
+    def add_conv2d(self, node):
+        assert node.inputsSize() == 7
+        assert node.outputsSize() == 1
+
+        (
+            jit_image,
+            jit_weight,
+            jit_bias,
+            jit_stride,
+            jit_pad,
+            jit_dilation,
+            jit_groups,
+        ) = node.inputs()
+
+        _, weight_tensor = self.get_constant_value(jit_weight, "TensorType")
+        bias_id, _bias_oper = self.get_optional_bias(jit_bias, weight_tensor)
+        args = self.get_conv_pool_args_2d_from_jit(
+            weight_tensor.shape[2:4], jit_stride, jit_pad, jit_dilation, jit_groups
+        )
+
+        return self.add_conv2d_common(
+            node.outputsAt(0),
+            0.0,
+            0,
+            jit_image,
+            weight_tensor,
+            bias_id,
+            args,
+            False,  # transpose
+            NNAPI_FuseCode.FUSED_NONE,
+        )
+
+    def add_conv_underscore(self, node):
+        assert node.inputsSize() == 13
+        assert node.outputsSize() == 1
+
+        (
+            jit_image,
+            jit_weight,
+            jit_bias,
+            jit_stride,
+            jit_pad,
+            jit_dilation,
+            jit_transpose,
+            _,
+            jit_groups,
+            _,
+            _,
+            _,
+            _,
+        ) = node.inputs()
+
+        _, weight_tensor = self.get_constant_value(jit_weight, "TensorType")
+        _, transpose = self.get_constant_value(jit_transpose)
+        bias_id, _bias_oper = self.get_optional_bias(jit_bias, weight_tensor, transpose)
+        args = self.get_conv_pool_args_2d_from_jit(
+            weight_tensor.shape[2:4], jit_stride, jit_pad, jit_dilation, jit_groups
+        )
+
+        return self.add_conv2d_common(
+            node.outputsAt(0),
+            0.0,
+            0,
+            jit_image,
+            weight_tensor,
+            bias_id,
+            args,
+            transpose,
+            NNAPI_FuseCode.FUSED_NONE,
+        )
+
+    def add_log_softmax(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+
+        jit_input, jit_dim, _jit_half_to_float = node.inputs()
+        input_id, input_oper = self.get_tensor_operand_by_jitval_fixed_size(jit_input)
+        _, dim = self.get_constant_value(jit_dim, "IntType")
+
+        out_shape = input_oper.shape
+
+        inputs = [None] * 3
+        inputs[0] = input_id
+        # specifying 1 as the scaling factor for the exponent, beta
+        inputs[1] = self.add_immediate_float_scalar(1)
+        inputs[2] = self.add_immediate_int_scalar(dim)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(
+            node.outputsAt(0), input_oper._replace(shape=out_shape)
+        )
+        self.add_operation(NNAPI_OperationCode.LOG_SOFTMAX, inputs, outputs)
+
+    def add_qconv2d(self, node, fuse_code, transpose=False):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+
+        (
+            jit_image,
+            jit_packed_weight,
+            jit_scale,
+            jit_zero_point,
+        ) = node.inputs()
+
+        _, out_scale = self.get_constant_value(jit_scale, "FloatType")
+        _, out_zero_point = self.get_constant_value(jit_zero_point, "IntType")
+        weight_ctype, packed_weight = self.get_constant_value(jit_packed_weight)
+        assert weight_ctype.name() == "Conv2dPackedParamsBase"
+        (
+            pack_version,
+            tensors,
+            opt_tensors,
+        ) = packed_weight.__getstate__()[0]
+        assert pack_version == "2"
+        packed_config, raw_weight = tensors
+        (raw_bias,) = opt_tensors
+        assert raw_bias is not None
+        args = self.get_conv_pool_args_2d_from_pack(
+            raw_weight.shape[2:4], packed_config
+        )
+
+        assert raw_weight.qscheme() == torch.per_tensor_affine
+        if raw_weight.dtype == torch.quint8:
+            unsigned_weight = raw_weight
+        else:
+            assert raw_weight.dtype == torch.qint8
+            unsigned_weight = torch._make_per_tensor_quantized_tensor(
+                (raw_weight.int_repr().int() + 128).to(torch.uint8),
+                scale=raw_weight.q_scale(),
+                zero_point=raw_weight.q_zero_point() + 128,
+            )
+        weight_scale = unsigned_weight.q_scale()
+        _, image_oper = self.get_tensor_operand_by_jitval(jit_image)
+        bias_scale = image_oper.scale * weight_scale
+        int_bias = torch.quantize_per_tensor(raw_bias, bias_scale, 0, torch.qint32)
+        bias_id = self.add_tensor_operand_for_weight(int_bias)
+
+        multiplier = image_oper.scale * weight_scale / out_scale
+        assert multiplier > 0
+        if multiplier >= 1:
+            raise Exception(  # noqa: TRY002
+                "Quantized convolution multiplier is greater than 1.  "
+                "This is supported by NNAPI, but not by most hardware backends.  "
+                "Try training a model without quantization-aware training.  "
+            )
+
+        return self.add_conv2d_common(
+            node.outputsAt(0),
+            out_scale,
+            out_zero_point,
+            jit_image,
+            unsigned_weight,
+            bias_id,
+            args,
+            transpose,
+            fuse_code,
+        )
+
+    def add_conv2d_common(
+        self,
+        jit_out,
+        out_scale,
+        out_zero_point,
+        jit_image,
+        weight_tensor,
+        bias_id,
+        args,
+        transpose,
+        fuse_code,
+    ):
+        image_id, image_oper = self.get_tensor_operand_by_jitval(jit_image)
+        in_c = image_oper.shape[1]
+
+        if args.group == 1:
+            # Full convolution
+            depthwise = False
+            if transpose:
+                weight_permutation = (1, 2, 3, 0)
+            else:
+                weight_permutation = (0, 2, 3, 1)
+        elif args.group == in_c:
+            # Depthwise convolution
+            depthwise = True
+            weight_permutation = (1, 2, 3, 0)
+        else:
+            raise Exception("Group convolution not supported yet.")  # noqa: TRY002
+
+        # TODO: Transform at load time to share weights with CPU model.
+        nnapi_weight_tensor = weight_tensor.permute(*weight_permutation).contiguous()
+        weight_id = self.add_tensor_operand_for_weight(nnapi_weight_tensor)
+        weight_oper = self.operands[weight_id]
+
+        bias_oper = self.operands[bias_id]
+
+        if image_oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32:
+            assert weight_oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32
+            assert bias_oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32
+        elif image_oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM:
+            assert weight_oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM
+            assert bias_oper.op_type == NNAPI_OperandCode.TENSOR_INT32
+            assert approx_equal(image_oper.scale * weight_oper.scale, bias_oper.scale)
+            assert bias_oper.zero_point == 0
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Unsupported input type for conv2d: {image_oper.op_type}"
+            )  # noqa: TRY002
+
+        assert len(image_oper.shape) == 4
+        assert len(weight_oper.shape) == 4
+        assert len(bias_oper.shape) == 1
+
+        if depthwise:
+            # Depthwise convolution
+            one, _kern_h, _kern_w, out_c = weight_oper.shape
+            assert one == 1
+            assert out_c % in_c == 0
+            channel_multiplier = out_c // in_c
+            assert channel_multiplier == 1  # Don't support multiplier
+            assert out_c == in_c
+        else:
+            # Full convolution
+            out_c, _kern_h, _kern_w, kern_d = weight_oper.shape
+            assert kern_d == in_c
+
+        assert out_c == bias_oper.shape[0]
+
+        use_nchw = image_oper.use_nchw()
+
+        if depthwise:
+            num_args = 12
+            opcode = NNAPI_OperationCode.DEPTHWISE_CONV_2D
+        else:
+            num_args = 11
+            if transpose:
+                opcode = NNAPI_OperationCode.TRANSPOSE_CONV_2D
+            else:
+                opcode = NNAPI_OperationCode.CONV_2D
+
+        inputs = [None] * num_args
+        inputs[0] = image_id
+        inputs[1] = weight_id
+        inputs[2] = bias_id
+        inputs[3] = self.add_immediate_int_scalar(args.pad_l)
+        inputs[4] = self.add_immediate_int_scalar(args.pad_r)
+        inputs[5] = self.add_immediate_int_scalar(args.pad_t)
+        inputs[6] = self.add_immediate_int_scalar(args.pad_b)
+        inputs[7] = self.add_immediate_int_scalar(args.stride_w)
+        inputs[8] = self.add_immediate_int_scalar(args.stride_h)
+        if depthwise:
+            inputs[9] = self.add_immediate_int_scalar(1)
+            inputs[10] = self.add_immediate_int_scalar(fuse_code)
+            inputs[11] = self.add_immediate_bool_scalar(use_nchw)
+        else:
+            inputs[9] = self.add_immediate_int_scalar(fuse_code)
+            inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        out_shape = get_conv_pool_shape(image_oper.shape, args, out_c, transpose)
+        out_oper = image_oper._replace(
+            shape=out_shape,
+            scale=out_scale,
+            zero_point=out_zero_point,
+        )
+        out_id = self.add_tensor_operand(jit_out, out_oper)
+        self._handle_conv_pool_flexible_input(out_id, jit_image, args, transpose)
+
+        outputs[0] = out_id
+        self.add_operation(opcode, inputs, outputs)
+
+    def _handle_conv_pool_flexible_input(self, out_id, jit_image, args, transpose):
+        image_id, image_oper = self.get_tensor_operand_by_jitval(jit_image)
+        batch, in_ch, in_h, in_w = image_oper.shape
+
+        if batch == 0:
+            self.forward_operand_shape(out_id, 0, image_id, 0)
+        if in_ch == 0:
+            raise Exception("Input channels can't be flexible")  # noqa: TRY002
+        # H & W
+        if transpose:
+            if in_h == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    2,
+                    f"({flex_name(image_id, 2)} - 1) * {args.stride_h} + {args.kernel_h} - {args.pad_t} - {args.pad_b}",
+                )
+            if in_w == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    3,
+                    f"({flex_name(image_id, 3)} - 1) * {args.stride_w} + {args.kernel_w} - {args.pad_l} - {args.pad_r}",
+                )
+        else:
+            if in_h == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    2,
+                    f"({flex_name(image_id, 2)} - {args.kernel_h} + {args.pad_t} + {args.pad_b}) // {args.stride_h} + 1",
+                )
+            if in_w == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    3,
+                    f"({flex_name(image_id, 3)} - {args.kernel_w} + {args.pad_l} + {args.pad_r}) // {args.stride_w} + 1",
+                )
+
+
+def serialize_model(
+    module, inputs, *, config=None, return_shapes=None, use_int16_for_qint16=False
+):
+    """Convert to NNAPI and serialize torchscript module.
+
+    Parameters:
+        module: Torchscript module to convert
+        inputs: Tensors used to specify input details for NNAPI
+        config (optional): Optional config to attach to module
+        return_shapes (optional): Specify shape of outputs if
+            your module uses runtime flexible shapes to set output
+            buffer size for NNAPI
+        use_int16_for_qint16 (optional): Use Pytorch int16 to represent NNAPI qint16 values
+    """
+    return _NnapiSerializer(config, use_int16_for_qint16).serialize_model(
+        module, inputs, return_shapes
+    )

tool_server/.venv/lib/python3.12/site-packages/torch/backends/cpu/__init__.py

@@ -0,0 +1,21 @@
+import torch
+
+
+__all__ = [
+    "get_cpu_capability",
+]
+
+
+def get_cpu_capability() -> str:
+    r"""Return cpu capability as a string value.
+
+    Possible values:
+    - "DEFAULT"
+    - "VSX"
+    - "Z VECTOR"
+    - "NO AVX"
+    - "AVX2"
+    - "AVX512"
+    - "SVE256"
+    """
+    return torch._C._get_cpu_capability()

tool_server/.venv/lib/python3.12/site-packages/torch/backends/cuda/__init__.py

@@ -0,0 +1,536 @@
+# mypy: allow-untyped-defs
+import contextlib
+from typing import Union
+from typing_extensions import deprecated
+
+import torch
+
+
+__all__ = [
+    "is_built",
+    "cuFFTPlanCacheAttrContextProp",
+    "cuFFTPlanCache",
+    "cuFFTPlanCacheManager",
+    "cuBLASModule",
+    "preferred_linalg_library",
+    "preferred_blas_library",
+    "preferred_rocm_fa_library",
+    "cufft_plan_cache",
+    "matmul",
+    "SDPAParams",
+    "enable_cudnn_sdp",
+    "cudnn_sdp_enabled",
+    "enable_flash_sdp",
+    "flash_sdp_enabled",
+    "enable_mem_efficient_sdp",
+    "mem_efficient_sdp_enabled",
+    "math_sdp_enabled",
+    "enable_math_sdp",
+    "allow_fp16_bf16_reduction_math_sdp",
+    "fp16_bf16_reduction_math_sdp_allowed",
+    "is_flash_attention_available",
+    "can_use_flash_attention",
+    "can_use_efficient_attention",
+    "can_use_cudnn_attention",
+    "sdp_kernel",
+]
+
+
+def is_built():
+    r"""
+    Return whether PyTorch is built with CUDA support.
+
+    Note that this doesn't necessarily mean CUDA is available; just that if this PyTorch
+    binary were run on a machine with working CUDA drivers and devices, we would be able to use it.
+    """
+    return torch._C._has_cuda
+
+
+class cuFFTPlanCacheAttrContextProp:
+    # Like regular ContextProp, but uses the `.device_index` attribute from the
+    # calling object as the first argument to the getter and setter.
+    def __init__(self, getter, setter):
+        self.getter = getter
+        self.setter = setter
+
+    def __get__(self, obj, objtype):
+        return self.getter(obj.device_index)
+
+    def __set__(self, obj, val):
+        if isinstance(self.setter, str):
+            raise RuntimeError(self.setter)
+        self.setter(obj.device_index, val)
+
+
+class cuFFTPlanCache:
+    r"""
+    Represent a specific plan cache for a specific `device_index`.
+
+    The attributes `size` and `max_size`, and method `clear`, can fetch and/ or
+    change properties of the C++ cuFFT plan cache.
+    """
+
+    def __init__(self, device_index):
+        self.device_index = device_index
+
+    size = cuFFTPlanCacheAttrContextProp(
+        torch._cufft_get_plan_cache_size,
+        ".size is a read-only property showing the number of plans currently in the "
+        "cache. To change the cache capacity, set cufft_plan_cache.max_size.",
+    )
+
+    max_size = cuFFTPlanCacheAttrContextProp(
+        torch._cufft_get_plan_cache_max_size, torch._cufft_set_plan_cache_max_size
+    )
+
+    def clear(self):
+        return torch._cufft_clear_plan_cache(self.device_index)
+
+
+class cuFFTPlanCacheManager:
+    r"""
+    Represent all cuFFT plan caches, return the cuFFTPlanCache for a given device when indexed.
+
+    Finally, this object, when used directly as a `cuFFTPlanCache` object (e.g.,
+    setting the `.max_size`) attribute, the current device's cuFFT plan cache is
+    used.
+    """
+
+    __initialized = False
+
+    def __init__(self):
+        self.caches = []
+        self.__initialized = True
+
+    def __getitem__(self, device):
+        index = torch.cuda._utils._get_device_index(device)
+        if index < 0 or index >= torch.cuda.device_count():
+            raise RuntimeError(
+                f"cufft_plan_cache: expected 0 <= device index < {torch.cuda.device_count()}, but got "
+                f"device with index {index}"
+            )
+        if len(self.caches) == 0:
+            self.caches.extend(
+                cuFFTPlanCache(index) for index in range(torch.cuda.device_count())
+            )
+        return self.caches[index]
+
+    def __getattr__(self, name):
+        return getattr(self[torch.cuda.current_device()], name)
+
+    def __setattr__(self, name, value):
+        if self.__initialized:
+            return setattr(self[torch.cuda.current_device()], name, value)
+        else:
+            return super().__setattr__(name, value)
+
+
+class cuBLASModule:
+    def __getattr__(self, name):
+        if name == "allow_tf32":
+            return torch._C._get_cublas_allow_tf32()
+        elif name == "allow_fp16_reduced_precision_reduction":
+            return torch._C._get_cublas_allow_fp16_reduced_precision_reduction()
+        elif name == "allow_bf16_reduced_precision_reduction":
+            return torch._C._get_cublas_allow_bf16_reduced_precision_reduction()
+        elif name == "allow_fp16_accumulation":
+            return torch._C._get_cublas_allow_fp16_accumulation()
+        raise AttributeError("Unknown attribute " + name)
+
+    def __setattr__(self, name, value):
+        if name == "allow_tf32":
+            return torch._C._set_cublas_allow_tf32(value)
+        elif name == "allow_fp16_reduced_precision_reduction":
+            return torch._C._set_cublas_allow_fp16_reduced_precision_reduction(value)
+        elif name == "allow_bf16_reduced_precision_reduction":
+            return torch._C._set_cublas_allow_bf16_reduced_precision_reduction(value)
+        elif name == "allow_fp16_accumulation":
+            return torch._C._set_cublas_allow_fp16_accumulation(value)
+        raise AttributeError("Unknown attribute " + name)
+
+
+_LinalgBackends = {
+    "default": torch._C._LinalgBackend.Default,
+    "cusolver": torch._C._LinalgBackend.Cusolver,
+    "magma": torch._C._LinalgBackend.Magma,
+}
+_LinalgBackends_str = ", ".join(_LinalgBackends.keys())
+
+
+def preferred_linalg_library(
+    backend: Union[None, str, torch._C._LinalgBackend] = None
+) -> torch._C._LinalgBackend:
+    r"""
+    Override the heuristic PyTorch uses to choose between cuSOLVER and MAGMA for CUDA linear algebra operations.
+
+    .. warning:: This flag is experimental and subject to change.
+
+    When PyTorch runs a CUDA linear algebra operation it often uses the cuSOLVER or MAGMA libraries,
+    and if both are available it decides which to use with a heuristic.
+    This flag (a :class:`str`) allows overriding those heuristics.
+
+    * If `"cusolver"` is set then cuSOLVER will be used wherever possible.
+    * If `"magma"` is set then MAGMA will be used wherever possible.
+    * If `"default"` (the default) is set then heuristics will be used to pick between
+      cuSOLVER and MAGMA if both are available.
+    * When no input is given, this function returns the currently preferred library.
+    * User may use the environment variable TORCH_LINALG_PREFER_CUSOLVER=1 to set the preferred library to cuSOLVER
+      globally.
+      This flag only sets the initial value of the preferred library and the preferred library
+      may still be overridden by this function call later in your script.
+
+    Note: When a library is preferred other libraries may still be used if the preferred library
+    doesn't implement the operation(s) called.
+    This flag may achieve better performance if PyTorch's heuristic library selection is incorrect
+    for your application's inputs.
+
+    Currently supported linalg operators:
+
+    * :func:`torch.linalg.inv`
+    * :func:`torch.linalg.inv_ex`
+    * :func:`torch.linalg.cholesky`
+    * :func:`torch.linalg.cholesky_ex`
+    * :func:`torch.cholesky_solve`
+    * :func:`torch.cholesky_inverse`
+    * :func:`torch.linalg.lu_factor`
+    * :func:`torch.linalg.lu`
+    * :func:`torch.linalg.lu_solve`
+    * :func:`torch.linalg.qr`
+    * :func:`torch.linalg.eigh`
+    * :func:`torch.linalg.eighvals`
+    * :func:`torch.linalg.svd`
+    * :func:`torch.linalg.svdvals`
+    """
+    if backend is None:
+        pass
+    elif isinstance(backend, str):
+        if backend not in _LinalgBackends:
+            raise RuntimeError(
+                "Unknown input value. " f"Choose from: {_LinalgBackends_str}."
+            )
+        torch._C._set_linalg_preferred_backend(_LinalgBackends[backend])
+    elif isinstance(backend, torch._C._LinalgBackend):
+        torch._C._set_linalg_preferred_backend(backend)
+    else:
+        raise RuntimeError("Unknown input value type.")
+
+    return torch._C._get_linalg_preferred_backend()
+
+
+_BlasBackends = {
+    "default": torch._C._BlasBackend.Default,
+    "cublas": torch._C._BlasBackend.Cublas,
+    "hipblas": torch._C._BlasBackend.Cublas,  # alias
+    "cublaslt": torch._C._BlasBackend.Cublaslt,
+    "hipblaslt": torch._C._BlasBackend.Cublaslt,  # alias
+    "ck": torch._C._BlasBackend.Ck,
+}
+_BlasBackends_str = ", ".join(_BlasBackends.keys())
+
+
+def preferred_blas_library(
+    backend: Union[None, str, torch._C._BlasBackend] = None
+) -> torch._C._BlasBackend:
+    r"""
+    Override the library PyTorch uses for BLAS operations. Choose between cuBLAS, cuBLASLt, and CK [ROCm-only].
+
+    .. warning:: This flag is experimental and subject to change.
+
+    When PyTorch runs a CUDA BLAS operation it defaults to cuBLAS even if both cuBLAS and cuBLASLt are available.
+    For PyTorch built for ROCm, hipBLAS, hipBLASLt, and CK may offer different performance.
+    This flag (a :class:`str`) allows overriding which BLAS library to use.
+
+    * If `"cublas"` is set then cuBLAS will be used wherever possible.
+    * If `"cublaslt"` is set then cuBLASLt will be used wherever possible.
+    * If `"ck"` is set then CK will be used wherever possible.
+    * If `"default"` (the default) is set then heuristics will be used to pick between the other options.
+    * When no input is given, this function returns the currently preferred library.
+    * User may use the environment variable TORCH_BLAS_PREFER_CUBLASLT=1 to set the preferred library to cuBLASLt
+      globally.
+      This flag only sets the initial value of the preferred library and the preferred library
+      may still be overridden by this function call later in your script.
+
+    Note: When a library is preferred other libraries may still be used if the preferred library
+    doesn't implement the operation(s) called.
+    This flag may achieve better performance if PyTorch's library selection is incorrect
+    for your application's inputs.
+
+    """
+    if backend is None:
+        pass
+    elif isinstance(backend, str):
+        if backend not in _BlasBackends:
+            raise RuntimeError(
+                "Unknown input value. " f"Choose from: {_BlasBackends_str}."
+            )
+        torch._C._set_blas_preferred_backend(_BlasBackends[backend])
+    elif isinstance(backend, torch._C._BlasBackend):
+        torch._C._set_blas_preferred_backend(backend)
+    else:
+        raise RuntimeError("Unknown input value type.")
+
+    return torch._C._get_blas_preferred_backend()
+
+
+_ROCmFABackends = {
+    "default": torch._C._ROCmFABackend.Default,
+    "aotriton": torch._C._ROCmFABackend.AOTriton,
+    "ck": torch._C._ROCmFABackend.Ck,
+}
+_ROCmFABackends_str = ", ".join(_ROCmFABackends.keys())
+
+
+from torch._C import _SDPAParams as SDPAParams, _SDPBackend as SDPBackend
+
+
+def preferred_rocm_fa_library(
+    backend: Union[None, str, torch._C._ROCmFABackend] = None
+) -> torch._C._ROCmFABackend:
+    r"""
+    [ROCm-only]
+    Override the backend PyTorch uses in ROCm environments for Flash Attention. Choose between AOTriton and CK
+
+    .. warning:: This flag is experimeental and subject to change.
+
+    When Flash Attention is enabled and desired, PyTorch defaults to using AOTriton as the backend.
+    This flag (a :class:`str`) allows users to override this backend to use composable_kernel
+
+    * If `"default"` is set then the default backend will be used wherever possible. Currently AOTriton.
+    * If `"aotriton"` is set then AOTriton will be used wherever possible.
+    * If `"ck"` is set then CK will be used wherever possible.
+    * When no input is given, this function returns the currently preferred library.
+    * User may use the environment variable TORCH_ROCM_FA_PREFER_CK=1 to set the preferred library to CK
+      globally.
+
+    Note: When a library is preferred other libraries may still be used if the preferred library
+    doesn't implement the operation(s) called.
+    This flag may achieve better performance if PyTorch's library selection is incorrect
+    for your application's inputs.
+    """
+    if backend is None:
+        pass
+    elif isinstance(backend, str):
+        if backend not in _ROCmFABackends:
+            raise RuntimeError(
+                "Unknown input value. " f"Choose from: {_ROCmFABackends_str}."
+            )
+        torch._C._set_rocm_fa_preferred_backend(_ROCmFABackends[backend])
+    elif isinstance(backend, torch._C._ROCmFABackend):
+        torch._C._set_rocm_fa_preferred_backend(backend)
+    else:
+        raise ValueError("Unknown input value. " f"Choose from: {_ROCmFABackends_str}.")
+
+    return torch._C._get_rocm_fa_preferred_backend()
+
+
+# Set the __module__ attribute
+SDPAParams.__module__ = "torch.backends.cuda"
+SDPAParams.__name__ = "SDPAParams"
+
+
+def flash_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether flash scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_flash_sdp_enabled()
+
+
+def enable_flash_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables flash scaled dot product attention.
+    """
+    torch._C._set_sdp_use_flash(enabled)
+
+
+def mem_efficient_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether memory efficient scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_mem_efficient_sdp_enabled()
+
+
+def enable_mem_efficient_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables memory efficient scaled dot product attention.
+    """
+    torch._C._set_sdp_use_mem_efficient(enabled)
+
+
+def math_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether math scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_math_sdp_enabled()
+
+
+def enable_math_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables math scaled dot product attention.
+    """
+    torch._C._set_sdp_use_math(enabled)
+
+
+def allow_fp16_bf16_reduction_math_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables fp16/bf16 reduction in math scaled dot product attention.
+    """
+    torch._C._set_math_sdp_allow_fp16_bf16_reduction(enabled)
+
+
+def fp16_bf16_reduction_math_sdp_allowed():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether fp16/bf16 reduction in math scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_math_sdp_allow_fp16_bf16_reduction()
+
+
+def is_flash_attention_available() -> bool:
+    r"""Check if PyTorch was built with FlashAttention for scaled_dot_product_attention.
+
+    Returns:
+        True if FlashAttention is built and available; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._is_flash_attention_available()
+
+
+def can_use_flash_attention(params: SDPAParams, debug: bool = False) -> bool:
+    r"""Check if FlashAttention can be utilized in scaled_dot_product_attention.
+
+    Args:
+        params: An instance of SDPAParams containing the tensors for query,
+                key, value, an optional attention mask, dropout rate, and
+                a flag indicating if the attention is causal.
+        debug: Whether to logging.warn debug information as to why FlashAttention could not be run.
+            Defaults to False.
+
+    Returns:
+        True if FlashAttention can be used with the given parameters; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._can_use_flash_attention(params, debug)
+
+
+def can_use_efficient_attention(params: SDPAParams, debug: bool = False) -> bool:
+    r"""Check if efficient_attention can be utilized in scaled_dot_product_attention.
+
+    Args:
+        params: An instance of SDPAParams containing the tensors for query,
+                key, value, an optional attention mask, dropout rate, and
+                a flag indicating if the attention is causal.
+        debug: Whether to logging.warn with information as to why efficient_attention could not be run.
+            Defaults to False.
+
+    Returns:
+        True if efficient_attention can be used with the given parameters; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._can_use_mem_efficient_attention(params, debug)
+
+
+def can_use_cudnn_attention(params: SDPAParams, debug: bool = False) -> bool:
+    r"""Check if cudnn_attention can be utilized in scaled_dot_product_attention.
+
+    Args:
+        params: An instance of SDPAParams containing the tensors for query,
+                key, value, an optional attention mask, dropout rate, and
+                a flag indicating if the attention is causal.
+        debug: Whether to logging.warn with information as to why cuDNN attention could not be run.
+            Defaults to False.
+
+    Returns:
+        True if cuDNN can be used with the given parameters; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._can_use_cudnn_attention(params, debug)
+
+
+def cudnn_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether cuDNN scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_cudnn_sdp_enabled()
+
+
+def enable_cudnn_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables cuDNN scaled dot product attention.
+    """
+    torch._C._set_sdp_use_cudnn(enabled)
+
+
+@contextlib.contextmanager
+@deprecated(
+    (
+        "`torch.backends.cuda.sdp_kernel()` is deprecated. "
+        "In the future, this context manager will be removed. "
+        "Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, "
+        "with updated signature."
+    ),
+    category=FutureWarning,
+)
+def sdp_kernel(
+    enable_flash: bool = True,
+    enable_math: bool = True,
+    enable_mem_efficient: bool = True,
+    enable_cudnn: bool = True,
+):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    This context manager can be used to temporarily enable or disable any of the three backends for scaled dot product attention.
+    Upon exiting the context manager, the previous state of the flags will be restored.
+    """
+    from torch.nn.attention import sdpa_kernel
+
+    backend_list = []
+    if enable_flash:
+        backend_list.append(SDPBackend.FLASH_ATTENTION)
+    if enable_mem_efficient:
+        backend_list.append(SDPBackend.EFFICIENT_ATTENTION)
+    if enable_math:
+        backend_list.append(SDPBackend.MATH)
+    if enable_cudnn:
+        backend_list.append(SDPBackend.CUDNN_ATTENTION)
+
+    with sdpa_kernel(backend_list) as context:
+        try:
+            yield context
+        finally:
+            pass
+
+
+cufft_plan_cache = cuFFTPlanCacheManager()
+matmul = cuBLASModule()

tool_server/.venv/lib/python3.12/site-packages/torch/backends/cudnn/__init__.py

@@ -0,0 +1,208 @@
+# mypy: allow-untyped-defs
+import os
+import sys
+import warnings
+from contextlib import contextmanager
+from typing import Optional
+
+import torch
+from torch.backends import __allow_nonbracketed_mutation, ContextProp, PropModule
+
+
+try:
+    from torch._C import _cudnn
+except ImportError:
+    _cudnn = None  # type: ignore[assignment]
+
+# Write:
+#
+#   torch.backends.cudnn.enabled = False
+#
+# to globally disable CuDNN/MIOpen
+
+__cudnn_version: Optional[int] = None
+
+if _cudnn is not None:
+
+    def _init():
+        global __cudnn_version
+        if __cudnn_version is None:
+            __cudnn_version = _cudnn.getVersionInt()
+            runtime_version = _cudnn.getRuntimeVersion()
+            compile_version = _cudnn.getCompileVersion()
+            runtime_major, runtime_minor, _ = runtime_version
+            compile_major, compile_minor, _ = compile_version
+            # Different major versions are always incompatible
+            # Starting with cuDNN 7, minor versions are backwards-compatible
+            # Not sure about MIOpen (ROCm), so always do a strict check
+            if runtime_major != compile_major:
+                cudnn_compatible = False
+            elif runtime_major < 7 or not _cudnn.is_cuda:
+                cudnn_compatible = runtime_minor == compile_minor
+            else:
+                cudnn_compatible = runtime_minor >= compile_minor
+            if not cudnn_compatible:
+                if os.environ.get("PYTORCH_SKIP_CUDNN_COMPATIBILITY_CHECK", "0") == "1":
+                    return True
+                base_error_msg = (
+                    f"cuDNN version incompatibility: "
+                    f"PyTorch was compiled  against {compile_version} "
+                    f"but found runtime version {runtime_version}. "
+                    f"PyTorch already comes bundled with cuDNN. "
+                    f"One option to resolving this error is to ensure PyTorch "
+                    f"can find the bundled cuDNN. "
+                )
+
+                if "LD_LIBRARY_PATH" in os.environ:
+                    ld_library_path = os.environ.get("LD_LIBRARY_PATH", "")
+                    if any(
+                        substring in ld_library_path for substring in ["cuda", "cudnn"]
+                    ):
+                        raise RuntimeError(
+                            f"{base_error_msg}"
+                            f"Looks like your LD_LIBRARY_PATH contains incompatible version of cudnn. "
+                            f"Please either remove it from the path or install cudnn {compile_version}"
+                        )
+                    else:
+                        raise RuntimeError(
+                            f"{base_error_msg}"
+                            f"one possibility is that there is a "
+                            f"conflicting cuDNN in LD_LIBRARY_PATH."
+                        )
+                else:
+                    raise RuntimeError(base_error_msg)
+
+        return True
+
+else:
+
+    def _init():
+        return False
+
+
+def version():
+    """Return the version of cuDNN."""
+    if not _init():
+        return None
+    return __cudnn_version
+
+
+CUDNN_TENSOR_DTYPES = {
+    torch.half,
+    torch.float,
+    torch.double,
+}
+
+
+def is_available():
+    r"""Return a bool indicating if CUDNN is currently available."""
+    return torch._C._has_cudnn
+
+
+def is_acceptable(tensor):
+    if not torch._C._get_cudnn_enabled():
+        return False
+    if tensor.device.type != "cuda" or tensor.dtype not in CUDNN_TENSOR_DTYPES:
+        return False
+    if not is_available():
+        warnings.warn(
+            "PyTorch was compiled without cuDNN/MIOpen support. To use cuDNN/MIOpen, rebuild "
+            "PyTorch making sure the library is visible to the build system."
+        )
+        return False
+    if not _init():
+        warnings.warn(
+            "cuDNN/MIOpen library not found. Check your {libpath}".format(
+                libpath={"darwin": "DYLD_LIBRARY_PATH", "win32": "PATH"}.get(
+                    sys.platform, "LD_LIBRARY_PATH"
+                )
+            )
+        )
+        return False
+    return True
+
+
+def set_flags(
+    _enabled=None,
+    _benchmark=None,
+    _benchmark_limit=None,
+    _deterministic=None,
+    _allow_tf32=None,
+):
+    orig_flags = (
+        torch._C._get_cudnn_enabled(),
+        torch._C._get_cudnn_benchmark(),
+        None if not is_available() else torch._C._cuda_get_cudnn_benchmark_limit(),
+        torch._C._get_cudnn_deterministic(),
+        torch._C._get_cudnn_allow_tf32(),
+    )
+    if _enabled is not None:
+        torch._C._set_cudnn_enabled(_enabled)
+    if _benchmark is not None:
+        torch._C._set_cudnn_benchmark(_benchmark)
+    if _benchmark_limit is not None and is_available():
+        torch._C._cuda_set_cudnn_benchmark_limit(_benchmark_limit)
+    if _deterministic is not None:
+        torch._C._set_cudnn_deterministic(_deterministic)
+    if _allow_tf32 is not None:
+        torch._C._set_cudnn_allow_tf32(_allow_tf32)
+    return orig_flags
+
+
+@contextmanager
+def flags(
+    enabled=False,
+    benchmark=False,
+    benchmark_limit=10,
+    deterministic=False,
+    allow_tf32=True,
+):
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(
+            enabled, benchmark, benchmark_limit, deterministic, allow_tf32
+        )
+    try:
+        yield
+    finally:
+        # recover the previous values
+        with __allow_nonbracketed_mutation():
+            set_flags(*orig_flags)
+
+
+# The magic here is to allow us to intercept code like this:
+#
+#   torch.backends.<cudnn|mkldnn>.enabled = True
+
+
+class CudnnModule(PropModule):
+    def __init__(self, m, name):
+        super().__init__(m, name)
+
+    enabled = ContextProp(torch._C._get_cudnn_enabled, torch._C._set_cudnn_enabled)
+    deterministic = ContextProp(
+        torch._C._get_cudnn_deterministic, torch._C._set_cudnn_deterministic
+    )
+    benchmark = ContextProp(
+        torch._C._get_cudnn_benchmark, torch._C._set_cudnn_benchmark
+    )
+    benchmark_limit = None
+    if is_available():
+        benchmark_limit = ContextProp(
+            torch._C._cuda_get_cudnn_benchmark_limit,
+            torch._C._cuda_set_cudnn_benchmark_limit,
+        )
+    allow_tf32 = ContextProp(
+        torch._C._get_cudnn_allow_tf32, torch._C._set_cudnn_allow_tf32
+    )
+
+
+# This is the sys.modules replacement trick, see
+# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
+sys.modules[__name__] = CudnnModule(sys.modules[__name__], __name__)
+
+# Add type annotation for the replaced module
+enabled: bool
+deterministic: bool
+benchmark: bool
+allow_tf32: bool
+benchmark_limit: int

tool_server/.venv/lib/python3.12/site-packages/torch/backends/cudnn/rnn.py

@@ -0,0 +1,64 @@
+# mypy: allow-untyped-defs
+import torch.cuda
+
+
+try:
+    from torch._C import _cudnn
+except ImportError:
+    # Uses of all the functions below should be guarded by torch.backends.cudnn.is_available(),
+    # so it's safe to not emit any checks here.
+    _cudnn = None  # type: ignore[assignment]
+
+
+def get_cudnn_mode(mode):
+    if mode == "RNN_RELU":
+        return int(_cudnn.RNNMode.rnn_relu)
+    elif mode == "RNN_TANH":
+        return int(_cudnn.RNNMode.rnn_tanh)
+    elif mode == "LSTM":
+        return int(_cudnn.RNNMode.lstm)
+    elif mode == "GRU":
+        return int(_cudnn.RNNMode.gru)
+    else:
+        raise Exception(f"Unknown mode: {mode}")  # noqa: TRY002
+
+
+# NB: We don't actually need this class anymore (in fact, we could serialize the
+# dropout state for even better reproducibility), but it is kept for backwards
+# compatibility for old models.
+class Unserializable:
+    def __init__(self, inner):
+        self.inner = inner
+
+    def get(self):
+        return self.inner
+
+    def __getstate__(self):
+        # Note: can't return {}, because python2 won't call __setstate__
+        # if the value evaluates to False
+        return "<unserializable>"
+
+    def __setstate__(self, state):
+        self.inner = None
+
+
+def init_dropout_state(dropout, train, dropout_seed, dropout_state):
+    dropout_desc_name = "desc_" + str(torch.cuda.current_device())
+    dropout_p = dropout if train else 0
+    if (dropout_desc_name not in dropout_state) or (
+        dropout_state[dropout_desc_name].get() is None
+    ):
+        if dropout_p == 0:
+            dropout_state[dropout_desc_name] = Unserializable(None)
+        else:
+            dropout_state[dropout_desc_name] = Unserializable(
+                torch._cudnn_init_dropout_state(  # type: ignore[call-arg]
+                    dropout_p,
+                    train,
+                    dropout_seed,
+                    self_ty=torch.uint8,
+                    device=torch.device("cuda"),
+                )
+            )
+    dropout_ts = dropout_state[dropout_desc_name].get()
+    return dropout_ts

tool_server/.venv/lib/python3.12/site-packages/torch/backends/cusparselt/__init__.py

@@ -0,0 +1,57 @@
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+
+
+__all__ = [
+    "version",
+    "is_available",
+    "get_max_alg_id",
+]
+
+try:
+    from torch._C import _cusparselt
+except ImportError:
+    _cusparselt = None  # type: ignore[assignment]
+
+__cusparselt_version: Optional[int] = None
+__MAX_ALG_ID: Optional[int] = None
+
+if _cusparselt is not None:
+
+    def _init():
+        global __cusparselt_version
+        global __MAX_ALG_ID
+        if __cusparselt_version is None:
+            __cusparselt_version = _cusparselt.getVersionInt()
+            if __cusparselt_version == 400:
+                __MAX_ALG_ID = 4
+            elif __cusparselt_version == 502:
+                __MAX_ALG_ID = 5
+            elif __cusparselt_version == 602:
+                __MAX_ALG_ID = 37
+        return True
+
+else:
+
+    def _init():
+        return False
+
+
+def version() -> Optional[int]:
+    """Return the version of cuSPARSELt"""
+    if not _init():
+        return None
+    return __cusparselt_version
+
+
+def is_available() -> bool:
+    r"""Return a bool indicating if cuSPARSELt is currently available."""
+    return torch._C._has_cusparselt
+
+
+def get_max_alg_id() -> Optional[int]:
+    if not _init():
+        return None
+    return __MAX_ALG_ID