diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/distributions/von_mises.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/distributions/von_mises.py new file mode 100644 index 0000000000000000000000000000000000000000..47588ffedd0b57f14b0fef6aca0346f9042d7bc8 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/distributions/von_mises.py @@ -0,0 +1,223 @@ +# mypy: allow-untyped-defs +import math +from typing import Optional + +import torch +import torch.jit +from torch import Tensor +from torch.distributions import constraints +from torch.distributions.distribution import Distribution +from torch.distributions.utils import broadcast_all, lazy_property + + +__all__ = ["VonMises"] + + +def _eval_poly(y, coef): + coef = list(coef) + result = coef.pop() + while coef: + result = coef.pop() + y * result + return result + + +_I0_COEF_SMALL = [ + 1.0, + 3.5156229, + 3.0899424, + 1.2067492, + 0.2659732, + 0.360768e-1, + 0.45813e-2, +] +_I0_COEF_LARGE = [ + 0.39894228, + 0.1328592e-1, + 0.225319e-2, + -0.157565e-2, + 0.916281e-2, + -0.2057706e-1, + 0.2635537e-1, + -0.1647633e-1, + 0.392377e-2, +] +_I1_COEF_SMALL = [ + 0.5, + 0.87890594, + 0.51498869, + 0.15084934, + 0.2658733e-1, + 0.301532e-2, + 0.32411e-3, +] +_I1_COEF_LARGE = [ + 0.39894228, + -0.3988024e-1, + -0.362018e-2, + 0.163801e-2, + -0.1031555e-1, + 0.2282967e-1, + -0.2895312e-1, + 0.1787654e-1, + -0.420059e-2, +] + +_COEF_SMALL = [_I0_COEF_SMALL, _I1_COEF_SMALL] +_COEF_LARGE = [_I0_COEF_LARGE, _I1_COEF_LARGE] + + +def _log_modified_bessel_fn(x, order=0): + """ + Returns ``log(I_order(x))`` for ``x > 0``, + where `order` is either 0 or 1. + """ + assert order == 0 or order == 1 + + # compute small solution + y = x / 3.75 + y = y * y + small = _eval_poly(y, _COEF_SMALL[order]) + if order == 1: + small = x.abs() * small + small = small.log() + + # compute large solution + y = 3.75 / x + large = x - 0.5 * x.log() + _eval_poly(y, _COEF_LARGE[order]).log() + + result = torch.where(x < 3.75, small, large) + return result + + +@torch.jit.script_if_tracing +def _rejection_sample(loc, concentration, proposal_r, x): + done = torch.zeros(x.shape, dtype=torch.bool, device=loc.device) + # pyrefly: ignore [bad-assignment] + while not done.all(): + u = torch.rand((3,) + x.shape, dtype=loc.dtype, device=loc.device) + u1, u2, u3 = u.unbind() + z = torch.cos(math.pi * u1) + f = (1 + proposal_r * z) / (proposal_r + z) + c = concentration * (proposal_r - f) + accept = ((c * (2 - c) - u2) > 0) | ((c / u2).log() + 1 - c >= 0) + if accept.any(): + # pyrefly: ignore [no-matching-overload] + x = torch.where(accept, (u3 - 0.5).sign() * f.acos(), x) + done = done | accept + return (x + math.pi + loc) % (2 * math.pi) - math.pi + + +class VonMises(Distribution): + """ + A circular von Mises distribution. + + This implementation uses polar coordinates. The ``loc`` and ``value`` args + can be any real number (to facilitate unconstrained optimization), but are + interpreted as angles modulo 2 pi. + + Example:: + >>> # xdoctest: +IGNORE_WANT("non-deterministic") + >>> m = VonMises(torch.tensor([1.0]), torch.tensor([1.0])) + >>> m.sample() # von Mises distributed with loc=1 and concentration=1 + tensor([1.9777]) + + :param torch.Tensor loc: an angle in radians. + :param torch.Tensor concentration: concentration parameter + """ + + # pyrefly: ignore [bad-override] + arg_constraints = {"loc": constraints.real, "concentration": constraints.positive} + support = constraints.real + has_rsample = False + + def __init__( + self, + loc: Tensor, + concentration: Tensor, + validate_args: Optional[bool] = None, + ) -> None: + self.loc, self.concentration = broadcast_all(loc, concentration) + batch_shape = self.loc.shape + event_shape = torch.Size() + super().__init__(batch_shape, event_shape, validate_args) + + def log_prob(self, value): + if self._validate_args: + self._validate_sample(value) + log_prob = self.concentration * torch.cos(value - self.loc) + log_prob = ( + log_prob + - math.log(2 * math.pi) + - _log_modified_bessel_fn(self.concentration, order=0) + ) + return log_prob + + @lazy_property + def _loc(self) -> Tensor: + return self.loc.to(torch.double) + + @lazy_property + def _concentration(self) -> Tensor: + return self.concentration.to(torch.double) + + @lazy_property + def _proposal_r(self) -> Tensor: + kappa = self._concentration + # pyrefly: ignore [unsupported-operation] + tau = 1 + (1 + 4 * kappa**2).sqrt() + rho = (tau - (2 * tau).sqrt()) / (2 * kappa) + # pyrefly: ignore [unsupported-operation] + _proposal_r = (1 + rho**2) / (2 * rho) + # second order Taylor expansion around 0 for small kappa + _proposal_r_taylor = 1 / kappa + kappa + return torch.where(kappa < 1e-5, _proposal_r_taylor, _proposal_r) + + @torch.no_grad() + def sample(self, sample_shape=torch.Size()): + """ + The sampling algorithm for the von Mises distribution is based on the + following paper: D.J. Best and N.I. Fisher, "Efficient simulation of the + von Mises distribution." Applied Statistics (1979): 152-157. + + Sampling is always done in double precision internally to avoid a hang + in _rejection_sample() for small values of the concentration, which + starts to happen for single precision around 1e-4 (see issue #88443). + """ + shape = self._extended_shape(sample_shape) + x = torch.empty(shape, dtype=self._loc.dtype, device=self.loc.device) + return _rejection_sample( + self._loc, self._concentration, self._proposal_r, x + ).to(self.loc.dtype) + + def expand(self, batch_shape, _instance=None): + try: + return super().expand(batch_shape) + except NotImplementedError: + validate_args = self.__dict__.get("_validate_args") + loc = self.loc.expand(batch_shape) + concentration = self.concentration.expand(batch_shape) + return type(self)(loc, concentration, validate_args=validate_args) + + @property + def mean(self) -> Tensor: + """ + The provided mean is the circular one. + """ + return self.loc + + @property + def mode(self) -> Tensor: + return self.loc + + @lazy_property + def variance(self) -> Tensor: # type: ignore[override] + """ + The provided variance is the circular one. + """ + return ( + 1 + - ( + _log_modified_bessel_fn(self.concentration, order=1) + - _log_modified_bessel_fn(self.concentration, order=0) + ).exp() + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/distributions/weibull.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/distributions/weibull.py new file mode 100644 index 0000000000000000000000000000000000000000..dfc440296ad6cead7942526da0ab58ed8e42b249 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/distributions/weibull.py @@ -0,0 +1,97 @@ +# mypy: allow-untyped-defs +from typing import Optional, Union + +import torch +from torch import Tensor +from torch.distributions import constraints +from torch.distributions.exponential import Exponential +from torch.distributions.gumbel import euler_constant +from torch.distributions.transformed_distribution import TransformedDistribution +from torch.distributions.transforms import AffineTransform, PowerTransform +from torch.distributions.utils import broadcast_all + + +__all__ = ["Weibull"] + + +class Weibull(TransformedDistribution): + r""" + Samples from a two-parameter Weibull distribution. + + Example: + + >>> # xdoctest: +IGNORE_WANT("non-deterministic") + >>> m = Weibull(torch.tensor([1.0]), torch.tensor([1.0])) + >>> m.sample() # sample from a Weibull distribution with scale=1, concentration=1 + tensor([ 0.4784]) + + Args: + scale (float or Tensor): Scale parameter of distribution (lambda). + concentration (float or Tensor): Concentration parameter of distribution (k/shape). + validate_args (bool, optional): Whether to validate arguments. Default: None. + """ + + arg_constraints = { + "scale": constraints.positive, + "concentration": constraints.positive, + } + # pyrefly: ignore [bad-override] + support = constraints.positive + + def __init__( + self, + scale: Union[Tensor, float], + concentration: Union[Tensor, float], + validate_args: Optional[bool] = None, + ) -> None: + self.scale, self.concentration = broadcast_all(scale, concentration) + self.concentration_reciprocal = self.concentration.reciprocal() + base_dist = Exponential( + torch.ones_like(self.scale), validate_args=validate_args + ) + transforms = [ + PowerTransform(exponent=self.concentration_reciprocal), + AffineTransform(loc=0, scale=self.scale), + ] + # pyrefly: ignore [bad-argument-type] + super().__init__(base_dist, transforms, validate_args=validate_args) + + def expand(self, batch_shape, _instance=None): + new = self._get_checked_instance(Weibull, _instance) + new.scale = self.scale.expand(batch_shape) + new.concentration = self.concentration.expand(batch_shape) + new.concentration_reciprocal = new.concentration.reciprocal() + base_dist = self.base_dist.expand(batch_shape) + transforms = [ + PowerTransform(exponent=new.concentration_reciprocal), + AffineTransform(loc=0, scale=new.scale), + ] + super(Weibull, new).__init__(base_dist, transforms, validate_args=False) + new._validate_args = self._validate_args + return new + + @property + def mean(self) -> Tensor: + return self.scale * torch.exp(torch.lgamma(1 + self.concentration_reciprocal)) + + @property + def mode(self) -> Tensor: + return ( + self.scale + * ((self.concentration - 1) / self.concentration) + ** self.concentration.reciprocal() + ) + + @property + def variance(self) -> Tensor: + return self.scale.pow(2) * ( + torch.exp(torch.lgamma(1 + 2 * self.concentration_reciprocal)) + - torch.exp(2 * torch.lgamma(1 + self.concentration_reciprocal)) + ) + + def entropy(self): + return ( + euler_constant * (1 - self.concentration_reciprocal) + + torch.log(self.scale * self.concentration_reciprocal) + + 1 + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/distributions/wishart.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/distributions/wishart.py new file mode 100644 index 0000000000000000000000000000000000000000..2d230d48533b1b586f1f2c141e2bd5b36a4c4fbc --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/distributions/wishart.py @@ -0,0 +1,347 @@ +# mypy: allow-untyped-defs +import math +import warnings +from typing import Optional, Union + +import torch +from torch import nan, Tensor +from torch.distributions import constraints +from torch.distributions.exp_family import ExponentialFamily +from torch.distributions.multivariate_normal import _precision_to_scale_tril +from torch.distributions.utils import lazy_property +from torch.types import _Number, _size, Number + + +__all__ = ["Wishart"] + +_log_2 = math.log(2) + + +def _mvdigamma(x: Tensor, p: int) -> Tensor: + assert x.gt((p - 1) / 2).all(), "Wrong domain for multivariate digamma function." + return torch.digamma( + x.unsqueeze(-1) + - torch.arange(p, dtype=x.dtype, device=x.device).div(2).expand(x.shape + (-1,)) + ).sum(-1) + + +def _clamp_above_eps(x: Tensor) -> Tensor: + # We assume positive input for this function + return x.clamp(min=torch.finfo(x.dtype).eps) + + +class Wishart(ExponentialFamily): + r""" + Creates a Wishart distribution parameterized by a symmetric positive definite matrix :math:`\Sigma`, + or its Cholesky decomposition :math:`\mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top` + + Example: + >>> # xdoctest: +SKIP("FIXME: scale_tril must be at least two-dimensional") + >>> m = Wishart(torch.Tensor([2]), covariance_matrix=torch.eye(2)) + >>> m.sample() # Wishart distributed with mean=`df * I` and + >>> # variance(x_ij)=`df` for i != j and variance(x_ij)=`2 * df` for i == j + + Args: + df (float or Tensor): real-valued parameter larger than the (dimension of Square matrix) - 1 + covariance_matrix (Tensor): positive-definite covariance matrix + precision_matrix (Tensor): positive-definite precision matrix + scale_tril (Tensor): lower-triangular factor of covariance, with positive-valued diagonal + Note: + Only one of :attr:`covariance_matrix` or :attr:`precision_matrix` or + :attr:`scale_tril` can be specified. + Using :attr:`scale_tril` will be more efficient: all computations internally + are based on :attr:`scale_tril`. If :attr:`covariance_matrix` or + :attr:`precision_matrix` is passed instead, it is only used to compute + the corresponding lower triangular matrices using a Cholesky decomposition. + 'torch.distributions.LKJCholesky' is a restricted Wishart distribution.[1] + + **References** + + [1] Wang, Z., Wu, Y. and Chu, H., 2018. `On equivalence of the LKJ distribution and the restricted Wishart distribution`. + [2] Sawyer, S., 2007. `Wishart Distributions and Inverse-Wishart Sampling`. + [3] Anderson, T. W., 2003. `An Introduction to Multivariate Statistical Analysis (3rd ed.)`. + [4] Odell, P. L. & Feiveson, A. H., 1966. `A Numerical Procedure to Generate a SampleCovariance Matrix`. JASA, 61(313):199-203. + [5] Ku, Y.-C. & Bloomfield, P., 2010. `Generating Random Wishart Matrices with Fractional Degrees of Freedom in OX`. + """ + + support = constraints.positive_definite + has_rsample = True + _mean_carrier_measure = 0 + + @property + def arg_constraints(self): + return { + "covariance_matrix": constraints.positive_definite, + "precision_matrix": constraints.positive_definite, + "scale_tril": constraints.lower_cholesky, + "df": constraints.greater_than(self.event_shape[-1] - 1), + } + + def __init__( + self, + df: Union[Tensor, Number], + covariance_matrix: Optional[Tensor] = None, + precision_matrix: Optional[Tensor] = None, + scale_tril: Optional[Tensor] = None, + validate_args: Optional[bool] = None, + ) -> None: + assert (covariance_matrix is not None) + (scale_tril is not None) + ( + precision_matrix is not None + ) == 1, ( + "Exactly one of covariance_matrix or precision_matrix or scale_tril may be specified." + ) + + param = next( + p + for p in (covariance_matrix, precision_matrix, scale_tril) + if p is not None + ) + + if param.dim() < 2: + raise ValueError( + "scale_tril must be at least two-dimensional, with optional leading batch dimensions" + ) + + if isinstance(df, _Number): + batch_shape = torch.Size(param.shape[:-2]) + self.df = torch.tensor(df, dtype=param.dtype, device=param.device) + else: + batch_shape = torch.broadcast_shapes(param.shape[:-2], df.shape) + self.df = df.expand(batch_shape) + event_shape = param.shape[-2:] + + if self.df.le(event_shape[-1] - 1).any(): + raise ValueError( + f"Value of df={df} expected to be greater than ndim - 1 = {event_shape[-1] - 1}." + ) + + if scale_tril is not None: + # pyrefly: ignore [read-only] + self.scale_tril = param.expand(batch_shape + (-1, -1)) + elif covariance_matrix is not None: + # pyrefly: ignore [read-only] + self.covariance_matrix = param.expand(batch_shape + (-1, -1)) + elif precision_matrix is not None: + # pyrefly: ignore [read-only] + self.precision_matrix = param.expand(batch_shape + (-1, -1)) + + if self.df.lt(event_shape[-1]).any(): + warnings.warn( + "Low df values detected. Singular samples are highly likely to occur for ndim - 1 < df < ndim.", + stacklevel=2, + ) + + super().__init__(batch_shape, event_shape, validate_args=validate_args) + self._batch_dims = [-(x + 1) for x in range(len(self._batch_shape))] + + if scale_tril is not None: + self._unbroadcasted_scale_tril = scale_tril + elif covariance_matrix is not None: + self._unbroadcasted_scale_tril = torch.linalg.cholesky(covariance_matrix) + else: # precision_matrix is not None + self._unbroadcasted_scale_tril = _precision_to_scale_tril(precision_matrix) + + # Chi2 distribution is needed for Bartlett decomposition sampling + self._dist_chi2 = torch.distributions.chi2.Chi2( + df=( + self.df.unsqueeze(-1) + - torch.arange( + self._event_shape[-1], + dtype=self._unbroadcasted_scale_tril.dtype, + device=self._unbroadcasted_scale_tril.device, + ).expand(batch_shape + (-1,)) + ) + ) + + def expand(self, batch_shape, _instance=None): + new = self._get_checked_instance(Wishart, _instance) + batch_shape = torch.Size(batch_shape) + cov_shape = batch_shape + self.event_shape + new._unbroadcasted_scale_tril = self._unbroadcasted_scale_tril.expand(cov_shape) + new.df = self.df.expand(batch_shape) + + new._batch_dims = [-(x + 1) for x in range(len(batch_shape))] + + if "covariance_matrix" in self.__dict__: + new.covariance_matrix = self.covariance_matrix.expand(cov_shape) + if "scale_tril" in self.__dict__: + new.scale_tril = self.scale_tril.expand(cov_shape) + if "precision_matrix" in self.__dict__: + new.precision_matrix = self.precision_matrix.expand(cov_shape) + + # Chi2 distribution is needed for Bartlett decomposition sampling + new._dist_chi2 = torch.distributions.chi2.Chi2( + df=( + new.df.unsqueeze(-1) + - torch.arange( + self.event_shape[-1], + dtype=new._unbroadcasted_scale_tril.dtype, + device=new._unbroadcasted_scale_tril.device, + ).expand(batch_shape + (-1,)) + ) + ) + + super(Wishart, new).__init__(batch_shape, self.event_shape, validate_args=False) + new._validate_args = self._validate_args + return new + + @lazy_property + def scale_tril(self) -> Tensor: + return self._unbroadcasted_scale_tril.expand( + self._batch_shape + self._event_shape + ) + + @lazy_property + def covariance_matrix(self) -> Tensor: + return ( + self._unbroadcasted_scale_tril + @ self._unbroadcasted_scale_tril.transpose(-2, -1) + ).expand(self._batch_shape + self._event_shape) + + @lazy_property + def precision_matrix(self) -> Tensor: + identity = torch.eye( + self._event_shape[-1], + device=self._unbroadcasted_scale_tril.device, + dtype=self._unbroadcasted_scale_tril.dtype, + ) + return torch.cholesky_solve(identity, self._unbroadcasted_scale_tril).expand( + self._batch_shape + self._event_shape + ) + + @property + def mean(self) -> Tensor: + return self.df.view(self._batch_shape + (1, 1)) * self.covariance_matrix + + @property + def mode(self) -> Tensor: + factor = self.df - self.covariance_matrix.shape[-1] - 1 + factor[factor <= 0] = nan + return factor.view(self._batch_shape + (1, 1)) * self.covariance_matrix + + @property + def variance(self) -> Tensor: + V = self.covariance_matrix # has shape (batch_shape x event_shape) + diag_V = V.diagonal(dim1=-2, dim2=-1) + return self.df.view(self._batch_shape + (1, 1)) * ( + V.pow(2) + torch.einsum("...i,...j->...ij", diag_V, diag_V) + ) + + def _bartlett_sampling(self, sample_shape=torch.Size()): + p = self._event_shape[-1] # has singleton shape + + # Implemented Sampling using Bartlett decomposition + noise = _clamp_above_eps( + self._dist_chi2.rsample(sample_shape).sqrt() + ).diag_embed(dim1=-2, dim2=-1) + + i, j = torch.tril_indices(p, p, offset=-1) + noise[..., i, j] = torch.randn( + torch.Size(sample_shape) + self._batch_shape + (int(p * (p - 1) / 2),), + dtype=noise.dtype, + device=noise.device, + ) + chol = self._unbroadcasted_scale_tril @ noise + return chol @ chol.transpose(-2, -1) + + def rsample( + self, sample_shape: _size = torch.Size(), max_try_correction=None + ) -> Tensor: + r""" + .. warning:: + In some cases, sampling algorithm based on Bartlett decomposition may return singular matrix samples. + Several tries to correct singular samples are performed by default, but it may end up returning + singular matrix samples. Singular samples may return `-inf` values in `.log_prob()`. + In those cases, the user should validate the samples and either fix the value of `df` + or adjust `max_try_correction` value for argument in `.rsample` accordingly. + """ + + if max_try_correction is None: + max_try_correction = 3 if torch._C._get_tracing_state() else 10 + + sample_shape = torch.Size(sample_shape) + sample = self._bartlett_sampling(sample_shape) + + # Below part is to improve numerical stability temporally and should be removed in the future + is_singular = self.support.check(sample) + if self._batch_shape: + is_singular = is_singular.amax(self._batch_dims) + + if torch._C._get_tracing_state(): + # Less optimized version for JIT + for _ in range(max_try_correction): + sample_new = self._bartlett_sampling(sample_shape) + sample = torch.where(is_singular, sample_new, sample) + + is_singular = ~self.support.check(sample) + if self._batch_shape: + is_singular = is_singular.amax(self._batch_dims) + + else: + # More optimized version with data-dependent control flow. + if is_singular.any(): + warnings.warn("Singular sample detected.", stacklevel=2) + + for _ in range(max_try_correction): + sample_new = self._bartlett_sampling(is_singular[is_singular].shape) + sample[is_singular] = sample_new + + is_singular_new = ~self.support.check(sample_new) + if self._batch_shape: + is_singular_new = is_singular_new.amax(self._batch_dims) + is_singular[is_singular.clone()] = is_singular_new + + if not is_singular.any(): + break + + return sample + + def log_prob(self, value): + if self._validate_args: + self._validate_sample(value) + nu = self.df # has shape (batch_shape) + p = self._event_shape[-1] # has singleton shape + return ( + -nu + * ( + p * _log_2 / 2 + + self._unbroadcasted_scale_tril.diagonal(dim1=-2, dim2=-1) + .log() + .sum(-1) + ) + - torch.mvlgamma(nu / 2, p=p) + + (nu - p - 1) / 2 * torch.linalg.slogdet(value).logabsdet + - torch.cholesky_solve(value, self._unbroadcasted_scale_tril) + .diagonal(dim1=-2, dim2=-1) + .sum(dim=-1) + / 2 + ) + + def entropy(self): + nu = self.df # has shape (batch_shape) + p = self._event_shape[-1] # has singleton shape + return ( + (p + 1) + * ( + p * _log_2 / 2 + + self._unbroadcasted_scale_tril.diagonal(dim1=-2, dim2=-1) + .log() + .sum(-1) + ) + + torch.mvlgamma(nu / 2, p=p) + - (nu - p - 1) / 2 * _mvdigamma(nu / 2, p=p) + + nu * p / 2 + ) + + @property + def _natural_params(self) -> tuple[Tensor, Tensor]: + nu = self.df # has shape (batch_shape) + p = self._event_shape[-1] # has singleton shape + return -self.precision_matrix / 2, (nu - p - 1) / 2 + + # pyrefly: ignore [bad-override] + def _log_normalizer(self, x, y): + p = self._event_shape[-1] + return (y + (p + 1) / 2) * ( + -torch.linalg.slogdet(-2 * x).logabsdet + _log_2 * p + ) + torch.mvlgamma(y + (p + 1) / 2, p=p) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9865ecf9c95bc36f4784fd3f0f63f0d9bdf27dbd --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/__init__.py @@ -0,0 +1,611 @@ +import logging +import os +import warnings +import zipfile +from collections.abc import Callable, Mapping +from typing import Any +from typing_extensions import deprecated + +import torch +import torch.utils._pytree as pytree +from torch.fx.passes.infra.pass_base import PassResult +from torch.types import FileLike + + +__all__ = [ + "AdditionalInputs", + "Constraint", + "CustomDecompTable", + "default_decompositions", + "Dim", + "dims", + "draft_export", + "export_for_training", + "export", + "ExportBackwardSignature", + "ExportedProgram", + "ExportGraphSignature", + "FlatArgsAdapter", + "load", + "ModuleCallEntry", + "ModuleCallSignature", + "register_dataclass", + "save", + "ShapesCollection", + "unflatten", + "UnflattenedModule", +] + +# To make sure export specific custom ops are loaded +import torch.export.custom_ops + +from .decomp_utils import CustomDecompTable +from .dynamic_shapes import AdditionalInputs, Constraint, Dim, dims, ShapesCollection +from .exported_program import ( + default_decompositions, + ExportedProgram, + ModuleCallEntry, + ModuleCallSignature, +) +from .graph_signature import ExportBackwardSignature, ExportGraphSignature +from .unflatten import FlatArgsAdapter, unflatten, UnflattenedModule + + +PassType = Callable[[torch.fx.GraphModule], PassResult | None] + +log: logging.Logger = logging.getLogger(__name__) + + +@deprecated( + "`torch.export.export_for_training` is deprecated and will be removed in PyTorch 2.10. " + "Please use `torch.export.export` instead, which is functionally equivalent.", + category=FutureWarning, +) +def export_for_training( + mod: torch.nn.Module, + args: tuple[Any, ...], + kwargs: Mapping[str, Any] | None = None, + *, + dynamic_shapes: dict[str, Any] | tuple[Any, ...] | list[Any] | None = None, + strict: bool = False, + preserve_module_call_signature: tuple[str, ...] = (), + prefer_deferred_runtime_asserts_over_guards: bool = False, +) -> ExportedProgram: + """ + :func:`export_for_training` takes any nn.Module along with example inputs, and produces a traced graph representing + only the Tensor computation of the function in an Ahead-of-Time (AOT) fashion, + which can subsequently be executed with different inputs or serialized. The + traced graph (1) produces normalized operators in the all ATen operator set + (as well as any user-specified custom operators), (2) has eliminated all Python control + flow and data structures (with certain exceptions), and (3) records the set of + shape constraints needed to show that this normalization and control-flow elimination + is sound for future inputs. This API is intended for PT2 quantization training use cases + and will soon be the default IR of torch.export.export in the near future. To read further about + the motivation behind this change, please refer to + https://dev-discuss.pytorch.org/t/why-pytorch-does-not-need-a-new-standardized-operator-set/2206 + With this API, and :func:`run_decompositions()`, you should be able to get inference IR with + your custom decomposition behaviour. + + **Soundness Guarantee** + + See :func:`export()` docstring for more details. + + Args: + mod: We will trace the forward method of this module. + + args: Example positional inputs. + + kwargs: Optional example keyword inputs. + + dynamic_shapes: + An optional argument where the type should either be: + 1) a dict from argument names of ``f`` to their dynamic shape specifications, + 2) a tuple that specifies dynamic shape specifications for each input in original order. + If you are specifying dynamism on keyword args, you will need to pass them in the order that + is defined in the original function signature. + + The dynamic shape of a tensor argument can be specified as either + (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is + not required to include static dimension indices in this dict, but when they are, + they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None, + where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions + are denoted by None. Arguments that are dicts or tuples / lists of tensors are + recursively specified by using mappings or sequences of contained specifications. + + strict: When enabled (default), the export function will trace the program through + TorchDynamo which will ensure the soundness of the resulting graph. Otherwise, the + exported program will not validate the implicit assumptions baked into the graph and + may cause behavior divergence between the original model and the exported one. This is + useful when users need to workaround bugs in the tracer, or simply want incrementally + enable safety in their models. Note that this does not affect the resulting IR spec + to be different and the model will be serialized in the same way regardless of what value + is passed here. + WARNING: This option is experimental and use this at your own risk. + + preserve_module_call_signature: A list of submodule paths for which the original + calling conventions are preserved as metadata. The metadata will be used when calling + torch.export.unflatten to preserve the original calling conventions of modules. + + Returns: + An :class:`ExportedProgram` containing the traced callable. + + **Acceptable input/output types** + + Acceptable types of inputs (for ``args`` and ``kwargs``) and outputs include: + + - Primitive types, i.e. ``torch.Tensor``, ``int``, ``float``, ``bool`` and ``str``. + - Dataclasses, but they must be registered by calling :func:`register_dataclass` first. + - (Nested) Data structures comprising of ``dict``, ``list``, ``tuple``, ``namedtuple`` and + ``OrderedDict`` containing all above types. + + """ + from ._trace import _export_for_training + + if not isinstance(mod, torch.nn.Module): + raise ValueError( + f"Expected `mod` to be an instance of `torch.nn.Module`, got {type(mod)}." + ) + if isinstance(mod, torch.jit.ScriptModule): + raise ValueError( + "Exporting a ScriptModule is not supported. " + "Maybe try converting your ScriptModule to an ExportedProgram " + "using `TS2EPConverter(mod, args, kwargs).convert()` instead." + ) + return _export_for_training( + mod, + args, + kwargs, + dynamic_shapes, + strict=strict, + preserve_module_call_signature=preserve_module_call_signature, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + ) + + +def export( + mod: torch.nn.Module, + args: tuple[Any, ...], + kwargs: Mapping[str, Any] | None = None, + *, + dynamic_shapes: dict[str, Any] | tuple[Any, ...] | list[Any] | None = None, + strict: bool = False, + preserve_module_call_signature: tuple[str, ...] = (), + prefer_deferred_runtime_asserts_over_guards: bool = False, +) -> ExportedProgram: + """ + :func:`export` takes any nn.Module along with example inputs, and produces a traced graph representing + only the Tensor computation of the function in an Ahead-of-Time (AOT) fashion, + which can subsequently be executed with different inputs or serialized. The + traced graph (1) produces normalized operators in the functional ATen operator set + (as well as any user-specified custom operators), (2) has eliminated all Python control + flow and data structures (with certain exceptions), and (3) records the set of + shape constraints needed to show that this normalization and control-flow elimination + is sound for future inputs. + + **Soundness Guarantee** + + While tracing, :func:`export()` takes note of shape-related assumptions + made by the user program and the underlying PyTorch operator kernels. + The output :class:`ExportedProgram` is considered valid only when these + assumptions hold true. + + Tracing makes assumptions on the shapes (not values) of input tensors. + Such assumptions must be validated at graph capture time for :func:`export` + to succeed. Specifically: + + - Assumptions on static shapes of input tensors are automatically validated without additional effort. + - Assumptions on dynamic shape of input tensors require explicit specification + by using the :func:`Dim` API to construct dynamic dimensions and by associating + them with example inputs through the ``dynamic_shapes`` argument. + + If any assumption can not be validated, a fatal error will be raised. When that happens, + the error message will include suggested fixes to the specification that are needed + to validate the assumptions. For example :func:`export` might suggest the + following fix to the definition of a dynamic dimension ``dim0_x``, say appearing in the + shape associated with input ``x``, that was previously defined as ``Dim("dim0_x")``:: + + dim = Dim("dim0_x", max=5) + + This example means the generated code requires dimension 0 of input ``x`` to be less + than or equal to 5 to be valid. You can inspect the suggested fixes to dynamic dimension + definitions and then copy them verbatim into your code without needing to change the + ``dynamic_shapes`` argument to your :func:`export` call. + + Args: + mod: We will trace the forward method of this module. + + args: Example positional inputs. + + kwargs: Optional example keyword inputs. + + dynamic_shapes: + An optional argument where the type should either be: + 1) a dict from argument names of ``f`` to their dynamic shape specifications, + 2) a tuple that specifies dynamic shape specifications for each input in original order. + If you are specifying dynamism on keyword args, you will need to pass them in the order that + is defined in the original function signature. + + The dynamic shape of a tensor argument can be specified as either + (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is + not required to include static dimension indices in this dict, but when they are, + they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None, + where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions + are denoted by None. Arguments that are dicts or tuples / lists of tensors are + recursively specified by using mappings or sequences of contained specifications. + + strict: When disabled (default), the export function will trace the program through + Python runtime, which by itself will not validate some of the implicit assumptions + baked into the graph. It will still validate most critical assumptions like shape + safety. When enabled (by setting ``strict=True``), the export function will trace + the program through TorchDynamo which will ensure the soundness of the resulting + graph. TorchDynamo has limited Python feature coverage, thus you may experience more + errors. Note that toggling this argument does not affect the resulting IR spec to be + different and the model will be serialized in the same way regardless of what value + is passed here. + + preserve_module_call_signature: A list of submodule paths for which the original + calling conventions are preserved as metadata. The metadata will be used when calling + torch.export.unflatten to preserve the original calling conventions of modules. + + Returns: + An :class:`ExportedProgram` containing the traced callable. + + **Acceptable input/output types** + + Acceptable types of inputs (for ``args`` and ``kwargs``) and outputs include: + + - Primitive types, i.e. ``torch.Tensor``, ``int``, ``float``, ``bool`` and ``str``. + - Dataclasses, but they must be registered by calling :func:`register_dataclass` first. + - (Nested) Data structures comprising of ``dict``, ``list``, ``tuple``, ``namedtuple`` and + ``OrderedDict`` containing all above types. + + """ + from ._trace import _export + + if not isinstance(mod, torch.nn.Module): + raise ValueError( + f"Expected `mod` to be an instance of `torch.nn.Module`, got {type(mod)}." + ) + if isinstance(mod, torch.jit.ScriptModule): + raise ValueError( + "Exporting a ScriptModule is not supported. " + "Maybe try converting your ScriptModule to an ExportedProgram " + "using `TS2EPConverter(mod, args, kwargs).convert()` instead." + ) + + try: + return _export( + mod, + args, + kwargs, + dynamic_shapes, + strict=strict, + preserve_module_call_signature=preserve_module_call_signature, + pre_dispatch=True, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + ) + except Exception as e: + draft_export_msg = ( + "The error above occurred when calling torch.export.export. If you would " + "like to view some more information about this error, and get a list " + "of all other errors that may occur in your export call, you can " + "replace your `export()` call with `draft_export()`." + ) + + # For errors that we know can be caught by draft-export, add the message + # to ask users to try out draft-export + if isinstance( + e, + ( + torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode, + torch._subclasses.fake_tensor.UnsupportedOperatorException, + torch._dynamo.exc.UserError, + torch.fx.experimental.symbolic_shapes.ConstraintViolationError, + ), + ): + new_msg = str(e) + "\n\n" + draft_export_msg + e.args = (new_msg,) + elif isinstance(e, RuntimeError) and "no fake impl registered" in str(e): + new_msg = str(e) + "\n\n" + draft_export_msg + e.args = (new_msg,) + raise e + + +DEFAULT_PICKLE_PROTOCOL = 2 + + +def save( + ep: ExportedProgram, + f: FileLike, + *, + extra_files: dict[str, Any] | None = None, + opset_version: dict[str, int] | None = None, + pickle_protocol: int = DEFAULT_PICKLE_PROTOCOL, +) -> None: + """ + + .. warning:: + Under active development, saved files may not be usable in newer versions + of PyTorch. + + Saves an :class:`ExportedProgram` to a file-like object. It can then be + loaded using the Python API :func:`torch.export.load `. + + Args: + ep (ExportedProgram): The exported program to save. + + f (str | os.PathLike[str] | IO[bytes]) A file-like object (has to + implement write and flush) or a string containing a file name. + + extra_files (Optional[Dict[str, Any]]): Map from filename to contents + which will be stored as part of f. + + opset_version (Optional[Dict[str, int]]): A map of opset names + to the version of this opset + + pickle_protocol: can be specified to override the default protocol + + Example:: + + import torch + import io + + + class MyModule(torch.nn.Module): + def forward(self, x): + return x + 10 + + + ep = torch.export.export(MyModule(), (torch.randn(5),)) + + # Save to file + torch.export.save(ep, "exported_program.pt2") + + # Save to io.BytesIO buffer + buffer = io.BytesIO() + torch.export.save(ep, buffer) + + # Save with extra files + extra_files = {"foo.txt": b"bar".decode("utf-8")} + torch.export.save(ep, "exported_program.pt2", extra_files=extra_files) + + """ + if not isinstance(ep, ExportedProgram): + raise TypeError( + f"The 'ep' parameter must be an instance of 'ExportedProgram', got '{type(ep).__name__}' instead." + ) + + from torch.export.pt2_archive._package import package_pt2 + + package_pt2( + f, + exported_programs={"model": ep}, + extra_files=extra_files, + pickle_protocol=pickle_protocol, + opset_version=opset_version, + ) + + +def load( + f: FileLike, + *, + extra_files: dict[str, Any] | None = None, + expected_opset_version: dict[str, int] | None = None, +) -> ExportedProgram: + """ + + .. warning:: + Under active development, saved files may not be usable in newer versions + of PyTorch. + + .. warning:: + :func:`torch.export.load()` uses pickle under the hood to load models. **Never load data from an untrusted source.** + + Loads an :class:`ExportedProgram` previously saved with + :func:`torch.export.save `. + + Args: + f (str | os.PathLike[str] | IO[bytes]): A file-like object (has to + implement write and flush) or a string containing a file name. + + extra_files (Optional[Dict[str, Any]]): The extra filenames given in + this map would be loaded and their content would be stored in the + provided map. + + expected_opset_version (Optional[Dict[str, int]]): A map of opset names + to expected opset versions + + Returns: + An :class:`ExportedProgram` object + + Example:: + + import torch + import io + + # Load ExportedProgram from file + ep = torch.export.load("exported_program.pt2") + + # Load ExportedProgram from io.BytesIO object + with open("exported_program.pt2", "rb") as f: + buffer = io.BytesIO(f.read()) + buffer.seek(0) + ep = torch.export.load(buffer) + + # Load with extra files. + extra_files = {"foo.txt": ""} # values will be replaced with data + ep = torch.export.load("exported_program.pt2", extra_files=extra_files) + print(extra_files["foo.txt"]) + print(ep(torch.randn(5))) + """ + if isinstance(f, (str, os.PathLike)): + f = os.fspath(f) + + extra_files = extra_files or {} + + from torch.export.pt2_archive._package import load_pt2, PT2ArchiveContents + + try: + pt2_contents = load_pt2( + f, + expected_opset_version=expected_opset_version, + ) + except RuntimeError: + log.warning("Ran into the following error when deserializing", exc_info=True) + pt2_contents = PT2ArchiveContents({}, {}, {}) + + if len(pt2_contents.exported_programs) > 0 or len(pt2_contents.extra_files) > 0: + for k, v in pt2_contents.extra_files.items(): + extra_files[k] = v + + return pt2_contents.exported_programs["model"] + + # TODO: For backward compatibility, we support loading a zip file from 2.7. Delete this path in 2.9(?) + with zipfile.ZipFile(f, "r") as zipf: + if "version" not in zipf.namelist(): + raise RuntimeError( + "We ran into an error when deserializing the saved file. " + "Please check the warnings above for possible errors. " + ) + + log.warning( + "Trying to deserialize for the older format. This version of file is " + "deprecated. Please generate a new pt2 saved file." + ) + + # Check the version + version = zipf.read("version").decode().split(".") + from torch._export.serde.schema import ( + SCHEMA_VERSION, # todo change archive version to schema version + ) + + assert len(version) == len(SCHEMA_VERSION), ( + "Version in the saved file has incorrect length, double check if the file is generated by torch.export.save()" + ) + if version[0] != str(SCHEMA_VERSION[0]): + raise RuntimeError( + f"Serialized version {version} does not match our current " + f"schema version {SCHEMA_VERSION}." + ) + + from torch._export.serde.serialize import deserialize, SerializedArtifact + + # Load serialized_ep and serialized_state_dict from the zip file + + serialized_exported_program: bytes | None = None + serialized_state_dict: bytes | None = None + serialized_constants: bytes | None = None + serialized_example_inputs: bytes | None = None + + for file_info in zipf.infolist(): + file_content = zipf.read(file_info.filename) + + if file_info.filename == "serialized_exported_program.json": + serialized_exported_program = file_content + elif file_info.filename == "serialized_state_dict.json": + warnings.warn("This version of file is deprecated", stacklevel=2) + serialized_state_dict = file_content + elif file_info.filename == "serialized_constants.json": + warnings.warn("This version of file is deprecated", stacklevel=2) + serialized_constants = file_content + elif file_info.filename == "serialized_state_dict.pt": + serialized_state_dict = file_content + elif file_info.filename == "serialized_constants.pt": + serialized_constants = file_content + elif file_info.filename == "serialized_example_inputs.pt": + serialized_example_inputs = file_content + elif file_info.filename.startswith("extra_files"): + filename = file_info.filename.split("/", 1)[1] + extra_files[filename] = file_content.decode("utf-8") + + assert serialized_exported_program is not None + assert serialized_state_dict is not None + assert serialized_constants is not None + assert serialized_example_inputs is not None + artifact: SerializedArtifact = SerializedArtifact( + serialized_exported_program, + serialized_state_dict, + serialized_constants, + serialized_example_inputs, + ) + + # Deserialize ExportedProgram + ep = deserialize(artifact, expected_opset_version) + + return ep + + +def draft_export( + mod: torch.nn.Module, + args: tuple[Any, ...], + kwargs: Mapping[str, Any] | None = None, + *, + dynamic_shapes: dict[str, Any] | tuple[Any, ...] | list[Any] | None = None, + preserve_module_call_signature: tuple[str, ...] = (), + strict: bool = False, + prefer_deferred_runtime_asserts_over_guards: bool = False, +) -> ExportedProgram: + """ + A version of torch.export.export which is designed to consistently produce + an ExportedProgram, even if there are potential soundness issues, and to + generate a report listing the issues found. + """ + from ._draft_export import draft_export + + return draft_export( + mod=mod, + args=args, + kwargs=kwargs, + dynamic_shapes=dynamic_shapes, + preserve_module_call_signature=preserve_module_call_signature, + strict=strict, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + ) + + +def register_dataclass( + cls: type[Any], + *, + serialized_type_name: str | None = None, +) -> None: + """ + Registers a dataclass as a valid input/output type for :func:`torch.export.export`. + + Args: + cls: the dataclass type to register + serialized_type_name: The serialized name for the dataclass. This is + required if you want to serialize the pytree TreeSpec containing this + dataclass. + + Example:: + + import torch + from dataclasses import dataclass + + + @dataclass + class InputDataClass: + feature: torch.Tensor + bias: int + + + @dataclass + class OutputDataClass: + res: torch.Tensor + + + torch.export.register_dataclass(InputDataClass) + torch.export.register_dataclass(OutputDataClass) + + + class Mod(torch.nn.Module): + def forward(self, x: InputDataClass) -> OutputDataClass: + res = x.feature + x.bias + return OutputDataClass(res=res) + + + ep = torch.export.export(Mod(), (InputDataClass(torch.ones(2, 2), 1),)) + print(ep) + + """ + pytree.register_dataclass(cls, serialized_type_name=serialized_type_name) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_draft_export.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_draft_export.py new file mode 100644 index 0000000000000000000000000000000000000000..3eb2621b707bb4bc047ce2cad284a0c9e33b6fb4 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_draft_export.py @@ -0,0 +1,544 @@ +import getpass +import json +import logging +import os +import re +import tempfile +import time +from collections.abc import Callable, Mapping +from dataclasses import dataclass +from enum import IntEnum +from typing import Any + +import torch +import torch._logging._internal +import torch.utils._pytree as pytree +from torch._dynamo.exc import UserError, UserErrorType +from torch._export.passes.insert_custom_op_guards import ( + get_op_profiles, + insert_custom_op_guards, + OpProfile, +) +from torch._utils_internal import log_draft_export_usage + +from ._trace import _export, get_ep_stats +from .dynamic_shapes import _DimHint, _DimHintType, Dim +from .exported_program import ExportedProgram + + +log = logging.getLogger(__name__) + + +class FailureType(IntEnum): + MISSING_FAKE_KERNEL = 1 + DATA_DEPENDENT_ERROR = 2 + GUARD_ADDED = 3 + MISMATCHED_FAKE_KERNEL = 4 + + def __str__(self) -> str: + return self.name + + +def prettify_stack(stack: list[dict[str, str]], str_to_filename: dict[int, str]) -> str: + res = "" + for frame in stack: + if frame["filename"] not in str_to_filename: + continue + + res += f""" + File {str_to_filename[frame["filename"]]}, lineno {frame["line"]}, in {frame["name"]}""" # type: ignore[index] + + res += f"\n {stack[-1]['loc']}" + return res + + +def prettify_frame_locals( + loc: str, locals: dict[str, Any], symbols: dict[str, Any] +) -> str: + local_str = "\n".join(f" {k}: {v}" for k, v in locals.items()) + res = f""" + Locals: +{local_str} +""" + if any(v is not None for v in symbols.values()): + symbol_str = "\n".join( + f" {k}: {v}" for k, v in symbols.items() if v is not None + ) + res += f""" + Symbols: +{symbol_str} +""" + return res + + +def get_loc(filename: str, lineno: int) -> str | None: + try: + with open(filename) as f: + for i, line in enumerate(f): + if i == lineno - 1: + return line.strip() + except FileNotFoundError: + pass + return None + + +class FailureReport: + def __init__( + self, failure_type: FailureType, data: dict[str, Any], xfail: bool = False + ) -> None: + self.failure_type: FailureType = failure_type + self.data: dict[str, Any] = data + self.xfail: bool = xfail + + def __repr__(self) -> str: + return f"FailureReport(failure_type={self.failure_type}, xfail={self.xfail}, data={self.data})" + + def print(self, str_to_filename: dict[int, str]) -> str: + if self.failure_type == FailureType.MISSING_FAKE_KERNEL: + op = self.data["op"] + + return f"""Missing fake kernel. + torch.ops.{op} is missing a fake kernel implementation. + + Please refer to https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU/edit#heading=h.ahugy69p2jmz for more detailed instructions on how to write a meta implementation. +""" # noqa: B950 + + elif self.failure_type == FailureType.GUARD_ADDED: + locals_info = ( + prettify_frame_locals(**self.data["frame_locals"]) + if self.data["frame_locals"] + else "" + ) + return f"""Guard Added. + A guard was added during tracing, which might've resulted in some incorrect + tracing or constraint violation error. + Specifically, this guard was added: {self.data["expr"]}, where {self.data["symbol_to_sources"]}. + This occurred at the following stacktrace: {prettify_stack(self.data["user_stack"], str_to_filename)}: + {locals_info} + And the following framework stacktrace: {prettify_stack(self.data["stack"], str_to_filename)}\n + Because of this, we have modified the dynamic shapes structure to be the + following. You can also use torch.export.Dim.AUTO instead to specify your + dynamic shapes, and we will automatically infer the dynamism for you. + ``` + dynamic_shapes = {self.data["new_dynamic_shapes"]} + ``` +""" + + elif self.failure_type == FailureType.DATA_DEPENDENT_ERROR: + locals_info = ( + prettify_frame_locals(**self.data["frame_locals"]) + if self.data["frame_locals"] + else "" + ) + return f"""Data dependent error. + When exporting, we were unable to evaluate the value of `{self.data["expr"]}`. + This was encountered {self.data["occurrences"]} times. + This occurred at the following user stacktrace: {prettify_stack(self.data["user_stack"], str_to_filename)} + {locals_info} + And the following framework stacktrace: {prettify_stack(self.data["stack"], str_to_filename)}\n + As a result, it was specialized to a constant (e.g. `{self.data["result"]}` in the 1st occurrence), and asserts were inserted into the graph. + + Please add `torch._check(...)` to the original code to assert this data-dependent assumption. + Please refer to https://docs.google.com/document/d/1kZ_BbB3JnoLbUZleDT6635dHs88ZVYId8jT-yTFgf3A/edit#heading=h.boi2xurpqa0o for more details. +""" # noqa: B950 + + elif self.failure_type == FailureType.MISMATCHED_FAKE_KERNEL: + op = self.data["op"] + reason = self.data["reason"] + return f"""Mismatched fake kernel. + torch.ops.{op} has a fake kernel implementation, but it has incorrect behavior, based on the real kernel. + The reason for the mismatch is: {reason}. + + Please refer to https://docs.google.com/document/d/1_W62p8WJOQQUzPsJYa7s701JXt0qf2OfLub2sbkHOaU/edit#heading=h.ahugy69p2jmz for more detailed instructions on how to write a fake implementation. +""" # noqa: B950 + + else: + raise ValueError(f"Unknown failure type: {self.failure_type}") + + +class DraftExportReport: + def __init__( + self, + failures: list[FailureReport], + str_to_filename: dict[int, str], + expressions_created: dict[int, dict[str, Any]], + op_profiles: dict[str, set[OpProfile]], + ): + self.failures: list[FailureReport] = failures + self.str_to_filename = str_to_filename + self.expressions_created: dict[int, dict[str, Any]] = expressions_created + self.op_profiles = op_profiles + + def successful(self) -> bool: + return len(self.failures) == 0 or all( + failure.xfail for failure in self.failures + ) + + def __repr__(self) -> str: + return f"DraftExportReport({self.failures})" + + def __str__(self) -> str: + WARNING_COLOR = "\033[93m" + GREEN_COLOR = "\033[92m" + END_COLOR = "\033[0m" + + if self.successful(): + return f"""{GREEN_COLOR} +############################################################################################## +Congratuations: No issues are found during export, and it was able to soundly produce a graph. +You can now change back to torch.export.export() +############################################################################################## +{END_COLOR}""" + + error = f"""{WARNING_COLOR} +################################################################################################### +WARNING: {len(self.failures)} issue(s) found during export, and it was not able to soundly produce a graph. +Please follow the instructions to fix the errors. +################################################################################################### + +""" + + for i, failure in enumerate(self.failures): + error += f"{i + 1}. {failure.print(self.str_to_filename)}\n" + error += END_COLOR + return error + + def apply_suggested_fixes(self) -> None: + raise NotImplementedError("Not implemented yet") + + +@dataclass +class ExpressionCreatedNode: + result_id: int + argument_ids: list[int] + record: dict[str, object] + visited: bool = False + + +class LogRecord: + def __init__(self) -> None: + self.log_count: dict[int, int] = {} + self.logs: list[tuple[str, dict[str, Any]]] = [] + + def _hash(self, element: tuple[str, dict[str, Any]]) -> int: + key, data = element + + if key == "missing_fake_kernel": + return hash((key, data["op"])) + elif key == "mismatched_fake_kernel": + return hash((key, data["op"], data["reason"])) + elif key == "propagate_real_tensors_provenance": + return hash((key, json.dumps(data["user_stack"]))) + elif key == "guard_added": + return hash((key, json.dumps(data["user_stack"]))) + elif key == "create_unbacked_symbol": + return hash((key, json.dumps(data["user_stack"]))) + + return hash((key, json.dumps(data))) + + def try_add(self, element: tuple[str, dict[str, str]]) -> bool: + hash_value = self._hash(element) + if hash_value in self.log_count: + self.log_count[hash_value] += 1 + return False + + self.log_count[hash_value] = 1 + self.logs.append(element) + return True + + def get_log_count(self, element: tuple[str, dict[str, Any]]) -> int: + return self.log_count[self._hash(element)] + + +class CaptureStructuredTrace(torch._logging._internal.LazyTraceHandler): + def __init__(self) -> None: + self.specific_log_keys = [ + "str", + "exported_program", + "propagate_real_tensors_provenance", + "guard_added", + "missing_fake_kernel", + "mismatched_fake_kernel", + "expression_created", + "create_unbacked_symbol", + ] + self.log_record: LogRecord = LogRecord() + self.expression_created_logs: dict[int, ExpressionCreatedNode] = {} + self.symbol_to_expressions: dict[str, list[dict[str, Any]]] = {} + self.logger = logging.getLogger("torch.__trace") + self.prev_get_dtrace = False + + if root_dir := os.environ.get(torch._logging._internal.DTRACE_ENV_VAR): + super().__init__(root_dir) + else: + sanitized_username = re.sub(r'[\\/:*?"<>|]', "_", getpass.getuser()) + root_dir = os.path.join( + tempfile.gettempdir(), + "export_" + sanitized_username, + ) + super().__init__(root_dir) + + self.setFormatter(torch._logging._internal.TorchLogsFormatter(trace=True)) + + def __enter__(self) -> "CaptureStructuredTrace": + self.log_record = LogRecord() + self.expression_created_logs = {} + + # Remove the lazy trace handler if it exists + possible_lazy_trace_handlers = [ + handler + for handler in self.logger.handlers + if isinstance(handler, torch._logging._internal.LazyTraceHandler) + ] + for handler in possible_lazy_trace_handlers: + self.logger.removeHandler(handler) + + self.logger.addHandler(self) + self.prev_get_dtrace = torch._logging._internal.GET_DTRACE_STRUCTURED + # pyrefly: ignore [bad-assignment] + torch._logging._internal.GET_DTRACE_STRUCTURED = True + return self + + def __exit__(self, exc_type, exc_value, traceback) -> None: # type: ignore[no-untyped-def] + self.log_record = LogRecord() + self.expression_created_logs = {} + self.logger.removeHandler(self) + # pyrefly: ignore [bad-assignment] + torch._logging._internal.GET_DTRACE_STRUCTURED = self.prev_get_dtrace + self.prev_get_dtrace = False + + def emit(self, record: Any) -> None: + def _log_expression_created( + emit_func: Callable[[Any], None], sym_node_id: int + ) -> None: + # Log all the relevant expression_created logs + if sym_node_id is None: + return + if res := self.expression_created_logs.get(sym_node_id, None): + # Don't log the expression if we have already + # printed it beforehand + if not res.visited: + res.visited = True + for arg in res.argument_ids: + _log_expression_created(emit_func, arg) + + emit_func(res.record) + + metadata = record.metadata + for key in self.specific_log_keys: + if key in metadata: + if self.log_record.try_add((key, metadata[key])): + if key == "expression_created": + # We don't want to log all expression_created logs, only + # the ones that are relevant to the + # guards/propagate_real_tensor + self.expression_created_logs[metadata[key]["result_id"]] = ( + ExpressionCreatedNode( + metadata[key]["result_id"], + metadata[key].get("argument_ids", []), + record, + ) + ) + return + + elif key == "propagate_real_tensors_provenance": + _log_expression_created( + super().emit, metadata[key].get("expr_node_id") + ) + + elif key == "guard_added": + if len(metadata[key]["symbol_to_sources"]) == 0: + # We only want to include guards added that are relevant to + # the symbolic shapes corresponding to the inputs which were + # specified in the dynamic_shapes arg. These have a source. + return + elif metadata[key]["prefix"] == "runtime_assert": + # This should've been captured by a + # propagate_real_tensors log + return + + _log_expression_created( + super().emit, metadata[key].get("expr_node_id") + ) + + super().emit(record) + + +def draft_export( + mod: torch.nn.Module, + args: tuple[Any, ...], + kwargs: Mapping[str, Any] | None = None, + *, + dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any] | None = None, + preserve_module_call_signature: tuple[str, ...] = (), + strict: bool = False, + pre_dispatch: bool = True, + prefer_deferred_runtime_asserts_over_guards: bool = False, +) -> ExportedProgram: + start_time = time.time() + kwargs = kwargs or {} + dynamic_shapes = dynamic_shapes or {} + + constraint_violation_msg = None + capture_structured_log = CaptureStructuredTrace() + + with ( + torch._functorch.config.patch( + fake_tensor_propagate_real_tensors=True, + generate_fake_kernels_from_real_mismatches=True, + ), + capture_structured_log, + ): + try: + new_shapes = None + ep = _export( + mod, + args, + kwargs, + dynamic_shapes=dynamic_shapes, + strict=strict, + pre_dispatch=pre_dispatch, + preserve_module_call_signature=preserve_module_call_signature, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + ) + except Exception as exc: + if ( + isinstance(exc, UserError) + and exc.error_type == UserErrorType.CONSTRAINT_VIOLATION + ): + constraint_violation_msg = exc.msg + + def convert_dim_to_auto(dim: Any) -> Any: + if isinstance(dim, Dim): + return Dim.AUTO(min=dim.min, max=dim.max) + elif isinstance(dim, _DimHint) and dim.type == _DimHintType.DYNAMIC: + return Dim.AUTO(min=dim.min, max=dim.max) + return dim + + new_shapes = pytree.tree_map(convert_dim_to_auto, dynamic_shapes) + ep = _export( + mod, + args, + kwargs, + dynamic_shapes=new_shapes, + strict=strict, + pre_dispatch=pre_dispatch, + preserve_module_call_signature=preserve_module_call_signature, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + ) + else: + log_draft_export_usage( + error=True, + export_time=time.time() - start_time, + strict=strict, + message=str(exc), + type=f"{type(exc).__name__}.{type(exc).__qualname__}", + ) + raise exc + + torch._logging.dtrace_structured("exported_program", payload_fn=lambda: str(ep)) + + str_to_filename: dict[int, str] = {} + failures: list[FailureReport] = [] + incorrect_custom_ops: set[str] = set() + expressions_created: dict[int, dict[str, Any]] = {} + + for log_name, log_contents in capture_structured_log.log_record.logs: + failure_type = None + + if log_name == "str": + str_to_filename[log_contents[1]] = log_contents[0] # type: ignore[index] + continue + + elif log_name == "propagate_real_tensors_provenance": + log_contents["occurrences"] = ( + capture_structured_log.log_record.get_log_count( + (log_name, log_contents) + ) + ) + + failure_type = FailureType.DATA_DEPENDENT_ERROR + + elif log_name == "guard_added": + if new_shapes is None: + continue + + failure_type = FailureType.GUARD_ADDED + log_contents["new_dynamic_shapes"] = new_shapes + elif log_name == "missing_fake_kernel": + failure_type = FailureType.MISSING_FAKE_KERNEL + incorrect_custom_ops.add(log_contents["op"]) + + elif log_name == "mismatched_fake_kernel": + failure_type = FailureType.MISMATCHED_FAKE_KERNEL + incorrect_custom_ops.add(log_contents["op"]) + + else: + continue + + assert failure_type is not None + failures.append( + FailureReport( + failure_type, + log_contents, + ) + ) + + for k, v in capture_structured_log.expression_created_logs.items(): + if v.visited: + expressions_created[k] = v.record + + op_profiles = get_op_profiles(ep.graph_module, incorrect_custom_ops) + report = DraftExportReport( + failures, str_to_filename, expressions_created, op_profiles + ) + + # Add asserts around custom ops + insert_custom_op_guards(ep.graph_module, incorrect_custom_ops) + + ep._report = report + if not report.successful(): + log_filename = capture_structured_log.stream.name + + warning_msg = f""" +################################################################################################### +WARNING: {len(report.failures)} issue(s) found during export, and it was not able to soundly produce a graph. +To view the report of failures in an html page, please run the command: + `tlparse {log_filename} --export` +Or, you can view the errors in python by inspecting `print(ep._report)`. +""" + + if len(report.op_profiles) > 0: + warning_msg += f""" +While tracing we found {len(report.op_profiles)} operator(s) which do not have a fake kernel registered. +If you intend to retrace the exported graph or run it with fake tensors, please run it under the +following context manager, which will register a fake kernel for those operators. +``` +with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): + # run with fake tensors +``` +""" + + warning_msg += """#################################################################################################""" + + log.warning(warning_msg) + + else: + log.info( + """ +############################################################################################## +Congratuations: No issues are found during export, and it was able to soundly produce a graph. +You can now change back to torch.export.export() +############################################################################################## + """ + ) + + log_draft_export_usage( + error=False, + export_time=time.time() - start_time, + strict=strict, + constraint_violations=constraint_violation_msg, + report=ep._report, + **get_ep_stats(ep), + ) + return ep diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_leakage_detection_utils.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_leakage_detection_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fe211e1dc079c844748edda14ba269917e7e7847 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_leakage_detection_utils.py @@ -0,0 +1,112 @@ +import gc +import types +import typing +import weakref + +import torch + + +""" +These functions are used to detect potential fake tensor leakage when using PT2 export. +See NOTE [export non-strict fake tensor leak detection] + +There are some complications that made this logic overly complicated: +1) Python 3.10 and Python 3.12 have different ways of implementing referrer so + we need to account for whether it is ref.__dict__ or the real ref object + +2) There are some internal PT2 references to fake tensors like `TrackedFake` +3) closures, generators, and bound methods can hold fake tensors. +4) global object can hold onto a fake tensor + +In general, these utils are our last resort to detect fake tensors. if the leak happens +within the model attributes, we have a separate mechanism to detect. This tool relies a bit +on garbage collector internal details, so I think it is unsafe to turn on by default, hence +this tool should be used as debugging tool. +""" + + +# Things we never want to flag as leaks +_SKIP_TYPES = ( + types.FrameType, + types.ModuleType, +) + + +def _is_globals_or_locals(obj: typing.Any) -> bool: + # These comparisons only make sense within this frame; still cheap to check. + return obj is globals() or obj is locals() + + +def _is_tracked_fake(obj: typing.Any) -> bool: + return isinstance(obj, torch.fx.experimental.symbolic_shapes.TrackedFake) + + +def _is_gm_meta_like_dict(d: dict, o: typing.Any) -> bool: + # Hope gm.meta was a custom dict we can assert on + return d.get("val") is o + + +def _dict_is_attr_of_tracked_fake(d: dict) -> bool: + """ + Python 3.10 quirk: sometimes the referrer is obj.__dict__ instead of obj. + Check if this dict is exactly the __dict__ of a TrackedFake. + """ + for parent in gc.get_referrers(d): + if ( + hasattr(parent, "__dict__") + and parent.__dict__ is d + and _is_tracked_fake(parent) + ): + return True + return False + + +def find_legit_leaks_from_referrers(active_fakes: weakref.WeakSet) -> weakref.WeakSet: + legit_leak: weakref.WeakSet = weakref.WeakSet() + + # This is so that we don't falsely flag generator to be holding fake tensor + fake_list = list(active_fakes) + fake_list_id = id(fake_list) + + for act in fake_list: + # Track by id to avoid processing duplicate referrers + seen = set() + # Assume it's a leak unless we find only ignorable referrers + flagged = False + + for r in gc.get_referrers(act): + rid = id(r) + if rid in seen: + continue + seen.add(rid) + + # Skip our own fake_list + if rid == fake_list_id: + continue + + # Fast-path: skip obvious non-owners + if _is_globals_or_locals(r): + continue + if isinstance(r, _SKIP_TYPES): + continue + if _is_tracked_fake(r): + # TrackedFake should be ignored + continue + + # Handle dicts carefully (Python 3.10 sometimes shows __dict__) + if isinstance(r, dict): + if _is_gm_meta_like_dict(r, act): + continue + if _dict_is_attr_of_tracked_fake(r): + continue + flagged = True + break + + # Any other referrer we don't explicitly whitelist counts as a leak + flagged = True + break + + if flagged: + legit_leak.add(act) + + return legit_leak diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_remove_auto_functionalized_pass.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_remove_auto_functionalized_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..1b4833927656767940f3795d37f0e75b93b647da --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_remove_auto_functionalized_pass.py @@ -0,0 +1,52 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + + +import torch +from torch._higher_order_ops.auto_functionalize import ( + auto_functionalized, + auto_functionalized_v2, +) +from torch._inductor.fx_passes.post_grad import decompose_auto_functionalized +from torch.export import ExportedProgram +from torch.fx import Graph + + +def remove_self_clone(graph: Graph) -> None: + for node in graph.nodes: + if node.target is torch.ops.aten.copy_.default and node.args[0] == node.args[1]: + node.replace_all_uses_with(node.args[0]) + graph.erase_node(node) + + +def unsafe_remove_auto_functionalized_pass( + ep: ExportedProgram, +) -> ExportedProgram: + """ + This pass removes an instances of the higher order op 'auto_functionalized', + and modifies the calling EP inplace to have the original mutator op. + This pass doesn't perform safety checks to make sure that this inplace mutation is safe. + """ + + with ep.graph_module._set_replace_hook(ep.graph_signature.get_replace_hook()): + for module in ep.graph_module.modules(): + if not isinstance(module, torch.fx.GraphModule): + continue + for node in ep.graph.nodes: + if ( + node.op == "call_function" and node.target is auto_functionalized + ) or ( + node.op == "call_function" and node.target is auto_functionalized_v2 + ): + func = node.args[0] + assert isinstance(func, torch._ops.OpOverload) + # re-inplace everything + node.meta["only_clone_these_tensors"] = [] + decompose_auto_functionalized(ep.graph) + remove_self_clone(ep.graph) + ep.graph.eliminate_dead_code() + + return ep diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_remove_effect_tokens_pass.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_remove_effect_tokens_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..8504d1cbdb71fd6f2199776fb7712768893afed1 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_remove_effect_tokens_pass.py @@ -0,0 +1,212 @@ +# mypy: allow-untyped-defs +import operator + +import torch +from torch._higher_order_ops.effects import _get_schema, with_effects + +from .exported_program import ExportedProgram +from .graph_signature import ( + CustomObjArgument, + InputKind, + InputSpec, + OutputKind, + OutputSpec, + TokenArgument, +) + + +def _get_custom_obj_for_node(node, inputs_to_lifted_custom_objs, constants): + """Extract the custom object from a node's arguments.""" + custom_obj_node = node + custom_obj_meta = custom_obj_node.meta["val"] # type: ignore[union-attr] + assert isinstance(custom_obj_meta, CustomObjArgument) + + if custom_obj_meta.fake_val: + return custom_obj_meta.fake_val + elif custom_obj_node.name in inputs_to_lifted_custom_objs: # type: ignore[union-attr] + return constants[inputs_to_lifted_custom_objs[custom_obj_node.name]] # type: ignore[union-attr] + else: + raise RuntimeError(f"Unable to find custom obj for node {node}") + + +def _replace_with_effects_node( + node, ep, inputs_to_lifted_custom_objs, output_tokens, input_tokens, module +): + """Replace a with_effects node with the underlying function call.""" + # Get the input nodes + token_node, func, *node_args = node.args + if token_node.op == "placeholder": + input_tokens.append(token_node) + + assert isinstance(func, (torch._ops.OpOverload, torch._ops.HigherOrderOperator)) + + # Get the schema for the function + if func is torch.ops.higher_order.call_torchbind: + custom_obj = _get_custom_obj_for_node( + node_args[0], inputs_to_lifted_custom_objs, ep.constants + ) + schema = _get_schema(func, [custom_obj] + node_args[1:]) + else: + schema = _get_schema(func, node_args) + + # Create the replacement node + with module.graph.inserting_before(node): + new_node = module.graph.call_function(func, tuple(node_args), node.kwargs) + + # Update getitem nodes that extract outputs from with_effects + for user in list(node.users.keys()): + assert user.target is operator.getitem + # getitem(with_effects, 0) is the token node + if user.args[1] == 0: + for user_user in list(user.users.keys()): + if user_user.op == "output": + output_tokens.append(user) + + # Copy metadata from old node to new node + for k, v in node.meta.items(): + new_node.meta[k] = v + if k == "unbacked_bindings": + # Remove the extra layer for effect token + old_bindings = new_node.meta[k] + new_bindings = { + k: path[1:] if path else path for k, path in old_bindings.items() + } + new_node.meta[k] = new_bindings + + # Fix up the getitem nodes based on return count + if len(schema.returns) == 1: + # Single return: replace getitem(with_effects, 1) with the node itself + for user in list(node.users.keys()): + if user.args[1] == 1: + user.replace_all_uses_with(new_node) + new_node.meta["val"] = node.meta["val"][1] + elif len(schema.returns) > 1: + # Multiple returns: shift getitem indices down by 1 + for user in list(node.users.keys()): + if user.args[1] >= 1: + user.args = (new_node, user.args[1] - 1) + new_node.meta["val"] = node.meta["val"][1:] + else: + # No returns + assert len(schema.returns) == 0 + assert len(new_node.users) == 0 + new_node.meta["val"] = None + + +def _replace_invoke_subgraph_node(node, module, output_tokens, input_tokens): + """Replace an invoke_subgraph node to remove the token argument.""" + assert node.args[0].op == "get_attr" + submod = getattr(module, node.args[0].target) + if not submod.meta.get("has_with_effects", False): + return + + # Remove token from inputs + subgraph, identifier, token, *operands = node.args + node.args = (subgraph, identifier, *operands) + if token.op == "placeholder": + input_tokens.append(token) + + # Update getitem nodes to account for removed token output + for user in list(node.users.keys()): + if user.args[1] >= 1: + user.args = (node, user.args[1] - 1) + elif user.args[1] == 0: + for user_user in list(user.users.keys()): + if user_user.op == "output": + output_tokens.append(user) + + +def _remove_effect_tokens(ep: ExportedProgram) -> ExportedProgram: + """ + Removes the existence of tokens from the exported program, including: + - Removes the input and output tokens + - Replaces with_effects(token, func, args) with just func(args) + + This function does an inplace modification on the given ExportedProgram. + """ + inputs_to_lifted_custom_objs = ep.graph_signature.inputs_to_lifted_custom_objs + + # mark submodules with effects as having effects. This will be used in the following pass to remove effects from subgraphs + for _, module in ep.graph_module.named_modules(): + if not isinstance(module, torch.fx.GraphModule): + continue + + with_effect_nodes = [ + node for node in module.graph.nodes if node.target is with_effects + ] + if len(with_effect_nodes) > 0: + module.meta["has_with_effects"] = True + + # Process each module with the replace hook to ensure graph signature is updated + with ep.graph_module._set_replace_hook(ep.graph_signature.get_replace_hook()): + for _, module in ep.graph_module.named_modules(): + if not isinstance(module, torch.fx.GraphModule): + continue + + input_tokens = [] + output_tokens = [] + + # Process with_effects and invoke_subgraph nodes + for node in module.graph.nodes: + if node.target is with_effects: + _replace_with_effects_node( + node, + ep, + inputs_to_lifted_custom_objs, + output_tokens, + input_tokens, + module, + ) + elif node.target is torch.ops.higher_order.invoke_subgraph: + _replace_invoke_subgraph_node( + node, module, output_tokens, input_tokens + ) + + # Remove tokens from the output node + if len(output_tokens) > 0: + output_node = next(reversed(module.graph.find_nodes(op="output"))) + output_args = output_node.args[0] + assert len(output_args) >= len(output_tokens), ( + f"{output_args} output arguments found\n" + f"{output_tokens} output tokens found\n" + f"{module.graph}" + ) + output_node.args = (tuple(output_args[len(output_tokens) :]),) + + module.graph.eliminate_dead_code() + + # Remove tokens from the input placeholders + for node in module.graph.nodes: + if node.op == "placeholder" and node in input_tokens: + module.graph.erase_node(node) + + module.recompile() + + num_tokens: int = 0 + input_token_names: list[str] = [] + new_input_specs: list[InputSpec] = [] + for inp in ep.graph_signature.input_specs: + if inp.kind == InputKind.TOKEN: + num_tokens += 1 + assert isinstance(inp.arg, TokenArgument) + input_token_names.append(inp.arg.name) + else: + new_input_specs.append(inp) + + num_out_tokens: int = 0 + new_output_specs: list[OutputSpec] = [] + output_token_names: list[OutputSpec] = [] + for out in ep.graph_signature.output_specs: + if out.kind == OutputKind.TOKEN: + num_out_tokens += 1 + output_token_names.append(out.arg.name) + else: + new_output_specs.append(out) + + # Update graph signature + ep.graph_signature.input_specs = new_input_specs + ep.graph_signature.output_specs = new_output_specs + + assert num_tokens == num_out_tokens + + return ep diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_safeguard.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_safeguard.py new file mode 100644 index 0000000000000000000000000000000000000000..76f22f369c566a97062fc60696ad7972dc2b260c --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_safeguard.py @@ -0,0 +1,44 @@ +# mypy: allow-untyped-defs +import torch +from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode +from torch.overrides import TorchFunctionMode + + +class AutogradStateOpsFailSafeguard(TorchFunctionMode): + """ + Detect grad state ops during exporting the graph and fail the process by + raising an error, to avoid unexpected behavior. Those grad mode ops could be: + `torch.no_grad` + `torch.enable_grad` + `torch.set_grad_enabled` + + Export with predispatch mode is exempted. + """ + + def __torch_function__(self, func, types, args=(), kwargs=None): + kwargs = kwargs or {} + unsupported_grad_mode_ops = [ + torch._C._set_grad_enabled, + ] + # It's only enabled while tracing, by confirming the torch dispatch mode is + # any active PROXY. This is to allow the autograd ops out of tracing. + current_state = torch._C.is_grad_enabled() + if func in unsupported_grad_mode_ops: + assert len(args) == 1 + changed_state = args[0] + mode = torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.PROXY) + # Intend to check if it's not the pre_dispatch mode. It's allowed to use + # autograd ops in pre_dispatch mode, e.g. `torch.no_grad` + if ( + mode + and isinstance(mode, ProxyTorchDispatchMode) + and not mode.pre_dispatch + and changed_state != current_state + ): + raise RuntimeError( + f"Encountered autograd state manager op {func} trying to change global autograd state " + "while exporting. This is unsafe because we don't capture this op in torch.export " + "today, hence we can't reflect the user intention soundly. You can fix this by " + "adding a torch.no_grad() context around the export call." + ) + return func(*args, **kwargs) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_swap.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_swap.py new file mode 100644 index 0000000000000000000000000000000000000000..f5aca6305c7de783b82d3848d58bec12a77a6fb8 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_swap.py @@ -0,0 +1,439 @@ +import logging +import operator +import types +from collections import defaultdict + +import torch +import torch.fx._pytree as fx_pytree +import torch.utils._pytree as pytree +from torch.export.exported_program import ( + ConstantArgument, + ExportedProgram, + ModuleCallSignature, +) +from torch.fx.passes.tools_common import legalize_graph, NodeList +from torch.fx.passes.utils.fuser_utils import erase_nodes, fuse_as_graphmodule + + +log = logging.getLogger(__name__) + + +def _get_getitem_users(node: torch.fx.Node) -> set[torch.fx.Node]: + node_users = list(node.users.keys()) + getitem_users = set() + for user in node_users: + if user.op == "output": + continue + + assert user.op == "call_function" and user.target is operator.getitem, ( + f"Expected getitem node as user for {node}, instead got {user}" + ) + getitem_users.update(list(user.users.keys())) + return getitem_users + + +def _try_remove_connecting_pytrees(curr_module_node: torch.fx.Node) -> None: + """ + We want to try to remove extraneous pytree flatten/unflatten calls between modules + calls. Instead of having the following: + graph(): + ... + %foo : [num_users=1] = call_module[target=foo](args = (%getitem_1, %getitem_2), kwargs = {}) + %tree_flatten_spec : [num_users=1] = call_function[target=torch.fx._pytree.tree_flatten_spec](args = (%foo, %_spec_1), kwargs = {}) + %getitem_4 : [num_users=1] = call_function[target=operator.getitem](args = (%tree_flatten_spec, 0), kwargs = {}) + %tree_unflatten_1 : [num_users=2] = call_function[target=torch.utils._pytree.tree_unflatten](args = ([%getitem_4], %_spec_2), kwargs = {}) + %getitem_5 : [num_users=1] = call_function[target=operator.getitem](args = (%tree_unflatten_1, 0), kwargs = {}) + %getitem_7 : [num_users=0] = call_function[target=operator.getitem](args = (%tree_unflatten_1, 1), kwargs = {}) + %getitem_6 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem_5, 0), kwargs = {}) + %bar : [num_users=1] = call_module[target=bar](args = (%getitem_6,), kwargs = {}) + ... + + We could do the following, if we know that all the outputs of `foo` feed into `bar`: + graph(): + ... + %foo : [num_users=1] = call_module[target=foo](args = (%getitem_1, %getitem_2), kwargs = {}) + %bar : [num_users=1] = call_module[target=bar](args = (%getitem_6,), kwargs = {}) + ... + + Currently this optimization only works for the case where all of the outputs + of `foo` go directly into `bar`, and `bar` has no other inputs. + """ # noqa: B950 + + log.debug("Trying to remove pytrees for module call %s", curr_module_node) + + curr_module_users = list(curr_module_node.users.keys()) + assert len(curr_module_users) == 1, ( + f"Expected only one user for module node, instead got {list(curr_module_users)}" + ) + flatten_node = curr_module_users[0] + assert ( + flatten_node.op == "call_function" + and flatten_node.target is fx_pytree.tree_flatten_spec + ) + + flatten_getitem_users = _get_getitem_users(flatten_node) + if len(flatten_getitem_users) != 1: + log.debug( + "More than one user found for flatten node, %s: %s. " + "Unable to fuse it with another unflatten call.", + flatten_node, + flatten_getitem_users, + ) + return + + unflatten_node = next(iter(flatten_getitem_users)) + if not ( + unflatten_node.op == "call_function" + and unflatten_node.target is pytree.tree_unflatten + ): + log.debug( + "Flatten node %s's user is not a pytree.tree_unflatten. " + "Instead it is: %s. Passing...", + flatten_node, + unflatten_node, + ) + return + + for i, arg in enumerate(unflatten_node.args[0]): # type: ignore[union-attr,arg-type] + if arg not in flatten_node.users: + log.debug( + "Module %s's outputs are not all directly used as inputs to " + "the subsequent module. Unable to fuse the connecting " + "flatten/unflatten. The inputs to the subsequent module are: %s. ", + curr_module_node, + unflatten_node.args[0], + ) + return + + if not ( + # pyrefly: ignore [missing-attribute] + arg.op == "call_function" + # pyrefly: ignore [missing-attribute] + and arg.target is operator.getitem + # pyrefly: ignore [missing-attribute] + and arg.args[1] == i + ): + log.debug( + "Module %s's outputs are not all directly used in the same " + "order as outputted. Unable to fuse the connecting " + "flatten/unflatten. The inputs to the " + "subsequent module are: %s. ", + curr_module_node, + unflatten_node.args[0], + ) + return + + # Unflatten has two levels of getitem, because it gets the args and kwargs + unflatten_getitem_getitem_users = set() + unflatten_getitem_users = _get_getitem_users(unflatten_node) + for unflatten_getitem_user in unflatten_getitem_users: + unflatten_getitem_getitem_users.update( + list(unflatten_getitem_user.users.keys()) + ) + + if len(unflatten_getitem_getitem_users) != 1: + log.debug( + "More than one user found for unflatten node, %s: %s. " + "Unable to fuse it with another flatten call.", + unflatten_node, + unflatten_getitem_getitem_users, + ) + return + + next_module_node = next(iter(unflatten_getitem_getitem_users)) + if next_module_node.op != "call_module": + log.debug( + "Unflatten node %s's user is not a call_module. " + "Instead it is: %s. Passing...", + unflatten_node, + next_module_node, + ) + return + + # Directly put the outputs of the current module into the next module + next_module_node.args = (curr_module_node,) + + +def _remove_extraneous_pytrees(gm: torch.fx.GraphModule) -> None: + """ + Remove extraneous pytree flatten/unflatten calls. + + We try a couple of optimizations here: + 1. Remove pytree flatten/unflatten calls between modules + 2. TODO: Remove module's in_spec + initial unflatten call + 3. TODO: Remove module's out_spec + final flatten call + """ + + for node in gm.graph.nodes: + if node.op == "call_module" and node.target != "_guards_fn": + _try_remove_connecting_pytrees(node) + + gm.graph.eliminate_dead_code() + + +def _construct_inputs( + gm: torch.fx.GraphModule, + signature: ModuleCallSignature, + node_name_map: dict[str, torch.fx.Node], +) -> tuple[list[torch.fx.Node], dict[str, torch.fx.Node]]: + tree_unflatten_args: list[torch.fx.Node | None] = [] + for input_ in signature.inputs: + if isinstance(input_, ConstantArgument) and input_.value is None: + # Constants should be directly embedded into the graph and not used + # as inputs + tree_unflatten_args.append(None) + elif input_.name not in node_name_map: + # For unused inputs + tree_unflatten_args.append(None) + else: + tree_unflatten_args.append(node_name_map[input_.name]) + + # Insert unflatten call + from .unflatten import _generate_unflatten + + unflatten_node = _generate_unflatten(gm, tree_unflatten_args, signature.in_spec) + + assert signature.in_spec.num_children == 2 + assert signature.in_spec.type is tuple + args_spec, kwargs_spec = signature.in_spec.children() + assert args_spec.type is tuple + assert kwargs_spec.type is dict + + args_node = gm.graph.call_function(operator.getitem, (unflatten_node, 0)) + args_nodes = [ + gm.graph.call_function(operator.getitem, (args_node, i)) + for i in range(args_spec.num_children) + ] + kwargs_node = gm.graph.call_function(operator.getitem, (unflatten_node, 1)) + kwargs_nodes = { + k: gm.graph.call_function(operator.getitem, (kwargs_node, k)) + for k in kwargs_spec.context + } + return args_nodes, kwargs_nodes + + +def _insert_call_module( + gm: torch.fx.GraphModule, + args_nodes: list[torch.fx.Node], + kwargs_nodes: dict[str, torch.fx.Node], + module_to_swap: torch.nn.Module, + name: str, +) -> torch.fx.Node: + from .unflatten import _assign_attr, _AttrKind + + _assign_attr(module_to_swap, gm, name, _AttrKind.MODULE) + module_node = gm.graph.call_module(name, tuple(args_nodes), kwargs_nodes) # type: ignore[arg-type] + return module_node + + +def _deconstruct_outputs( + gm: torch.fx.GraphModule, + signature: ModuleCallSignature, + module_node: torch.fx.Node, + node_name_map: dict[str, torch.fx.Node], + orig_outputs: tuple[torch.fx.Node, ...], +) -> None: + from .unflatten import _generate_flatten_spec + + flatten_node = _generate_flatten_spec(gm, module_node, signature.out_spec) + + for i, orig_output in enumerate(orig_outputs): + # Use Proxy to record getitem access. + proxy_out = torch.fx.Proxy(flatten_node)[i].node # type: ignore[index] + orig_output.replace_all_uses_with(proxy_out, propagate_meta=True) + + node_name_map[orig_output.name] = proxy_out + + +def _swap_module_helper( + gm: torch.fx.GraphModule, + modules_to_swap: dict[str, torch.nn.Module], + module_call_graph: dict[str, ModuleCallSignature], +) -> torch.fx.GraphModule: + log.debug("Starting graph:") + log.debug(gm.graph) + + legalize_graph(gm) + + partitions: dict[str, NodeList] = defaultdict(list) + + node_name_map: dict[str, torch.fx.Node] = { + node.name: node for node in gm.graph.nodes + } + + # TODO: Handle the duplicate module case + for node in gm.graph.nodes: + if nn_module_stack := node.meta.get("nn_module_stack"): + for path, _ in nn_module_stack.values(): + if path in modules_to_swap: + partitions[path].append(node) + break + + for name, nodes in partitions.items(): + """ + Given a graph like the following, and we want to swap out the submodule "foo": + graph(): + %x : [num_users=1] = placeholder[target=x] + %y : [num_users=2] = placeholder[target=y] + %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%y, %x), kwargs = {}), nn_module_stack = {"foo": ("foo", torch.nn.Module)} + %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%y, %add), kwargs = {}), nn_module_stack = {"bar": ("bar", torch.nn.Module)} + return (sub,) + + We will first partition out foo's subgraph: + graph(): + %x : [num_users=1] = placeholder[target=x] + %y : [num_users=2] = placeholder[target=y] + %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%y, %x), kwargs = {}) + return add + + And then insert an unflatten + call_module + flatten to replace the subgraph: + graph(): + %x : [num_users=1] = placeholder[target=x] + %y : [num_users=1] = placeholder[target=y] + + %_spec_0 : [num_users=1] = get_attr[target=_spec_0] + %tree_unflatten : [num_users=2] = call_function[target=torch.utils._pytree.tree_unflatten](args = ([%x, %y], %_spec_0), kwargs = {}) + %getitem : [num_users=2] = call_function[target=operator.getitem](args = (%tree_unflatten, 0), kwargs = {}) + %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem, 0), kwargs = {}) + %getitem_2 : [num_users=1] = call_function[target=operator.getitem](args = (%getitem, 1), kwargs = {}) + %getitem_3 : [num_users=0] = call_function[target=operator.getitem](args = (%tree_unflatten, 1), kwargs = {}) + %foo : [num_users=0] = call_module[target=foo](args = (%getitem_1, %getitem_2), kwargs = {}) + %_spec_1 : [num_users=1] = get_attr[target=_spec_1] + %tree_flatten_spec : [num_users=1] = call_function[target=torch.fx._pytree.tree_flatten_spec](args = (None, %_spec_1), kwargs = {}) + %getitem_4 : [num_users=1] = call_function[target=operator.getitem](args = (%tree_flatten_spec, 0), kwargs = {}) + + %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%y, %getitem_4), kwargs = {}) + return (%sub,) + + The `tree_unflatten` call will construct tensor inputs into the input + format needed by the swapped eager module. + The `call_module` node should now reference the swapped torch.nn.Module. + The `tree_flatten_spec` call will deconstruct the eager outputs of the + swapped module into tensors. + """ # noqa: B950 + + submod_name = name.replace(".", "_") + sub_gm, orig_inputs, orig_outputs = fuse_as_graphmodule( + gm, nodes, f"fused_{submod_name}" + ) + + log.debug("Fused subgraph nodes:") + log.debug(sub_gm.graph) + + signature: ModuleCallSignature = module_call_graph[name] + + args_nodes, kwargs_nodes = _construct_inputs(gm, signature, node_name_map) + module_node = _insert_call_module( + gm, args_nodes, kwargs_nodes, modules_to_swap[name], name + ) + _deconstruct_outputs(gm, signature, module_node, node_name_map, orig_outputs) + + erase_nodes(gm, nodes) + + log.debug("Swapped graph:") + log.debug(gm.graph) + + legalize_graph(gm) + + log.debug("Before removing extraneous pytrees:") + log.debug(gm.graph) + + _remove_extraneous_pytrees(gm) + log.debug("After removing extraneous pytrees:") + log.debug(gm.graph) + + gm.recompile() + + return gm + + +def _fix_input_output_signature( + gm: torch.fx.GraphModule, signature: ModuleCallSignature +) -> None: + """ + Given the unlifted module from calling ep.module(), we want to remove the + pytree processing from the graph module's PyTreeCodeGen and instead make it + nodes inside of the graph. This allows us to do some optimizations, like + remove these pytree calls if it is unnecessary, and makes the PyTree part + more obvious to graph passes. + """ + from torch.export.unflatten import _generate_flatten, _generate_unflatten + + # Remove the registered pytree codegen because we will take care of it + # through inserting pytree nodes into the graph + gm.graph._codegen = torch.fx.graph.CodeGen() + + old_placeholders = [node for node in gm.graph.nodes if node.op == "placeholder"] + + new_placeholders = [] + forward_arg_names = signature.forward_arg_names + if forward_arg_names is None: + forward_arg_names = [] + assert signature.in_spec.num_children == 2 + arg_spec = signature.in_spec.child(0) + kwarg_spec = signature.in_spec.child(1) + assert arg_spec.type is tuple + assert kwarg_spec.type is dict + for i in range(arg_spec.num_children): + forward_arg_names.append(f"arg_{i}") + forward_arg_names.extend(kwarg_spec.context) + + for arg in forward_arg_names: + with gm.graph.inserting_before(old_placeholders[0]): + new_placeholders.append(gm.graph.placeholder(arg)) + + # Insert flatten call for the inputs + with gm.graph.inserting_before(old_placeholders[0]): + flat_node = _generate_flatten(gm, tuple(new_placeholders)) + for i, old_placeholder in enumerate(old_placeholders): + old_placeholder.op = "call_function" + old_placeholder.target = operator.getitem + old_placeholder.args = (flat_node, i) + + # Insert unflatten call for the outputs + output_node = next(node for node in gm.graph.nodes if node.op == "output") + with gm.graph.inserting_before(output_node): + unflat = _generate_unflatten(gm, output_node.args[0], signature.out_spec) + output_node.args = (unflat,) + + gm.recompile() + + +def _swap_modules( + ep: ExportedProgram, modules_to_swap: dict[str, torch.nn.Module] +) -> torch.fx.GraphModule: + """ + Unlifts the given ExportedProgram into a fx.GraphModule, and then swaps + previously traced modules with new eager modules specified. Returns a + fx.GraphModule with a custom forward function. + + Args: + ep (ExportedProgram): Exported program to modify + modules_to_swap (Dict[str, torch.nn.Module]): Mapping from module fqn to + eager module to swap with. The specified module fqn should have also + been specified in the `preserve_module_call_signature` argument to + torch.export so that we know how to restore the calling convention + to this argument. + run_with_interpreter: Whether or not to run the graph using + fx.Interpreter. Setting to true will help result in better error + messages and easier debugging, but it has found to result in a QPS + drop. + """ + module_call_graph = { + entry.fqn: entry.signature for entry in ep.module_call_graph if entry.signature + } + + gm = ep.module() + gm.validate_inputs = False # type: ignore[assignment] + gm.graph.eliminate_dead_code() # type: ignore[operator, union-attr] + assert isinstance(gm, torch.fx.GraphModule) + _fix_input_output_signature(gm, ep.module_call_graph[0].signature) + + gm.module_call_graph = ep.module_call_graph + gm.train = types.MethodType(type(gm).train, gm) # type: ignore[assignment] + gm.eval = types.MethodType(type(gm).eval, gm) # type: ignore[assignment] + + assert isinstance(gm, torch.fx.GraphModule) + gm = _swap_module_helper(gm, modules_to_swap, module_call_graph) + + return gm diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_trace.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_trace.py new file mode 100644 index 0000000000000000000000000000000000000000..ac401dc433bdaa04333917551e03900b7250ccc1 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_trace.py @@ -0,0 +1,2463 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import dataclasses +import functools +import inspect +import logging +import re +import sys +import time +import warnings +from collections.abc import Callable +from contextlib import contextmanager, ExitStack, nullcontext +from itertools import chain +from typing import Any, TYPE_CHECKING, TypeAlias +from unittest import mock + + +if TYPE_CHECKING: + import weakref + +import torch +import torch._dynamo +import torch.fx +import torch.utils._pytree as pytree +from torch._dispatch.python import enable_python_dispatcher +from torch._dynamo.exc import UserError, UserErrorType +from torch._export.db.logging import ( + exportdb_error_message, + get_class_if_classified_error, +) +from torch._export.non_strict_utils import ( + _fakify_module_inputs, + _fakify_script_objects, + _gather_constant_attrs, + _NonStrictTorchFunctionHandler, + _override_builtin_ops, + make_constraints, + make_fake_inputs, + produce_guards_and_solve_constraints, +) +from torch._export.passes.collect_tracepoints_pass import CollectTracepointsPass +from torch._export.passes.lift_constants_pass import ( + _materialize_and_lift_constants, + ConstantAttrMap, +) +from torch._export.utils import ( + _collect_param_buffer_metadata, + _compiling_state_context, + _fakify_params_buffers, + _populate_param_buffer_metadata_to_new_gm, + _update_gm_meta_if_possible, + apply_runtime_assertion_pass, + placeholder_naming_pass, + placeholder_prefixes, +) +from torch._export.verifier import SpecViolationError +from torch._export.wrappers import _wrap_submodules +from torch._functorch._aot_autograd.graph_capture_wrappers import create_functional_call +from torch._functorch._aot_autograd.input_output_analysis import ( + _graph_input_names, + _graph_output_names, +) +from torch._functorch._aot_autograd.schemas import GraphSignature +from torch._functorch._aot_autograd.subclass_utils import get_subclass_typing_container +from torch._functorch._aot_autograd.utils import ( + create_tree_flattened_fn, + register_buffer_assignment_hook, +) +from torch._functorch.aot_autograd import ( + _detect_attribute_assignment, + aot_export_joint_with_descriptors, +) +from torch._guards import detect_fake_mode, tracing, TracingContext +from torch._library.fake_class_registry import FakeScriptObject +from torch._logging import dtrace_structured +from torch._subclasses.fake_tensor import FakeTensorMode +from torch._utils_internal import log_export_usage +from torch.export._leakage_detection_utils import find_legit_leaks_from_referrers +from torch.export._unlift import _check_input_constraints_pre_hook +from torch.export.dynamic_shapes import ( + _check_dynamic_shapes, + _combine_args, + _DimHintType, + _IntWrapper, + _process_dynamic_shapes, +) +from torch.export.exported_program import OutputKind +from torch.fx._symbolic_trace import _ConstantAttributeType +from torch.fx.experimental.proxy_tensor import ( + get_proxy_slot, + make_fx, + PreDispatchTorchFunctionMode, + track_tensor_tree, +) +from torch.fx.experimental.symbolic_shapes import ( + ConstraintViolationError, + free_unbacked_symbols, + GuardOnDataDependentSymNode, + ShapeEnv, +) +from torch.fx.graph import _PyTreeInfo +from torch.utils._pytree import TreeSpec +from torch.utils._sympy.value_ranges import ValueRangeError + +from .exported_program import ( + _disable_prexisiting_fake_mode, + ExportedProgram, + InputKind, + ModuleCallEntry, + ModuleCallSignature, +) +from .graph_signature import _convert_to_export_graph_signature, ExportGraphSignature + + +log = logging.getLogger(__name__) + +# Type alias for dynamic shapes specification +_DynamicShapesSpec: TypeAlias = dict[str, Any] | tuple[Any, ...] | list[Any] + + +@dataclasses.dataclass +class ExportDynamoConfig: + """ + Manage Export-specific configurations of Dynamo. + """ + + allow_rnn: bool = True + reorderable_logging_functions: set[Callable] = dataclasses.field( + default_factory=set + ) + # Emit runtime asserts after AOTAutograd instead. + # This isn't really necessary, and isn't much more efficient since the runtime asserts pass does CSE, + # but if we want to reason more about what guards/runtime asserts to emit, + # this makes it a bit cleaner to do from the export side. Also no real point in running this twice. + do_not_emit_runtime_asserts: bool = True + specialize_int: bool = True + specialize_float: bool = True + assume_static_by_default: bool = False + automatic_dynamic_shapes: bool = False + capture_dynamic_output_shape_ops: bool = True + capture_scalar_outputs: bool = True + prefer_deferred_runtime_asserts_over_guards: bool = False + replay_side_effects: bool = False + side_effect_replay_policy: str = "warn" + + +@dataclasses.dataclass +class ATenExportArtifact: + gm: torch.fx.GraphModule + sig: ExportGraphSignature + constants: dict[str, _ConstantAttributeType] + + +@dataclasses.dataclass(frozen=True) +class ExportArtifact: + aten: ATenExportArtifact + in_spec: TreeSpec + out_spec: TreeSpec + fake_mode: FakeTensorMode + module_call_specs: dict[str, dict[str, pytree.TreeSpec]] + + +DEFAULT_EXPORT_DYNAMO_CONFIG = ExportDynamoConfig() +DEFAULT_EXPORT_DYNAMO_CONFIG.reorderable_logging_functions = { + logging.critical, + logging.debug, + logging.error, + logging.exception, + logging.info, + logging.log, + logging.warning, + print, + warnings.warn, +} + + +@contextmanager +def _ignore_backend_decomps(): + orig_mkldnn_flag = torch.backends.mkldnn.set_flags(False) + orig_nnpack_flag = torch.backends.nnpack.set_flags(False) + try: + yield + finally: + torch.backends.mkldnn.set_flags(*orig_mkldnn_flag) + torch.backends.nnpack.set_flags(*orig_nnpack_flag) + + +@contextmanager +def _disable_custom_triton_op_functional_decomposition(): + old = torch._functorch.config.decompose_custom_triton_ops + try: + # pyrefly: ignore [bad-assignment] + torch._functorch.config.decompose_custom_triton_ops = False + yield torch._functorch.config.decompose_custom_triton_ops + finally: + torch._functorch.config.decompose_custom_triton_ops = old + + +def custom_triton_ops_decomposition_disabled(): + return not torch._functorch.config.decompose_custom_triton_ops + + +def _fixup_key(x): + return "L__self__" + _strip_root(x) + + +def _strip_root(x): + if isinstance(x, str) and x.startswith("_export_root"): + stripped = x[len("_export_root") :] + return stripped.removeprefix(".") + return x + + +def _is_bogus_const_name(name: str): + splitted_names = name.split(".") + if len(splitted_names) < 1: + return True + + return splitted_names[-1].startswith("lifted_tensor") + + +def _rewrite_tracepoint_node(gm: torch.fx.GraphModule): + """ + In-place modify input graph module by replacing the export tracepoint with a new node + that has the same target and args, but with the _export_root stripped from path. + """ + for node in gm.graph.nodes: + if node.target is torch.ops.higher_order._export_tracepoint: + if "path" in node.kwargs: + path = _strip_root(node.kwargs["path"]) + with gm.graph.inserting_before(node): + new_node = gm.graph.create_node( + "call_function", + torch.ops.higher_order._export_tracepoint, + args=node.args, + kwargs={ + "path": path, + "kind": node.kwargs["kind"], + }, + ) + new_node.meta = node.meta + node.replace_all_uses_with(new_node) + gm.graph.erase_node(node) + + +def detect_shape_env(inputs: Any = None): + shape_envs = [] + + for i, flat_input in enumerate(inputs): + if isinstance(flat_input, torch.SymInt): + shape_envs.append((flat_input.node.shape_env, "symint input", i)) + + if shape_envs: + shape_env, desc1, i1 = shape_envs[0] + for m, desc2, i2 in shape_envs[1:]: + assert shape_env is m, ( + f"shape env ({shape_env}) from {desc1} {i1} doesn't match mode ({m}) from {desc2} {i2}\n\n" + f"shape env from {desc1} {i1} allocated at:\n{shape_env.stack}\n" + f"shape env from {desc2} {i2} allocated at:\n{m.stack}" + ) + return shape_env + else: + return None + + +def _extract_fake_inputs(gm, args, kwargs): + """ + Given a graph module, extract fakified input tensors from the metadata of + its placeholders, and map them to the structure of given args and kwargs. + Also return the fake mode used to fakify those inputs. + """ + fake_inps: list[Any] = [] + fake_vals: list[Any] = [] + for node in gm.graph.nodes: + if node.op == "placeholder": + fake_inps.append(node.meta.get("val")) + else: + fake_vals.append(node.meta.get("example_value")) + + if in_shuffle_graph := getattr(gm, "_in_shuffle_graph", None): + flat_args = pytree.tree_leaves((args, kwargs)) + node_map = { + node: i + for i, node in enumerate( + next(iter(reversed(in_shuffle_graph.graph.nodes))).args[0] + ) + if node.op == "placeholder" + } + new_fake_inps: list[Any] = [] + for i, node in enumerate( + in_shuffle_graph.graph.find_nodes(op="placeholder")[1:] + ): + if node in node_map: + new_fake_inps.append(fake_inps[node_map[node]]) + else: + new_fake_inps.append(flat_args[i]) + fake_inps = new_fake_inps + # We get both because now we might have a combination of symint and tensor + # inputs, and we want to check that the shape env is consistent between + # both. Unfortunately we can't see what fake mode is attached to the shape + # env, then we can just compare fake modes. + detected_fake_mode = detect_fake_mode(fake_inps + fake_vals) + detected_shape_env = detect_shape_env(fake_inps + fake_vals) + + if detected_fake_mode: + if detected_shape_env: + assert detected_shape_env is detected_fake_mode.shape_env, ( + "Detected shape env does not match fake mode's shape env" + ) + fake_mode = detected_fake_mode + elif detected_shape_env: + fake_mode = FakeTensorMode(shape_env=detected_shape_env, export=True) + else: + fake_mode = FakeTensorMode(shape_env=ShapeEnv(), export=True) + + count = 0 + + def lookup_fake(x): + nonlocal count + val = fake_inps[count] if isinstance(x, (int, torch.Tensor)) else x + count += 1 + return val + + fake_args = pytree.tree_map(lookup_fake, args) + fake_kwargs = pytree.tree_map(lookup_fake, kwargs) + + return fake_args, fake_kwargs, fake_mode + + +def _replace_param_buffer_names(param_buffer_table, sig): + for spec in sig.input_specs: + if spec.kind in ( + InputKind.PARAMETER, + InputKind.BUFFER, + ): + spec.target = param_buffer_table[spec.target] + for spec in sig.output_specs: + if spec.kind in ( + OutputKind.BUFFER_MUTATION, + OutputKind.GRADIENT_TO_PARAMETER, + ): + spec.target = param_buffer_table[spec.target] + + +def _convert_to_positional_args(orig_arg_names, args, kwargs): + assert len(orig_arg_names) == len(args) + len(kwargs), ( + f"Total number of arg names is expected to be {len(orig_arg_names)} " + f"but got {len(args)} positional args, {len(kwargs)} kwargs." + ) + reordered_kwargs = [kwargs[kw_name] for kw_name in orig_arg_names[len(args) :]] + return ( + *args, + *reordered_kwargs, + ) + + +def _normalize_nn_module_stack(gm_torch_level, root_cls): + # Append a root module to every nn_module_stack. + root = "L['self']" + root_key = re.sub(r"[^a-zA-Z0-9]", "_", root) + for gm in gm_torch_level.modules(): + if not isinstance(gm, torch.fx.GraphModule): + continue + for node in gm.graph.nodes: + if node.op in ["placeholder", "output"]: + continue + add_root = True + if nn_module_stack := node.meta.get("nn_module_stack", {}): + path, ty = next(iter(nn_module_stack.values())) + # After deserializing the class `ty` might not exist anymore so + # it could be a string + if inspect.isclass(ty) and issubclass(ty, torch.nn.Module): + # TODO Figure out why sometimes we have root sometimes we don't. + if path == root and ty is root_cls: + add_root = False + else: + assert isinstance(ty, str) + if add_root: + + def normalize_path(path): + if path == "L['self']": + return "" + if path.startswith("L['self']."): + return path[len("L['self'].") :] + return path + + nn_module_stack = { + root_key: (root, root_cls.__module__ + "." + root_cls.__qualname__), + # pyrefly: ignore [unbound-name] + **nn_module_stack, + } + node.meta["nn_module_stack"] = { + key: (normalize_path(path), ty) + for key, (path, ty) in nn_module_stack.items() + } + + +def _get_param_buffer_mapping( + original_module: torch.nn.Module, + traced_module: torch.nn.Module, +) -> dict[str, str]: + """ + Returns a mapping of parameter/buffer names from the new module to the + original model. This is to help with restoring the FQN for parameter/buffers + of a traced module to what the original module contains. + """ + + param_lookup: dict[int, str] = {} + buffer_lookup: dict[int, str] = {} + for name, param in original_module.named_parameters(remove_duplicate=False): + if param_lookup.get(id(param)) is None: + # we only want to keep the first occurrence of a parameter to guarantee parity of original and traced module. + param_lookup[id(param)] = name + for name, buffer in original_module.named_buffers(remove_duplicate=False): + buffer_lookup[id(buffer)] = name + + param_buffer_table: dict[str, str] = {} + for dynamo_name, dynamo_param in traced_module.named_parameters( + remove_duplicate=False + ): + assert dynamo_name not in param_buffer_table + if id(dynamo_param) in param_lookup: + param_buffer_table[dynamo_name] = param_lookup[id(dynamo_param)] + + for dynamo_name, dynamo_buffer in traced_module.named_buffers( + remove_duplicate=False + ): + assert dynamo_name not in param_buffer_table + if id(dynamo_buffer) in buffer_lookup: + param_buffer_table[dynamo_name] = buffer_lookup[id(dynamo_buffer)] + + return param_buffer_table + + +def _preserve_requires_grad_pass( + gm: torch.fx.GraphModule, + sig: ExportGraphSignature, + fake_params_buffers: dict[str, torch.Tensor], + constants: dict[str, _ConstantAttributeType], + flat_fake_args: list[Any], +): + placeholders = [node for node in gm.graph.nodes if node.op == "placeholder"] + assert len(sig.input_specs) == len(placeholders) + i = 0 + for node, spec in zip(placeholders, sig.input_specs): + if spec.kind in ( + InputKind.PARAMETER, + InputKind.BUFFER, + ): + assert spec.target is not None + node.meta["val"].requires_grad = fake_params_buffers[ + spec.target + ].requires_grad + elif spec.kind == InputKind.USER_INPUT: + fake_arg = flat_fake_args[i] + if isinstance(fake_arg, torch.Tensor): + node.meta["val"].requires_grad = fake_arg.requires_grad + i += 1 + elif spec.kind == InputKind.CONSTANT_TENSOR: + assert spec.target is not None + constant = constants[spec.target] + if isinstance(constant, torch.Tensor): + # If the tensor is not leaf, it should already have a correct requires grad field + if node.meta["val"].is_leaf: + node.meta["val"].requires_grad = constant.requires_grad + else: + assert node.meta["val"].requires_grad == constant.requires_grad + elif spec.kind in (InputKind.CUSTOM_OBJ, InputKind.TOKEN): + continue + else: + raise AssertionError(spec.kind) + + +def _remap_constants( + orig_constant_attrs: ConstantAttrMap, + graph_signature: ExportGraphSignature, + constants: dict[str, _ConstantAttributeType], +) -> None: + """Rewrite the graph signature and constants table to use the FQN from the original module.""" + remap_table: dict[str, list[str]] = {} + for name, value in constants.items(): + if value in orig_constant_attrs: + remap_table[name] = orig_constant_attrs[value] + + for spec in graph_signature.input_specs: + if spec.kind in ( + InputKind.CONSTANT_TENSOR, + InputKind.CUSTOM_OBJ, + ): + orig_target = spec.target + assert orig_target is not None + targets = remap_table.get(orig_target, [orig_target]) + spec.target = targets[0] + + constant = constants[orig_target] + del constants[orig_target] + for target in targets: + constants[target] = constant + + +def _replace_unbacked_bindings(gm: torch.fx.GraphModule) -> None: + """ + When we run an interpreter-based pass over a GraphModule, execution of data-dependent operators + will produce example values with new unbacked symbols. To track that the new/old symbols are equivalent, + we used to rely on the unbacked_renamings mapping. This led to problematic metadata where the unbacked_bindings + keys mapped new symbols (u2) to paths containing old symbols (u0) in the example values, or worse, backed symbols + or constants (e.g. if the original unbacked was replaced/specialized). Additionally this created problems with + de/serialized programs, since we didn't comprehensively serialize ShapeEnv/unbacked renamings/node bindings. + + This pass attempts a simpler way of handling these for export, by throwing away the previously computed bindings, and re-running + the pattern match used in compute_unbacked_bindings. This ensures we keep the original symbols contained in the example values, + or delete bindings if they've been replaced/specialized. + """ + from torch._export.utils import _get_shape_env_from_gm + from torch.fx.experimental.symbolic_shapes import _free_unbacked_symbols_with_path + from torch.utils._sympy.symbol import symbol_is_type, SymT + + if (shape_env := _get_shape_env_from_gm(gm)) is None: + return + + base_unbacked_symbols = { + symbol + for symbol in shape_env.var_to_range + if symbol_is_type(symbol, (SymT.UNBACKED_INT, SymT.UNBACKED_FLOAT)) + and symbol not in shape_env.unbacked_renamings + } + for node in gm.graph.nodes: + node.meta.pop("unbacked_bindings", None) + if (val := node.meta.get("val")) is not None and ( + unbacked_bindings := _free_unbacked_symbols_with_path( + val, + (), + shape_env=shape_env, + pending=base_unbacked_symbols, + simplify=True, + ) + ): + node.meta["unbacked_bindings"] = unbacked_bindings + + +def _produce_aten_artifact( + *, + gm: torch.fx.GraphModule, + mod, + constant_attrs, + graph_signature, + pre_dispatch, + fake_args, + fake_kwargs, + fake_params_buffers, + _prettify_placeholder_names=True, +) -> ATenExportArtifact: + """ + This is a helper function that is shared between export_to_aten_ir and export_to_aten_ir_make_fx + to produce the aten artifact. (export compatible graph module + signature) + + It does: + 1. Applies runtime assertion pass + 2. Recompute unbacked_bindings pass + 3. Populate meta val when missing + 4. Lift constants as placeholders + 5. Replace raw autograd and autocast ops with HOPs + 6. Prettify names for placeholders + 7. Preserve requires_grad value on node meta val + """ + # Run runtime asserts pass before creating input/output specs, since size-related CSE/DCE might affect output signature. + # Overwrite output specs afterwards. + flat_fake_args = pytree.tree_leaves((fake_args, fake_kwargs)) + gm, graph_signature = apply_runtime_assertion_pass(gm, graph_signature) + + # Simplify unbacked_bindings by recomputing them. + # Useful for any pass that's interpreter-based and might call rebind_unbacked(), + # e.g. AOTAutograd in this case. + _replace_unbacked_bindings(gm) + + total_non_user_inputs = ( + len(graph_signature.parameters) + + len(graph_signature.buffers) + + len(graph_signature.input_tokens) + ) + set_missing_meta_vals(gm, flat_fake_args, total_non_user_inputs) + + export_graph_signature: ExportGraphSignature | None + export_graph_signature = _convert_to_export_graph_signature( + graph_signature, gm, _get_non_persistent_buffers(mod) + ) + + # script objects are always stored in constants no matter whether they're initial inputs or + # they're lifted in aot" before rewrite_script_object_meta + constants = _materialize_and_lift_constants( + gm, export_graph_signature, constant_attrs + ) + + if pre_dispatch: + from torch._export.passes.replace_autocast_with_hop_pass import ( + replace_autocast_with_hop_pass, + ) + from torch._export.passes.replace_set_grad_with_hop_pass import ( + replace_set_grad_with_hop_pass, + ) + + # Note: replace_set_grad_with_hop_pass need to be after lift_constant_pass because + # a getattr of a constant tensor doesn't have meta["val"] until after lift_constant_pass. + # If replace_set_grad_with_hop_pass is before lift_constant_pass, + # and the constant_tensor is passed as input of the set grad hop, the placeholder's + # meta["val"] will be None and fails our verifier for placeholder. + gm, export_graph_signature = replace_set_grad_with_hop_pass( + gm, export_graph_signature + ) + + gm, export_graph_signature = replace_autocast_with_hop_pass( + gm, export_graph_signature + ) + + # Remove nn_module_stack, stack_trace metadata from all placeholders/inputs nodes. + for _mod in gm.modules(): + if not isinstance(_mod, torch.fx.GraphModule): + continue + for node in _mod.graph.nodes: + if node.op in ["placeholder", "output"]: + node.meta.pop("nn_module_stack", None) + node.meta.pop("stack_trace", None) + + # Prettify names for placeholder nodes. + assert export_graph_signature is not None + if _prettify_placeholder_names: + placeholder_naming_pass( + gm, + export_graph_signature, + mod, + fake_args, + fake_kwargs, + fake_params_buffers, + constants, + ) + + _preserve_requires_grad_pass( + gm, export_graph_signature, fake_params_buffers, constants, flat_fake_args + ) + + return ATenExportArtifact( + gm, + export_graph_signature, + constants, + ) + + +def _rename_constants_nodes( + gm: torch.fx.GraphModule, + graph_signature: ExportGraphSignature, +) -> None: + """ + For strict mode, rename constants nodes that were previously annotated as buffers. + """ + # handle name collisions with existing constants + node_names = {node.name for node in gm.graph.nodes} + + def rename_constant(name): + if name in node_names: + n = 1 + while (dup_name := f"{name}_{n}") in node_names: + n += 1 + name = dup_name + node_names.add(name) + return name + + # use input specs to map names from buffers to constants + buffer_prefix = placeholder_prefixes[InputKind.BUFFER] + const_prefix = placeholder_prefixes[InputKind.CONSTANT_TENSOR] + buffer_to_constant = {} + for spec in graph_signature.input_specs: + if spec.kind == InputKind.CONSTANT_TENSOR and not spec.arg.name.startswith( + const_prefix + ): + if spec.arg.name.startswith(buffer_prefix): # map from buffer to constants + c_name = rename_constant( + const_prefix + spec.arg.name[len(buffer_prefix) :] + ) + else: # lifted constant + c_name = rename_constant(const_prefix + spec.arg.name) + buffer_to_constant[spec.arg.name] = c_name + spec.arg.name = c_name + for spec in graph_signature.output_specs: + if spec.arg.name in buffer_to_constant: + spec.arg.name = buffer_to_constant[spec.arg.name] + + # Rename constants nodes for all modules + for mod in gm.modules(): + if not isinstance(mod, torch.fx.GraphModule): + continue + for node in mod.graph.nodes: + if node.name in buffer_to_constant: + node.name = node.target = buffer_to_constant[node.name] + mod.recompile() + + +def _restore_state_dict( + original_module: torch.nn.Module, traced_module: torch.fx.GraphModule +) -> None: + """ + Restores the state dict of the traced module to that of the original module. + """ + param_buffer_table = _get_param_buffer_mapping(original_module, traced_module) + # Don't want to change the convention of previous call. + param_buffer_table_reverse = {v: k for k, v in param_buffer_table.items()} + + # Replace state dict attr names with the fqn + for name, _ in list( + chain( + original_module.named_parameters(remove_duplicate=False), + # pyrefly: ignore [bad-argument-type] + original_module.named_buffers(remove_duplicate=False), + ) + ): + if name in param_buffer_table_reverse: + dynamo_name = param_buffer_table_reverse[name] + param = torch.fx.graph_module._get_attr(traced_module, dynamo_name) + torch.fx.graph_module._assign_attr(param, traced_module, name) + torch.fx.graph_module._del_attr(traced_module, dynamo_name) + + # Replace graph getattr nodes with the correct name + for node in traced_module.graph.nodes: + if node.op == "get_attr": + attr_name = node.target + if attr_name in param_buffer_table: + node.target = param_buffer_table[attr_name] + + traced_module.recompile() + + +def _get_module_hierarchy(mod: torch.nn.Module) -> dict[str, str]: + return { + name: type(m).__name__ for name, m in mod.named_modules(remove_duplicate=False) + } + + +def _make_module_call_graph( + in_spec: TreeSpec, + out_spec: TreeSpec, + module_call_signatures: dict[str, ModuleCallSignature], + forward_arg_names: list[str] | None = None, +) -> list[ModuleCallEntry]: + original = [ + ModuleCallEntry(fqn=fqn, signature=module_call_signatures.get(fqn)) + for fqn in _EXPORT_MODULE_HIERARCHY # type: ignore[union-attr] + ] + assert original[0].fqn == "" + original[0].signature = ModuleCallSignature( + inputs=[], + outputs=[], + in_spec=in_spec, + out_spec=out_spec, + forward_arg_names=forward_arg_names, + ) + additional = [ + ModuleCallEntry(fqn=fqn, signature=signature) + for fqn, signature in module_call_signatures.items() + if fqn not in _EXPORT_MODULE_HIERARCHY # type: ignore[operator] + ] + return [*original, *additional] + + +class _ExportModuleSpecTrackerDict(dict): + pass + + +def _export_to_torch_ir( + f: Callable, + args: tuple[Any, ...], + kwargs: dict[str, Any] | None = None, + dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any] | None = None, + *, + preserve_module_call_signature: tuple[str, ...] = (), + disable_constraint_solver: bool = False, + prefer_deferred_runtime_asserts_over_guards: bool = False, + restore_fqn: bool = True, + _log_export_usage: bool = True, + same_signature: bool = True, +) -> torch.fx.GraphModule: + """ + Traces either an nn.Module's forward function or just a callable with PyTorch + operations inside and produce a torch.fx.GraphModule in torch IR. + """ + + if _log_export_usage: + log_export_usage(event="export.private_api", flags={"_export_to_torch_ir"}) + + if not isinstance(args, tuple): + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expecting `args` to be a tuple of example positional inputs, got {type(args)}", + ) + + kwargs = kwargs or {} + + # Map ints to a wrapper structure to help us mark it as dynamic, if it is + # dynamic. We will unwrap ints in fakify later. + args, kwargs = pytree.tree_map_only(int, _IntWrapper, (args, kwargs)) + + combined_args = _combine_args(f, args, kwargs) + _check_dynamic_shapes(combined_args, dynamic_shapes) + constraints = _process_dynamic_shapes(combined_args, dynamic_shapes) + + # Unwrap static ints -- in the case where we have an empty graph + # containing just integer computation, dynamo will run its generated + # bytecode with these args/kwargs, which will error because we cannot + # directly apply int operations on IntWrapper. So we will just unwrap + # them here. + args, kwargs = pytree.tree_map_only( + _IntWrapper, + lambda a: a.val + if a.dynamism is None or a.dynamism.type == _DimHintType.STATIC + else a, + (args, kwargs), + ) + + dynamo_cfg = dataclasses.replace( + DEFAULT_EXPORT_DYNAMO_CONFIG, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + ) + + def use_legacy_dynamo_graph_capture() -> bool: + return bool( + constraints # dynamic shape + or dynamic_shapes # dynamic shape + or isinstance(f, torch.fx.GraphModule) # retracing + or preserve_module_call_signature # unflatten + or torch._functorch.config.fake_tensor_propagate_real_tensors # draft + or torch._export.config.use_legacy_dynamo_graph_capture + ) + + with torch._dynamo.config.patch(dataclasses.asdict(dynamo_cfg)): + try: + module_call_specs: dict[str, dict[str, pytree.TreeSpec]] = ( + _ExportModuleSpecTrackerDict() + ) + ctx = nullcontext() + if not isinstance(f, torch.fx.GraphModule): + ctx = _wrap_submodules( # type: ignore[assignment] + f, preserve_module_call_signature, module_call_specs + ) + with ctx, _ignore_backend_decomps(): + if torch._export.config.use_new_tracer_experimental: + from torch._dynamo.functional_export import ( + _dynamo_graph_capture_for_export, + dynamo_graph_capture_for_export, + ) + + if use_legacy_dynamo_graph_capture(): + dynamo_graph_capture = _dynamo_graph_capture_for_export( + f, constraints=constraints, dynamic_shapes=dynamic_shapes + ) + else: + dynamo_graph_capture = torch._dynamo.config.patch( + replay_side_effects=False + )(dynamo_graph_capture_for_export(f)) + # We can't serialize entire fake mode yet, so this is to make sure + # things like copy.deepcopy(ep.graph_module) not crash. + # see test_export.py::test_custom_tag_metadata_re_export + # Once we delete the old strict export, we can use + gm_torch_level = dynamo_graph_capture(*args, **kwargs) + # We can't serialize entire fake mode yet, so this is to make sure + # things like copy.deepcopy(ep.graph_module) not crash. + # see test_export.py::test_custom_tag_metadata_re_export + # Once we delete the old strict export, we can use this fake mode in the + # subsequent logic when lowering to aten IR. + del gm_torch_level.meta["fake_mode"] + + else: + gm_torch_level, _ = torch._dynamo.export( + f, + dynamic_shapes=dynamic_shapes, # type: ignore[arg-type] + constraints=constraints, # type: ignore[arg-type] + assume_static_by_default=True, + tracing_mode="symbolic", + disable_constraint_solver=disable_constraint_solver, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + _log_export_usage=_log_export_usage, + same_signature=same_signature, + )( + *args, + **kwargs, + ) + gm_torch_level.meta["module_call_specs"] = module_call_specs + except (ConstraintViolationError, ValueRangeError) as e: + raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e)) # noqa: B904 + except GuardOnDataDependentSymNode as e: + raise UserError( # noqa: B904 + UserErrorType.ANTI_PATTERN, + f"Consider annotating your code using torch._check*(). {str(e)}", + case_name="constrain_as_size_example", + ) + + if isinstance(f, torch.nn.Module) and restore_fqn: + _restore_state_dict(f, gm_torch_level) + + return gm_torch_level + + +def _aot_export_joint_with_descriptors( + stack, + mod, + args, + *, + kwargs, + decompositions, + fake_params_buffers, + _record_nn_module_stack=True, +): + from torch._functorch._aot_autograd.graph_compile import aot_stage2_export + from torch._functorch._aot_autograd.input_output_analysis import ( + create_graph_signature, + ) + + joint_with_descriptors = aot_export_joint_with_descriptors( + stack, + mod, + args, + kwargs=kwargs, + decompositions=decompositions, + _record_nn_module_stack=_record_nn_module_stack, + ) + # Convert JointWithDescriptors to graph module and ViewAndMutationMeta + gm, fw_metadata = aot_stage2_export( + joint_with_descriptors._aot_state, + joint_with_descriptors._aot_graph_capture, + ) + + assert isinstance(gm, torch.fx.GraphModule) + + # Create GraphSignature from the metadata + graph_signature = create_graph_signature( + gm, + fw_metadata, + joint_with_descriptors.in_spec, + joint_with_descriptors.out_spec, + user_args_flat=pytree.tree_leaves((args, kwargs)), + params_and_buffers_flat=list(fake_params_buffers.values()), + param_names=joint_with_descriptors.params_spec, + buffer_names=joint_with_descriptors.buffers_spec, + trace_joint=False, + num_user_fw_outs=None, + loss_index=None, + ) + return gm, graph_signature + + +def _export_to_aten_ir( + mod: torch.nn.Module, + fake_args, + fake_kwargs, + fake_params_buffers, + constant_attrs: ConstantAttrMap, + produce_guards_callback=None, + *, + transform=lambda x: x, # TODO(zhxchen17) Revisit if this is needed later. + pre_dispatch=False, + decomp_table=None, + _prettify_placeholder_names: bool = True, + decompose_custom_triton_ops: bool = False, +) -> ATenExportArtifact: + custom_triton_ops_decomposition_ctx = ( + nullcontext + if decompose_custom_triton_ops + else _disable_custom_triton_op_functional_decomposition + ) + # This _reparameterize_module makes sure inputs and module.params/buffers have the same fake_mode, + # otherwise aot_export_module will error out because it sees a mix of fake_modes. + # And we want aot_export_module to use the fake_tensor mode in dynamo to keep the pipeline easy to reason about. + with ExitStack() as stack: + stack.enter_context( + torch.nn.utils.stateless._reparametrize_module( + mod, + fake_params_buffers, + tie_weights=True, + strict=True, + stack_weights=True, + ) + ) + stack.enter_context(_ignore_backend_decomps()) + stack.enter_context(_compiling_state_context()) + stack.enter_context(custom_triton_ops_decomposition_ctx()) + stack.enter_context(torch.no_grad()) + + gm, graph_signature = transform(_aot_export_joint_with_descriptors)( + stack, + mod, + fake_args, + kwargs=fake_kwargs, + decompositions=decomp_table, + fake_params_buffers=fake_params_buffers, + _record_nn_module_stack=True, + ) + + def _maybe_fixup_gm_and_output_node_meta(old_gm, new_gm): + if isinstance(old_gm, torch.fx.GraphModule): + if hasattr(old_gm, "meta"): + new_gm.meta.update(old_gm.meta) + old_output_node = list(old_gm.graph.nodes)[-1] + new_output_node = list(new_gm.graph.nodes)[-1] + assert old_output_node.op == "output" and new_output_node.op == "output" + # make sure we don't override any meta + if "desc" in new_output_node.meta: + del new_output_node.meta["desc"] + new_output_node.meta.update(old_output_node.meta) + + # TODO unfortunately preserving graph-level metadata and output node's meta + # is not working well with aot_export. So we manually copy it. + # (The node-level meta is addressed above.) + _maybe_fixup_gm_and_output_node_meta(mod, gm) + + # Run produce guards before we handle runtime asserts. + # This means we run the export solver before the runtime asserts pass. + # Right now this doesn't mean much - the export solver is only there for suggested fixes, + # and we won't even get to constraint solving if that's needed. + # But if in future we want to control what runtime asserts are emitted for export, + # or rely on produce_guards + solver for some simplification on runtime asserts, this probably makes sense. + if produce_guards_callback: + try: + produce_guards_callback(gm) + except (ConstraintViolationError, ValueRangeError) as e: + raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e)) # noqa: B904 + + return _produce_aten_artifact( + gm=gm, + mod=mod, + constant_attrs=constant_attrs, + graph_signature=graph_signature, + pre_dispatch=pre_dispatch, + fake_args=fake_args, + fake_kwargs=fake_kwargs, + fake_params_buffers=fake_params_buffers, + _prettify_placeholder_names=_prettify_placeholder_names, + ) + + +def _get_forward_arg_names( + mod: torch.nn.Module, + args: tuple[Any, ...], + kwargs: dict[str, Any] | None = None, +) -> list[str]: + """ + Gets the argument names to forward that are used, for restoring the + original signature when unlifting the exported program module. + - Positional args: retain the original argument names, and enumerate + *args as args_0, args_1, ... + - Keyword args: retain the original kwarg names in the order specified + by the user. This order seems to matter for the current state of + export lifted modules. + """ + sig = inspect.signature(mod.forward) + _args = sig.bind_partial(*args).arguments + + names: list[str] = [] + for name, value in _args.items(): + # handle variable number of positional args + if sig.parameters[name].kind == inspect._ParameterKind.VAR_POSITIONAL: + names.extend([f"{name}_{i}" for i, _ in enumerate(value)]) + else: + names.append(name) + # order of kwargs matters for input spec + if kwargs: + names.extend([kwarg for kwarg, _ in kwargs.items()]) + + return names + + +def _get_non_persistent_buffers(mod: torch.nn.Module) -> set[str]: + """ + Returns set of non-persistent buffers in a module and its submodules. + """ + result: set[str] = set() + for name, m in mod.named_modules(remove_duplicate=False): + if name: + result.update(f"{name}.{b}" for b in m._non_persistent_buffers_set) + else: + result.update(m._non_persistent_buffers_set) + return result + + +def _rewrite_dynamo_tensor_constants( + orig_mod_buffers: set[torch.Tensor], + traced_mod_buffers: dict[str, torch.Tensor], + graph_signature: ExportGraphSignature, + constants: dict[str, _ConstantAttributeType], +) -> None: + """ + Dynamo erroneously marks tensor attributes on modules as buffers. + Rewrite them to be tensor constants. + """ + for spec in graph_signature.input_specs: + if spec.kind == InputKind.BUFFER: + assert spec.target is not None + value = traced_mod_buffers[spec.target] + if value not in orig_mod_buffers: + # This was a tensor constant erroneously marked as a buffer. + # Convert it into a constant in the graph signature, and add its + # value to the constants table. + spec.kind = InputKind.CONSTANT_TENSOR + constants[spec.target] = value # type: ignore[arg-type] + + +def _move_non_persistent_buffers_to_tensor_constants( + orig_mod: torch.nn.Module, + graph_signature: ExportGraphSignature, + constants: dict[str, _ConstantAttributeType], +) -> None: + """ + Moves non-persistent buffers to tensor constants. + """ + for spec in graph_signature.input_specs: + if spec.kind == InputKind.BUFFER and not spec.persistent: + assert spec.target is not None + assert spec.target not in constants + constants[spec.target] = orig_mod.get_buffer(spec.target) # type: ignore[arg-type] + + +def _verify_nn_module_stack(graph_module: torch.fx.GraphModule) -> None: + """ + Perform nn_module_stack checks on the graph. + Current constraints: + For the top level graph: + - populated for 'call_function', 'get_attr' + - None for 'placeholder', 'output' + For submodule graphs: + - None for 'placeholder', output' + + TODO(pianpwk): make this a consistent node-level check once nn_module_stack is populated for cond submodules. + """ + # Check top-level graph for all nodes, all graphs for placeholder & output nodes + for i, mod in enumerate([graph_module] + list(graph_module.modules())): + if not isinstance(mod, torch.fx.GraphModule): + continue + for node in mod.graph.nodes: + if node.op in ["call_function", "get_attr"]: + if i == 0: + if ( + nn_module_stack := node.meta.get("nn_module_stack", None) + ) is None: + raise SpecViolationError( + f"Node {node} of type {node.op} is missing nn_module_stack metadata" + ) + if not all( + isinstance(k, str) + and isinstance(v, tuple) + and len(v) == 2 + and all(isinstance(x, str) for x in v) + for k, v in nn_module_stack.items() + ): + raise SpecViolationError( + f"Node {node} of type {node.op} has incorrect nn_module_stack metadata format" + f"expected Dict[str, Tuple[str, str]], but got {nn_module_stack}" + ) + elif node.op in ["placeholder", "output"]: + if node.meta.get("nn_module_stack", None): + raise SpecViolationError( + f"Node {node} of type {node.op} contains nn_module_stack metadata, this should be None" + ) + + +def _verify_stack_trace(graph_module: torch.fx.GraphModule) -> None: + """ + Perform stack trace checks on the graph. + Constraints: + - None or non-empty str for 'call_function', 'get_attr' + - None for 'placeholder', 'output' + """ + for mod in [graph_module, *graph_module.modules()]: + if not isinstance(mod, torch.fx.GraphModule): + continue + for node in graph_module.graph.nodes: + stack_trace = node.meta.get("stack_trace", None) + if node.op in ["call_function", "get_attr"]: + if not (stack_trace is None or isinstance(stack_trace, str)): + raise SpecViolationError( + f"Node {node} of type {node.op} has invalid stack_trace metadata, " + f"expected a string or None but instead found: {stack_trace}" + ) + elif node.op in ["placeholder", "output"]: + if stack_trace: + raise SpecViolationError( + f"Node {node} of type {node.op} contains stack_trace metadata, " + f"expected None but instead found: {stack_trace}" + ) + + +def _verify_placeholder_names( + gm: torch.fx.GraphModule, sig: ExportGraphSignature +) -> None: + """ + Performs a sanity check on the placeholder node names. + - User input nodes: no restrictions, should match the original forward() signature + - Params/buffers/constants/custom_obj/token nodes: should start with prefixes defined in + """ + name_to_kind = {spec.arg.name: spec.kind for spec in sig.input_specs} + for mod in gm.modules(): + if not isinstance(mod, torch.fx.GraphModule): + continue + for node in mod.graph.nodes: + if node.op == "placeholder": + if node.name not in name_to_kind: + continue + node_kind = name_to_kind[node.name] + prefix = placeholder_prefixes[node_kind] + if not node.name.startswith(prefix): + raise SpecViolationError( + f"Placeholder node name {node.name} does not follow spec for {node_kind}, name should have prefix: {prefix}" + ) + + +def get_ep_stats(ep: ExportedProgram) -> dict[str, Any]: + op_count = 0 + op_set = set() + for m in ep.graph_module.modules(): + if not isinstance(m, torch.fx.GraphModule): + continue + for node in m.graph.nodes: + if node.op != "call_function": + continue + op_count += 1 + assert hasattr(node.target, "__module__") + assert hasattr(node.target, "__name__") + op_set.add(f"{node.target.__module__}.{node.target.__name__}") + return {"op_count": op_count, "op_set": op_set} + + +_EXPORT_FLAGS: set[str] | None = None +_EXPORT_MODULE_HIERARCHY: dict[str, str] | None = None + + +def _log_export_wrapper(fn): + @functools.wraps(fn) + def wrapper(*args, **kwargs): + global _EXPORT_FLAGS, _EXPORT_MODULE_HIERARCHY + try: + start = time.time() + ep = fn(*args, **kwargs) + end = time.time() + log_export_usage( + event="export.time", + metrics=end - start, + flags=_EXPORT_FLAGS, + **get_ep_stats(ep), + ) + except Exception as e: + t = type(e) + error_type = t.__module__ + "." + t.__qualname__ + case_name = get_class_if_classified_error(e) + if case_name is not None: + log.error(exportdb_error_message(case_name)) + log_export_usage( + event="export.error.classified", + type=error_type, + message=str(e), + flags=_EXPORT_FLAGS, + ) + else: + log_export_usage( + event="export.error.unclassified", + type=error_type, + message=str(e), + flags=_EXPORT_FLAGS, + ) + + if hasattr(e, "partial_fx_graph"): + print( + e.partial_fx_graph, + file=sys.stderr, + ) + + raise e + finally: + _EXPORT_FLAGS = None + _EXPORT_MODULE_HIERARCHY = None + + return ep + + return wrapper + + +def _process_jit_trace_inputs_for_export(example_inputs, example_kwarg_inputs): + if not isinstance(example_inputs, (tuple, list, dict)): + example_inputs = (example_inputs,) + + elif isinstance(example_inputs, list): + example_inputs = tuple(example_inputs) + + elif ( + isinstance(example_inputs, (torch.Tensor, dict)) + and example_kwarg_inputs is None + ): + example_inputs = (example_inputs,) + + if example_kwarg_inputs is None: + example_kwarg_inputs = {} + return example_inputs, example_kwarg_inputs + + +def _get_original_state_dict(mod: torch.nn.Module) -> dict[str, Any]: + # Explicitly not calling mode.state_dict() as we do not want the module state for serialization + # but the running module state so we can always match by id() the entries here with the graph inputs + named_parameters = dict(mod.named_parameters(remove_duplicate=False)) + named_buffers = dict(mod.named_buffers(remove_duplicate=False)) + original_state_dict = named_parameters | named_buffers + + non_persistent_buffers = _get_non_persistent_buffers(mod) + for k in non_persistent_buffers: + original_state_dict.pop(k, None) + + return original_state_dict + + +def _process_export_inputs( + mod: torch.nn.Module, + args: tuple[object, ...], + kwargs: dict[str, object] | None, + dynamic_shapes: _DynamicShapesSpec + | torch.export.AdditionalInputs + | torch.export.ShapesCollection + | None, +) -> tuple[ + tuple[object, ...], + dict[str, object], + TreeSpec, + _DynamicShapesSpec | None, + Callable[[ExportedProgram], None], +]: + """ + Process and validate export inputs for the torch.export API. + + This function validates the input arguments, normalizes kwargs, computes input tree specs, + and handles special dynamic shapes cases like AdditionalInputs and ShapesCollection. + + Args: + mod: The PyTorch module to be exported. + args: Tuple of example positional inputs for the module. + kwargs: Optional dictionary of example keyword inputs. + dynamic_shapes: Optional specification for dynamic shapes. Can be: + - dict mapping argument names to dynamic shape specifications + - tuple/list specifying dynamic shapes for each input in order + - torch.export.AdditionalInputs object with verification callback + - torch.export.ShapesCollection object + + Returns: + A tuple containing: + - args: Validated tuple of positional inputs + - kwargs: Normalized dictionary of keyword inputs (empty dict if None was passed) + - original_in_spec: TreeSpec representing the flattened input structure + - dynamic_shapes: Processed dynamic shapes specification + - verify_additional_inputs: Callback function for additional input verification + + Raises: + UserError: If args is not a tuple. + """ + if not isinstance(args, tuple): + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expecting `args` to be a tuple of example positional inputs, got {type(args)}", + ) + kwargs = kwargs if kwargs is not None else {} + if pytree.is_namedtuple_instance(args): + args = tuple(args) + + _, original_in_spec = pytree.tree_flatten((args, kwargs)) + + verify_additional_inputs: Callable[[ExportedProgram], None] + out_dynamic_shapes: _DynamicShapesSpec | None + if isinstance(dynamic_shapes, torch.export.AdditionalInputs): + verify_additional_inputs = dynamic_shapes.verify # type: ignore[assignment] + out_dynamic_shapes = dynamic_shapes.dynamic_shapes(mod, args, kwargs) # type: ignore[assignment] + else: + verify_additional_inputs = lambda ep: None # noqa: E731 + if isinstance(dynamic_shapes, torch.export.ShapesCollection): + out_dynamic_shapes = dynamic_shapes.dynamic_shapes(mod, args, kwargs) # type: ignore[assignment] + else: + out_dynamic_shapes = dynamic_shapes + + return args, kwargs, original_in_spec, out_dynamic_shapes, verify_additional_inputs + + +def _get_module_call_graph( + export_artifact: ExportArtifact, + preserve_module_call_signature: tuple[str, ...], + strict_mode_export: bool, + forward_arg_names: list[str] | None = None, +) -> tuple[torch.fx.GraphModule, list[ModuleCallEntry]]: + """ + In-place modify the graph module in export_artifact, remove _export_tracepoint nodes and + return module_call_graph. + """ + gm: torch.fx.GraphModule = export_artifact.aten.gm + export_graph_signature: ExportGraphSignature = export_artifact.aten.sig + module_call_specs: dict[str, dict[str, TreeSpec]] = ( + export_artifact.module_call_specs + ) + in_spec: TreeSpec = export_artifact.in_spec + out_spec: TreeSpec = export_artifact.out_spec + + # Make module signatures. + module_call_signatures: dict[str, ModuleCallSignature] = {} + for fqn, specs in module_call_specs.items(): + mod_fqn = _strip_root(fqn) if not strict_mode_export else fqn + module_call_signatures[mod_fqn] = ModuleCallSignature( + inputs=[], + outputs=[], + in_spec=specs["in_spec"], + out_spec=specs["out_spec"], + forward_arg_names=None, # we only propagate forward_arg_names for the top level module + ) + + if len(preserve_module_call_signature) > 0: + if not strict_mode_export: + _rewrite_tracepoint_node(gm) + res = CollectTracepointsPass(module_call_signatures, export_graph_signature)(gm) + assert res is not None + gm = res.graph_module + + assert _EXPORT_MODULE_HIERARCHY is not None + module_call_graph = _make_module_call_graph( + in_spec, + out_spec, + module_call_signatures, + forward_arg_names, + ) + return gm, module_call_graph + + +def _get_range_constraints( + mod: torch.nn.Module, + export_artifact: ExportArtifact, + args, + kwargs, + dynamic_shapes, +): + gm: torch.fx.GraphModule = export_artifact.aten.gm + export_graph_signature: ExportGraphSignature = export_artifact.aten.sig + fake_mode: FakeTensorMode = export_artifact.fake_mode + num_lifted = next( + ( + i + for i, s in enumerate(export_graph_signature.input_specs) + if s.kind == InputKind.USER_INPUT + ), + len(export_graph_signature.input_specs), + ) + combined_args = _combine_args(mod, args, kwargs) + + # This is because we trace based on the kwargs passed in from user + # not based on the signature. I feel it would be better to just enforce + # one ordering at the start of tracing to avoid confusions, but that is + # bigger refactor, so do this to unblock for now. + combined_args_traced_order = {} + for arg in combined_args: + if arg not in kwargs: + combined_args_traced_order[arg] = combined_args[arg] + + for key in kwargs: + combined_args_traced_order[key] = kwargs[key] + + combined_args = combined_args_traced_order + + range_constraints = make_constraints( + fake_mode, + gm, + combined_args, + dynamic_shapes, + num_lifted, + ) + return range_constraints + + +def _get_inline_constraints(fake_mode: FakeTensorMode): + assert fake_mode.shape_env is not None + return { + k: v + for k, v in fake_mode.shape_env.var_to_range.items() + if free_unbacked_symbols(k) + } + + +@contextmanager +def patch_forward(obj: torch.nn.Module, new_method): + """Helper method to make it easier to cleanly torch.export() a method on a + module that is not `forward`. + """ + # Save the original method + original_method = obj.forward + + # Patch the method + obj.forward = new_method.__get__(obj, obj.__class__) + + try: + yield + finally: + # Restore the original method + obj.forward = original_method + + +@contextmanager +def _temp_disable_texpr_fuser(): + original_state = torch._C._jit_texpr_fuser_enabled() + torch._C._jit_set_texpr_fuser_enabled(False) + try: + yield + finally: + torch._C._jit_set_texpr_fuser_enabled(original_state) + + +def _strict_export( + mod: torch.nn.Module, + args: tuple[Any, ...], + kwargs: dict[str, Any], + dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any] | None, + preserve_module_call_signature: tuple[str, ...], + orig_in_spec: TreeSpec, + prefer_deferred_runtime_asserts_over_guards: bool, + _to_aten_func: Callable, +) -> ExportArtifact: + """ + _to_aten_func can either be `_export_to_aten_ir_make_fx` or `_export_to_aten_ir` + """ + + gm_torch_level = _export_to_torch_ir( + mod, + args, + kwargs, + dynamic_shapes, + preserve_module_call_signature=preserve_module_call_signature, + restore_fqn=False, # don't need to restore because we will do it later + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + _log_export_usage=False, + ) + + # We detect the fake_mode by looking at gm_torch_level's placeholders, this is the fake_mode created in dynamo. + ( + fake_args, + fake_kwargs, + dynamo_fake_mode, + ) = _extract_fake_inputs(gm_torch_level, args, kwargs) + + fake_params_buffers = _fakify_params_buffers(dynamo_fake_mode, gm_torch_level) + + # First, we want to pass through the graph to try populating + # val field for getattr if there is anything missing. + # This can happen when quantization adds extra params and forgets + # to update "val" + for node in gm_torch_level.graph.nodes: + if node.op == "get_attr" and "val" not in node.meta: + attr = getattr(gm_torch_level, node.target) + # Checks if it is not a HigherOrderOp branch or a module + if not isinstance(attr, torch.nn.Module): + assert dynamo_fake_mode is not None, ( + "Cannot find dynamo_fake_mode. This could be due to the exported graph module have no placeholders." + ) + node.meta["val"] = dynamo_fake_mode.from_tensor( + attr, static_shapes=True + ) + + # Fix the graph output signature to be tuple if scalar + + # gm_torch_level.graph._codegen is made a _PyTreeCodeGen in rewrite_signature in eval_frame.py + assert isinstance(gm_torch_level.graph._codegen, torch.fx.graph._PyTreeCodeGen) + + # Calling gm_torch_level._out_spec is not safe because gm_torch_level might be + # a _LazyGraphModule, which does not populate _out_spec when calling recompile(). + # TODO: Fix recompile() in _LazyGraphModule. T207713214 + out_spec = orig_out_spec = gm_torch_level.graph._codegen.pytree_info.out_spec + + # Used to get rid of lint type error. + assert out_spec is not None + assert orig_out_spec is not None + + # aot_export expect the return type to always be a tuple. + if out_spec.type not in (list, tuple): + out_spec = pytree.treespec_tuple([out_spec]) + + orig_arg_names = gm_torch_level.graph._codegen.pytree_info.orig_args # type: ignore[attr-defined] + + gm_torch_level.graph._codegen.pytree_info = _PyTreeInfo( + orig_arg_names, + gm_torch_level._in_spec, + out_spec, + ) + gm_torch_level.recompile() + + _normalize_nn_module_stack(gm_torch_level, type(mod)) + + params_buffers_to_node_meta = _collect_param_buffer_metadata(gm_torch_level) + + # When aot_export lifts the params, we lose metadata (e.g. source_fn_stack, stack_trace) + # from the param nodes as they are treated as fresh inputs + # Therefore, we manually extract them before calling into aot_export + # params_buffers_to_node_meta = _collect_param_buffer_metadata(gm_torch_level) + + constant_attrs = _gather_constant_attrs(mod) + param_buffer_table: dict[str, str] = _get_param_buffer_mapping(mod, gm_torch_level) + + # Dynamo does not track which buffers were registered as non-persistent. This info + # is available in the original module, so we transfer it to the traced module. Also, + # since we didn't restore original param/buffer names yet, we must use traced names. + non_persistent_buffers = _get_non_persistent_buffers(mod) + reverse_name_lookup = {orig: traced for traced, orig in param_buffer_table.items()} + gm_torch_level._non_persistent_buffers_set = { + reverse_name_lookup[name] + for name in non_persistent_buffers + if name in reverse_name_lookup + } + + tx = TracingContext(dynamo_fake_mode) + with ( + dynamo_fake_mode, + tracing(tx), + mock.patch.object(dynamo_fake_mode, "allow_non_fake_inputs", True), + ): + aten_export_artifact = _to_aten_func( + gm_torch_level, + # NOTE: graph module expects only positional args + _convert_to_positional_args(orig_arg_names, fake_args, fake_kwargs), + {}, + fake_params_buffers, + constant_attrs, + ) + + # Decompose for readability. + gm = aten_export_artifact.gm + export_graph_signature = aten_export_artifact.sig + constants = aten_export_artifact.constants + + _populate_param_buffer_metadata_to_new_gm( + params_buffers_to_node_meta, gm, export_graph_signature + ) + + # Do some cleanups on the graph module to restore the state dict to the + # expected form. Each of these steps should probably get fixed upstream. + # 1. Remove tensor constants that were added as buffers. + _rewrite_dynamo_tensor_constants( + orig_mod_buffers=set(mod.buffers()), + traced_mod_buffers=dict(gm_torch_level.named_buffers()), + graph_signature=export_graph_signature, + constants=constants, + ) + # 2. Restore FQN of param/buffers + _replace_param_buffer_names(param_buffer_table, export_graph_signature) + + # 3. Move non-persistent buffers to tensor constants + _move_non_persistent_buffers_to_tensor_constants( + mod, export_graph_signature, constants + ) + + # 4. Rewrite constants to have the same FQN as the original module. + _remap_constants(constant_attrs, export_graph_signature, constants) + + # 5. Rename constants nodes in graph module from buffers to constants + _rename_constants_nodes(gm, export_graph_signature) + + return ExportArtifact( + aten=aten_export_artifact, + in_spec=orig_in_spec, + out_spec=orig_out_spec, + fake_mode=dynamo_fake_mode, + module_call_specs=gm_torch_level.meta["module_call_specs"], + ) + + +def _export_to_aten_ir_make_fx( + mod: torch.nn.Module, + fake_args, + fake_kwargs, + fake_params_buffers, + constant_attrs: ConstantAttrMap, + produce_guards_callback=None, + transform=lambda x: x, +) -> ATenExportArtifact: + def _make_fx_helper(stack, mod, args, kwargs, **flags): + kwargs = kwargs or {} + + named_parameters = dict(mod.named_parameters(remove_duplicate=False)) + named_buffers = dict(mod.named_buffers(remove_duplicate=False)) + + params_and_buffers = {**named_parameters, **named_buffers} + params_and_buffers_flat, params_spec = pytree.tree_flatten(params_and_buffers) + params_and_buffers_flat = tuple(params_and_buffers_flat) + + param_len = len(named_parameters) + buffer_len = len(named_buffers) + params_len = len(params_and_buffers) + + functional_call = create_functional_call( + mod, params_spec, params_len, store_orig_mod=True + ) + + params_buffers_args: list[Any] = [] + params_buffers_args.extend(params_and_buffers_flat) + params_buffers_args.extend(args) + + flat_fn, out_spec = create_tree_flattened_fn( + functional_call, params_buffers_args, kwargs + ) + flat_args, in_spec = pytree.tree_flatten((params_buffers_args, kwargs)) + + @functools.wraps(flat_fn) + def wrapped_fn(*args): + return tuple(flat_fn(*args)) + + with enable_python_dispatcher(): + ctx = nullcontext() + non_strict_root = getattr(mod, "_export_root", None) + if non_strict_root is not None: + ctx = _detect_attribute_assignment(non_strict_root) # type: ignore[assignment] + + # For any buffer that is assigned, we want to associate it to the final proxy node + # that it is assigned to. This node can then be copied into the buffer. + assigned_buffers: dict[str, str] = {} + hook = register_buffer_assignment_hook( + non_strict_root, assigned_buffers + ) + + def custom_getattribute(self, attr, *, original_getattr, attrs_to_proxy): + """ + The idea here is that we override subclass getattr methods to proxy + inner tensors and metadata. Because of infinite loop shenanigans, we have + to manually construct the getattr proxy nodes without relying on torch function + system. + """ + out = original_getattr(self, attr) + if attr in attrs_to_proxy: + if torch._C._is_torch_function_mode_enabled(): + if isinstance(out, torch.Tensor): + # When we get here there is no guarantee that we will hit the + # PreDispatchTorchFunctionMode, so we manually peak into the torch + # function mode list and tweak the PreDispatchTorchFunctionMode. + # This has side effect of proxying stuff like + # proxy.node.meta["val"] = extract_val(val) because at that time, torch function + # mode is still active. It seems bad to turn it off inside proxy_tensor.py, so + # I guess we will just rely on DCE for now to remove extra stuff like detach + torch_function_mode_stack = ( + torch.overrides._get_current_function_mode_stack() + ) + for mode in torch_function_mode_stack: + if isinstance(mode, PreDispatchTorchFunctionMode): + tracer = mode.tracer + proxy = get_proxy_slot(self, tracer).proxy + inner_proxy = tracer.create_proxy( + "call_function", + torch.ops.export.access_subclass_inner_tensor.default, + (proxy, attr), + {}, + ) + track_tensor_tree( + out, inner_proxy, constant=None, tracer=tracer + ) + return out + + @contextmanager + def override_getattribute_for_subclasses(args): + """ + Context manager that temporarily monkey patches + tensor.__getattribute__ so that we can intercept it at + torch_function layer. + """ + + # Dictionary that tracks subclass type to original getattr function + # and the attributes we can proxy. + tensor_type_to_old_getattribute: dict[ + type[torch.Tensor], tuple[Callable, set[str]] + ] = {} + for arg in args: + subclass_types_to_instances: dict[ + type[torch.Tensor], list[type[torch.Tensor]] + ] = get_subclass_typing_container(arg) + for subclass_type in subclass_types_to_instances: + if subclass_type not in tensor_type_to_old_getattribute: + assert len(subclass_types_to_instances[subclass_type]) > 0 + instance = subclass_types_to_instances[subclass_type][0] + # Query subclass specific attrs + attrs_to_proxy = set(dir(instance)) - set(dir(torch.Tensor)) + tensor_type_to_old_getattribute[subclass_type] = ( + subclass_type.__getattribute__, # type: ignore[attr-defined] + attrs_to_proxy, + ) + + try: + for k, ( + old_getattr, + attrs_to_proxy, + ) in tensor_type_to_old_getattribute.items(): + custom = functools.partialmethod( + custom_getattribute, + original_getattr=old_getattr, + attrs_to_proxy=attrs_to_proxy, + ) + k.__getattribute__ = custom # type: ignore[assignment, attr-defined] + yield + finally: + for k, (old_getattr, _) in tensor_type_to_old_getattribute.items(): + k.__getattribute__ = old_getattr # type: ignore[method-assign, attr-defined] + + @contextmanager + def _maybe_restore_grad_state(): + """ + When pre-dispatch export accidentally change grad state, we restore it back. + This can happen when we are calling torch._C._set_grad_enabled directly in the + forward. + """ + old_state = torch.is_grad_enabled() + try: + yield + finally: + torch._C._set_grad_enabled(old_state) + + with ( + ctx, + override_getattribute_for_subclasses(flat_args), + _maybe_restore_grad_state(), + ): + gm = make_fx( + wrapped_fn, + record_module_stack=True, + pre_dispatch=True, + )(*flat_args) + + if non_strict_root is not None: + input_names = _graph_input_names(gm) + buffer_input_names = { + name: input_names[param_len + i] + for i, (name, buf) in enumerate(non_strict_root._buffers.items()) + if buf is not None + } + output_node = list(gm.graph.nodes)[-1] + # We copy nodes corresponding to buffer assignments to buffers in the graph. + for buf, name in assigned_buffers.items(): # type: ignore[possibly-undefined] + buf_node = _find_node(gm, buffer_input_names[buf]) + name_node = _find_node(gm, name) + with gm.graph.inserting_before(output_node): + new_node = gm.graph.create_node( + "call_function", + torch.ops.aten.copy_.default, + args=(buf_node, name_node), + ) + new_node.meta = name_node.meta + + hook.remove() # type: ignore[possibly-undefined] + + def _is_impure(node): + if node.op == "call_function" and node.target in ( + # In export, we ignore any op that is related to + # eager mode profiling call. The expectation is + # that either runtimes provide their own profiling + # OR user wrap the compiled region on a profiling in + # later stage. + torch.ops.profiler._record_function_enter.default, + torch.ops.profiler._record_function_enter_new.default, + torch.ops.profiler._record_function_exit._RecordFunction, + # In theory, we could fix this dead detach and getattr nodes + # from subclass tensors if we carefully rewrite track_tensor_tree + # in a way that it doesn't do any tensor methods. + torch.ops.aten.detach.default, + torch.ops.export.access_subclass_inner_tensor.default, + ): + return False + return True + + gm.graph.eliminate_dead_code(_is_impure) + + # create graph signature + assert out_spec.spec is not None, "out_spec.spec is None!" + input_names = _graph_input_names(gm) + output_names = _graph_output_names(gm) + sig = GraphSignature( + parameters=list(named_parameters), + buffers=list(named_buffers), + user_inputs=input_names[params_len:], + user_outputs=output_names, + inputs_to_parameters=dict(zip(input_names[0:param_len], named_parameters)), + inputs_to_buffers=dict( + zip(input_names[param_len : param_len + buffer_len], named_buffers) + ), + buffers_to_mutate={}, + parameters_to_mutate={}, + user_inputs_to_mutate={}, + in_spec=in_spec, + out_spec=out_spec.spec, + backward_signature=None, + input_tokens=[], + output_tokens=[], + ) + return gm, sig + + # This _reparameterize_module makes sure inputs and module.params/buffers have the same fake_mode, + # otherwise aot_export_module will error out because it sees a mix of fake_modes. + # And we want aot_export_module to use the fake_tensor mode in dynamo to keep the pipeline easy to reason about. + with ExitStack() as stack: + stack.enter_context( + torch.nn.utils.stateless._reparametrize_module( + mod, + fake_params_buffers, + tie_weights=True, + strict=True, + stack_weights=True, + ) + ) + stack.enter_context(_ignore_backend_decomps()) + stack.enter_context(_compiling_state_context()) + gm, graph_signature = transform(_make_fx_helper)( + stack, + mod, + fake_args, + trace_joint=False, + kwargs=fake_kwargs, + ) + + # [NOTE] In training IR, we don't run + # any DCE as a result we preserve constant + # nodes in the graph. make_fx invariant is that + # they don't guarantee every node gets a meta['val'] + # field. Since the actual value is already hardcoded in + # graph, the node.meta here actually doesn't matter. But + # we do this to make spec verifier happy. + for node in gm.graph.nodes: + if ( + node.op == "call_function" + and len(node.users) == 0 + and "val" not in node.meta + ): + node.meta["val"] = None + + if isinstance(mod, torch.fx.GraphModule) and hasattr(mod, "meta"): + gm.meta.update(mod.meta) + + # See comment in _export_to_aten_ir() + if produce_guards_callback: + try: + produce_guards_callback(gm) + except (ConstraintViolationError, ValueRangeError) as e: + raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e)) # noqa: B904 + + return _produce_aten_artifact( + gm=gm, + mod=mod, + constant_attrs=constant_attrs, + graph_signature=graph_signature, + pre_dispatch=True, + fake_args=fake_args, + fake_kwargs=fake_kwargs, + fake_params_buffers=fake_params_buffers, + ) + + +def set_missing_meta_vals(gm, flat_args, num_params_buffers): + # Sets missing metadata to address two problems: + # 1. aot_export adds symint metadata for placeholders with int values; since + # these become specialized, we replace such metadata with the original values. + # 2. any tensor attributes that are not params / buffers, i.e., are constants + # need to have their metadata set before lifting them because it is needed + # for computing the exported program's signature. + index = 0 + for node in gm.graph.nodes: + if node.op == "placeholder": + if index >= num_params_buffers: + user_arg = flat_args[index - num_params_buffers] + if not isinstance(user_arg, torch.Tensor): + node.meta["val"] = user_arg + index += 1 + + +def _find_node(gm: torch.fx.GraphModule, name: str) -> torch.fx.Node: + return next(iter(node for node in gm.graph.nodes if node.name == name)) + + +def _non_strict_export( + mod: torch.nn.Module, + args: tuple[Any, ...], + kwargs: dict[str, Any], + dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any] | None, + preserve_module_call_signature: tuple[str, ...], + orig_in_spec: TreeSpec, + prefer_deferred_runtime_asserts_over_guards: bool, + _to_aten_func: Callable, +) -> ExportArtifact: + """ + _to_aten_func can either be `_export_to_aten_ir_make_fx` or `_export_to_aten_ir` + """ + + out_spec: TreeSpec | None = None + in_spec: TreeSpec | None = None + + module_call_specs: dict[str, dict[str, pytree.TreeSpec]] = {} + + def _tuplify_outputs(aot_export): + def _aot_export_non_strict(stack, mod, args, *, kwargs=None, **flags): + kwargs = kwargs or {} + + class Wrapper(torch.nn.Module): + def __init__(self, mod): + super().__init__() + self._export_root = mod + + def forward(self, *args, **kwargs): + nonlocal out_spec + nonlocal in_spec + mod = self._export_root + _, in_spec = pytree.tree_flatten((args, kwargs)) + if isinstance(mod, torch.fx.GraphModule): + # NOTE: We're going to run this graph module with an fx interpreter, + # which will not run any forward hooks. Thus, ideally, we should run + # all forward hooks here. But the general logic for running them is + # complicated (see nn/module.py), and probably not worth duplicating. + # Instead we only look for, and run, an export-specific forward hook. + if ( + _check_input_constraints_pre_hook + in mod._forward_pre_hooks.values() + ): + _check_input_constraints_pre_hook(mod, args, kwargs) + with torch.fx.traceback.preserve_node_meta(): + args = (*args, *kwargs.values()) + tree_out = torch.fx.Interpreter(mod).run(*args) + else: + tree_out = mod(*args, **kwargs) + flat_outs, out_spec = pytree.tree_flatten(tree_out) + return tuple(flat_outs) + + wrapped_mod = Wrapper(mod) + # Patch export_root to the signatures so that wrapper module correctly populates the + # in/out spec + new_preserved_call_signatures = [ + "_export_root." + i for i in preserve_module_call_signature + ] + ctx = nullcontext() + if not isinstance(mod, torch.fx.GraphModule): + ctx = _wrap_submodules( # type: ignore[assignment] + wrapped_mod, new_preserved_call_signatures, module_call_specs + ) + with ctx: + gm, sig = aot_export(stack, wrapped_mod, args, kwargs=kwargs, **flags) + log.debug("Exported program from AOTAutograd:\n%s", gm) + + sig.parameters = pytree.tree_map(_strip_root, sig.parameters) + sig.buffers = pytree.tree_map(_strip_root, sig.buffers) + sig.inputs_to_buffers = pytree.tree_map(_strip_root, sig.inputs_to_buffers) + sig.inputs_to_parameters = pytree.tree_map( + _strip_root, sig.inputs_to_parameters + ) + sig.buffers_to_mutate = pytree.tree_map(_strip_root, sig.buffers_to_mutate) + sig.parameters_to_mutate = pytree.tree_map( + _strip_root, sig.parameters_to_mutate + ) + + for node in gm.graph.nodes: + if "nn_module_stack" in node.meta: + nn_module_stack = node.meta["nn_module_stack"] + node.meta["nn_module_stack"] = { + _fixup_key(key): val + for key, val in pytree.tree_map( + _strip_root, nn_module_stack + ).items() + } + + return gm, sig + + return _aot_export_non_strict + + ( + fake_mode, + fake_args, + fake_kwargs, + equalities_inputs, + original_signature, + dynamic_shapes, + ) = make_fake_inputs( + mod, + args, + kwargs, + dynamic_shapes, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, # for shape env initialization + ) + + fake_params_buffers = _fakify_params_buffers(fake_mode, mod) + + def _produce_guards_callback(gm): + return produce_guards_and_solve_constraints( + fake_mode=fake_mode, + gm=gm, + dynamic_shapes=dynamic_shapes, + equalities_inputs=equalities_inputs, + original_signature=original_signature, + ) + + tx = TracingContext(fake_mode) + + # We also need to attach dynamo configs as these will be used in HOOs that + # use torch.compile, like cond + dynamo_config = dataclasses.asdict(DEFAULT_EXPORT_DYNAMO_CONFIG) + dynamo_config["do_not_emit_runtime_asserts"] = ( + False # We want to emit runtime asserts + ) + + with ( + fake_mode, + _NonStrictTorchFunctionHandler(), + tracing(tx), + torch._dynamo.config.patch(dynamo_config), + ): + with ( + _fakify_script_objects(mod, fake_args, fake_kwargs, fake_mode) as ( + patched_mod, + new_fake_args, + new_fake_kwargs, + new_fake_constant_attrs, + map_fake_to_real, + ), + _fakify_module_inputs(fake_args, fake_kwargs, fake_mode), + _override_builtin_ops(), + ): + # _to_aten_func is _export_to_aten_ir when using the default non-strict export + # We need to pass positional args correctly + aten_export_artifact = _to_aten_func( + patched_mod, + new_fake_args, + new_fake_kwargs, + fake_params_buffers, + new_fake_constant_attrs, + produce_guards_callback=_produce_guards_callback, + transform=_tuplify_outputs, + ) + # aten_export_artifact.constants contains only fake script objects, we need to map them back + aten_export_artifact.constants = { + fqn: map_fake_to_real[obj] if isinstance(obj, FakeScriptObject) else obj + for fqn, obj in aten_export_artifact.constants.items() + } + + _move_non_persistent_buffers_to_tensor_constants( + mod, aten_export_artifact.sig, aten_export_artifact.constants + ) + + assert out_spec is not None + assert in_spec is not None + + return ExportArtifact( + aten=aten_export_artifact, + in_spec=in_spec, + out_spec=out_spec, + fake_mode=fake_mode, + module_call_specs=module_call_specs, + ) + + +@_log_export_wrapper +@_disable_prexisiting_fake_mode +def _export_for_training( + mod: torch.nn.Module, + args: tuple[Any, ...], + kwargs: dict[str, Any] | None = None, + dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any] | None = None, + *, + strict: bool = True, + preserve_module_call_signature: tuple[str, ...] = (), + prefer_deferred_runtime_asserts_over_guards: bool = False, +) -> ExportedProgram: + global _EXPORT_MODULE_HIERARCHY + _EXPORT_MODULE_HIERARCHY = _get_module_hierarchy(mod) + + ( + args, + kwargs, + orig_in_spec, + dynamic_shapes, + verify_additional_inputs, + ) = _process_export_inputs(mod, args, kwargs, dynamic_shapes) + + original_state_dict = _get_original_state_dict(mod) + + has_ambient_mode = False + if not strict: + flat_args, _ = pytree.tree_flatten((args, kwargs)) + has_ambient_mode = torch._guards.detect_fake_mode(flat_args) is not None + + # Call the appropriate export function based on the strictness of tracing. + export_func = _strict_export if strict else _non_strict_export + + if not strict and torch._export.config.detect_non_strict_fake_tensor_leaks: + from torch._subclasses.fake_tensor import fake_tensor_tls + + fake_tensor_tls.non_strict_export_fake_tensor_tracker.clear() + + export_artifact = export_func( + mod=mod, + args=args, + kwargs=kwargs, + dynamic_shapes=dynamic_shapes, + preserve_module_call_signature=preserve_module_call_signature, + orig_in_spec=orig_in_spec, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + _to_aten_func=_export_to_aten_ir_make_fx, + ) + + # If we are tracing with fake inputs, it is expected to + # see fake tensor constants. + if not strict and not has_ambient_mode: + for const, val in export_artifact.aten.constants.items(): + if isinstance( + val, torch._subclasses.fake_tensor.FakeTensor + ) and _is_bogus_const_name(const): + error_msg = ( + f"We found a fake tensor in the exported program constant's list. " + f"This typically means our tracing system encountered an op that " + f"we can't trace through. For the potential source, you can refer to " + f"following model attribute: {const}. " + f"Please file an issue on github. " + ) + if torch._export.config.error_on_lifted_constant_tensors: + raise RuntimeError(error_msg) + else: + warnings.warn(error_msg, stacklevel=2) + + export_graph_signature = export_artifact.aten.sig + + forward_arg_names = _get_forward_arg_names(mod, args, kwargs) + inline_constraints = _get_inline_constraints(export_artifact.fake_mode) + # The unbacked symint symbols are updated in aot_export + # so we serialize them here instead of inside dynamo. + # Note: _get_range_constraints depends on "inline_constraints" to be set. + export_artifact.aten.gm.meta["inline_constraints"] = inline_constraints + range_constraints = _get_range_constraints( + mod, + export_artifact, + args, + kwargs, + dynamic_shapes, + ) + # The returned the gm is in-place modified + gm, module_call_graph = _get_module_call_graph( + export_artifact, + preserve_module_call_signature, + strict, + forward_arg_names, + ) + + _verify_nn_module_stack(gm) + _verify_stack_trace(gm) + _verify_placeholder_names(gm, export_graph_signature) + + _update_gm_meta_if_possible(gm, mod) + + from torch._export.verifier import TrainingIRVerifier + + exported_program = ExportedProgram( + root=gm, + graph=gm.graph, + graph_signature=export_graph_signature, + state_dict=original_state_dict, + range_constraints=range_constraints, + module_call_graph=module_call_graph, + example_inputs=(args, kwargs), + constants=export_artifact.aten.constants, + verifiers=[TrainingIRVerifier], + ) + + verify_additional_inputs(exported_program) + + if not strict and torch._export.config.detect_non_strict_fake_tensor_leaks: + # See NOTE [export non-strict fake tensor leak detection] + from torch._subclasses.fake_tensor import fake_tensor_tls + from torch.fx.experimental.proxy_tensor import ( + _FAKE_TENSOR_ID_TO_PROXY_MAP_FOR_EXPORT, + ) + + active_fakes = fake_tensor_tls.non_strict_export_fake_tensor_tracker + legit_leak: weakref.WeakSet = find_legit_leaks_from_referrers(active_fakes) + leak_sources: list[str] = [] + if len(legit_leak) > 0: + for fake_val in legit_leak: + if id(fake_val) in _FAKE_TENSOR_ID_TO_PROXY_MAP_FOR_EXPORT: + stack_trace = _FAKE_TENSOR_ID_TO_PROXY_MAP_FOR_EXPORT[ + id(fake_val) + ].meta.get("stack_trace", "") + + # Get shape and dtype info + shape_info = f"shape={fake_val.shape}, dtype={fake_val.dtype}" + leak_info = f"FakeTensor({shape_info}): {stack_trace}" + leak_sources.append(leak_info) + + # Format the warning message more nicely + leak_details = "\n ".join(leak_sources) + warnings.warn( + f"Detected {len(legit_leak)} fake tensors that are still alive after export.\n" + f"This is likely result of torch.export.export not being able to track side effects " + f"that is happening outside of model scope.\n\n" + f"Leaked tensors:\n {leak_details}\n\n" + f"Alternatively, please file a bug report to PyTorch team for further debugging help.", + stacklevel=2, + ) + + del legit_leak + + return exported_program + + +@_log_export_wrapper +@_disable_prexisiting_fake_mode +def _export( + mod: torch.nn.Module, + args: tuple[Any, ...], + kwargs: dict[str, Any] | None = None, + dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any] | None = None, + *, + strict: bool = True, + preserve_module_call_signature: tuple[str, ...] = (), + pre_dispatch: bool = False, + prefer_deferred_runtime_asserts_over_guards: bool = False, +) -> ExportedProgram: + """ + Traces either an nn.Module's forward function or just a callable with PyTorch + operations inside and produce a ExportedProgram. + + Args: + mod: the `nn.Module` to trace. + + args: example positional inputs. + + kwargs: optional example keyword inputs. + + dynamic_shapes: + An optional argument where the type should either be: + 1) a dict from argument names of ``f`` to their dynamic shape specifications, + 2) a tuple that specifies dynamic shape specifications for each input in original order. + If you are specifying dynamism on keyword args, you will need to pass them in the order that + is defined in the original function signature. + + The dynamic shape of a tensor argument can be specified as either + (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is + not required to include static dimension indices in this dict, but when they are, + they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None, + where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions + are denoted by None. Arguments that are dicts or tuples / lists of tensors are + recursively specified by using mappings or sequences of contained specifications. + + preserve_module_call_signature: A list of submodule paths for which the original + calling conventions are preserved as metadata. + + prefer_deferred_runtime_asserts_over_guards: + With the current dynamic shapes language for dims and derived dims, we can run into constraints + that are not expressible with the language. For example, flattening a matrix and adding to a vector, + both fully dynamic (i.e. x.reshape([-1]) + y) emits a guard s0 * s1 = s2, which is not expressible. + By default, we either raise a constraint violation error or specialize to static values. + If this flag is set to True, we avoid erroring out and instead allow complex constraints to exist as runtime + assertions in the graph. The sympy interpreter (torch/utils/_sympy/interp.py) will produce the math ops + required to compute and assert the value of the guard (e.g. sym_size_int, eq, _assert_scalar). + Additionally, if TORCH_DYNAMO_DO_NOT_EMIT_RUNTIME_ASSERTS=1 is specified, we will allow complex constraints + while not emitting runtime asserts, returning a cleaner graph with lesser guarantees around dynamic shapes. + + Returns: + An ExportedProgram containing the traced module. + """ + + from torch._utils_internal import export_training_ir_rollout_check + + global _EXPORT_FLAGS, _EXPORT_MODULE_HIERARCHY + _EXPORT_MODULE_HIERARCHY = _get_module_hierarchy(mod) + + flags = set() + flags.add("strict" if strict else "non_strict") + flags.add("pre_dispatch" if pre_dispatch else "aot_dispatch") + _EXPORT_FLAGS = flags + + log_export_usage(event="export.enter", flags=_EXPORT_FLAGS) + + dtrace_structured("export", payload_fn=lambda: "start!") + + # NOTE Export training IR rollout + # Old export calls export._trace(pre_dispatch=True) + # and there are still lot of internal/OSS callsites that + # use export._trace(pre_dispatch=True) directly. Therefore, + # it makes more sense to do the switch here. + # export_training_ir_rollout_check returns True in OSS + # while internally it returns False UNLESS otherwise specified. + if pre_dispatch and export_training_ir_rollout_check(): + ep = _export_for_training( + mod, + args, + kwargs, + dynamic_shapes, + strict=strict, + preserve_module_call_signature=preserve_module_call_signature, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + ) + dtrace_structured("exported_program", payload_fn=lambda: str(ep)) + return ep + + ( + args, + kwargs, + original_in_spec, + dynamic_shapes, + verify_additional_inputs, + ) = _process_export_inputs(mod, args, kwargs, dynamic_shapes) + + original_state_dict = _get_original_state_dict(mod) + + # Call the appropriate export function based on the strictness of tracing. + export_func = _strict_export if strict else _non_strict_export + + export_artifact = export_func( # type: ignore[operator] + mod=mod, + args=args, + kwargs=kwargs, + dynamic_shapes=dynamic_shapes, + preserve_module_call_signature=preserve_module_call_signature, + orig_in_spec=original_in_spec, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + _to_aten_func=functools.partial( + _export_to_aten_ir, + pre_dispatch=pre_dispatch, + ), + ) + export_graph_signature: ExportGraphSignature = export_artifact.aten.sig + + forward_arg_names = _get_forward_arg_names(mod, args, kwargs) + inline_constraints = _get_inline_constraints(export_artifact.fake_mode) + # The unbacked symint symbols are updated in aot_export + # so we serialize them here instead of inside dynamo. + # Note: this step must be before _get_range_constraints. + export_artifact.aten.gm.meta["inline_constraints"] = inline_constraints + range_constraints = _get_range_constraints( + mod, + export_artifact, + args, + kwargs, + dynamic_shapes, + ) + gm, module_call_graph = _get_module_call_graph( + export_artifact, + preserve_module_call_signature, + strict, + forward_arg_names, + ) + + _verify_nn_module_stack(gm) + _verify_stack_trace(gm) + _verify_placeholder_names(gm, export_graph_signature) + + # Remove Proxy because they cannot be deepcopied or pickled. + torch._export.utils.remove_proxy_from_state_dict(original_state_dict, in_place=True) + + from torch._export.verifier import Verifier + + _update_gm_meta_if_possible(gm, mod) + + exported_program = ExportedProgram( + root=gm, + graph=gm.graph, + graph_signature=export_graph_signature, + state_dict=original_state_dict, + range_constraints=range_constraints, + module_call_graph=module_call_graph, + example_inputs=(args, kwargs), + constants=export_artifact.aten.constants, + verifiers=[Verifier], + ) + + dtrace_structured("exported_program", payload_fn=lambda: str(exported_program)) + + verify_additional_inputs(exported_program) + return exported_program diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_tree_utils.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_tree_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fd3f55c30afeb2e05dd6dbaebb42b3149dd32e93 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_tree_utils.py @@ -0,0 +1,65 @@ +from collections.abc import Callable +from typing import Any + +from torch.utils._pytree import Context, TreeSpec + + +def reorder_kwargs(user_kwargs: dict[str, Any], spec: TreeSpec) -> dict[str, Any]: + """Reorder user-provided kwargs to match the order in `spec`. `spec` is + expected to be the in_spec of an exported program, i.e. the spec that + results from flattening `(args, kwargs)`. + + We need this to provide consistent input ordering, such so that users can + pass in foo(a=a, b=b) OR foo(b=b, a=a) and receive the same result. + """ + # Make sure that the spec is actually shaped like (args, kwargs) + assert spec.type is tuple + assert spec.num_children == 2 + kwargs_spec = spec.child(1) + assert kwargs_spec.type is dict + + if set(user_kwargs) != set(kwargs_spec.context): + raise ValueError( + f"Ran into a kwarg keyword mismatch: " + f"Got the following keywords {list(user_kwargs)} but expected {kwargs_spec.context}" + ) + + reordered_kwargs = {} + for kw in kwargs_spec.context: + reordered_kwargs[kw] = user_kwargs[kw] + + return reordered_kwargs + + +def is_equivalent( + spec1: TreeSpec, + spec2: TreeSpec, + equivalence_fn: Callable[[type | None, Context, type | None, Context], bool], +) -> bool: + """Customizable equivalence check for two TreeSpecs. + + Arguments: + spec1: The first TreeSpec to compare + spec2: The second TreeSpec to compare + equivalence_fn: A function to determine the equivalence of two + TreeSpecs by examining their types and contexts. It will be called like: + + equivalence_fn(spec1.type, spec1.context, spec2.type, spec2.context) + + This function will be applied recursively to all children. + + Returns: + True if the two TreeSpecs are equivalent, False otherwise. + """ + if not equivalence_fn(spec1.type, spec1.context, spec2.type, spec2.context): + return False + + # Recurse on children + if spec1.num_children != spec2.num_children: + return False + + for child_spec1, child_spec2 in zip(spec1.children(), spec2.children()): + if not is_equivalent(child_spec1, child_spec2, equivalence_fn): + return False + + return True diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_unlift.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_unlift.py new file mode 100644 index 0000000000000000000000000000000000000000..84e4313d395b7a100784601ebce020760299f9ca --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_unlift.py @@ -0,0 +1,878 @@ +# mypy: allow-untyped-defs +import copy +import inspect +import math +import warnings +from collections.abc import Sequence +from itertools import chain +from typing import Any + +import sympy + +import torch +import torch.utils._pytree as pytree +from torch._export.non_strict_utils import ( + _enter_enable_graph_inputs_of_type_nn_module, + _exit_enable_graph_inputs_of_type_nn_module, + _get_graph_inputs_of_type_nn_module, +) +from torch._export.passes.add_runtime_assertions_for_constraints_pass import ( + _convert_range_to_int, +) +from torch._export.utils import _check_input_constraints_for_graph +from torch.export.unflatten import _assign_attr, _AttrKind +from torch.fx.experimental.proxy_tensor import _pytree_subclasses_that_lose_info +from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo +from torch.fx.traceback import NodeSource, NodeSourceAction +from torch.utils._sympy.solve import try_solve +from torch.utils._sympy.value_ranges import ValueRanges + +from ._remove_effect_tokens_pass import _remove_effect_tokens +from ._tree_utils import reorder_kwargs +from .exported_program import ( + ExportedProgram, + ExportGraphSignature, + InputKind, + OutputKind, +) + + +def eq_spec(self: pytree.TreeSpec, other: pytree.TreeSpec) -> bool: + """ + Refinement of TreeSpec.__eq__ where, e.g., torch.Size(...) matches tuple(...). + See _pytree_subclasses_that_lose_info in proxy_tensor.py for more details. + """ + + def _normalize_type(t): + return str(_pytree_subclasses_that_lose_info.get(t, t)) + + def _match_normalized_structure(a, b): + if a is b: + return True + if _normalize_type(a.type) != _normalize_type(b.type): + return False + if a.type is dict and b.type is dict: + # in the case of dict, the context is list of keys and we allow the keys to be in any order + if set(a.context) != set(b.context): + return False + elif a.context != b.context: + return False + if a.num_children != b.num_children: + return False + return all( + _match_normalized_structure(a, b) + for a, b in zip(a.children(), b.children()) + ) + + return _match_normalized_structure(self, other) + + +def _check_inputs_match(args, kwargs, in_spec: pytree.TreeSpec) -> list: + reordered_kwargs = reorder_kwargs(kwargs, in_spec) + flat_args_with_path, received_spec = pytree.tree_flatten_with_path( + (args, reordered_kwargs) + ) + + if not eq_spec(received_spec, in_spec): + raise ValueError( # noqa: B904 + "Trying to flatten user inputs with exported input tree spec: \n" + f"{in_spec}\n" + "but actually got inputs with tree spec of: \n" + f"{received_spec}.\n" + "Please check that the inputs have the same number and type of " + "args and kwargs as the ones you used when tracing." + ) + + return flat_args_with_path + + +def _force_ep_signature_match(ep_guards_code: list[str], input_paths): + # TODO (tmanlaibaatar) + # This is band-aid solution to export new tracer replacing + # shape env sources to flat_args. The real fix should be replacing + # shape env sources to original user sources but this is quite + # involved because you need to carefully construct new sources using + # dynamo and replace all instances of it inside shape env. But it is + # lot easier to manipulate after we turn them into strings and only + # time we use these guards is during retracing or running exported program, + # so it is probably ok to have "not useful" guards on ep for now. + name_mapping = {} + for idx, path in enumerate(input_paths): + name_mapping[f"L['flat_args'][{idx}]"] = f"L{pytree.keystr(path)}" + + new_guards_code = [] + for guard in ep_guards_code: + for old_name, new_name in name_mapping.items(): + guard = guard.replace(old_name, new_name) + new_guards_code.append(guard) + + return new_guards_code + + +def _force_gm_signature_match(ep_guards_code: list[str], signature): + """ + The signature of the originally exported module may not match + the signature of the unlifted graph module extracted from the + exported program. The guards code extracted from the exported + program is based on the former, but the generated guards fn is + based on the latter; thus we need to reconcile any such diff. + """ + + import re + + # Handle case where signatures may differ in var args. + orig_arg_names = set() + for g in ep_guards_code: + # match substrings of the form L[''][] + orig_arg_names.update(re.findall(r"L\[\'([^\']+)\'\]\[([0-9]+)\]", g)) + + sig_arg_names = set() + for n in signature.parameters: + # match substrings of the form _ + sig_arg_names.update(re.findall(r"(.+)_([0-9]+)", n)) + + # replace L[''][] with L['_'] + new_guards_code = ep_guards_code + for match in orig_arg_names: + if match in sig_arg_names: + base, idx = match + new_guards_code = [ + g.replace(f"L['{base}'][{idx}]", f"L['{base}_{idx}']") + for g in new_guards_code + ] + + return new_guards_code + + +def _convert_guards_code_to_fn( + guards_code: list[str], + paths_of_placeholders: list[pytree.KeyPath], +): + """ + Generates Python code given guards code and paths of placeholders. + We assume that, based on source information, + - the tracer generates the guards code + - the input spec generates the paths of placeholders. + + Example: + + Suppose we are given the guards code "L['z']['k'].size()[1] == 3" + and we are given that ['z']['k'] is the path of placeholder #2. + Then we will generate: + ``` + torch._assert( + args[2].size()[0] == 3, + "Guard failed: z['k'].size()[0] == 3", + ) + ``` + + FAQ: Why do we generate code based on (flattened) args instead of + the original (unflattened) inputs? Because this would require + inserting an additional pytree.unflatten call in our graph. + + FAQ: Why do we not emit RuntimeError on guard failure as we used to? + Because it is inconvenient :/, get used to AssertionError instead. + """ + + import ast + + from torch.fx.experimental.symbolic_shapes import SYMPY_INTERP + + actual_guards_code = [] + shadow_guards_code = [] + for c in guards_code: + a, s = c, c + for idx, path in enumerate(paths_of_placeholders): + # e.g., replace L['z']['k'] with args[2] for Python code (actual) + a = a.replace("L" + pytree.keystr(path), f"args[{idx}]") + # e.g., replace L['z']['k'] with z['k'] for error message (shadow) + s = s.replace( + "L" + pytree.keystr(path), + path[0].key + pytree.keystr(path[1:]), # type: ignore[attr-defined] + ) + actual_guards_code.append(a) + shadow_guards_code.append(s.replace("\n", "")) + + # generate function code as str + code_str = "\ndef _(*args):\n" + for actual, shadow in zip(actual_guards_code, shadow_guards_code): + # printing guards code may potentially introduce redundant parens; + # we can normalize them out for readability by parsing/unparsing + # NOTE: this is not necessary for correctness, just deemed desirable + _shadow = ast.unparse(ast.parse(shadow, mode="eval")) + # actual code and shadow error message + code_str += f' torch._assert({actual}, "Guard failed: {_shadow}")\n' + code_str += " return\n" + + # populate namespace with sympy globals, materialize function (named `_`) + namespace = {**SYMPY_INTERP} + exec(code_str, namespace) + + # create and return a module whose forward is the materialized function + # NOTE: we want Dynamo to trace through this module, to repopulate guards: + # otherwise we would lose them when retracing + # NOTE: calling this module will be a side effect (no users): so it must + # be marked impure to avoid being not cleaned up by DCE + guards_fn = GuardsFn() + guards_fn.forward = torch._dynamo.dont_skip_tracing(namespace["_"]) # type: ignore[call-overload, method-assign] + guards_fn._is_impure = True # type: ignore[assignment] + return guards_fn + + +@torch._dynamo.disable +def _check_input_constraints_for_module(self, args, kwargs): + flat_args_with_path = _check_inputs_match(args, kwargs, self._in_spec) + _check_input_constraints_for_graph( + self.graph.find_nodes(op="placeholder"), + flat_args_with_path, + self.range_constraints, + ) + + +def _check_input_constraints_pre_hook(self, args, kwargs): + # preserve current behavior for clients that do not want any validation + if not self.validate_inputs: + return + + # when a guards function exists, assume that the graph does calls it! + # so we do not need to check input constraints...but we still want + # to check inputs match, otherwise we'd get obscure pytree errors + if hasattr(self, "_guards_fn"): + _check_inputs_match(args, kwargs, self._in_spec) + return + + # NOTE: for some reason, Dynamo is tracing into this, we should see why and + # put compile at the right place. Until then, we can skip the input + # constraint checks. + if not torch.compiler.is_dynamo_compiling(): + _check_input_constraints_for_module(self, args, kwargs) + + +def _unlift_inputs_as_getattr( + gm: torch.fx.GraphModule, + lifted_inputs: Sequence[str | None], +) -> tuple[dict[str, torch.fx.Node], dict[str, torch.fx.Node]]: + """ + Unlift inputs referring to params/buffers/constants as getattr nodes in the + graph + """ + unlifted_name_to_node = {} + input_name_to_node = {} + + placeholder_nodes = [node for node in gm.graph.nodes if node.op == "placeholder"] + assert len(lifted_inputs) == len(placeholder_nodes) + for input_node, lifted_node in zip(placeholder_nodes, lifted_inputs): + if lifted_node is None: + input_name_to_node[input_node.name] = input_node + + else: + with gm.graph.inserting_after(input_node): + # It is fine to ignore this warning because + # it is guaranteed that we will populate this + # attr later. + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + getattr_node = gm.graph.get_attr(lifted_node) + input_node.replace_all_uses_with(getattr_node) + metadata = input_node.meta + gm.graph.erase_node(input_node) + getattr_node.meta = metadata + getattr_node.meta["from_node"] = [ + NodeSource( + input_node, + "ExportedProgram.module().unlift()", + [NodeSourceAction.CREATE, NodeSourceAction.REPLACE], + ) + ] + unlifted_name_to_node[lifted_node] = getattr_node + + return unlifted_name_to_node, input_name_to_node + + +def _insert_copy_for_mutations( + gm: torch.fx.GraphModule, + mutated_outputs: Sequence[str | None], + unlifted_name_to_node: dict[str, torch.fx.Node], + input_name_to_node: dict[str, torch.fx.Node], +) -> None: + """ + Find the all the buffers and inputs that were mutated and insert copy_ + operators to reflect mutations. + """ + output_node = gm.graph.output_node() + outputs = pytree.tree_flatten(output_node.args)[0] + assert len(outputs) == len(mutated_outputs) + + user_output_nodes = [] + return_nodes_to_copy = {} + for return_node, mutated_node_name in zip(outputs, mutated_outputs): + if mutated_node_name is None: + user_output_nodes.append(return_node) + continue + + if mutated_node_name in unlifted_name_to_node: + mutated_node = unlifted_name_to_node[mutated_node_name] + elif mutated_node_name in input_name_to_node: + mutated_node = input_name_to_node[mutated_node_name] + else: + raise RuntimeError( + f"Could not find {mutated_node_name} in either buffer or input nodes" + ) + + with gm.graph.inserting_before(output_node): + copy_node = gm.graph.call_function( + torch.ops.aten.copy_.default, (mutated_node, return_node) + ) + return_nodes_to_copy[return_node] = copy_node + + output_args = tuple( + return_nodes_to_copy.get(node, node) for node in user_output_nodes + ) + with gm.graph.inserting_before(output_node): + # Only return user outputs + new_output = gm.graph.output(output_args) + output_node.replace_all_uses_with(new_output) + gm.graph.erase_node(output_node) + new_output.name = output_node.name + new_output.meta.update(output_node.meta) + new_output.meta["from_node"] = [ + NodeSource( + output_node, + "ExportedProgram.module().unlift()", + [NodeSourceAction.CREATE, NodeSourceAction.REPLACE], + ) + ] + + +def _get_codegen( + in_spec: pytree.TreeSpec, + out_spec: pytree.TreeSpec | None, + forward_arg_names: list[str] | None = None, +) -> _PyTreeCodeGen: + """ + Create the codegen for the graph module based on the in/out specs + """ + if forward_arg_names: + names = forward_arg_names + elif ( + in_spec.type is tuple + and in_spec.num_children == 2 + and in_spec.child(0).type is tuple + and in_spec.child(1).type is dict + ): + # if in_spec contains the args (tuple) and kwargs (dict) + names = [f"arg_{i}" for i in range(in_spec.child(0).num_children)] + # add kwarg names + names.extend(in_spec.child(1).context) + else: + names = [f"arg_{i}" for i in range(in_spec.num_children)] + + return _PyTreeCodeGen( + _PyTreeInfo( + names, + in_spec, + out_spec, + ) + ) + + +def _unlift( + gm: torch.fx.GraphModule, + lifted_inputs: Sequence[str | None], + mutated_outputs: Sequence[str | None], + in_spec: pytree.TreeSpec, + out_spec: pytree.TreeSpec | None, + forward_arg_names: list[str] | None = None, +): + """ + Args: + lifted_inputs: A list matching the graph module's input nodes. For + an input node that is referring to a lifted parameter/buffer, this + list will contain the fqn the corresponding attribute. Otherwise, this + list will contain None. This is used to unlift the lifted parameters as + get_attr nodes. + + mutated_outputs: A list matching the graph module's output nodes. For + an output node that is referring to a mutated buffer or user input, this + list will contain the name of the corresponding buffer or user input + that needs to be mutated. Otherwise, this list will contain None. This + is used to re-insert an inplace copy_ operator to copy the mutated + values back to the original node. + """ + unlifted_name_to_node, input_name_to_node = _unlift_inputs_as_getattr( + gm, lifted_inputs + ) + _insert_copy_for_mutations( + gm, mutated_outputs, unlifted_name_to_node, input_name_to_node + ) + gm.graph._codegen = _get_codegen(in_spec, out_spec, forward_arg_names) + gm.graph.lint() + gm.recompile() + return gm + + +def _register_attrs_to_new_gm( + new_gm: torch.fx.GraphModule, + graph_signature: ExportGraphSignature, + state_dict: dict[str, Any], + constants: dict[str, Any], +) -> None: + non_persistent_buffers = set(graph_signature.non_persistent_buffers) + for name in graph_signature.buffers: + if name in non_persistent_buffers: + persistent = False + value = constants[name] + else: + persistent = True + value = state_dict[name] + _assign_attr( + value, new_gm, name, attr_kind=_AttrKind.BUFFER, persistent=persistent + ) + for name in graph_signature.parameters: + value = state_dict[name] + _assign_attr( + value, + new_gm, + name, + attr_kind=_AttrKind.PARAMETER, + ) + + # Technically this doesn't account for the aliased multiple constants but + # it is ok because we have a separate pass later in the stack that populates + # the final gm. + for name in chain( + graph_signature.lifted_custom_objs, graph_signature.lifted_tensor_constants + ): + value = constants[name] + _assign_attr( + value, + new_gm, + name, + attr_kind=_AttrKind.CONSTANT, + ) + + +class _StatefulGraphModuleFactory(type): + """ + Metaclass that ensures a private constructor for _StatefulGraphModule + """ + + def __call__(cls, *args, **kwargs): + raise TypeError( + f"{cls.__module__}.{cls.__qualname__} has no public constructor. " + ) + + def _create(cls, root, graph, range_constraints=None): + return super().__call__( + root, + graph, + range_constraints=range_constraints, + ) + + +class _StatefulGraphModule(torch.fx.GraphModule, metaclass=_StatefulGraphModuleFactory): + def __init__(self, root, graph, range_constraints=None): + super().__init__(root, graph) + # Need to fix up non-persistent buffers. + self.range_constraints = range_constraints or [] + self.validate_inputs = True + + +def _create_stateful_graph_module( + plain_graph_module: torch.fx.GraphModule, + range_constraints, + ep: ExportedProgram, +) -> _StatefulGraphModule: + stateful_gm = _StatefulGraphModule._create( + plain_graph_module, + plain_graph_module.graph, + range_constraints=range_constraints, + ) + + module_types = _get_graph_inputs_of_type_nn_module(ep.example_inputs) + stateful_gm.register_forward_pre_hook( + lambda *args, **kwargs: _enter_enable_graph_inputs_of_type_nn_module( + module_types + ) + ) + stateful_gm.register_forward_pre_hook( + _check_input_constraints_pre_hook, with_kwargs=True + ) + + stateful_gm.register_forward_hook( + lambda *args, **kwargs: _exit_enable_graph_inputs_of_type_nn_module( + module_types + ), + always_call=True, + ) + + # When we have a constant that has requires_grad=True, we need to detach it + # when we unlift as the tensors that require gradients should be registered + # via parameters. But this is problematic when we have aliasing two constants + # because when we call detach, they will become different tensors. This dict + # keeps track of this logic. + original_tensor_to_detached_tensor = {} + + # Fix up lifted tensor constants. + # fx.GraphModule() constructor silently turns a constant attribute of plain_graph_module + # into a buffer in stateful_gm and creates an inconsistency with graph_signature. + # We fix this by de-registering these buffers in lifted_tensor_constants + # and call _assign_attr(attr_kind=CONSTANT) to register them as constants. + for constant_fqn in ep.graph_signature.lifted_tensor_constants: + # Sometimes, the constant can require gradient, this is probably a bug in user code, + # e.g. `self.const = torch.randn(2, 2, requires_grad=True)`. + # We call detach on the constant_val since they're tensor constants and we don't need to + # compute their gradients anyway. + # Users should properly register it as parameter if they want it to require gradient. + buffer = stateful_gm.get_buffer(constant_fqn) + if buffer.requires_grad: + warnings.warn( + f"A model attribute `{constant_fqn}` requires gradient. " + f"but it's not properly registered as a parameter. " + f"torch.export will detach it and treat it as a constant tensor " + f"but please register it as parameter instead.", + stacklevel=2, + ) + detached_buffer = buffer.detach() + original_tensor_to_detached_tensor[buffer] = detached_buffer + buffer = detached_buffer + *prefix, field = constant_fqn.rsplit(".") + submod = torch.fx.graph_module._get_attr_via_attr_list(stateful_gm, prefix) + delattr(submod, field) + _assign_attr(buffer, stateful_gm, constant_fqn, attr_kind=_AttrKind.CONSTANT) + + # Constants are not preserved well when we create a new GraphModule unlike param/buffers + for const_name, value in ep.constants.items(): + if not torch.fx.graph_module._has_attr(stateful_gm, const_name): + if isinstance(value, torch.Tensor): + if value.requires_grad: + warnings.warn( + f"A model attribute `{const_name}` requires gradient " + f"but it's not properly registered as a parameter. " + f"torch.export will detach it and treat it as a constant tensor " + f"but please register it as parameter instead.", + stacklevel=2, + ) + if value in original_tensor_to_detached_tensor: + value = original_tensor_to_detached_tensor[value] + else: + detached_value = value.detach() + original_tensor_to_detached_tensor[value] = detached_value + value = detached_value + _assign_attr( + value, + stateful_gm, + const_name, + attr_kind=_AttrKind.CONSTANT, + ) + + # Fix up non-persistent buffers. torch.fx does not distinguish between + # persistent and non-persistent buffers, so we must restore that distinction + # here. + for buffer in ep.graph_signature.non_persistent_buffers: + _assign_attr( + plain_graph_module.get_buffer(buffer), + stateful_gm, + buffer, + attr_kind=_AttrKind.BUFFER, + persistent=False, + ) + + return stateful_gm + + +def _get_input_paths(example_inputs, signature): + """ + Generate paths of placeholders, needed for generating the guards function. + + NOTE: Here we make use of the example inputs used for export as well as + the signature of the unlifted graph module (not preserved by export). + """ + + args, kwargs = example_inputs + binded = signature.bind(*args, **kwargs) + binded.apply_defaults() + ctx = binded.arguments + flat_example_inputs_with_paths = pytree.tree_leaves_with_path(ctx) + return [path for path, _ in flat_example_inputs_with_paths] + + +def _replace_sources(result_str: str, flat_input_paths: list[Any]): + """ + Given user specified input paths, maybe fix up the guard string + to reflect user path instead of tracer path. + """ + name_mapping = {} + for idx, path in enumerate(flat_input_paths): + name_mapping[f"L['flat_args'][{idx}]"] = f"L{pytree.keystr(path)}" + + replace = result_str + for key, val in name_mapping.items(): + replace = replace.replace(key, val) + return replace + + +def _get_input_guards_for_graph( + placeholders: list[torch.fx.Node], + range_constraints: dict[sympy.Symbol, ValueRanges], + paths_for_placeholders: list[pytree.KeyPath], +): + """ + Guards generated by the tracer include conditions observed in code, but + but do not include some additional checks we typically do in export. + For example, when dynamic shapes get specialized, are specified to be + within a range, or are specified to be in some equational relation, + corresponding input invalidation is done within a pre_hook, specifically, + `_check_input_constraints_for_graph`. + + Here we generate guards corresponding to the checks that happen in + `_check_input_constraints_for_graph`, and add them to the guards already + generated by the tracer. In the future, it may be worthwhile to separate + them so that we can allow clients to turn off one but not the other. + (Looking at you, AOTI.) + + NOTE: We should eventually reconcile this logic with `build_guards` that + is used by AOT Precompile. + """ + + deferred_expressions = [] + new_guards_code = [] + sources: dict[sympy.Expr, str] = {} + + def handle_symint(expr, src): + if len(expr.free_symbols) == 1: + # complex equations (e.g., involving derived dims) need to + # handled later, since we may not have enough information + # just as we are passing through the placeholders in order + deferred_expressions.append((src, expr)) + if expr in sources: + # expressions that appear in multiple sources should force + # inputs corresponding to those sources to be equal + # e.g., x.shape[0] == y.shape[1] + orig_src = sources[expr] + new_guards_code.append(f"{src} == {orig_src}") + else: + sources[expr] = src + # process value ranges as elsewhere in export + min_val, max_val = _convert_range_to_int(range_constraints[expr]) + if min_val > 2: + new_guards_code.append(f"{src} >= {min_val}") + if max_val < math.inf: + new_guards_code.append(f"{src} <= {max_val}") + + for placeholder, path in zip(placeholders, paths_for_placeholders): + src = "L" + pytree.keystr(path) + meta = placeholder.meta["val"] + # specializations + if isinstance(meta, int): + new_guards_code.append(f"{src} == {meta}") + if isinstance(meta, float): + if meta == math.inf: + new_guards_code.append(f"{src} == math.inf") + elif meta == -math.inf: + new_guards_code.append(f"{src} == -math.inf") + else: + new_guards_code.append(f"{src} == {meta}") + elif isinstance(meta, str): + new_guards_code.append(f"{src} == '{meta}'") + # range constraints and equalities + elif isinstance(meta, torch.SymInt) and meta.node.expr in range_constraints: + handle_symint(meta.node.expr, src) + elif isinstance(meta, torch.Tensor): + for i, dim in enumerate(meta.shape): + src = "L" + pytree.keystr(path) + f".size()[{i}]" + if isinstance(dim, int): + # specializations + new_guards_code.append(f"{src} == {dim}") + elif ( + isinstance(dim, torch.SymInt) and dim.node.expr in range_constraints + ): + # range constraints and equalities + handle_symint(dim.node.expr, src) + + unification_map: dict[sympy.Symbol, sympy.Expr] = {} + py_printer = torch.utils._sympy.printers.PythonPrinter() + + # process complex equations (e.g., involving derived dims) + for src, expr in deferred_expressions: + # we know this is the only symbol in expr (see check above) + symbol = next(iter(expr.free_symbols)) + if symbol in sources: + # if s0 is already known to be directly sourced from inputs, + # e.g., z.shape[2], we do not need to do anything further + # (assume we have already processed constraints on s0 above) + continue + + # otherwise s0 has some "hidden" source like 'dim' + # example: src = y.shape[1], expr = s0 + 1 + if symbol in unification_map: + # suppose that we already know that s0 = x.shape[0] * 2 + # so we can emit the guard: x.shape[0] * 2 + 1 = y.shape[1] + substitution = expr.subs(unification_map) + new_guards_code.append( + py_printer.doprint(sympy.Eq(substitution, sympy.Symbol(src))) + ) + else: + # we do not yet know what s0 is, but given s0 + 1 = y.shape[1], + # we can solve for s0...now knowing that s0 = y.shape[1] - 1 + solution = try_solve(sympy.Eq(expr, sympy.Symbol(src)), symbol) + if solution is not None: + definition = solution[1] + unification_map[symbol] = definition + + return new_guards_code + + +def _ok_to_generate_guards_fn(): + patterns = [ + "executorch", + "modai", + "on_device_ai", + "torchao", + ] + # force check_guards=False for files matching `patterns` + # because they have too many calls to .module() and + # do not like any call modules in the graph + # TODO: fix these files to handle guard fns + frame = inspect.currentframe() + while frame is not None: + if any(path in frame.f_code.co_filename for path in patterns): + return False + frame = frame.f_back + + return True + + +def _unlift_exported_program_lifted_states( + ep: ExportedProgram, check_guards=True +) -> torch.fx.GraphModule: + check_guards = check_guards and _ok_to_generate_guards_fn() + + source_node_dict = { + node.name: node for node in ep.graph.nodes if node.op != "placeholder" + } + # placeholder node name might change after deepcopy + placeholder_source_node_dict = { + node.target: node for node in ep.graph.nodes if node.op == "placeholder" + } + + new_gm = torch.fx.GraphModule(ep.graph_module, copy.deepcopy(ep.graph)) + new_gm.meta.update(ep.graph_module.meta) + ep = copy.copy(ep) + ep._graph_module = new_gm + + # TODO T206340015 + if ep.verifiers[0].dialect != "TRAINING": + ep = _remove_effect_tokens(ep) + + _register_attrs_to_new_gm(new_gm, ep.graph_signature, ep.state_dict, ep.constants) + forward_arg_names = ( + sig.forward_arg_names if (sig := ep.module_call_graph[0].signature) else None + ) + lifted_inputs: list[str | None] = [ + ( + in_spec.target + if in_spec.kind + in ( + InputKind.BUFFER, + InputKind.CONSTANT_TENSOR, + InputKind.PARAMETER, + InputKind.CUSTOM_OBJ, + ) + else None + ) + for in_spec in ep.graph_signature.input_specs + ] + + mutated_outputs: list[str | None] = [ + ( + out_spec.target + if out_spec.kind + in ( + OutputKind.BUFFER_MUTATION, + OutputKind.USER_INPUT_MUTATION, + OutputKind.PARAMETER_MUTATION, + ) + else None + ) + for out_spec in ep.graph_signature.output_specs + ] + + for node in new_gm.graph.nodes: + source_node = None + if node.op == "placeholder": + source_node = placeholder_source_node_dict.get(node.target) + else: + if node.name in source_node_dict: + source_node = source_node_dict.get(node.name) + node.meta["from_node"] = [ + NodeSource( + source_node, + "ExportedProgram.module()", + NodeSourceAction.CREATE, + ) + ] + + assert ep.call_spec.in_spec is not None + new_gm = _unlift( + new_gm, + lifted_inputs, + mutated_outputs, + ep.call_spec.in_spec, + ep.call_spec.out_spec, + forward_arg_names=forward_arg_names, + ) + unlift_gm = _create_stateful_graph_module(new_gm, ep.range_constraints, ep) + unlift_gm.meta.update(ep.graph_module.meta) + + # create a _guards_fn submodule and insert a call to it after placeholders + graph = unlift_gm.graph + placeholders = graph.find_nodes(op="placeholder") + if check_guards and placeholders and ep.example_inputs: + sig = inspect.signature(unlift_gm.forward) + input_paths = _get_input_paths( + ep.example_inputs, + sig, + ) + + # TODO (tmanlaibaatar) + # This is band-aid solution to export new tracer replacing + # shape env sources to flat_args. The real fix should be replacing + # shape env sources to original user sources but this is quite + # involved because you need to carefully construct new sources using + # dynamo and replace all instances of it inside shape env. But it is + # lot easier to manipulate after we turn them into strings and only + # time we use these guards is during retracing or running exported program, + # so it is probably ok to have "not useful" guards on ep for now. + ep_guards = [] + for guard in ep._guards_code: + ep_guards.append(_replace_sources(guard, input_paths)) + + guards_code = _get_input_guards_for_graph( + placeholders, ep.range_constraints, input_paths + ) + + ep_guards_code = _force_ep_signature_match(ep._guards_code, input_paths) + ep_guards_code = _force_gm_signature_match(ep_guards_code, sig) + guards_code.extend(ep_guards_code) + unlift_gm._guards_fn = _convert_guards_code_to_fn(guards_code, input_paths) + + root_nn_module_stack = torch.fx._utils.first_call_function_nn_module_stack( + graph + ) + with graph.inserting_after(placeholders[-1]): + node = graph.call_module("_guards_fn", tuple(placeholders)) + node.meta["nn_module_stack"] = root_nn_module_stack + + unlift_gm.recompile() + + return unlift_gm + + +class GuardsFn(torch.nn.Module): + """ + Module class for guard functions. + """ + + def forward(self, *args): + pass diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_wrapper_utils.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_wrapper_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..bc27a8575a0a0d4d90fe9bcbc1a65180f0afdd18 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/_wrapper_utils.py @@ -0,0 +1,10 @@ +import torch + + +class _WrapperModule(torch.nn.Module): + def __init__(self, f): # type: ignore[no-untyped-def] + super().__init__() + self.f = f + + def forward(self, *args, **kwargs): # type: ignore[no-untyped-def] + return self.f(*args, **kwargs) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/custom_obj.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/custom_obj.py new file mode 100644 index 0000000000000000000000000000000000000000..8e7f2080a4ee705a2621386c9b69a089d507544a --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/custom_obj.py @@ -0,0 +1,16 @@ +from dataclasses import dataclass + + +__all__ = ["ScriptObjectMeta"] + + +@dataclass +class ScriptObjectMeta: + """ + Metadata which is stored on nodes representing ScriptObjects. + """ + + # Key into constants table to retrieve the real ScriptObject. + constant_name: str + + class_fqn: str diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/custom_ops.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/custom_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..9df7988da9314c4b18863c88e503ad5b04ae07d4 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/custom_ops.py @@ -0,0 +1,49 @@ +# mypy: allow-untyped-defs +import importlib + +import torch + + +lib = torch.library.Library("export", "FRAGMENT") # noqa: TOR901 + +lib.define( + "access_subclass_inner_tensor(Tensor src_subclass_tensor, str attr) -> Tensor" +) + + +@torch.library.impl(lib, "access_subclass_inner_tensor", "Autograd") +# When running under torch.inference_mode(), we seem to skip AUtograd key +# so we should desugar this op as soon as we start tracing to post-dispatch. +@torch.library.impl(lib, "access_subclass_inner_tensor", "Python") +def _access_subclass_inner_tensor( + src_subclass_tensor: torch.Tensor, attr: str +) -> torch.Tensor: + from torch.utils._python_dispatch import is_traceable_wrapper_subclass + + assert is_traceable_wrapper_subclass(src_subclass_tensor) + val = getattr(src_subclass_tensor, attr, None) + if val is None or not isinstance(val, torch.Tensor): + raise RuntimeError( + f"Attribute {attr} is not a tensor or doesn't exist in {src_subclass_tensor}" + ) + return val + + +def _call_custom_autograd_function_in_pre_dispatch(function_cls_name, *args, **kwargs): + """ + Import a custom autograd function by string name and call it. This is pretty bad + because: + 1) There is no schema + + Ideally we should automatically wrap custom autograd functions with a custom op, but + that is too much work because we need to schematize custom autograd functions. For now, + we just hackily put it in the IR. + """ + # Parse module and class name + module_name, class_name = function_cls_name.rsplit(".", 1) + + # Import the module and get the class + module = importlib.import_module(module_name) + function_cls = getattr(module, class_name) + assert hasattr(function_cls, "apply") + return function_cls.apply(*args, **kwargs) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/decomp_utils.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/decomp_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d3097734c8a35adecf0423633452989c07f68e90 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/decomp_utils.py @@ -0,0 +1,160 @@ +# mypy: allow-untyped-defs +from collections.abc import Callable + +import torch +from torch._export.utils import ( + _collect_all_valid_cia_ops, + _collect_all_valid_cia_ops_for_aten_namespace, + _get_decomp_for_cia, + _is_aten_op, +) + + +__all__ = ["CustomDecompTable"] + + +""" +Core ATen ops with Composite Implicit Autograd dispatch that should be excluded from decomposition +by default. The decomposition logic should eventually exclude all core-tagged CIA ops, but until all +backends are ready, this list allows opt-in one at a time. +""" +PRESERVED_ATEN_CIA_OPS = { + torch.ops.aten.upsample_bilinear2d.vec, + torch.ops.aten.upsample_nearest2d.vec, + # NB: don't use the C++ decomp, because it is not functional! + torch.ops.aten.silu_backward.default, + torch.ops.aten.mish_backward.default, + torch.ops.aten._fused_rms_norm.default, +} + + +class CustomDecompTable(dict[torch._ops.OperatorBase, Callable]): + """ + This is a custom dictionary that is specifically used for handling decomp_table in export. + The reason we need this is because in the new world, you can only *delete* an op from decomp + table to preserve it. This is problematic for custom ops because we don't know when the custom + op will actually be loaded to the dispatcher. As a result, we need to record the custom ops operations + until we really need to materialize it (which is when we run decomposition pass.) + + Invariants we hold are: + 1. All aten decomp is loaded at the init time + 2. We materialize ALL ops when user ever reads from the table to make it more likely + that dispatcher picks up the custom op. + 3. If it is write operation, we don't necessarily materialize + 4. We load the final time during export, right before calling run_decompositions() + + """ + + def __init__(self): + super().__init__() + from torch._decomp import _core_aten_decompositions_post_autograd + + # For aten ops, we load them up in the beginning + self.decomp_table = _core_aten_decompositions_post_autograd() + + for op in _collect_all_valid_cia_ops_for_aten_namespace(): + if op not in PRESERVED_ATEN_CIA_OPS and op not in self.decomp_table: + self.decomp_table[op] = _get_decomp_for_cia(op) + + # This is to track the *pending* deleted custom ops that haven't been materialized yet + self.deleted_custom_ops = set() + # When this is true, there shouldn't be any pending operations in the table. + self.has_materialized = False + + def __getitem__(self, key): + self._materialize_if_needed() + return self.decomp_table.__getitem__(key) + + def __setitem__(self, key, value) -> None: + self.decomp_table.__setitem__(key, value) + + if key in self.deleted_custom_ops: + self.deleted_custom_ops.remove(key) + + def keys(self): + self._materialize_if_needed() + return self.decomp_table.keys() + + def __delitem__(self, key) -> None: + self.pop(key) + + def update(self, other_dict): # type: ignore[override] + for k, v in other_dict.items(): + self.decomp_table.__setitem__(k, v) + + def __missing__(self, key) -> bool: + return not self.__contains__(key) + + def __contains__(self, key) -> bool: + self._materialize_if_needed() + return self.decomp_table.__contains__(key) + + def __len__(self) -> int: + self._materialize_if_needed() + return self.decomp_table.__len__() + + def __iter__(self): + self._materialize_if_needed() + return self.decomp_table.__iter__() + + def __reversed__(self): + self._materialize_if_needed() + return self.decomp_table.__reversed__() + + def copy(self) -> "CustomDecompTable": + new_dict = CustomDecompTable() + new_dict.decomp_table = self.decomp_table.copy() + new_dict.deleted_custom_ops = self.deleted_custom_ops.copy() + new_dict.has_materialized = self.has_materialized + return new_dict + + def pop(self, *args): + def _pop_if_can(key): + if _is_aten_op(key): + return self.decomp_table.pop(key) + + if key in self.decomp_table: + # Even if we materialized it, we should add it to the deleted + # custom ops list so that when we materialize next time, + # we should respect user's intention. + self.deleted_custom_ops.add(key) + return self.decomp_table.pop(key) + + if key in self.deleted_custom_ops: + raise KeyError(f"{key} doesn't exist in the table") + + self.deleted_custom_ops.add(key) + # We would come here when user pops off something that is + # not in the table. In this case, we just pretend that it + # was in the table. + return _get_decomp_for_cia(key) + + if len(args) == 1: + return _pop_if_can(args[0]) + + if len(args) == 2: + try: + return _pop_if_can(args[0]) + except KeyError: + return args[1] + + def items(self): + self._materialize_if_needed() + return self.decomp_table.items() + + def materialize(self) -> dict[torch._ops.OperatorBase, Callable]: + for op in _collect_all_valid_cia_ops(): + if _is_aten_op(op): + continue + elif op in self.decomp_table: + continue + elif op not in self.deleted_custom_ops: + self.decomp_table[op] = _get_decomp_for_cia(op) + + self.has_materialized = True + self.deleted_custom_ops = set() + return {**self.decomp_table} + + def _materialize_if_needed(self) -> None: + if not self.has_materialized: + self.materialize() diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/dynamic_shapes.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/dynamic_shapes.py new file mode 100644 index 0000000000000000000000000000000000000000..fe2e12ba0810b703dd2ccae1dc6da447132d3b5a --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/dynamic_shapes.py @@ -0,0 +1,1374 @@ +# mypy: allow-untyped-defs +import dataclasses +import inspect +import logging +import sys +from collections import defaultdict +from collections.abc import Callable +from enum import auto, Enum +from typing import Any, TYPE_CHECKING, Union + +import torch +from torch.utils._pytree import ( + _get_node_type, + BUILTIN_TYPES, + KeyPath, + keystr, + MappingKey, + SequenceKey, + SUPPORTED_NODES, + tree_iter, + tree_map, + tree_map_with_path, + tree_structure, + TreeSpec, +) + +from .exported_program import ExportedProgram + + +if TYPE_CHECKING: + from sympy import Symbol + + from torch._guards import Source + from torch.fx.experimental.symbolic_shapes import ShapeEnv, StrictMinMaxConstraint + +__all__ = [ + "Constraint", + "Dim", + "dims", + "refine_dynamic_shapes_from_suggested_fixes", + "AdditionalInputs", +] + + +log = logging.getLogger(__name__) + + +class _DimHintType(Enum): + """ + Enum for dynamic shape hints. + - AUTO means automatic inference of shape (static or dynamic). + - STATIC means static shape (always specialized). + - DYNAMIC means dynamic, will error out if specialized. + """ + + AUTO = auto() + STATIC = auto() + DYNAMIC = auto() + + +@dataclasses.dataclass +class _DimHint: + """ + Internal class for dynamic shape hints. + - min and max are optional. + - _factory is for UX only, below example: + auto_hint = _DimHint.AUTO() # _factory=True + bounded_hint = auto_hint(min=10, max=100) # Returns new instance with _factory=False + bounded_hint(min=5, max=50) # Will fail, non-factory instance cannot be called + """ + + type: _DimHintType + min: int | None = None + max: int | None = None + _factory: bool | None = True + + @staticmethod + def AUTO(): + return _DimHint(_DimHintType.AUTO) + + @staticmethod + def DYNAMIC(): + return _DimHint(_DimHintType.DYNAMIC) + + @staticmethod + def STATIC(): + return _DimHint(_DimHintType.STATIC) + + def __call__(self, min=None, max=None) -> "_DimHint": + if not self._factory: + raise TypeError(f"'{type(self)}' object is not callable") + assert min is None or min >= 0, "min must be non-negative" + assert max is None or max >= 0, "max must be non-negative" + assert min is None or max is None or min <= max, "min must be <= max" + return _DimHint(self.type, min=min, max=max, _factory=False) + + def __repr__(self): + parts = [self.type.name] + if self.min is not None: + parts.append(f"min={self.min}") + if self.max is not None: + parts.append(f"max={self.max}") + return f"DimHint({', '.join(parts)})" + + +class Dim: + """ + The ``Dim`` class allows users to specify dynamism in their exported + programs. By marking a dimension with a ``Dim``, the compiler associates the + dimension with a symbolic integer containing a dynamic range. + + The API can be used in 2 ways: Dim hints (i.e. automatic dynamic shapes: + ``Dim.AUTO``, ``Dim.DYNAMIC``, ``Dim.STATIC``), or named Dims (i.e. + ``Dim("name", min=1, max=2)``). + + Dim hints provide the lowest barrier to exportability, with the user only + needing to specify if a dimension if dynamic, static, or left for the + compiler to decide (``Dim.AUTO``). The export process will automatically + infer the remaining constraints on min/max ranges and relationships between + dimensions. + + Example:: + + class Foo(nn.Module): + def forward(self, x, y): + assert x.shape[0] == 4 + assert y.shape[0] >= 16 + return x @ y + + + x = torch.randn(4, 8) + y = torch.randn(8, 16) + dynamic_shapes = { + "x": {0: Dim.AUTO, 1: Dim.AUTO}, + "y": {0: Dim.AUTO, 1: Dim.AUTO}, + } + ep = torch.export(Foo(), (x, y), dynamic_shapes=dynamic_shapes) + + Here, export would raise an exception if we replaced all uses of ``Dim.AUTO`` with ``Dim.DYNAMIC``, + as ``x.shape[0]`` is constrained to be static by the model. + + More complex relations between dimensions may also be codegened as runtime assertion nodes by the compiler, + e.g. ``(x.shape[0] + y.shape[1]) % 4 == 0``, to be raised if runtime inputs do not satisfy such constraints. + + You may also specify min-max bounds for Dim hints, e.g. ``Dim.AUTO(min=16, max=32)``, ``Dim.DYNAMIC(max=64)``, + with the compiler inferring the remaining constraints within the ranges. An exception will be raised if + the valid range is entirely outside the user-specified range. + + Named Dims provide a stricter way of specifying dynamism, where exceptions are raised if the compiler + infers constraints that do not match the user specification. For example, exporting the previous + model, the user would need the following ``dynamic_shapes`` argument:: + + s0 = Dim("s0") + s1 = Dim("s1", min=16) + dynamic_shapes = { + "x": {0: 4, 1: s0}, + "y": {0: s0, 1: s1}, + } + ep = torch.export(Foo(), (x, y), dynamic_shapes=dynamic_shapes) + + Named Dims also allow specification of relationships between dimensions, up + to univariate linear relations. For example, the following indicates one + dimension is a multiple of another plus 4:: + + s0 = Dim("s0") + s1 = 3 * s0 + 4 + + """ + + AUTO = _DimHint.AUTO() + DYNAMIC = _DimHint.DYNAMIC() + STATIC = _DimHint.STATIC() + + def __init__(self, name: str, *, min: int | None = None, max: int | None = None): + from torch.utils._sympy.numbers import int_oo + + _min = 0 if min is None else min + _max = int_oo if max is None else max + assert _max > _min, f"Cannot create Dim with inconsistent min={min}, max={max}" + assert name.isidentifier(), f"Dim name must be a valid identifier, got {name}" + self.__name__ = name + self.min = _min + self.max = _max + + def __add__(self, other) -> "Dim": + # e.g., dim + 1 + if type(other) is not int: + raise NotImplementedError( + f"Attempted to add {other} to {self.__name__}, where an integer was expected. " + "(Only increasing linear operations with integer coefficients are supported.)" + ) + return self._derive(lambda x: x + other) + + def __radd__(self, other) -> "Dim": + return self + other + + def __sub__(self, other) -> "Dim": + # e.g., dim - 1 + if type(other) is not int: + raise NotImplementedError( + f"Attempted to subtract {other} from {self.__name__}, where an integer was expected. " + "(Only increasing linear operations with integer coefficients are supported.)" + ) + return self._derive(lambda x: x - other) + + def __rsub__(self, other) -> "Dim": + raise NotImplementedError( + f"Attempted to negate {self.__name__}. " + "(Only increasing linear operations with integer coefficients are supported.)" + ) + + def __mul__(self, other) -> "Dim": + # e.g., dim * 2 + if type(other) is not int or other <= 0: + raise NotImplementedError( + f"Attempted to multiply {other} with {self.__name__}, where a positive integer was expected. " + "(Only increasing linear operations with integer coefficients are supported.)" + ) + return self._derive(lambda x: x * other) + + def __rmul__(self, other) -> "Dim": + return self * other + + def _derived_name(self, fn) -> str: + from sympy import sympify + + return str(fn(sympify(self.__name__))) + + def _derive(self, fn) -> "Dim": + return _DerivedDim(self._derived_name(fn), self, fn) + + @staticmethod + def _readable(name: str, min_: int, max_: int) -> str: + from torch.utils._sympy.numbers import int_oo + + if min_ == 2: + min_ = None # type: ignore[assignment] + if max_ == int_oo: + max_ = None # type: ignore[assignment] + if min_ is None and max_ is None: + return f"Dim('{name}')" + if min_ is None: + return f"Dim('{name}', max={max_})" + if max_ is None: + return f"Dim('{name}', min={min_})" + return f"Dim('{name}', min={min_}, max={max_})" + + def __repr__(self): + return Dim._readable(self.__name__, self.min, self.max) + + +_Dim = Dim # TODO(pianpwk): remove after it's no longer internally breaking + + +class _StaticDim(Dim): + """ + Class for static :func:`Dim` types. + + This class is only for setting and checking static dim constraints, + and the user should never interact with it. + """ + + def __init__(self, value: int): + self.__name__ = str(value) + self.value = value + + @property + def min(self): # type: ignore[override] + return self.value # type: ignore[attr-defined] + + @property + def max(self): # type: ignore[override] + return self.value # type: ignore[attr-defined] + + +class _DerivedDim(Dim): + """ + Class for derived :func:`Dim` types. + + Currently we only support increasing linear expressions with integer coefficients. + In other words, a derived Dim can always be written in the form Ax + B, where + x is a regular Dim (i.e., non-derived Dim), A and B are integers, and A is positive. + (In particular, the latter ensures that x < y => Ax + B < Ay + B.) + These restrictions on the form of derived Dims makes the metatheory simpler: e.g., + it simplifies computing ranges for derived Dims, solving for underlying regular Dims, + deciding equalities between derived Dims, and so on. + + The function lambda x: Ax + B is expressed by `fn`, where x is a normal Dim, `root`. + The range of a derived Dim is computed by mapping `fn` over the range of its `root`. + """ + + def __init__(self, name: str, root: Dim, fn: Callable): + self.__name__ = name + self.root = root + self.fn = fn + + @property + def min(self): # type: ignore[override] + # assume that self.fn is an increasing function + # TODO(avik): use sympy value range analysis instead? + from sympy import Integer + + from torch.utils._sympy.numbers import int_oo + + if self.root.min is -int_oo: # type: ignore[attr-defined] + return -int_oo # fn not needed cuz increasing + + _min_symint = self.fn(Integer(self.root.min)) # type: ignore[attr-defined] + root = self.root # type: ignore[attr-defined] + assert _min_symint >= 0, ( + f"Expected derived min value of {self.__name__} to be >= 0. " + f"Please specify an appropriate min value for {root.__name__} " + f"(currently {root.min})." + ) + return int(_min_symint) + + @property + def max(self): # type: ignore[override] + # assume that self.fn is an increasing function + # TODO(avik): use sympy value range analysis instead? + from sympy import Integer + + from torch.utils._sympy.numbers import int_oo + + if self.root.max is int_oo: # type: ignore[attr-defined] + return int_oo # fn not needed cuz increasing + + _max_symint = self.fn(Integer(self.root.max)) # type: ignore[attr-defined] + root = self.root # type: ignore[attr-defined] + assert _max_symint <= sys.maxsize - 1, ( + f"Expected derived max value of {self.__name__} to be <= {sys.maxsize - 1}. " + f"Please specify an appropriate max value for {root.__name__} " + f"(currently {root.max})." + ) + return int(_max_symint) + + def _derive(self, fn): + # We support nesting, e.g., 2*dim + 1. + # This is implemented by composing operations on the same root. + # As a consequence, roots are always regular Dims (i.e., not derived Dims). + return _DerivedDim( + self._derived_name(fn), + self.root, + lambda x: fn(self.fn(x)), + ) + + def __repr__(self): + return self.__name__ + + +def dims( + *names: str, min: int | None = None, max: int | None = None +) -> tuple[Dim, ...]: + """ + Util to create multiple :func:`Dim` types. + + Returns: + A tuple of :func:`Dim` types. + """ + return tuple(Dim(name, min=min, max=max) for name in names) # type: ignore[misc] + + +@dataclasses.dataclass +class _ConstraintTarget: + """ + This represents input tensor dimensions. + """ + + t_id: int + dim: int + + +@dataclasses.dataclass +class _Constraint(_ConstraintTarget): + """ + This represents a Dim describing a constraint target. + + `name` is the name of the Dim. + `constraint_range` contains the min/max bounds of the Dim. + """ + + name: str + constraint_range: "StrictMinMaxConstraint" + + def _clone_with_range(self, lower=0, upper=None): + # Import sympy locally + from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint + from torch.utils._sympy.numbers import int_oo + from torch.utils._sympy.value_ranges import ValueRanges + + if upper is None: + upper = int_oo + + constraint_range = StrictMinMaxConstraint( + vr=self.constraint_range.vr & ValueRanges(lower=lower, upper=upper), + warn_only=False, + ) + return _Constraint( + self.t_id, + self.dim, + self.name, + constraint_range, + ) + + def __ge__(self, lower): + return self._clone_with_range(lower=lower) + + def __gt__(self, lower): + return self._clone_with_range(lower=lower + 1) + + def __le__(self, upper): + return self._clone_with_range(upper=upper) + + def __lt__(self, upper): + return self._clone_with_range(upper=upper - 1) + + def __bool__(self): + # NOTE(avik): We do not support compound expressions like a <= x <= b. + # This is because Python implicitly desugars them into bool(a <= x) and bool(x <= b), + # and moreover, enforces that any overload of __bool__ must return True or False. + # FWIW, sympy also raises TypeError in this case. + raise TypeError( + "Cannot determine truth value of _Constraint. " + "If you are trying to combine _Constraint's with logical connectives, " + "you can specify them separately instead." + ) + + @property + def serializable_spec(self): + # We need a serialization compatible format of the constraint so that it + # can be savedin the graph module w/o breaking the module serialization. + # The saved constraints will be used directly for the post-exporting pass + # that converts constraints to runtime assertion. The saved constraints + # will not be saved in the serialized module. + # TODO: A better way is needed. Currently we use 't_id' to map the constraint, + # which is not reliable + return { + "t_id": self.t_id, + "dim": self.dim, + "min": self.constraint_range.vr.lower, + "max": self.constraint_range.vr.upper, + } + + +@dataclasses.dataclass +class _PhantomRoot: + """ + This represents the root of a derived Dim where the root does not directly + specify the shape of any input dimension, but the derived Dim does. + + e.g., the input shapes 2*dim and dim + 1 are related via a "phantom" dim. + + The fields `name`, `constraint_range`, and `val` carried by a phantom root + help create a symbol for it. Any derived dims with this phantom root are + backed by expressions over this symbol. + """ + + name: str + constraint_range: "StrictMinMaxConstraint" + val: int + + +@dataclasses.dataclass +class _DerivedConstraint(_ConstraintTarget): + """ + This represents a derived Dim, whose root is either a regular constraint target + (which directly specifies the shape of some input dimension) or a phantom root + (which does so indirectly). + + It can be thought of as a subclass of `_Constraint`, except that it does not + support <, <=, >, >= operations. + """ + + name: str + constraint_range: "StrictMinMaxConstraint" + root: _ConstraintTarget | _PhantomRoot + fn: Callable + + @property + def serializable_spec(self): + # same as _Constraint.serializable_spec + return { + "t_id": self.t_id, + "dim": self.dim, + "min": self.constraint_range.vr.lower, + "max": self.constraint_range.vr.upper, + } + + +@dataclasses.dataclass +class _RelaxedConstraint(_ConstraintTarget): + """ + This represents a dim marked with Dim.AUTO/DYNAMIC (i.e. mark_dynamic() or maybe_mark_dynamic()), + which leaves relations & min/max ranges for inference, instead of requiring explicit specification. + The intention is for constraint violations to not be raised if produce_guards() finds equalities or + relations between a _RelaxedConstraint and another type of _Constraint. + """ + + @property + def serializable_spec(self): + return { + "t_id": self.t_id, + "dim": self.dim, + } + + +Constraint = _Constraint | _DerivedConstraint | _RelaxedConstraint + + +@dataclasses.dataclass +class _IntWrapper: + """ + Dummy wrapper class to wrap around integer inputs so that when we parse the + dynamic_shapes structure, we can mark if any of the integers were marked as + dynamic. + """ + + val: int + # Disallow specifying dynamism + dynamism: _DimHint | int | None = dataclasses.field(init=False, default=None) + + +def _process_equalities( + constraint: Constraint, + get_sources: Callable[[int, int], list["Source"]], + shape_env: "ShapeEnv", + names: dict[str, tuple[int, int]], + source_pairs: list[tuple["Source", "Source"]], + derived_equalities: list[tuple["Source", Union["Source", "Symbol"], Callable]], + phantom_symbols: dict[str, "Symbol"], + relaxed_sources: set["Source"], +): + """ + Updates `source_pairs`, `derived_equalities`, and `phantom_symbols` (which become + fields of `EqualityConstraint`) based on a given input `constraint`. + """ + + sources = get_sources(constraint.t_id, constraint.dim) + if not sources: # empty sources due to unused shapes + return + + source, *other_sources = sources + # When t.size()[dim] maps to src0, src1, ..., srcN, we add + # constraints that make src0 "equal" to src1, ..., srcN. + source_pairs.extend((source, other_source) for other_source in other_sources) + if isinstance(constraint, _Constraint): + if constraint.name in names: + shared_t_id, shared_dim = names[constraint.name] + other_sources = get_sources(shared_t_id, shared_dim) + source_pairs.extend( + (source, other_source) for other_source in other_sources + ) + else: + names[constraint.name] = (constraint.t_id, constraint.dim) + elif isinstance(constraint, _DerivedConstraint): + # branch based on the root of the _DerivedConstraint + if not isinstance(constraint.root, _PhantomRoot): + # either root points to an input source + root = get_sources(constraint.root.t_id, constraint.root.dim)[0] + else: + # or root points to a phantom symbol + if constraint.root.name in phantom_symbols: + root = phantom_symbols[constraint.root.name] + else: + # create a phantom symbol in the shape env based on the _PhantomRoot + root = shape_env.create_symbol( + val=constraint.root.val, + source=torch._dynamo.source.ConstantSource(constraint.root.name), + dynamic_dim=torch.fx.experimental.symbolic_shapes.DimDynamic.DYNAMIC, + constraint_dim=constraint.root.constraint_range, + ) + phantom_symbols[constraint.root.name] = root + + fn = constraint.fn + # A derived equality (source, root, fn) informally corresponds to source = fn(root). + # Here source describes an input and root might describe another input or a phantom symbol. + derived_equalities.append((source, root, fn)) + elif isinstance(constraint, _RelaxedConstraint): + relaxed_sources.add(source) + + +def _tree_map_with_path( + func: Callable[..., Any], + tree: Any, + *dynamic_shapes: Any, + tree_name: str | None = None, +) -> Any: + """ + Customized tree_map for mapping pytrees to dynamic_shapes. + + For built-in types (e.g., standard collections) this behaves exactly like tree_map. + + OTOH for a user-defined class C registered with pytree, we cannot assume that a C + containing tensors can be mapped to a C containing dynamic shapes (i.e., C may not + be a polymorphic container). In that case we use the flattened form of C instead. + Thus a C(**tensors) that flattens to (**tensors) will map to (**dynamic_shapes). + + Args: + func: function to apply to each (int, float, str, bool, None, torch.Tensor) + tree: input pytree + dynamic_shapes: zero or more (typically one) dynamic_shapes to match + + Returns: + output pytree mapping func to each (int, float, str, bool, None, torch.Tensor) + """ + + def is_leaf(t): + # BUILTIN_TYPES is a subset of SUPPORTED_NODES, the latter being all types + # registered with pytree. Types *not* in BUILTIN_TYPES include primitive types + # (int, float, str, bool, None, torch.Tensor), which are not in SUPPORTED_NODES, + # as well as user-defined classes registered with pytree, which are. + return _get_node_type(t) not in BUILTIN_TYPES + + def f(path, t, *dynamic_shapes): + typ = _get_node_type(t) + # typ is not in BUILTIN_TYPES + if typ in SUPPORTED_NODES: + # thus typ is a user-defined class registered with pytree, + # in which case flatten and recurse + return tree_map_with_path( + f, + SUPPORTED_NODES[typ].flatten_fn(t)[0], + *dynamic_shapes, + is_leaf=is_leaf, + ) + else: + return func(path, t, *dynamic_shapes) + + try: + return tree_map_with_path(f, tree, *dynamic_shapes, is_leaf=is_leaf) + except ValueError as e: + if "mismatch" in e.args[0]: + # When PyTree finds a structural mismatch between tree and dynamic_shapes, + # the error message is unfortunately quite horrible. Let's fix that. + assert dynamic_shapes, "Cannot be a mismatch if there is no dynamic_shapes" + assert tree_name, "Must provide a tree_name when there might be a mismatch" + + def _key(type_, context, i): + # derive a PyTree key given the type, context, and child # of a TreeSpec + if type_ is dict: + return MappingKey(context[i]) + if type_ in (list, tuple): + assert context is None + return SequenceKey(i) + raise AssertionError(f"Did not expect type {type_}") + + def raise_mismatch_error(msg): + from torch._dynamo.exc import UserError, UserErrorType + + raise UserError( + UserErrorType.INVALID_INPUT, + f"Detected mismatch between the structure of `{tree_name}` and `dynamic_shapes`: {msg}", + case_name="dynamic_shapes_validation", + ) + + def _compare( + treespec: TreeSpec, other_treespec: TreeSpec, path: KeyPath + ) -> None: + # raise an error at the point where tree and dynamic_shapes differ, + # including the path to that point and the reason for the difference + rendered_path = keystr(path) + if treespec.is_leaf(): + return + if other_treespec.is_leaf(): + raise_mismatch_error( + f"`{tree_name}{rendered_path}` is a {treespec.type}, " + f"but `dynamic_shapes{rendered_path}` is not" + ) + if treespec.type != other_treespec.type: + raise_mismatch_error( + f"`{tree_name}{rendered_path}` is a {treespec.type}, " + f"but `dynamic_shapes{rendered_path}` is a {other_treespec.type}" + ) + if treespec.num_children != other_treespec.num_children: + raise_mismatch_error( + f"`{tree_name}{rendered_path}` has {treespec.num_children} elements, " + f"but `dynamic_shapes{rendered_path}` has {other_treespec.num_children} elements" + ) + if treespec.type is dict: + # context, children could be out of order + if set(treespec.context) != set(other_treespec.context): + raise_mismatch_error( + f"`{tree_name}{rendered_path}` has keys {treespec.context}, " + f"but `dynamic_shapes{rendered_path}` has keys {other_treespec.context}" + ) + _remap = dict( + zip(other_treespec.context, other_treespec.children()) + ) + other_children = [_remap[k] for k in treespec.context] + else: + other_children = other_treespec.children() + for i, (child, other_child) in enumerate( + zip(treespec.children(), other_children) + ): + _compare( + child, + other_child, + path + (_key(treespec.type, treespec.context, i),), + ) + + treespec = tree_structure(tree, is_leaf=is_leaf) + for other_tree in dynamic_shapes: + other_treespec = tree_structure(other_tree, is_leaf) + _compare(treespec, other_treespec, ()) + raise + + +def _combine_args(f, args, kwargs) -> dict[str, Any]: + # combine args and kwargs following the signature of f, as it happens + # in the body of f when called with *args, **kwargs + if isinstance(f, ExportedProgram): + f = f.module() + + signature = ( + inspect.signature(f.forward) + if isinstance(f, torch.nn.Module) + else inspect.signature(f) + ) + kwargs = kwargs if kwargs is not None else {} + return signature.bind(*args, **kwargs).arguments + + +class ShapesCollection: + """ + Builder for dynamic_shapes. + Used to assign dynamic shape specifications to tensors that appear in inputs. + + This is useful particularly when :func:`args` is a nested input structure, and it's + easier to index the input tensors, than to replicate the structure of :func:`args` in + the :func:`dynamic_shapes` specification. + + Example:: + + args = {"x": tensor_x, "others": [tensor_y, tensor_z]} + + dim = torch.export.Dim(...) + dynamic_shapes = torch.export.ShapesCollection() + dynamic_shapes[tensor_x] = (dim, dim + 1, 8) + dynamic_shapes[tensor_y] = {0: dim * 2} + # This is equivalent to the following (now auto-generated): + # dynamic_shapes = {"x": (dim, dim + 1, 8), "others": [{0: dim * 2}, None]} + + torch.export(..., args, dynamic_shapes=dynamic_shapes) + + To specify dynamism for integers, we need to first wrap the integers using + _IntWrapper so that we have a "unique identification tag" for each integer. + + Example:: + + args = {"x": tensor_x, "others": [int_x, int_y]} + # Wrap all ints with _IntWrapper + mapped_args = pytree.tree_map_only(int, lambda a: _IntWrapper(a), args) + + dynamic_shapes = torch.export.ShapesCollection() + dynamic_shapes[tensor_x] = (dim, dim + 1, 8) + dynamic_shapes[mapped_args["others"][0]] = Dim.DYNAMIC + + # This is equivalent to the following (now auto-generated): + # dynamic_shapes = {"x": (dim, dim + 1, 8), "others": [Dim.DYNAMIC, None]} + + torch.export(..., args, dynamic_shapes=dynamic_shapes) + """ + + def __init__(self): + self._shapes = {} + + def __setitem__(self, t, shape): + assert isinstance(t, (torch.Tensor, _IntWrapper)), ( + f"Cannot assign shape to non-tensor or non-_IntWrapper type {type(t)}" + ) + + # TODO(avik): check that shape is indeed a Shape + + t_id = id(t) + if t_id in self._shapes: + _shape = self._shapes[t_id] + assert shape == _shape, ( + f"Shapes assigned to input do not match: expected {_shape}, got {shape}" + ) + else: + self._shapes[id(t)] = shape + + def __getitem__(self, t): + t_id = id(t) + if t_id not in self._shapes: + self._shapes[t_id] = {} + return self._shapes[t_id] + + def __len__(self): + return len(self._shapes) + + def dynamic_shapes(self, m, args, kwargs=None): + """ + Generates the :func:`dynamic_shapes` pytree structure according to :func:`args` and :func:`kwargs`. + """ + + t_ids = set() + + def find_shape(path, t): + t_id = id(t) + if t_id in self._shapes: + t_ids.add(t_id) + return self._shapes[t_id] + else: + return None + + combined_args = _combine_args(m, args, kwargs) + dynamic_shapes = _tree_map_with_path(find_shape, combined_args) + if any(t_id not in t_ids for t_id in self._shapes): + raise ValueError( + "Some tensors that were assigned shapes were not found in args. " + "Maybe such tensors were copied when passing them as args? " + "Maybe such tensors are contained in classes that were not registered with pytree?" + ) + return dynamic_shapes + + +class AdditionalInputs: + """ + Infers dynamic_shapes based on additional inputs. + + This is useful particularly for deployment engineers who, on the one hand, may + have access to ample testing or profiling data that can provide a fair sense of + representative inputs for a model, but on the other hand, may not know enough + about the model to guess which input shapes should be dynamic. + + Input shapes that are different than the original are considered dynamic; conversely, + those that are the same as the original are considered static. Moreover, we verify + that the additional inputs are valid for the exported program. This guarantees that + tracing with them instead of the original would have generated the same graph. + + Example:: + + args0, kwargs0 = ... # example inputs for export + + # other representative inputs that the exported program will run on + dynamic_shapes = torch.export.AdditionalInputs() + dynamic_shapes.add(args1, kwargs1) + ... + dynamic_shapes.add(argsN, kwargsN) + + torch.export(..., args0, kwargs0, dynamic_shapes=dynamic_shapes) + """ + + def __init__(self): + self._examples = [] + + def add(self, args, kwargs=None): + """ + Additional input :func:`args` and :func:`kwargs`. + """ + + assert type(args) is tuple, f"Representative args {args} must be a tuple" + assert kwargs is None or type(kwargs) is dict, ( + f"Representative kwargs {kwargs} must be None or a dict" + ) + self._examples.append((args, kwargs)) + + def dynamic_shapes(self, m, args, kwargs=None): + """ + Infers a :func:`dynamic_shapes` pytree structure by merging shapes of the + original input :func:`args` and :func:`kwargs` and of each additional input + args and kwargs. + """ + + dynamic_shapes, *other_dynamic_shapes = [ + _tree_map_with_path( + lambda path, t: tuple(t.shape) if isinstance(t, torch.Tensor) else t, + _combine_args(m, args, kwargs), + ) + for args, kwargs in [(args, kwargs), *self._examples] + ] + + def _mark_dynamism(v, *other_vs): + if not all(type(v) is type(other) for other in other_vs): + raise ValueError( + "The following inputs were found to have differing types, " + f"so they cannot be marked as dynamic: {(v,) + other_vs}." + ) + + if isinstance(v, int) and not isinstance(v, bool): + if all(other_v == v for other_v in other_vs): + return None + else: + return Dim.DYNAMIC + else: + if not all(other_v == v for other_v in other_vs): + raise ValueError( + "The following inputs were found to have differing values, " + f"but they cannot be marked as dynamic: {(v,) + other_vs}." + ) + return None + + return tree_map( + _mark_dynamism, + dynamic_shapes, + *other_dynamic_shapes, + is_leaf=lambda i: type(i) is int, + ) + + def verify(self, ep): + """ + Verifies that an exported program is valid for each additional input. + """ + + epm = ep.module() + for args, kwargs in self._examples: + torch.export._unlift._check_input_constraints_for_module( + epm, args, kwargs or {} + ) + + +def _warn_on_None_dynamic_shape_dimension(): + msg = ( + "Using None as a dynamic shape dimension is deprecated. " + "Please use Dim.STATIC instead" + ) + # TODO(avik): raise an error in the future + log.warning(msg) + + +def _check_dynamic_shapes( + combined_args: dict[str, Any], + dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any] | None, +): + """ + Checks the dynamic_shapes specification for correctness, + using combined args + kwargs as reference for inputs structure. + """ + from torch._dynamo.exc import UserError, UserErrorType + + if dynamic_shapes is None or len(dynamic_shapes) == 0: + return + if isinstance(dynamic_shapes, (tuple, list)): + combined_args = type(dynamic_shapes)(combined_args.values()) # type: ignore[assignment, misc] + + bounds: dict[str, tuple[int, int]] = {} + + def check_same_bounds(dim): + if dim.__name__ in bounds: + min_, max_ = bounds[dim.__name__] + if dim.min != min_ or dim.max != max_: + this_ = Dim._readable(dim.__name__, min_, max_) + that_ = Dim._readable(dim.__name__, dim.min, dim.max) + raise UserError( + UserErrorType.INVALID_INPUT, + f"Found different definitions {this_} and {that_} " + f"for the same symbolic dimension {dim}!", + ) + else: + bounds[dim.__name__] = (dim.min, dim.max) + + def check_symbols(path, tensor, shape): + if isinstance(shape, dict): + for i, dim in shape.items(): + if isinstance(dim, Dim): + check_same_bounds(dim) + elif dim is None: + _warn_on_None_dynamic_shape_dimension() + elif not (isinstance(dim, (int, _DimHint))): + raise UserError( + UserErrorType.INVALID_INPUT, + f"Unexpected dimension mapped to index {i} in input tensor shape {shape} " + f"specified at `dynamic_shapes{keystr(path)}` " + f"(expected None, an int, a Dim, Dim.AUTO, Dim.STATIC, or Dim.DYNAMIC, " + f" but got {dim!r} instead)", + case_name="dynamic_shapes_validation", + ) + elif isinstance(shape, (tuple, list)): + if len(shape) != len(tensor.shape): + raise UserError( + UserErrorType.INVALID_INPUT, + f"Expected dynamic shape spec {shape} specified at `dynamic_shapes{keystr(path)}` " + f"to have the same length as the actual tensor shape {tensor.shape} " + f"(expected {len(tensor.shape)}, but got {len(shape)} instead)", + case_name="dynamic_shapes_validation", + ) + for i, dim in enumerate(shape): + if isinstance(dim, Dim): + check_same_bounds(dim) + elif dim is None: + _warn_on_None_dynamic_shape_dimension() + elif not (isinstance(dim, (int, _DimHint))): + raise UserError( + UserErrorType.INVALID_INPUT, + f"Unexpected dimension #{i} in input tensor shape {shape} " + f"specified at `dynamic_shapes{keystr(path)}` " + f"(expected None, an int, a Dim, Dim.AUTO, Dim.STATIC, or Dim.DYNAMIC, " + f"but got {dim!r} instead)", + case_name="dynamic_shapes_validation", + ) + elif shape is not None: + raise UserError( + UserErrorType.INVALID_INPUT, + f"Unexpected input tensor shape {shape} specified at `dynamic_shapes{keystr(path)}` " + f"(expected either a list/tuple of dimensions, or a dict mapping indices to dimensions," + f" where each dimension is an int, a Dim, Dim.AUTO, Dim.STATIC, or Dim.DYNAMIC)", + case_name="dynamic_shapes_validation", + ) + + assert isinstance(dynamic_shapes, (dict, tuple, list)) + if isinstance(dynamic_shapes, dict): + got_keys = list(dynamic_shapes.keys()) + expected_arg_names = list(combined_args.keys()) + if sorted(got_keys) != sorted(expected_arg_names): + msg = ( + f"When `dynamic_shapes` is specified as a dict, its top-level keys " + f"must be the arg names {expected_arg_names} of `inputs`, but " + f"here they are {got_keys}. " + ) + if ( + len(combined_args) == 1 + and expected_arg_names[0] not in got_keys + and isinstance(combined_args[expected_arg_names[0]], dict) + ): + msg += ( + "Since here `inputs` is a list/tuple enclosing a single dict, " + "maybe you just forgot to enclose `dynamic_shapes` in a list/tuple?" + ) + else: + msg += ( + "Alternatively, you could also ignore arg names entirely " + "and specify `dynamic_shapes` as a list/tuple matching `inputs`." + ) + raise UserError( + UserErrorType.INVALID_INPUT, msg, case_name="dynamic_shapes_validation" + ) + + def check_shape(path, t, dynamic_shape): + if isinstance(t, torch.Tensor): + check_symbols(path, t, dynamic_shape) + elif isinstance(t, _IntWrapper): + if isinstance(dynamic_shape, _Dim): + raise ValueError( + "Unable to specify input integers as dynamic through named " + "Dims. Please use Dim.AUTO/DYNAMIC instead." + ) + assert dynamic_shape is None or isinstance(dynamic_shape, (int, _DimHint)) + else: + if dynamic_shape is not None: + rendered_path = keystr(path) + raise UserError( + UserErrorType.INVALID_INPUT, + f"Cannot associate shape {dynamic_shape} specified at `dynamic_shapes{rendered_path}` " + f"to non-tensor type {type(t)} at `inputs{rendered_path}` (expected None)", + case_name="dynamic_shapes_validation", + ) + + _tree_map_with_path(check_shape, combined_args, dynamic_shapes, tree_name="inputs") + + +def _process_dynamic_shapes( + combined_args: dict[str, Any], + dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any] | None, +) -> list[Constraint]: + """ + Reads the dynamic_shapes specification and produces a list of constraints. + """ + from torch._dynamo.exc import UserError, UserErrorType + + if dynamic_shapes is None or len(dynamic_shapes) == 0: + # we run with dynamic by default, so no need to produce constraints + return [] + if isinstance(dynamic_shapes, (tuple, list)): + combined_args = type(dynamic_shapes)(combined_args.values()) # type: ignore[assignment, misc] + + # map of Dim names representing input shape dimensions to constraints on them + symbols: dict[str, list[Constraint]] = defaultdict(list) + # track roots that do not directly represent input shape dimensions + phantom_roots: dict[str, _PhantomRoot] = {} + derived_constraints_with_phantom_root: list[_DerivedConstraint] = [] + # list of constraints to return + constraints: list[Constraint] = [] + + def to_constraint(dim, tensor, i): + import sympy + + from torch.fx.experimental.symbolic_shapes import StrictMinMaxConstraint + from torch.utils._sympy.solve import try_solve + from torch.utils._sympy.value_ranges import ValueRanges + + def root_value(): + # given tensor.shape[i] is the value of dim = fn(root), + # find the value of root + symbol = sympy.Symbol(dim.root.__name__, integer=True) + expr = dim.fn(symbol) + solution = try_solve(sympy.Eq(expr, tensor.shape[i]), symbol) + if solution is not None: + return int(solution[1]) + else: + raise UserError( # noqa: B904 + UserErrorType.CONSTRAINT_VIOLATION, + f"Expected shape[{i}] = {tensor.shape[i]} of input Tensor to be " + f"of the form {expr}, where {symbol} is an integer", + ) + + if isinstance(dim, _DerivedDim): + # generate a _DerivedConstraint where the root is: + # - either a _ConstraintTarget (if dim.root directly describes an input shape) + # - or a _PhantomRoot (otherwise) + dim_root = dim.root # type: ignore[attr-defined] + if dim_root.__name__ in symbols: + # root represents an input shape dimension + root_constraint = symbols[dim_root.__name__][0] + root = _ConstraintTarget( + root_constraint.t_id, + root_constraint.dim, + ) + elif dim_root.__name__ not in phantom_roots: + # create a phantom root + root = _PhantomRoot( # type: ignore[assignment] + name=dim_root.__name__, + constraint_range=StrictMinMaxConstraint( + vr=ValueRanges(lower=dim_root.min, upper=dim_root.max), + warn_only=False, + ), + val=root_value(), + ) + phantom_roots[dim_root.__name__] = root # type: ignore[assignment] + else: + root = phantom_roots[dim_root.__name__] # type: ignore[assignment] + constraint = _DerivedConstraint( + id(tensor), + i, + dim.__name__, + StrictMinMaxConstraint( + vr=ValueRanges(lower=dim.min, upper=dim.max), + warn_only=False, + ), + root, + dim.fn, # type: ignore[attr-defined] + ) + if isinstance(root, _PhantomRoot): + # NOTE(avik): since we have not processed all inputs yet, we may replace this + # with a root that does represent an input shape dimension later (see below) + derived_constraints_with_phantom_root.append(constraint) + elif isinstance(dim, _StaticDim): + constraint = _Constraint( # type: ignore[assignment] + id(tensor), + i, + dim.__name__, + StrictMinMaxConstraint( + vr=ValueRanges(lower=dim.value, upper=dim.value), # type: ignore[attr-defined] + warn_only=False, + ), + ) + else: + assert isinstance(dim, Dim) + constraint = _Constraint( # type: ignore[assignment] + id(tensor), + i, + dim.__name__, + StrictMinMaxConstraint( + vr=ValueRanges(lower=dim.min, upper=dim.max), # type: ignore[attr-defined] + warn_only=False, + ), + ) + return constraint + + def _parse_tensor_dim(tensor, idx, dim) -> None: + def _create_static_dim(tensor, i, value): + return _StaticDim(value) + + if isinstance(dim, (int, Dim)): + if isinstance(dim, int): + dim = _create_static_dim(tensor, idx, dim) + constraint = to_constraint(dim, tensor, idx) + symbols[dim.__name__].append(constraint) + elif isinstance(dim, _DimHint): + if dim.type == _DimHintType.AUTO: + torch._dynamo.maybe_mark_dynamic(tensor, idx) + elif dim.type == _DimHintType.STATIC: + torch._dynamo.mark_static(tensor, idx) + elif dim.type == _DimHintType.DYNAMIC: + torch._dynamo.mark_dynamic(tensor, idx) + constraints.append(_RelaxedConstraint(id(tensor), idx)) + elif dim is None: + torch._dynamo.mark_static(tensor, idx) + + def update_symbols(path, tensor, shape): + # clean out decorators from user side, or previous export call + # we also delete these attributes in non_strict_utils.py/make_constraints() + tensor._dynamo_weak_dynamic_indices = set() + tensor._dynamo_dynamic_indices = set() + tensor._dynamo_dynamic_range = set() + tensor._dynamo_static_indices = set() + tensor._dynamo_unbacked_indices = set() + + if isinstance(shape, dict): + for i, dim in shape.items(): + _parse_tensor_dim(tensor, i, dim) + elif isinstance(shape, (tuple, list)): + for i, dim in enumerate(shape): + _parse_tensor_dim(tensor, i, dim) + elif shape is None: + for i in range(tensor.dim()): + _parse_tensor_dim(tensor, i, None) + + def assoc_shape(path, t, dynamic_shape): + if isinstance(t, torch.Tensor): + update_symbols(path, t, dynamic_shape) + elif isinstance(t, _IntWrapper): + # If tensor dimensions are marked as dynamic, the tensors themselves + # get marked using mark_dynamic. However since we can't mark + # integers as dynamic, we first wrap integers in this class, and + # then set the `dim` field of the class with the dynamic shapes dim + # to mark the integer as dynamic. + t.dynamism = dynamic_shape + + _tree_map_with_path(assoc_shape, combined_args, dynamic_shapes, tree_name="inputs") + + for derived_constraint_with_phantom_root in derived_constraints_with_phantom_root: + phantom_root_name = derived_constraint_with_phantom_root.root.name # type: ignore[union-attr] + if phantom_root_name in symbols: + # We found an input shape dimension corresponding to this name, so we + # do not need a phantom symbol for it after all. + # NOTE(avik): Overall we want to maintain the invariant that roots that + # are phantom symbols are really "phantom," i.e., they cannot be represented + # by any input source. This is important when we are deciding derived equalities, + # since we can focus our attention exclusively on input sources: deciding + # derived equalities involving phantom symbols are, in comparison, trivial. + derived_constraint_with_phantom_root.root = symbols[phantom_root_name][0] + + for dynamic_dims in symbols.values(): + constraints.extend(dynamic_dims) + + return constraints + + +def _get_dim_name_mapping( + dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any] | None, +): + name_to_dim = {} + for dim in tree_iter(dynamic_shapes, is_leaf=lambda x: isinstance(x, Dim)): + if dim is None: + # NOTE: this must denote a non-Tensor or automatic at this point. + continue + if isinstance(dim, int): + continue + elif isinstance(dim, Dim): + name_to_dim[dim.__name__] = dim + if isinstance(dim, _DerivedDim): + name_to_dim[dim.root.__name__] = dim.root # type: ignore[attr-defined] + else: + assert isinstance(dim, _DimHint) + return name_to_dim + + +def refine_dynamic_shapes_from_suggested_fixes( + msg: str, + dynamic_shapes: dict[str, Any] | tuple[Any] | list[Any], +) -> dict[str, Any] | tuple[Any] | list[Any]: + """ + When exporting with :func:`dynamic_shapes`, export may fail with a ConstraintViolation error if the specification + doesn't match the constraints inferred from tracing the model. The error message may provide suggested fixes - + changes that can be made to :func:`dynamic_shapes` to export successfully. + + Example ConstraintViolation error message:: + + Suggested fixes: + + dim = Dim('dim', min=3, max=6) # this just refines the dim's range + dim = 4 # this specializes to a constant + dy = dx + 1 # dy was specified as an independent dim, but is actually tied to dx with this relation + + This is a helper function that takes the ConstraintViolation error message and the original :func:`dynamic_shapes` spec, + and returns a new :func:`dynamic_shapes` spec that incorporates the suggested fixes. + + Example usage:: + + try: + ep = export(mod, args, dynamic_shapes=dynamic_shapes) + except torch._dynamo.exc.UserError as exc: + new_shapes = refine_dynamic_shapes_from_suggested_fixes( + exc.msg, dynamic_shapes + ) + ep = export(mod, args, dynamic_shapes=new_shapes) + + """ + + import re + + import sympy + + from torch._dynamo.exc import UserError, UserErrorType + from torch.fx.experimental.symbolic_shapes import _is_supported_equivalence + + try: + shape_fixes_msg = msg.split("Suggested fixes:")[1].strip() + except Exception as exc: + raise UserError( + UserErrorType.INVALID_INPUT, + "Suggested fixes not found in error message given to refine_dynamic_shapes_from_suggested_fixes()", + ) from exc + + # build shape_fixes dictionary + shape_fixes = {} + for fix in shape_fixes_msg.split("\n"): + fix = fix.strip() + if match := re.match(r"(.*) = Dim\('(.*)'.*\)", fix): + name = match.group(1) + _min, _max = None, None + if match_min := re.match(r".* = Dim\('.*', min\=([0-9]+).*\)", fix): + _min = int(match_min.group(1)) + if match_max := re.match(r".* = Dim\('.*'.*max\=([0-9]+)\)", fix): + _max = int(match_max.group(1)) + shape_fixes[name] = Dim(name, min=_min, max=_max) + else: + name, expr = fix.split(" = ") + expr = sympy.sympify(expr) + if isinstance(expr, sympy.Number): + # static, integer + shape_fixes[name] = int(expr) # type: ignore[assignment] + else: + # relation or derived dim + shape_fixes[name] = expr + + name_to_dim = _get_dim_name_mapping(dynamic_shapes) + + # track derived dim roots + roots: set[str] = set() + for k, c in shape_fixes.items(): + assert isinstance(c, (int, Dim, _DerivedDim, sympy.Expr)) + if isinstance(c, sympy.Expr): # check dim/derived dim expression + assert _is_supported_equivalence(c) + shape_fixes[k] = c + roots.add(str(next(iter(c.free_symbols)))) + if isinstance(c, _DerivedDim): + roots.add(c.root.__name__) # type: ignore[attr-defined] + + # check keys are existing dims or new roots + for k in shape_fixes: + assert k in name_to_dim or k in roots + + # cache so we don't produce multiple derived dim objects + derived_dim_cache: dict[str, _DerivedDim] = {} + + def apply_fixes(path, dim, dummy): + if dim is None or isinstance(dim, int): # not dynamic + return dim + elif dim.__name__ in shape_fixes: # directly fix + fix = shape_fixes[dim.__name__] + if isinstance(fix, sympy.Expr): # now derived or related + if str(fix) in derived_dim_cache: + return derived_dim_cache[str(fix)] + else: + symbol = next(iter(fix.free_symbols)) + # try to locate symbol + if symbol.name in shape_fixes: + root = shape_fixes[symbol.name] + else: + assert symbol.name in name_to_dim + root = name_to_dim[symbol.name] + # figure out value of fix + modulus, remainder = sympy.polys.polytools.div(fix, symbol) + dim = root + if modulus != 1: + dim = int(modulus) * dim + if remainder != 0: + dim = dim + int(remainder) + derived_dim_cache[str(fix)] = dim + return dim + else: + return fix + elif isinstance(dim, _DerivedDim) and dim.root.__name__ in shape_fixes: # type: ignore[attr-defined] + if dim.__name__ in derived_dim_cache: + return derived_dim_cache[dim.__name__] + else: # evaluate new derived value based on root + _dim = dim.fn(shape_fixes[dim.root.__name__]) # type: ignore[attr-defined] + derived_dim_cache[dim.__name__] = _dim + return _dim + return dim # unchanged dim + + return _tree_map_with_path(apply_fixes, dynamic_shapes, dynamic_shapes) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/experimental/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/experimental/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..14399a7bfdadd7d7a35781892dd60e8809a6d5b7 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/experimental/__init__.py @@ -0,0 +1,430 @@ +import copy +import dataclasses +import functools +import os +import types +import typing +import typing_extensions +import zipfile +from pathlib import Path + +import torch +from torch.export.experimental._utils import _get_main_cpp_file, _get_make_file +from torch.export.exported_program import _decompose_exported_program + + +_InputT = typing_extensions.ParamSpec("_InputT") +_RetT = typing.TypeVar("_RetT") + + +__all__ = [] # type: ignore[var-annotated] + + +def _copy_graph_module_and_signature( + ep: torch.export.ExportedProgram, +) -> tuple[torch.fx.GraphModule, torch.export.graph_signature.ExportGraphSignature]: + # copy.deepcopy lets the objects override __deepcopy__ methods with graph_copy() and node_copy(), + # and this can break placeholder names in some particular cases. + # For example, node copying will avoid Python keywords like 'input', suffixing and renaming to 'input_1'. + # So we manually overwrite placeholder names by reading the old graph. + gm = copy.deepcopy(ep.graph_module) + new_graph_signature = copy.deepcopy(ep.graph_signature) + + # iterate over old/new graph modules + for old_gm, new_gm in zip(ep.graph_module.modules(), gm.modules()): # type: ignore[union-attr] + old_phs = [node for node in old_gm.graph.nodes if node.op == "placeholder"] + new_phs = [node for node in new_gm.graph.nodes if node.op == "placeholder"] + # iterate over placeholders + assert len(old_phs) == len(new_phs) + for old_node, new_node in zip(old_phs, new_phs): + new_node.name = old_node.name + + return gm, new_graph_signature + + +def _remove_detach_pass( + gm: torch.fx.GraphModule, sig: torch.export.graph_signature.ExportGraphSignature +) -> None: + with gm._set_replace_hook(sig.get_replace_hook()): + for node in list(reversed(gm.graph.nodes)): + if node.op != "call_function": + continue + if ( + node.target is torch.ops.aten.detach.default + and len(node.users) == 1 + and next(iter(node.users)).target is torch.ops.aten.detach.default + ): + next(iter(node.users)).replace_all_uses_with(node) + + gm.graph.eliminate_dead_code() + gm.recompile() + + +def _export_forward_backward( + ep: torch.export.ExportedProgram, joint_loss_index: int = 0 +) -> torch.export.ExportedProgram: + """ + WARNING: This API is highly unstable and will be subject to change in the future. + """ + from torch._decomp import core_aten_decompositions + + ep = _decompose_exported_program( + ep, + cia_to_decomp={}, + python_decomp_table=core_aten_decompositions(), + joint_loss_index=joint_loss_index, + # For serialization purpose, we don't want to decompose custom triton ops. + # If users would like to decompose custom triton ops, they could do it + # with run_decompositions() API. + decompose_custom_triton_ops=False, + ) + gm, new_graph_signature = _copy_graph_module_and_signature(ep) + _remove_detach_pass(gm, new_graph_signature) + + return ep._update(gm, new_graph_signature) + + +def _sticky_export( + forward_func: typing.Callable[_InputT, _RetT], + dynamic_shapes_callback: typing.Callable[ + _InputT, list[typing.Any] | dict[str, typing.Any] | tuple[typing.Any, ...] + ] + | None = None, +) -> typing.Callable[_InputT, _RetT]: + """ + Lazily export the model on first forward call. + Usage: + model.forward = _sticky_export(model.forward, dynamic_shapes_callback=callback) + """ + model = forward_func.__self__ # type: ignore[attr-defined] + original_forward = forward_func.__func__ # type: ignore[attr-defined] + + @functools.wraps(forward_func) + def wrapper(*args: _InputT.args, **kwargs: _InputT.kwargs) -> _RetT: + # Unpatch forward to avoid recursion during export + model.forward = types.MethodType(original_forward, model) + + dynamic_shapes_spec = None + if dynamic_shapes_callback: + dynamic_shapes_spec = dynamic_shapes_callback(*args, **kwargs) + + try: + exported = torch.export.export( + model, + args, + kwargs, + dynamic_shapes=dynamic_shapes_spec, + ).module() + wrapper._exported_artifact = exported # type: ignore[attr-defined] + finally: + # Restore the wrapper after export + model.forward = wrapper + + return exported(*args, **kwargs) + + return wrapper + + +@dataclasses.dataclass +class _ExportMethod: + overloads: dict[str, torch.export.ExportedProgram] + fallbacks: list[torch.export.ExportedProgram] + + +class _ExportPackage: + """ + An export package is a collection of torch.export()-ed PyTorch models consisting of + a list of exported methods and their corresponding overloads. ExportPackage is introduced + on top of torch.export() to support the following use cases: + - Exporting a model with multiple methods if a model has multiple independent parts. + - Exporting a function with multiple overloads based on tensor shapes or other metadata. + + ExportPackage is designed to contain multiple methods (associated with method names) and for + each method, it can have multiple overloads (associated with overload names). + + Here is an example of the data structure for an ExportPackage: + ``` + ExportPackage( + methods={ + "decoder": ExportMethod( + overloads={ + "prefill": ExportedProgram(...), + "decode": ExportedProgram(...), + }, + fallbacks=[], + ), + "encoder": ExportMethod(overloads={}, fallbacks=[ExportedProgram(...)]), + }, + ) + ``` + + To export a model into an ExportPackage, users can use the exporter API provided by ExportPackage. + Exporter is a decorator that takes a callable and returns a wrapper. The wrapper will export the + function into an ExportPackage, when it's invoked with some sample inputs (similar to how + torch.compile() works). For more details, please refer to the document on .exporter() method. + + This design allows users to decouple the exported callables from the actual sample inputs which can + be helpful for use cases where the exported callable is hidden behind helper functions or when sample + inpusts are hard to get. + + NOTE: This is an experimental API and anything can be changed in the future. + + Example usage: + ``` + def fn(x): + return x + 1 + + def main(f, x): + x += 1 + ret = f(x) + return ret + 1 + + package = ExportPackage() + main(package.exporter(fn), torch.randn(3, 2)) + ``` + + """ + + def __init__(self) -> None: + self.methods: dict[str, _ExportMethod] = {} + + def _exporter( + self, + method: str, + fn: typing.Callable[_InputT, _RetT], + *, + fallback: str = "once", + ) -> typing.Callable[_InputT, _RetT]: + """ + A function/module decorator that sets up a callable to be exported later invoked. + By default the exporter will only trigger torch.export for once and error on + later invocations. To customize this behavior, users have the following two options: + 1. Call .define_overload() method on the returned wrapper to define an overload. + 2. Adjust the fallback policy using `fallback` argument. + + An "overload" is a named branch for an ExportMethod with a user defined precondition, + typically based on input tensor shapes. It's up to a downstream backend implementation + of ExportMethod to respect the precondition later in inference. + + define_overload() takes arguments like the following: + - A name, for indexing purposes in a backend. + - A callable (spec) that: + - Has the same model input signature as the original model code. + - Returns an optional dynamic shape spec. + + Exporter will only export an overload when the spec callable successfully returns + a result without raising AssertionError. + + For example: + ``` + package = ExportPackage() + + + def prefill(x, xa, kv_cache): + assert x.shape[1] == 3 + assert kv_cache == {} + + + def decode(x, xa, kv_cache): + assert x.shape[1] > 1 + assert len(kv_cache) > 0 + return {...} # dynamic shape specs here + + + exporter = ( + package.exporter(decoder) + .define_overload("prefill", prefill) + .define_overload("decode", decode) + ) + ``` + + A "fallback" is exported when no overload precondition matches a given set of sample + inputs. Overloads should + Fallbacks don't have names and are ordered in a list. It's up to a backend to decide + which fallback is used amony multiple ones. + + A reference backend implementation of ExportMethod may look like the following: + ``` + def execute(method: ExportMethod, *args, **kwargs): + for overload in method.overloads: + if match_precondition(overload, *args, **kwargs): + return execute_overload(overload, *args, **kwargs) + for fallback in method.fallbacks: + if match_precondition(fallback, *args, **kwargs): + return execute_fallback(fallback, *args, **kwargs) + ``` + + Args: + method(str): The method name for an exported part of PyTorch model. This + will be saved together with the exported/compiled artifacts + in any serialization format and can be used as the key to + index ExportPackage methods later. + fn(callable): A PyTorch function/module to be exported. + fallback(str): The fallback policy to decide when to call torch.export + - "once" is the default policy. Under this policy a PyTorch program is assumed + to be only called once later and an error will be raised for subsequent + runs. + - "error" means the ExportMethod will never have any fallbacks, meaning + users should define all the possible overloads ahead of time. + + """ + + fallbacks: list[torch.export.ExportedProgram] = [] + specs: dict[str, typing.Callable[_InputT, typing.Any]] = {} + overloads: dict[str, torch.export.ExportedProgram] = {} + self.methods[method] = _ExportMethod(fallbacks=fallbacks, overloads=overloads) + + @functools.wraps(fn) + def _exporter_context(*args, **kwargs): # type: ignore[no-untyped-def] + import torch.export._wrapper_utils + + model: torch.nn.Module + if not isinstance(fn, torch.nn.Module): + model = torch.export._wrapper_utils._WrapperModule(fn) + else: + model = fn + + for k, v in specs.items(): + try: + if isinstance(fn, torch.nn.Module): + dynamic_shapes = v(fn, *args, **kwargs) # type: ignore[arg-type] + else: + # pyrefly: ignore [invalid-param-spec] + dynamic_shapes = v(*args, **kwargs) + except AssertionError: + continue + if k not in overloads: + ep = torch.export.export( + model, args, kwargs, dynamic_shapes=dynamic_shapes + ) + overloads[k] = ep + ep = overloads[k] + return ep.module()(*args, **kwargs) + + if fallback == "error": + raise RuntimeError( + f"Exporter: Cannot export fallback {fn} when fallback policy is set to 'error'," + + "please specify an overload or adjust the fallback policy." + ) + elif fallback == "once": + if len(fallbacks) > 0: + raise RuntimeError( + f"Exporter: Cannot export {fn} more than once, " + + "please specify an overload or adjust the fallback policy." + ) + else: + raise RuntimeError(f"Unknown fallback policy: {fallback}") + ep = torch.export.export(model, args, kwargs) + + fallbacks.append(ep) + return ep.module()(*args, **kwargs) + + if isinstance(fn, torch.nn.Module): + _exporter_context = torch._dynamo.eval_frame.OptimizedModule( # type: ignore[assignment] # noqa: F811 + fn, + lambda _: _exporter_context, # type: ignore[arg-type] + ) + + def _define_overload( + overload: str, spec: typing.Callable[_InputT, typing.Any] + ) -> typing.Any: + assert overload not in specs + assert callable(spec) + assert overload.isidentifier() + specs[overload] = spec + return _exporter_context + + assert not hasattr(fn, "_define_overload") + _exporter_context._define_overload = _define_overload # type: ignore[attr-defined] + + # pyrefly: ignore [bad-return] + return _exporter_context + + @property + def _method_overloads( + self, + ) -> typing.Iterator[tuple[str, torch.export.ExportedProgram]]: + for method, method_data in self.methods.items(): + for overload, ep in method_data.overloads.items(): + yield f"{method}:{overload}", ep + + def _compiled_and_package( + self, + f: torch.types.FileLike, + standalone: bool = False, + package_example_inputs: bool = False, + ) -> None: + options: dict[str, typing.Any] = { + "aot_inductor.package": True, + "aot_inductor.package_cpp_only": True, + "always_keep_tensor_constants": True, + # we'll change this back to False once we enable weight deduping for standalone mode + "aot_inductor.package_constants_in_so": standalone, + "aot_inductor_mode.compile_standalone": standalone, + } + aoti_files_map = {} + model_names = [] + for name, ep in self._method_overloads: + name = name.replace(":", "__") + model_names.append(name) + options["aot_inductor.model_name_for_generated_files"] = name + aoti_files = torch._inductor.aot_compile( + ep.module(), # type: ignore[arg-type] + ep.example_inputs[0], + kwargs=ep.example_inputs[1], + options=options, + ) + # pyrefly: ignore [unsupported-operation] + aoti_files_map[name] = aoti_files + + from torch._inductor.package import package + + pt2_path = package.package_aoti( + f, + aoti_files_map, # type: ignore[arg-type] + ) + + if not standalone: + return + + assert isinstance(pt2_path, str) + base_directory = os.path.dirname(pt2_path) + package_name = os.path.basename(pt2_path)[:-4] + with ( + zipfile.ZipFile(pt2_path, "r") as zip_ref, + ): + zip_ref.extractall(base_directory) + + example_inputs_map: dict[str, int] | None = ( + {} if package_example_inputs else None + ) + use_cuda = False + for name, ep in self._method_overloads: + name = name.replace(":", "__") + # TODO: also dump kwargs + # TODO: currently only support list of Tensors and they need to be on the same device + if not ep.example_inputs: + continue + for inp in ep.example_inputs[0]: + if isinstance(inp, torch.Tensor) and inp.device.type == "cuda": + # TODO: more carefully determine the device type + use_cuda = True + if package_example_inputs: + assert example_inputs_map is not None + example_inputs_map[name] = len(ep.example_inputs[0]) + for i, t in enumerate(ep.example_inputs[0]): + path = Path(base_directory) / f"{name}_input_{i}.pt" + torch.save(t, path) + + # Detect if ROCm is being used + is_hip = torch.version.hip is not None + cmake_file_str = _get_make_file(package_name, model_names, use_cuda, is_hip) + + with open(Path(base_directory) / "CMakeLists.txt", "w") as file: + file.write(cmake_file_str) + + main_file_str = _get_main_cpp_file( + package_name, model_names, use_cuda, example_inputs_map, is_hip + ) + with open(Path(base_directory) / "main.cpp", "w") as file: + file.write(main_file_str) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/experimental/_utils.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/experimental/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1005effe2f299a2bd33ac0517e24b46d840bf675 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/experimental/_utils.py @@ -0,0 +1,238 @@ +import logging + +from torch._inductor.utils import IndentedBuffer + + +__all__ = [] # type: ignore[var-annotated] +logger = logging.getLogger(__name__) + + +def _get_main_cpp_file( + package_name: str, + model_names: list[str], + cuda: bool, + example_inputs_map: dict[str, int] | None, + is_hip: bool, +) -> str: + """ + Generates a main.cpp file for AOTInductor standalone models in the specified package. + + Args: + package_name (str): Name of the package containing the models. + model_names (List[str]): List of model names to include in the generated main.cpp. + cuda (bool): Whether to generate code with CUDA support. + example_inputs_map (Optional[Dict[str, List[Tensor]]]): A mapping from model name to + its list of example input tensors. If provided, the generated main.cpp will + load and run these inputs. + + Returns: + str: The contents of the generated main.cpp file as a string. + """ + + ib = IndentedBuffer() + + ib.writelines( + [ + "#include ", + "#include ", + "#include ", + "#include ", + "#include ", + "#include ", + "#include ", + ] + ) + if cuda: + if is_hip: + ib.writelines( + [ + "#include ", + ] + ) + + else: + ib.writelines( + [ + "#include ", + "#include ", + ] + ) + + for model_name in model_names: + ib.writeline( + f'#include "{package_name}/data/aotinductor/{model_name}/{model_name}.h"' + ) + + ib.newline() + for model_name in model_names: + ib.writeline(f"using torch::aot_inductor::AOTInductorModel{model_name};") + + ib.writelines( + [ + "using torch::aot_inductor::ConstantHandle;", + "using torch::aot_inductor::ConstantMap;", + "", + "int main(int argc, char* argv[]) {", + ] + ) + + with ib.indent(): + ib.writeline(f'std::string device_str = "{"cuda" if cuda else "cpu"}";') + ib.writeline("try {") + + with ib.indent(): + ib.writeline("c10::Device device(device_str);") + + if example_inputs_map is not None: + # TODO: add device + for i, model_name in enumerate(model_names): + num_inputs = example_inputs_map[model_name] + + ib.writeline(f"// Load input tensors for model {model_name}") + ib.writeline(f"std::vector input_tensors{i + 1};") + ib.writeline(f"for (int j = 0; j < {num_inputs}; ++j) {{") + with ib.indent(): + ib.writeline( + f'std::string filename = "{model_name}_input_" + std::to_string(j) + ".pt";' + ) + ib.writeline("std::ifstream in(filename, std::ios::binary);") + ib.writeline("if (!in.is_open()) {") + with ib.indent(): + ib.writeline( + 'std::cerr << "Failed to open file: " << filename << std::endl;' + ) + ib.writeline("return 1;") + ib.writeline("}") + ib.writeline( + "std::vector buffer((std::istreambuf_iterator(in)), std::istreambuf_iterator());" + ) + ib.writeline( + "torch::IValue ivalue = torch::pickle_load(buffer);" + ) + ib.writeline( + f"input_tensors{i + 1}.push_back(ivalue.toTensor().to(device));" + ) + ib.writeline("}") + ib.newline() + + ib.newline() + ib.writeline("\n// Create array of input handles") + for i in range(len(model_names)): + ib.writelines( + [ + f"auto input_handles{i + 1} =", + f" torch::aot_inductor::unsafe_alloc_new_handles_from_tensors(input_tensors{i + 1});", + ] + ) + + ib.writeline("\n// Create array for output handles") + for i in range(len(model_names)): + ib.writeline(f"AtenTensorHandle output_handle{i + 1};") + + ib.writeline("\n// Create and load models") + for i, model_name in enumerate(model_names): + ib.writelines( + [ + f"auto constants_map{i + 1} = std::make_shared();", + f"auto constants_array{i + 1} = std::make_shared>();", + f"auto model{i + 1} = std::make_unique(", + f" std::move(constants_map{i + 1}),", + f" std::move(constants_array{i + 1}),", + " device_str,", + f' "{package_name}/data/aotinductor/{model_name}/");', + f"model{i + 1}->load_constants();", + ] + ) + + if example_inputs_map is not None: + ib.writeline("\n// Run the models") + for i in range(len(model_names)): + ib.writeline( + f"torch::aot_inductor::DeviceStreamType stream{i + 1} = nullptr;" + ) + ib.writeline( + f"model{i + 1}->run(&input_handles{i + 1}[0], &output_handle{i + 1}, stream{i + 1}, nullptr);" + ) + + ib.writeline("\n// Convert output handles to tensors") + for i in range(len(model_names)): + ib.writelines( + [ + f"auto output_tensor{i + 1} =", + f" torch::aot_inductor::alloc_tensors_by_stealing_from_handles(&output_handle{i + 1}, 1);", + ] + ) + + ib.writeline("\n// Validate outputs") + for i in range(len(model_names)): + ib.writeline( + f"""std::cout << "output_tensor{i + 1}\\n" << output_tensor{i + 1} << std::endl;""" + ) + ib.writeline( + f"""torch::save(output_tensor{i + 1}, "output_tensor{i + 1}.pt");""" + ) + + ib.writeline("return 0;") + + ib.writelines( + [ + "} catch (const std::exception &e) {", + ] + ) + with ib.indent(): + ib.writeline('std::cerr << "Error: " << e.what() << std::endl;') + ib.writeline("return 1;") + + ib.writeline("}") + ib.writeline("}") + + return ib.getvalue() + + +def _get_make_file( + package_name: str, model_names: list[str], cuda: bool, is_hip: bool +) -> str: + ib = IndentedBuffer() + + ib.writelines( + [ + "cmake_minimum_required(VERSION 3.10)", + "project(TestProject)", + "", + "set(CMAKE_CXX_STANDARD 17)", + "", + ] + ) + + from torch._inductor.config import test_configs + + if test_configs.use_libtorch: + ib.writeline("find_package(Torch REQUIRED)") + + if cuda: + if is_hip: + ib.writeline("find_package(hip REQUIRED)") + else: + ib.writeline("find_package(CUDA REQUIRED)") + + ib.newline() + for model_name in model_names: + ib.writeline(f"add_subdirectory({package_name}/data/aotinductor/{model_name}/)") + + ib.writeline("\nadd_executable(main main.cpp)") + if cuda: + if is_hip: + ib.writeline("target_compile_definitions(main PRIVATE USE_HIP)") + else: + ib.writeline("target_compile_definitions(main PRIVATE USE_CUDA)") + + model_libs = " ".join(model_names) + ib.writeline(f"target_link_libraries(main PRIVATE torch {model_libs})") + + if cuda: + if is_hip: + ib.writeline("target_link_libraries(main PRIVATE hip::host)") + else: + ib.writeline("target_link_libraries(main PRIVATE cuda ${CUDA_LIBRARIES})") + + return ib.getvalue() diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/exported_program.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/exported_program.py new file mode 100644 index 0000000000000000000000000000000000000000..47385fa2f088209d0e6d1c0aa8094885c39f6c5e --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/exported_program.py @@ -0,0 +1,1719 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import contextlib +import copy +import dataclasses +import functools +import operator +import types +import warnings +from collections import defaultdict +from collections.abc import Callable, Iterator +from contextlib import contextmanager +from typing import Any, final, NamedTuple, TYPE_CHECKING + +from torch._guards import tracing, TracingContext +from torch._higher_order_ops.utils import autograd_not_implemented +from torch._library.fake_class_registry import FakeScriptObject +from torch._subclasses.fake_impls import ( + _deregister_op_impl, + _is_op_registered_to_fake_rule, + register_op_impl, +) +from torch._subclasses.fake_tensor import FakeTensorMode +from torch.fx._symbolic_trace import _ConstantAttributeType +from torch.fx._utils import first_call_function_nn_module_stack +from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo +from torch.fx.immutable_collections import immutable_dict, immutable_list +from torch.fx.passes.runtime_assert import insert_deferred_runtime_asserts + + +if TYPE_CHECKING: + # Import the following modules during type checking to enable code intelligence features, + # such as auto-completion in tools like pylance, even when these modules are not explicitly + # imported in user code. + + import sympy + + from torch.utils._sympy.value_ranges import ValueRanges + +import torch +import torch.utils._pytree as pytree +from torch._export.utils import ( + _build_cache, + _collect_all_valid_cia_ops, + _collect_and_set_constant_attrs, + _collect_param_buffer_metadata, + _detect_fake_mode_from_gm, + _fakify_params_buffers, + _get_decomp_for_cia, + _is_preservable_cia_op, + _name_hoo_subgraph_placeholders, + _override_graph_signature_for_temp_registered_constants, + _overwrite_signature_for_non_persistent_buffers, + _populate_param_buffer_metadata_to_new_gm, + _register_constants_as_buffers, + _rename_without_collisions, + _special_op_to_preserve_cia, + placeholder_naming_pass, +) +from torch._export.verifier import Verifier +from torch._guards import detect_fake_mode +from torch._subclasses.fake_tensor import unset_fake_temporarily +from torch.export._tree_utils import is_equivalent, reorder_kwargs +from torch.export.decomp_utils import CustomDecompTable +from torch.fx._compatibility import compatibility +from torch.fx.passes.infra.pass_base import PassResult +from torch.fx.passes.infra.pass_manager import PassManager + +from .graph_signature import ( # noqa: F401 + ArgumentSpec, + ConstantArgument, + CustomObjArgument, + ExportGraphSignature, + InputKind, + InputSpec, + OutputKind, + OutputSpec, + SymBoolArgument, + SymFloatArgument, + SymIntArgument, + TensorArgument, + TokenArgument, +) + + +__all__ = [ + "ExportedProgram", + "ModuleCallEntry", + "ModuleCallSignature", + "default_decompositions", +] + + +PassType = Callable[[torch.fx.GraphModule], PassResult | None] + + +@dataclasses.dataclass +class ModuleCallSignature: + inputs: list[ArgumentSpec] + outputs: list[ArgumentSpec] + in_spec: pytree.TreeSpec + out_spec: pytree.TreeSpec + forward_arg_names: list[str] | None = None + + def replace_all_uses_with(self, original_node, new_node): + for i in self.inputs: + if i.name == original_node.name: + i.name = new_node.name + for o in self.outputs: + if o.name == original_node.name: + o.name = new_node.name + + +@dataclasses.dataclass +class ModuleCallEntry: + fqn: str + signature: ModuleCallSignature | None = None + + +def _disable_prexisiting_fake_mode(fn): + @functools.wraps(fn) + def wrapper(*args, **kwargs): + with unset_fake_temporarily(): + return fn(*args, **kwargs) + + return wrapper + + +def _fx_collection_equivalence_fn( + spec1_type: type | None, + spec1_context: pytree.Context, + spec2_type: type | None, + spec2_context: pytree.Context, +) -> bool: + """Treat containers and their immutable variants as the same type. Otherwise + compare as normal. + """ + if spec1_type is None or spec2_type is None: + return spec1_type is spec2_type and spec1_context == spec2_context + + if issubclass(spec1_type, (dict, immutable_dict)) and issubclass( + spec2_type, (dict, immutable_dict) + ): + return spec1_context == spec2_context + + if issubclass(spec1_type, (list, immutable_list)) and issubclass( + spec2_type, (list, immutable_list) + ): + return spec1_context == spec2_context + + return spec1_type is spec2_type and spec1_context == spec2_context + + +# This list is compiled from DispatchKey.cpp. +# The idea is that we use these keys to override +# CIA decomp in export +_AUTOGRAD_ALIAS_BACKEND_KEYS_TO_OVERRIDE = [ + torch._C.DispatchKey.AutogradCPU, + torch._C.DispatchKey.AutogradCUDA, + torch._C.DispatchKey.AutogradMeta, + torch._C.DispatchKey.AutogradXLA, + torch._C.DispatchKey.AutogradLazy, + torch._C.DispatchKey.AutogradIPU, + torch._C.DispatchKey.AutogradXPU, + torch._C.DispatchKey.AutogradMPS, + torch._C.DispatchKey.AutogradHPU, + torch._C.DispatchKey.AutogradPrivateUse1, + torch._C.DispatchKey.AutogradPrivateUse2, + torch._C.DispatchKey.AutogradPrivateUse3, +] + + +# This list is compiled from DispatchKey.cpp. +# The idea is that we use these keys to add +# python kernels that directly uses default +# CIA decomp +# See NOTE Registering old CIA to Backend kernel +_BACKEND_KEYS_TO_OVERRIDE = [ + torch._C.DispatchKey.CPU, + torch._C.DispatchKey.CUDA, + torch._C.DispatchKey.Meta, + torch._C.DispatchKey.XLA, + torch._C.DispatchKey.Lazy, + torch._C.DispatchKey.IPU, + torch._C.DispatchKey.XPU, + torch._C.DispatchKey.MPS, + torch._C.DispatchKey.HPU, +] + + +@contextmanager +def _override_composite_implicit_decomp(cia_ops_to_callable): + # This function overrides CompositeImplicitAutograd decomp for + # functional composite ops that user specified. Ideally we want to not-decompose + # ALL composite ops but today's C++ functinalization relies on + # the fact that it is working with the opset after decomp is run. + # Hence we can only do it for functional ops. One caveat is that + # there are some composite ops that lie about their schema (claimed to be + # functional but not really aka dropout), for these cases, we just decompose. + saved_tables = {} + patched_ops = set() + for op_overload, decomp_callable in cia_ops_to_callable.items(): + saved_tables[op_overload] = op_overload.py_kernels.copy() + patched_ops.add(op_overload) + for override_dispatch_key in _AUTOGRAD_ALIAS_BACKEND_KEYS_TO_OVERRIDE: + if override_dispatch_key not in op_overload.py_kernels: + # TODO (tmanlaibaatar)https://github.com/pytorch/pytorch/issues/129430 + op_overload.py_impl(override_dispatch_key)( + autograd_not_implemented(op_overload, deferred_error=True) + ) + # See NOTE: Registering old CIA to Backend kernel + # It is important that we cache this before we override py_kernels. + orig_cia_callable = _get_decomp_for_cia(op_overload) + if torch._C.DispatchKey.CompositeImplicitAutograd in op_overload.py_kernels: + del op_overload.py_kernels[torch._C.DispatchKey.CompositeImplicitAutograd] + + op_overload.py_impl(torch._C.DispatchKey.CompositeImplicitAutograd)( + decomp_callable + ) + + # [NOTE] Directly registering fake tensor rule to CIA ops + # The problem we are facing here is if your CIA custom rule + # says we want to preserve the op, we will return NotImplemented. + # Unfortunately, this will invoke meta device tracing in fake tensor + # resulting in divergent behaviour for CIA kernels that has device based + # branching (one case is torch.ops.aten.scaled_dot_product.attention) + # To get around this issue, we register direct fake impl so that we + # run the kernel before we actually try to decompose the op in FakeTensorMode. + # Note that is a no-op in most cases, because: + # 1) In post dispatch tracing, CIA would have already decomposed + # 2) Most CIA impl are device agnostic. + def _force_dispatch_to_orig_cia_callable(fake_tensor_mode, op, *args, **kwargs): + orig_cia_callable = kwargs["original_callable"] + del kwargs["original_callable"] + with fake_tensor_mode: + return orig_cia_callable(*args, **kwargs) + + if not _is_op_registered_to_fake_rule(op_overload): + register_op_impl(op_overload)( + functools.partial( + _force_dispatch_to_orig_cia_callable, + original_callable=orig_cia_callable, + ) + ) + + for key in _BACKEND_KEYS_TO_OVERRIDE: + if key not in op_overload.py_kernels: + # [NOTE] Registering old CIA to Backend kernel + # We always register original CIA behavior to the backend keys kernel + # The reason is when we are fake tensor prop-ing or executing real kernel, + # we end up calling an operator on respective backend, which in python dispatcher, + # will resolve into CIA key. (see resolve_key in torch/_ops.py) + # As a result, this CIA now will call into the custom user defined + # CIA which can cause a problem. + # To make it more concrete, the case we are handling is: + # (1) there is a tensor constant we are performing constant propagation + # on during tracing + # (2) we invoke an op underneath autograd (either because we are below autograd, + # or we are tracing in inference mode), so one of the backend keys gets hit + # (3) the op we are invoking has a CIA impl that normally runs in eager mode + # (and the user wants to tweak this CIA impl during tracing, but during + # const-prop we want the original CIA to run + op_overload.py_impl(key)(orig_cia_callable) + + try: + yield + finally: + for op in patched_ops: + op.py_kernels.clear() + op.py_kernels.update(saved_tables[op]) + op._dispatch_cache.clear() + _deregister_op_impl(op) + + +def _split_decomp_table_to_cia_and_python_decomp( + decomp_table: dict[torch._ops.OperatorBase, Callable], +) -> tuple[dict[torch._ops.OperatorBase, Callable], ...]: + all_preservable_cia_ops = set(_collect_all_valid_cia_ops()) + cia_ops_to_callable = {} + + for op in list(decomp_table.keys()): + # TODO we are silently allowing non-safe(non-functional) ops through a crack + # due to core aten decomp table having non-functional entries. Once we have + # a tighter check around core aten decomp, we should warn users about them. + # Tracking issue: (https://github.com/pytorch/pytorch/issues/135759) + + # if it is a valid CIA op we can mess with in export, we check if it is: + # 1. Has been marked as to be decomposed. Example: + # decomp_table = decomp_table_to_core_aten() + # del decomp_table[aten.linear] + # In this case, user says decompose everything except for aten.linear + # 2. Has been marked with custom decomp behaviour. Example: + # decomp_table = {aten.linear: some_op} + # For (1), we want to remove all the CIA ops that weren't handled by user as + # it suggests they are safe to decompose, so we should remove from preservable_list. + # for (2), we just plumb the custom decomp to AOTDIspatcher. + # In both cases, we want to remove this CIA op from the decomp_table as it is special + # handled. + if op in all_preservable_cia_ops: + cia_ops_to_callable[op] = decomp_table[op] + all_preservable_cia_ops.remove(op) + del decomp_table[op] + # If it is a custom op, we want to still preserve or do whatever + # with it if it is a functional CIA. The reason we don't remove + # from CIA list is because we don't query custom ops. + elif _is_preservable_cia_op(op): + op_name = op.name() + assert not op_name.startswith("aten"), "This should be a custom op" + cia_ops_to_callable[op] = decomp_table[op] + + # If we reached here, it means user intentionally deleted these CIA ops from + # decomp table. + for k in all_preservable_cia_ops: + cia_ops_to_callable[k] = _special_op_to_preserve_cia + + return cia_ops_to_callable, decomp_table + + +def default_decompositions() -> "CustomDecompTable": + """ + This is the default decomposition table which contains decomposition of + all ATEN operators to core aten opset. Use this API together with + :func:`run_decompositions()` + """ + return CustomDecompTable() + + +def _decompose_and_get_gm_with_new_signature_constants( + ep: "ExportedProgram", + *, + cia_to_decomp: dict[torch._ops.OperatorBase, Callable], + python_decomp_table: dict[torch._ops.OperatorBase, Callable], + joint_loss_index: int | None, + decompose_custom_triton_ops, +): + from torch._export.passes.lift_constants_pass import _materialize_and_lift_constants + from torch._functorch.aot_autograd import aot_export_module + from torch.export._trace import ( + _disable_custom_triton_op_functional_decomposition, + _export_to_aten_ir, + _ignore_backend_decomps, + _verify_nn_module_stack, + _verify_placeholder_names, + _verify_stack_trace, + ) + from torch.fx.experimental.symbolic_shapes import ShapeEnv + + def _is_joint_ir_decomp(ep, joint_loss_index): + return ( + joint_loss_index is not None + or ep.graph_signature.backward_signature is not None + ) + + if not _is_joint_ir_decomp(ep, joint_loss_index): + mod = ep.module() + + wrapped_params_buffers = { + **dict(mod.named_parameters(remove_duplicate=False)), + **dict(mod.named_buffers(remove_duplicate=False)), + } + + from torch._functorch._aot_autograd.subclass_parametrization import ( + unwrap_tensor_subclass_parameters, + ) + + # [NOTE] Unwrapping subclasses AOT + # In torch.compile, the subclass unwrapping/wrapping happen at runtime + # but at export, this is impossible as it is intended to be run on + # C++ environment. As a result, we unwrap subclass parameters AOT. After this, + # ExportedProgram state_dict won't be same as eager model because eager model + # could have subclass weights while ExportedProgram will have desugared versions. + # This is fine because run_decompositions is supposed to specialize to post-autograd + # graph where the subclass desugaring is supposed to happen. + unwrap_tensor_subclass_parameters(mod) + unwrapped_params_buffers = { + **dict(mod.named_parameters(remove_duplicate=False)), + **dict(mod.named_buffers(remove_duplicate=False)), + } + + # TODO T204030333 + fake_mode = _detect_fake_mode_from_gm(ep.graph_module) + if fake_mode is None: + fake_mode = FakeTensorMode(shape_env=ShapeEnv(), export=True) + + # Fix the graph output signature to be tuple if scalar + out_spec = mod._out_spec + + assert isinstance(mod.graph._codegen, _PyTreeCodeGen) + orig_arg_names = mod.graph._codegen.pytree_info.orig_args + + # aot_export expect the return type to always be a tuple. + assert out_spec is not None + if out_spec.type not in (list, tuple): + out_spec = pytree.treespec_tuple([out_spec]) + + mod.graph._codegen = _PyTreeCodeGen( + _PyTreeInfo( + orig_arg_names, + mod._in_spec, + out_spec, + ) + ) + + mod.recompile() + + # the exported module will store constants & non-persistent buffers such that + # retracing treats them as persistent buffers, so we inform the constants lifting pass + # and overwrite the new graph signature using the previous program. + _collect_and_set_constant_attrs(ep.graph_signature, ep.constants, mod) + + # When we have a module with constant attributes, AotDispatcher doesn't actually + # wrap them as functional tensors, because dynamo would have already made it buffer. + # In non-strict case, however, AotDispatcher can intercept constants, causing it to not + # functionalize the operators that are operating on constant tensors. Since dynamo already + # wraps constants as buffers, we temporarily register the constants as buffers and undo this + # operation after AOTDispatcher is done. + temp_registered_constants = _register_constants_as_buffers( + mod, ep.state_dict, ep.graph_signature.non_persistent_buffers + ) + + # get params & buffers after excluding constants + fake_params_buffers = _fakify_params_buffers(fake_mode, mod) + + params_buffers_to_node_meta = _collect_param_buffer_metadata(mod) + + # TODO (tmanlaibaatar) Ideally run_decomp should just call _non_strict_export + # but due to special handling of constants as non-persistent buffers make it little + # difficult. But we should unify this code path together. T206837815 + from torch._export.non_strict_utils import ( + _enable_graph_inputs_of_type_nn_module, + _fakify_script_objects, + ) + + retracing_args = [] + for node in mod.graph.nodes: + if node.op == "placeholder": + if isinstance(node.meta["val"], CustomObjArgument): + real_script_obj = None + if node.meta["val"].fake_val is None: + real_script_obj = ep.constants[node.meta["val"].name] + else: + real_script_obj = node.meta["val"].fake_val.real_obj + retracing_args.append(real_script_obj) + else: + retracing_args.append(node.meta["val"]) + + tx = TracingContext(fake_mode) + + with ( + fake_mode, + _override_composite_implicit_decomp( + cia_to_decomp, + ), + _enable_graph_inputs_of_type_nn_module(ep.example_inputs), + tracing(tx), + ): + retracing_args_unwrapped = pytree.tree_unflatten( + retracing_args, mod._in_spec + ) + # this requires empty kwargs, but not in pytree.flattened format + with _fakify_script_objects( + mod, + ( + *retracing_args_unwrapped[0], + *retracing_args_unwrapped[1].values(), + ), + {}, + fake_mode, + ) as ( + patched_mod, + new_fake_args, + new_fake_kwargs, + new_fake_constant_attrs, + map_fake_to_real, + ): + aten_export_artifact = _export_to_aten_ir( + patched_mod, + new_fake_args, + new_fake_kwargs, + fake_params_buffers, + new_fake_constant_attrs, + decomp_table=python_decomp_table, + _prettify_placeholder_names=False, + decompose_custom_triton_ops=decompose_custom_triton_ops, + ) + + # aten_export_artifact.constants contains only fake script objects, we need to map them back + aten_export_artifact.constants = { + fqn: ( + map_fake_to_real[obj] + if isinstance(obj, FakeScriptObject) + else obj + ) + for fqn, obj in aten_export_artifact.constants.items() + } + + gm = aten_export_artifact.gm + new_graph_signature = aten_export_artifact.sig + + # In the previous step, we assume constants as buffers for AOTDispatcher to + # functianalize properly, so undo that here + new_graph_signature = ( + _override_graph_signature_for_temp_registered_constants( + new_graph_signature, temp_registered_constants + ) + ) + + _populate_param_buffer_metadata_to_new_gm( + params_buffers_to_node_meta, gm, new_graph_signature + ) + + # overwrite signature for non-persistent buffers + new_graph_signature = _overwrite_signature_for_non_persistent_buffers( + ep.graph_signature, new_graph_signature + ) + + constants = _materialize_and_lift_constants( + gm, new_graph_signature, new_fake_constant_attrs + ) + + placeholder_naming_pass( + gm, + new_graph_signature, + patched_mod, + new_fake_args, + new_fake_kwargs, + fake_params_buffers, + constants, + ) + + _verify_nn_module_stack(gm) + _verify_stack_trace(gm) + _verify_placeholder_names(gm, new_graph_signature) + + gm, new_graph_signature = _remove_unnecessary_copy_op_pass( + gm, new_graph_signature + ) + + # When we apply parameterization rule to unwrap + # subclasses, the state dict will now have different + # desugared parameters. We need to manually filter those + # and update the ep.state_dict. Ideally, we should just return + # the state dict of ep.module but ep.module only stores params + # buffers that participate in forward. If we undo this behavior, + # it would break some downstream users. + new_state_dict = { + **ep.state_dict, + **{ + name: p + for name, p in unwrapped_params_buffers.items() + if name not in wrapped_params_buffers + }, + } + + for name, p in wrapped_params_buffers.items(): + # Buffers can be persistent/non-persistent + if name not in new_state_dict: + assert not isinstance(p, torch.nn.Parameter) + + if name in new_state_dict: + if name not in unwrapped_params_buffers: + new_state_dict.pop(name) + + return gm, new_graph_signature, new_state_dict + + old_placeholders = [ + node for node in ep.graph_module.graph.nodes if node.op == "placeholder" + ] + fake_args = [node.meta["val"] for node in old_placeholders] + + buffers_to_remove = [name for name, _ in ep.graph_module.named_buffers()] + for name in buffers_to_remove: + delattr(ep.graph_module, name) + + # TODO(zhxhchen17) Return the new graph_signature directly. + fake_mode_det = detect_fake_mode(fake_args) + fake_mode_ctx = contextlib.nullcontext() if fake_mode_det is None else fake_mode_det # type: ignore[assignment] + custom_triton_ops_decomposition_ctx = ( + contextlib.nullcontext + if decompose_custom_triton_ops + else _disable_custom_triton_op_functional_decomposition + ) + with ( + _ignore_backend_decomps(), + fake_mode_ctx, + _override_composite_implicit_decomp(cia_to_decomp), + custom_triton_ops_decomposition_ctx(), + ): + gm, graph_signature = aot_export_module( + ep.graph_module, + fake_args, + decompositions=python_decomp_table, + trace_joint=joint_loss_index is not None, + output_loss_index=( + joint_loss_index if joint_loss_index is not None else None + ), + ) + gm.graph.eliminate_dead_code() + + # Update the signatures with the new placeholder names in case they + # changed when calling aot_export + def update_arg(old_arg, new_ph): + if isinstance(old_arg, ConstantArgument): + return old_arg + elif isinstance(old_arg, TensorArgument): + return TensorArgument(name=new_ph.name) + elif isinstance(old_arg, SymIntArgument): + return SymIntArgument(name=new_ph.name) + elif isinstance(old_arg, SymFloatArgument): + return SymFloatArgument(name=new_ph.name) + elif isinstance(old_arg, SymBoolArgument): + return SymBoolArgument(name=new_ph.name) + raise RuntimeError(f"Type of old_arg not supported: {type(old_arg)}") + + new_placeholders = [node for node in gm.graph.nodes if node.op == "placeholder"] + new_outputs: tuple[torch.fx.Node, ...] = tuple(gm.graph.output_node().args[0]) # type: ignore[arg-type] + + # rename the placeholders + assert len(new_placeholders) == len(old_placeholders) + for old_ph, new_ph in zip(old_placeholders, new_placeholders): + new_ph.name = new_ph.target = old_ph.name + + # handle name collisions with newly decomposed graph nodes + name_map = {} + find_available: dict[str, int] = defaultdict(int) + used_names: set[str] = set() + for ph in new_placeholders: + name_map[ph.name] = ph.name + _build_cache(ph.name, find_available, used_names) + for node in gm.graph.nodes: + if node.op == "placeholder": + continue + node.name = _rename_without_collisions( + name_map, find_available, used_names, node.name, node.name + ) + + # propagate names to higher order op subgraphs + _name_hoo_subgraph_placeholders(gm) + + # Run this pass before creating input/output specs, since size-related CSE/DCE might affect output signature. + # Overwrite output specs afterwards. + from torch._export.passes._node_metadata_hook import ( + _node_metadata_hook, + _set_node_metadata_hook, + ) + from torch._functorch._aot_autograd.input_output_analysis import _graph_output_names + + if not torch._dynamo.config.do_not_emit_runtime_asserts: + stack_trace = ( + 'File "torch/fx/passes/runtime_assert.py", line 24, ' + "in insert_deferred_runtime_asserts" + ) + shape_env = _get_shape_env(gm) + if shape_env is not None: + with _set_node_metadata_hook( + gm, + functools.partial( + _node_metadata_hook, metadata={"stack_trace": stack_trace} + ), + ): + insert_deferred_runtime_asserts( + gm, + shape_env, + f"exported program: {first_call_function_nn_module_stack(gm.graph)}", + export=True, + ) + + # update output specs + gm.recompile() + for output, name in zip(new_outputs, _graph_output_names(gm)): + if name is not None: + output.name = name + + # To match the output target with correct input for input mutations + # need to find the old to new placeholder map + old_new_placeholder_map = { + spec.arg.name: new_placeholders[i].name + for i, spec in enumerate(ep.graph_signature.input_specs) + if not isinstance(spec.arg, ConstantArgument) + } + + input_specs = [ + InputSpec( + spec.kind, + update_arg(spec.arg, new_placeholders[i]), + spec.target, + spec.persistent, + ) + for i, spec in enumerate(ep.graph_signature.input_specs) + ] + + output_specs = [] + + # handle buffer & input mutations; these appear before loss output & gradients + # (1) ep.graph_signature.input_specs tells us types of inputs + # (2) graph_signature.user_inputs tells us node input names in order + # (3) graph_signature.user_inputs_to_mutate tells us buffer & input mutations + # map (3) -> (2) for input order, -> (1) for input type + user_inputs_index = {name: i for i, name in enumerate(graph_signature.user_inputs)} + mutation_names = list(graph_signature.user_inputs_to_mutate.keys()) + assert mutation_names == [node.name for node in new_outputs[: len(mutation_names)]] + for output_name, input_name in graph_signature.user_inputs_to_mutate.items(): + i = user_inputs_index[input_name] + input_spec = ep.graph_signature.input_specs[i] + assert input_spec.kind in (InputKind.USER_INPUT, InputKind.BUFFER) + output_kind = ( + OutputKind.BUFFER_MUTATION + if input_spec.kind == InputKind.BUFFER + else OutputKind.USER_INPUT_MUTATION + ) + target = ( + input_spec.target + if input_spec.kind == InputKind.BUFFER + else input_spec.arg.name + ) + output_specs.append( + OutputSpec( + kind=output_kind, + arg=TensorArgument(name=output_name), + target=target, + ) + ) + + # handle actual user outputs + for i, spec in enumerate(ep.graph_signature.output_specs): + output_specs.append( + OutputSpec( + OutputKind.LOSS_OUTPUT if i == joint_loss_index else spec.kind, + update_arg(spec.arg, new_outputs[len(mutation_names) + i]), + old_new_placeholder_map.get(spec.target, spec.target), + ) + ) + + if joint_loss_index is not None: + assert graph_signature.backward_signature is not None + gradients = graph_signature.backward_signature.gradients_to_user_inputs + assert len(graph_signature.user_inputs) == len(ep.graph_signature.input_specs) + specs = { + graph_signature.user_inputs[i]: spec + for i, spec in enumerate(ep.graph_signature.input_specs) + if isinstance(spec.arg, TensorArgument) + } + for node in new_outputs[len(output_specs) :]: + source = gradients[node.name] + spec = specs[source] # type: ignore[index] + if spec.kind == InputKind.PARAMETER: + kind = OutputKind.GRADIENT_TO_PARAMETER + target = spec.target + elif spec.kind == InputKind.USER_INPUT: + kind = OutputKind.GRADIENT_TO_USER_INPUT + target = source + else: + raise AssertionError(f"Unknown input kind: {spec.kind}") + output_specs.append( + OutputSpec( + kind, + TensorArgument(name=node.name), + target, + ) + ) + + assert len(new_placeholders) == len(old_placeholders) + + new_graph_signature = ExportGraphSignature( + input_specs=input_specs, output_specs=output_specs + ) + # NOTE: aot_export adds symint metadata for placeholders with int + # values; since these become specialized, we replace such metadata with + # the original values. + # Also, set the param/buffer metadata back to the placeholders. + for old_node, new_node in zip(old_placeholders, new_placeholders): + if not isinstance(old_node.meta["val"], torch.Tensor): + new_node.meta["val"] = old_node.meta["val"] + + if ( + new_node.target in new_graph_signature.inputs_to_parameters + or new_node.target in new_graph_signature.inputs_to_buffers + ): + for k, v in old_node.meta.items(): + new_node.meta[k] = v + return gm, new_graph_signature, ep.state_dict + + +def _remove_unnecessary_copy_op_pass( + gm: torch.fx.GraphModule, new_graph_signature: ExportGraphSignature +) -> tuple[torch.fx.GraphModule, ExportGraphSignature]: + """ + Removes redundant copy_ node that was introduced due to mutated buffer. + """ + with gm._set_replace_hook(new_graph_signature.get_replace_hook()): + for node in gm.graph.nodes: + if node.op == "output": + args, _ = pytree.tree_flatten(node.args) + for out in args: + if isinstance(out, torch.fx.Node) and ( + out.name in new_graph_signature.buffers_to_mutate + or out.name in new_graph_signature.parameters_to_mutate + ): + if ( + out.op == "call_function" + and out.target is torch.ops.aten.copy.default + ): + out.replace_all_uses_with(out.args[1]) # type: ignore[arg-type] + gm.graph.erase_node(out) + gm.recompile() + return gm, new_graph_signature + + +def _common_getitem_elimination_pass( + gm: torch.fx.GraphModule, graph_signature, module_call_graph +): + with gm._set_replace_hook(graph_signature.get_replace_hook()): + for module in gm.modules(): + if not isinstance(module, torch.fx.GraphModule): + continue + + node_id: dict[torch.fx.Node, str] = {} + getitems: dict[str, torch.fx.Node] = {} + for node in list(module.graph.nodes): + if node.op == "call_function" and node.target is operator.getitem: + source, idx = node.args + new_id = f"{node_id[source]}.{idx}" + if new_id in getitems: + node.replace_all_uses_with(getitems[new_id]) + for entry in module_call_graph: + if entry.signature is not None: + entry.signature.replace_all_uses_with( + node, getitems[new_id] + ) + module.graph.erase_node(node) + else: + getitems[new_id] = node + node_id[node] = new_id + else: + node_id[node] = node.name + + +def _get_updated_module_call_graph( + old_gm: torch.fx.GraphModule, + old_graph_signature: ExportGraphSignature, + gm: torch.fx.GraphModule, + graph_signature: ExportGraphSignature, + old_module_call_graph: list[ModuleCallEntry], +): + new_module_call_graph = copy.deepcopy(old_module_call_graph) + + old_nodes = {node.name: node for node in old_gm.graph.nodes} + + old_graph_params_buffers = { + **old_graph_signature.inputs_to_parameters, + **old_graph_signature.inputs_to_buffers, + } + new_graph_params_buffers = { + **graph_signature.inputs_to_parameters, + **graph_signature.inputs_to_buffers, + } + + # use node-level provenance metadata to create a map + # from old node names to new node names + provenance: dict[str, str] = {} + + user_input_counter = 0 + old_user_input_names = [ + node.target for node in old_gm.graph.nodes if node.op == "placeholder" + ] + old_user_input_names = list( + filter( + lambda x: x not in old_graph_params_buffers + and x not in old_graph_signature.input_tokens, + old_user_input_names, + ) + ) + new_user_input_names = [ + node.target for node in gm.graph.nodes if node.op == "placeholder" + ] + + for node in gm.graph.nodes: + if history := node.meta.get("from_node", []): + provenance[history[-1].name] = node.name + + # For params and buffers, we might have applied parameterizaiton rule + # so that the names might have changed. But for user inputs, we know we + # must preserve the old name. + elif node.op == "placeholder": + if not ( + node.target in new_graph_params_buffers + or node.target in graph_signature.input_tokens + ): + if node.target in new_user_input_names: + assert isinstance(node.name, str) + old_name = old_user_input_names[user_input_counter] + assert isinstance(old_name, str) + provenance[old_name] = node.name + user_input_counter += 1 + + # For all the parameters and buffers, we first see + # if they are result of parametrizations and if they + # are, we log them and error later + old_param_to_desugared = defaultdict(list) + for name, target in new_graph_params_buffers.items(): + # if the parameters are not parametrized, the naming won't change. + if not target.startswith("parametrizations."): + # If we are in strict mode, we can't just reuse the param names + if name in old_graph_params_buffers: + provenance[name] = name + else: + old_target = ".".join(target.split(".")[1:-1]) + old_param_to_desugared[old_target].append(name) + + # map old names to new names in module call signatures + for entry in new_module_call_graph: + signature = entry.signature + if signature is None: + continue + for x in [*signature.inputs, *signature.outputs]: + # We noticed that submodule is taking subclass as input. we can't + # preserve signature here. + if x.name in old_param_to_desugared: + raise ValueError( + f"It looks like {x.name} is a tensor subclass. " + f"Preserving submodule that takes subclass parameter is not supported" + f" in inference IR because we desugar them, resulting in more tensors" + ) + + if x.name in provenance: + x.name = provenance[x.name] + + # This can happen when aten.to is called at graph boundaries. + # Basically aten.to at post-dispatch level can either be copy + # or alias. In the alias case, we will no-op it so it will + # disappear from the graph. If we detect such case, we should + # reuse the input to aten.to as the new input to the submodule. + # Technically this can happen for other maybe aliasing ops, + # but aten.to is probably the most common one. + elif x.name in old_nodes: + old_node = old_nodes[x.name] + if old_node.op == "call_function" and old_node.target in [ + torch.ops.aten.to.dtype_layout, + torch.ops.aten.to.device, + torch.ops.aten.to.dtype, + ]: + old_target = old_node.args[0].name + if old_target not in provenance: + raise ValueError( + f"It looks like {old_target} is a tensor subclass. " + f"Preserving submodule that takes subclass parameter is not supported" + f" in inference IR because we desugar them, resulting in more tensors" + ) + + x.name = provenance[old_target] + + return new_module_call_graph + + +def _decompose_exported_program( + ep, + *, + cia_to_decomp: dict[torch._ops.OperatorBase, Callable], + python_decomp_table: dict[torch._ops.OperatorBase, Callable], + joint_loss_index: int | None, + decompose_custom_triton_ops: bool, +): + ( + gm, + new_graph_signature, + state_dict, + ) = _decompose_and_get_gm_with_new_signature_constants( + ep, + cia_to_decomp=cia_to_decomp, + python_decomp_table=python_decomp_table, + joint_loss_index=joint_loss_index, + decompose_custom_triton_ops=decompose_custom_triton_ops, + ) + + # The signatures of ep.module_call_graph refer to input / output nodes of + # the original graph module. However, the new graph module may have + # new nodes due to decompositions. So we need to update these signatures + # in the decomposed exported program's module_call_graph. + new_module_call_graph = _get_updated_module_call_graph( + ep.graph_module, + ep.graph_signature, + gm, + new_graph_signature, + ep.module_call_graph, + ) + + # TODO unfortunately preserving graph-level metadata is not + # working well with aot_export. So we manually copy it. + # (The node-level meta is addressed above.) + gm.meta.update(ep.graph_module.meta) + + new_range_constraints = _get_updated_range_constraints( + gm, + ep.range_constraints, + ) + + exported_program = ExportedProgram( + root=gm, + graph=gm.graph, + graph_signature=new_graph_signature, + state_dict=state_dict, + range_constraints=new_range_constraints, + module_call_graph=new_module_call_graph, + example_inputs=ep.example_inputs, + constants=ep.constants, + ) + return exported_program + + +class ExportedProgram: + """ + Package of a program from :func:`export`. It contains + an :class:`torch.fx.Graph` that represents Tensor computation, a state_dict containing + tensor values of all lifted parameters and buffers, and various metadata. + + You can call an ExportedProgram like the original callable traced by + :func:`export` with the same calling convention. + + To perform transformations on the graph, use ``.module`` property to access + an :class:`torch.fx.GraphModule`. You can then use + `FX transformation `_ + to rewrite the graph. Afterwards, you can simply use :func:`export` + again to construct a correct ExportedProgram. + """ + + _graph_module: torch.fx.GraphModule + """The underlying GraphModule containing the exported computation graph.""" + + _graph_signature: ExportGraphSignature + """The signature containing input/output specifications for the graph.""" + + _state_dict: dict[str, Any] + """Dictionary containing parameter and buffer values from the original module.""" + + _range_constraints: "dict[sympy.Symbol, ValueRanges]" + """Symbolic shape constraints for dynamic shapes in the graph.""" + + _module_call_graph: list[ModuleCallEntry] + """Call graph information tracking module hierarchy and signatures.""" + + _example_inputs: tuple[tuple[Any, ...], dict[str, Any]] | None + """Example inputs used during export, stored as (args, kwargs) tuple.""" + + _constants: dict[str, _ConstantAttributeType] + """Dictionary of constant values used in the graph.""" + + _verifiers: list[type[Verifier]] + """List of verifier classes used to validate the exported program.""" + + _guards_code: list[str] + + def __init__( + self, + root: torch.nn.Module | dict[str, Any], + graph: torch.fx.Graph, + graph_signature: ExportGraphSignature, + state_dict: dict[str, torch.Tensor | torch.nn.Parameter], + range_constraints: "dict[sympy.Symbol, Any]", + module_call_graph: list[ModuleCallEntry], + example_inputs: tuple[tuple[Any, ...], dict[str, Any]] | None = None, + constants: dict[str, _ConstantAttributeType] | None = None, + *, + verifiers: list[type[Verifier]] | None = None, + ): + # Remove codegen related things from the graph. It should just be a flat graph. + graph._codegen = torch.fx.graph.CodeGen() + self._graph_module = _create_graph_module_for_export(root, graph) + if isinstance(root, torch.fx.GraphModule): + self._graph_module.meta.update(root.meta) + + _common_getitem_elimination_pass( + self._graph_module, graph_signature, module_call_graph + ) + self._graph_signature: ExportGraphSignature = graph_signature + self._state_dict: dict[str, Any] = state_dict + self._range_constraints: dict[sympy.Symbol, ValueRanges] = range_constraints + assert module_call_graph is not None + self._module_call_graph: list[ModuleCallEntry] = module_call_graph + self._example_inputs = example_inputs + + self._constants = constants or {} + + verifiers = verifiers or [Verifier] + assert all(issubclass(v, Verifier) for v in verifiers) + self._verifiers = verifiers + # Validate should be always the last step of the constructor. + self.validate() + + self._guards_code = _convert_guards_to_code(self._graph_module) + + @property + @compatibility(is_backward_compatible=False) + def graph_module(self): + return self._graph_module + + @graph_module.setter + @compatibility(is_backward_compatible=False) + def graph_module(self, value): + raise RuntimeError("Unable to set ExportedProgram's graph_module attribute.") + + @property + @compatibility(is_backward_compatible=False) + def graph(self): + return self.graph_module.graph + + @graph.setter + @compatibility(is_backward_compatible=False) + def graph(self, value): + raise RuntimeError("Unable to set ExportedProgram's graph attribute.") + + @property + @compatibility(is_backward_compatible=False) + def graph_signature(self): + return self._graph_signature + + @graph_signature.setter + @compatibility(is_backward_compatible=False) + def graph_signature(self, value): + raise RuntimeError("Unable to set ExportedProgram's graph_signature attribute.") + + @property + @compatibility(is_backward_compatible=False) + def state_dict(self): + return self._state_dict + + @state_dict.setter + @compatibility(is_backward_compatible=False) + def state_dict(self, value): + raise RuntimeError("Unable to set ExportedProgram's state_dict attribute.") + + @compatibility(is_backward_compatible=False) + def parameters(self) -> Iterator[torch.nn.Parameter]: + """ + Returns an iterator over original module's parameters. + """ + for _, param in self.named_parameters(): + yield param + + @compatibility(is_backward_compatible=False) + def named_parameters(self) -> Iterator[tuple[str, torch.nn.Parameter]]: + """ + Returns an iterator over original module parameters, yielding + both the name of the parameter as well as the parameter itself. + """ + for param_name in self.graph_signature.parameters: + yield param_name, self.state_dict[param_name] + + @compatibility(is_backward_compatible=False) + def buffers(self) -> Iterator[torch.Tensor]: + """ + Returns an iterator over original module buffers. + """ + for _, buf in self.named_buffers(): + yield buf + + @compatibility(is_backward_compatible=False) + def named_buffers(self) -> Iterator[tuple[str, torch.Tensor]]: + """ + Returns an iterator over original module buffers, yielding + both the name of the buffer as well as the buffer itself. + """ + non_persistent_buffers = set(self.graph_signature.non_persistent_buffers) + for buffer_name in self.graph_signature.buffers: + if buffer_name in non_persistent_buffers: + yield buffer_name, self.constants[buffer_name] + else: + yield buffer_name, self.state_dict[buffer_name] + + @property + @compatibility(is_backward_compatible=False) + def range_constraints(self): + return self._range_constraints + + @range_constraints.setter + @compatibility(is_backward_compatible=False) + def range_constraints(self, value): + raise RuntimeError( + "Unable to set ExportedProgram's range_constraints attribute." + ) + + @property + @compatibility(is_backward_compatible=False) + def module_call_graph(self): + return self._module_call_graph + + @module_call_graph.setter + @compatibility(is_backward_compatible=False) + def module_call_graph(self, value): + raise RuntimeError( + "Unable to set ExportedProgram's module_call_graph attribute." + ) + + @property + @compatibility(is_backward_compatible=False) + def example_inputs(self): + return self._example_inputs + + @example_inputs.setter + @compatibility(is_backward_compatible=False) + def example_inputs(self, value): + # This is allowed + + if value is None: + self._example_inputs = value + return + + if not ( + isinstance(value, tuple) + and len(value) == 2 + and isinstance(value[0], tuple) + and isinstance(value[1], dict) + ): + raise ValueError( + "Example inputs should be a tuple containing example arguments (as " + "a tuple), and example kwargs (as a dictionary)." + ) + + args, kwargs = value + from ._unlift import _check_inputs_match + + _check_inputs_match(args, kwargs, self.call_spec.in_spec) + + self._example_inputs = value + + @property + @compatibility(is_backward_compatible=False) + def call_spec(self): + class CallSpec(NamedTuple): + in_spec: pytree.TreeSpec | None + out_spec: pytree.TreeSpec | None + + if len(self.module_call_graph) == 0: + return CallSpec(in_spec=None, out_spec=None) + assert self.module_call_graph[0].fqn == "" + return CallSpec( + in_spec=self.module_call_graph[0].signature.in_spec, + out_spec=self.module_call_graph[0].signature.out_spec, + ) + + @call_spec.setter + @compatibility(is_backward_compatible=False) + def call_spec(self, value): + raise RuntimeError("Unable to set ExportedProgram's call_spec attribute.") + + @property + @compatibility(is_backward_compatible=False) + def verifier(self) -> Any: + return self._verifiers[0] + + @verifier.setter + @compatibility(is_backward_compatible=False) + def verifier(self, value): + raise RuntimeError("Unable to set ExportedProgram's verifier attribute.") + + @property + @compatibility(is_backward_compatible=False) + def dialect(self) -> str: + assert self._verifiers is not None + return self._verifiers[0].dialect + + @dialect.setter + @compatibility(is_backward_compatible=False) + def dialect(self, value): + raise RuntimeError("Unable to set ExportedProgram's dialect attribute.") + + @property + @compatibility(is_backward_compatible=False) + def verifiers(self): + return self._verifiers + + @verifiers.setter + @compatibility(is_backward_compatible=False) + def verifiers(self, value): + raise RuntimeError("Unable to set ExportedProgram's verifiers attribute.") + + @property + @compatibility(is_backward_compatible=False) + def tensor_constants(self): + return self._constants + + @tensor_constants.setter + @compatibility(is_backward_compatible=False) + def tensor_constants(self, value): + raise RuntimeError( + "Unable to set ExportedProgram's tensor_constants attribute." + ) + + @property + @compatibility(is_backward_compatible=False) + def constants(self): + return self._constants + + @constants.setter + @compatibility(is_backward_compatible=False) + def constants(self, value): + raise RuntimeError("Unable to set ExportedProgram's constants attribute.") + + def _get_flat_args_with_check(self, args, kwargs): + """Flatten args, kwargs using pytree, then, check specs. + + Args: + args: List[Any] original args passed to __call__ + kwargs: Dict[str, Any] original kwargs passed to __call + + Returns: + A tuple of (flat_args, received_spec) + flat_args is flattened args / kwargs + received_spec is the pytree spec produced while flattening the + tuple (args, kwargs) + """ + in_spec = self.call_spec.in_spec + if in_spec is not None: + kwargs = reorder_kwargs(kwargs, in_spec) + flat_args_with_path, received_spec = pytree.tree_flatten_with_path( + (args, kwargs) + ) + self._check_input_constraints(flat_args_with_path) + flat_args = tuple(x[1] for x in flat_args_with_path) + return flat_args, received_spec + + def _graph_module_flat_inputs(self, args: Any, kwargs: Any) -> Any: + """Transform args, kwargs of __call__ to args for graph_module. + + self.graph_module takes stuff from state dict as inputs. + The invariant is for ep: ExportedProgram is + ep(args, kwargs) == + ep.postprocess(ep.graph_module(ep.graph_module_flat_inputs(args, kwargs))) + """ + + in_spec = self.call_spec.in_spec + flat_args, received_spec = self._get_flat_args_with_check(args, kwargs) + if in_spec is not None and not is_equivalent( + received_spec, in_spec, _fx_collection_equivalence_fn + ): + raise ValueError( + "Trying to flatten user inputs with exported input tree spec: \n" + f"{in_spec}\n" + "but actually got inputs with tree spec of: \n" + f"{received_spec}" + ) + + additional_inputs = [] + for input_ in self.graph_signature.input_specs: + if input_.kind == InputKind.USER_INPUT: + continue + elif input_.kind in ( + InputKind.PARAMETER, + InputKind.BUFFER, + ): + if input_.persistent is False: + # This is a non-persistent buffer, grab it from our + # constants instead of the state dict. + additional_inputs.append(self.constants[input_.target]) + else: + additional_inputs.append(self.state_dict[input_.target]) + elif input_.kind in ( + InputKind.CONSTANT_TENSOR, + InputKind.CUSTOM_OBJ, + ): + additional_inputs.append(self.constants[input_.target]) + additional_inputs = tuple(additional_inputs) + + # NOTE: calling convention is first params, then buffers, then args as user supplied them. + # See: torch/_functorch/aot_autograd.py#L1034 + return additional_inputs + flat_args + + def __call__(self, *args: Any, **kwargs: Any) -> Any: + raise RuntimeError( + "Unable to call ExportedProgram directly. " + "You should use `exported_program.module()` instead." + ) + + def __str__(self) -> str: + graph_module = self.graph_module.print_readable( + print_output=False, colored=False + ).replace("\n", "\n ") + graph_signature = str(self.graph_signature).replace("\n", "\n ") + string = ( + "ExportedProgram:\n" + f" {graph_module}\n" + f"Graph signature: {graph_signature}\n" + f"Range constraints: {self.range_constraints}\n" + ) + return string + + def module(self, check_guards=True) -> torch.fx.GraphModule: + """ + Returns a self contained GraphModule with all the parameters/buffers inlined. + + - When `check_guards=True` (default), a `_guards_fn` submodule is generated + and a call to a `_guards_fn` submodule is inserted right after placeholders + in the graph. This module checks guards on inputs. + - When `check_guards=False`, a subset of these checks are performed by a + forward pre-hook on the graph module. No `_guards_fn` submodule is generated. + + """ + from ._unlift import _unlift_exported_program_lifted_states + + module = _unlift_exported_program_lifted_states(self, check_guards=check_guards) + + def _train(self, mode: bool = True): + raise NotImplementedError("Calling train() is not supported yet.") + + def _eval(self, mode: bool = True): + raise NotImplementedError("Calling eval() is not supported yet.") + + module.train = types.MethodType(_train, module) # type: ignore[method-assign] + module.eval = types.MethodType(_eval, module) # type: ignore[method-assign] + return module + + def _num_lifted_params_buffers(self): + return next( + ( + i + for i, s in enumerate(self._graph_signature.input_specs) + if s.kind == InputKind.USER_INPUT + ), + len(self._graph_signature.input_specs), + ) + + @_disable_prexisiting_fake_mode + def run_decompositions( + self, + decomp_table: dict[torch._ops.OperatorBase, Callable] | None = None, + decompose_custom_triton_ops: bool = False, + ) -> "ExportedProgram": + """ + Run a set of decompositions on the exported program and returns a new + exported program. By default we will run the Core ATen decompositions to + get operators in the + `Core ATen Operator Set `_. + + For now, we do not decompose joint graphs. + + Args: + decomp_table: + An optional argument that specifies decomp behaviour for Aten ops + (1) If None, we decompose to core aten decompositions + (2) If empty, we don't decompose any operator + + + Some examples: + + If you don't want to decompose anything + + .. code-block:: python + + ep = torch.export.export(model, ...) + ep = ep.run_decompositions(decomp_table={}) + + If you want to get a core aten operator set except for certain operator, you can do following: + + .. code-block:: python + + ep = torch.export.export(model, ...) + decomp_table = torch.export.default_decompositions() + decomp_table[your_op] = your_custom_decomp + ep = ep.run_decompositions(decomp_table=decomp_table) + """ + _decomp_table = ( + default_decompositions() if decomp_table is None else dict(decomp_table) + ) + + if isinstance(_decomp_table, CustomDecompTable): + _decomp_table = _decomp_table.materialize() + + # Note [Separating decomp_table into CIA decomps and non-CIA decomps] + # At this point, we have a decomp_table that contains decomp behaviour for + # both CIA and post-autograd ops. + # We need to separate the op into two categories: + # 1. CIA op: These are the ops that we want to override + # CompositeImplicitAutograd decomp for. For them, we need to use _override_composite_implicit_decomp + # context manager to plumb it through AOTDispatcher + # 2. Non-CIA op: These ops are only relevant after AOTDIspatcher runs, so just + # checking if they are statically functional is enough. + # For joint IR case tho, we need to use the old path because we can't register + # custom decomps this way because we can't use context manager as it installs + # autograd_error node. + ( + cia_to_decomp, + python_decomp_table, + ) = _split_decomp_table_to_cia_and_python_decomp(_decomp_table) + + return _decompose_exported_program( + self, + cia_to_decomp=cia_to_decomp, + python_decomp_table=python_decomp_table, + joint_loss_index=None, + decompose_custom_triton_ops=decompose_custom_triton_ops, + ) + + def _transform_do_not_use(self, *passes: PassType) -> "ExportedProgram": + pm = PassManager(list(passes)) + # Since we abstractly run the passes, we need to disable backend decomp here + # again. + from torch.export._trace import _ignore_backend_decomps + + with _ignore_backend_decomps(): + res = pm(self.graph_module) + transformed_gm = res.graph_module if res is not None else self.graph_module + assert transformed_gm is not None + + # pyrefly: ignore [missing-attribute] + if transformed_gm is self.graph_module and not res.modified: + return self + + # TODO(zhxchen17) Remove this. + def _get_updated_graph_signature( + old_signature: ExportGraphSignature, + new_gm: torch.fx.GraphModule, + ) -> ExportGraphSignature: + """ + Update the graph signature's user_input/user_outputs. + """ + new_input_specs = [] + for i, node in enumerate(new_gm.graph.nodes): + if node.op != "placeholder": + break + + assert i < len(old_signature.input_specs), ( + "Number of inputs changed after transformation" + ) + old_input_spec = old_signature.input_specs[i] + arg = ( + old_input_spec.arg + if isinstance( + old_input_spec.arg, (ConstantArgument, CustomObjArgument) + ) + else type(old_input_spec.arg)(node.name) + ) + new_input_specs.append( + InputSpec( + old_input_spec.kind, + arg, + old_input_spec.target, + old_input_spec.persistent, + ) + ) + + output_node = list(new_gm.graph.nodes)[-1] + assert output_node.op == "output" + + new_output_specs = [] + for i, node in enumerate(output_node.args[0]): + assert i < len(old_signature.output_specs), ( + "Number of outputs changed after transformation" + ) + old_output_spec = old_signature.output_specs[i] + arg = ( + old_output_spec.arg + if isinstance( + old_output_spec.arg, (ConstantArgument, CustomObjArgument) + ) + else type(old_output_spec.arg)(node.name) + ) + new_output_specs.append( + OutputSpec(old_output_spec.kind, arg, old_output_spec.target) + ) + + new_signature = ExportGraphSignature( + input_specs=new_input_specs, output_specs=new_output_specs + ) + return new_signature + + transformed_ep = ExportedProgram( + root=transformed_gm, + graph=transformed_gm.graph, + graph_signature=_get_updated_graph_signature( + self.graph_signature, transformed_gm + ), + state_dict=self.state_dict, + range_constraints=_get_updated_range_constraints( + transformed_gm, + self.range_constraints, + ), + module_call_graph=copy.deepcopy(self._module_call_graph), + example_inputs=self.example_inputs, + constants=self.constants, + verifiers=self.verifiers, + ) + transformed_ep.graph_module.meta.update(self.graph_module.meta) + # pyrefly: ignore [missing-attribute] + transformed_ep.graph_module.meta.update(res.graph_module.meta) + return transformed_ep + + def _check_input_constraints(self, flat_args_with_path): + from torch._export.utils import _check_input_constraints_for_graph + + placeholders = [p for p in self.graph.nodes if p.op == "placeholder"] + input_placeholders = [ + p + for p, s in zip(placeholders, self.graph_signature.input_specs) + if s.kind == InputKind.USER_INPUT + ] + _check_input_constraints_for_graph( + input_placeholders, flat_args_with_path, self.range_constraints + ) + + @compatibility(is_backward_compatible=False) + def validate(self): + self._validate() + + # TODO: remove this + @final + def _validate(self): + assert len(self.verifiers) > 0, ( + "ExportedProgram must have at least one verifier." + ) + for v in self.verifiers: + v().check(self) + + # TODO(zhxchen17) Formalize this. + def _update( + self, + graph_module, + graph_signature, + *, + state_dict=None, + constants=None, + verifiers=None, + ) -> "ExportedProgram": + return ExportedProgram( + root=graph_module, + graph=graph_module.graph, + graph_signature=graph_signature, + state_dict=state_dict if state_dict is not None else self.state_dict, + range_constraints=copy.deepcopy(self.range_constraints), + module_call_graph=copy.deepcopy(self._module_call_graph), + example_inputs=self.example_inputs, + constants=constants if constants is not None else self.constants, + verifiers=verifiers if verifiers is not None else self.verifiers, + ) + + +def _get_shape_env(gm): + vals = [ + node.meta["val"] + for node in gm.graph.nodes + if node.meta.get("val", None) is not None + ] + from torch._guards import detect_fake_mode + + fake_mode = detect_fake_mode(vals) + if fake_mode is not None: + return fake_mode.shape_env + for v in vals: + if isinstance(v, torch.SymInt): + return v.node.shape_env + + +def _get_updated_range_constraints( + gm: torch.fx.GraphModule, + old_range_constraints: "dict[sympy.Symbol, Any] | None" = None, +) -> "dict[sympy.Symbol, Any]": + assert old_range_constraints is not None + + shape_env = _get_shape_env(gm) + if shape_env is None: + return {} + + range_constraints = copy.copy(old_range_constraints) + range_constraints = { + k: v for k, v in range_constraints.items() if k not in shape_env.replacements + } + # Only when we have an unbacked symint, and it's used as constructor inputs, + # runtime_var_to_range will make a difference compated to var_to_range. + # e.g. [2, oo) -> [0, oo) + for k, v in shape_env.var_to_range.items(): + if k not in shape_env.replacements and k not in range_constraints: + range_constraints[k] = v + return range_constraints + + +def _create_graph_module_for_export(root, graph): + try: + gm = torch.fx.GraphModule(root, graph) + except SyntaxError: + # If custom objects stored in memory are being used in the graph, + # the generated python code will result in a syntax error on the custom + # object, since it is unable to parse the in-memory object. However + # we can still run the graph eagerly through torch.fx.Interpreter, + # so we will bypass this error. + warnings.warn( + "Unable to execute the generated python source code from " + "the graph. The graph module will no longer be directly callable, " + "but you can still run the ExportedProgram, and if needed, you can " + "run the graph module eagerly using torch.fx.Interpreter.", + stacklevel=2, + ) + gm = torch.fx.GraphModule(root, torch.fx.Graph()) + gm._graph = graph + + return gm + + +def _convert_guards_to_code(graph_module): + shape_env = _get_shape_env(graph_module) + if shape_env is None: + return [] + + local_vars = { + var + for var, sources in shape_env.var_to_sources.items() + if all( + not isinstance(source, torch._dynamo.source.ConstantSource) + for source in sources + ) + } + py_printer = torch.fx.experimental.symbolic_shapes.ShapeGuardPythonPrinter( + shape_env.var_to_sources, lambda s: s.name, shape_env.var_to_sources + ) + ret = [ + py_printer.doprint(guard.expr) + for guard in shape_env.guards + if guard.expr.free_symbols.issubset(local_vars) + ] + # TODO Figure out how to resolve guards containing weight sizes. + # This is not a big deal as _guards_code is mostly empty today. + return [guard for guard in ret if "L['self']" not in guard] diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/graph_signature.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/graph_signature.py new file mode 100644 index 0000000000000000000000000000000000000000..5311b7beb47ef318e5f5f646fe70b43327672270 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/graph_signature.py @@ -0,0 +1,729 @@ +# mypy: allow-untyped-defs +import dataclasses +from collections.abc import Collection, Mapping +from enum import auto, Enum +from typing import TYPE_CHECKING, Union + +from torch._library.fake_class_registry import FakeScriptObject +from torch._library.opaque_object import get_opaque_type_name, is_opaque_type +from torch._subclasses.fake_tensor import is_fake + + +if TYPE_CHECKING: + import torch + from torch._functorch._aot_autograd.schemas import GraphSignature + +__all__ = [ + "ConstantArgument", + "CustomObjArgument", + "ExportBackwardSignature", + "ExportGraphSignature", + "InputKind", + "InputSpec", + "OutputKind", + "OutputSpec", + "SymIntArgument", + "SymFloatArgument", + "SymBoolArgument", + "TensorArgument", +] + + +@dataclasses.dataclass +class TensorArgument: + name: str + + +@dataclasses.dataclass +class TokenArgument: + name: str + + +@dataclasses.dataclass +class SymIntArgument: + name: str + + +@dataclasses.dataclass +class SymFloatArgument: + name: str + + +@dataclasses.dataclass +class SymBoolArgument: + name: str + + +@dataclasses.dataclass +class CustomObjArgument: + name: str + class_fqn: str + fake_val: FakeScriptObject | None = None + + +@dataclasses.dataclass +class ConstantArgument: + name: str + value: int | float | bool | str | None + + +ArgumentSpec = Union[ + TensorArgument, + SymIntArgument, + SymFloatArgument, + SymBoolArgument, + ConstantArgument, + CustomObjArgument, + TokenArgument, +] + + +class InputKind(Enum): + USER_INPUT = auto() + PARAMETER = auto() + BUFFER = auto() + CONSTANT_TENSOR = auto() + CUSTOM_OBJ = auto() + TOKEN = auto() + + +@dataclasses.dataclass +class InputSpec: + kind: InputKind + arg: ArgumentSpec + target: str | None + persistent: bool | None = None + + def __post_init__(self): + if self.kind == InputKind.BUFFER: + assert self.persistent is not None, ( + "Failed to specify persistent flag on BUFFER." + ) + assert isinstance( + self.arg, + ( + TensorArgument, + SymIntArgument, + SymFloatArgument, + SymBoolArgument, + ConstantArgument, + CustomObjArgument, + TokenArgument, + ), + ), f"got {type(self.arg)}" + + def __str__(self): + target = "" if self.target is None else f" target='{self.target}'" + persistent = "" if self.persistent is None else f" persistent={self.persistent}" + return f"{str(self.arg.name)}: {str(self.kind.name)}{target}{persistent}" + + +class OutputKind(Enum): + USER_OUTPUT = auto() + LOSS_OUTPUT = auto() + BUFFER_MUTATION = auto() + PARAMETER_MUTATION = auto() + GRADIENT_TO_PARAMETER = auto() + GRADIENT_TO_USER_INPUT = auto() + USER_INPUT_MUTATION = auto() + TOKEN = auto() + + +@dataclasses.dataclass +class OutputSpec: + kind: OutputKind + arg: ArgumentSpec + target: str | None + + def __post_init__(self): + assert isinstance( + self.arg, + ( + TensorArgument, + SymIntArgument, + SymFloatArgument, + SymBoolArgument, + ConstantArgument, + TokenArgument, + CustomObjArgument, + ), + ), self.arg + + def __str__(self): + target = "" if self.target is None else f" target='{self.target}'" + return f"{str(self.arg.name)}: {str(self.kind.name)}{target}" + + +@dataclasses.dataclass +class ExportBackwardSignature: + gradients_to_parameters: dict[str, str] + gradients_to_user_inputs: dict[str, str] + loss_output: str + + +@dataclasses.dataclass +class ExportGraphSignature: + """ + :class:`ExportGraphSignature` models the input/output signature of Export Graph, + which is a fx.Graph with stronger invariants guarantees. + + Export Graph is functional and does not access "states" like parameters + or buffers within the graph via ``getattr`` nodes. Instead, :func:`export` + guarantees that parameters, buffers, and constant tensors are lifted out of + the graph as inputs. Similarly, any mutations to buffers are not included + in the graph either, instead the updated values of mutated buffers are + modeled as additional outputs of Export Graph. + + The ordering of all inputs and outputs are:: + + Inputs = [*parameters_buffers_constant_tensors, *flattened_user_inputs] + Outputs = [*mutated_inputs, *flattened_user_outputs] + + e.g. If following module is exported:: + + class CustomModule(nn.Module): + def __init__(self) -> None: + super(CustomModule, self).__init__() + + # Define a parameter + self.my_parameter = nn.Parameter(torch.tensor(2.0)) + + # Define two buffers + self.register_buffer("my_buffer1", torch.tensor(3.0)) + self.register_buffer("my_buffer2", torch.tensor(4.0)) + + def forward(self, x1, x2): + # Use the parameter, buffers, and both inputs in the forward method + output = ( + x1 + self.my_parameter + ) * self.my_buffer1 + x2 * self.my_buffer2 + + # Mutate one of the buffers (e.g., increment it by 1) + self.my_buffer2.add_(1.0) # In-place addition + + return output + + + mod = CustomModule() + ep = torch.export.export(mod, (torch.tensor(1.0), torch.tensor(2.0))) + + Resulting Graph is non-functional:: + + graph(): + %p_my_parameter : [num_users=1] = placeholder[target=p_my_parameter] + %b_my_buffer1 : [num_users=1] = placeholder[target=b_my_buffer1] + %b_my_buffer2 : [num_users=2] = placeholder[target=b_my_buffer2] + %x1 : [num_users=1] = placeholder[target=x1] + %x2 : [num_users=1] = placeholder[target=x2] + %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x1, %p_my_parameter), kwargs = {}) + %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %b_my_buffer1), kwargs = {}) + %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%x2, %b_my_buffer2), kwargs = {}) + %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {}) + %add_ : [num_users=0] = call_function[target=torch.ops.aten.add_.Tensor](args = (%b_my_buffer2, 1.0), kwargs = {}) + return (add_1,) + + Resulting ExportGraphSignature of the non-functional Graph would be:: + + # inputs + p_my_parameter: PARAMETER target='my_parameter' + b_my_buffer1: BUFFER target='my_buffer1' persistent=True + b_my_buffer2: BUFFER target='my_buffer2' persistent=True + x1: USER_INPUT + x2: USER_INPUT + + # outputs + add_1: USER_OUTPUT + + To get a functional Graph, you can use :func:`run_decompositions`:: + + mod = CustomModule() + ep = torch.export.export(mod, (torch.tensor(1.0), torch.tensor(2.0))) + ep = ep.run_decompositions() + + Resulting Graph is functional:: + + graph(): + %p_my_parameter : [num_users=1] = placeholder[target=p_my_parameter] + %b_my_buffer1 : [num_users=1] = placeholder[target=b_my_buffer1] + %b_my_buffer2 : [num_users=2] = placeholder[target=b_my_buffer2] + %x1 : [num_users=1] = placeholder[target=x1] + %x2 : [num_users=1] = placeholder[target=x2] + %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%x1, %p_my_parameter), kwargs = {}) + %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %b_my_buffer1), kwargs = {}) + %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%x2, %b_my_buffer2), kwargs = {}) + %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {}) + %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%b_my_buffer2, 1.0), kwargs = {}) + return (add_2, add_1) + + Resulting ExportGraphSignature of the functional Graph would be:: + + # inputs + p_my_parameter: PARAMETER target='my_parameter' + b_my_buffer1: BUFFER target='my_buffer1' persistent=True + b_my_buffer2: BUFFER target='my_buffer2' persistent=True + x1: USER_INPUT + x2: USER_INPUT + + # outputs + add_2: BUFFER_MUTATION target='my_buffer2' + add_1: USER_OUTPUT + + """ + + input_specs: list[InputSpec] + output_specs: list[OutputSpec] + + # A list of parameters uniquely identified by mangled fully qualified name + @property + def parameters(self) -> Collection[str]: + return tuple( + s.target + for s in self.input_specs + if s.kind == InputKind.PARAMETER + if isinstance(s.target, str) + ) + + # A list of buffers uniquely identified by mangled fully qualified name + @property + def buffers(self) -> Collection[str]: + return tuple( + s.target + for s in self.input_specs + if s.kind == InputKind.BUFFER + if isinstance(s.target, str) + ) + + @property + def non_persistent_buffers(self) -> Collection[str]: + return tuple( + s.target + for s in self.input_specs + if s.kind == InputKind.BUFFER + if s.persistent is False + if isinstance(s.target, str) + ) + + # A list of lifted constant tensors + @property + def lifted_tensor_constants(self) -> Collection[str]: + return tuple( + s.target + for s in self.input_specs + if s.kind == InputKind.CONSTANT_TENSOR + if isinstance(s.target, str) + ) + + @property + def lifted_custom_objs(self) -> Collection[str]: + return tuple( + s.target + for s in self.input_specs + if s.kind == InputKind.CUSTOM_OBJ + if isinstance(s.target, str) + ) + + # Graph node names of pytree-flattened inputs of original program + @property + def user_inputs(self) -> Collection[int | float | bool | None | str]: + user_inputs: list[int | float | bool | None | str] = [] + for s in self.input_specs: + if s.kind != InputKind.USER_INPUT: + continue + + if isinstance( + s.arg, + ( + TensorArgument, + SymIntArgument, + SymFloatArgument, + SymBoolArgument, + CustomObjArgument, + ), + ): + user_inputs.append(s.arg.name) + elif isinstance(s.arg, ConstantArgument): + user_inputs.append(s.arg.value) + else: + raise RuntimeError(f"{s.arg} is not a valid user inputs") + return tuple(user_inputs) + + # Graph node names of pytree-flattened outputs of original program + # For joint-graph purposes, will include the loss output. + @property + def user_outputs(self) -> Collection[int | float | bool | None | str]: + user_outputs: list[int | float | bool | None | str] = [] + for s in self.output_specs: + if s.kind not in [ + OutputKind.USER_OUTPUT, + OutputKind.LOSS_OUTPUT, + ]: + continue + + if isinstance( + s.arg, + (TensorArgument, SymIntArgument, SymFloatArgument, SymBoolArgument), + ): + user_outputs.append(s.arg.name) + elif isinstance(s.arg, ConstantArgument): + user_outputs.append(s.arg.value) + elif isinstance(s.arg, CustomObjArgument): + user_outputs.append(s.arg.name) + else: + raise RuntimeError(f"{s.arg} is not a valid user output") + return tuple(user_outputs) + + # A dictionary mapping graph input node names to parameters. If a graph input + # name is found in this dictionary, it is guaranteed to be a lifted parameter. + @property + def inputs_to_parameters(self) -> Mapping[str, str]: + return _immutable_dict( + (s.arg.name, s.target) + for s in self.input_specs + if s.kind == InputKind.PARAMETER + and isinstance(s.arg, TensorArgument) + and isinstance(s.target, str) + ) + + # A dictionary mapping graph input node names to buffers. If a graph input + # name is found in this dictionary, it is guaranteed to be a lifted buffer. + @property + def inputs_to_buffers(self) -> Mapping[str, str]: + return _immutable_dict( + (s.arg.name, s.target) # type: ignore[union-attr, misc] + for s in self.input_specs + if s.kind == InputKind.BUFFER + and isinstance(s.arg, TensorArgument) + and isinstance(s.target, str) + ) + + # A dictionary mapping graph output node names to buffers that are mutated in the + # original program. Buffers that are not mutated will not be found in this dictionary. + @property + def buffers_to_mutate(self) -> Mapping[str, str]: + return _immutable_dict( + (s.arg.name, s.target) + for s in self.output_specs + if s.kind == OutputKind.BUFFER_MUTATION + and isinstance(s.arg, TensorArgument) + and isinstance(s.target, str) + ) + + @property + def parameters_to_mutate(self) -> Mapping[str, str]: + return _immutable_dict( + (s.arg.name, s.target) + for s in self.output_specs + if s.kind == OutputKind.PARAMETER_MUTATION + and isinstance(s.arg, TensorArgument) + and isinstance(s.target, str) + ) + + @property + def user_inputs_to_mutate(self) -> Mapping[str, str]: + return _immutable_dict( + (s.arg.name, s.target) + for s in self.output_specs + if s.kind == OutputKind.USER_INPUT_MUTATION + and isinstance(s.arg, TensorArgument) + and isinstance(s.target, str) + ) + + # A dictionary mapping graph input node names to lifted tensor constants. + @property + def inputs_to_lifted_tensor_constants(self) -> Mapping[str, str]: + return _immutable_dict( + (s.arg.name, s.target) + for s in self.input_specs + if s.kind == InputKind.CONSTANT_TENSOR + and isinstance(s.arg, TensorArgument) + and isinstance(s.target, str) + ) + + @property + def inputs_to_lifted_custom_objs(self) -> Mapping[str, str]: + return _immutable_dict( + (s.arg.name, s.target) + for s in self.input_specs + if s.kind == InputKind.CUSTOM_OBJ + and isinstance(s.arg, CustomObjArgument) + and isinstance(s.target, str) + ) + + @property + def backward_signature(self) -> ExportBackwardSignature | None: + loss_output = None + gradients_to_parameters: dict[str, str] = {} + gradients_to_user_inputs: dict[str, str] = {} + for spec in self.output_specs: + if spec.kind == OutputKind.LOSS_OUTPUT: + assert loss_output is None + assert isinstance(spec.arg, TensorArgument) + loss_output = spec.arg.name + elif spec.kind == OutputKind.GRADIENT_TO_PARAMETER: + assert isinstance(spec.target, str) + assert isinstance(spec.arg, TensorArgument) + gradients_to_parameters[spec.arg.name] = spec.target + elif spec.kind == OutputKind.GRADIENT_TO_USER_INPUT: + assert isinstance(spec.target, str) + assert isinstance(spec.arg, TensorArgument) + gradients_to_user_inputs[spec.arg.name] = spec.target + + if loss_output is None: + return None + + return ExportBackwardSignature( + loss_output=loss_output, + gradients_to_parameters=gradients_to_parameters, + gradients_to_user_inputs=gradients_to_user_inputs, + ) + + # Map from assertion dependency token index to assertion dep token output + # name in output. The shape of output after aot_autograd will be like: + # (updated_inputs, user_outputs, dep_token). + @property + def assertion_dep_token(self) -> Mapping[int, str] | None: + return None + + @property + def input_tokens(self) -> Collection[str]: + input_tokens = [] + for s in self.input_specs: + if s.kind == InputKind.TOKEN: + assert isinstance(s.arg, TokenArgument) + input_tokens.append(s.arg.name) + return tuple(input_tokens) + + @property + def output_tokens(self) -> Collection[str]: + output_tokens = [] + for s in self.output_specs: + if s.kind == OutputKind.TOKEN: + assert isinstance(s.arg, TokenArgument) + output_tokens.append(s.arg.name) + return tuple(output_tokens) + + def __post_init__(self) -> None: + assertion_dep_token = self.assertion_dep_token + if assertion_dep_token is None: + return + assert len(assertion_dep_token) == 1 + assertion_dep_token_index = next(iter(assertion_dep_token.keys())) + assert ( + len(self.user_outputs) + len(self.buffers_to_mutate) + == assertion_dep_token_index + ) + + def replace_all_uses(self, old: str, new: str): + """ + Replace all uses of the old name with new name in the signature. + """ + assert isinstance(old, str) + assert isinstance(new, str) + arg_types = ( + TensorArgument, + SymIntArgument, + SymFloatArgument, + SymBoolArgument, + CustomObjArgument, + TokenArgument, + ) + for o in self.output_specs: + if isinstance(o.arg, arg_types): + if o.arg.name == old: + o.arg.name = new + for i in self.input_specs: + if isinstance(i.arg, arg_types): + if i.arg.name == old: + i.arg.name = new + + def get_replace_hook(self, replace_inputs=False): + def _(old, new, user): + if user.op == "output": + self.replace_all_uses(old.name, new) + if replace_inputs and old.op == "placeholder": + self.replace_all_uses(old.name, new) + + return _ + + def __str__(self): + input_specs = "\n".join(str(s) for s in self.input_specs) + output_specs = "\n".join(str(s) for s in self.output_specs) + return f"\n# inputs\n{input_specs}\n\n# outputs\n{output_specs}\n" + + +def _immutable_dict(items): + """ + Creates a mapping where items cannot be added, deleted, or updated. + NOTE: The immutability is shallow (like tuple is an immutable collection). + """ + from types import MappingProxyType + + return MappingProxyType(dict(items)) + + +def _make_argument_spec(node, token_names) -> ArgumentSpec: + from torch import ScriptObject, SymBool, SymFloat, SymInt + from torch._library.fake_class_registry import FakeScriptObject + + if isinstance(node, (int, bool, float, type(None), str)): + # For const outputs we just directly return this + return ConstantArgument(name="", value=node) + + assert "val" in node.meta, ( + f"{node} is not a constant or a node with a 'val' metadata field" + ) + val = node.meta["val"] + if node.name in token_names: + return TokenArgument(name=node.name) + elif is_fake(val): + return TensorArgument(name=node.name) + elif isinstance(val, SymInt): + return SymIntArgument(name=node.name) + elif isinstance(val, SymFloat): + return SymFloatArgument(name=node.name) + elif isinstance(val, SymBool): + return SymBoolArgument(name=node.name) + elif isinstance(val, ScriptObject): + return CustomObjArgument(name=node.name, class_fqn=val._type().qualified_name()) # type: ignore[attr-defined] + elif isinstance(val, FakeScriptObject): + return CustomObjArgument( + name=node.name, class_fqn=val.script_class_name, fake_val=val + ) + elif is_opaque_type(type(val)): + return CustomObjArgument( + name=node.name, class_fqn=get_opaque_type_name(type(val)), fake_val=val + ) + elif isinstance(val, (int, bool, str, float, type(None))): + return ConstantArgument(name=node.name, value=val) + else: + raise AssertionError( + f"Encountered an unsupported object of type {type(val)} " + f"while writing the metadata for exported program" + ) + + +def _convert_to_export_graph_signature( + graph_signature: "GraphSignature", + gm: "torch.fx.GraphModule", + non_persistent_buffers: set[str], +) -> "ExportGraphSignature": + from torch.utils import _pytree as pytree + + is_joint = graph_signature.backward_signature is not None + + # unpack objects + user_inputs = set(graph_signature.user_inputs) + inputs_to_parameters = graph_signature.inputs_to_parameters + inputs_to_buffers = graph_signature.inputs_to_buffers + user_outputs = set(graph_signature.user_outputs) + buffer_mutations = graph_signature.buffers_to_mutate + parameter_mutations = graph_signature.parameters_to_mutate + user_input_mutations = graph_signature.user_inputs_to_mutate + grad_params = ( + graph_signature.backward_signature.gradients_to_parameter # type: ignore[union-attr] + if is_joint + else {} + ) + grad_user_inputs = ( + graph_signature.backward_signature.gradients_to_user_inputs # type: ignore[union-attr] + if is_joint + else {} + ) + loss_output = ( + graph_signature.backward_signature.loss_output # type: ignore[union-attr] + if is_joint + else None + ) + input_tokens = graph_signature.input_tokens + output_tokens = graph_signature.output_tokens + + inputs = [ + _make_argument_spec(node, input_tokens) + for node in gm.graph.nodes + if node.op == "placeholder" + ] + outputs = [ + _make_argument_spec(node, output_tokens) + for node in pytree.tree_leaves(next(iter(reversed(gm.graph.nodes))).args) + ] + + def to_input_spec(inp: ArgumentSpec) -> InputSpec: + if isinstance(inp, TokenArgument): + return InputSpec(kind=InputKind.TOKEN, arg=inp, target=None) + + if not isinstance(inp, TensorArgument): + return InputSpec(kind=InputKind.USER_INPUT, arg=inp, target=None) + name = inp.name + if name in user_inputs: + return InputSpec(kind=InputKind.USER_INPUT, arg=inp, target=None) + elif name in inputs_to_parameters: + return InputSpec( + kind=InputKind.PARAMETER, + arg=inp, + target=inputs_to_parameters[name], # type: ignore[index] + ) + elif name in inputs_to_buffers: + return InputSpec( + kind=InputKind.BUFFER, + arg=inp, + target=inputs_to_buffers[name], # type: ignore[index] + persistent=(inputs_to_buffers[name] not in non_persistent_buffers), # type: ignore[index] + ) + else: + raise AssertionError(f"Unknown tensor input kind: {name}") + + def to_output_spec(idx: int, o: ArgumentSpec) -> OutputSpec: + if isinstance(o, TokenArgument): + return OutputSpec(kind=OutputKind.TOKEN, arg=o, target=None) + + if not isinstance(o, TensorArgument): + return OutputSpec(kind=OutputKind.USER_OUTPUT, arg=o, target=None) + name = o.name + if idx < len(buffer_mutations) + len(parameter_mutations) + len( + user_input_mutations + ) + len(output_tokens): + if name in buffer_mutations: + return OutputSpec( + kind=OutputKind.BUFFER_MUTATION, + arg=o, + target=buffer_mutations[name], # type: ignore[index] + ) + elif name in parameter_mutations: + return OutputSpec( + kind=OutputKind.PARAMETER_MUTATION, + arg=o, + target=parameter_mutations[name], # type: ignore[index] + ) + elif name in user_input_mutations: + return OutputSpec( + kind=OutputKind.USER_INPUT_MUTATION, + arg=o, + target=user_input_mutations[name], # type: ignore[index] + ) + else: + raise AssertionError(f"Unknown tensor mutation kind: {name}") + else: + if name in user_outputs: + return OutputSpec(kind=OutputKind.USER_OUTPUT, arg=o, target=None) + + elif name in grad_params: + return OutputSpec( + kind=OutputKind.GRADIENT_TO_PARAMETER, + arg=o, + target=grad_params[name], + ) + elif name in grad_user_inputs: + return OutputSpec( + kind=OutputKind.GRADIENT_TO_USER_INPUT, + arg=o, + target=grad_user_inputs[name], + ) + elif name == loss_output: + return OutputSpec(kind=OutputKind.LOSS_OUTPUT, arg=o, target=None) + + else: + raise AssertionError(f"Unknown tensor output kind: {name}") + + input_specs = [to_input_spec(inp) for inp in inputs] + output_specs = [to_output_spec(idx, o) for idx, o in enumerate(outputs)] + return ExportGraphSignature(input_specs=input_specs, output_specs=output_specs) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/passes/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/passes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ca5634bd4eadb7a80ddb7521ec0dae26fb2cfec5 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/passes/__init__.py @@ -0,0 +1,97 @@ +from typing import Union + +import torch +import torch.utils._pytree as pytree +from torch.export.exported_program import ExportedProgram + + +__all__ = ["move_to_device_pass"] + + +def move_to_device_pass( + ep: ExportedProgram, location: torch.device | str | dict[str, str] +) -> ExportedProgram: + """ + Move the exported program to the given device. + + Args: + ep (ExportedProgram): The exported program to move. + location (Union[torch.device, str, Dict[str, str]]): The device to move the exported program to. + If a string, it is interpreted as a device name. + If a dict, it is interpreted as a mapping from + the existing device to the intended one + + Returns: + ExportedProgram: The moved exported program. + """ + + def _get_new_device( + curr_device: torch.device, + location: torch.device | str | dict[str, str], + ) -> str: + if isinstance(location, dict): + if str(curr_device) in location: + return location[str(curr_device)] + else: + return str(curr_device) + else: + return str(location) + + # move all the state_dict + for k, v in ep.state_dict.items(): + if isinstance(v, torch.nn.Parameter): + ep._state_dict[k] = torch.nn.Parameter( + v.to(_get_new_device(v.device, location)), + v.requires_grad, + ) + else: + ep._state_dict[k] = v.to(_get_new_device(v.device, location)) + + # move all the constants + for k, v in ep.constants.items(): + if isinstance(v, torch.Tensor): + ep._constants[k] = v.to(_get_new_device(v.device, location)) + + # move example_inputs if they exist + if ep.example_inputs is not None: + args, kwargs = ep.example_inputs + moved_args = pytree.tree_map_only( + torch.Tensor, + lambda tensor: tensor.to(_get_new_device(tensor.device, location)), + args, + ) + moved_kwargs = pytree.tree_map_only( + torch.Tensor, + lambda tensor: tensor.to(_get_new_device(tensor.device, location)), + kwargs, + ) + ep._example_inputs = (moved_args, moved_kwargs) + + for m in ep.graph_module.modules(): + if isinstance(m, torch.fx.GraphModule): + for node in m.graph.nodes: + # move all the nodes kwargs with burnt-in device + if "device" in node.kwargs: + kwargs = node.kwargs.copy() + kwargs["device"] = _get_new_device(kwargs["device"], location) + node.kwargs = kwargs + + if ( + node.op == "call_function" + and node.target is torch.ops.aten.to.device + ): + args = list(node.args) + # pyrefly: ignore [unsupported-operation] + args[1] = _get_new_device(args[1], location) + node.args = tuple(args) + + # move all the tensor metadata + node.meta["val"] = pytree.tree_map( + lambda v: v.to(_get_new_device(v.device, location)) + if isinstance(v, torch.Tensor) + else v, + node.meta.get("val"), + ) + + ep.validate() + return ep diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b2bf26a275d9eef91f4b6807ac472b2cd0c30b0f --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/__init__.py @@ -0,0 +1,4 @@ +from ._package import is_pt2_package, PT2ArchiveReader, PT2ArchiveWriter + + +__all__ = ["PT2ArchiveWriter", "PT2ArchiveReader", "is_pt2_package"] diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/_package.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/_package.py new file mode 100644 index 0000000000000000000000000000000000000000..1b46db0958d28b37a602686b34e400c17cecacb3 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/_package.py @@ -0,0 +1,1204 @@ +import glob +import io +import json +import logging +import os +import tempfile +import zipfile +from dataclasses import dataclass +from typing import Any, IO, TYPE_CHECKING, TypeAlias + +import torch +import torch.utils._pytree as pytree +from torch._export.serde import schema +from torch._export.serde.serialize import ( + _dataclass_to_dict, + _dict_to_dataclass, + deserialize_device, + deserialize_scalar_type, + deserialize_size, + deserialize_storage_offset, + deserialize_stride, + ExportedProgramDeserializer, + serialize, + serialize_tensor_meta, + SerializedArtifact, +) +from torch._inductor.cpp_builder import normalize_path_separator +from torch._subclasses.fake_tensor import FakeTensor +from torch.export import ExportedProgram +from torch.export._tree_utils import reorder_kwargs +from torch.export.pt2_archive._package_weights import ( + get_complete, + group_weights, + TensorProperties, + Weights, +) +from torch.export.pt2_archive.constants import ( + AOTINDUCTOR_DIR, + ARCHIVE_FORMAT_PATH, + ARCHIVE_FORMAT_VALUE, + ARCHIVE_VERSION_PATH, + ARCHIVE_VERSION_VALUE, + CONSTANTS_CONFIG_FILENAME_FORMAT, + CONSTANTS_DIR, + CUSTOM_OBJ_FILENAME_PREFIX, + EXECUTORCH_DIR, + EXTRA_DIR, + MODELS_DIR, + MODELS_FILENAME_FORMAT, + SAMPLE_INPUTS_FILENAME_FORMAT, + TENSOR_CONSTANT_FILENAME_PREFIX, + WEIGHT_FILENAME_PREFIX, + WEIGHTS_CONFIG_FILENAME_FORMAT, + WEIGHTS_DIR, +) +from torch.types import FileLike + + +if TYPE_CHECKING: + from torch.utils._ordered_set import OrderedSet + + +DEFAULT_PICKLE_PROTOCOL = 2 +AOTI_FILES: TypeAlias = list[str | Weights] | dict[str, list[str | Weights]] + + +logger: logging.Logger = logging.getLogger(__name__) + + +def is_pt2_package(serialized_model: bytes | str) -> bool: + """ + Check if the serialized model is a PT2 Archive package. + """ + try: + with zipfile.ZipFile( + io.BytesIO(serialized_model) + if isinstance(serialized_model, bytes) + else serialized_model + ) as zip_reader: + root_folder = zip_reader.namelist()[0].split(os.path.sep)[0] + archive_format_path = f"{root_folder}/{ARCHIVE_FORMAT_PATH}" + if archive_format_path in zip_reader.namelist(): + return zip_reader.read(archive_format_path) == b"pt2" + except Exception: + logger.info("Model is not a PT2 package") + return False + + +class PT2ArchiveWriter: + """ + Context manager for writing a PT2 archive. + """ + + def __init__(self, archive_path_or_buffer: FileLike): + if isinstance(archive_path_or_buffer, str): + archive_path_or_buffer = normalize_path_separator(archive_path_or_buffer) + self.archive_file = torch._C.PyTorchFileWriter(archive_path_or_buffer) # type: ignore[arg-type] + # NOTICE: version here is different from the archive_version + # this is the version of zip file format, which is used by PyTorchFileWriter, which write to /.data/version + # archive_version is the version of the PT2 archive spec, which write to /archive_version + self.archive_file.set_min_version(6) + + def __enter__(self) -> "PT2ArchiveWriter": + return self + + def __exit__(self, *args: Any) -> None: + if not self.has_record(ARCHIVE_FORMAT_PATH): + self.write_string(ARCHIVE_FORMAT_PATH, ARCHIVE_FORMAT_VALUE) + + if not self.has_record(ARCHIVE_VERSION_PATH): + self.write_string(ARCHIVE_VERSION_PATH, ARCHIVE_VERSION_VALUE) + + self.close() + + def has_record(self, name: str) -> bool: + """ + Check if a record exists in the archive. + """ + return name in self.archive_file.get_all_written_records() + + def count_prefix(self, prefix: str) -> int: + """ + Count the number of records that start with a given prefix. + """ + return sum( + 1 + for record in self.archive_file.get_all_written_records() + if record.startswith(prefix) + ) + + def write_bytes(self, name: str, data: bytes) -> None: + """ + Write a bytes object to the archive. + name: The destination file inside the archive. + data: The bytes object to write. + """ + assert isinstance(data, bytes), f"Expected bytes but got {type(data)}" + self.archive_file.write_record(name, data, len(data)) + + def write_string(self, name: str, data: str) -> None: + """ + Write a string object to the archive. + name: The destination file inside the archive. + data: The string object to write. + """ + assert isinstance(data, str), f"Expected string but got {type(data)}" + data_bytes = data.encode() + self.write_bytes(name, data_bytes) + + def write_file(self, name: str, file_path: str) -> None: + """ + Copy a file into the archive. + name: The destination file inside the archive. + file_path: The source file on disk. + """ + assert os.path.isfile(file_path), f"{file_path} is not a valid file path" + + with open(file_path, "rb") as f: + file_bytes = f.read() + self.write_bytes(name, file_bytes) + + def write_folder(self, archive_dir: str, folder_dir: str) -> None: + """ + Copy a folder into the archive. + archive_dir: The destination folder inside the archive. + folder_dir: The source folder on disk. + """ + assert os.path.isdir(folder_dir), f"{folder_dir} is not a valid directory path" + + file_paths = filter( + os.path.isfile, glob.glob(f"{folder_dir}/**", recursive=True) + ) + for file_path in file_paths: + # pyrefly: ignore [no-matching-overload] + filename = os.path.relpath(file_path, folder_dir) + archive_path = os.path.join(archive_dir, filename) + # pyrefly: ignore [bad-argument-type] + self.write_file(archive_path, file_path) + + def close(self) -> None: + """ + Close the archive. + """ + self.archive_file.write_end_of_file() + + +class PT2ArchiveReader: + """ + Context manager for reading a PT2 archive. + """ + + def __init__(self, archive_path_or_buffer: FileLike): + if isinstance(archive_path_or_buffer, str): + archive_path_or_buffer = normalize_path_separator(archive_path_or_buffer) + self.archive_file = torch._C.PyTorchFileReader(archive_path_or_buffer) # type: ignore[arg-type] + assert self.read_string(ARCHIVE_FORMAT_PATH) == ARCHIVE_FORMAT_VALUE, ( + "Invalid archive format" + ) + + def __enter__(self) -> "PT2ArchiveReader": + return self + + def __exit__(self, *args: Any) -> None: + # torch._C.PyTorchFileReader doesn't have a close method + pass + + def read_bytes(self, name: str) -> bytes: + """ + Read a bytes object from the archive. + name: The source file inside the archive. + """ + return self.archive_file.get_record(name) + + def read_string(self, name: str) -> str: + """ + Read a string object from the archive. + name: The source file inside the archive. + """ + data = self.read_bytes(name) + return data.decode() + + def archive_version(self) -> int: + """ + Get the archive version. + """ + try: + archive_version = self.read_string(ARCHIVE_VERSION_PATH) + except Exception: + # if archive_version is not found, it means the archive is older than version 0. + # In this case, we assume the archive is version 0. + archive_version = "0" + + return int(archive_version) + + def get_file_names(self) -> list[str]: + """ + Get the file names in the archive. + """ + return self.archive_file.get_all_records() + + +is_pt2_package.__module__ = "torch.export.pt2_archive" +PT2ArchiveWriter.__module__ = "torch.export.pt2_archive" +PT2ArchiveReader.__module__ = "torch.export.pt2_archive" + + +def _package_aoti_files( + archive_writer: PT2ArchiveWriter, + aoti_files: AOTI_FILES | None, + pickle_protocol: int = DEFAULT_PICKLE_PROTOCOL, +) -> None: + if aoti_files is None: + return + + if isinstance(aoti_files, list): + aoti_files = {"model": aoti_files} + + assert isinstance(aoti_files, dict) + + all_weights: dict[str, Weights] = {} # model_name -> weight + weights_configs: dict[ + str, dict[str, Any] + ] = {} # model_name -> (weight_name -> (filename, shape, stride, offset)) + + for model_name, files in aoti_files.items(): + num_so_files = 0 + weights_configs[model_name] = {} + + for file in files: + if file == "": + continue + + if isinstance(file, Weights): + all_weights[model_name] = file + continue + + if file.endswith(".so"): + num_so_files += 1 + if num_so_files > 1: + raise RuntimeError( + f"Multiple .so files found in {files}. " + "You might need to clear your cache " + "directory before calling aoti_compile again." + ) + + filename = os.path.basename(file) + if filename.startswith(CUSTOM_OBJ_FILENAME_PREFIX): + new_filepath = os.path.join(CONSTANTS_DIR, filename) + else: + new_filepath = os.path.join(AOTINDUCTOR_DIR, model_name, filename) + logger.debug( + "Saving AOTI generated file %s to archive in %s", file, new_filepath + ) + archive_writer.write_file( + str(new_filepath), + file, + ) + + if len(all_weights) > 0: + # Dedup weights + grouped_tensors: list[OrderedSet[tuple[str, str]]] = group_weights(all_weights) + for idx, group in enumerate(grouped_tensors): + filename = f"{WEIGHT_FILENAME_PREFIX}{idx}" + model_name, weight_name = get_complete(group, all_weights) + complete_tensor, _ = all_weights[model_name].get_weight(weight_name) + buffer = io.BytesIO() + torch.save(complete_tensor, buffer, pickle_protocol=pickle_protocol) + archive_writer.write_bytes( + os.path.join(WEIGHTS_DIR, filename), buffer.getvalue() + ) + for model_name, weight_name in group: + _, w_property = all_weights[model_name].get_weight(weight_name) + weights_configs[model_name][weight_name] = ( + filename, + w_property.shape, + w_property.stride, + w_property.offset, + ) + + for model_name, weights_config in weights_configs.items(): + archive_writer.write_string( + os.path.join(AOTINDUCTOR_DIR, model_name, "weights_config.json"), + json.dumps(weights_config), + ) + logger.debug("packaging weights_config for model %s", model_name) + logger.debug(weights_config) + + +def _is_fake_tensor(t: torch.Tensor) -> bool: + return isinstance(t, FakeTensor) + + +def _is_tensor_subclass(t: torch.Tensor) -> bool: + return isinstance(t, torch.Tensor) and type(t.data) is not torch.Tensor + + +def _get_raw_tensor_bytes(value: torch.Tensor) -> bytes: + """ + Get the raw bytes of a tensor. This is used to save the tensor in pt2 archive. + """ + # NOTE: don't chain .cpu() with .data_ptr(). If an HtoD copy needs to be + # performed, the CPU copy needs to be kept alive when its underlying + # memory is accessed. + import ctypes + + if _is_fake_tensor(value): + value_bytes = b"" + elif value.data_ptr(): + cpu_tensor = value.cpu() + value_untyped_storage = cpu_tensor.untyped_storage() + # we store the raw bytes the untyped storage. Tensor metadata is stored separately + value_bytes = bytes( + ctypes.cast( + value_untyped_storage.data_ptr(), + ctypes.POINTER(ctypes.c_ubyte * value_untyped_storage.size()), + ).contents + ) + else: + # for empty tensor + value_bytes = b"" + return value_bytes + + +def _should_use_pickle(t: torch.Tensor) -> bool: + return _is_tensor_subclass(t) and not _is_fake_tensor(t) + + +def _save_pickled_tensors( + pickled_items: list[tuple[str, torch.Tensor]], + archive_writer: PT2ArchiveWriter, + config: dict[str, schema.PayloadMeta], + directory: str, + filename_prefix: str, + idx: int, + pickle_protocol: int = DEFAULT_PICKLE_PROTOCOL, +) -> int: + """Save pickled tensors and update config. Returns updated index.""" + for item_fqn, tensor in pickled_items: + path_name = f"{filename_prefix}{idx}" + archive_path = os.path.join(directory, path_name) + buffer = io.BytesIO() + torch.save(tensor, buffer, pickle_protocol=pickle_protocol) + archive_writer.write_bytes(archive_path, buffer.getvalue()) + + config[item_fqn] = schema.PayloadMeta( + path_name=path_name, + is_param=isinstance(tensor, torch.nn.Parameter), + use_pickle=True, + tensor_meta=serialize_tensor_meta(tensor), + ) + idx += 1 + return idx + + +def _save_raw_tensors( + raw_items: dict[str, tuple[torch.Tensor, TensorProperties]], + model_name: str, + archive_writer: PT2ArchiveWriter, + config: dict[str, schema.PayloadMeta], + directory: str, + filename_prefix: str, + idx: int, +) -> int: + """Save deduplicated raw tensor bytes and update config. Returns updated index.""" + if not raw_items: + return idx + + weights_dict = {model_name: Weights(raw_items)} + storage_groups = group_weights(weights_dict) + + for group in storage_groups: + # Find the complete tensor that covers all others in this storage group + model_name, complete_item_name = get_complete(group, weights_dict) + complete_tensor, _ = weights_dict[model_name].get_weight(complete_item_name) + + path_name = f"{filename_prefix}{idx}" + archive_path = os.path.join(directory, path_name) + tensor_bytes = _get_raw_tensor_bytes(complete_tensor) + archive_writer.write_bytes(archive_path, tensor_bytes) + idx += 1 + + for _, item_fqn in group: + tensor, _ = weights_dict[model_name].get_weight(item_fqn) + config[item_fqn] = schema.PayloadMeta( + path_name=path_name, + is_param=isinstance(tensor, torch.nn.Parameter), + use_pickle=False, + tensor_meta=serialize_tensor_meta(tensor), + ) + + return idx + + +def _package_state_dict( + model_name: str, + exported_program: ExportedProgram, + archive_writer: PT2ArchiveWriter, + pickle_protocol: int = DEFAULT_PICKLE_PROTOCOL, +) -> schema.PayloadConfig: + weights_config: dict[str, schema.PayloadMeta] = {} + + pickled_weights: list[tuple[str, torch.Tensor]] = [] + raw_weights: dict[str, tuple[torch.Tensor, TensorProperties]] = {} + + # Categorize weights + for weight_fqn, weight_tensor in exported_program.state_dict.items(): + assert isinstance(weight_tensor, torch.Tensor), ( + "only torch.Tensor is allowed in state_dict" + ) + if _should_use_pickle(weight_tensor): + pickled_weights.append((weight_fqn, weight_tensor)) + else: + raw_weights[weight_fqn] = (weight_tensor, TensorProperties(weight_tensor)) + + idx = archive_writer.count_prefix(os.path.join(WEIGHTS_DIR, WEIGHT_FILENAME_PREFIX)) + + # Save weights in pickle format + idx = _save_pickled_tensors( + pickled_weights, + archive_writer, + weights_config, + WEIGHTS_DIR, + WEIGHT_FILENAME_PREFIX, + idx, + pickle_protocol, + ) + + # Save weights in raw bytes format + _save_raw_tensors( + raw_weights, + model_name, + archive_writer, + weights_config, + WEIGHTS_DIR, + WEIGHT_FILENAME_PREFIX, + idx, + ) + + return schema.PayloadConfig(config=weights_config) + + +def _package_constants( + model_name: str, + exported_program: ExportedProgram, + archive_writer: PT2ArchiveWriter, + pickle_protocol: int = DEFAULT_PICKLE_PROTOCOL, +) -> schema.PayloadConfig: + constants_config: dict[str, schema.PayloadMeta] = {} + + pickled_constants: list[tuple[str, torch.Tensor]] = [] + raw_constants: dict[str, tuple[torch.Tensor, TensorProperties]] = {} + custom_objects: list[tuple[str, torch._C.ScriptObject]] = [] + + # Categorize constants + for constant_fqn, constant in exported_program.constants.items(): + if isinstance(constant, torch.Tensor): + if _should_use_pickle(constant): + pickled_constants.append((constant_fqn, constant)) + else: + raw_constants[constant_fqn] = (constant, TensorProperties(constant)) + + elif isinstance(constant, torch._C.ScriptObject): + custom_objects.append((constant_fqn, constant)) + + else: + raise RuntimeError(f"Unsupported constant type: {type(constant)}") + + tensor_idx = archive_writer.count_prefix( + os.path.join(CONSTANTS_DIR, TENSOR_CONSTANT_FILENAME_PREFIX) + ) + custom_obj_idx = archive_writer.count_prefix( + os.path.join(CONSTANTS_DIR, CUSTOM_OBJ_FILENAME_PREFIX) + ) + + # Save constants in pickle format + tensor_idx = _save_pickled_tensors( + pickled_constants, + archive_writer, + constants_config, + CONSTANTS_DIR, + TENSOR_CONSTANT_FILENAME_PREFIX, + tensor_idx, + pickle_protocol, + ) + + # Save constants in raw bytes format + _save_raw_tensors( + raw_constants, + model_name, + archive_writer, + constants_config, + CONSTANTS_DIR, + TENSOR_CONSTANT_FILENAME_PREFIX, + tensor_idx, + ) + + # Handle custom objects + for constant_fqn, constant in custom_objects: + path_name = f"{CUSTOM_OBJ_FILENAME_PREFIX}{custom_obj_idx}" + archive_path = os.path.join(CONSTANTS_DIR, path_name) + custom_obj_bytes = torch._C._pickle_save(constant) + archive_writer.write_bytes(archive_path, custom_obj_bytes) + + constants_config[constant_fqn] = schema.PayloadMeta( + path_name=path_name, + is_param=False, + use_pickle=True, + tensor_meta=None, + ) + custom_obj_idx += 1 + + return schema.PayloadConfig(config=constants_config) + + +def _package_payload_config( + archive_writer: PT2ArchiveWriter, + payload_config: schema.PayloadConfig, + config_file: str, +) -> None: + """ + Save the payload config as json file in the archive. + """ + archive_writer.write_string( + config_file, json.dumps(_dataclass_to_dict(payload_config)) + ) + + +def _package_exported_programs( + archive_writer: PT2ArchiveWriter, + exported_programs: ExportedProgram | dict[str, ExportedProgram] | None, + opset_version: dict[str, int] | None = None, + pickle_protocol: int = DEFAULT_PICKLE_PROTOCOL, +) -> None: + if exported_programs is None: + return + + if isinstance(exported_programs, ExportedProgram): + exported_programs = {"model": exported_programs} + + assert isinstance(exported_programs, dict) + + for model_name, ep in exported_programs.items(): + weights_config = _package_state_dict( + model_name, ep, archive_writer, pickle_protocol + ) + weights_config_file = WEIGHTS_CONFIG_FILENAME_FORMAT.format(model_name) + _package_payload_config(archive_writer, weights_config, weights_config_file) + + constants_config = _package_constants( + model_name, ep, archive_writer, pickle_protocol + ) + constants_config_file = CONSTANTS_CONFIG_FILENAME_FORMAT.format(model_name) + _package_payload_config(archive_writer, constants_config, constants_config_file) + + artifact: SerializedArtifact = serialize( + ep, + opset_version, + pickle_protocol, + ) + + archive_writer.write_bytes( + MODELS_FILENAME_FORMAT.format(model_name), artifact.exported_program + ) + archive_writer.write_bytes( + SAMPLE_INPUTS_FILENAME_FORMAT.format(model_name), + artifact.example_inputs, + ) + + +def _package_extra_files( + archive_writer: PT2ArchiveWriter, extra_files: dict[str, Any] | None +) -> None: + if extra_files is None: + return + + for extra_file_name, content in extra_files.items(): + archive_writer.write_string(f"{EXTRA_DIR}{extra_file_name}", content) + + +def _package_executorch_files( + archive_writer: PT2ArchiveWriter, executorch_files: dict[str, bytes] | None +) -> None: + if executorch_files is None: + return + + for file_name, content in executorch_files.items(): + archive_writer.write_bytes(f"{EXECUTORCH_DIR}{file_name}", content) + + +def package_pt2( + f: FileLike, + *, + exported_programs: ExportedProgram | dict[str, ExportedProgram] | None = None, + aoti_files: AOTI_FILES | None = None, + extra_files: dict[str, Any] | None = None, + opset_version: dict[str, int] | None = None, + pickle_protocol: int = DEFAULT_PICKLE_PROTOCOL, + executorch_files: dict[str, bytes] | None = None, +) -> FileLike: + r""" + Saves the artifacts to a PT2Archive format. The artifact can then be loaded + using ``load_pt2``. + + Args: + f (str | os.PathLike[str] | IO[bytes]): A file-like object (has to + implement write and flush) or a string containing a file name. + + exported_programs (Union[ExportedProgram, dict[str, ExportedProgram]]): + The exported program to save, or a dictionary mapping model name to an + exported program to save. The exported program will be saved under + models/\*.json. If only one ExportedProgram is specified, this will + automatically be named "model". + + aoti_files (Union[list[str], dict[str, list[str]]]): A list of files + generated by AOTInductor via + ``torch._inductor.aot_compile(..., {"aot_inductor.package": True})``, + or a dictionary mapping model name to its AOTInductor generated files. + If only one set of files is specified, this will automatically be named + "model". + + extra_files (Optional[Dict[str, Any]]): Map from filename to contents + which will be stored as part of the pt2. + + opset_version (Optional[Dict[str, int]]): A map of opset names + to the version of this opset + + pickle_protocol: can be specified to override the default protocol + + executorch_files (Optional[dict[str, bytes]]): Optional executorch + artifacts to save. + + """ + assert not ( + exported_programs is None and aoti_files is None and extra_files is None + ), ( + "No value passed in for `exported_programs`, `aoti_files`, and " + "`extra_files`, implying that you do not plan on saving anything." + ) + + if not ( + (isinstance(f, (io.IOBase, IO)) and f.writable() and f.seekable()) + or (isinstance(f, (str, os.PathLike)) and os.fspath(f).endswith(".pt2")) + or (isinstance(f, tempfile._TemporaryFileWrapper) and f.name.endswith(".pt2")) + ): + # TODO: turn this into an error + logger.warning( + "Expect archive file to be a file ending in .pt2, or is a buffer. " + "Instead got {%s}", + f, + ) + + if isinstance(f, (str, os.PathLike)): + f = os.fspath(f) + + # pyrefly: ignore [bad-argument-type] + with PT2ArchiveWriter(f) as archive_writer: + _package_exported_programs( + archive_writer, exported_programs, pickle_protocol=pickle_protocol + ) + _package_aoti_files( + archive_writer, + aoti_files, + pickle_protocol=pickle_protocol, + ) + _package_extra_files(archive_writer, extra_files) + _package_executorch_files(archive_writer, executorch_files) + + if isinstance(f, (io.IOBase, IO)): + f.seek(0) + # pyrefly: ignore [bad-return] + return f + + +class AOTICompiledModel: + """ + Callable AOT Inductor loaded model from a .pt2 + """ + + def __init__(self, loader: torch._C._aoti.AOTIModelPackageLoader) -> None: + self.loader = loader + + def __call__(self, *args, **kwargs): # type: ignore[no-untyped-def] + call_spec = self.loader.get_call_spec() + in_spec = pytree.treespec_loads(call_spec[0]) + out_spec = pytree.treespec_loads(call_spec[1]) + flat_inputs = pytree.tree_flatten((args, reorder_kwargs(kwargs, in_spec)))[0] + flat_inputs = [x for x in flat_inputs if isinstance(x, torch.Tensor)] + flat_outputs = self.loader.boxed_run(flat_inputs) + return pytree.tree_unflatten(flat_outputs, out_spec) + + def get_metadata(self) -> dict[str, str]: + return self.loader.get_metadata() + + def load_constants( + self, + constants_map: dict[str, torch.Tensor], + *, + check_full_update: bool, + user_managed: bool = False, + ) -> None: + """ + Given a mapping of constant fqns to tensors, load the constants into the model. + You can use ``get_constant_fqns`` to get the list of constant fqns that + are needed in the compiled model. + + Args: + constants_map: A mapping of constant fqns to tensors. + check_full_update: Whether to add check to see if all the constants + are updated and have values. + """ + self.loader.load_constants( + constants_map, False, check_full_update, user_managed + ) + + def get_constant_fqns(self) -> list[str]: + return self.loader.get_constant_fqns() + + def __deepcopy__(self, memo: dict[Any, Any] | None) -> "AOTICompiledModel": + logger.warning( + "AOTICompiledModel deepcopy warning: AOTICompiledModel.loader is not deepcopied." + ) + return AOTICompiledModel(self.loader) + + +@dataclass +class PT2ArchiveContents: + exported_programs: dict[str, ExportedProgram] + aoti_runners: dict[str, AOTICompiledModel] + extra_files: dict[str, Any] + + +def _create_flat_tensor_from_bytes( + tensor_bytes: bytes, + tensor_meta: schema.TensorMeta, +) -> torch.Tensor: + """ + Create a flat tensor from raw bytes with dtype, device and requires_grad. + It will be re-strided based on size, stride, and storage_offset later. + """ + dtype = deserialize_scalar_type(tensor_meta.dtype) + size = deserialize_size(tensor_meta.sizes) + device = deserialize_device(tensor_meta.device) + + if len(tensor_bytes) != 0: + tensor = torch.frombuffer( + tensor_bytes, dtype=dtype, requires_grad=tensor_meta.requires_grad + ).to(device) + else: + # cannot call torch.frombuffer() on empty bytes + logger.warning( + "Cannot call torch.frombuffer() on empty bytes. " + "Creating a tensor with zeros as workaround." + ) + tensor = torch.zeros(size, dtype=dtype, device=device) + + return tensor + + +def _build_file_map( + archive_reader: PT2ArchiveReader, + config: schema.PayloadConfig, + base_dir: str, +) -> dict[str, torch.Tensor]: + """ + Build a map from file path to the payload in flat tensor format. + """ + file_map: dict[str, torch.Tensor] = {} + for payload_meta in config.config.values(): + # skip pickled objects + if payload_meta.use_pickle: + continue + # skip files that already exist in the map + if payload_meta.path_name in file_map: + continue + + tensor_bytes = archive_reader.read_bytes( + os.path.join(base_dir, payload_meta.path_name) + ) + assert payload_meta.tensor_meta is not None + tensor = _create_flat_tensor_from_bytes(tensor_bytes, payload_meta.tensor_meta) + file_map[payload_meta.path_name] = tensor + + return file_map + + +def _load_payload_config( + archive_reader: PT2ArchiveReader, + config_file: str, +) -> schema.PayloadConfig: + """ + Load and parse a payload config from the archive. + """ + return _dict_to_dataclass( + schema.PayloadConfig, + json.loads(archive_reader.read_string(config_file)), + ) + + +def _load_state_dict( + archive_reader: PT2ArchiveReader, + model_name: str, +) -> dict[str, torch.Tensor] | bytes: + # Make it BC compatible with legacy weight files + legacy_weights_file = f"{WEIGHTS_DIR}{model_name}.pt" + if legacy_weights_file in archive_reader.get_file_names(): + logger.warning( + "You are loading weight from the legacy format. " + "Please generate a new pt2 file using torch.export.save()." + ) + return archive_reader.read_bytes(legacy_weights_file) + else: + weights_config_file = WEIGHTS_CONFIG_FILENAME_FORMAT.format(model_name) + assert weights_config_file in archive_reader.get_file_names(), ( + f"{weights_config_file} not found in PT2 archive" + ) + weights_config = _load_payload_config(archive_reader, weights_config_file) + # construct the mapping from file name (e.g. weight_0) to flat weight payload + state_dict_file_map = _build_file_map( + archive_reader, weights_config, WEIGHTS_DIR + ) + # chain the mapping weight FQN -> weight file name -> strided weight payload + # so that the aliasing of weights is preserved + state_dict: dict[str, torch.Tensor] = {} + for weight_fqn, payload_meta in weights_config.config.items(): + if payload_meta.use_pickle: + weight_bytes = archive_reader.read_bytes( + os.path.join(WEIGHTS_DIR, payload_meta.path_name) + ) + state_dict[weight_fqn] = torch.load( + io.BytesIO(weight_bytes), weights_only=False + ) + else: + tensor_meta = payload_meta.tensor_meta + assert tensor_meta is not None + weight_tensor = torch.as_strided( + input=state_dict_file_map[payload_meta.path_name], + size=deserialize_size(tensor_meta.sizes), + stride=deserialize_stride(tensor_meta.strides), + storage_offset=deserialize_storage_offset( + tensor_meta.storage_offset + ), + ) + if payload_meta.is_param: + state_dict[weight_fqn] = torch.nn.Parameter( + weight_tensor, requires_grad=tensor_meta.requires_grad + ) + else: + state_dict[weight_fqn] = weight_tensor + + return state_dict + + +def _load_constants( + archive_reader: PT2ArchiveReader, + model_name: str, +) -> dict[str, torch.Tensor] | bytes: + # Make it BC compatible with legacy constant files + legacy_constants_file = f"{CONSTANTS_DIR}{model_name}.pt" + if legacy_constants_file in archive_reader.get_file_names(): + logger.warning( + "You are loading constant from the legacy format. " + "Please generate a new pt2 file using torch.export.save()." + ) + return archive_reader.read_bytes(legacy_constants_file) + else: + constants_config_file = CONSTANTS_CONFIG_FILENAME_FORMAT.format(model_name) + assert constants_config_file in archive_reader.get_file_names(), ( + f"{constants_config_file} not found in PT2 archive" + ) + constants_config = _load_payload_config(archive_reader, constants_config_file) + # construct the mapping from file name (e.g. constant_0) to constant payload + constant_file_map = _build_file_map( + archive_reader, constants_config, CONSTANTS_DIR + ) + # chain the mapping constant FQN -> constant file name -> strided constant payload + # so that the aliasing of constants is preserved + constants: dict[str, torch.Tensor] = {} + for constant_fqn, payload_meta in constants_config.config.items(): + path_name = payload_meta.path_name + if path_name.startswith(TENSOR_CONSTANT_FILENAME_PREFIX): + if payload_meta.use_pickle: + constant_bytes = archive_reader.read_bytes( + os.path.join(CONSTANTS_DIR, path_name) + ) + constants[constant_fqn] = torch.load( + io.BytesIO(constant_bytes), weights_only=False + ) + else: + tensor_meta = payload_meta.tensor_meta + assert tensor_meta is not None + constant_tensor = torch.as_strided( + input=constant_file_map[path_name], + size=deserialize_size(tensor_meta.sizes), + stride=deserialize_stride(tensor_meta.strides), + storage_offset=deserialize_storage_offset( + tensor_meta.storage_offset + ), + ) + constants[constant_fqn] = constant_tensor + + elif path_name.startswith(CUSTOM_OBJ_FILENAME_PREFIX): + constant_bytes = archive_reader.read_bytes( + os.path.join(CONSTANTS_DIR, path_name) + ) + constants[constant_fqn] = torch._C._pickle_load_obj(constant_bytes) + + else: + raise RuntimeError(f"Unsupported constant type: {path_name}") + + return constants + + +def _load_exported_programs( + archive_reader: PT2ArchiveReader, + file_names: list[str], + expected_opset_version: dict[str, int] | None, +) -> dict[str, ExportedProgram]: + exported_program_files = [ + file for file in file_names if file.startswith(MODELS_DIR) + ] + exported_programs = {} + for file in exported_program_files: + prefix, suffix = MODELS_FILENAME_FORMAT.split( + "{}" + ) # split "models/{}.json" into "models/" and "json" + model_name = file[ + len(prefix) : -len(suffix) + ] # given "models/foo.json" we can now get "foo" + + sample_inputs_file = SAMPLE_INPUTS_FILENAME_FORMAT.format(model_name) + serialized_sample_inputs = archive_reader.read_bytes(sample_inputs_file) + + from torch._export.serde.serialize import _bytes_to_dataclass + + exported_program_bytes = archive_reader.read_bytes(file) + serialized_exported_program = _bytes_to_dataclass( + schema.ExportedProgram, exported_program_bytes + ) + state_dict = _load_state_dict(archive_reader, model_name) + constants = _load_constants(archive_reader, model_name) + + ep = ExportedProgramDeserializer(expected_opset_version).deserialize( + serialized_exported_program, + state_dict, + constants, + serialized_sample_inputs, + ) + + exported_programs[model_name] = ep + + return exported_programs + + +def _load_extra_files( + archive_reader: PT2ArchiveReader, file_names: list[str] +) -> dict[str, Any]: + extra_files = [file for file in file_names if file.startswith(EXTRA_DIR)] + + extra_file_contents: dict[str, Any] = {} + for file in extra_files: + contents = archive_reader.read_string(file) + extra_file_contents[file[len(EXTRA_DIR) :]] = contents + + return extra_file_contents + + +def _load_aoti( + file: str, + model_name: str, + run_single_threaded: bool, + num_runners: int, + device_idx: int, +) -> AOTICompiledModel: + loaded_metadata = torch._C._aoti.AOTIModelPackageLoader.load_metadata_from_package( # type: ignore[attr-defined] + file, model_name + ) + + device = loaded_metadata["AOTI_DEVICE_KEY"] + current_device_info = torch._inductor.codecache.get_device_information(device) + + for k, v in current_device_info.items(): + if k in loaded_metadata: + if v != loaded_metadata[k]: + logger.warning( + "Device information mismatch for %s: %s vs %s. " + "This could cause some issues when loading the AOTInductor compiled artifacts.", + k, + v, + loaded_metadata[k], + ) + + aoti_compiled_model = AOTICompiledModel( + torch._C._aoti.AOTIModelPackageLoader( + file, + model_name, + run_single_threaded, + num_runners, + device_idx, + ) + ) + + return aoti_compiled_model + + +def load_pt2( + f: FileLike, + *, + expected_opset_version: dict[str, int] | None = None, + run_single_threaded: bool = False, + num_runners: int = 1, + device_index: int = -1, + load_weights_from_disk: bool = False, +) -> PT2ArchiveContents: # type: ignore[type-arg] + """ + Loads all the artifacts previously saved with ``package_pt2``. + + Args: + f (str | os.PathLike[str] | IO[bytes]): A file-like object (has to + implement write and flush) or a string containing a file name. + + expected_opset_version (Optional[Dict[str, int]]): A map of opset names + to expected opset versions + + num_runners (int): Number of runners to load AOTInductor artifacts + + run_single_threaded (bool): Whether the model should be run without + thread synchronization logic. This is useful to avoid conflicts with + CUDAGraphs. + + device_index (int): The index of the device to which the PT2 package is + to be loaded. By default, `device_index=-1` is used, which corresponds + to the device `cuda` when using CUDA. Passing `device_index=1` would + load the package to `cuda:1`, for example. + + Returns: + A ``PT2ArchiveContents`` object which contains all the objects in the PT2. + """ + + from torch._inductor.cpp_builder import normalize_path_separator + + if not ( + (isinstance(f, (io.IOBase, IO)) and f.readable() and f.seekable()) + or (isinstance(f, (str, os.PathLike)) and os.fspath(f).endswith(".pt2")) + ): + # TODO: turn this into an error in 2.9 + logger.warning( + "Unable to load package. f must be a buffer or a file ending in " + ".pt2. Instead got {%s}", + f, + ) + + if isinstance(f, (str, os.PathLike)): + f = os.fspath(f) + + weights = {} + weight_maps = {} + # pyrefly: ignore [bad-argument-type] + with PT2ArchiveReader(f) as archive_reader: + version = archive_reader.read_string(ARCHIVE_VERSION_PATH) + if version != ARCHIVE_VERSION_VALUE: + raise ValueError( + f"Saved archive version {version} does not match our current " + f"archive version {ARCHIVE_VERSION_VALUE}." + ) + + file_names = archive_reader.get_file_names() + + exported_programs = _load_exported_programs( + archive_reader, file_names, expected_opset_version + ) + extra_files = _load_extra_files(archive_reader, file_names) + + # Get a list of AOTI model names + aoti_model_names: set[str] = set() + for file in file_names: + if file.startswith(AOTINDUCTOR_DIR): + file_end = file[ + len(AOTINDUCTOR_DIR) : + ] # remove data/aotinductor/ prefix + file_end = normalize_path_separator( + file_end + ) # Win32 need normalize path before split. + model_name = file_end.split("/")[ + 0 + ] # split "model_name/...cpp" into "model_name" + aoti_model_names.add(model_name) + if load_weights_from_disk and file.endswith("weights_config.json"): + weight_map = json.loads(archive_reader.read_string(file)) + weight_maps[model_name] = weight_map + elif load_weights_from_disk and file.startswith(WEIGHTS_DIR): + weight_file_name = file[ + len(WEIGHTS_DIR) : + ] # remove data/weights/ prefix + weight_bytes = archive_reader.read_bytes(file) + loaded_weight = torch.load(io.BytesIO(weight_bytes)) + weights[weight_file_name] = loaded_weight + + if isinstance(f, (io.IOBase, IO)): + if len(aoti_model_names) > 0: + # Workaround for AOTIModelPackageLoader not reading buffers + with tempfile.NamedTemporaryFile(suffix=".pt2") as tf: + f.seek(0) + tf.write(f.read()) + f.seek(0) + logger.debug("Writing buffer to tmp file located at %s.", tf.name) + + aoti_runners = { + model_name: _load_aoti( + tf.name, + model_name, + run_single_threaded, + num_runners, + device_index, + ) + for model_name in aoti_model_names + } + else: + aoti_runners = {} + else: + aoti_runners = { + model_name: _load_aoti( + f, + model_name, + run_single_threaded, + num_runners, + device_index, + ) + for model_name in aoti_model_names + } + + if weight_maps: + for model_name in aoti_model_names: + model_weights = {} + for weight_name, (file, shape, stride, storage_offset) in weight_maps[ + model_name + ].items(): + weight = weights[file] + model_weights[weight_name] = weight.as_strided( + shape, stride, storage_offset + ) + + # user_managed=True ensures the weights updates are shared by all runners. + aoti_runners[model_name].load_constants( + model_weights, check_full_update=True, user_managed=True + ) + + return PT2ArchiveContents(exported_programs, aoti_runners, extra_files) + + +def load_weights_to_pt2_contents( + pt2_contents: PT2ArchiveContents, weights_map: dict[str, Any] +) -> None: + """ + Load weights into the models in PT2 archive contents + + Args: + pt2_contents (PT2ArchiveContents): The contents of the PT2 archive. + """ + for model_name, weights in weights_map.items(): + if model_name not in pt2_contents.aoti_runners: + raise RuntimeError(f"Model {model_name} not found in PT2 archive contents.") + pt2_contents.aoti_runners[model_name].load_constants( + weights, check_full_update=True, user_managed=True + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/_package_weights.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/_package_weights.py new file mode 100644 index 0000000000000000000000000000000000000000..5acd86feebf0a691d7e527e4ea382e7b4aaabf9c --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/_package_weights.py @@ -0,0 +1,135 @@ +import collections +import warnings + +import torch +from torch._subclasses.fake_tensor import FakeTensor +from torch.utils._ordered_set import OrderedSet + + +def _end_ptr(tensor: torch.Tensor) -> int: + if tensor.nelement(): + stop = tensor.view(-1)[-1].data_ptr() + tensor.element_size() + else: + stop = tensor.data_ptr() + return stop + + +class TensorProperties: + def __init__(self, tensor: torch.Tensor): + self.is_fake = isinstance(tensor, FakeTensor) + self.is_contiguous = tensor.is_contiguous() + self.storage_ptr = None + self.storage_size = None + self.start = None + self.end = None + + if not self.is_fake: + # only get the storage pointer for real tensors + # pyrefly: ignore [bad-assignment] + self.storage_ptr = tensor.untyped_storage().data_ptr() + if self.is_contiguous: + # only get storage size and start/end pointers for contiguous tensors + # pyrefly: ignore [bad-assignment] + self.storage_size = tensor.untyped_storage().nbytes() + # pyrefly: ignore [bad-assignment] + self.start = tensor.data_ptr() + # pyrefly: ignore [bad-assignment] + self.end = _end_ptr(tensor) + + # info to recover tensor + self.shape = tensor.shape + self.stride = tensor.stride() + self.offset = tensor.storage_offset() + + def is_complete(self) -> bool: + """ + Whether the tensor completely overlaps with its underlying storage + """ + if self.is_fake: + # Theoretically, fake tensors should not appear in weights + # But we handle this corner case to make it always complete + return True + if not self.is_contiguous: + return False + + assert self.storage_ptr is not None + assert self.storage_size is not None + assert self.start is not None + assert self.end is not None + return ( + self.start == self.storage_ptr + and self.end == self.storage_ptr + self.storage_size + ) + + +class Weights(dict): + """ + A dictionary mapping from weight name to a tuple of (tensor, TensorProperties). + tensor represents the actual initial value of the weight. + TensorProperties represents the properties of the weight that are needed to recover the weight. + + We use two separate entries because `tensor` could be a clone of the original weight tensor, + so it doesn't have the same property as the original weight (such as underlying storage pointer). + """ + + def __init__(self, weight_dict: dict[str, tuple[torch.Tensor, TensorProperties]]): + super().__init__(weight_dict) + + def get_weight(self, name: str) -> tuple[torch.Tensor, TensorProperties]: + return self[name] + + def get_weight_properties(self, name: str) -> TensorProperties: + return self[name][1] + + +def get_complete( + group: OrderedSet[tuple[str, str]], models_weights: dict[str, Weights] +) -> tuple[str, str]: + """ + `group` is a (model_name, weight_name) tuple. + `model_weights` is a dictionary mapping from model name to its Weights. + + One of the tensor in `group` must be complete and they must share the + same underlying storage. + + Returns the name of the complete tensor in the `group`. If multiple + tensors are complete, returns an arbitrary one. + """ + + def get_tensor_properties(name_tuple: tuple[str, str]) -> TensorProperties: + # returns the tensor properties + (model_name, weight_name) = name_tuple + return models_weights[model_name].get_weight_properties(weight_name) + + for name_tuple in group: + tensor_property = get_tensor_properties(name_tuple) + if tensor_property.is_complete(): + return name_tuple + + warnings.warn( + "No complete tensor found in the group! Returning the first one. " + "This may cause issues when your weights are not on CPU.", + stacklevel=2, + ) + assert len(group) > 0 + return next(iter(group)) + + +def group_weights(all_weights: dict[str, Weights]) -> list[OrderedSet[tuple[str, str]]]: + """ + Group weights that share the same underlying storage. + + Returns a list of sets, each set contains a tuple of (model_name, weight_name). + """ + + weights_dict: dict[tuple[int, torch.dtype], OrderedSet[tuple[str, str]]] = ( + collections.defaultdict(OrderedSet) + ) # (storage_key, dtype) -> set(weight) + + for model_name, weights in all_weights.items(): + for weight_name, (tensor, properties) in weights.items(): + weights_dict[(properties.storage_ptr, tensor.dtype)].add( + (model_name, weight_name) + ) + + return list(weights_dict.values()) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/constants.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..4b05e257b8f3dfc387b553f0aeecc7a0e1653528 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/pt2_archive/constants.py @@ -0,0 +1,35 @@ +# Defined in torch/csrc/export/pt2_archive_constants.h +from torch._C._export import pt2_archive_constants + + +AOTINDUCTOR_DIR: str = pt2_archive_constants.AOTINDUCTOR_DIR +ARCHIVE_FORMAT_PATH: str = pt2_archive_constants.ARCHIVE_FORMAT_PATH +ARCHIVE_FORMAT_VALUE: str = pt2_archive_constants.ARCHIVE_FORMAT_VALUE +ARCHIVE_ROOT_NAME: str = pt2_archive_constants.ARCHIVE_ROOT_NAME +ARCHIVE_VERSION_PATH: str = pt2_archive_constants.ARCHIVE_VERSION_PATH +ARCHIVE_VERSION_VALUE: str = pt2_archive_constants.ARCHIVE_VERSION_VALUE +CONSTANTS_DIR: str = pt2_archive_constants.CONSTANTS_DIR +CONSTANTS_CONFIG_FILENAME_FORMAT: str = ( + pt2_archive_constants.CONSTANTS_CONFIG_FILENAME_FORMAT +) +CUSTOM_OBJ_FILENAME_PREFIX: str = pt2_archive_constants.CUSTOM_OBJ_FILENAME_PREFIX +EXECUTORCH_DIR: str = pt2_archive_constants.EXECUTORCH_DIR +EXTRA_DIR: str = pt2_archive_constants.EXTRA_DIR +MODELS_DIR: str = pt2_archive_constants.MODELS_DIR +MODELS_FILENAME_FORMAT: str = pt2_archive_constants.MODELS_FILENAME_FORMAT +MODULE_INFO_PATH: str = pt2_archive_constants.MODULE_INFO_PATH +MTIA_DIR: str = pt2_archive_constants.MTIA_DIR +SAMPLE_INPUTS_DIR: str = pt2_archive_constants.SAMPLE_INPUTS_DIR +SAMPLE_INPUTS_FILENAME_FORMAT: str = pt2_archive_constants.SAMPLE_INPUTS_FILENAME_FORMAT +TENSOR_CONSTANT_FILENAME_PREFIX: str = ( + pt2_archive_constants.TENSOR_CONSTANT_FILENAME_PREFIX +) +WEIGHTS_CONFIG_FILENAME_FORMAT: str = ( + pt2_archive_constants.WEIGHTS_CONFIG_FILENAME_FORMAT +) +WEIGHT_FILENAME_PREFIX: str = pt2_archive_constants.WEIGHT_FILENAME_PREFIX +WEIGHTS_DIR: str = pt2_archive_constants.WEIGHTS_DIR +XL_MODEL_WEIGHTS_DIR: str = pt2_archive_constants.XL_MODEL_WEIGHTS_DIR +XL_MODEL_WEIGHTS_PARAM_CONFIG_PATH: str = ( + pt2_archive_constants.XL_MODEL_WEIGHTS_PARAM_CONFIG_PATH +) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/unflatten.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/unflatten.py new file mode 100644 index 0000000000000000000000000000000000000000..680b0a907512971da77e505818989035929d20aa --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/export/unflatten.py @@ -0,0 +1,1803 @@ +# mypy: allow-untyped-defs +import abc +import copy +import logging +import operator +import re +from collections import defaultdict +from collections.abc import Callable +from contextlib import contextmanager +from copy import deepcopy +from dataclasses import dataclass +from enum import Enum +from typing import Any, cast + +import torch +import torch.fx._pytree as fx_pytree +import torch.utils._pytree as pytree +from torch._library.fake_class_registry import FakeScriptObject +from torch.export import ExportedProgram +from torch.export._tree_utils import reorder_kwargs +from torch.export.exported_program import ( + ConstantArgument, + ExportGraphSignature, + InputKind, + ModuleCallSignature, + SymBoolArgument, + SymFloatArgument, + SymIntArgument, + TensorArgument, +) +from torch.fx._symbolic_trace import is_fx_symbolic_tracing +from torch.fx.graph_module import _get_attr, _get_attr_via_attr_list, _print_readable +from torch.utils._pytree import GetAttrKey, SequenceKey + +from ._remove_effect_tokens_pass import _remove_effect_tokens + + +log = logging.getLogger(__name__) + + +__all__ = [ + "FlatArgsAdapter", + "InterpreterModule", + "InterpreterModuleDispatcher", + "UnflattenedModule", + "unflatten", +] + + +class _AttrKind(Enum): + PARAMETER = "parameter" + BUFFER = "buffer" + CONSTANT = "constant" + MODULE = "module" + + +@dataclass(frozen=True) +class _TensorID: + """Custom tensor identifier containing storage, stride, and size information.""" + + untyped_storage: torch.UntypedStorage + stride: tuple + size: tuple + storage_offset: int + + +RUN_WITH_INTERPRETER = True + + +@contextmanager +def _disable_interpreter(): + global RUN_WITH_INTERPRETER + old_flag = RUN_WITH_INTERPRETER + RUN_WITH_INTERPRETER = False + try: + yield + finally: + RUN_WITH_INTERPRETER = old_flag + + +# Assign attribute 'from_obj' to the qualified name 'target' on 'to_module +# This installs empty Modules where none exist yet if they are subpaths of target +def _assign_attr( + from_obj: torch.Tensor | torch.ScriptObject | torch.nn.Module, + to_module: torch.nn.Module, + target: str, + attr_kind: _AttrKind, + persistent: bool = True, +): + *prefix, field = target.split(".") + # We need to generate all submodules of `to_module` that are at `prefix` and + # variants of `prefix` that differ only by call name. All of these submodules + # will then be assigned `from_obj` at `field` so that they can share this attribute. + # For example, if target is foo.bar.f, foo has another call name foo@1, + # and bar has other call names bar@1, bar@2, then we will assign f to + # foo.bar, foo.bar@1, foo.bar@2, foo@1.bar, foo@1.bar@1, foo@1.bar@2. + to_modules = {to_module} + for item in prefix: + ts: set[torch.nn.Module] = set() + for to_module in to_modules: + if not hasattr(to_module, item): + setattr(to_module, item, torch.nn.Module()) + ts.update( + t_call # type: ignore[misc] + for k, t_call in to_module._modules.items() + if _is_call_name(k, item) + ) + to_modules = ts + + for to_module in to_modules: + if attr_kind == _AttrKind.PARAMETER: + assert isinstance(from_obj, torch.nn.Parameter) + to_module.register_parameter(field, from_obj) + elif attr_kind == _AttrKind.BUFFER: + assert isinstance(from_obj, torch.Tensor) + to_module.register_buffer(field, from_obj, persistent=persistent) + elif attr_kind == _AttrKind.CONSTANT: + assert not isinstance(from_obj, FakeScriptObject), ( + "FakeScriptObject should only exist during tracing." + ) + assert isinstance( + from_obj, + ( + torch.Tensor, + torch.ScriptObject, + ), + ) + setattr(to_module, field, from_obj) + elif attr_kind == _AttrKind.MODULE: + assert isinstance(from_obj, torch.nn.Module) + setattr(to_module, field, from_obj) + + +class _SubmoduleBase: + _ty: str | None + + def type_name(self) -> str | None: + """ + Subclass of this class - InterpreterModule, InterpreterModuleDispatcher, represents + corresponding model in eager model. To get this type information for those modules + in eager model we need to use this method. + """ + return self._ty + + +class InterpreterModule(_SubmoduleBase, torch.nn.Module): + """A module that uses torch.fx.Interpreter to execute instead of the usual + codegen that GraphModule uses. This provides better stack trace information + and makes it easier to debug execution. + """ + + graph_module: torch.fx.GraphModule | None + + def __init__( + self, + graph: torch.fx.Graph, + ty: str | None = None, + ): + super().__init__() + self.graph = graph + self._ty = ty + self.graph.owning_module = self # type: ignore[assignment] + self._run_with_interpreter = RUN_WITH_INTERPRETER + + def forward(self, *args, **kwargs): + assert self.graph_module is not None, "Didn't finalize this InterpreterModule" + if not is_fx_symbolic_tracing() and ( + torch.compiler.is_dynamo_compiling() or not self._run_with_interpreter + ): + # Dynamo cannot trace through torch.fx.Interpreter, so fall back to + # GraphModule codegen in this instance. + # Patch the codegened forward to run with this InterpreterModule, + # so attribute accesses, etc. are on this module instead. + return type(self.graph_module).forward(self, *args, **kwargs) + else: + if kwargs: + # Handle **kwargs. FX only natively supports positional + # arguments (through placeholders). So in order to pass in + # kwargs, we must correspond the names of the placeholders with + # the keys in the kwarg dict. + arg_list = list(args) + kwarg_names = self.arg_names[len(arg_list) :] + arg_list.extend( + kwargs[kwarg_name] + for kwarg_name in kwarg_names + if kwarg_name in kwargs + ) + + # Assert that the kwargs passed in exactly match the positional + # arguments specified by the GraphModule. This should be + # guaranteed by the unflattening process. + assert len(kwarg_names) == len(kwargs) + assert len(arg_list) == len(self.arg_names) + args = tuple(arg_list) + + return torch.fx.Interpreter(self, graph=self.graph).run( + *args, enable_io_processing=False + ) + + def finalize(self): + # We need to "finalize" because GraphModule populates its own state_dict + # based on the get_attrs observed in the graph. So we need to fully + # construct the graph and call _sink_params before generating this + # GraphModule. + + # need to set `graph_module` directly on the dict to avoid it getting + # registered as a submodule. + self.__dict__["graph_module"] = torch.fx.GraphModule(self, self.graph) + self.graph.lint() + + # Cache arg names for kwarg handling (see forward()) + self.arg_names = [] + for node in self.graph.nodes: + if node.op == "placeholder": + self.arg_names.append(node.target) + + def print_readable( + self, + print_output=True, + include_stride=False, + include_device=False, + colored=False, + ): + return _print_readable( + self, + "InterpreterModule", + print_output, + include_stride, + include_device, + colored, + ) + + +class InterpreterModuleDispatcher(_SubmoduleBase, torch.nn.Module): + """ + A module that carries a sequence of InterpreterModules corresponding to + a sequence of calls of that module. Each call to the module dispatches + to the next InterpreterModule, and wraps back around after the last. + """ + + def __init__(self, attrs: set[str], call_modules: list[InterpreterModule]): + super().__init__() + assert call_modules + self._modules = call_modules[0]._modules + for accessor in attrs: + setattr(self, accessor, getattr(call_modules[0], accessor)) + self._ty = call_modules[0]._ty + self._call_modules = call_modules + self._num_calls = 0 + + def forward(self, *args, **kwargs): + call_module = self._call_modules[self._num_calls] + self._num_calls = (self._num_calls + 1) % len(self._call_modules) + try: + return call_module(*args, **kwargs) + except Exception: + self._num_calls = 0 + raise + + def call_modules(self): + return self._call_modules + + def print_readable( + self, + print_output=True, + include_stride=False, + include_device=False, + colored=False, + ): + outputs = [ + mod.print_readable( + print_output, + include_stride, + include_device, + colored, + ) + for mod in self._call_modules + ] + return "\n".join(outputs) + + +class FlatArgsAdapter(abc.ABC): + """ + Adapts input arguments with ``input_spec`` to align ``target_spec``. + """ + + @abc.abstractmethod + def adapt( + self, + target_spec: pytree.TreeSpec, + input_spec: pytree.TreeSpec, + input_args: list[Any], + metadata: dict[str, Any] | None = None, + obj: Any | None = None, + ) -> list[Any]: + """NOTE: This adapter may mutate given ``input_args_with_path``.""" + ... + + def get_flat_arg_paths(self) -> list[str]: + """Returns a list of paths that are used to access the flat args.""" + return [] + + +class UnflattenedModule(_SubmoduleBase, torch.nn.Module): + def __init__( + self, + export_module: ExportedProgram, + flat_args_adapter: FlatArgsAdapter | None = None, + ): + super().__init__() + if export_module.graph_signature.backward_signature is not None: + raise ValueError("Unflattening on JointExportModule NYI") + + def _id(obj): + """Returns _TensorID dataclass for tensors, otherwise id().""" + if isinstance(obj, torch.Tensor): + return _TensorID( + untyped_storage=obj.untyped_storage(), + stride=obj.stride(), + size=obj.size(), + storage_offset=obj.storage_offset(), # type: ignore[arg-type] + ) + return id(obj) + + fqn_list = [entry.fqn for entry in export_module.module_call_graph] + assert fqn_list[0] == "" + export_graph = deepcopy(export_module.graph) + self.graph_signature = deepcopy(export_module.graph_signature) + self.graph = torch.fx.Graph() + self.graph.owning_module = self # type: ignore[assignment] + self.module_call_graph = deepcopy(export_module.module_call_graph) + self.flat_args_adapter = flat_args_adapter + + self.meta = export_module.graph_module.meta + self.meta["unflattened_module"] = self + + # Flag to indicate whether args have been adapted. + self.adapted = False + self._run_with_interpreter = RUN_WITH_INTERPRETER + + _inplace_buffer_and_input_mutations(export_graph, self.graph_signature) + _fix_nn_module_stacks(export_graph) + self._ty = _root_module_type(export_graph) + + self.ivals = _IVals() + # for any intermediate value of a mutation that is read, track the mutation + seen_modules, seen_attrs = _outline_submodules(export_graph, self) + # for each read intermediate value of a mutation, find where it was created, + # and perform the mutation + self.ivals.update(seen_modules.values()) + # move attributes that correspond to graph arguments for HOPs + # from exported program to unflattened submodules + _copy_graph_attrs(export_module._graph_module, self, seen_attrs) + + self.range_constraints = export_module.range_constraints + self.equality_constraints: list = [] + + # aliasing/unused param or buffer issues: + # in strict-mode export, dynamo export will deduplicate aliased tensors, + # and ignore unused tensors. For aliasing, this causes issues when some aliases + # are unused, and we're unable to match the placeholder node to the correct FQN. + # This leads to the graph signature potentially having the wrong target FQN, + # and downstream issues where parameters are assigned to the wrong target attribute, + # mismatching the relevant placeholder node in the unflattened module. + # To resolve this we restore (_assign_attr) all aliased/unused tensors in + # the state_dict as module attributes, but only keep the used tensors in the + # graph's forward pass (_sink_params). + state_dict = export_module.state_dict + assigned_params: set[str] = set() # tracking unused params + id_to_param: dict[ + int | _TensorID, torch.nn.Parameter + ] = {} # handling weight-sharing + for name in self.graph_signature.parameters: # this loop adds used params + param = state_dict[name] + if _id(param) not in id_to_param: + id_to_param[_id(param)] = torch.nn.Parameter( + param.clone(), requires_grad=param.requires_grad + ) + + _assign_attr( + id_to_param[_id(param)], + self, + name, + attr_kind=_AttrKind.PARAMETER, + ) + assigned_params.add(name) + + non_persistent_buffers = set(self.graph_signature.non_persistent_buffers) + assigned_buffers: set[str] = set() # tracking unused buffers + id_to_buffer: dict[int | _TensorID, tuple[torch.nn.Parameter, bool]] = {} + for name in self.graph_signature.buffers: # this loop adds used buffers + if name in non_persistent_buffers: + persistent = False + buffer = export_module.constants[name] + else: + persistent = True + buffer = state_dict[name] + + if _id(buffer) not in id_to_buffer: + id_to_buffer[_id(buffer)] = (buffer.clone(), persistent) + + _assign_attr( + id_to_buffer[_id(buffer)][0], + self, + name, + attr_kind=_AttrKind.BUFFER, + persistent=persistent, + ) + assigned_buffers.add(name) + + # restore aliased/unused params and buffers + # these appear in state dict but not graph signature + for name, tensor in state_dict.items(): + if name in assigned_params or name in assigned_buffers: # already assigned + continue + + is_buffer = False + if _id(tensor) in id_to_buffer or not isinstance( + tensor, torch.nn.Parameter + ): # aliased buffer + is_buffer = True + + if is_buffer: + if ( + _id(tensor) not in id_to_buffer + ): # this is completely unused (not weight-sharing) + id_to_buffer[_id(tensor)] = ( + tensor, + True, + ) # assign to respect original model + _assign_attr( + id_to_buffer[_id(tensor)][0], + self, + name, + attr_kind=_AttrKind.BUFFER, + persistent=True, + ) + else: + if _id(tensor) not in id_to_param: # this is unused + id_to_param[_id(tensor)] = tensor + _assign_attr( + id_to_param[_id(tensor)], + self, + name, + attr_kind=_AttrKind.PARAMETER, + ) + + # use id map so we don't double-clone aliased constants + id_to_const: dict[int | _TensorID, torch.Tensor | torch._C.ScriptObject] = {} + for fqn, constant in export_module.constants.items(): + if _id(constant) not in id_to_const: + if isinstance(constant, torch.Tensor): + constant = constant.clone() + id_to_const[_id(constant)] = constant + _constant = id_to_const[_id(constant)] + _assign_attr( + _constant, + self, + fqn, + attr_kind=_AttrKind.CONSTANT, + ) + + # This is to handle parameters/buffers that point to the same tensor + # object id -> list of (node_name, target_name) + consts_map: dict[int | _TensorID, list[tuple[str, str]]] = defaultdict(list) + consts_targets: set[str] = set() + + def add_to_consts_map(obj_id, node_name, target_name): + name_list = consts_map[obj_id] + name_list.append((node_name, target_name)) + + # track aliased/unused params, buffers + # prefer using untyped_storage() over id() when it's available + added_params_buffers: set[str] = set() + for s in self.graph_signature.input_specs: + if s.kind == InputKind.PARAMETER or ( + s.kind == InputKind.BUFFER and s.persistent + ): + assert hasattr(s.arg, "name") + assert isinstance(s.target, str) + add_to_consts_map( + _id(export_module.state_dict[s.target]), + s.arg.name, + s.target, + ) + consts_targets.add(s.target) + added_params_buffers.add(s.target) + elif ( + s.kind == InputKind.BUFFER + and not s.persistent + or s.kind == InputKind.CONSTANT_TENSOR + or s.kind == InputKind.CUSTOM_OBJ + ): + assert hasattr(s.arg, "name") + assert isinstance(s.target, str) + add_to_consts_map( + _id(export_module.constants[s.target]), + s.arg.name, + s.target, + ) + consts_targets.add(s.target) + + # add constants that are aliased and don't appear in graph signature + for const_name, const in export_module.constants.items(): + if const_name not in consts_targets: + const_id = _id(const) + assert const_id in consts_map + ph_name, _ = consts_map[const_id][0] + add_to_consts_map(const_id, ph_name, const_name) + added_params_buffers.add(s.target) + + # add aliased/unused params and buffers that don't appear in graph signature + for fqn, tensor in export_module.state_dict.items(): + if fqn not in added_params_buffers: + tensor_id = _id(tensor) + if tensor_id not in consts_map: + # completely unused (no weight-sharing), ignore. + # this weight doesn't appear in graph module, + # so won't cause FQN assignment issues + continue + ph_name, _ = consts_map[tensor_id][0] + add_to_consts_map(tensor_id, ph_name, fqn) + + # node name -> list of possible targets + inputs_to_state: dict[str, list[str]] = {} + for node_target in consts_map.values(): + targets = [t[1] for t in node_target] + for n, _ in node_target: + inputs_to_state[n] = targets + + _sink_params(self, inputs_to_state, []) + + redirected_call_indices = _deduplicate_modules(seen_modules.values()) + fqn_list = [fqn for fqn in fqn_list if fqn not in redirected_call_indices] + + self._dispatch_modules(redirected_call_indices, consts_targets) + fqn_list = [fqn for fqn in fqn_list if "@" not in fqn] + + # Cache so we don't have to compute this every time. + # NOTE: this needs to be kept in sync with the placeholders in + # self.graph, but currently we have no way to guarantee that. + self.input_placeholders = [ + node for node in self.graph.nodes if node.op == "placeholder" + ] + self.check_input_constraints = True + # TODO(zhxchen17) We can register modules ahead of time instead of reorder later. + fqn_order = {fqn: i for i, fqn in enumerate(fqn_list)} + # In the case of legacy IR, we might be missing some modules from metadata. + for name, _ in self.named_modules(remove_duplicate=False): + if name not in fqn_order: + fqn_order[name] = len(fqn_order) + _reorder_submodules(self, fqn_order) + self.graph.lint() + self.finalize() + + def _print_graph(self): + for fqn, mod in self.named_modules(): + print(fqn + ":") + if hasattr(mod, "graph") and isinstance(mod.graph, torch.fx.Graph): + print(mod.graph) + + def _adapt_flat_args(self, flat_args, in_spec, input): + signature = self.module_call_graph[0].signature + if in_spec == signature.in_spec: + return flat_args + + if self.flat_args_adapter is None: + raise TypeError( + "There is no flat args adapter specified. " + "Are you sure you are calling this with the right arguments? " + ) + else: + flat_args = self.flat_args_adapter.adapt( + target_spec=signature.in_spec, + input_spec=in_spec, + input_args=flat_args, + metadata=self.meta, + obj=input, + ) + + if len(flat_args) != signature.in_spec.num_leaves: + raise TypeError( + f"Flat args adaption failed, number of args mismatch " + f"Adatped: {len(flat_args)} \n" + f"Exported module: {signature.in_spec.num_leaves}" + ) + return flat_args + + def process_forward_inputs(self, *args, **kwargs): + signature = self.module_call_graph[0].signature + + reordered_kwargs = kwargs + if kwargs: + reordered_kwargs = reorder_kwargs(kwargs, signature.in_spec) + + flat_args_with_path, in_spec = pytree.tree_flatten_with_path( + (args, reordered_kwargs) + ) + flat_args = [x[1] for x in flat_args_with_path] + + if is_fx_symbolic_tracing(): + return flat_args + + if in_spec != signature.in_spec: + if not self.adapted: + print( + "Input treespec does not match with exported module's: \n" + f"Input treespec: {in_spec}. ", + f"Exported module treespec: {signature.in_spec}", + ) + print("Adapting flat arg to match exported module's treespec") + flat_args = self._adapt_flat_args(flat_args, in_spec, args) + self.adapted = True + + if self.check_input_constraints: + # Import here to avoid an unfortunate circular dependency. + # TODO(suo): untangle this. + from torch._export.utils import _check_input_constraints_for_graph + + if self.adapted is True: + flat_arg_paths = ( + self.flat_args_adapter.get_flat_arg_paths() + if self.flat_args_adapter + else [] + ) + assert not flat_arg_paths or len(flat_arg_paths) == len(flat_args) + new_flat_args_with_path = [ # type: ignore[var-annotated] + ( + ( + SequenceKey(idx=idx), + GetAttrKey( + name=flat_arg_paths[idx] + if flat_arg_paths + else "" + ), + ), + arg, + ) + for idx, arg in enumerate(flat_args) + ] + else: + new_flat_args_with_path = flat_args_with_path # type: ignore[assignment] + + _check_input_constraints_for_graph( + self.input_placeholders, new_flat_args_with_path, self.range_constraints + ) + + return flat_args + + def forward(self, *args, **kwargs): + flat_args = self.process_forward_inputs(*args, **kwargs) + signature = self.module_call_graph[0].signature + + if is_fx_symbolic_tracing(): + return_val = torch.fx.Interpreter(self, graph=self.graph).run( + *flat_args, enable_io_processing=False + ) + # For scalar return value, fx.Graph wraps in a tuple + if isinstance(return_val, tuple) and len(return_val) == 1: + return return_val[0] + return return_val + + if torch.compiler.is_dynamo_compiling() or not self._run_with_interpreter: + tree_out = type(self.graph_module).forward(self, *flat_args) # type: ignore[union-attr] + else: + tree_out = torch.fx.Interpreter(self, graph=self.graph).run( + *flat_args, enable_io_processing=False + ) + return pytree.tree_unflatten(tree_out, signature.out_spec) + + def finalize(self): + self.__dict__["graph_module"] = torch.fx.GraphModule(self, self.graph) + self.graph.lint() + + def _dispatch_modules(self, redirected_call_indices, consts_targets): + """For a module whose call signatures are preserved, replace + multiple modules corresponding to multiple calls to that module + with a single dispatcher module that tracks which module to call. + """ + + # for each fqn whose module call signature is preserved, + # map that fqn to a list of called modules + called_modules = defaultdict(list) + for entry in self.module_call_graph: + if entry.fqn and entry.signature: + # some modules were removed and their fqns redirected to other + # fqns during deduplication + fqn = entry.fqn + mod = _get_attr(self, redirected_call_indices.get(fqn, fqn)) + base, idx = fqn.split("@") if "@" in fqn else [fqn, "0"] + called_modules[base].append((int(idx), mod)) + + attrs_map = defaultdict(set) + for target in consts_targets: + if "." in target: + orig_fqn, name = target.rsplit(".", 1) + attrs_map[orig_fqn].add(name) + else: + attrs_map[""].add(target) + + # replace multiple call modules with a single dispatcher module + for orig_fqn, indexed_call_modules in called_modules.items(): + call_modules = [mod for _, mod in sorted(indexed_call_modules)] + if len(call_modules) > 1: + for i in range(len(call_modules)): + fqn = _call_name(orig_fqn, i + 1) + if fqn not in redirected_call_indices: + *prefix, name = fqn.split(".") + _get_attr_via_attr_list(self, prefix)._modules.pop(name) + self.set_submodule( + orig_fqn, + InterpreterModuleDispatcher(attrs_map[orig_fqn], call_modules), + ) + + # elide call indices in call modules because they are + # tracked automatically inside the dispatcher module + def elide_call_indices(prefix, graph): + for node in graph.nodes: + if node.op == "call_module": + fqn = node.target.split("@")[0] + path = f"{prefix}.{fqn}" if prefix else fqn + if path in called_modules: + node.target = fqn + + for fqn, mod in self.named_modules(remove_duplicate=False): + if hasattr(mod, "graph"): + elide_call_indices(fqn, mod.graph) + elif hasattr(mod, "_call_modules"): + for mod_ in mod._call_modules: + assert hasattr(mod_, "graph") + elide_call_indices(fqn, mod_.graph) + + def print_readable( + self, + print_output=True, + include_stride=False, + include_device=False, + colored=False, + ): + return _print_readable( + self, + "UnflattenedModule", + print_output, + include_stride, + include_device, + colored, + ) + + +def unflatten( + module: ExportedProgram, flat_args_adapter: FlatArgsAdapter | None = None +) -> UnflattenedModule: + """Unflatten an ExportedProgram, producing a module with the same module + hierarchy as the original eager module. This can be useful if you are trying + to use :mod:`torch.export` with another system that expects a module + hierarchy instead of the flat graph that :mod:`torch.export` usually produces. + + .. note:: The args/kwargs of unflattened modules will not necessarily match + the eager module, so doing a module swap (e.g. :code:`self.submod = + new_mod`) will not necessarily work. If you need to swap a module out, you + need to set the :code:`preserve_module_call_signature` parameter of + :func:`torch.export.export`. + + Args: + module (ExportedProgram): The ExportedProgram to unflatten. + flat_args_adapter (Optional[FlatArgsAdapter]): Adapt flat args if input TreeSpec does not match with exported module's. + + Returns: + An instance of :class:`UnflattenedModule`, which has the same module + hierarchy as the original eager module pre-export. + """ + module = _remove_effect_tokens(module) + m = UnflattenedModule(module, flat_args_adapter) + + # Disable process_forward_inputs as the adapter has many + # non-dynamo-traceable behavior. + m.process_forward_inputs = torch._dynamo.disable( # type: ignore[method-assign] + m.process_forward_inputs, + reason="do not trace into preprocessing the inputs", + recursive=True, + ) + + return m + + +def _inplace_buffer_and_input_mutations( + graph: torch.fx.Graph, + graph_signature: ExportGraphSignature, +) -> None: + """Transform buffer and input mutations from their functionalized form + into copy_ nodes in the graph. + + Functionalization represents a buffer mutation by passing the buffer as + an input and output. For example, consider the eager code: + def forward(self, x): + self.buffer += x + return x * x + + This corresponds to a graph that looks like: + def forward(self, buffer, x): + mutated_buffer = aten.add(buffer, x) + mul = aten.mul(x, x) + return (mutated_buffer, mul) + + We want to inplace this into something that looks like the original + eager code: + def forward(self, buffer, x): + mutated_buffer = aten.add(buffer, x) + buffer.copy_(mutated_buffer) + mul = aten.mul(x, x) + return (mul,) + + Input mutations are handled similarly. + """ + output_node = next(iter(reversed(graph.nodes))) + assert output_node.op == "output" and len(output_node.args) == 1 + return_args = output_node.args[0] + + input_name_to_node = { + node.name: node for node in graph.nodes if node.op == "placeholder" + } + mutation_name_to_input_name = {} + + # Collect mutated buffers. + buffer_fqn_to_input_name = { + buffer_fqn: k for k, buffer_fqn in graph_signature.inputs_to_buffers.items() + } + mutation_name_to_input_name = { + k: buffer_fqn_to_input_name[buffer_fqn] + for k, buffer_fqn in graph_signature.buffers_to_mutate.items() + } + # Collect mutated user inputs. + mutation_name_to_input_name.update(graph_signature.user_inputs_to_mutate) + + num_mutations = len(mutation_name_to_input_name) + + for mutation in return_args[:num_mutations]: + input_name = mutation_name_to_input_name[mutation.name] + input_node = input_name_to_node[input_name] + + with graph.inserting_after(mutation): + # Create a copy_ node that inplaces the mutation. + new_node = graph.create_node( + "call_function", torch.ops.aten.copy_.default, (input_node, mutation) + ) + for k, v in mutation.meta.items(): + new_node.meta[k] = v + # Replace all uses of the previously functional mutation with + # our copy_ node. + mutation.replace_all_uses_with(new_node, lambda x: x is not new_node) + + # Remove the mutated buffer / input from the graph outputs, since we don't + # need to thread it through anymore. + user_outputs = tuple(return_args[num_mutations:]) + output_node.args = ((user_outputs),) + + +def _root_module_type(graph: torch.fx.Graph) -> str | None: + for node in graph.nodes: + if "nn_module_stack" not in node.meta: + continue + + for path, ty in node.meta["nn_module_stack"].values(): + if not path: + return ty + return None + + +def _fix_nn_module_stacks(graph): + # For each nn module stack in the graph, check if the fqns in it represent a stack: + # 1. Each fqn must be a prefix of the next fqn. + # 2. If not, remove the entries starting from the next fqn, emitting a warning. + for node in graph.nodes: + if "nn_module_stack" not in node.meta: + continue + + nn_module_stack = node.meta["nn_module_stack"] + fqns = [ + fqn.split("@")[0] if "@" in fqn else fqn + for fqn, _t in nn_module_stack.values() + ] + + # Check if each FQN is a prefix of the next one + prev_fqn, *next_fqns = fqns + num_valid_indices = 1 # root FQN + for curr_fqn in next_fqns: + # Check if the previous FQN is a prefix of the current one + if _is_prefix(prev_fqn, curr_fqn): + num_valid_indices += 1 + prev_fqn = curr_fqn + else: + # Found a non-prefix FQN, stop here + break + + # If we need to remove entries, create a new stack with only valid entries + if num_valid_indices < len(nn_module_stack): + log.warning( + "nn_module_stack fqns %s at node %s do not form a stack! dropping last %d entries", + fqns, + node, + len(nn_module_stack) - num_valid_indices, + ) + node.meta["nn_module_stack"] = dict( + list(nn_module_stack.items())[:num_valid_indices] + ) + + +def _is_prefix(candidate, target): + """Check whether `candidate` is a prefix of `target`.""" + return len(candidate) < len(target) and target[: len(candidate)] == candidate + + +def _compute_accessor(parent_fqn: str, child_fqn: str) -> str: + if parent_fqn == "": + # Handle the root module correctly. + return child_fqn + + parent_split = parent_fqn.split(".") + child_split = child_fqn.split(".") + + # TODO: support skip connection by inlining the child module. + if child_split[: len(parent_split)] != parent_split: + raise RuntimeError( + f"Child module '{child_fqn}' is not a descendant of parent module '{parent_fqn}'." + "This is currently unsupported." + "Please try to make child module attach to parent module directly." + ) + return ".".join(child_split[len(parent_split) :]) + + +def _check_graph_equivalence(x: torch.nn.Module, y: torch.nn.Module): + def graph_dump(graph: torch.fx.Graph) -> str: + ret = [] + nodes_idx: dict[int, int] = {} + + def arg_dump(arg) -> str: + if isinstance(arg, torch.fx.Node): + return "%" + str(nodes_idx[id(arg)]) + return str(arg) + + for i, node in enumerate(graph.nodes): + args_dump = [str(arg) for arg in pytree.tree_map(arg_dump, node.args)] + args_dump += [ + f"{key}={value}" + for key, value in pytree.tree_map(arg_dump, node.kwargs).items() + ] + target = node.target if node.op in ("call_function", "get_attr") else "" + # pyrefly: ignore [bad-argument-type] + ret.append(f"{i}: {node.op}[{target}]({', '.join(args_dump)})") + nodes_idx[id(node)] = i + return "\n".join(ret) + + assert isinstance(x.graph, torch.fx.Graph) + assert isinstance(y.graph, torch.fx.Graph) + return graph_dump(x.graph) == graph_dump(y.graph) + + +def _add_spec(gm: torch.nn.Module, spec) -> str: + i = 0 + while hasattr(gm, f"_spec_{i}"): + i += 1 + name = f"_spec_{i}" + setattr(gm, name, spec) + return name + + +def _generate_flatten(gm: torch.fx.GraphModule, node) -> torch.fx.Node: + flatten = gm.graph.call_function(pytree.tree_flatten, (node,)) + getitem_0 = gm.graph.call_function(operator.getitem, (flatten, 0)) + return getitem_0 + + +def _generate_flatten_spec( + gm: torch.fx.GraphModule | InterpreterModule | UnflattenedModule, node, spec +) -> torch.fx.Node: + name = _add_spec(gm, spec) + spec_node = gm.graph.get_attr(name) + return gm.graph.call_function(fx_pytree.tree_flatten_spec, (node, spec_node)) + + +def _generate_unflatten( + gm: torch.fx.GraphModule | InterpreterModule | UnflattenedModule, nodes, spec +) -> torch.fx.Node: + name = _add_spec(gm, spec) + spec_node = gm.graph.get_attr(name) + return gm.graph.call_function(pytree.tree_unflatten, (nodes, spec_node)) + + +def _get_submodule(mod: torch.nn.Module, target: str): + *prefix, field = target.split(".") + + for item in prefix: + submod = getattr(mod, item, None) + + if submod is None: + return None + + if not isinstance(submod, torch.nn.Module): + return None + + mod = submod + + return getattr(mod, field, None) + + +def _add_submodule( + mod: torch.nn.Module, + target: str, + module_to_add: torch.nn.Module, + create_module: Callable[[str], torch.nn.Module] | None = None, +): + *prefix, field = target.split(".") + + for i, item in enumerate(prefix): + submod = getattr(mod, item, None) + + if submod is None: + if create_module is not None: + submod = create_module(".".join(prefix[: i + 1])) + else: + submod = torch.nn.Module() + setattr(mod, item, submod) + + if not isinstance(submod, torch.nn.Module): + return False + + mod = submod + + mod.add_module(field, module_to_add) + + +def _call_name(base: str, n: int) -> str: + # Given n >= 0, generate call names to a submodule `base` of the form + # `base`, `base@1`, `base@2`, etc. + return base if n == 1 else f"{base}@{n - 1}" + + +def _is_call_name(call_name: str, base: str) -> bool: + # Recognize when call_name = _call_name(base, n) for some n >= 0. + return re.match(re.escape(base) + r"(@\d+)?$", call_name) is not None + + +class _ModuleFrame: + def __init__( + self, + flat_graph: torch.fx.Graph, + nodes: tuple[torch.fx.Node, ...], + seen_nodes, + seen_modules, + seen_attrs, + created_modules, + parent, + module_stack: list[tuple[str, str | None, int]], + module_id, + module_call_graph: dict[str, ModuleCallSignature], + module: torch.fx.GraphModule | UnflattenedModule | None = None, + ): + self.flat_graph = flat_graph + self.nodes = nodes + self.seen_nodes = seen_nodes + self.seen_modules = seen_modules + self.seen_attrs = seen_attrs + self.created_modules = created_modules + self.parent = parent + self.module_stack = module_stack + self.module_id = module_id + + self.module_call_graph = module_call_graph + self.verbose = False + + self.fqn, ty, num_calls = self.module_stack[-1] + # generate call name for self.fqn + self.child_fqn = _call_name(self.fqn, num_calls + 1) + + self.module: torch.fx.GraphModule | UnflattenedModule | InterpreterModule + if module is not None: + self.module = module + self.ivals = module.ivals if hasattr(module, "ivals") else {} # type: ignore[var-annotated] + else: + self.module = self.created_modules.get( + self.fqn, + InterpreterModule(torch.fx.Graph(), ty=ty), + ) + self.ivals = parent.ivals + + self.graph = self.module.graph + + # Mapping of nodes in the flat graph to nodes in this graph. + self.node_map: dict[torch.fx.Node, torch.fx.Node] = {} + self.node_to_placeholder = {} + + self.parent_call_module: torch.fx.Node | None = None + if parent is not None: + accessor = _compute_accessor(parent.fqn, self.child_fqn) + + def create_module(fqn): + path = f"{parent.fqn}.{fqn}" if parent.fqn else fqn + if path in self.created_modules: + return self.created_modules[path] + submod = InterpreterModule(torch.fx.Graph(), ty=ty) + self.created_modules[path] = submod + return submod + + _add_submodule(parent.module, accessor, self.module, create_module) + self.parent_call_module = parent.graph.call_module(accessor) + if self.seen_modules[self.module_id]: + base_module_frame = self.seen_modules[self.module_id][0] + self.module._modules = base_module_frame.module._modules + self.seen_modules[self.module_id].append( + _SubmoduleEntry( + parent_fqn=self.parent.fqn, + parent_module=self.parent.module, + parent_call_module=self.parent_call_module, + fqn=self.fqn, + call_idx=num_calls + 1, + module=self.module, + ) + ) + + signature = module_call_graph.get(self.child_fqn) + if signature is not None and self.parent is not None: + assert signature.in_spec.num_children == 2 + assert signature.in_spec.type is tuple + args_spec, kwargs_spec = signature.in_spec.children() + assert args_spec.type is tuple + assert kwargs_spec.type is dict + + with self.graph.inserting_after(None): + arg_nodes = [ + self.graph.placeholder(f"_positional_arg_{idx}") + for idx in range(args_spec.num_children) + ] + kwarg_nodes = {} + for name in kwargs_spec.context: + kwarg_nodes[name] = self.graph.placeholder(name) + flat_args = _generate_flatten_spec( + self.module, + (tuple(arg_nodes), kwarg_nodes), + signature.in_spec, + ) + for idx, arg in enumerate(signature.inputs): + flat_arg_node = self.graph.create_node( + op="call_function", + target=operator.getitem, + args=(flat_args, idx), + name=( + arg.name + if not isinstance(arg, ConstantArgument) + else f"_constant_{idx}" + ), + ) + if isinstance(arg, ConstantArgument): + continue + + if arg.name in self.seen_nodes: + flat_arg_node.meta = copy.copy(self.seen_nodes[arg.name].meta) + self.node_to_placeholder[self.seen_nodes[arg.name]] = ( + flat_arg_node + ) + + with self.parent.graph.inserting_before(self.parent_call_module): + input_nodes: list[torch.fx.Node | None] = [] + for input in signature.inputs: + if isinstance(input, ConstantArgument): + input_nodes.append(input.value) # type: ignore[arg-type] + elif input.name not in self.seen_nodes: + input_nodes.append(None) + else: + assert isinstance( + input, + ( + TensorArgument, + SymIntArgument, + SymBoolArgument, + SymFloatArgument, + ), + ) + input_nodes.append( + self.parent.remap_input(self.seen_nodes[input.name]) + ) + + inputs_node = _generate_unflatten( + self.parent.module, + input_nodes, + signature.in_spec, + ) + + args_node = self.parent.graph.call_function( + operator.getitem, (inputs_node, 0) + ) + kwargs_node = self.parent.graph.call_function( + operator.getitem, (inputs_node, 1) + ) + arg_nodes = [ + self.parent.graph.call_function(operator.getitem, (args_node, i)) + for i in range(args_spec.num_children) + ] + kwarg_nodes = { + k: self.parent.graph.call_function( + operator.getitem, (kwargs_node, k) + ) + for k in kwargs_spec.context + } + assert self.parent_call_module is not None + # pyrefly: ignore [bad-assignment] + self.parent_call_module.args = tuple(arg_nodes) + self.parent_call_module.kwargs = kwarg_nodes # type: ignore[assignment] + + def add_placeholder(self, x): + assert self.fqn != "", f"Cannot add placeholder {x} to root module" + assert x.graph is self.flat_graph + # x is not in subgraph, create a new placeholder for subgraph + with self.graph.inserting_before(None): + placeholder_node = self.graph.placeholder(x.name, type_expr=x.type) + # copy all meta fields, even if some fields might be irrelevant for + # the placeholder node + placeholder_node.meta = copy.copy(x.meta) + self.node_to_placeholder[x] = placeholder_node + + def copy_sym_call_function(self, x): + # This only exists because we deduplicate sym_size nodes in the flat export graph, + # and if preserve_module_call_signature is set, we may not be able to pass sym_size + # nodes, or their downstream users, as inputs to submodule calls. + # To avoid this we copy these call_function nodes with sym_type results. + # This should however only be done for sym_type nodes - call_function nodes on tensors + # should not be deduplicated in the first place. + args = pytree.tree_map_only(torch.fx.Node, self.remap_input, x.args) + kwargs = pytree.tree_map_only(torch.fx.Node, self.remap_input, x.kwargs) + node = self.graph.call_function(x.target, args, kwargs) + node.meta = copy.copy(x.meta) + self.node_map[x] = node + return node + + def remap_input(self, x): + assert x.graph is self.flat_graph + if x in self.node_map: + return self.node_map[x] + self.print(f"remap_input({x})") + if x in self.node_to_placeholder: + return self.node_to_placeholder[x] + elif ( + x.op == "placeholder" or self.module_call_graph.get(self.fqn) is None + # allow placeholder creation if we are not preserving module call signature + ): + self.add_placeholder(x) + if self.parent_call_module is not None: + # Important to *prepend* the output to match how we are + # inserting placeholder nodes. + with self.parent.graph.inserting_before(self.parent_call_module): + self.parent_call_module.insert_arg(0, self.parent.remap_input(x)) + return self.node_to_placeholder[x] + elif x.op == "call_function" and ( + x.target + in ( + torch.ops.aten.sym_size.int, + torch.ops.aten.item.default, + torch.ops.aten.unbind.int, + torch.ops.aten.sum.dim_IntList, + torch.ops.aten.view.default, + torch.ops.aten.diff.default, + ) + or (hasattr(x.target, "__module__") and x.target.__module__ == "_operator") + ): + # export deduplicates sym_size nodes, and may need to re-copy them + # if module call signature needs to be preserved + self.copy_sym_call_function(x) + return self.node_map[x] + elif self.module_call_graph.get(self.fqn) is not None: + # x is reading the intermediate value of a mutation, so record it; + # later we will find where it was created and perform the update + return self.ivals.read(self, x) # type: ignore[operator, union-attr] + else: + raise RuntimeError( + f"Could not run remap_input() on op type: {x.op} for node {x}" + ) + + def uplift_common_custom_metadata(self) -> None: + # Copy custom metadata if all nodes have same custom metadata + custom_meta = None + for node in self.node_map.values(): + curr_meta = node.meta.get("custom", {}) + if custom_meta is None: + # first node + custom_meta = curr_meta + continue + + if curr_meta != custom_meta: + custom_meta = {} + break + + if custom_meta: + # Lift common custom metadata to parent node and clear children node's custom metadata + assert self.parent_call_module is not None + self.parent_call_module.meta["custom"] = custom_meta + for node in self.node_map.values(): + del node.meta["custom"] + + def finalize_outputs(self): + self.created_modules.pop(self.fqn, None) + + orig_outputs = [] + + signature = self.module_call_graph.get(self.child_fqn) + if signature is not None and self.parent is not None: + for output in signature.outputs: + if isinstance( + output, + ( + TensorArgument, + SymIntArgument, + SymBoolArgument, + SymFloatArgument, + ConstantArgument, + ), + ): + if output.name in self.seen_nodes: + orig_outputs.append(self.seen_nodes[output.name]) + else: + orig_outputs.append(None) + else: + raise RuntimeError( + f"Unsupported data type for output node: {output}" + ) + + def get_actual_output_node(output): + if output is None: + return None + + seen_node = self.seen_nodes[output.name] + if seen_node in self.node_map: + return self.node_map[seen_node] + elif seen_node in self.node_to_placeholder: + return self.node_to_placeholder[seen_node] + else: + raise RuntimeError( + f"Could not find output node {output}. Graph: {self.graph}" + ) + + tree_out_node = _generate_unflatten( + self.module, + tuple(get_actual_output_node(output) for output in orig_outputs), + signature.out_spec, + ) + parent_out: torch.fx.Node | None = _generate_flatten_spec( + self.parent.module, self.parent_call_module, signature.out_spec + ) + graph_outputs: torch.fx.Node | list[torch.fx.Node] = tree_out_node + else: + graph_outputs = [] + # Iterate through nodes we have copied into self.graph. + for orig_node in self.node_map: + for user_node in orig_node.users: + if user_node.name not in self.seen_nodes: + # external user node, need to expose as an output + orig_outputs.append(orig_node) + graph_outputs.append(self.node_map[orig_node]) + break + + parent_out = self.parent_call_module + if len(graph_outputs) == 1: + graph_outputs = graph_outputs[0] + + assert isinstance(graph_outputs, (list, torch.fx.Node)) + + self.graph.output(graph_outputs) + + # Rewrite outputs in parent module + if parent_out is None: + return + + parent_out.meta["val"] = ( + graph_outputs.meta.get("val") + if isinstance(graph_outputs, torch.fx.Node) + else [o.meta.get("val") for o in graph_outputs] + ) + self.uplift_common_custom_metadata() + + if len(orig_outputs) == 1 and signature is None: + self.parent.node_map[orig_outputs[0]] = parent_out + else: + for i, orig_output in enumerate(orig_outputs): + if orig_output is None: + continue + # Use Proxy to record getitem access. + proxy_out = torch.fx.Proxy(parent_out)[i].node # type: ignore[index] + proxy_out.meta["val"] = orig_output.meta.get("val") + self.parent.node_map[orig_output] = proxy_out + + def copy_node(self, node): + self.print("copying", node.format_node()) + self.node_map[node] = self.graph.node_copy(node, self.remap_input) + self.seen_nodes[node.name] = node + + def run_outer(self): + for i, node in enumerate(self.flat_graph.nodes): + self.print(i, node.meta.get("nn_module_stack"), node.format_node()) + + # Copy all graph inputs + node_idx: int = 0 + node = self.nodes[node_idx] + while node.op == "placeholder": + self.copy_node(node) + node_idx += 1 + node = self.nodes[node_idx] + + self.run_from(node_idx) + + # Copy graph outputs + for node in self.flat_graph.nodes: + if node.op == "output": + self.copy_node(node) + + def print(self, *args, **kwargs): + if self.verbose: + # pyrefly: ignore [not-iterable] + print(*args, **kwargs) + + def run_from(self, node_idx): + module_idx = 0 + # Walk through the graph, building up a new graph with the right submodules + while node_idx < len(self.nodes): + node = self.nodes[node_idx] + assert node.op != "placeholder" + + self.print() + self.print("STEP", node_idx, node.format_node()) + self.print(self.module_stack) + depth = len(self.module_stack) + if node.op == "output": + if depth == 1: + # We want the output node of the original graph to be handled + # specially by the outermost stack frame (in run_outer). So + # skip finalization here. + return node_idx + + # We've reached the end of the graph. Wrap up all the existing stack frames. + self.finalize_outputs() + return node_idx + + if len(node.meta.get("nn_module_stack", {})) == 0: + raise RuntimeError(f"Unable to find nn_module_stack for node {node}") + + nn_module_stack = node.meta["nn_module_stack"] + from torch._export.passes._node_metadata_hook import ( + _EMPTY_NN_MODULE_STACK_KEY, + ) + + if ( + len(nn_module_stack) == 1 + and _EMPTY_NN_MODULE_STACK_KEY in nn_module_stack + ): + # Empty case from the node_metadata_hook + node_module_stack = self.module_stack + else: + node_module_stack = [ + ( + path, + ty if path else None, + int(k.split("@")[-1]) if "@" in k else 0, + ) + for k, (path, ty) in node.meta["nn_module_stack"].items() + ] + + if node_module_stack[:depth] != self.module_stack: + # This means that the current module is done executing and the + # current node is the beginning of a new module. + # + # In this case, we should finalize this module and return without + # incrementing the node counter. + self.finalize_outputs() + self.print("outlining", self.fqn) + self.print(self.graph) + return node_idx + + assert node_module_stack is not None + + if _is_prefix(self.module_stack, node_module_stack): + # This means that the current node represents the execution of a new + # module. + next_module = node_module_stack[depth] + self.print("Creating new stack frame for", next_module) + # Run a nested version of module outliner from the current node + # counter. Once it is complete, continue from that point. + next_module_key = list(node.meta["nn_module_stack"].keys())[depth] + node_idx = _ModuleFrame( + self.flat_graph, + self.nodes, + self.seen_nodes, + self.seen_modules, + self.seen_attrs, + self.created_modules, + self, + self.module_stack + [next_module], + next_module_key.split("@")[0], + self.module_call_graph, + ).run_from(node_idx) + module_idx += 1 + continue + + # The only remaining possibility is that we are in the right stack + # frame. Copy the node into this frame's graph and increment the node counter. + assert node_module_stack == self.module_stack + + if node.op == "get_attr": + # this must be a graph argument for a HOP + self.seen_attrs[self.child_fqn].add(node.target) + + self.copy_node(node) + # pyrefly: ignore [unsupported-operation] + node_idx += 1 + + +@dataclass +class _SubmoduleEntry: + parent_fqn: str + parent_module: torch.nn.Module + parent_call_module: torch.fx.Node + fqn: str + call_idx: int + module: torch.nn.Module + + +def _outline_submodules(orig_graph: torch.fx.Graph, root_module: UnflattenedModule): + seen_nodes: dict[str, torch.fx.Node] = {} + seen_modules: dict[int, list[_SubmoduleEntry]] = defaultdict(list) + seen_attrs: dict[str, set[str]] = defaultdict(set) + created_modules: dict[str, torch.nn.Module] = {} + _ModuleFrame( + orig_graph, + tuple(orig_graph.nodes), + seen_nodes, + seen_modules, + seen_attrs, + created_modules, + None, + [("", None, 0)], + "", + { + entry.fqn: entry.signature + for entry in root_module.module_call_graph + if entry.signature + }, + module=root_module, + ).run_outer() + return seen_modules, seen_attrs + + +def _reorder_submodules( + parent: torch.nn.Module, fqn_order: dict[str, int], prefix: str = "" +): + # TODO Can be optimized by adding submodules ahead of time. + if prefix == "": + for fqn in list(fqn_order.keys())[1:]: + if _get_submodule(parent, fqn) is None: + _add_submodule(parent, fqn, torch.nn.Module()) + + children = [] + for name, child in list(parent._modules.items()): + if child is None: + continue + fqn = prefix + name + _reorder_submodules(child, fqn_order, prefix=fqn.split("@")[0] + ".") + delattr(parent, name) + children.append((fqn_order[fqn], name, child)) + children.sort(key=operator.itemgetter(0)) + for _, name, child in children: + parent.register_module(name, child) + + +class _IVals: + """ + Collect the intermediate values of mutations in a graph. + + Example: in the following graph, suppose that buf_in and buf_out + are the input and output values of a buffer. + + buf_in = placeholder() + ... + ival1 = f0(buf_in, ...) # inside self.n0(...) + ... + ival2 = f1(ival1, ...) # inside self.n1(...) + ... + buf_out = f2(ival2, ...) # inside self.n2(...) + return buf_out, ... + + Here ival1 and ival2 are intermediate values created inside + calls to n0 and n1 respectively, and used inside calls to + n1 and n2 respectively. + """ + + def __init__(self): + # for each fqn, set of node names corresponding to intermediate values + self.node_names_by_fqn = defaultdict(set) + + def _is_mutable(self, target): + if isinstance(target, torch._ops.OpOverload): + return target._schema.is_mutable + return False + + def read(self, mf, node): + """ + Read state corresponding to a given intermediate value. + """ + # we can assume that the node must be from a mutation + assert node.op == "call_function" + b = self._is_mutable(node.target) + print("Checking mutability", node.target, b) + if not b: + # so the mutation was functionalized; + # we will apply the original mutation later (see below) + fqn, _ = next(reversed(node.meta["nn_module_stack"].values())) + self.node_names_by_fqn[fqn].add(node.name) + return mf.remap_input(node.args[0]) + + def update(self, partitions): + """ + Update states corresponding to intermediate values that were read. + """ + for shared_submodules in partitions: + for entry in shared_submodules: + graph = entry.module.graph + node_names = self.node_names_by_fqn[entry.fqn] + nodes = [n for n in graph.nodes if n.name in node_names] + for node in nodes: + # so node must be from a functionalized mutation; + # we perform the original mutation now + with graph.inserting_after(node): + new_node = graph.create_node( + "call_function", + torch.ops.aten.copy_.default, + (node.args[0], node), + ) + new_node.meta = copy.copy(node.meta) + + +def _copy_graph_attrs( + gm: torch.fx.GraphModule, + root_module: UnflattenedModule, + seen_attrs: dict[str, set[str]], +): + for child_fqn, names in seen_attrs.items(): + module = _get_attr(root_module, child_fqn) if child_fqn else root_module + for name in names: + val = getattr(gm, name) + setattr(module, name, val) + + +def _deduplicate_modules(partitions): + redirected_call_indices = {} + for shared_submodules in partitions: + for i, entry in enumerate(shared_submodules): + child_fqn = _call_name(entry.fqn, entry.call_idx) + target = _compute_accessor(entry.parent_fqn, child_fqn) + deduplicated = False + # Iterate over all previously seen modules, and deduplicate if possible + for seen in shared_submodules[:i]: + if _check_graph_equivalence(seen.module, entry.module): + parent = entry.parent_module + # Since graphs are equivalent, we can deduplicate. + # There are two cases. + if seen.fqn == entry.fqn: + # Case 1: The current module has the same fqn as the seen module. + # In this case we have generated a call name that can be optimized away. + # So we remove the current module from the hierarchy and replace + # the current call name with the seen call name in the parent graph. + *prefix, name = target.split(".") + _get_attr_via_attr_list(parent, prefix)._modules.pop(name) + seen_child_fqn = _call_name(seen.fqn, seen.call_idx) + seen_target = _compute_accessor( + entry.parent_fqn, seen_child_fqn + ) + entry.parent_call_module.target = seen_target + redirected_call_indices[child_fqn] = seen_child_fqn + break + elif not deduplicated: + # Case 2: The current module has a different fqn than the seen module. + # In this case we replace the current module with the seen module. + # There should be nothing pointing to the current module any more, + # so it can be garbage collected. + # NOTE: We *do not* replace the current call name with the seen call name + # in the parent graph, because this will lose information on which fqn + # was actually called. However, it is possible that the current call name + # will be optimized away when we find another seen module with the same fqn, + # so we do not break out of the loop yet. + parent.set_submodule(target, seen.module) + deduplicated = True + + return redirected_call_indices + + +def _sink_params( + module: torch.nn.Module, + inputs_to_state: dict[str, list[str]], + scope: list[str], + module_id_to_inputs_removed: dict[int, set[str]] | None = None, +): + """Sink params, buffers, and constants from graph inputs into get_attr nodes. + + Exported modules are purely functional, so they pass their parameters and + buffers in as inputs to the graph. + + To replicate eager's semantics, we need to get them from the module state + via get_attr instead. + + module: GraphModule, potentially containing nested submodules. + inputs_to_state: mapping graph input names to the corresponding key in the state_dict. + scope: tracks where we are in the module hierarchy, so that we can emit the + right `getattr(self, "foo.bar")` calls, etc. + module_id_to_inputs_removed: records inputs removed by child modules, mapping + the module object id to the list of placeholder node names in the child module + that were removed. + """ + if module_id_to_inputs_removed is None: + module_id_to_inputs_removed = defaultdict(set) + + if id(module) in module_id_to_inputs_removed: + return {id(module): module_id_to_inputs_removed[id(module)]} + + # We need to use _modules here instead of named_children(), because we + # explicitly want duplicate modules to show up in the traversal. + for name, submodule in module._modules.items(): + submod_id_to_inputs_removed = _sink_params( + cast("torch.nn.Module", submodule), + inputs_to_state, + scope + [name], + module_id_to_inputs_removed, + ) + for k, v in submod_id_to_inputs_removed.items(): + module_id_to_inputs_removed[k].update(v) + + graph = getattr(module, "graph", None) + if graph is None or len(graph.nodes) == 0: + # Not all modules have graphs defined, if they are empty modules with no operations (like ParameterList) + return module_id_to_inputs_removed + + assert isinstance(graph, torch.fx.Graph) + + inputs = list(filter(lambda n: n.op == "placeholder", graph.nodes)) + the_last_input = None if len(inputs) == 0 else inputs[-1] + + # Also remove from call_module nodes + call_module_nodes = filter(lambda n: n.op == "call_module", graph.nodes) + for node in call_module_nodes: + submodule = _get_attr(module, node.target) + # remove placeholder from call_module node arguments, only if we've + # erased the placeholder node in the corresponding _sink_params() call + if submodule is not None and id(submodule) in module_id_to_inputs_removed: + node.args = tuple( + filter( + lambda n: n.name not in module_id_to_inputs_removed[id(submodule)], + node.args, + ) + ) + + # Filter out inputs_to_state corresponding to current scope. + inputs_to_state_of_scope: dict[torch.fx.Node, list[str]] = {} + for node in inputs: + if node.name not in inputs_to_state: + continue + + state_name = None + for sn in inputs_to_state[node.name]: + sn_split = sn.split(".") + if sn_split[: len(scope)] == [x.split("@")[0] for x in scope]: + state_name = sn_split + break + + # If there's a mismatch between scope name and state name, then + # there must be multiple scopes pointing to the same state name, + # meaning some modules are shared. In such case, we can simply skip + # updating the current node because another later iteration will + # take care of this input node when the unique match between scope + # and state name occurs. To make sure this always happen, we should + # enforce the invariant that no placeholder node in the unflattened + # graph appears in inputs_to_state dict, which means all the extra + # input nodes have been handled. + if state_name is None: + continue + + inputs_to_state_of_scope[node] = state_name + + # Record name of remove inputs for return purpose. + inputs_removed: set[str] = set() + + for node, state_name in inputs_to_state_of_scope.items(): + if len(node.users) > 0: + attr_path = state_name[len(scope) :] + state_attr = _get_attr_via_attr_list(module, attr_path) + assert isinstance(state_attr, (torch.Tensor, torch.ScriptObject)) + + # Make sure the newly created get_attr node is placed after the last placeholder node + with graph.inserting_after(the_last_input): + new_node = graph.create_node("get_attr", ".".join(attr_path)) + + node.replace_all_uses_with(new_node, propagate_meta=True) + + graph.erase_node(node) + inputs_removed.add(node.name) + + if isinstance(module, InterpreterModule): + module.finalize() + + return {id(module): inputs_removed} diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fft/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fft/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b48cd28bb17df5194e27af9b8c53c0d53a856e03 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fft/__init__.py @@ -0,0 +1,1442 @@ +import torch +from torch._C import _add_docstr, _fft # type: ignore[attr-defined] +from torch._torch_docs import common_args, factory_common_args + + +__all__ = [ + "fft", + "ifft", + "fft2", + "ifft2", + "fftn", + "ifftn", + "rfft", + "irfft", + "rfft2", + "irfft2", + "rfftn", + "irfftn", + "hfft", + "ihfft", + "fftfreq", + "rfftfreq", + "fftshift", + "ifftshift", + "Tensor", +] + +Tensor = torch.Tensor + +# Note: This not only adds the doc strings for the spectral ops, but +# connects the torch.fft Python namespace to the torch._C._fft builtins. + +fft = _add_docstr( + _fft.fft_fft, + r""" +fft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor + +Computes the one dimensional discrete Fourier transform of :attr:`input`. + +Note: + The Fourier domain representation of any real signal satisfies the + Hermitian property: `X[i] = conj(X[-i])`. This function always returns both + the positive and negative frequency terms even though, for real inputs, the + negative frequencies are redundant. :func:`~torch.fft.rfft` returns the + more compact one-sided representation where only the positive frequencies + are returned. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimension. + +Args: + input (Tensor): the input tensor + n (int, optional): Signal length. If given, the input will either be zero-padded + or trimmed to this length before computing the FFT. + dim (int, optional): The dimension along which to take the one dimensional FFT. + norm (str, optional): Normalization mode. For the forward transform + (:func:`~torch.fft.fft`), these correspond to: + + * ``"forward"`` - normalize by ``1/n`` + * ``"backward"`` - no normalization + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal) + + Calling the backward transform (:func:`~torch.fft.ifft`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.ifft` + the exact inverse. + + Default is ``"backward"`` (no normalization). + +Keyword args: + {out} + +Example: + + >>> t = torch.arange(4) + >>> t + tensor([0, 1, 2, 3]) + >>> torch.fft.fft(t) + tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j]) + + >>> t = torch.tensor([0.+1.j, 2.+3.j, 4.+5.j, 6.+7.j]) + >>> torch.fft.fft(t) + tensor([12.+16.j, -8.+0.j, -4.-4.j, 0.-8.j]) +""".format(**common_args), +) + +ifft = _add_docstr( + _fft.fft_ifft, + r""" +ifft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor + +Computes the one dimensional inverse discrete Fourier transform of :attr:`input`. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimension. + +Args: + input (Tensor): the input tensor + n (int, optional): Signal length. If given, the input will either be zero-padded + or trimmed to this length before computing the IFFT. + dim (int, optional): The dimension along which to take the one dimensional IFFT. + norm (str, optional): Normalization mode. For the backward transform + (:func:`~torch.fft.ifft`), these correspond to: + + * ``"forward"`` - no normalization + * ``"backward"`` - normalize by ``1/n`` + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal) + + Calling the forward transform (:func:`~torch.fft.fft`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.ifft` + the exact inverse. + + Default is ``"backward"`` (normalize by ``1/n``). + +Keyword args: + {out} + +Example: + + >>> t = torch.tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j]) + >>> torch.fft.ifft(t) + tensor([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]) +""".format(**common_args), +) + +fft2 = _add_docstr( + _fft.fft_fft2, + r""" +fft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor + +Computes the 2 dimensional discrete Fourier transform of :attr:`input`. +Equivalent to :func:`~torch.fft.fftn` but FFTs only the last two dimensions by default. + +Note: + The Fourier domain representation of any real signal satisfies the + Hermitian property: ``X[i, j] = conj(X[-i, -j])``. This + function always returns all positive and negative frequency terms even + though, for real inputs, half of these values are redundant. + :func:`~torch.fft.rfft2` returns the more compact one-sided representation + where only the positive frequencies of the last dimension are returned. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the FFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Default: ``s = [input.size(d) for d in dim]`` + dim (Tuple[int], optional): Dimensions to be transformed. + Default: last two dimensions. + norm (str, optional): Normalization mode. For the forward transform + (:func:`~torch.fft.fft2`), these correspond to: + + * ``"forward"`` - normalize by ``1/n`` + * ``"backward"`` - no normalization + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal) + + Where ``n = prod(s)`` is the logical FFT size. + Calling the backward transform (:func:`~torch.fft.ifft2`) with the same + normalization mode will apply an overall normalization of ``1/n`` + between the two transforms. This is required to make + :func:`~torch.fft.ifft2` the exact inverse. + + Default is ``"backward"`` (no normalization). + +Keyword args: + {out} + +Example: + + >>> x = torch.rand(10, 10, dtype=torch.complex64) + >>> fft2 = torch.fft.fft2(x) + + The discrete Fourier transform is separable, so :func:`~torch.fft.fft2` + here is equivalent to two one-dimensional :func:`~torch.fft.fft` calls: + + >>> two_ffts = torch.fft.fft(torch.fft.fft(x, dim=0), dim=1) + >>> torch.testing.assert_close(fft2, two_ffts, check_stride=False) + +""".format(**common_args), +) + +ifft2 = _add_docstr( + _fft.fft_ifft2, + r""" +ifft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor + +Computes the 2 dimensional inverse discrete Fourier transform of :attr:`input`. +Equivalent to :func:`~torch.fft.ifftn` but IFFTs only the last two dimensions by default. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the IFFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Default: ``s = [input.size(d) for d in dim]`` + dim (Tuple[int], optional): Dimensions to be transformed. + Default: last two dimensions. + norm (str, optional): Normalization mode. For the backward transform + (:func:`~torch.fft.ifft2`), these correspond to: + + * ``"forward"`` - no normalization + * ``"backward"`` - normalize by ``1/n`` + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal) + + Where ``n = prod(s)`` is the logical IFFT size. + Calling the forward transform (:func:`~torch.fft.fft2`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.ifft2` + the exact inverse. + + Default is ``"backward"`` (normalize by ``1/n``). + +Keyword args: + {out} + +Example: + + >>> x = torch.rand(10, 10, dtype=torch.complex64) + >>> ifft2 = torch.fft.ifft2(x) + + The discrete Fourier transform is separable, so :func:`~torch.fft.ifft2` + here is equivalent to two one-dimensional :func:`~torch.fft.ifft` calls: + + >>> two_iffts = torch.fft.ifft(torch.fft.ifft(x, dim=0), dim=1) + >>> torch.testing.assert_close(ifft2, two_iffts, check_stride=False) + +""".format(**common_args), +) + +fftn = _add_docstr( + _fft.fft_fftn, + r""" +fftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor + +Computes the N dimensional discrete Fourier transform of :attr:`input`. + +Note: + The Fourier domain representation of any real signal satisfies the + Hermitian property: ``X[i_1, ..., i_n] = conj(X[-i_1, ..., -i_n])``. This + function always returns all positive and negative frequency terms even + though, for real inputs, half of these values are redundant. + :func:`~torch.fft.rfftn` returns the more compact one-sided representation + where only the positive frequencies of the last dimension are returned. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the FFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Default: ``s = [input.size(d) for d in dim]`` + dim (Tuple[int], optional): Dimensions to be transformed. + Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given. + norm (str, optional): Normalization mode. For the forward transform + (:func:`~torch.fft.fftn`), these correspond to: + + * ``"forward"`` - normalize by ``1/n`` + * ``"backward"`` - no normalization + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal) + + Where ``n = prod(s)`` is the logical FFT size. + Calling the backward transform (:func:`~torch.fft.ifftn`) with the same + normalization mode will apply an overall normalization of ``1/n`` + between the two transforms. This is required to make + :func:`~torch.fft.ifftn` the exact inverse. + + Default is ``"backward"`` (no normalization). + +Keyword args: + {out} + +Example: + + >>> x = torch.rand(10, 10, dtype=torch.complex64) + >>> fftn = torch.fft.fftn(x) + + The discrete Fourier transform is separable, so :func:`~torch.fft.fftn` + here is equivalent to two one-dimensional :func:`~torch.fft.fft` calls: + + >>> two_ffts = torch.fft.fft(torch.fft.fft(x, dim=0), dim=1) + >>> torch.testing.assert_close(fftn, two_ffts, check_stride=False) + +""".format(**common_args), +) + +ifftn = _add_docstr( + _fft.fft_ifftn, + r""" +ifftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor + +Computes the N dimensional inverse discrete Fourier transform of :attr:`input`. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the IFFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Default: ``s = [input.size(d) for d in dim]`` + dim (Tuple[int], optional): Dimensions to be transformed. + Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given. + norm (str, optional): Normalization mode. For the backward transform + (:func:`~torch.fft.ifftn`), these correspond to: + + * ``"forward"`` - no normalization + * ``"backward"`` - normalize by ``1/n`` + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal) + + Where ``n = prod(s)`` is the logical IFFT size. + Calling the forward transform (:func:`~torch.fft.fftn`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.ifftn` + the exact inverse. + + Default is ``"backward"`` (normalize by ``1/n``). + +Keyword args: + {out} + +Example: + + >>> x = torch.rand(10, 10, dtype=torch.complex64) + >>> ifftn = torch.fft.ifftn(x) + + The discrete Fourier transform is separable, so :func:`~torch.fft.ifftn` + here is equivalent to two one-dimensional :func:`~torch.fft.ifft` calls: + + >>> two_iffts = torch.fft.ifft(torch.fft.ifft(x, dim=0), dim=1) + >>> torch.testing.assert_close(ifftn, two_iffts, check_stride=False) + +""".format(**common_args), +) + +rfft = _add_docstr( + _fft.fft_rfft, + r""" +rfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor + +Computes the one dimensional Fourier transform of real-valued :attr:`input`. + +The FFT of a real signal is Hermitian-symmetric, ``X[i] = conj(X[-i])`` so +the output contains only the positive frequencies below the Nyquist frequency. +To compute the full output, use :func:`~torch.fft.fft` + +Note: + Supports torch.half on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimension. + +Args: + input (Tensor): the real input tensor + n (int, optional): Signal length. If given, the input will either be zero-padded + or trimmed to this length before computing the real FFT. + dim (int, optional): The dimension along which to take the one dimensional real FFT. + norm (str, optional): Normalization mode. For the forward transform + (:func:`~torch.fft.rfft`), these correspond to: + + * ``"forward"`` - normalize by ``1/n`` + * ``"backward"`` - no normalization + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the FFT orthonormal) + + Calling the backward transform (:func:`~torch.fft.irfft`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.irfft` + the exact inverse. + + Default is ``"backward"`` (no normalization). + +Keyword args: + {out} + +Example: + + >>> t = torch.arange(4) + >>> t + tensor([0, 1, 2, 3]) + >>> torch.fft.rfft(t) + tensor([ 6.+0.j, -2.+2.j, -2.+0.j]) + + Compare against the full output from :func:`~torch.fft.fft`: + + >>> torch.fft.fft(t) + tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j]) + + Notice that the symmetric element ``T[-1] == T[1].conj()`` is omitted. + At the Nyquist frequency ``T[-2] == T[2]`` is it's own symmetric pair, + and therefore must always be real-valued. +""".format(**common_args), +) + +irfft = _add_docstr( + _fft.fft_irfft, + r""" +irfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor + +Computes the inverse of :func:`~torch.fft.rfft`. + +:attr:`input` is interpreted as a one-sided Hermitian signal in the Fourier +domain, as produced by :func:`~torch.fft.rfft`. By the Hermitian property, the +output will be real-valued. + +Note: + Some input frequencies must be real-valued to satisfy the Hermitian + property. In these cases the imaginary component will be ignored. + For example, any imaginary component in the zero-frequency term cannot + be represented in a real output and so will always be ignored. + +Note: + The correct interpretation of the Hermitian input depends on the length of + the original data, as given by :attr:`n`. This is because each input shape + could correspond to either an odd or even length signal. By default, the + signal is assumed to be even length and odd signals will not round-trip + properly. So, it is recommended to always pass the signal length :attr:`n`. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimension. + With default arguments, size of the transformed dimension should be (2^n + 1) as argument + `n` defaults to even output size = 2 * (transformed_dim_size - 1) + +Args: + input (Tensor): the input tensor representing a half-Hermitian signal + n (int, optional): Output signal length. This determines the length of the + output signal. If given, the input will either be zero-padded or trimmed to this + length before computing the real IFFT. + Defaults to even output: ``n=2*(input.size(dim) - 1)``. + dim (int, optional): The dimension along which to take the one dimensional real IFFT. + norm (str, optional): Normalization mode. For the backward transform + (:func:`~torch.fft.irfft`), these correspond to: + + * ``"forward"`` - no normalization + * ``"backward"`` - normalize by ``1/n`` + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real IFFT orthonormal) + + Calling the forward transform (:func:`~torch.fft.rfft`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.irfft` + the exact inverse. + + Default is ``"backward"`` (normalize by ``1/n``). + +Keyword args: + {out} + +Example: + + >>> t = torch.linspace(0, 1, 5) + >>> t + tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]) + >>> T = torch.fft.rfft(t) + >>> T + tensor([ 2.5000+0.0000j, -0.6250+0.8602j, -0.6250+0.2031j]) + + Without specifying the output length to :func:`~torch.fft.irfft`, the output + will not round-trip properly because the input is odd-length: + + >>> torch.fft.irfft(T) + tensor([0.1562, 0.3511, 0.7812, 1.2114]) + + So, it is recommended to always pass the signal length :attr:`n`: + + >>> roundtrip = torch.fft.irfft(T, t.numel()) + >>> torch.testing.assert_close(roundtrip, t, check_stride=False) + +""".format(**common_args), +) + +rfft2 = _add_docstr( + _fft.fft_rfft2, + r""" +rfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor + +Computes the 2-dimensional discrete Fourier transform of real :attr:`input`. +Equivalent to :func:`~torch.fft.rfftn` but FFTs only the last two dimensions by default. + +The FFT of a real signal is Hermitian-symmetric, ``X[i, j] = conj(X[-i, -j])``, +so the full :func:`~torch.fft.fft2` output contains redundant information. +:func:`~torch.fft.rfft2` instead omits the negative frequencies in the last +dimension. + +Note: + Supports torch.half on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the real FFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Default: ``s = [input.size(d) for d in dim]`` + dim (Tuple[int], optional): Dimensions to be transformed. + Default: last two dimensions. + norm (str, optional): Normalization mode. For the forward transform + (:func:`~torch.fft.rfft2`), these correspond to: + + * ``"forward"`` - normalize by ``1/n`` + * ``"backward"`` - no normalization + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real FFT orthonormal) + + Where ``n = prod(s)`` is the logical FFT size. + Calling the backward transform (:func:`~torch.fft.irfft2`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.irfft2` + the exact inverse. + + Default is ``"backward"`` (no normalization). + +Keyword args: + {out} + +Example: + + >>> t = torch.rand(10, 10) + >>> rfft2 = torch.fft.rfft2(t) + >>> rfft2.size() + torch.Size([10, 6]) + + Compared against the full output from :func:`~torch.fft.fft2`, we have all + elements up to the Nyquist frequency. + + >>> fft2 = torch.fft.fft2(t) + >>> torch.testing.assert_close(fft2[..., :6], rfft2, check_stride=False) + + The discrete Fourier transform is separable, so :func:`~torch.fft.rfft2` + here is equivalent to a combination of :func:`~torch.fft.fft` and + :func:`~torch.fft.rfft`: + + >>> two_ffts = torch.fft.fft(torch.fft.rfft(t, dim=1), dim=0) + >>> torch.testing.assert_close(rfft2, two_ffts, check_stride=False) + +""".format(**common_args), +) + +irfft2 = _add_docstr( + _fft.fft_irfft2, + r""" +irfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor + +Computes the inverse of :func:`~torch.fft.rfft2`. +Equivalent to :func:`~torch.fft.irfftn` but IFFTs only the last two dimensions by default. + +:attr:`input` is interpreted as a one-sided Hermitian signal in the Fourier +domain, as produced by :func:`~torch.fft.rfft2`. By the Hermitian property, the +output will be real-valued. + +Note: + Some input frequencies must be real-valued to satisfy the Hermitian + property. In these cases the imaginary component will be ignored. + For example, any imaginary component in the zero-frequency term cannot + be represented in a real output and so will always be ignored. + +Note: + The correct interpretation of the Hermitian input depends on the length of + the original data, as given by :attr:`s`. This is because each input shape + could correspond to either an odd or even length signal. By default, the + signal is assumed to be even length and odd signals will not round-trip + properly. So, it is recommended to always pass the signal shape :attr:`s`. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + With default arguments, the size of last dimension should be (2^n + 1) as argument + `s` defaults to even output size = 2 * (last_dim_size - 1) + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the real FFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Defaults to even output in the last dimension: + ``s[-1] = 2*(input.size(dim[-1]) - 1)``. + dim (Tuple[int], optional): Dimensions to be transformed. + The last dimension must be the half-Hermitian compressed dimension. + Default: last two dimensions. + norm (str, optional): Normalization mode. For the backward transform + (:func:`~torch.fft.irfft2`), these correspond to: + + * ``"forward"`` - no normalization + * ``"backward"`` - normalize by ``1/n`` + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real IFFT orthonormal) + + Where ``n = prod(s)`` is the logical IFFT size. + Calling the forward transform (:func:`~torch.fft.rfft2`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.irfft2` + the exact inverse. + + Default is ``"backward"`` (normalize by ``1/n``). + +Keyword args: + {out} + +Example: + + >>> t = torch.rand(10, 9) + >>> T = torch.fft.rfft2(t) + + Without specifying the output length to :func:`~torch.fft.irfft2`, the output + will not round-trip properly because the input is odd-length in the last + dimension: + + >>> torch.fft.irfft2(T).size() + torch.Size([10, 8]) + + So, it is recommended to always pass the signal shape :attr:`s`. + + >>> roundtrip = torch.fft.irfft2(T, t.size()) + >>> roundtrip.size() + torch.Size([10, 9]) + >>> torch.testing.assert_close(roundtrip, t, check_stride=False) + +""".format(**common_args), +) + +rfftn = _add_docstr( + _fft.fft_rfftn, + r""" +rfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor + +Computes the N-dimensional discrete Fourier transform of real :attr:`input`. + +The FFT of a real signal is Hermitian-symmetric, +``X[i_1, ..., i_n] = conj(X[-i_1, ..., -i_n])`` so the full +:func:`~torch.fft.fftn` output contains redundant information. +:func:`~torch.fft.rfftn` instead omits the negative frequencies in the +last dimension. + +Note: + Supports torch.half on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the real FFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Default: ``s = [input.size(d) for d in dim]`` + dim (Tuple[int], optional): Dimensions to be transformed. + Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given. + norm (str, optional): Normalization mode. For the forward transform + (:func:`~torch.fft.rfftn`), these correspond to: + + * ``"forward"`` - normalize by ``1/n`` + * ``"backward"`` - no normalization + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real FFT orthonormal) + + Where ``n = prod(s)`` is the logical FFT size. + Calling the backward transform (:func:`~torch.fft.irfftn`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.irfftn` + the exact inverse. + + Default is ``"backward"`` (no normalization). + +Keyword args: + {out} + +Example: + + >>> t = torch.rand(10, 10) + >>> rfftn = torch.fft.rfftn(t) + >>> rfftn.size() + torch.Size([10, 6]) + + Compared against the full output from :func:`~torch.fft.fftn`, we have all + elements up to the Nyquist frequency. + + >>> fftn = torch.fft.fftn(t) + >>> torch.testing.assert_close(fftn[..., :6], rfftn, check_stride=False) + + The discrete Fourier transform is separable, so :func:`~torch.fft.rfftn` + here is equivalent to a combination of :func:`~torch.fft.fft` and + :func:`~torch.fft.rfft`: + + >>> two_ffts = torch.fft.fft(torch.fft.rfft(t, dim=1), dim=0) + >>> torch.testing.assert_close(rfftn, two_ffts, check_stride=False) + +""".format(**common_args), +) + +irfftn = _add_docstr( + _fft.fft_irfftn, + r""" +irfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor + +Computes the inverse of :func:`~torch.fft.rfftn`. + +:attr:`input` is interpreted as a one-sided Hermitian signal in the Fourier +domain, as produced by :func:`~torch.fft.rfftn`. By the Hermitian property, the +output will be real-valued. + +Note: + Some input frequencies must be real-valued to satisfy the Hermitian + property. In these cases the imaginary component will be ignored. + For example, any imaginary component in the zero-frequency term cannot + be represented in a real output and so will always be ignored. + +Note: + The correct interpretation of the Hermitian input depends on the length of + the original data, as given by :attr:`s`. This is because each input shape + could correspond to either an odd or even length signal. By default, the + signal is assumed to be even length and odd signals will not round-trip + properly. So, it is recommended to always pass the signal shape :attr:`s`. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + With default arguments, the size of last dimension should be (2^n + 1) as argument + `s` defaults to even output size = 2 * (last_dim_size - 1) + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the real FFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Defaults to even output in the last dimension: + ``s[-1] = 2*(input.size(dim[-1]) - 1)``. + dim (Tuple[int], optional): Dimensions to be transformed. + The last dimension must be the half-Hermitian compressed dimension. + Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given. + norm (str, optional): Normalization mode. For the backward transform + (:func:`~torch.fft.irfftn`), these correspond to: + + * ``"forward"`` - no normalization + * ``"backward"`` - normalize by ``1/n`` + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the real IFFT orthonormal) + + Where ``n = prod(s)`` is the logical IFFT size. + Calling the forward transform (:func:`~torch.fft.rfftn`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.irfftn` + the exact inverse. + + Default is ``"backward"`` (normalize by ``1/n``). + +Keyword args: + {out} + +Example: + + >>> t = torch.rand(10, 9) + >>> T = torch.fft.rfftn(t) + + Without specifying the output length to :func:`~torch.fft.irfft`, the output + will not round-trip properly because the input is odd-length in the last + dimension: + + >>> torch.fft.irfftn(T).size() + torch.Size([10, 8]) + + So, it is recommended to always pass the signal shape :attr:`s`. + + >>> roundtrip = torch.fft.irfftn(T, t.size()) + >>> roundtrip.size() + torch.Size([10, 9]) + >>> torch.testing.assert_close(roundtrip, t, check_stride=False) + +""".format(**common_args), +) + +hfft = _add_docstr( + _fft.fft_hfft, + r""" +hfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor + +Computes the one dimensional discrete Fourier transform of a Hermitian +symmetric :attr:`input` signal. + +Note: + + :func:`~torch.fft.hfft`/:func:`~torch.fft.ihfft` are analogous to + :func:`~torch.fft.rfft`/:func:`~torch.fft.irfft`. The real FFT expects + a real signal in the time-domain and gives a Hermitian symmetry in the + frequency-domain. The Hermitian FFT is the opposite; Hermitian symmetric in + the time-domain and real-valued in the frequency-domain. For this reason, + special care needs to be taken with the length argument :attr:`n`, in the + same way as with :func:`~torch.fft.irfft`. + +Note: + Because the signal is Hermitian in the time-domain, the result will be + real in the frequency domain. Note that some input frequencies must be + real-valued to satisfy the Hermitian property. In these cases the imaginary + component will be ignored. For example, any imaginary component in + ``input[0]`` would result in one or more complex frequency terms which + cannot be represented in a real output and so will always be ignored. + +Note: + The correct interpretation of the Hermitian input depends on the length of + the original data, as given by :attr:`n`. This is because each input shape + could correspond to either an odd or even length signal. By default, the + signal is assumed to be even length and odd signals will not round-trip + properly. So, it is recommended to always pass the signal length :attr:`n`. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimension. + With default arguments, size of the transformed dimension should be (2^n + 1) as argument + `n` defaults to even output size = 2 * (transformed_dim_size - 1) + +Args: + input (Tensor): the input tensor representing a half-Hermitian signal + n (int, optional): Output signal length. This determines the length of the + real output. If given, the input will either be zero-padded or trimmed to this + length before computing the Hermitian FFT. + Defaults to even output: ``n=2*(input.size(dim) - 1)``. + dim (int, optional): The dimension along which to take the one dimensional Hermitian FFT. + norm (str, optional): Normalization mode. For the forward transform + (:func:`~torch.fft.hfft`), these correspond to: + + * ``"forward"`` - normalize by ``1/n`` + * ``"backward"`` - no normalization + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian FFT orthonormal) + + Calling the backward transform (:func:`~torch.fft.ihfft`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.ihfft` + the exact inverse. + + Default is ``"backward"`` (no normalization). + +Keyword args: + {out} + +Example: + + Taking a real-valued frequency signal and bringing it into the time domain + gives Hermitian symmetric output: + + >>> t = torch.linspace(0, 1, 5) + >>> t + tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]) + >>> T = torch.fft.ifft(t) + >>> T + tensor([ 0.5000-0.0000j, -0.1250-0.1720j, -0.1250-0.0406j, -0.1250+0.0406j, + -0.1250+0.1720j]) + + Note that ``T[1] == T[-1].conj()`` and ``T[2] == T[-2].conj()`` is + redundant. We can thus compute the forward transform without considering + negative frequencies: + + >>> torch.fft.hfft(T[:3], n=5) + tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000]) + + Like with :func:`~torch.fft.irfft`, the output length must be given in order + to recover an even length output: + + >>> torch.fft.hfft(T[:3]) + tensor([0.1250, 0.2809, 0.6250, 0.9691]) +""".format(**common_args), +) + +ihfft = _add_docstr( + _fft.fft_ihfft, + r""" +ihfft(input, n=None, dim=-1, norm=None, *, out=None) -> Tensor + +Computes the inverse of :func:`~torch.fft.hfft`. + +:attr:`input` must be a real-valued signal, interpreted in the Fourier domain. +The IFFT of a real signal is Hermitian-symmetric, ``X[i] = conj(X[-i])``. +:func:`~torch.fft.ihfft` represents this in the one-sided form where only the +positive frequencies below the Nyquist frequency are included. To compute the +full output, use :func:`~torch.fft.ifft`. + +Note: + Supports torch.half on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimension. + +Args: + input (Tensor): the real input tensor + n (int, optional): Signal length. If given, the input will either be zero-padded + or trimmed to this length before computing the Hermitian IFFT. + dim (int, optional): The dimension along which to take the one dimensional Hermitian IFFT. + norm (str, optional): Normalization mode. For the backward transform + (:func:`~torch.fft.ihfft`), these correspond to: + + * ``"forward"`` - no normalization + * ``"backward"`` - normalize by ``1/n`` + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the IFFT orthonormal) + + Calling the forward transform (:func:`~torch.fft.hfft`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.ihfft` + the exact inverse. + + Default is ``"backward"`` (normalize by ``1/n``). + +Keyword args: + {out} + +Example: + + >>> t = torch.arange(5) + >>> t + tensor([0, 1, 2, 3, 4]) + >>> torch.fft.ihfft(t) + tensor([ 2.0000-0.0000j, -0.5000-0.6882j, -0.5000-0.1625j]) + + Compare against the full output from :func:`~torch.fft.ifft`: + + >>> torch.fft.ifft(t) + tensor([ 2.0000-0.0000j, -0.5000-0.6882j, -0.5000-0.1625j, -0.5000+0.1625j, + -0.5000+0.6882j]) +""".format(**common_args), +) + +hfft2 = _add_docstr( + _fft.fft_hfft2, + r""" +hfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor + +Computes the 2-dimensional discrete Fourier transform of a Hermitian symmetric +:attr:`input` signal. Equivalent to :func:`~torch.fft.hfftn` but only +transforms the last two dimensions by default. + +:attr:`input` is interpreted as a one-sided Hermitian signal in the time +domain. By the Hermitian property, the Fourier transform will be real-valued. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + With default arguments, the size of last dimension should be (2^n + 1) as argument + `s` defaults to even output size = 2 * (last_dim_size - 1) + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the Hermitian FFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Defaults to even output in the last dimension: + ``s[-1] = 2*(input.size(dim[-1]) - 1)``. + dim (Tuple[int], optional): Dimensions to be transformed. + The last dimension must be the half-Hermitian compressed dimension. + Default: last two dimensions. + norm (str, optional): Normalization mode. For the forward transform + (:func:`~torch.fft.hfft2`), these correspond to: + + * ``"forward"`` - normalize by ``1/n`` + * ``"backward"`` - no normalization + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian FFT orthonormal) + + Where ``n = prod(s)`` is the logical FFT size. + Calling the backward transform (:func:`~torch.fft.ihfft2`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.ihfft2` + the exact inverse. + + Default is ``"backward"`` (no normalization). + +Keyword args: + {out} + +Example: + + Starting from a real frequency-space signal, we can generate a + Hermitian-symmetric time-domain signal: + >>> T = torch.rand(10, 9) + >>> t = torch.fft.ihfft2(T) + + Without specifying the output length to :func:`~torch.fft.hfftn`, the + output will not round-trip properly because the input is odd-length in the + last dimension: + + >>> torch.fft.hfft2(t).size() + torch.Size([10, 10]) + + So, it is recommended to always pass the signal shape :attr:`s`. + + >>> roundtrip = torch.fft.hfft2(t, T.size()) + >>> roundtrip.size() + torch.Size([10, 9]) + >>> torch.allclose(roundtrip, T) + True + +""".format(**common_args), +) + +ihfft2 = _add_docstr( + _fft.fft_ihfft2, + r""" +ihfft2(input, s=None, dim=(-2, -1), norm=None, *, out=None) -> Tensor + +Computes the 2-dimensional inverse discrete Fourier transform of real +:attr:`input`. Equivalent to :func:`~torch.fft.ihfftn` but transforms only the +two last dimensions by default. + +Note: + Supports torch.half on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the Hermitian IFFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Default: ``s = [input.size(d) for d in dim]`` + dim (Tuple[int], optional): Dimensions to be transformed. + Default: last two dimensions. + norm (str, optional): Normalization mode. For the backward transform + (:func:`~torch.fft.ihfft2`), these correspond to: + + * ``"forward"`` - no normalization + * ``"backward"`` - normalize by ``1/n`` + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian IFFT orthonormal) + + Where ``n = prod(s)`` is the logical IFFT size. + Calling the forward transform (:func:`~torch.fft.hfft2`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.ihfft2` + the exact inverse. + + Default is ``"backward"`` (normalize by ``1/n``). + +Keyword args: + {out} + +Example: + + >>> T = torch.rand(10, 10) + >>> t = torch.fft.ihfft2(t) + >>> t.size() + torch.Size([10, 6]) + + Compared against the full output from :func:`~torch.fft.ifft2`, the + Hermitian time-space signal takes up only half the space. + + >>> fftn = torch.fft.ifft2(t) + >>> torch.allclose(fftn[..., :6], rfftn) + True + + The discrete Fourier transform is separable, so :func:`~torch.fft.ihfft2` + here is equivalent to a combination of :func:`~torch.fft.ifft` and + :func:`~torch.fft.ihfft`: + + >>> two_ffts = torch.fft.ifft(torch.fft.ihfft(t, dim=1), dim=0) + >>> torch.allclose(t, two_ffts) + True + +""".format(**common_args), +) + +hfftn = _add_docstr( + _fft.fft_hfftn, + r""" +hfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor + +Computes the n-dimensional discrete Fourier transform of a Hermitian symmetric +:attr:`input` signal. + +:attr:`input` is interpreted as a one-sided Hermitian signal in the time +domain. By the Hermitian property, the Fourier transform will be real-valued. + +Note: + :func:`~torch.fft.hfftn`/:func:`~torch.fft.ihfftn` are analogous to + :func:`~torch.fft.rfftn`/:func:`~torch.fft.irfftn`. The real FFT expects + a real signal in the time-domain and gives Hermitian symmetry in the + frequency-domain. The Hermitian FFT is the opposite; Hermitian symmetric in + the time-domain and real-valued in the frequency-domain. For this reason, + special care needs to be taken with the shape argument :attr:`s`, in the + same way as with :func:`~torch.fft.irfftn`. + +Note: + Some input frequencies must be real-valued to satisfy the Hermitian + property. In these cases the imaginary component will be ignored. + For example, any imaginary component in the zero-frequency term cannot + be represented in a real output and so will always be ignored. + +Note: + The correct interpretation of the Hermitian input depends on the length of + the original data, as given by :attr:`s`. This is because each input shape + could correspond to either an odd or even length signal. By default, the + signal is assumed to be even length and odd signals will not round-trip + properly. It is recommended to always pass the signal shape :attr:`s`. + +Note: + Supports torch.half and torch.chalf on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + With default arguments, the size of last dimension should be (2^n + 1) as argument + `s` defaults to even output size = 2 * (last_dim_size - 1) + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the real FFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Defaults to even output in the last dimension: + ``s[-1] = 2*(input.size(dim[-1]) - 1)``. + dim (Tuple[int], optional): Dimensions to be transformed. + The last dimension must be the half-Hermitian compressed dimension. + Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given. + norm (str, optional): Normalization mode. For the forward transform + (:func:`~torch.fft.hfftn`), these correspond to: + + * ``"forward"`` - normalize by ``1/n`` + * ``"backward"`` - no normalization + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian FFT orthonormal) + + Where ``n = prod(s)`` is the logical FFT size. + Calling the backward transform (:func:`~torch.fft.ihfftn`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.ihfftn` + the exact inverse. + + Default is ``"backward"`` (no normalization). + +Keyword args: + {out} + +Example: + + Starting from a real frequency-space signal, we can generate a + Hermitian-symmetric time-domain signal: + >>> T = torch.rand(10, 9) + >>> t = torch.fft.ihfftn(T) + + Without specifying the output length to :func:`~torch.fft.hfftn`, the + output will not round-trip properly because the input is odd-length in the + last dimension: + + >>> torch.fft.hfftn(t).size() + torch.Size([10, 10]) + + So, it is recommended to always pass the signal shape :attr:`s`. + + >>> roundtrip = torch.fft.hfftn(t, T.size()) + >>> roundtrip.size() + torch.Size([10, 9]) + >>> torch.allclose(roundtrip, T) + True + +""".format(**common_args), +) + +ihfftn = _add_docstr( + _fft.fft_ihfftn, + r""" +ihfftn(input, s=None, dim=None, norm=None, *, out=None) -> Tensor + +Computes the N-dimensional inverse discrete Fourier transform of real :attr:`input`. + +:attr:`input` must be a real-valued signal, interpreted in the Fourier domain. +The n-dimensional IFFT of a real signal is Hermitian-symmetric, +``X[i, j, ...] = conj(X[-i, -j, ...])``. :func:`~torch.fft.ihfftn` represents +this in the one-sided form where only the positive frequencies below the +Nyquist frequency are included in the last signal dimension. To compute the +full output, use :func:`~torch.fft.ifftn`. + +Note: + Supports torch.half on CUDA with GPU Architecture SM53 or greater. + However it only supports powers of 2 signal length in every transformed dimensions. + +Args: + input (Tensor): the input tensor + s (Tuple[int], optional): Signal size in the transformed dimensions. + If given, each dimension ``dim[i]`` will either be zero-padded or + trimmed to the length ``s[i]`` before computing the Hermitian IFFT. + If a length ``-1`` is specified, no padding is done in that dimension. + Default: ``s = [input.size(d) for d in dim]`` + dim (Tuple[int], optional): Dimensions to be transformed. + Default: all dimensions, or the last ``len(s)`` dimensions if :attr:`s` is given. + norm (str, optional): Normalization mode. For the backward transform + (:func:`~torch.fft.ihfftn`), these correspond to: + + * ``"forward"`` - no normalization + * ``"backward"`` - normalize by ``1/n`` + * ``"ortho"`` - normalize by ``1/sqrt(n)`` (making the Hermitian IFFT orthonormal) + + Where ``n = prod(s)`` is the logical IFFT size. + Calling the forward transform (:func:`~torch.fft.hfftn`) with the same + normalization mode will apply an overall normalization of ``1/n`` between + the two transforms. This is required to make :func:`~torch.fft.ihfftn` + the exact inverse. + + Default is ``"backward"`` (normalize by ``1/n``). + +Keyword args: + {out} + +Example: + + >>> T = torch.rand(10, 10) + >>> ihfftn = torch.fft.ihfftn(T) + >>> ihfftn.size() + torch.Size([10, 6]) + + Compared against the full output from :func:`~torch.fft.ifftn`, we have all + elements up to the Nyquist frequency. + + >>> ifftn = torch.fft.ifftn(t) + >>> torch.allclose(ifftn[..., :6], ihfftn) + True + + The discrete Fourier transform is separable, so :func:`~torch.fft.ihfftn` + here is equivalent to a combination of :func:`~torch.fft.ihfft` and + :func:`~torch.fft.ifft`: + + >>> two_iffts = torch.fft.ifft(torch.fft.ihfft(t, dim=1), dim=0) + >>> torch.allclose(ihfftn, two_iffts) + True + +""".format(**common_args), +) + +fftfreq = _add_docstr( + _fft.fft_fftfreq, + r""" +fftfreq(n, d=1.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + +Computes the discrete Fourier Transform sample frequencies for a signal of size :attr:`n`. + +Note: + By convention, :func:`~torch.fft.fft` returns positive frequency terms + first, followed by the negative frequencies in reverse order, so that + ``f[-i]`` for all :math:`0 < i \leq n/2`` in Python gives the negative + frequency terms. For an FFT of length :attr:`n` and with inputs spaced in + length unit :attr:`d`, the frequencies are:: + + f = [0, 1, ..., (n - 1) // 2, -(n // 2), ..., -1] / (d * n) + +Note: + For even lengths, the Nyquist frequency at ``f[n/2]`` can be thought of as + either negative or positive. :func:`~torch.fft.fftfreq` follows NumPy's + convention of taking it to be negative. + +Args: + n (int): the FFT length + d (float, optional): The sampling length scale. + The spacing between individual samples of the FFT input. + The default assumes unit spacing, dividing that result by the actual + spacing gives the result in physical frequency units. + +Keyword Args: + {out} + {dtype} + {layout} + {device} + {requires_grad} + +Example: + + >>> torch.fft.fftfreq(5) + tensor([ 0.0000, 0.2000, 0.4000, -0.4000, -0.2000]) + + For even input, we can see the Nyquist frequency at ``f[2]`` is given as + negative: + + >>> torch.fft.fftfreq(4) + tensor([ 0.0000, 0.2500, -0.5000, -0.2500]) + +""".format(**factory_common_args), +) + +rfftfreq = _add_docstr( + _fft.fft_rfftfreq, + r""" +rfftfreq(n, d=1.0, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor + +Computes the sample frequencies for :func:`~torch.fft.rfft` with a signal of size :attr:`n`. + +Note: + :func:`~torch.fft.rfft` returns Hermitian one-sided output, so only the + positive frequency terms are returned. For a real FFT of length :attr:`n` + and with inputs spaced in length unit :attr:`d`, the frequencies are:: + + f = torch.arange((n + 1) // 2) / (d * n) + +Note: + For even lengths, the Nyquist frequency at ``f[n/2]`` can be thought of as + either negative or positive. Unlike :func:`~torch.fft.fftfreq`, + :func:`~torch.fft.rfftfreq` always returns it as positive. + +Args: + n (int): the real FFT length + d (float, optional): The sampling length scale. + The spacing between individual samples of the FFT input. + The default assumes unit spacing, dividing that result by the actual + spacing gives the result in physical frequency units. + +Keyword Args: + {out} + {dtype} + {layout} + {device} + {requires_grad} + +Example: + + >>> torch.fft.rfftfreq(5) + tensor([0.0000, 0.2000, 0.4000]) + + >>> torch.fft.rfftfreq(4) + tensor([0.0000, 0.2500, 0.5000]) + + Compared to the output from :func:`~torch.fft.fftfreq`, we see that the + Nyquist frequency at ``f[2]`` has changed sign: + >>> torch.fft.fftfreq(4) + tensor([ 0.0000, 0.2500, -0.5000, -0.2500]) + +""".format(**factory_common_args), +) + +fftshift = _add_docstr( + _fft.fft_fftshift, + r""" +fftshift(input, dim=None) -> Tensor + +Reorders n-dimensional FFT data, as provided by :func:`~torch.fft.fftn`, to have +negative frequency terms first. + +This performs a periodic shift of n-dimensional data such that the origin +``(0, ..., 0)`` is moved to the center of the tensor. Specifically, to +``input.shape[dim] // 2`` in each selected dimension. + +Note: + By convention, the FFT returns positive frequency terms first, followed by + the negative frequencies in reverse order, so that ``f[-i]`` for all + :math:`0 < i \leq n/2` in Python gives the negative frequency terms. + :func:`~torch.fft.fftshift` rearranges all frequencies into ascending order + from negative to positive with the zero-frequency term in the center. + +Note: + For even lengths, the Nyquist frequency at ``f[n/2]`` can be thought of as + either negative or positive. :func:`~torch.fft.fftshift` always puts the + Nyquist term at the 0-index. This is the same convention used by + :func:`~torch.fft.fftfreq`. + +Args: + input (Tensor): the tensor in FFT order + dim (int, Tuple[int], optional): The dimensions to rearrange. + Only dimensions specified here will be rearranged, any other dimensions + will be left in their original order. + Default: All dimensions of :attr:`input`. + +Example: + + >>> f = torch.fft.fftfreq(4) + >>> f + tensor([ 0.0000, 0.2500, -0.5000, -0.2500]) + + >>> torch.fft.fftshift(f) + tensor([-0.5000, -0.2500, 0.0000, 0.2500]) + + Also notice that the Nyquist frequency term at ``f[2]`` was moved to the + beginning of the tensor. + + This also works for multi-dimensional transforms: + + >>> x = torch.fft.fftfreq(5, d=1/5) + 0.1 * torch.fft.fftfreq(5, d=1/5).unsqueeze(1) + >>> x + tensor([[ 0.0000, 1.0000, 2.0000, -2.0000, -1.0000], + [ 0.1000, 1.1000, 2.1000, -1.9000, -0.9000], + [ 0.2000, 1.2000, 2.2000, -1.8000, -0.8000], + [-0.2000, 0.8000, 1.8000, -2.2000, -1.2000], + [-0.1000, 0.9000, 1.9000, -2.1000, -1.1000]]) + + >>> torch.fft.fftshift(x) + tensor([[-2.2000, -1.2000, -0.2000, 0.8000, 1.8000], + [-2.1000, -1.1000, -0.1000, 0.9000, 1.9000], + [-2.0000, -1.0000, 0.0000, 1.0000, 2.0000], + [-1.9000, -0.9000, 0.1000, 1.1000, 2.1000], + [-1.8000, -0.8000, 0.2000, 1.2000, 2.2000]]) + + :func:`~torch.fft.fftshift` can also be useful for spatial data. If our + data is defined on a centered grid (``[-(N//2), (N-1)//2]``) then we can + use the standard FFT defined on an uncentered grid (``[0, N)``) by first + applying an :func:`~torch.fft.ifftshift`. + + >>> x_centered = torch.arange(-5, 5) + >>> x_uncentered = torch.fft.ifftshift(x_centered) + >>> fft_uncentered = torch.fft.fft(x_uncentered) + + Similarly, we can convert the frequency domain components to centered + convention by applying :func:`~torch.fft.fftshift`. + + >>> fft_centered = torch.fft.fftshift(fft_uncentered) + + The inverse transform, from centered Fourier space back to centered spatial + data, can be performed by applying the inverse shifts in reverse order: + + >>> x_centered_2 = torch.fft.fftshift(torch.fft.ifft(torch.fft.ifftshift(fft_centered))) + >>> torch.testing.assert_close(x_centered.to(torch.complex64), x_centered_2, check_stride=False) + + +""", +) + +ifftshift = _add_docstr( + _fft.fft_ifftshift, + r""" +ifftshift(input, dim=None) -> Tensor + +Inverse of :func:`~torch.fft.fftshift`. + +Args: + input (Tensor): the tensor in FFT order + dim (int, Tuple[int], optional): The dimensions to rearrange. + Only dimensions specified here will be rearranged, any other dimensions + will be left in their original order. + Default: All dimensions of :attr:`input`. + +Example: + + >>> f = torch.fft.fftfreq(5) + >>> f + tensor([ 0.0000, 0.2000, 0.4000, -0.4000, -0.2000]) + + A round-trip through :func:`~torch.fft.fftshift` and + :func:`~torch.fft.ifftshift` gives the same result: + + >>> shifted = torch.fft.fftshift(f) + >>> torch.fft.ifftshift(shifted) + tensor([ 0.0000, 0.2000, 0.4000, -0.4000, -0.2000]) + +""", +) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/func/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/func/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..35743fcf429ae779d5608314bcd89fc0af333d38 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/func/__init__.py @@ -0,0 +1,31 @@ +from torch._functorch.apis import grad, grad_and_value, vmap +from torch._functorch.batch_norm_replacement import replace_all_batch_norm_modules_ +from torch._functorch.eager_transforms import ( + debug_unwrap, + functionalize, + hessian, + jacfwd, + jacrev, + jvp, + linearize, + vjp, +) +from torch._functorch.functional_call import functional_call, stack_module_state + + +__all__ = [ + "grad", + "grad_and_value", + "vmap", + "replace_all_batch_norm_modules_", + "functionalize", + "hessian", + "jacfwd", + "jacrev", + "jvp", + "linearize", + "vjp", + "functional_call", + "stack_module_state", + "debug_unwrap", +] diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/futures/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/futures/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d265a56914290014f94f7c92e4e0edd4bd2323a9 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/futures/__init__.py @@ -0,0 +1,336 @@ +# mypy: allow-untyped-defs +# pylint: disable=useless-parent-delegation +from __future__ import annotations + +from typing import cast, Generic, Optional, TYPE_CHECKING, TypeVar, Union + +import torch + + +if TYPE_CHECKING: + from collections.abc import Callable + + +__all__ = ["Future", "collect_all", "wait_all"] + + +T = TypeVar("T") +S = TypeVar("S") + + +class Future(torch._C.Future, Generic[T]): + r""" + Wrapper around a ``torch._C.Future`` which encapsulates an asynchronous + execution of a callable, e.g. :meth:`~torch.distributed.rpc.rpc_async`. It + also exposes a set of APIs to add callback functions and set results. + + .. warning:: GPU support is a beta feature, subject to changes. + """ + + def __init__( + self, *, devices: Optional[list[Union[int, str, torch.device]]] = None + ): + r""" + Create an empty unset ``Future``. If the future is intended to hold + values containing CUDA tensors, (a superset of) their CUDA devices must + be specified at construction. (This is only supported if + ``torch.cuda.is_available()`` returns ``True``). This is needed to + ensure proper CUDA stream synchronization. The child futures, returned + by the ``then`` method, will inherit these devices. + + Args: + devices(``List[Union[int, str, torch.device]]``, optional): the set + of devices on which tensors contained in this future's value are + allowed to reside and on which callbacks are allowed to operate. + """ + if devices is None: + devices = [] + super().__init__([torch.device(d) for d in devices]) + + def done(self) -> bool: + r""" + Return ``True`` if this ``Future`` is done. A ``Future`` is done if it + has a result or an exception. + + If the value contains tensors that reside on GPUs, ``Future.done()`` + will return ``True`` even if the asynchronous kernels that are + populating those tensors haven't yet completed running on the device, + because at such stage the result is already usable, provided one + performs the appropriate synchronizations (see :meth:`wait`). + """ + return super().done() + + def wait(self) -> T: + r""" + Block until the value of this ``Future`` is ready. + + If the value contains tensors that reside on GPUs, then an additional + synchronization is performed with the kernels (executing on the device) + which may be asynchronously populating those tensors. Such sync is + non-blocking, which means that ``wait()`` will insert the necessary + instructions in the current streams to ensure that further operations + enqueued on those streams will be properly scheduled after the async + kernels but, once that is done, ``wait()`` will return, even if those + kernels are still running. No further synchronization is required when + accessing and using the values, as long as one doesn't change streams. + + Returns: + The value held by this ``Future``. If the function (callback or RPC) + creating the value has thrown an error, this ``wait`` method will + also throw an error. + """ + return super().wait() + + def value(self) -> T: + r""" + Obtain the value of an already-completed future. + + This method should only be called after a call to :meth:`wait` has + completed, or inside a callback function passed to :meth:`then`. In + other cases this ``Future`` may not yet hold a value and calling + ``value()`` could fail. + + If the value contains tensors that reside on GPUs, then this method will + *not* perform any additional synchronization. This should be done + beforehand, separately, through a call to :meth:`wait` (except within + callbacks, for which it's already being taken care of by :meth:`then`). + + Returns: + The value held by this ``Future``. If the function (callback or RPC) + creating the value has thrown an error, this ``value()`` method will + also throw an error. + """ + return super().value() + + def then(self, callback: Callable[[Future[T]], S]) -> Future[S]: + r""" + Append the given callback function to this ``Future``, which will be run + when the ``Future`` is completed. Multiple callbacks can be added to + the same ``Future``, but the order in which they will be executed cannot + be guaranteed (to enforce a certain order consider chaining: + ``fut.then(cb1).then(cb2)``). The callback must take one argument, which + is the reference to this ``Future``. The callback function can use the + :meth:`value` method to get the value. Note that if this ``Future`` is + already completed, the given callback will be run immediately inline. + + If the ``Future``'s value contains tensors that reside on GPUs, the + callback might be invoked while the async kernels that are populating + those tensors haven't yet finished executing on the device. However, the + callback will be invoked with some dedicated streams set as current + (fetched from a global pool) which will be synchronized with those + kernels. Hence any operation performed by the callback on these tensors + will be scheduled on the device after the kernels complete. In other + words, as long as the callback doesn't switch streams, it can safely + manipulate the result without any additional synchronization. This is + similar to the non-blocking behavior of :meth:`wait`. + + Similarly, if the callback returns a value that contains tensors that + reside on a GPU, it can do so even if the kernels that are producing + these tensors are still running on the device, as long as the callback + didn't change streams during its execution. If one wants to change + streams, one must be careful to re-synchronize them with the original + streams, that is, those that were current when the callback was invoked. + + Args: + callback(``Callable``): a ``Callable`` that takes this ``Future`` as + the only argument. + + Returns: + A new ``Future`` object that holds the return value of the + ``callback`` and will be marked as completed when the given + ``callback`` finishes. + + .. note:: Note that if the callback function throws, either + through the original future being completed with an exception and + calling ``fut.wait()``, or through other code in the callback, the + future returned by ``then`` will be marked appropriately with the + encountered error. However, if this callback later completes + additional futures, those futures are not marked as completed with + an error and the user is responsible for handling completion/waiting + on those futures independently. + + Example:: + + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) + >>> def callback(fut): + ... print(f"RPC return value is {fut.wait()}.") + >>> fut = torch.futures.Future() + >>> # The inserted callback will print the return value when + >>> # receiving the response from "worker1" + >>> cb_fut = fut.then(callback) + >>> chain_cb_fut = cb_fut.then( + ... lambda x : print(f"Chained cb done. {x.wait()}") + ... ) + >>> fut.set_result(5) + RPC return value is 5. + Chained cb done. None + """ + return cast(Future[S], super().then(callback)) + + def add_done_callback(self, callback: Callable[[Future[T]], None]) -> None: + r""" + Append the given callback function to this ``Future``, which will be run + when the ``Future`` is completed. Multiple callbacks can be added to + the same ``Future``, but the order in which they will be executed cannot + be guaranteed. The callback must take one argument, which is the + reference to this ``Future``. The callback function can use the + :meth:`value` method to get the value. Note that if this ``Future`` is + already completed, the given callback will be run inline. + + We recommend that you use the :meth:`then` method as it provides a way + to synchronize after your callback has completed. ``add_done_callback`` + can be cheaper if your callback does not return anything. But both + :meth:`then` and ``add_done_callback`` use the same callback + registration API under the hood. + + With respect to GPU tensors, this method behaves in the same way as + :meth:`then`. + + Args: + callback(``Future``): a ``Callable`` that takes in one argument, + which is the reference to this ``Future``. + + .. note:: Note that if the callback function throws, either + through the original future being completed with an exception and + calling ``fut.wait()``, or through other code in the callback, + error handling must be carefully taken care of. For example, if + this callback later completes additional futures, those futures are + not marked as completed with an error and the user is responsible + for handling completion/waiting on those futures independently. + + Example:: + + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) + >>> def callback(fut): + ... print("This will run after the future has finished.") + ... print(fut.wait()) + >>> fut = torch.futures.Future() + >>> fut.add_done_callback(callback) + >>> fut.set_result(5) + This will run after the future has finished. + 5 + """ + super().add_done_callback(callback) + + def set_result(self, result: T) -> None: + r""" + Set the result for this ``Future``, which will mark this ``Future`` as + completed and trigger all attached callbacks. Note that a ``Future`` + cannot be marked completed twice. + + If the result contains tensors that reside on GPUs, this method can be + called even if the asynchronous kernels that are populating those + tensors haven't yet completed running on the device, provided that the + streams on which those kernels were enqueued are set as the current ones + when this method is called. Put simply, it's safe to call this method + immediately after launching those kernels, without any additional + synchronization, as long as one doesn't change streams in between. This + method will record events on all the relevant current streams and will + use them to ensure proper scheduling for all the consumers of this + ``Future``. + + Args: + result (object): the result object of this ``Future``. + + Example:: + + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) + >>> import threading + >>> import time + >>> def slow_set_future(fut, value): + ... time.sleep(0.5) + ... fut.set_result(value) + >>> fut = torch.futures.Future() + >>> t = threading.Thread( + ... target=slow_set_future, + ... args=(fut, torch.ones(2) * 3) + ... ) + >>> t.start() + >>> print(fut.wait()) + tensor([3., 3.]) + >>> t.join() + """ + super().set_result(result) + + def set_exception(self, result: T) -> None: + r""" + Set an exception for this ``Future``, which will mark this ``Future`` as + completed with an error and trigger all attached callbacks. Note that + when calling wait()/value() on this ``Future``, the exception set here + will be raised inline. + + Args: + result (BaseException): the exception for this ``Future``. + + Example:: + + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) + >>> fut = torch.futures.Future() + >>> fut.set_exception(ValueError("foo")) + >>> fut.wait() + Traceback (most recent call last): + ... + ValueError: foo + """ + assert isinstance(result, Exception), ( + f"{result} is of type {type(result)}, not an Exception." + ) + + def raise_error(fut_result): + raise fut_result + + super()._set_unwrap_func(raise_error) + self.set_result(result) # type: ignore[arg-type] + + +def collect_all(futures: list[Future]) -> Future[list[Future]]: + r""" + Collects the provided :class:`~torch.futures.Future` objects into a single + combined :class:`~torch.futures.Future` that is completed when all of the + sub-futures are completed. + + Args: + futures (list): a list of :class:`~torch.futures.Future` objects. + + Returns: + Returns a :class:`~torch.futures.Future` object to a list of the passed + in Futures. + + Example:: + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) + >>> fut0 = torch.futures.Future() + >>> fut1 = torch.futures.Future() + >>> fut = torch.futures.collect_all([fut0, fut1]) + >>> fut0.set_result(0) + >>> fut1.set_result(1) + >>> fut_list = fut.wait() + >>> print(f"fut0 result = {fut_list[0].wait()}") + fut0 result = 0 + >>> print(f"fut1 result = {fut_list[1].wait()}") + fut1 result = 1 + """ + return cast( + Future[list[Future]], + torch._C._collect_all(cast(list[torch._C.Future], futures)), + ) + + +def wait_all(futures: list[Future]) -> list: + r""" + Waits for all provided futures to be complete, and returns + the list of completed values. If any of the futures encounters an error, + the method will exit early and report the error not waiting for other + futures to complete. + + Args: + futures (list): a list of :class:`~torch.futures.Future` object. + + Returns: + A list of the completed :class:`~torch.futures.Future` results. This + method will throw an error if ``wait`` on any + :class:`~torch.futures.Future` throws. + """ + return [ + fut.wait() + for fut in torch._C._collect_all(cast(list[torch._C.Future], futures)).wait() + ] diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c048b4fdd8f8940d46ff75a265af3bc7587255c7 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/__init__.py @@ -0,0 +1,116 @@ +r''' +FX is a toolkit for developers to use to transform ``nn.Module`` +instances. FX consists of three main components: a **symbolic tracer,** +an **intermediate representation**, and **Python code generation**. A +demonstration of these components in action: + +:: + + import torch + + + # Simple module for demonstration + class MyModule(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.param = torch.nn.Parameter(torch.rand(3, 4)) + self.linear = torch.nn.Linear(4, 5) + + def forward(self, x): + return self.linear(x + self.param).clamp(min=0.0, max=1.0) + + + module = MyModule() + + from torch.fx import symbolic_trace + + # Symbolic tracing frontend - captures the semantics of the module + symbolic_traced: torch.fx.GraphModule = symbolic_trace(module) + + # High-level intermediate representation (IR) - Graph representation + print(symbolic_traced.graph) + """ + graph(): + %x : [num_users=1] = placeholder[target=x] + %param : [num_users=1] = get_attr[target=param] + %add : [num_users=1] = call_function[target=operator.add](args = (%x, %param), kwargs = {}) + %linear : [num_users=1] = call_module[target=linear](args = (%add,), kwargs = {}) + %clamp : [num_users=1] = call_method[target=clamp](args = (%linear,), kwargs = {min: 0.0, max: 1.0}) + return clamp + """ + + # Code generation - valid Python code + print(symbolic_traced.code) + """ + def forward(self, x): + param = self.param + add = x + param; x = param = None + linear = self.linear(add); add = None + clamp = linear.clamp(min = 0.0, max = 1.0); linear = None + return clamp + """ + +The **symbolic tracer** performs "symbolic execution" of the Python +code. It feeds fake values, called Proxies, through the code. Operations +on these Proxies are recorded. More information about symbolic tracing +can be found in the :func:`symbolic_trace` and :class:`Tracer` +documentation. + +The **intermediate representation** is the container for the operations +that were recorded during symbolic tracing. It consists of a list of +Nodes that represent function inputs, callsites (to functions, methods, +or :class:`torch.nn.Module` instances), and return values. More information +about the IR can be found in the documentation for :class:`Graph`. The +IR is the format on which transformations are applied. + +**Python code generation** is what makes FX a Python-to-Python (or +Module-to-Module) transformation toolkit. For each Graph IR, we can +create valid Python code matching the Graph's semantics. This +functionality is wrapped up in :class:`GraphModule`, which is a +:class:`torch.nn.Module` instance that holds a :class:`Graph` as well as a +``forward`` method generated from the Graph. + +Taken together, this pipeline of components (symbolic tracing -> +intermediate representation -> transforms -> Python code generation) +constitutes the Python-to-Python transformation pipeline of FX. In +addition, these components can be used separately. For example, +symbolic tracing can be used in isolation to capture a form of +the code for analysis (and not transformation) purposes. Code +generation can be used for programmatically generating models, for +example from a config file. There are many uses for FX! + +Several example transformations can be found at the +`examples `__ +repository. +''' + +from torch.fx import immutable_collections +from torch.fx._symbolic_trace import ( # noqa: F401 + PH, + ProxyableClassMeta, + symbolic_trace, + Tracer, + wrap, +) +from torch.fx.graph import CodeGen, Graph # noqa: F401 +from torch.fx.graph_module import GraphModule +from torch.fx.interpreter import Interpreter, Transformer +from torch.fx.node import has_side_effect, map_arg, Node +from torch.fx.proxy import Proxy +from torch.fx.subgraph_rewriter import replace_pattern + + +__all__ = [ + "symbolic_trace", + "Tracer", + "wrap", + "Graph", + "GraphModule", + "Interpreter", + "Transformer", + "Node", + "Proxy", + "replace_pattern", + "has_side_effect", + "map_arg", +] diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_compatibility.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_compatibility.py new file mode 100644 index 0000000000000000000000000000000000000000..c07dd1b51bc05a7b1288efdd331a9bec4926845f --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_compatibility.py @@ -0,0 +1,40 @@ +import textwrap +from collections.abc import Callable +from typing import Any, TypeVar + + +_BACK_COMPAT_OBJECTS: dict[Any, None] = {} +_MARKED_WITH_COMPATIBILITY: dict[Any, None] = {} + + +_T = TypeVar("_T") + + +def compatibility(is_backward_compatible: bool) -> Callable[[_T], _T]: + if is_backward_compatible: + + def mark_back_compat(fn: _T) -> _T: + docstring = textwrap.dedent(getattr(fn, "__doc__", None) or "") + docstring += """ +.. note:: + Backwards-compatibility for this API is guaranteed. +""" + fn.__doc__ = docstring + _BACK_COMPAT_OBJECTS.setdefault(fn) + _MARKED_WITH_COMPATIBILITY.setdefault(fn) + return fn + + return mark_back_compat + else: + + def mark_not_back_compat(fn: _T) -> _T: + docstring = textwrap.dedent(getattr(fn, "__doc__", None) or "") + docstring += """ +.. warning:: + This API is experimental and is *NOT* backward-compatible. +""" + fn.__doc__ = docstring + _MARKED_WITH_COMPATIBILITY.setdefault(fn) + return fn + + return mark_not_back_compat diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_graph_pickler.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_graph_pickler.py new file mode 100644 index 0000000000000000000000000000000000000000..eb6465680570b3300407cb5297f3c0cba6420276 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_graph_pickler.py @@ -0,0 +1,647 @@ +import dataclasses +import importlib +import io +import pickle +from abc import abstractmethod +from collections.abc import Callable +from typing import Any, NewType, Optional, TypeVar, Union +from typing_extensions import override, Self + +import torch +import torch.utils._pytree as pytree +from torch._guards import TracingContext +from torch._inductor.standalone_compile import AOTCompiledArtifact +from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode, Tensor +from torch._subclasses.meta_utils import ( + MetaConverter, + MetaTensorDesc, + MetaTensorDescriber, +) +from torch.fx.experimental.sym_node import SymNode +from torch.fx.experimental.symbolic_shapes import ShapeEnv +from torch.utils._mode_utils import no_dispatch + + +_SymNodeT = TypeVar("_SymNodeT", torch.SymInt, torch.SymFloat) + + +def _ops_filter_safe(name: str) -> bool: + """ + An ops filter which allows pickle-safe ops. Pickle-safe ops are built-in + ones where it will be possible to unpickle on any machine which has PyTorch. + """ + # TODO: This list is pretty pessimistic right now. What's the full list? + return name.startswith( + ( + "torch.ops.aten", + "torch.ops.fbgemm", + ) + ) + + +@dataclasses.dataclass +class Options: + # A filter for which ops will cause the pickler to raise a + # BypassFxGraphCache exception. If None then all ops are allowed. + ops_filter: Optional[Callable[[str], bool]] = _ops_filter_safe + + +class GraphPickler(pickle.Pickler): + """ + GraphPickler is a Pickler which helps pickling fx graph - in particular + GraphModule. + """ + + def __init__(self, file: io.BytesIO, options: Optional[Options] = None) -> None: + super().__init__(file) + self.options = options or Options() + + # This abomination is so we can pass external decoding state to the + # unpickler functions. We serialize _unpickle_state as a persistent + # external item and when we deserialize it we return the common state + # object. + self._unpickle_state = _UnpickleStateToken(object()) + + # This is used to describe tensors. It needs to be common across the + # pickle so that duplicates and views are properly handled. + self._meta_tensor_describer = MetaTensorDescriber(copy_data=False) + + @override + # pyrefly: ignore [bad-override] + def reducer_override( + self, obj: object + ) -> tuple[Callable[..., Any], tuple[Any, ...]]: + # This function is supposed to return either NotImplemented (meaning to + # do the default pickle behavior) or a pair of (unpickle callable, data + # to pass to unpickle). + + # We could instead teach individual classes how to pickle themselves but + # that has a few problems: + # + # 1. If we have some special needs (maybe for this use-case we don't + # want to fully serialize every field) then we're adding private + # details to a public interface. + # + # 2. If we need to have some common shared data (such as a + # FakeTensorMode) which is passed to each value it's harder to + # support. + + # These are the types that need special handling. See the individual + # *PickleData classes for details on pickling that particular type. + if isinstance(obj, FakeTensor): + return _TensorPickleData.reduce_helper(self, obj) + elif isinstance(obj, torch.fx.GraphModule): + return _GraphModulePickleData.reduce_helper(self, obj) + elif isinstance(obj, (torch._ops.OperatorBase, torch._ops.OpOverloadPacket)): + return _OpPickleData.reduce_helper(self, obj) + elif isinstance(obj, ShapeEnv): + return _ShapeEnvPickleData.reduce_helper(self, obj) + elif isinstance(obj, torch.SymInt): + return _SymNodePickleData.reduce_helper(self, obj) + elif isinstance(obj, torch._guards.TracingContext): + return _TracingContextPickleData.reduce_helper(self, obj) + else: + # We should never get a raw Node! + assert not isinstance(obj, torch.fx.Node) + if reduce := _TorchNumpyPickleData.reduce_helper(self, obj): + return reduce + + # returning `NotImplemented` causes pickle to revert to the default + # behavior for this object. + return NotImplemented + + @override + def persistent_id(self, obj: object) -> Optional[str]: + if obj is self._unpickle_state: + return "unpickle_state" + else: + return None + + @classmethod + def dumps(cls, obj: object, options: Optional[Options] = None) -> bytes: + """ + Pickle an object. + """ + with io.BytesIO() as stream: + pickler = cls(stream, options) + pickler.dump(obj) + return stream.getvalue() + + @staticmethod + def loads(data: bytes, fake_mode: FakeTensorMode) -> object: + """ + Unpickle an object. + """ + state = _UnpickleState(fake_mode) + with io.BytesIO(data) as stream: + unpickler = _GraphUnpickler(stream, state) + return unpickler.load() + + +class _UnpickleState: + def __init__(self, fake_mode: FakeTensorMode) -> None: + self.fake_mode = fake_mode + self.meta_converter: MetaConverter[FakeTensor] = MetaConverter() + + +# This token is passed when pickling to indicate that we want to use the +# unpickler's _UnpickleState as a parameter in that position. +_UnpickleStateToken = NewType("_UnpickleStateToken", object) + + +class _GraphUnpickler(pickle.Unpickler): + def __init__(self, stream: io.BytesIO, unpickle_state: _UnpickleState) -> None: + super().__init__(stream) + self._unpickle_state = unpickle_state + + @override + def persistent_load(self, pid: object) -> object: + if pid == "unpickle_state": + return self._unpickle_state + else: + raise pickle.UnpicklingError("Invalid persistent ID") + + +class _ShapeEnvPickleData: + data: dict[str, object] + + @classmethod + def reduce_helper( + cls, pickler: GraphPickler, obj: ShapeEnv + ) -> tuple[ + Callable[[Self, _UnpickleState], ShapeEnv], tuple[Self, _UnpickleStateToken] + ]: + return cls.unpickle, (cls(obj), pickler._unpickle_state) + + def __init__(self, env: ShapeEnv) -> None: + # In theory pickle should recognize that a given ShapeEnv was already + # pickled and reuse the resulting _ShapeEnvPickleData (so two objects + # pointing at the same ShapeEnv get the same ShapeEnv out). + assert not env._translation_validation_enabled + self.data = env.__dict__.copy() + del self.data["tracked_fakes"] + del self.data["fake_tensor_cache"] + + def unpickle(self, unpickle_state: _UnpickleState) -> ShapeEnv: + # Fill in the existing ShapeEnv rather than creating a new one + assert unpickle_state.fake_mode + assert unpickle_state.fake_mode.shape_env + + for k, v in self.data.items(): + setattr(unpickle_state.fake_mode.shape_env, k, v) + + return unpickle_state.fake_mode.shape_env + + +class _SymNodePickleData: + @classmethod + def reduce_helper( + cls, + pickler: GraphPickler, + obj: _SymNodeT, + ) -> tuple[ + Callable[[Self, _UnpickleState], _SymNodeT], tuple[Self, _UnpickleStateToken] + ]: + args = (cls(obj.node), pickler._unpickle_state) + if isinstance(obj, torch.SymInt): + # pyrefly: ignore [bad-return] + return _SymNodePickleData.unpickle_sym_int, args + else: + raise NotImplementedError(f"Unhandled SymNode type {type(obj)}") + + def __init__(self, node: SymNode) -> None: + self.expr = node._expr + self.shape_env = node.shape_env + self.pytype = node.pytype + self.hint = node._hint + + def _to_sym_node(self) -> SymNode: + assert self.shape_env is not None + return SymNode(self.expr, self.shape_env, self.pytype, self.hint) + + def unpickle_sym_int(self, unpickle_state: _UnpickleState) -> torch.SymInt: + return torch.SymInt(self._to_sym_node()) + + +class _TensorPickleData: + metadata: MetaTensorDesc[FakeTensor] + + @classmethod + def reduce_helper( + cls, pickler: GraphPickler, obj: FakeTensor + ) -> tuple[ + Callable[[Self, _UnpickleState], FakeTensor], tuple[Self, _UnpickleStateToken] + ]: + return cls.unpickle, ( + cls(pickler._meta_tensor_describer, obj), + pickler._unpickle_state, + ) + + def __init__(self, describer: MetaTensorDescriber, t: Tensor) -> None: + # THINGS TO WORRY ABOUT: + # 1. Need to make sure that two tensors with the same id end up with the + # same id on the other side of the wire. + + metadata = describer.describe_tensor(t) + + # view_func is fine if it's either None or a _FakeTensorViewFunc. A + # custom one (which is basically a lambda) can't be serialized. + assert not metadata.view_func or isinstance( + metadata.view_func, torch._subclasses.meta_utils._FakeTensorViewFunc + ) + self.metadata = dataclasses.replace(metadata, fake_mode=None) + + # Some debugging/verification + for k in MetaTensorDesc._UNSERIALIZABLE: + if k in ("fake_mode", "view_func"): + continue + assert getattr(self.metadata, k) is None, ( + f"not None: {k}: {getattr(self.metadata, k)}" + ) + + def unpickle(self, unpickle_state: _UnpickleState) -> FakeTensor: + # TODO: make common w/ _output_from_cache_entry() in fake_tensor.py? + metadata = dataclasses.replace( + self.metadata, + fake_mode=unpickle_state.fake_mode, + ) + + # also need to set the fake_mode on the base of a tensor if it's a view + if metadata.is_view and metadata.base is not None: + new_base = dataclasses.replace( + metadata.base, + fake_mode=unpickle_state.fake_mode, + ) + metadata = dataclasses.replace(metadata, base=new_base) + + def with_fake( + make_meta_t: Callable[[], torch.Tensor], device: Union[torch.device, str] + ) -> FakeTensor: + with no_dispatch(): + return FakeTensor( + unpickle_state.fake_mode, + make_meta_t(), + # pyrefly: ignore [bad-argument-type] + device, + ) + + return unpickle_state.meta_converter.meta_tensor( + metadata, + unpickle_state.fake_mode.shape_env, + with_fake, + None, + None, + ) + + +class _TorchNumpyPickleData: + @classmethod + def reduce_helper( + cls, pickler: GraphPickler, obj: object + ) -> Optional[ + tuple[ + Callable[[Self, _UnpickleState], object], tuple[Self, _UnpickleStateToken] + ] + ]: + if data := cls.from_object(obj): + return (cls.unpickle, (data, pickler._unpickle_state)) + else: + return None + + def __init__(self, mod: str, name: str) -> None: + self.mod = mod + self.name = name + + def unpickle(self, unpickle_state: _UnpickleState) -> Callable[..., object]: + np = getattr(importlib.import_module(self.mod), self.name) + return torch._dynamo.variables.misc.get_np_to_tnp_map()[np] + + @classmethod + def from_object(cls, tnp: object) -> Optional[Self]: + if not callable(tnp): + return None + + tnp_to_np = torch._dynamo.variables.misc.get_tnp_to_np_map() + try: + if not (np := tnp_to_np.get(tnp)): + return None + except TypeError: + return None + + if not (mod := getattr(np, "__module__", None)): + mod = "numpy" + + if not (name := getattr(np, "__name__", None)): + return None + + # pyrefly: ignore [unbound-name] + assert np == getattr(importlib.import_module(mod), name) + # pyrefly: ignore [unbound-name] + return cls(mod, name) + + +class _GraphModulePickleData: + @classmethod + def reduce_helper( + cls, pickler: GraphPickler, obj: torch.fx.GraphModule + ) -> tuple[ + Callable[[Self, _UnpickleState], torch.fx.GraphModule], + tuple[Self, _UnpickleStateToken], + ]: + return cls.unpickle, ( + cls(obj, pickler.options), + pickler._unpickle_state, + ) + + def __init__(self, gm: torch.fx.GraphModule, options: Options) -> None: + # Need to do this to ensure the code is created for later pickling. + if isinstance(gm, torch.fx._lazy_graph_module._LazyGraphModule): + _python_code = gm._real_recompile() + else: + _python_code = gm.recompile() + self.gm_dict = gm.__dict__.copy() + del self.gm_dict["_graph"] + self.graph = _GraphPickleData(gm._graph, options) + + def unpickle(self, unpickle_state: _UnpickleState) -> torch.fx.GraphModule: + gm = torch.fx.GraphModule.__new__(torch.fx.GraphModule) + gm.__dict__ = self.gm_dict + gm._graph = self.graph.unpickle(gm, unpickle_state) + return gm + + +class _NodePickleData: + def __init__( + self, + node: torch.fx.Node, + mapping: dict[torch.fx.Node, "_NodePickleData"], + options: Options, + ) -> None: + self.args = pytree.tree_map_only(torch.fx.Node, lambda n: mapping[n], node.args) + self.kwargs = pytree.tree_map_only( + torch.fx.Node, lambda n: mapping[n], node.kwargs + ) + # -- self.graph = node.graph + self.name = node.name + self.op = node.op + self.target = _OpPickleData.pickle(node.target, options) + # self.input_nodes = node._input_nodes + # self.users = node.users + self.type = node.type + # self.sort_key = node._sort_key + # self.repr_fn = node._repr_fn + # self.meta = node.meta + self.meta = node.meta + + def unpickle( + self, + graph: torch.fx.Graph, + mapping: dict["_NodePickleData", torch.fx.Node], + unpickle_state: _UnpickleState, + ) -> torch.fx.Node: + args = pytree.tree_map_only(_NodePickleData, lambda n: mapping[n], self.args) + kwargs = pytree.tree_map_only( + _NodePickleData, lambda n: mapping[n], self.kwargs + ) + target = self.target.unpickle(unpickle_state) + assert callable(target) or isinstance(target, str) + node = graph.create_node(self.op, target, args, kwargs, self.name, self.type) + node.meta = self.meta + return node + + +class _OpPickleData: + @classmethod + def reduce_helper( + cls, pickler: GraphPickler, op: object + ) -> tuple[Callable[[_UnpickleState], object], tuple[_UnpickleStateToken]]: + result = cls.pickle(op, pickler.options) + return (result.unpickle, (pickler._unpickle_state,)) + + @classmethod + def pickle(cls, op: object, options: Options) -> "_OpPickleData": + if isinstance(op, str): + return _OpStrPickleData(op) + + if isinstance(getattr(op, "__wrapped__", None), AOTCompiledArtifact): + assert hasattr(op, "__wrapped__") + artifact = op.__wrapped__ + assert isinstance(artifact, AOTCompiledArtifact) + return _OpPrecompiledPickleData(artifact) + + name = torch.fx.Node._pretty_print_target(op) + + if isinstance(op, torch._ops.OpOverload): + return cls._pickle_op(name, _OpOverloadPickleData, options) + elif isinstance(op, torch._ops.OpOverloadPacket): + return cls._pickle_op(name, _OpOverloadPacketPickleData, options) + elif name.startswith(_OpFunctionPickleData.SUPPORTED_ROOTS): + root, detail = name.split(".", 1) + return _OpFunctionPickleData(root, detail) + else: + # TODO: raise a BypassFxGraphCache so we will just bypass this one... + raise NotImplementedError(f"TARGET: {type(op)} {op} {name}") + + @staticmethod + def _pickle_op( + name: str, + datacls: Union[ + type["_OpOverloadPickleData"], type["_OpOverloadPacketPickleData"] + ], + options: Options, + ) -> "_OpPickleData": + if (ops_filter := options.ops_filter) and not ops_filter(name): + from torch._inductor.codecache import BypassFxGraphCache + + raise BypassFxGraphCache(f"Unable to pickle non-standard op: {name}") + return datacls(name) + + @abstractmethod + def unpickle(self, unpickle_state: _UnpickleState) -> object: + pass + + @classmethod + def _lookup_global_by_name(cls, name: str) -> object: + """ + Like `globals()[name]` but supports dotted names. + """ + if "." in name: + mod, rest = name.split(".", 1) + root = globals()[mod] + return cls._getattr_by_name(root, rest) + else: + return globals()[name] + + @staticmethod + def _getattr_by_name(root: object, name: str) -> object: + """ + Like `getattr(root, name)` but supports dotted names. + """ + while "." in name: + mod, name = name.split(".", 1) + root = getattr(root, mod) + return getattr(root, name) + + +class _OpStrPickleData(_OpPickleData): + def __init__(self, name: str) -> None: + self.name = name + + def unpickle(self, unpickle_state: _UnpickleState) -> str: + return self.name + + +class _OpOverloadPickleData(_OpPickleData): + def __init__(self, name: str) -> None: + self.name = name + + def unpickle(self, unpickle_state: _UnpickleState) -> torch._ops.OpOverload: + obj = self._lookup_global_by_name(self.name) + assert isinstance(obj, torch._ops.OpOverload) + return obj + + +class _OpOverloadPacketPickleData(_OpPickleData): + def __init__(self, name: str) -> None: + self.name = name + + def unpickle(self, unpickle_state: _UnpickleState) -> torch._ops.OpOverloadPacket: + obj = self._lookup_global_by_name(self.name) + assert isinstance(obj, torch._ops.OpOverloadPacket) + return obj + + +class _OpPrecompiledPickleData(_OpPickleData): + def __init__(self, artifact: AOTCompiledArtifact) -> None: + self.contents = artifact.serialize() + + def unpickle(self, unpickle_state: _UnpickleState) -> object: + precompiled_artifact = AOTCompiledArtifact.deserialize(self.contents) + import functools + + @functools.wraps(precompiled_artifact) + def wrapped(*args: Any) -> Any: + return precompiled_artifact(*args) + + return wrapped + + +class _OpFunctionPickleData(_OpPickleData): + """ + Supports pickling a set of standard/common functions + These must be prefixed with the full namespace in order to properly + be pickled (i.e `einops.rearrange` and not `from einops import rearrange`) + """ + + # Static variable listing supported root names + SUPPORTED_ROOTS = ("builtins.", "math.", "torch.", "operator.", "einops.") + + def __init__(self, root: str, name: str) -> None: + self.root = root + self.name = name + + def unpickle(self, unpickle_state: _UnpickleState) -> object: + if self.root == "builtins": + return __builtins__.get(self.name) # type: ignore[attr-defined] + elif self.root == "math": + import math + + return self._getattr_by_name(math, self.name) + elif self.root == "torch": + return self._getattr_by_name(torch, self.name) + elif self.root == "operator": + import operator + + return self._getattr_by_name(operator, self.name) + elif self.root == "einops": + import einops + + return self._getattr_by_name(einops, self.name) + else: + raise NotImplementedError + + +class _GraphPickleData: + def __init__(self, graph: torch.fx.Graph, options: Options) -> None: + self.tracer_cls = graph._tracer_cls + self.tracer_extras = graph._tracer_extras + + nodes: dict[torch.fx.Node, _NodePickleData] = {} + for node in graph.nodes: + nodes[node] = _NodePickleData(node, nodes, options) + self.nodes = tuple(nodes.values()) + + # Unpickled variables: + # self._used_names = graph._used_names + # -- self._insert = self._root.prepend + # self._len = graph._len + # self._graph_namespace = graph._graph_namespace + # self._owning_module = graph._owning_module + # self._codegen = graph._codegen + # self._co_fields: Dict[str, Any] = graph._co_fields + # -- self._find_nodes_lookup_table = _FindNodesLookupTable() + + def unpickle( + self, gm: torch.fx.GraphModule, unpickle_state: _UnpickleState + ) -> torch.fx.Graph: + graph = torch.fx.Graph(gm, self.tracer_cls, self.tracer_extras) + + nodes: dict[_NodePickleData, torch.fx.Node] = {} + for nd in self.nodes: + nodes[nd] = nd.unpickle(graph, nodes, unpickle_state) + + return graph + + +class _TracingContextPickleData: + @classmethod + def reduce_helper( + cls, pickler: GraphPickler, obj: torch._guards.TracingContext + ) -> tuple[ + Callable[[Self, _UnpickleState], torch._guards.TracingContext], + tuple[Self, _UnpickleStateToken], + ]: + return ( + cls.unpickle, + ( + cls(obj), + pickler._unpickle_state, + ), + ) + + def __init__(self, context: TracingContext) -> None: + # TODO: Do we really need all of this? + self.module_context = context.module_context + self.frame_summary_stack = context.frame_summary_stack + self.loc_in_frame = context.loc_in_frame + self.aot_graph_name = context.aot_graph_name + self.params_flat = context.params_flat + self.params_flat_unwrap_subclasses = context.params_flat_unwrap_subclasses + self.params_unwrapped_to_flat_index = context.params_unwrapped_to_flat_index + self.output_strides = context.output_strides + self.force_unspec_int_unbacked_size_like = ( + context.force_unspec_int_unbacked_size_like + ) + # Not saved (because it's difficult and maybe not needed?): + # self.fw_metadata = context.fw_metadata + # self.guards_context = None + # self.global_context = None + # self.fake_mode = None + # self.fakify_first_call = None + # self.hop_dispatch_set_cache = None + # self.tensor_to_context = context.tensor_to_context + + def unpickle(self, unpickle_state: _UnpickleState) -> TracingContext: + context = TracingContext(unpickle_state.fake_mode) + context.module_context = self.module_context + context.frame_summary_stack = self.frame_summary_stack + context.loc_in_frame = self.loc_in_frame + context.aot_graph_name = self.aot_graph_name + context.params_flat = self.params_flat + context.params_flat_unwrap_subclasses = self.params_flat_unwrap_subclasses + context.params_unwrapped_to_flat_index = self.params_unwrapped_to_flat_index + context.output_strides = self.output_strides + context.force_unspec_int_unbacked_size_like = ( + self.force_unspec_int_unbacked_size_like + ) + return context diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_lazy_graph_module.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_lazy_graph_module.py new file mode 100644 index 0000000000000000000000000000000000000000..83ce51fddd0405213ce86e95bafb2c61503e262b --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_lazy_graph_module.py @@ -0,0 +1,180 @@ +# mypy: allow-untyped-defs +from contextlib import contextmanager + +from torch.fx.graph_module import ( + _format_import_block, + GraphModule, + reduce_graph_module, + reduce_package_graph_module, +) +from torch.package import PackageExporter, sys_importer + +from ._compatibility import compatibility + + +_use_lazy_graph_module_flag = False +_force_skip_lazy_graph_module_flag = False + + +@compatibility(is_backward_compatible=False) +@contextmanager +def _force_skip_lazy_graph_module(): + """ + Skip using lazy graph module disregarding the setting of _use_lazy_graph_module. + Use to skip _LazyGraphModule when testing inductor torchscript related backend. + + torch.jit.script a _LazyGraphModule results in following error: + https://gist.github.com/shunting314/5143654c8084aed84ecd19b818258a69 + """ + try: + global _force_skip_lazy_graph_module_flag + prior = _force_skip_lazy_graph_module_flag + _force_skip_lazy_graph_module_flag = True + yield + finally: + _force_skip_lazy_graph_module_flag = prior + + +@compatibility(is_backward_compatible=False) +@contextmanager +def _use_lazy_graph_module(should_use: bool): + try: + global _use_lazy_graph_module_flag + prior = _use_lazy_graph_module_flag + _use_lazy_graph_module_flag = ( + should_use and not _force_skip_lazy_graph_module_flag + ) + yield + finally: + _use_lazy_graph_module_flag = prior + + +@compatibility(is_backward_compatible=False) +def _get_graph_module_cls(): + return _LazyGraphModule if _use_lazy_graph_module_flag else GraphModule + + +def _make_graph_module(*args, graph_module_cls=None, **kwargs): + if graph_module_cls is None: + graph_module_cls = _get_graph_module_cls() + + return graph_module_cls(*args, **kwargs) + + +@compatibility(is_backward_compatible=False) +class _LazyGraphModule(GraphModule): + """ + The main difference between _LazyGraphModule and GraphModule is how recompile happens. + GraphModule will do a 'recompile' call to generate python code and the forward method when it's + constructed. Later on if the graph get updated, recompile method can be called again to refresh + the saved python code and forward method. + + However in some cases especially in inductor, the recompilation can be a waste since we never + check the python code for the graph module or call its forward method. A few more concreate + examples regarding pattern matching fx passes in inductor: + 1. some passes will update the graph to be compiled and then call recompile on the GraphModule. + 2. some passes will trace small pattern function to search it in the graph being compiled and + replace the match with the traced graph of a replacement function. The pattern graph and + replacement graph are quite small but there are large amount of them. Doing GraphModule.recompile + for them in GraphModule.__init__ is also a waste of time. + + However simply skip calling GraphModule.recompile in these scenarios is also dangeruous. + People may want to check the python code or call the GraphModule's forward method for debugging purposes. + + The way _LazyGraphModule solves it is, we override the recompile method to just mark the + need for recompilation but does not do the actual recompilation. Later on if people really + access the compiled python code or call the GraphModule's forward method, we do the real + recompilation. + """ + + @classmethod + def from_graphmodule(cls, gm: GraphModule): + if isinstance(gm, _LazyGraphModule): + return gm + else: + return _LazyGraphModule(gm, gm.graph) + + @staticmethod + def force_recompile(gm): + """ + Sometimes we need force a recompile as a workaround + - we want to do the real recompilation before symbolic_trace to avoid error: + https://gist.github.com/shunting314/75549c2e82ae07ac1139c94a3583d259 + """ + if isinstance(gm, _LazyGraphModule): + gm.real_recompile() + + def real_recompile(self): + if self._needs_recompile(): + self._real_recompile() + + @classmethod + def _needs_recompile(cls): + return cls.forward is cls._lazy_forward + + def _lazy_forward(self, *args, **kwargs): + # Call self.real_recompile() rather than self._real_recompile() here. + # The _lazy_forward method may be saved and call repeatedly. + # Calling self.real_recompile can make sure we skip recompilation if + # we have already done so. + self.real_recompile() + assert not self._needs_recompile() + + # call `__call__` rather than 'forward' since recompilation may + # install a wrapper for `__call__` to provide a customized error + # message. + return self(*args, **kwargs) + + forward = _lazy_forward + + def __reduce_package__(self, exporter: PackageExporter): + """ + Follow GraphModule.__reduce__ but call 'self._real_recompile' rather + than 'self.recompile' since for a _LazyGraphModule, self.recompile just + mark the need of recompilation and does not return the PythonCode object. + """ + python_code = self._real_recompile() + dict_without_graph = self.__dict__.copy() + dict_without_graph["_graphmodule_cls_name"] = self.__class__.__name__ + del dict_without_graph["_graph"] + + generated_module_name = f"fx-generated._{exporter.get_unique_id()}" + import_block = _format_import_block(python_code.globals, exporter.importer) + module_code = import_block + self.code + exporter.save_source_string(generated_module_name, module_code) + return ( + reduce_package_graph_module, + (dict_without_graph, generated_module_name), + ) + + def __reduce__(self): + """ + Follow GraphModule.__reduce__ but call 'self._real_recompile' rather + than 'self.recompile' since for a _LazyGraphModule, self.recompile just + mark the need of recompilation and does not return the PythonCode object. + """ + python_code = self._real_recompile() + dict_without_graph = self.__dict__.copy() + import_block = _format_import_block(python_code.globals, sys_importer) + del dict_without_graph["_graph"] + return (reduce_graph_module, (dict_without_graph, import_block)) + + def _real_recompile(self): + return super().recompile() + + @classmethod + def recompile(cls): + cls.forward = cls._lazy_forward + + @property + def code(self) -> str: + self.real_recompile() + return super().code + + def __str__(self) -> str: + """ + str(GraphModule) will access the _code attribute. Make sure recompile + happens so _code attribute is available. + """ + self.real_recompile() + return super().__str__() diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_pytree.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_pytree.py new file mode 100644 index 0000000000000000000000000000000000000000..bfb62f871eb6f5ba7788fd4920e292dda95738c6 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_pytree.py @@ -0,0 +1,114 @@ +from collections import namedtuple +from collections.abc import Callable +from typing import Any, Optional, TypeVar +from typing_extensions import NamedTuple + +import torch.return_types +from torch.utils._pytree import PyTree, tree_flatten, TreeSpec + + +FlattenFuncSpec = Callable[[PyTree, TreeSpec], list] +FlattenFuncExactMatchSpec = Callable[[PyTree, TreeSpec], bool] + +SUPPORTED_NODES: dict[type[Any], FlattenFuncSpec] = {} +SUPPORTED_NODES_EXACT_MATCH: dict[type[Any], Optional[FlattenFuncExactMatchSpec]] = {} + +_T = TypeVar("_T") +_K = TypeVar("_K") +_V = TypeVar("_V") + + +def register_pytree_flatten_spec( + cls: type[Any], + flatten_fn_spec: FlattenFuncSpec, + flatten_fn_exact_match_spec: Optional[FlattenFuncExactMatchSpec] = None, +) -> None: + SUPPORTED_NODES[cls] = flatten_fn_spec + SUPPORTED_NODES_EXACT_MATCH[cls] = flatten_fn_exact_match_spec + + +def _deregister_pytree_flatten_spec( + cls: type[Any], +) -> None: + del SUPPORTED_NODES[cls] + del SUPPORTED_NODES_EXACT_MATCH[cls] + + +def tree_flatten_spec( + pytree: PyTree, + spec: TreeSpec, +) -> list[Any]: + if spec.is_leaf(): + return [pytree] + # I guess these exist for BC, FC reasons. + # In general, we should be able to directly + # use pytree tree flattener to flatten them, + # as export serializes the pytree separately. + # Will remove it in follow up PR. + if spec.type in SUPPORTED_NODES: + flatten_fn_spec = SUPPORTED_NODES[spec.type] + child_pytrees = flatten_fn_spec(pytree, spec) + result = [] + for child, child_spec in zip(child_pytrees, spec.children()): + flat = tree_flatten_spec(child, child_spec) + result += flat + return result + flat_result, real_spec = tree_flatten(pytree) + if spec != real_spec: + raise RuntimeError( + f"Real spec {real_spec} of object {pytree} is different from expected spec {spec}. " + f"Please file an issue at https://github.com/pytorch/pytorch/issues/new?template=bug-report.yml" + ) + return flat_result + + +def _dict_flatten_spec(d: dict[_K, _V], spec: TreeSpec) -> list[_V]: + return [d[k] for k in spec.context] + + +def _list_flatten_spec(d: list[_T], spec: TreeSpec) -> list[_T]: + return [d[i] for i in range(spec.num_children)] + + +def _tuple_flatten_spec(d: tuple[_T, ...], spec: TreeSpec) -> list[_T]: + return [d[i] for i in range(spec.num_children)] + + +def _namedtuple_flatten_spec(d: NamedTuple, spec: TreeSpec) -> list[Any]: + return [d[i] for i in range(spec.num_children)] + + +def _dict_flatten_spec_exact_match(d: dict[_K, _V], spec: TreeSpec) -> bool: + return len(d) == spec.num_children + + +def _list_flatten_spec_exact_match(d: list[_T], spec: TreeSpec) -> bool: + return len(d) == spec.num_children + + +def _tuple_flatten_spec_exact_match(d: tuple[_T, ...], spec: TreeSpec) -> bool: + return len(d) == spec.num_children + + +def _namedtuple_flatten_spec_exact_match(d: NamedTuple, spec: TreeSpec) -> bool: + return len(d) == spec.num_children + + +register_pytree_flatten_spec(dict, _dict_flatten_spec, _dict_flatten_spec_exact_match) +register_pytree_flatten_spec(list, _list_flatten_spec, _list_flatten_spec_exact_match) +register_pytree_flatten_spec( + tuple, + _tuple_flatten_spec, + _tuple_flatten_spec_exact_match, +) +for return_type in torch.return_types.all_return_types: + register_pytree_flatten_spec( + return_type, + _tuple_flatten_spec, + _tuple_flatten_spec_exact_match, + ) +register_pytree_flatten_spec( + namedtuple, # type: ignore[arg-type] + _namedtuple_flatten_spec, + _namedtuple_flatten_spec_exact_match, +) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py new file mode 100644 index 0000000000000000000000000000000000000000..dd3482f3e04602ef800222e6a6f88727c681705d --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py @@ -0,0 +1,1368 @@ +# mypy: allow-untyped-defs +import builtins +import collections +import contextlib +import copy +import functools +import inspect +import logging +import math +import os +import warnings +from collections.abc import Callable +from itertools import chain +from types import CodeType, FunctionType, ModuleType +from typing import Any, get_args, NamedTuple, Optional, TypeAlias, Union + +import torch +import torch.utils._pytree as pytree +from torch._C import ScriptObject # type: ignore[attr-defined] +from torch._library.fake_class_registry import FakeScriptObject +from torch._library.opaque_object import is_opaque_reference_type, is_opaque_type + +from ._compatibility import compatibility +from ._lazy_graph_module import _make_graph_module +from .graph import _PyTreeCodeGen, _PyTreeInfo, Graph +from .graph_module import GraphModule +from .node import Argument, base_types, map_aggregate +from .proxy import ParameterProxy, Proxy, Scope, ScopeContextManager, TracerBase + + +log = logging.getLogger(__name__) + +HAS_VARSTUFF = inspect.CO_VARARGS | inspect.CO_VARKEYWORDS + +# These need to run in global scope to handle nested calls correctly +_orig_module_call: Callable = torch.nn.Module.__call__ +_orig_module_getattr: Callable = torch.nn.Module.__getattr__ + +_proxyable_classes: dict[type, None] = {} + +_is_fx_tracing_flag = False + +_ConstantAttributeType: TypeAlias = Union[ + torch.Tensor, torch.ScriptObject, FakeScriptObject, pytree.TreeSpec +] + +_constant_attribute_types = get_args(_ConstantAttributeType) + + +# We only want to print this once to avoid flooding logs +@functools.lru_cache +def is_fx_tracing_warning(): + log.warning( + "is_fx_tracing will return true for both fx.symbolic_trace and " + "torch.export. Please use " + "is_fx_tracing_symbolic_tracing() for specifically fx.symbolic_trace " + "or torch.compiler.is_compiling() for specifically torch.export/compile." + ) + + +def is_fx_tracing(): + is_fx_tracing_warning() + return _is_fx_tracing_flag + + +def is_fx_symbolic_tracing(): + return _is_fx_tracing_flag and not torch.compiler.is_compiling() + + +@compatibility(is_backward_compatible=True) +class ProxyableClassMeta(type): + """ + ProxyableClassMeta allows you to make construction of a given Python class + symbolically traceable. For example:: + + import torch + import torch.fx + + + class TensorPair(metaclass=torch.fx.ProxyableClassMeta): + def __init__(self, left, right): + self.left, self.right = left, right + + def add(self, other): + l = self.left + other.left + r = self.right + other.right + return TensorPair(l, r) + + def mul(self, other): + l = self.left * other.left + r = self.right * other.right + return TensorPair(l, r) + + + def use_tensor_pair_ctor(x: TensorPair, y: torch.Tensor): + s = x.add(TensorPair(y, y)) + return s.mul(x) + + + x = TensorPair(torch.randn(5, 3), torch.randn(5, 3)) + y = torch.randn(5, 3) + ref_out = use_tensor_pair_ctor(x, y) + + traced = torch.fx.symbolic_trace(use_tensor_pair_ctor) + print(traced.code) + ''' + def forward(self, x : __main___TensorPair, y : torch.Tensor): + tensor_pair = __main___TensorPair(y, y); y = None + add = x.add(tensor_pair); tensor_pair = None + mul = add.mul(x); add = x = None + return mul + ''' + + From this example, we can see that construction of a class (``TensorPair``) + defined with ``ProxyableClassMeta`` as metaclass can be recorded in symbolic + tracing. + """ + + def __init__(cls, name, bases, attrs): + _proxyable_classes.setdefault(cls) + super().__init__(name, bases, attrs) + + def __call__(cls, *args, **kwargs): + instance = cls.__new__(cls) # type: ignore[call-overload] + + if not is_fx_tracing(): + cls.__init__(instance, *args, **kwargs) # type: ignore[misc] + return instance + + found_proxies = [] + + def check_proxy(a): + if isinstance(a, Proxy): + found_proxies.append(a) + + map_aggregate(args, check_proxy) + map_aggregate(kwargs, check_proxy) + + if len(found_proxies) != 0: + tracer = found_proxies[0].tracer + return tracer.create_proxy("call_function", cls, args, kwargs) + else: + cls.__init__(instance, *args, **kwargs) # type: ignore[misc] + return instance + + +def _patch_function(fn: FunctionType, nargs: int) -> FunctionType: + co = fn.__code__ + co_flags = co.co_flags & ~HAS_VARSTUFF + co_args: tuple + if hasattr(co, "co_qualname"): + # Python-3.11+ code signature + co_args = ( + nargs, + 0, + 0, + co.co_nlocals, + co.co_stacksize, + co_flags, + co.co_code, + co.co_consts, + co.co_names, + co.co_varnames, + co.co_filename, + co.co_name, + co.co_qualname, # type: ignore[attr-defined] + co.co_firstlineno, + co.co_linetable, + co.co_exceptiontable, # type: ignore[attr-defined] + co.co_freevars, + co.co_cellvars, + ) + elif hasattr(co, "co_posonlyargcount"): + co_args = ( + nargs, + 0, + 0, + co.co_nlocals, + co.co_stacksize, + co_flags, + co.co_code, + co.co_consts, + co.co_names, + co.co_varnames, + co.co_filename, + co.co_name, + co.co_firstlineno, + co.co_lnotab, + co.co_freevars, + co.co_cellvars, + ) + else: + co_args = ( + nargs, + 0, + co.co_nlocals, + co.co_stacksize, + co_flags, + co.co_code, + co.co_consts, + co.co_names, + co.co_varnames, + co.co_filename, + co.co_name, + co.co_firstlineno, + co.co_lnotab, + co.co_freevars, + co.co_cellvars, + ) + new_code = CodeType(*co_args) # type: ignore[arg-type] + return FunctionType( + new_code, fn.__globals__, fn.__name__, fn.__defaults__, fn.__closure__ + ) + + # we need to insert placeholder nodes for *args and **kwargs + # we can't call this function normally, otherwise it would try to unpack them + # instead, let's make python think that args and kwargs are normal variables + + +@compatibility(is_backward_compatible=False) +class PHBase: + """ + Object representing an input placeholder to `concrete_args` + """ + + def __repr__(self): + return "PH" + + +PH = PHBase() + + +@compatibility(is_backward_compatible=False) +class PHWithMeta(PHBase): + """ + Object representing an input placeholder to `concrete_args` + """ + + def __init__(self, ph_key: Optional[str] = None): + super().__init__() + + # Provide a hey for user to identify placeholder node during analysis + self.ph_key = ph_key + + +def _transfer_attrs(fr, to): + for attr_name in dir(fr): + attr_val = getattr(fr, attr_name) + if ( + not callable(attr_val) + and not attr_name.startswith("__") + and not hasattr(to, attr_name) + ): + setattr(to, attr_name, attr_val) + + +@compatibility(is_backward_compatible=True) +class Tracer(TracerBase): + # Reference: https://github.com/pytorch/pytorch/issues/54354 + # The first line of this docstring overrides the one Sphinx generates for the + # documentation. We need it so that Sphinx doesn't leak `math`s path from the + # build environment (e.g. ` None: + # This method's signature is overridden by the first line of this class' + # docstring. If this method's signature is modified, the signature that + # overrides it also should be modified accordingly. + + """ + Construct a Tracer object. + + Args: + + autowrap_modules (Tuple[ModuleType]): defaults to `(math, )`, + Python modules whose functions should be wrapped automatically + without needing to use fx.wrap(). Backward-compatibility for + this parameter is guaranteed. + + autowrap_functions (Tuple[Callable, ...]): defaults to `()`, + Python functions that should be wrapped automatically without + needing to use fx.wrap(). Backward compatibility for this + parameter is guaranteed. + + param_shapes_constant (bool): When this flag is set, calls to shape, + size and a few other shape like attributes of a module's parameter + will be evaluated directly, rather than returning a new Proxy value + for an attribute access. Backward compatibility for this parameter + is guaranteed. + """ + + super().__init__() + + # Functions we will eagerly wrap when we see them while tracing + # this captures both `math.sqrt()` and `from math import sqrt` automatically + self._autowrap_function_ids: set[int] = { + id(value) + for name, value in chain.from_iterable( + m.__dict__.items() for m in autowrap_modules + ) + if not name.startswith("_") and callable(value) + } + self._autowrap_function_ids.update({id(f) for f in autowrap_functions}) + + # Python modules to apply autowrap to at the start, in addition to + # modules we see while tracing + self._autowrap_search: list[ModuleType] = list(autowrap_modules) + self.param_shapes_constant = param_shapes_constant + + self.submodule_paths: Optional[dict[torch.nn.Module, str]] = None + self.root_module_name: str = "" + # Maps the containing module's name to the operator name + self.scope = Scope("", None) + # Records the module call stack + self.module_stack = collections.OrderedDict() + self.num_calls: dict[str, int] = {} + # Mapping of node name to module scope + self.node_name_to_scope: dict[str, tuple[str, type]] = {} + + _qualname_counter: dict[str, int] = collections.defaultdict(int) + + @compatibility(is_backward_compatible=True) + def get_fresh_qualname(self, prefix: str) -> str: + """ + Gets a fresh name for a prefix and returns it. This function ensures + that it will not clash with an existing attribute on the graph. + """ + # The idea here is that if the module doesn't have this prefix at all we + # should reset the counter to start from the beginning + # It's a ... little bit hacky (doesn't cover all cases) but the precise + # naming of the prefixes isn't a correctness issue, just a niceness + # issue + qualname = f"{prefix}0" + if not hasattr(self.root, qualname): + self._qualname_counter[prefix] = 0 + return qualname + + i = self._qualname_counter[prefix] + while True: + qualname = f"{prefix}{i}" + i += 1 + if not hasattr(self.root, qualname): + break + self._qualname_counter[prefix] = i + + return qualname + + @compatibility(is_backward_compatible=True) + def create_arg(self, a: Any) -> "Argument": + """ + A method to specify the behavior of tracing when preparing values to + be used as arguments to nodes in the ``Graph``. + + By default, the behavior includes: + + #. Iterate through collection types (e.g. tuple, list, dict) and recursively + call ``create_args`` on the elements. + #. Given a Proxy object, return a reference to the underlying IR ``Node`` + #. Given a non-Proxy Tensor object, emit IR for various cases: + + * For a Parameter, emit a ``get_attr`` node referring to that Parameter + * For a non-Parameter Tensor, store the Tensor away in a special + attribute referring to that attribute. + + This method can be overridden to support more types. + + Args: + + a (Any): The value to be emitted as an ``Argument`` in the ``Graph``. + + + Returns: + + The value ``a`` converted into the appropriate ``Argument`` + """ + # The base tracer is used to construct Graphs when there is no associated + # module hierarchy, so it can never create parameter references. + # The default tracer adds the ability to refer to parameters when + # tracing modules. + if isinstance(a, torch.nn.Parameter): + for n, p in self.root.named_parameters(): + if a is p: + return self.create_node("get_attr", n, (), {}) + raise NameError("parameter is not a member of this module") + elif isinstance(a, torch.Tensor): + for n_, p_ in self.root.named_buffers(): + if a is p_: + return self.create_node("get_attr", n_, (), {}) + elif isinstance(a, torch.nn.Module): + for n_, p_ in self.root.named_modules(): + if a is p_: + return self.create_node("get_attr", n_, (), {}) + # For NamedTuple instances that appear literally as args, we emit + # a node to construct the NamedTuple and use that Node as the argument. + if isinstance(a, tuple) and hasattr(a, "_fields"): + args = tuple(self.create_arg(elem) for elem in a) + return self.create_node("call_function", a.__class__, args, {}) + + # Tensors do not have a reliable string repr() from which they can be + # constructed (and we probably don't want to rely on that, either), so + # for any constant Tensor values we encounter, first search for if they + # are an attribute of some module in the module hierarchy. If so, emit + # a get_attr to retrieve that tensor. Otherwise, we'll store away the + # tensor value into a special attribute on the Module s.t. we can + # retrieve it with a get_attr. + if isinstance(a, _constant_attribute_types) or ( + is_opaque_reference_type(type(a)) + ): + qualname: Optional[str] = self.tensor_attrs.get( + a + ) # pyrefly: ignore[no-matching-overload] + + # Tensor was not found in the Module hierarchy, stow it away in a + # special attribute and set the qualname to refer to that + if not qualname: + if isinstance(a, torch.Tensor): + base_name = "_tensor_constant" + elif isinstance(a, (FakeScriptObject, ScriptObject)): + base_name = "_torchbind_obj" + elif isinstance(a, pytree.TreeSpec): + base_name = "_tree_spec_constant" + elif is_opaque_type(type(a)): + base_name = "_opaque_obj" + else: + raise RuntimeError( + f"cannot create constant arg for {a} of type {type(a)}." + ) + qualname = self.get_fresh_qualname(base_name) + assert isinstance(qualname, str) + self.tensor_attrs[a] = ( # pyrefly: ignore[unsupported-operation] + qualname + ) + setattr(self.root, qualname, a) + + return self.create_node("get_attr", qualname, (), {}) + + if type(a) in _proxyable_classes: + # This is an instance of a proxyable class for which we did not + # witness its construction. Intern this as a constant attribute + + # TODO: binary search + qualname = self.get_fresh_qualname(f"_{a.__class__.__name__}_constant_") + assert isinstance(qualname, str) + setattr(self.root, qualname, a) + + return self.create_node("get_attr", qualname, (), {}) + return super().create_arg(a) + + @compatibility(is_backward_compatible=True) + def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool: + """ + A method to specify whether a given ``nn.Module`` is a "leaf" module. + + Leaf modules are the atomic units that appear in + the IR, referenced by ``call_module`` calls. By default, + Modules in the PyTorch standard library namespace (torch.nn) + are leaf modules. All other modules are traced through and + their constituent ops are recorded, unless specified otherwise + via this parameter. + + Args: + + m (Module): The module being queried about + module_qualified_name (str): The path to root of this module. For example, + if you have a module hierarchy where submodule ``foo`` contains + submodule ``bar``, which contains submodule ``baz``, that module will + appear with the qualified name ``foo.bar.baz`` here. + """ + return ( + m.__module__.startswith("torch.nn") + or m.__module__.startswith("torch.ao.nn") + ) and not isinstance(m, torch.nn.Sequential) + + @compatibility(is_backward_compatible=True) + def path_of_module(self, mod: torch.nn.Module) -> str: + """ + Helper method to find the qualified name of ``mod`` in the Module hierarchy + of ``root``. For example, if ``root`` has a submodule named ``foo``, which has + a submodule named ``bar``, passing ``bar`` into this function will return + the string "foo.bar". + + Args: + + mod (str): The ``Module`` to retrieve the qualified name for. + """ + # Prefer the O(1) algorithm + if self.submodule_paths: + path = self.submodule_paths.get(mod) + if path is None: + raise NameError("module is not installed as a submodule") + assert isinstance(path, str) + return path + # O(N^2) fallback in the case that we didn't store the submodule + # paths. + else: + for n, p in self.root.named_modules(): + if mod is p: + return n + raise NameError("module is not installed as a submodule") + + @compatibility(is_backward_compatible=True) + def call_module( + self, + m: torch.nn.Module, + forward: Callable[..., Any], + args: tuple[Any, ...], + kwargs: dict[str, Any], + ) -> Any: + """ + Method that specifies the behavior of this ``Tracer`` when it encounters + a call to an ``nn.Module`` instance. + + By default, the behavior is to check if the called module is a leaf module + via ``is_leaf_module``. If it is, emit a ``call_module`` node referring to + ``m`` in the ``Graph``. Otherwise, call the ``Module`` normally, tracing through + the operations in its ``forward`` function. + + This method can be overridden to--for example--create nested traced + GraphModules, or any other behavior you would want while tracing across + ``Module`` boundaries. + + Args: + + m (Module): The module for which a call is being emitted + forward (Callable): The forward() method of the ``Module`` to be invoked + args (Tuple): args of the module callsite + kwargs (Dict): kwargs of the module callsite + + Return: + + The return value from the Module call. In the case that a ``call_module`` + node was emitted, this is a ``Proxy`` value. Otherwise, it is whatever + value was returned from the ``Module`` invocation. + """ + module_qualified_name = self.path_of_module(m) + with ScopeContextManager( + self.scope, Scope(module_qualified_name, type(m)) + ) as _scope: + # module_stack is an ordered dict so writing then deleting the + # entry is equivalent to push/pop on a list + num_calls = self.num_calls.get(module_qualified_name, 0) + module_key = ( + f"{_scope.module_path}@{num_calls}" + if num_calls > 0 + else _scope.module_path + ) + self.module_stack[module_key] = (module_qualified_name, _scope.module_type) + self.num_calls[module_qualified_name] = num_calls + 1 + if not self.is_leaf_module(m, module_qualified_name): + ret_val = forward(*args, **kwargs) + else: + ret_val = self.create_proxy( + "call_module", module_qualified_name, args, kwargs + ) + key, _ = self.module_stack.popitem(last=True) + assert key == module_key, f" Unexpected key {key}" + + return ret_val + + @compatibility(is_backward_compatible=False) + def getattr(self, attr: str, attr_val: Any, parameter_proxy_cache: dict[str, Any]): + """ + Method that specifies the behavior of this ``Tracer`` when we call getattr + on a call to an ``nn.Module`` instance. + + By default, the behavior is to return a proxy value for the attribute. It + also stores the proxy value in the ``parameter_proxy_cache``, so that future + calls will reuse the proxy rather than creating a new one. + + This method can be overridden to --for example-- not return proxies when + querying parameters. + + Args: + + attr (str): The name of the attribute being queried + attr_val (Any): The value of the attribute + parameter_proxy_cache (Dict[str, Any]): A cache of attr names to proxies + + Return: + + The return value from the getattr call. + """ + + def maybe_get_proxy_for_attr( + attr_val, collection_to_search, parameter_proxy_cache + ): + for n, p in collection_to_search: + if attr_val is p: + if n not in parameter_proxy_cache: + kwargs = {} + if ( + "proxy_factory_fn" + in inspect.signature(self.create_proxy).parameters + ): + kwargs["proxy_factory_fn"] = ( + # pyrefly: ignore [unsupported-operation] + None + if not self.param_shapes_constant + else lambda node: ParameterProxy( + self, node, n, attr_val + ) + ) + val_proxy = self.create_proxy("get_attr", n, (), {}, **kwargs) # type: ignore[arg-type] + parameter_proxy_cache[n] = val_proxy + return parameter_proxy_cache[n] + return None + + if isinstance(attr_val, torch.nn.Parameter): + maybe_parameter_proxy = maybe_get_proxy_for_attr( + attr_val, self.root.named_parameters(), parameter_proxy_cache + ) + if maybe_parameter_proxy is not None: + return maybe_parameter_proxy + + if self.proxy_buffer_attributes and isinstance(attr_val, torch.Tensor): + maybe_buffer_proxy = maybe_get_proxy_for_attr( + attr_val, self.root.named_buffers(), parameter_proxy_cache + ) + if maybe_buffer_proxy is not None: + return maybe_buffer_proxy + + return attr_val + + # This method will be refactored + @compatibility(is_backward_compatible=False) + def create_args_for_root(self, root_fn, is_module, concrete_args=None): + """ + Create ``placeholder`` nodes corresponding to the signature of the ``root`` + Module. This method introspects root's signature and emits those + nodes accordingly, also supporting ``*args`` and ``**kwargs``. + """ + # In some cases, a function or method has been decorated with a wrapper + # defined via ``functools.wraps``. In this case, the outer code object + # will likely not contain the actual parameters we care about, so unwrap + # the function to get to the innermost callable. + fn_for_analysis = inspect.unwrap(root_fn) + co = fn_for_analysis.__code__ + total_args = co.co_argcount + co.co_kwonlyargcount + orig_args = list(co.co_varnames) + names_iter = iter(co.co_varnames) + args: list[Any] = [] + skip_arg_idx = 0 + if is_module: + if total_args == 0: + raise RuntimeError( + "``self`` argument cannot be part of *args expansion!" + ) + skip_arg_idx = 1 + next(names_iter) # skip self + args.append(self.root) + + sig = inspect.signature(fn_for_analysis) + + # This covers the very specific case where we are passing in flat + # concrete_args as a tuple, but our traced fn takes (*args, **kwargs). + # In this case, just take the concrete_args and pass them through. + name_idx = 0 + if ( + isinstance(concrete_args, tuple) + and len(concrete_args) > 0 + and (co.co_flags & HAS_VARSTUFF) + and total_args == 1 + ): + for concrete_arg in concrete_args: + out = self.create_proxy("placeholder", f"input_{name_idx}", (), {}) + if isinstance(concrete_arg, PHBase): + if concrete_arg != PH: + # Transfer attrs in the case where you're using a placeholder other + # than the singleton PH (PH has no attributes to transfer). + # Proxies were created out of the placeholders. + # Transfer any metadata (put on the placeholders in the form of + # attributes set by the user) from the placeholder to the + # underlying nodes (the proxy is unwrapped by the user, but + # the metadata should hold). + _transfer_attrs(fr=concrete_arg, to=out.node) + args.append(out) + name_idx += 1 + return root_fn, args + + arg_names = [next(names_iter) for idx in range(skip_arg_idx, total_args)] + if isinstance(concrete_args, tuple): + if len(arg_names) != len(concrete_args): + raise RuntimeError( + f"Tracing expected {len(arg_names)} arguments but got {len(concrete_args)} concrete arguments" + ) + concrete_args = dict(zip(arg_names, concrete_args)) + + def proxy_placeholder(name): + return self._proxy_placeholder(name, concrete_args, sig, fn_for_analysis) + + args.extend(proxy_placeholder(names) for names in arg_names) + + if co.co_kwonlyargcount > 0 or co.co_flags & HAS_VARSTUFF: + # TODO: type annotations for *args and **kwargs + if co.co_flags & inspect.CO_VARARGS: + args.append(proxy_placeholder("*" + next(names_iter))) + if co.co_flags & inspect.CO_VARKEYWORDS: + args.append(proxy_placeholder("**" + next(names_iter))) + root_fn = _patch_function(root_fn, len(args)) + + flat_args, in_spec = pytree.tree_flatten(tuple(args)) + if not all(child.is_leaf() for child in in_spec.children()): + # In the case that we have pytree-flattened inputs in + # `concrete_args`, generate a flattening wrapper around the + # original root function and return that. + self.graph._codegen = _PyTreeCodeGen( # type: ignore[has-type] + _PyTreeInfo(orig_args[:total_args], in_spec, None) + ) + + def flatten_fn(*args): + tree_args = pytree.tree_unflatten(list(args), in_spec) + tree_out = root_fn(*tree_args) + out_args, out_spec = pytree.tree_flatten(tree_out) + assert isinstance(self.graph._codegen, _PyTreeCodeGen) # type: ignore[has-type] + self.graph._codegen.pytree_info = ( + self.graph._codegen.pytree_info._replace(out_spec=out_spec) + ) + return out_args + + return flatten_fn, flat_args + return root_fn, args + + @compatibility(is_backward_compatible=True) + def trace( + self, + root: Union[torch.nn.Module, Callable[..., Any]], + concrete_args: Optional[dict[str, Any]] = None, + ) -> Graph: + """ + Trace ``root`` and return the corresponding FX ``Graph`` representation. ``root`` + can either be an ``nn.Module`` instance or a Python callable. + + Note that after this call, ``self.root`` may be different from the ``root`` passed + in here. For example, when a free function is passed to ``trace()``, we will + create an ``nn.Module`` instance to use as the root and add embedded constants + to. + + + Args: + + root (Union[Module, Callable]): Either a ``Module`` or a function to be + traced through. Backwards-compatibility for this parameter is + guaranteed. + concrete_args (Optional[Dict[str, any]]): Concrete arguments that should + not be treated as Proxies. This parameter is experimental and + its backwards-compatibility is *NOT* guaranteed. + + Returns: + + A ``Graph`` representing the semantics of the passed-in ``root``. + """ + global _is_fx_tracing_flag + old_is_fx_tracing_flag = _is_fx_tracing_flag + _is_fx_tracing_flag = True + try: + if isinstance(root, torch.nn.Module): + # do real recompilation for _LazyGraphModule before retracing since the trace + # method can not trace the _lazy_forward method. Got error: + # https://gist.github.com/shunting314/75549c2e82ae07ac1139c94a3583d259 + # without this. + from torch.fx._lazy_graph_module import _LazyGraphModule + + _LazyGraphModule.force_recompile(root) + + self.root = root + + assert hasattr(type(root), self.traced_func_name), ( + f"traced_func_name={self.traced_func_name} doesn't exist in {type(root).__name__}" + ) + + fn = getattr(type(root), self.traced_func_name) + self.root_module_name = root._get_name() + self.submodule_paths = {mod: name for name, mod in root.named_modules()} + else: + self.root = torch.nn.Module() + fn = root + + tracer_cls: Optional[type[Tracer]] = getattr(self, "__class__", None) + self.graph = Graph(tracer_cls=tracer_cls) + if hasattr(fn, "__code__"): + code = fn.__code__ + self.graph._co_fields = { + "co_name": code.co_name, + "co_filename": code.co_filename, + "co_firstlineno": code.co_firstlineno, + } + + # When we encounter a Tensor value that's not a parameter, we look if it + # is some other attribute on the model. Construct a dict mapping Tensor + # values to the qualified name here for efficiency. This is used downstream + # in create_arg + self.tensor_attrs: dict[ + _ConstantAttributeType, + str, + ] = {} + + def collect_tensor_attrs(m: torch.nn.Module, prefix_atoms: list[str]): + for k, v in m.__dict__.items(): + if isinstance(v, _constant_attribute_types): + self.tensor_attrs[v] = ".".join(prefix_atoms + [k]) + for k, v in m.named_children(): + collect_tensor_attrs(v, prefix_atoms + [k]) + + collect_tensor_attrs(self.root, []) + + assert isinstance(fn, FunctionType) + + fn_globals = fn.__globals__ # run before it gets patched + fn, args = self.create_args_for_root( + fn, isinstance(root, torch.nn.Module), concrete_args + ) + + parameter_proxy_cache: dict[ + str, Proxy + ] = {} # Reduce number of get_attr calls + + # Method dispatch on parameters is not recorded unless it's directly used. + # Thus, we need to insert a proxy when __getattr__ requests a parameter. + @functools.wraps(_orig_module_getattr) + def module_getattr_wrapper(mod, attr): + attr_val = _orig_module_getattr(mod, attr) + return self.getattr(attr, attr_val, parameter_proxy_cache) + + @functools.wraps(_orig_module_call) + def module_call_wrapper(mod, *args, **kwargs): + def forward(*args, **kwargs): + return _orig_module_call(mod, *args, **kwargs) + + _autowrap_check( + patcher, # type: ignore[has-type] + getattr(getattr(mod, "forward", mod), "__globals__", {}), + self._autowrap_function_ids, + ) + return self.call_module(mod, forward, args, kwargs) + + with _new_patcher() as patcher: + # allow duplicate patches to support the case of nested calls + patcher.patch_method( + torch.nn.Module, + "__getattr__", + module_getattr_wrapper, + deduplicate=False, + ) + patcher.patch_method( + torch.nn.Module, + "__call__", + module_call_wrapper, + deduplicate=False, + ) + _patch_wrapped_functions(patcher) + _autowrap_check(patcher, fn_globals, self._autowrap_function_ids) + for module in self._autowrap_search: + _autowrap_check( + patcher, module.__dict__, self._autowrap_function_ids + ) + ann = inspect.get_annotations(inspect.unwrap(fn)) + self.create_node( + "output", + "output", + (self.create_arg(fn(*args)),), + {}, + type_expr=ann.get("return", None), + ) + + self.submodule_paths = None + except RuntimeError as e: + if e.args and isinstance(e.args[0], str) and "data-dependent" in e.args[0]: + partial_fx_graph = self.graph.python_code( + root_module="self", + verbose=True, + ).src + e.partial_fx_graph = partial_fx_graph # type: ignore[attr-defined] + raise + + raise + finally: + _is_fx_tracing_flag = old_is_fx_tracing_flag + return self.graph + + def __deepcopy__(self, memo): + # _autowrap_search contains modules, which cannot be deepcopied. + new_tracer = Tracer.__new__(Tracer) + + for k, v in self.__dict__.items(): + if k == "_autowrap_search": + new_obj = copy.copy(v) + else: + new_obj = copy.deepcopy(v, memo) + + new_tracer.__dict__[k] = new_obj + + return new_tracer + + def _proxy_placeholder(self, name, concrete_args, sig, fn_for_analysis): + if concrete_args is not None and name in concrete_args: + cnt = 0 + + def replace_ph(x): + nonlocal cnt + cnt += 1 + param = sig.parameters[name] + default: tuple[Any, ...] = ( + () if param.default is inspect.Parameter.empty else (param.default,) + ) + out = self.create_proxy( + "placeholder", f"{name}_{str(cnt)}", default, {} + ) + if isinstance(x, PHBase): + if x != PH: + # Transfer attrs in the case where you're using a placeholder other + # than the singleton PH (PH has no attributes to transfer). + # Proxies were created out of the placeholders. + # Transfer any metadata (put on the placeholders in the form of + # attributes set by the user) from the placeholder to the + # underlying nodes (the proxy is unwrapped by the user, but + # the metadata should hold). + _transfer_attrs(fr=x, to=out.node) + + return out + # Union[int, bool] == bool in Python <= 3.6 + if ( + type(x) is bool + or type(x) in base_types + and type(x) is not torch.Tensor + ): + torch._assert( + out == x, + f"{name} has been specialized to have value {x} but got another value", + ) + elif x is None: + args = ( + out, + f"{name} has been specialized to have value None but got another value", + ) + self.create_proxy("call_function", _assert_is_none, args, {}) + else: + warnings.warn( + f"Was not able to add assertion to guarantee correct input {name} to " + f"specialized function. It is up to the user to make sure that your inputs match the " + f"inputs you specialized the function with." + ) + + return x + + return pytree.tree_map(replace_ph, concrete_args[name]) + if name[0] == "*": + default: tuple[Any, ...] = () + else: + param = sig.parameters[name] + default = ( # type: ignore[assignment] + () if param.default is inspect.Parameter.empty else (param.default,) + ) + return self.create_proxy( + "placeholder", + name, + default, + {}, + type_expr=fn_for_analysis.__annotations__.get(name, None), + ) + + +# Dictionary of (id(globals dict), function name) => globals_dict to patch for +# the purposes of the wrap() API. +# We key by the globals dict id and function name to ensure we're wrapping a given +# function only once. +_wrapped_fns_to_patch: dict[tuple[int, str], dict] = {} + +# List of methods on classes to wrap (class type, function name) +# this currently only works for Tensor.* methods that aren't traced properly +_wrapped_methods_to_patch: list[tuple[type, str]] = [] + +if os.environ.get("FX_PATCH_GETITEM") == "1": + # This change is needed to trace models like PositionalEmbedding from BERT: + # https://github.com/pytorch/benchmark/blob/master/torchbenchmark/models/BERT_pytorch/bert_pytorch/model/embedding/position.py + # but causes issues in quantization documented here: + # https://github.com/pytorch/pytorch/issues/50710 + # once that is fixed we can make this the default behavior. + _wrapped_methods_to_patch.append((torch.Tensor, "__getitem__")) + + +def _find_proxy(*objects_to_search): + """ + Recursively search a data structure for a Proxy() and return it, + return None if not found. + """ + proxy = None + + def find_proxy(x): + nonlocal proxy + if isinstance(x, Proxy): + proxy = x + + map_aggregate(objects_to_search, find_proxy) + return proxy + + +def _create_wrapped_func(orig_fn): + @functools.wraps(orig_fn) + def wrapped(*args, **kwargs): + """ + Given an closed-over ``orig_function`` to invoke, search the args and kwargs for + a Proxy object. If there is one, emit a ``call_function`` node to preserve the + call to this leaf function directly. Otherwise, just return the results of + this function call, as this function is not being traced. + """ + proxy = _find_proxy(args, kwargs) + if proxy is not None: + return_proxy = proxy.tracer.create_proxy( + "call_function", orig_fn, args, kwargs + ) + return_proxy.node.meta["is_wrapped"] = True + return return_proxy + return orig_fn(*args, **kwargs) + + return wrapped + + +def _create_wrapped_method(cls, name): + orig_fn = getattr(cls, name) + + @functools.wraps(orig_fn) + def wrapped(*args, **kwargs): + """ + Search the args and kwargs for a Proxy object. If there is one, + emit a ``call_method`` node to preserve the call to this method + directly. Otherwise, just return the results of this function + call, as this function is not being traced. + """ + proxy = _find_proxy(args, kwargs) + if proxy is not None: + return proxy.tracer.create_proxy("call_method", name, args, kwargs) + return orig_fn(*args, **kwargs) + + return wrapped + + +class _PatchedFn(NamedTuple): + frame_dict: Any + fn_name: str + orig_fn: Any + new_fn: Any + + def revert(self): + raise NotImplementedError + + def patch(self): + raise NotImplementedError + + +class _PatchedFnSetItem(_PatchedFn): + def revert(self): + self.frame_dict[self.fn_name] = self.orig_fn + + def patch(self): + self.frame_dict[self.fn_name] = self.new_fn + + +class _PatchedFnDel(_PatchedFn): + def revert(self): + del self.frame_dict[self.fn_name] + + def patch(self): + self.frame_dict[self.fn_name] = self.new_fn + + +class _PatchedFnSetAttr(_PatchedFn): + def revert(self): + setattr(self.frame_dict, self.fn_name, self.orig_fn) + + def patch(self): + setattr(self.frame_dict, self.fn_name, self.new_fn) + + +class _Patcher: + def __init__(self) -> None: + super().__init__() + self.patches_made: list[_PatchedFn] = [] + self.visited: set[int] = set() + + def patch( + self, + frame_dict: dict[str, Any], + name: str, + new_fn: Callable, + deduplicate: bool = True, + ): + """ + Replace frame_dict[name] with new_fn until we exit the context manager. + """ + new_fn.__fx_already_patched = deduplicate # type: ignore[attr-defined] + if name not in frame_dict and hasattr(builtins, name): + self.patches_made.append(_PatchedFnDel(frame_dict, name, None, new_fn)) + self.patches_made[-1].patch() + elif getattr(frame_dict[name], "__fx_already_patched", False): + return # already patched, no need to do it again + else: + self.patches_made.append( + _PatchedFnSetItem(frame_dict, name, frame_dict[name], new_fn) + ) + self.patches_made[-1].patch() + + def patch_method( + self, cls: type, name: str, new_fn: Callable, deduplicate: bool = True + ): + """ + Replace object_or_dict.name with new_fn until we exit the context manager. + """ + new_fn.__fx_already_patched = deduplicate # type: ignore[attr-defined] + orig_fn = getattr(cls, name) + if getattr(orig_fn, "__fx_already_patched", False): + return # already patched, no need to do it again + self.patches_made.append(_PatchedFnSetAttr(cls, name, orig_fn, new_fn)) + self.patches_made[-1].patch() + + def visit_once(self, thing: Any): + """Return True on the first call to with thing, otherwise false""" + idx = id(thing) + if idx in self.visited: + return False + self.visited.add(idx) + return True + + def revert_all_patches(self): + """ + Remove all the stored patcheds. It doesn't modify patches_made. + """ + for patch in self.patches_made: + patch.revert() + return self.patches_made + + def reapply_all_patches(self): + """ + Patch all the stored patcheds. It doesn't modify patches_made. + """ + for patch in self.patches_made: + patch.patch() + return self.patches_made + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + """ + Undo all the changes made via self.patch() and self.patch_method() + """ + while self.patches_made: + # unpatch in reverse order to handle duplicates correctly + self.patches_made.pop().revert() + self.visited.clear() + + +CURRENT_PATCHER: Optional[_Patcher] = None + + +@contextlib.contextmanager +def _new_patcher(): + global CURRENT_PATCHER + prior_patcher = CURRENT_PATCHER + try: + CURRENT_PATCHER = _Patcher() + yield CURRENT_PATCHER + finally: + # Clear all the patches made by when using current patcher. + assert CURRENT_PATCHER is not None + CURRENT_PATCHER.revert_all_patches() + CURRENT_PATCHER = prior_patcher + + +@contextlib.contextmanager +def _maybe_revert_all_patches(): + current_patcher = CURRENT_PATCHER + patches_made = None + patches_removed = None + try: + if current_patcher is not None: + patches_removed = current_patcher.revert_all_patches() + yield + finally: + if current_patcher is not None: + patches_made = current_patcher.reapply_all_patches() + assert patches_made == patches_removed, ( + "CURRENT_PATCHER was changed during a revert_all_patches" + ) + + +def _patch_wrapped_functions(patcher: _Patcher): + """ + Go through ``_wrapped_fn_patch_table`` and, for each frame object, wrap + the listed global functions in the `_create_wrapped_func` wrapper. + """ + for (_, name), frame_dict in _wrapped_fns_to_patch.copy().items(): + if name not in frame_dict and hasattr(builtins, name): + orig_fn = getattr(builtins, name) + else: + orig_fn = frame_dict[name] + patcher.patch(frame_dict, name, _create_wrapped_func(orig_fn)) + + for cls, name in _wrapped_methods_to_patch: + patcher.patch_method(cls, name, _create_wrapped_method(cls, name)) + + +def _autowrap_check( + patcher: _Patcher, frame_dict: dict[str, Any], function_ids: set[int] +): + """ + Some methods, like `math.sqrt` are common enough we want to automatically wrap them as we see them. + This method searches a scope for them and patches them if found. + """ + if patcher.visit_once(frame_dict): + for name, value in frame_dict.items(): + if ( + not name.startswith("_") + and callable(value) + and id(value) in function_ids + ): + patcher.patch(frame_dict, name, _create_wrapped_func(value)) + + +@compatibility(is_backward_compatible=True) +def wrap(fn_or_name: Union[str, Callable]): + """ + This function can be called at module-level scope to register fn_or_name as a "leaf function". + A "leaf function" will be preserved as a CallFunction node in the FX trace instead of being + traced through:: + + # foo/bar/baz.py + def my_custom_function(x, y): + return x * x + y * y + + + torch.fx.wrap("my_custom_function") + + + def fn_to_be_traced(x, y): + # When symbolic tracing, the below call to my_custom_function will be inserted into + # the graph rather than tracing it. + return my_custom_function(x, y) + + This function can also equivalently be used as a decorator:: + + # foo/bar/baz.py + @torch.fx.wrap + def my_custom_function(x, y): + return x * x + y * y + + A wrapped function can be thought of a "leaf function", analogous to the concept of + "leaf modules", that is, they are functions that are left as calls in the FX trace + rather than traced through. + + Args: + + fn_or_name (Union[str, Callable]): The function or name of the global function to insert into the + graph when it's called + """ + if not callable(fn_or_name) and not isinstance(fn_or_name, str): + raise RuntimeError( + "Unsupported type for global function! Must be either a callable or " + "string name" + ) + + if callable(fn_or_name): + assert not isinstance(fn_or_name, str) # to make mypy happy + fn_name = fn_or_name.__name__ + else: + assert isinstance(fn_or_name, str), ( + "fn_or_name must be a global function or string name" + ) + fn_name = fn_or_name + + currentframe = inspect.currentframe() + assert currentframe is not None + f = currentframe.f_back + assert f is not None + if f.f_code.co_name != "": + raise NotImplementedError("wrap must be called at the top level of a module") + + # consider implementing Callable version of this via _autowrap_function_ids / _autowrap_search + # semantics would be slightly different, but would add support `from x import wrapped_function` + _wrapped_fns_to_patch[(id(f.f_globals), fn_name)] = f.f_globals + return fn_or_name + + +@compatibility(is_backward_compatible=True) +def symbolic_trace( + root: Union[torch.nn.Module, Callable[..., Any]], + concrete_args: Optional[dict[str, Any]] = None, +) -> GraphModule: + """ + Symbolic tracing API + + Given an ``nn.Module`` or function instance ``root``, this function will return a ``GraphModule`` + constructed by recording operations seen while tracing through ``root``. + + ``concrete_args`` allows you to partially specialize your function, whether it's to remove control flow or data structures. + + For example:: + + def f(a, b): + if b == True: + return a + else: + return a * 2 + + FX can typically not trace through this due to the presence of control + flow. However, we can use `concrete_args` to specialize on the value of + `b` to trace through this:: + + f = fx.symbolic_trace(f, concrete_args={"b": False}) + assert f(3, False) == 6 + + Note that although you can still pass in different values of `b`, they will be ignored. + + We can also use `concrete_args` to eliminate data-structure handling from + our function. This will use pytrees to flatten your input. To avoid + overspecializing, pass in `fx.PH` for values that shouldn't be + specialized. For example:: + + def f(x): + out = 0 + for v in x.values(): + out += v + return out + + + f = fx.symbolic_trace( + f, concrete_args={"x": {"a": fx.PH, "b": fx.PH, "c": fx.PH}} + ) + assert f({"a": 1, "b": 2, "c": 4}) == 7 + + + Args: + root (Union[torch.nn.Module, Callable]): Module or function to be traced and converted + into a Graph representation. + concrete_args (Optional[Dict[str, any]]): Inputs to be partially specialized + + Returns: + GraphModule: a Module created from the recorded operations from ``root``. + """ + tracer = Tracer() + graph = tracer.trace(root, concrete_args) + name = ( + root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__ + ) + return _make_graph_module(tracer.root, graph, name) + + +@wrap +def _assert_is_none(value, msg): + assert value is None, msg diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_utils.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..25f1c51171734704c6c2ccea3a739de629ff5262 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/_utils.py @@ -0,0 +1,67 @@ +# mypy: allow-untyped-defs +import sys +from typing import Optional + +import torch +from torch._logging import LazyString + + +def lazy_format_graph_code(name, gm, maybe_id=None, **kwargs): + """ + Returns a LazyString that formats the graph code. + """ + + def format_name(): + if maybe_id is not None: + return f"{name} {maybe_id}" + else: + return name + + if "print_output" not in kwargs: + kwargs["print_output"] = False + + if "colored" in kwargs: + try: + if not sys.stdout.isatty(): + kwargs["colored"] = False + except AttributeError: + kwargs["colored"] = False + + return LazyString( + lambda: _format_graph_code( + f"===== {format_name()} =====\n", + gm.forward.__code__.co_filename, + gm.print_readable(**kwargs), + ) + ) + + +def _format_graph_code(name, filename, graph_str): + """ + Returns a string that formats the graph code. + """ + return f"TRACED GRAPH\n {name} {filename} {graph_str}\n" + + +def first_call_function_nn_module_stack(graph: torch.fx.Graph) -> Optional[dict]: + """ + Returns the nn_module_stack of the first call_function node. + """ + for node in graph.nodes: + if node.op == "call_function" and "nn_module_stack" in node.meta: + return node.meta["nn_module_stack"] + return None + + +def get_node_context(node, num_nodes=2) -> str: + """ + Returns a string of the last num_nodes nodes in the graph. + """ + node_contexts = [] + cur = node + for _ in range(num_nodes): + node_contexts.append(cur.format_node()) + if cur.op == "root": + break + cur = cur.prev + return "\n".join(node_contexts[::-1]) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/annotate.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/annotate.py new file mode 100644 index 0000000000000000000000000000000000000000..b3c5056066251df51542cd187652c5111749516f --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/annotate.py @@ -0,0 +1,36 @@ +# mypy: allow-untyped-defs +from torch.fx.proxy import Proxy + +from ._compatibility import compatibility + + +@compatibility(is_backward_compatible=False) +def annotate(val, type): + """ + Annotates a Proxy object with a given type. + + This function annotates a val with a given type if a type of the val is a torch.fx.Proxy object + Args: + val (object): An object to be annotated if its type is torch.fx.Proxy. + type (object): A type to be assigned to a given proxy object as val. + Returns: + The given val. + Raises: + RuntimeError: If a val already has a type in its node. + """ + if isinstance(val, Proxy): + if val.node.type: + raise RuntimeError( + f"Tried to annotate a value that already had a type on it!" + f" Existing type is {val.node.type} " + f"and new type is {type}. " + f"This could happen if you tried to annotate a function parameter " + f"value (in which case you should use the type slot " + f"on the function signature) or you called " + f"annotate on the same value twice" + ) + else: + val.node.type = type + return val + else: + return val diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/config.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/config.py new file mode 100644 index 0000000000000000000000000000000000000000..db06176c43e13c1fecd2e2e89a4b8371ca8d3bc5 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/config.py @@ -0,0 +1,6 @@ +# Whether to disable showing progress on compilation passes +# Need to add a new config otherwise will get a circular import if dynamo config is imported here +disable_progress = True + +# If True this also shows the node names in each pass, for small models this is great but larger models it's quite noisy +verbose_progress = False diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_backward_state.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_backward_state.py new file mode 100644 index 0000000000000000000000000000000000000000..9c742431857c33af22dbc1ad73b5bdfcf6124b9c --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_backward_state.py @@ -0,0 +1,27 @@ +import torch.fx + + +class BackwardState: + """ + BackwardState is used to pass Python hooks from the forwards pass + into the backwards pass in Dynamo+Compiled Autograd. + + It is created by TorchDynamo and has special handling there. + Dynamo will pass an empty BackwardState to the forwards, then populate + members on it (via setattr) only after the forwards graph is finished. + Later on, in CompileAutograd we will inline and add the needed guards + on the BackwardState. + + BackwardState is identified and has special handling in AOTAutograd. + During AOTAutograd: + 1) BackwardState is an input to the forwards graph + 2) It must only be used in the backwards + 3) It will be empty in the forwards + 4) In the forwards we add a wrapper to save it + 5) In the backwards it becomes an input + 6) There can only be one per graph + + BackwardState requires CompiledAutograd. + """ + + proxy: torch.fx.Proxy diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_config.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_config.py new file mode 100644 index 0000000000000000000000000000000000000000..a537978db3834d0bbb425bbd4214a8b17163db18 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_config.py @@ -0,0 +1,112 @@ +import os +import sys +from typing import Optional + +from torch.utils._config_module import Config, install_config_module + + +# [@compile_ignored: debug] Fails hard instead of graph breaking on guard on data dependent errors. +no_data_dependent_graph_break = ( + os.environ.get("TORCHDYNAMO_NO_DATA_DEPENDENT_GRAPH_BREAK", "0") == "1" +) +# [@compile_ignored: debug] Uses z3 for validating the guard optimizations transformations. +translation_validation = ( + os.environ.get("TORCHDYNAMO_TRANSLATION_VALIDATION", "0") == "1" +) +# Timeout (in milliseconds) for z3 finding a solution. +# [@compile_ignored: debug] +translation_validation_timeout = int( + os.environ.get("TORCHDYNAMO_TRANSLATION_VALIDATION_TIMEOUT", "600000") +) +# Disables bisection for translation validation. +# +# Translation validation bisection is enabled by default, if translation validation +# is also enabled. This should help finding guard simplification issues. However, +# since validation uses Z3 for bisecting, it might take a lot of time. +# +# Set this configuration option so as to avoid bisecting. +# [@compile_ignored: debug] +translation_validation_no_bisect = ( + os.environ.get("TORCHDYNAMO_TRANSLATION_NO_BISECT", "0") == "1" +) +# Checks whether replaying ShapeEnv events on a freshly constructed one yields +# the a ShapeEnv with the same state. This should be used only in testing. +check_shape_env_recorded_events = False + +# TODO: Perhaps consider allowing unions for the configs below (so you can hit +# multiple reps at the same time) + +# Give extended debug information if the string representation of a guard +# matches this. For example, set this to "Ne(s0, 10)" and whenever we issue +# this guard, we will generate full Python and C++ backtrace +# [@compile_ignored: debug] +extended_debug_guard_added = os.environ.get( + "TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED", None +) + +# Give extended debug information when a particular symbol is allocated. For +# example, set this to "u2" and whenever we create this symbol, we will +# generate full Python and C++ backtrace +# [@compile_ignored: debug] +extended_debug_create_symbol = os.environ.get( + "TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL", None +) + +# Give extended debug information (C++ backtrace) for all extended debug +# settings as well as errors. The C++ backtrace is slow and very spammy so we +# don't include it by default even when you're requesting extended debug. +# [@compile_ignored: debug] +extended_debug_cpp = os.environ.get("TORCHDYNAMO_EXTENDED_DEBUG_CPP", "") != "" + +# Give extended debug information (line of code) when a torch function +# is called during export. This is useful for showing progress and detecting +# where export might be stuck. Currently only works for strict=False. +# [@compile_ignored: debug] +extended_debug_current_loc = ( + os.environ.get("TORCHEXPORT_EXTENDED_DEBUG_CURRENT_LOC", "0") == "1" +) + +# [@compile_ignored: debug] Show a warning for every specialization +print_specializations = False + +# wraps (un)equalities with 'Not' class after recording the correct expression +# in the FX graph. This should incorrectly construct the divisible and replacement +# lists, and incorrectly issue guards. +inject_EVALUATE_EXPR_flip_equality_TESTING_ONLY = False + +# [@compile_ignored: debug] Validate that ShapeEnv's version key is updated correctly +validate_shape_env_version_key = False + +# If we produce more than this many guards on a symbol, force the symbol to +# get specialized and bail out if this many guards mention this particular +# symbol. This may be slightly more aggressive than the true number of guards +# issued (as we test if we've hit the limit on-the-fly, whereas we may +# do further simplifications at final guard issuance time that make guards +# irrelevant.) +symbol_guard_limit_before_specialize: Optional[int] = None + +# This flag changes whether we should use the same symbolic variable to represent input sizes that are the same. +use_duck_shape = True + +# Controls the registration of torch.nonzero() on the meta device. +# When True, nonzero returns a tensor with shape (self.numel(), self.dim()) +# assuming all elements are none-zero. +# Default is False to prevent unintended registration. Set to True to enable. +meta_nonzero_assume_all_nonzero = False + +# Applies size-oblivious reasoning to backed symbols. This allocates a [0, inf] range for backed size symbols, +# and relies on size-oblivious semantics to avoid 0/1 specialization guards by marking them size-like. +# Currently an experimental option for export. +backed_size_oblivious = False + +# Skip dtype check in meta registrations. Only used for systems that does its own dtype checking. +skip_dtype_check_in_meta_registrations = False + +# Experimental: If True, graph module will register fx metadata during recompile() +enrich_profiler_metadata: bool = Config( # type: ignore[var-annotated] + default=False, + env_name_default="TORCH_ENRICH_RPOFILER_STACK_TRACE", +) + + +install_config_module(sys.modules[__name__]) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_constant_symnode.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_constant_symnode.py new file mode 100644 index 0000000000000000000000000000000000000000..b3b40bda324c8fd6ad171d14ddb17f52508cb23a --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_constant_symnode.py @@ -0,0 +1,78 @@ +from typing import * # noqa: F403 + + +# Python version of c10/core/ConstantSymNodeImpl.cpp +# This needs to exist because the Python version of nested int is not compatible +# with the C++ version of constant symnode. +class ConstantIntNode: + def __init__(self, val: int): + self.val = val + + def is_constant(self) -> bool: + return True + + def maybe_as_int(self) -> int: + return self.val + + def is_int(self) -> bool: + return True + + def is_float(self) -> bool: + return False + + def is_bool(self) -> bool: + return False + + def is_nested_int(self) -> bool: + return False + + def clone(self) -> "ConstantIntNode": + return self + + def _str(self) -> str: + return str(self.val) + + def __str__(self) -> str: + return self._str() + + def __repr__(self) -> str: + return self._str() + + def _graph_repr(self) -> str: + return self._str() + + def add(self, other: Any) -> Any: + return other.add(self) + + def sub(self, other: Any) -> Any: + return other.neg().add(self.val) + + def mul(self, other: Any) -> Any: + return other.mul(self) + + def eq(self, other: Any) -> Any: + return other.eq(self) + + def ne(self, other: Any) -> Any: + return other.ne(self) + + def gt(self, other: Any) -> Any: + return other.lt(self) + + def lt(self, other: Any) -> Any: + return other.gt(self) + + def le(self, other: Any) -> Any: + return other.ge(self) + + def ge(self, other: Any) -> Any: + return other.le(self) + + def is_symbolic(self) -> bool: + return False + + def constant_int(self) -> int: + return self.val + + def guard_int(self, file: str, line: int) -> int: + return self.val diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_dynamism.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_dynamism.py new file mode 100644 index 0000000000000000000000000000000000000000..f6f30779ecc28df106658ca80fb103ee3735e5a1 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/_dynamism.py @@ -0,0 +1,119 @@ +import re +from collections.abc import Callable +from typing import Any, Union + +import torch +from torch.utils._pytree import tree_flatten_with_path, tree_map + + +KeyPath = tuple[Any, ...] +NonTensorShapeFn = Callable[[Union[int, float]], tuple[Any, ...]] + +__all__ = [ + "normalize_source_name", + "module_to_nested_dict", + "track_dynamism_across_examples", + "clone_and_convert_to_meta", +] + + +def normalize_source_name(name: str) -> str: + # Match attribute access like .x and replace with ['x'] + return re.sub(r"\.([a-zA-Z_][a-zA-Z0-9_]*)", r"['\1']", name) + + +def module_to_nested_dict(module: torch.nn.Module) -> dict[str, Any]: + """Recursively converts an nn.Module into a nested dictionary with explicit 'parameters' and 'modules' keys.""" + self_dict: dict[str, Any] = {} + + self_dict["_parameters"] = {} + self_dict["_modules"] = {} + + for attr_name in dir(module): + try: + if not attr_name.startswith("_") and not callable( + getattr(module, attr_name) + ): + attr_value = getattr(module, attr_name) + if ( + not isinstance(attr_value, torch.nn.Module) + and isinstance(attr_value, (int, float, torch.Tensor)) + and type(attr_value) is not bool + ): + self_dict[attr_name] = attr_value + except NotImplementedError: + # Skip attributes that raise NotImplementedError since they won't + # contain any dynamism anyways. + continue + + for name, param in module.named_parameters(recurse=False): + self_dict["_parameters"][name] = param + for name, buffer in module.named_buffers(recurse=False): + self_dict["_parameters"][name] = buffer + + for name, submodule in module.named_children(): + self_dict["_modules"][name] = module_to_nested_dict(submodule) + + return self_dict + + +def track_dynamism_across_examples( + example_inputs: list[Any], +) -> dict[Any, Any]: + """ + This function analyzes a list of example inputs to determine the dynamism of their shapes. + It tracks whether the dimensions of tensors or non-tensor values change across + different examples. The function returns a dictionary where each key represents + a path to a value in the input examples, and the corresponding value is a tuple + indicating which dimensions are dynamic (i.e., change across examples). This + helps in understanding how the structure of data varies across different instances. + """ + tracking: dict[KeyPath, tuple[list[set[Any]], bool]] = {} + + for ex in example_inputs: + if "self" in ex and isinstance(ex["self"], torch.nn.Module): + ex["self"] = module_to_nested_dict(ex["self"]) + leaves_with_paths, _ = tree_flatten_with_path(ex) + for key_path, value in leaves_with_paths: + if not isinstance(value, (int, float, torch.Tensor)): + continue + if isinstance(value, torch.Tensor): + shape: tuple[int | float, ...] = tuple(value.shape) + is_tensor = True + else: + shape = (value,) + is_tensor = False + if key_path not in tracking: + tracking[key_path] = ([set() for _ in range(len(shape))], is_tensor) + else: + dim_sets, flag = tracking[key_path] + if flag != is_tensor: + pass + while len(dim_sets) < len(shape): + dim_sets.append(set()) + for i, dim in enumerate(shape): + tracking[key_path][0][i].add(dim) + + output: dict[Any, Any] = {} + for key_path, (dim_sets, _is_tensor) in tracking.items(): + final_dyn = tuple(len(s) > 1 for s in dim_sets) + key_str = "L" + "".join(f"{str(k)}" for k in key_path) + key = key_path[0].key # type: ignore[attr-defined] + if key not in output: + output[key] = {} + output[key][key_str] = final_dyn + return output + + +def clone_and_convert_to_meta(example_input: Any) -> Any: + """ + This function takes a list of example inputs and for each tensor, clones it and converts it to device=meta. + For non-tensor values, it keeps the reference. It uses pytree to handle nested structures recursively. + """ + + def transform_fn(value: Any) -> Any: + if isinstance(value, torch.Tensor): + return value.clone().to(device="meta") + return value + + return tree_map(transform_fn, example_input) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/accelerator_partitioner.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/accelerator_partitioner.py new file mode 100644 index 0000000000000000000000000000000000000000..7cfd41b039e9ec6f9c456fd0240b18902756dc55 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/accelerator_partitioner.py @@ -0,0 +1,1085 @@ +# mypy: allow-untyped-defs +import operator +from collections import deque +from typing import NamedTuple + +import torch +from torch.fx.experimental.partitioner_utils import ( + Device, + get_extra_size_of, + get_latency_of_partitioned_graph, + get_partition_to_latency_mapping, + NodeLatency, + Partition, + PartitionerConfig, + PartitionMode, +) +from torch.fx.graph_module import GraphModule +from torch.fx.node import map_arg, Node +from torch.fx.passes.graph_manipulation import get_size_of_all_nodes +from torch.fx.passes.split_module import split_module + + +class DAGNode: + """DAGNode class maintains useful information for a partition (submodule), + and its input submodules and output submodules. + """ + + def __init__( + self, + submodule_node: Node, + input_nodes: list[Node], + output_nodes: list[Node], + logical_device_ids: list[int], + size_bytes: int, + ) -> None: + self.submodule_node: Node = submodule_node + self.input_nodes: list[Node] = input_nodes + self.output_nodes: list[Node] = output_nodes + self.logical_device_ids: list[int] = logical_device_ids + self.size_bytes = size_bytes + + def __str__(self) -> str: + return str(self.submodule_node) + + +class DAG: + """DAG class contains all the DAG nodes""" + + def __init__(self) -> None: + self.nodes: list[DAGNode] = [] + + def create_node( + self, + submodule_node: Node, + input_nodes: list[Node], + output_nodes: list[Node], + logical_devices: list[int], + size_bytes: int, + ) -> None: + node = DAGNode( + submodule_node, input_nodes, output_nodes, logical_devices, size_bytes + ) + self.nodes.append(node) + + +class PartitionResult(NamedTuple): + """NameTuple used for returning DAG and a new fx module""" + + dag: DAG + module_with_submodules: GraphModule + + +"""Followings are some helper functions for partition manipulation""" + + +def reset_partition_device(partitions): + for partition in partitions: + partition.logical_device_ids = [] + + +def combine_two_partitions( + partition_0: Partition, partition_1: Partition, partitions: list[Partition] +) -> None: + """Given a list of partitions and its two partitions, + combine these two partitions into a new one appending to the partitions + and remove the previous two partitions from the list of partitions + """ + partition = Partition(len(partitions)) + partition.nodes = partition_0.nodes.union(partition_1.nodes) + partition.recalculate_mem_size() + partitions.append(partition) + partitions.remove(partition_0) + partitions.remove(partition_1) + reorganize_partitions(partitions) + return + + +def set_parents_and_children(partitions: list[Partition]) -> None: + """Given a list of partitions, mark parents and children for each partition""" + # Go through all nodes in a partition. + # If a node's user is in other partition, + # then the other partition is this partition's children. + # This partition is the other partition's parent + for partition in partitions: + partition.children = set() + partition.parents = set() + for partition in partitions: + for node in partition.nodes: + # For each node in the current partition, find its users + users = node.users + for n in users: + # Find which the partition the user node belongs to. + # Note that if the node itself is also belongs to that partition, + # that partition is not the child of the current partition + for p in partitions: + if p != partition and n in p.nodes and node not in p.nodes: + partition.children.add(p) + p.parents.add(partition) + return + + +def reorganize_partitions(partitions: list[Partition]) -> None: + """Given a list of partitions, reorganize partition id, + its parents and its children for each partition + """ + # Rearrange partition ids + for i, partition in enumerate(partitions): + partition.partition_id = i + set_parents_and_children(partitions) + return + + +def get_bfs_level_partition(partitions: list[Partition]) -> None: + """Given a list of partitions, + mark the bfs level for each partition + """ + current_level: set[Partition] = set() + visited: set[Partition] = set() + for partition in partitions: + # If a partition has no parent, it should be in root level + if len(partition.parents) == 0: + current_level.add(partition) + next_level: set[Partition] = set() + level = 0 + # bfs + while current_level: + partition = current_level.pop() + partition.bfs_level = level + visited.add(partition) + children = partition.children + for child in children: + if child not in next_level: + next_level.add(child) + if not current_level: + current_level = next_level.copy() + next_level = set() + level += 1 + return + + +def get_node_to_partition_mapping(partitions: list[Partition]) -> dict[Node, int]: + """Given a list of partitions,return node to partition mapping""" + node_to_partition: dict[Node, int] = {} + for partition in partitions: + for node in partition.nodes: + node_to_partition[node] = partition.partition_id + return node_to_partition + + +def get_logical_id_to_device(devices: list[Device]) -> dict[int, Device]: + """Get a mapping from device logical ID to Device object.""" + logical_id_to_device: dict[int, Device] = {} + for d in devices: + logical_id_to_device[d.logical_id] = d + return logical_id_to_device + + +def get_device_partition_stats( + partitions: list[Partition], devices: list[Device] +) -> tuple[dict[Device, list[Partition]], dict[Device, int], list[Partition]]: + """Given a list of partitions and a list of devices, returns: + 1. A mapping from device to partitions on it; + 2. A mapping from device to its remaining memory size; + 3. A list of partitions that do not have a device. + """ + # logical id to device + logical_id_to_device = get_logical_id_to_device(devices) + # Track partitions on device + device_to_partitions: dict[Device, list[Partition]] = {} + # Track device's left mem size + device_to_left_mem_bytes: dict[Device, int] = {} + for d in devices: + device_to_partitions[d] = [] + device_to_left_mem_bytes[d] = d.available_mem_bytes + + # Deal with the partitions that already have a device + # and also collect all partitions without a device (no_device_partitions) + no_device_partitions = [] + for partition in partitions: + if partition.logical_device_ids != []: + for logical_id in partition.logical_device_ids: + device = logical_id_to_device[logical_id] + device_to_partitions[device].append(partition) + device_to_left_mem_bytes[device] -= partition.used_mem_bytes + else: + no_device_partitions.append(partition) + + return ( + device_to_partitions, + device_to_left_mem_bytes, + no_device_partitions, + ) + + +def get_device_to_partitions_mapping( + partitions: list[Partition], devices: list[Device] +): + """Given a list of partitions and a list of devices, + map each partition into a device. + """ + + def calculate_extra_mem_bytes_needed_for( + partition: Partition, partitions: list[Partition] + ): + all_nodes: set[Node] = set() + for p in partitions: + all_nodes = all_nodes.union(p.nodes) + if len(all_nodes) == 0: + return partition.used_mem_bytes + all_nodes = all_nodes.union(partition.nodes) + extra_size_needed = 0 + for node in partition.nodes: + extra_size_needed += get_extra_size_of(node, all_nodes) + return extra_size_needed + + def find_device_for(partition: Partition): + """Given a partition, find a logical device for the partition + The algorithm is to put the partition on the device + that has just enough mem left for that partition. + device_to_left_mem_bytes is a dictionary between device and its left mem size + sorted by its left mem size + """ + for d in device_to_left_mem_bytes: + extra_size_needed = calculate_extra_mem_bytes_needed_for( + partition, device_to_partitions[d] + ) + if extra_size_needed < device_to_left_mem_bytes[d]: + device_to_partitions[d].append(partition) + partition.logical_device_ids.append(d.logical_id) + device_to_left_mem_bytes[d] -= extra_size_needed + return True + return False + + ( + device_to_partitions, + device_to_left_mem_bytes, + no_device_partitions, + ) = get_device_partition_stats(partitions, devices) + + # Find devices for all the partitions without a device + found_device = True + for partition in no_device_partitions: + device_to_left_mem_bytes = dict( + sorted(device_to_left_mem_bytes.items(), key=operator.itemgetter(1)) + ) + found_device = find_device_for(partition) + if not found_device: + break + return found_device + + +def check_dependency(partition): + """Given a partition,check if there is a circular dependency on + this partition using bfs + """ + visited: set[Partition] = {partition} + queue: deque[Partition] = deque([partition]) + while queue: + p = queue.popleft() + for child in p.children: + if child == partition: + return True + else: + if child not in visited: + visited.add(child) + queue.append(child) + return False + + +class Partitioner: + """A fx module may not fit into one device. + Partitioner class helps partition one fx module into submodules (partitions), + so that the submodules can be executed crossing different accelerators. + The main function of this class is self.partition_graph. + It partitions the fx module based on the scheme specified in partition_config + A DAG structure is returned + along with a new fx module with submodule nodes. + """ + + def __init__(self) -> None: + self.partitions: list[Partition] = [] + self.node_to_partition: dict[Node, int] = {} + self.devices: list[Device] = [] + + def partition_graph( + self, + fx_module: GraphModule, + torch_module: torch.nn.Module, + partitioner_config: PartitionerConfig, + ) -> PartitionResult: + """Given the fx module, torch module and partitioner_config, + find the partitions, do the partitions, + and then return a DAG and a new fx module with submodule nodes (partitions) + """ + self.graph_module = fx_module + self.torch_module = torch_module + self.devices = partitioner_config.devices + if len(self.devices) == 0: + raise RuntimeError("No devices") + # Tag the size in bytes to all nodes in the graph_module. + get_size_of_all_nodes(self.graph_module) + # Check if there are op nodes in the fx module + nodes = self.graph_module.graph.nodes + if all(node.op in {"placeholder", "get_attr", "output"} for node in nodes): + raise RuntimeError("No Partition since no operations in the module") + # Calculate total size of the fx module + total_size_of_graph = 0 + for node in nodes: + if node.op == "output": + break + total_size_of_graph += node.size_bytes.total_size + # Find the device with the max mem size + device_with_max_mem = max(self.devices, key=lambda d: d.available_mem_bytes) + # AOT based partition + if partitioner_config.mode == PartitionMode.aot_based: + self.aot_based_partition( + partitioner_config.node_to_partition_mapping, + partitioner_config.partition_to_logical_device_mapping, + ) + # Single partition if the whole module can be fit into one device + elif total_size_of_graph <= device_with_max_mem.available_mem_bytes: + self.find_single_partition( + total_size_of_graph, logical_device_id=device_with_max_mem.logical_id + ) + elif total_size_of_graph > sum(d.available_mem_bytes for d in self.devices): + raise RuntimeError("Devices have no enough memory for the module") + else: + # Sparse nn based partition + if partitioner_config.mode == PartitionMode.sparse_nn: + available_mem_bytes = self.devices[0].available_mem_bytes + if not all( + device.available_mem_bytes == available_mem_bytes + for device in self.devices + ): + raise RuntimeError("All devices must have same memory size!") + # sparse_nn_partition only support same memory size + # TODO: add different size support for sparse_nn_partition + self.sparse_nn_partition(available_mem_bytes) + # Cost aware partition + elif partitioner_config.mode == PartitionMode.cost_aware: + self.cost_aware_partition( + partitioner_config.transfer_rate_bytes_per_sec, + partitioner_config.node_to_latency_mapping, + ) + # KL based partition + elif partitioner_config.mode == PartitionMode.kl_based: + self.kl_based_partition( + partitioner_config.transfer_rate_bytes_per_sec, + partitioner_config.node_to_latency_mapping, + ) + else: + self.size_based_partition() + + # Saturate host if possible. + if partitioner_config.saturate_host: + self.saturate_host() + + # Partition the graph module based on the partition assignment. + module_with_submodules = self.do_partition() + + # The DAG contains DAGNodes with info of each partition's input nodes, output nodes + # and how partitions are connected. + dag = self.dump_dag(module_with_submodules) + ret = PartitionResult(dag, module_with_submodules) + return ret + + def find_single_partition( + self, total_size_of_graph, logical_device_id: int = 0 + ) -> None: + """Fit the whole fx module into one device""" + partition_0 = self.create_partition() + for node in self.graph_module.graph.nodes: + if node.op == "output": + # Skip the output node, but there can + # be nodes after the output in certain cases. + continue + partition_0.nodes.add(node) + partition_0.used_mem_bytes = total_size_of_graph + partition_0.logical_device_ids = [logical_device_id] + # Get the node to partition mapping + self.node_to_partition = get_node_to_partition_mapping(self.partitions) + return + + def size_based_partition(self) -> None: + """This method is to partition the fx module based on memory size. + It uses greedy approach. The result may not be the best. + The basic idea is: + Step 1: + Find a device which has enough memory to fit the current node, create a empty partition + with the size of that device. + Then keep adding the following nodes into the partition until the partition is full. + Step 2: + Repeat Step 1 until no device left + Step 3: + If some nodes are left, create a partition for each left node (single node partition). + and then try to map those partitions into logical devices with enough mem left. + """ + + def find_device_based_on_size(node) -> Device: + """Given a node, this function is to find a logical device + that could fit the node. + """ + mem_size_needed = get_extra_size_of(node, set()) + device = Device("", -1, -1) + for d in self.devices: + if ( + d not in occupied_devices + and d.available_mem_bytes >= mem_size_needed + ): + device = d + break + if device.available_mem_bytes < 0: + raise RuntimeError(str(node) + "is too large to fit any device") + occupied_devices.append(device) + return device + + # Track partition and its left mem size + partition_to_left_mem_bytes: dict[Partition, int] = {} + # Track all the devices that have been used + occupied_devices: list[Device] = [] + partition = self.create_partition() + for node in self.graph_module.graph.nodes: + if node.op in {"call_module", "call_method", "call_function"}: + # Check if there are devices left + if len(self.partitions) <= len(self.devices): + total_size_of_input_nodes = get_extra_size_of(node, partition.nodes) + # Check if the current partition is the very first partition + if partition.used_mem_bytes == 0: + # Find a device to fit the first node, return available mem size + device = find_device_based_on_size(node) + occupied_devices.append(device) + # Update partition and its left mem size + partition_to_left_mem_bytes[partition] = ( + device.available_mem_bytes + ) + # Update available mem for the current partition + partition.logical_device_ids.append(device.logical_id) + else: + # The current partition is not the first partition + # Check if the current node can fit into current partition + if ( + partition_to_left_mem_bytes[partition] + < total_size_of_input_nodes + ): + # Check if no device is left + if len(self.partitions) == len(self.devices): + # No device is left + # Create the first single node partition for the current node + self.create_single_node_partition(node) + continue + # Some devices are still left + # Create a new partition with a mem size that is enough for the current node + device = find_device_based_on_size(node) + partition = self.create_partition() + total_size_of_input_nodes = get_extra_size_of( + node, partition.nodes + ) + partition_to_left_mem_bytes[partition] = ( + device.available_mem_bytes + ) + partition.logical_device_ids.append(device.logical_id) + partition.add_node(node) + partition_to_left_mem_bytes[partition] -= total_size_of_input_nodes + # Create single node partitions if no device is left + else: + self.create_single_node_partition(node) + reorganize_partitions(self.partitions) + # Get the node to partition mapping + self.node_to_partition = get_node_to_partition_mapping(self.partitions) + # Mapping all partitions into device + found_partition_to_device_mapping = get_device_to_partitions_mapping( + self.partitions, self.devices + ) + if not found_partition_to_device_mapping: + raise RuntimeError("Cannot Get a Valid Partition to Logical Device Mapping") + return + + def saturate_host(self) -> None: + """Saturate host by assigning replicates to unused devices with enough memory. + It uses a greedy approach to find a next available set of devices to place all split + partitions: For each used device, it searches for an idle device with minimal memory + size that can hold all the partition located on that device; If the search is successful + for all used devices, it then assigns the new devices' logical ID to the corresponding + partition. + """ + ( + device_to_partitions, + device_to_left_mem_bytes, + no_device_partitions, + ) = get_device_partition_stats(self.partitions, self.devices) + + assert len(no_device_partitions) == 0, ( + f"Expect no_device_partitions has 0 device, but get {len(no_device_partitions)}" + ) + + # Devices that hold partitions + used_devices = [d for d in self.devices if len(device_to_partitions[d]) > 0] + # Track replicates of the assigned devices + replicated_device_to_used_device: dict[Device, Device] = {} + + while len(used_devices) * 2 + len(replicated_device_to_used_device) <= len( + self.devices + ): + # Success flag for this round + success = True + # Devices that have not been assigned + idle_devices = [ + d + for d in self.devices + if d not in used_devices and d not in replicated_device_to_used_device + ] + # Temporary mapping from replicated device to original device + temp_replicate_mapping = {} + + # Find a new device to replicate all partitions on an used device + for used_device in used_devices: + # Idle devices that have enough memory + available_devices = [ + d + for d in idle_devices + if d.available_mem_bytes + >= used_device.available_mem_bytes + - device_to_left_mem_bytes[used_device] + ] + if len(available_devices) == 0: + success = False + break + new_device = min(available_devices, key=lambda d: d.available_mem_bytes) + idle_devices.remove(new_device) + temp_replicate_mapping[new_device] = used_device + + if not success: + break + replicated_device_to_used_device.update(temp_replicate_mapping) + + # Update logical device IDs assigned to the partitions + for ( + replicate_device, + original_device, + ) in replicated_device_to_used_device.items(): + logical_id = replicate_device.logical_id + for partition in device_to_partitions[original_device]: + partition.logical_device_ids.append(logical_id) + for p in self.partitions: + print(p.logical_device_ids) + + def do_partition(self) -> GraphModule: + """Return a new fx module with submodule nodes (partitions).""" + module_with_submodules = split_module( + self.graph_module, + self.torch_module, + lambda node: self.node_to_partition[node], + ) + return module_with_submodules + + def dump_dag(self, module_with_submodules: GraphModule) -> DAG: + """Return the dag structure and the new fx module with submodules.""" + dag = DAG() + for node in module_with_submodules.graph.nodes: + if node.op == "output": + break + if node.op in {"placeholder", "get_attr"}: + continue + if node.target is operator.__getitem__: + continue + input_nodes: dict[Node, None] = {} + map_arg(node.args, input_nodes.setdefault) + map_arg(node.kwargs, input_nodes.setdefault) + # When a node has two or more output nodes, + # it outputs its result to 'getitem' nodes. + # Those 'getitem' nodes are the output node for this node. + # Otherwise, the output node is this node itself. + if len(node.users) > 1: + output_nodes = list(node.users) + else: + output_nodes = [node] + partition_id = int(node.name.rsplit("_", 1)[-1]) + device_ids = self.partitions[partition_id].logical_device_ids + size_bytes = self.partitions[partition_id].used_mem_bytes + dag.create_node( + node, list(input_nodes), output_nodes, device_ids, size_bytes + ) + return dag + + def create_partition(self) -> Partition: + """Create a partition and append it to self.partitions.""" + partition_id = len(self.partitions) + partition = Partition(partition_id) + self.partitions.append(partition) + return partition + + def create_single_node_partition(self, node): + """Create a partition for a single node""" + partition = self.create_partition() + partition.add_node(node) + return + + def sparse_nn_partition(self, available_mem_bytes: int) -> None: + """This method partition a sparse nn module. + It is size based partition but different from size_based_partition, + it only works when all the devices have same memory size (available_mem_bytes). + In the future, devices with different mem sizes will be supported like size_based_partition. + It first traverse all the nodes and do the partitions based on the same memory size. + If the current partition has no enough memory left for a new op node + (call_module, call_method, call_function), a new partition is created. + When crossing the boundary between non-embedding nodes and embedding nodes, + a new partition is created regardlessly. + For example, if the current node is a non-embedding node but the next node is an + embedding node, a new partition is created for the next node. + After the partition, the partitions are combined as much as possible. + The rule is that a non-embedding partition only + combines with another non-embedding one. + So as the embedding partitions. + """ + + def combine_partitions_based_on_size( + partitions: list[Partition], available_mem_bytes: int + ) -> None: + """Combining small partitions together to keep as less partitions as possible. + Here is an example of the algorithm to do this: + Assume some partitions, we first sort them based on partition used memory size. + [(partition_4, 1), (partition_3, 1), (partition_2, 2), (partition_1, 7), (partition_0, 9)] + The available memory is 10. + step 1: self.find_partition_to_combine_based_on_size() + First, mark bfs level for each partition + Second, look the smallest partition, partition_4: 10 - 1 = 9 + It means any partition has a used memory equal or less than 9 could combine this partition + We go from the largest and selection partition_0. + Check the bfs level for two partitions, if the level difference is less than 2, + it can be combined. + step 2: repeat step 1 until no partitions can be combined + """ + find_combination = True + while find_combination: + # Sort partitions based on memory size + sorted_partitions = sorted(partitions, key=lambda p: p.used_mem_bytes) + # Mark bfs level + get_bfs_level_partition(self.partitions) + find_combination, partitions = find_partition_to_combine_based_on_size( + sorted_partitions, + available_mem_bytes, + # pyrefly: ignore [bad-argument-type] + partitions, + ) + return + + def calculate_mem_bytes_needed(p1, p2): + """Given two partitions, calculate how many mem bytes + are needed if two partitions are combined + """ + nodes = p1.nodes.union(p2.nodes) + mem_bytes_needed = 0 + for node in nodes: + mem_bytes_needed += get_extra_size_of(node, nodes) + return mem_bytes_needed + + def find_partition_to_combine_based_on_size( + sorted_partitions: list[Partition], + available_mem_bytes: int, + partitions: list[Partition], + ) -> tuple[bool, list[Partition]]: + """step 1 in combine_partition_based_on_size()""" + find_combination = False + smallest_partition = sorted_partitions.pop(0) + for p in sorted_partitions[::-1]: + if abs(smallest_partition.bfs_level - p.bfs_level) <= 1: + # Calculate how many bytes needed if combined + mem_bytes_needed = calculate_mem_bytes_needed(p, smallest_partition) + if mem_bytes_needed <= available_mem_bytes: + combine_two_partitions(p, smallest_partition, self.partitions) + partitions.remove(smallest_partition) + partitions.remove(p) + partitions.append(self.partitions[-1]) + find_combination = True + break + return find_combination, partitions + + def reset_partition_in_sparse_nn(partition, new_partition=True): + """If crossing the boundary between non-embedding nodes and + embedding nodes, create a new partition + """ + if in_embedding_region: + embedding_partitions.append(partition) + else: + non_embedding_partitions.append(partition) + if new_partition: + partition = self.create_partition() + # pyrefly: ignore [missing-attribute] + partition.left_mem_bytes = available_mem_bytes + return partition + return None + + def is_embedding_node(node: Node) -> bool: + """Check if a node is an embedding node""" + if node.op == "call_module": + submodule = self.graph_module + for atom in str(node.target).split("."): + if not hasattr(submodule, atom): + raise RuntimeError( + f"Module {submodule} has no attribute {atom}" + ) + submodule = getattr(submodule, atom) + if "Embedding" in str(submodule): + return True + return False + + # Track embedding partitions and non-embedding partitions separately + embedding_partitions: list[Partition] = [] + non_embedding_partitions: list[Partition] = [] + # A Flag to check the boundary + in_embedding_region: bool = False + partition = self.create_partition() + for node in self.graph_module.graph.nodes: + if node.op in {"call_module", "call_method", "call_function"}: + # Check if crossing the boundary between embedding nodes and non embedding nodes + if is_embedding_node(node) != in_embedding_region: + # Crossing the boundary + # Check if the current partition is an empty partition + if partition.used_mem_bytes != 0: + # The current partition isn't an empty partition. Create a new one. + partition = reset_partition_in_sparse_nn(partition) + in_embedding_region = not in_embedding_region + total_size_of_input_nodes = get_extra_size_of(node, partition.nodes) + if ( + total_size_of_input_nodes + partition.used_mem_bytes + > available_mem_bytes + ): + partition = reset_partition_in_sparse_nn(partition) + total_size_of_input_nodes = get_extra_size_of(node, partition.nodes) + if total_size_of_input_nodes > available_mem_bytes: + raise RuntimeError( + node.target + "is too large to fit into a device" + ) + partition.add_node(node) + reset_partition_in_sparse_nn(partition, new_partition=False) + # Set parents and children for partitions + set_parents_and_children(self.partitions) + # Combining non-embedding partitions + combine_partitions_based_on_size(non_embedding_partitions, available_mem_bytes) + # Combining embedding partitions + combine_partitions_based_on_size(embedding_partitions, available_mem_bytes) + total_size_of_non_embedding_partitions = 0 + for partition in non_embedding_partitions: + total_size_of_non_embedding_partitions += partition.used_mem_bytes + # Check if devices are enough for all partitions + if len(embedding_partitions) > len(self.devices): + msg = ( + "Need " + + str(len(embedding_partitions)) + + " devices, but only " + + str(len(self.devices)) + + " provided" + ) + raise RuntimeError(msg) + occupied_devices = [] + for i, partition in enumerate(embedding_partitions): + # Check if all non-embedding partitions can fit into embedding partition devices + if ( + total_size_of_non_embedding_partitions + partition.used_mem_bytes + > available_mem_bytes + ): + raise RuntimeError( + "partition_" + + str(partition.partition_id) + + "(embedding partition) and non embedding partitions can not fit into one device" + ) + else: + # Add logical device to the partition + partition.logical_device_ids = [self.devices[i].logical_id] + occupied_devices.append(self.devices[i].logical_id) + # Add logical devices to the non_embedding_partitions + for partition in non_embedding_partitions: + partition.logical_device_ids = occupied_devices + # Get the node to partition mapping + self.node_to_partition = get_node_to_partition_mapping(self.partitions) + return + + def cost_aware_partition( + self, + transfer_rate_bytes_per_sec: float, + node_to_latency_mapping: dict[Node, NodeLatency], + ) -> None: + """This method is to partition the fx module based on the cost. + The cost is the total latency of running the whole fx module. + In partitioner_utils.py, the cost model is built. + The cost aware partition algorithm is: + #1. At every beginning, each node is a partition. + Then we map all the partitions to the devices + and calculate the cost + #2. Then try to pre-combine any two of the partitions if the two + partitions can be combined. + (the bfs level is less than 2 or two partitions are connected and + can find partition to device mapping) + See if any partition pair could reduce the current cost. + Choose the pair that shows the minimum cost and then combine them + #3. Repeat #2 until the cost cannot be reduced. + """ + + def try_combining_partitions(p0_index, p1_index, partitions) -> float: + """Given two partitions and a list of partitions, combine these two partitions + and see what is the cost of the modified partition list + """ + p0 = partitions[p0_index] + p1 = partitions[p1_index] + """If two partitions' bfs level are less than 2 or two partitions are connected to each other, + then they can be combined + """ + if ( + (abs(p0.bfs_level - p1.bfs_level) <= 1) + or (p0 in p1.parents) + or p0 in (p1.children) + ): + combine_two_partitions(p0, p1, partitions) + # Check if a circular dependency exists after combining + if check_dependency(partitions[-1]): + return float("inf") + # Check if the modified partition list can be mapped to devices after combination + reset_partition_device(partitions) + found_deivce = get_device_to_partitions_mapping( + partitions, self.devices + ) + if not found_deivce: + return float("inf") + # Calculate the new cost + partition_to_latency_mapping = get_partition_to_latency_mapping( + partitions, node_to_latency_mapping + ) + cost = get_latency_of_partitioned_graph( + partitions, + partition_to_latency_mapping, + transfer_rate_bytes_per_sec, + ) + return cost + # If two partition can not be combined, the cost is inf + return float("inf") + + def search_combination( + transfer_rate_bytes_per_sec, node_to_latency_mapping + ) -> bool: + """Given transfer rate between partitions and each node's latency, + find two partitions to combine so the cost of the partitions can + be reduced. + The algorithm is : + 1. Go through all the partition pairs and see + if any pair of partitions can be combined. + 2. Calculate the cost after the combination. + 3. Select the minimum cost and combine its corresponding partition pair. + """ + partition_to_latency_mapping = get_partition_to_latency_mapping( + self.partitions, node_to_latency_mapping + ) + cost = get_latency_of_partitioned_graph( + self.partitions, + partition_to_latency_mapping, + transfer_rate_bytes_per_sec, + ) + if len(self.partitions) == 1: + return False + partition_pair: list[int] = [] + for i in range(len(self.partitions) - 1): + for j in range(i + 1, len(self.partitions)): + # Try to combine the partition pair + # and see the new cost after combination + new_cost = try_combining_partitions(i, j, self.partitions[:]) + if new_cost <= cost: + partition_pair = [i, j] + cost = new_cost + reorganize_partitions(self.partitions) + # If a partition pair is found, combine them + if len(partition_pair) != 0: + p0 = self.partitions[partition_pair[0]] + p1 = self.partitions[partition_pair[1]] + combine_two_partitions(p0, p1, self.partitions) + get_bfs_level_partition(self.partitions) + reset_partition_device(self.partitions) + get_device_to_partitions_mapping(self.partitions, self.devices) + return len(partition_pair) != 0 + + for node in self.graph_module.graph.nodes: + if node.op not in {"placeholder", "get_attr", "output"}: + self.create_single_node_partition(node) + # Set up parent partitions and children partitions for each partition + set_parents_and_children(self.partitions) + # Get bfs level for each partition + get_bfs_level_partition(self.partitions) + find_combination = True + while find_combination: + # Search for a pair partition to generate the minimum new cost, + # then combine them + find_combination = search_combination( + transfer_rate_bytes_per_sec, node_to_latency_mapping + ) + # Make sure all partitions are set up correctly + reorganize_partitions(self.partitions) + # Set up node to partition mapping + self.node_to_partition = get_node_to_partition_mapping(self.partitions) + return + + def kl_based_partition( + self, + transfer_rate_bytes_per_sec: float, + node_to_latency_mapping: dict[Node, NodeLatency], + ) -> None: + """This function is a cost aware partition based + on Kernighan-Lin algorithm. + First, the graph is partitioned using size_based_partition. + Then, each node is swapped with any other node in a different + partition, and at the same time, the cost is estimated after + the swapping. + For example, we have nodes n0, n1, n2, n3 and n4. + Using size_based_partition, n0 and n1 are in Partition p0. + n2, n3 and n4 in Partition p1. The current cost is estimated. + We first tried using n0 to swap with n2 from the other partition. + Then we see that swapping n0 and n2 shows a lower cost + than the current cost and it is the minimum among other pairs like + (n0, None)(This means moving n0 to Partition without swapping other nodes), + (n0, n3) and (n0, n4). We swap n0 and n2 and set the new cost + as the current cost. + Then We repeat this process for all the other nodes until all swapping pairs + are tried. + """ + + def swap_nodes(n0, n1, p0, p1): + # Either n0 or n1 could be None + # That means we simply move the node + # to another partition + if n0 is not None: + p0.remove_node(n0) + p1.add_node(n0) + if n1 is not None: + p0.add_node(n1) + p1.remove_node(n1) + + def try_swap_nodes( + n0, n1, p0, p1, node_to_latency_mapping, transfer_rate_per_sec + ): + cost = float("inf") + swap_nodes(n0, n1, p0, p1) + # Reorganize partitions after swapping + reorganize_partitions(self.partitions) + # Check if there is a circular dependency after swapping + if (not check_dependency(p0)) and (not check_dependency(p1)): + reset_partition_device(self.partitions) + partition_to_latency_mapping = get_partition_to_latency_mapping( + self.partitions, node_to_latency_mapping + ) + # Check if all partitions can be mapped to logical devices after swapping + found_device = get_device_to_partitions_mapping( + self.partitions, self.devices + ) + if not found_device: + cost = float("inf") + else: + cost = get_latency_of_partitioned_graph( + self.partitions, + partition_to_latency_mapping, + transfer_rate_bytes_per_sec, + ) + # Swap back and reset all partitions back to original + swap_nodes(n1, n0, p0, p1) + reorganize_partitions(self.partitions) + reset_partition_device(self.partitions) + get_device_to_partitions_mapping(self.partitions, self.devices) + return cost + + def swap_node_to_partition( + node, p0, p1, node_to_latency_mapping, transfer_rate_per_sec + ): + """This function helps to swap one node from partition p0 + with all the nodes in another partition p1 + """ + p1_nodes = list(p1.nodes) + [None] + min_cost = float("inf") + node_pair: list[Node] = [] + for n1 in p1_nodes: + # Ignore the node if it is not a op node + if n1 is not None and n1.op in {"placeholder", "get_attr"}: + continue + # Try swapping node in p0 with n1 in p1 + cost = try_swap_nodes( + node, n1, p0, p1, node_to_latency_mapping, transfer_rate_per_sec + ) + if cost < min_cost: + # pyrefly: ignore [bad-assignment] + node_pair = [node, n1] + min_cost = cost + return cost, node_pair # type: ignore[possibly-undefined] + + # First use size_base_partition + self.size_based_partition() + partition_to_latency_mapping = get_partition_to_latency_mapping( + self.partitions, node_to_latency_mapping + ) + # Calculate the cost of the partitions + cost = get_latency_of_partitioned_graph( + self.partitions, partition_to_latency_mapping, transfer_rate_bytes_per_sec + ) + # Keep tracking the node pair that shows the better cost + node_pair: list[Node] = [] + # Keep tracking the partition pair of node pair + partition_pair: list[Partition] = [] + # Collect all the op nodes from the graph + op_nodes = [ + n + for n in self.graph_module.graph.nodes + if n.op not in {"placeholder", "get_attr", "output"} + ] + for node in op_nodes: + # Find which partition the current node belongs + p0_index = self.node_to_partition[node] + p0 = self.partitions[p0_index] + # Go through all the other partitions to swap + # with other nodes from those partitions + for p1_index, _ in enumerate(self.partitions): + if p0_index != p1_index: + p1 = self.partitions[p1_index] + new_cost, new_node_pair = swap_node_to_partition( + node, + p0, + p1, + node_to_latency_mapping, + transfer_rate_bytes_per_sec, + ) + # Update the cost + # Track the swapped node pair and their partitions + if new_cost < cost: + cost = new_cost + node_pair = new_node_pair + partition_pair = [p0, p1] + # Do the swapping after trying all the nodes from a partition + if len(node_pair) != 0: + swap_nodes( + node_pair[0], node_pair[1], partition_pair[0], partition_pair[1] + ) + reorganize_partitions(self.partitions) + get_device_to_partitions_mapping(self.partitions, self.devices) + reorganize_partitions(self.partitions) + # Mapping the device to the partition + get_device_to_partitions_mapping(self.partitions, self.devices) + return + + def aot_based_partition( + self, node_to_partition_mapping, partition_to_logical_device_mapping + ): + """This function helps to rebuild the partitions given the nodes and its + corresponding partition id + """ + partition_id_to_partition_mapping: dict[int, Partition] = {} + self.node_to_partition = node_to_partition_mapping + for node in self.node_to_partition: + partition_id = self.node_to_partition[node] + # If the requested partition has not been created, create the partition + if partition_id not in partition_id_to_partition_mapping: + partition = Partition(partition_id) + self.partitions.append(partition) + partition_id_to_partition_mapping[partition_id] = partition + partition.logical_device_ids = partition_to_logical_device_mapping[ + partition_id + ] + else: + partition = partition_id_to_partition_mapping[ + self.node_to_partition[node] + ] + # Add the current node into the partition + partition.add_node(node) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/const_fold.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/const_fold.py new file mode 100644 index 0000000000000000000000000000000000000000..f494f11593410467623b680a7587e50a614be5a7 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/const_fold.py @@ -0,0 +1,354 @@ +# mypy: allow-untyped-defs +import re +from collections.abc import Callable +from typing import Optional, Union + +import torch.fx +from torch.fx.node import map_arg +from torch.fx.passes.split_module import split_module + + +__all__ = [ + "FoldedGraphModule", + "get_unique_attr_name_in_module", + "split_const_subgraphs", +] + + +class FoldedGraphModule(torch.fx.GraphModule): + """ + FoldedGraphModule is a GraphModule which also contains another + `const_subgraph_module` representing a subgraph which has all const attr + inputs and which can be run once before running the main standard + `graph`. The `const_output_names` are the ordered list names of attrs which + represent what each respective output from the const_subgraph should be set + on which attrs. + """ + + def __init__( + self, + root: torch.nn.Module, + graph: torch.fx.Graph, + const_subgraph: Optional[torch.fx.Graph] = None, + fx_const_folded_attrs_name: Optional[str] = None, + device_for_folded_attrs: str = "cuda", + ): + super().__init__(root, graph) + self.const_subgraph_module = ( + None + if const_subgraph is None + else torch.fx.GraphModule(root, const_subgraph) + ) + self.has_folding_been_run = False + self.fx_const_folded_attrs_name = fx_const_folded_attrs_name + self.device_for_folded_attrs = device_for_folded_attrs + + def __call__(self, *args, **kwargs): + if not self.has_folding_been_run: + self.run_folding() + return super().__call__(*args) + + def run_folding(self): + # If there's no const subgraph module or attr output names to use, return + # early as there is no const folding to perform. + if ( + self.const_subgraph_module is None + or self.fx_const_folded_attrs_name is None + ): + return + + assert not self.has_folding_been_run + self.has_folding_been_run = True + + # Actually run const folding subgraph. Note that single attr const fold + # subgraphs output a single Tensor while multiple outputs are returned as + # Tuple[Tensor,]. + folded_attrs = self.const_subgraph_module() + + def _create_param(i): + return torch.nn.Parameter( + i.detach().clone() + if not isinstance(i, int) + else torch.Tensor([i]).to(device=self.device_for_folded_attrs), + requires_grad=i.requires_grad if isinstance(i, torch.Tensor) else False, + ) + + params = ( + torch.nn.ParameterList([_create_param(i) for i in folded_attrs]) + if isinstance(folded_attrs, tuple) + else _create_param(folded_attrs) + ) + setattr(self, self.fx_const_folded_attrs_name, params) + + +def _inline_module(gm: torch.fx.GraphModule, inline_mod_name: str): + """ + Given `gm` and some graph module which is called with target name `inline_mod_name`, + this helper will inline all of the nodes from that called graph module into `gm`. + """ + # Fetch the inner graph module that we want to inline inside `gm`. + inline_mod = dict(gm.named_modules())[inline_mod_name] + assert isinstance(inline_mod, torch.fx.GraphModule) + call_mod_node_to_replace = None + for node in gm.graph.nodes: + if node.op == "call_module" and node.target == inline_mod_name: + call_mod_node_to_replace = node + break + assert call_mod_node_to_replace is not None + + # Now actually do the swap. Note that we have to keep track of new nodes that are + # copied into `gm` -- we do this via replacement_mapping. + call_mod_args = call_mod_node_to_replace.args + call_mod_kwargs = call_mod_node_to_replace.kwargs + + replacement_mapping: dict[torch.fx.Node, torch.fx.Node] = {} + ph_count = 0 + + def replacement_fn(node): + new_node = replacement_mapping[node] + new_node.meta = node.meta.copy() + return new_node + + for inline_node in inline_mod.graph.nodes: + if inline_node.op == "placeholder": + replacement_mapping[inline_node] = ( + call_mod_kwargs[inline_node.name] + if inline_node.name in call_mod_kwargs + else call_mod_args[ph_count] + ) + + ph_count += 1 + continue + + if inline_node.op == "output": + outputs = inline_node.args[0] + output_replacements = map_arg(outputs, replacement_fn) + call_mod_node_to_replace.replace_all_uses_with(output_replacements) + continue + + with gm.graph.inserting_before(call_mod_node_to_replace): + new_node = gm.graph.node_copy(inline_node, replacement_fn) + replacement_mapping[inline_node] = new_node + + # Explicitly remove the module that was just inlined, + # this module may contain impure ops so cannot be dead code eliminated, + # this module is unneeded as it's just inlined back to main graph. + gm.graph.erase_node(call_mod_node_to_replace) + gm.graph.eliminate_dead_code() + + +def get_unique_attr_name_in_module(mod_traced: torch.fx.GraphModule, name: str) -> str: + """ + Make sure the name is unique (in a module) and can represents an attr. + """ + # Delete all characters that are illegal in a Python identifier. + name = re.sub("[^0-9a-zA-Z_]+", "_", name) + if name[0].isdigit(): + name = f"_{name}" + # Now make sure it is in fact unique to the module by incrementing suffix value. + while hasattr(mod_traced, name): + match = re.match(r"(.*)_(\d+)$", name) + if match is None: + name = name + "_1" + else: + base, num = match.group(1, 2) + name = f"{base}_{int(num) + 1}" + + return name + + +def split_const_subgraphs( + module: Union[torch.nn.Module, torch.fx.GraphModule], + skip_folding_node_fn: Optional[Callable[[torch.fx.Node], bool]] = None, + device_for_folded_attrs: str = "cpu", +) -> FoldedGraphModule: + """ + Looks through `module` for any nodes that have all constant attribute inputs + and separates them out into their own constant subgraph, and returns a + FoldedGraphModule which runs that constant subgraph on the first run to set + attributes on the module prior to running the non-constant portion of the + graph. + """ + + import sympy + + if not isinstance(module, torch.fx.GraphModule): + mod_traced = torch.fx.symbolic_trace(module) + else: + mod_traced = module + + def _subgraph_has_impure_ops(module: torch.fx.GraphModule) -> bool: + """ + Return True if a GraphModule type subgraph contains any impure op, else False. + """ + assert isinstance(module, torch.fx.GraphModule), ( + "caller should only pass GraphModule to subgraph_has_impure_ops check" + ) + for node in module.graph.nodes: + if node.op == "call_function" and node.is_impure(): + return True + if ( + # pyrefly: ignore [invalid-argument] + node.op == "call_module" + # pyrefly: ignore [not-callable] + and (submodule := module.get_submodule(node.target)) + and isinstance(submodule, torch.fx.GraphModule) + ): + return _subgraph_has_impure_ops(submodule) + return False + + # Build up a list of const_nodes, defined as nodes that are themselves + # get_attrs, or have all get_attr or other constant node inputs. + const_nodes: set[torch.fx.Node] = set() + found_const_folding = False + for node in mod_traced.graph.nodes: + # Skip over placeholders/outputs because they can't be const folded and + # we don't want to add tags to them. + if node.op in {"placeholder", "output"}: + continue + + # If the node itself is constant, or all of its inputs are constant, + # then tag it as constant. + if node.op != "get_attr" and not set(node.all_input_nodes).issubset( + const_nodes + ): + continue + + # If provided skip folding function says to skip, then skip. + if skip_folding_node_fn and skip_folding_node_fn(node): + continue + + # Skip folding side-effectful functions + if node.is_impure(): + continue + + # Skip folding nodes that have symbolic fill_value + if isinstance(node.kwargs.get("fill_value", None), sympy.Expr): + continue + + # Skip folding submodules that have impure ops + if ( + # pyrefly: ignore [invalid-argument] + node.op == "call_module" + # pyrefly: ignore [not-callable] + and (target_mod := mod_traced.get_submodule(node.target)) + and isinstance(target_mod, torch.fx.GraphModule) + and _subgraph_has_impure_ops(target_mod) + ): + continue + + # Must be a constant foldable node at this point. + const_nodes.add(node) + if node.op != "get_attr": + found_const_folding = True + + # If we did not find any const folding then return early without a const fold subgraph. + if not found_const_folding: + return FoldedGraphModule(mod_traced, mod_traced.graph) + + # Partition the module into two: submod_0 for constant folding subgraph, and + # submod_1 for the rest. + def mod_partition(node: torch.fx.Node): + return 0 if node in const_nodes else 1 + + split = split_module(mod_traced, module, mod_partition) + + const_mod_name, non_const_mod_name = "submod_0", "submod_1" + # Safely get submod_1 in case there are no non-const nodes + const_gm, non_const_gm = split.submod_0, getattr(split, non_const_mod_name, None) + + # The module that a call_module node refers to gets copied to submodules during split. + # The path to the module also gets inlined, i.e. mod.a.b -> mod_a_b. Here we need to + # attach inlined modules to `split` as it's the owning module now. + for node in non_const_gm.graph.nodes if non_const_gm else []: + if node.op == "call_module": + setattr(split, node.target, getattr(non_const_gm, node.target)) + for node in const_gm.graph.nodes: + if node.op == "call_module": + setattr(split, node.target, getattr(const_gm, node.target)) + + # split_module currently does not use get_attrs for attrs. Instead it passes + # them in as args from the parent module, which used get_attrs. Here we set + # them as get_attrs inside const_gm, allowing for running folding without + # somehow a priori knowing the attrs that should be passed as args. We can + # unconditionally do this for all placeholders because we know all + # placeholders to const_gm must be constants accessible via get_attr. + call_const_gm_args = None + for node in split.graph.nodes: + if node.op == "call_module": + if node.target == const_mod_name: + call_const_gm_args = node.args + break + assert call_const_gm_args is not None + + # Here we do the actual replacement of placeholders to get_attrs. Note that here we + # set the const_gm.graph into a new root_const_gm with split as the root module, + # because we are fetching attributes directly from the root module, instead of + # fetching them from const_gm. Example: The const_gm must have some format like: + # graph(): + # %inp : [num_users=1] = placeholder[target=const_inp] + # %add : [num_users=1] = call_function[target=operator.add](args = (%inp, %inp), kwargs = {}) + # return add + # We replace that with the following, which does not have any placeholders: + # graph(): + # %inp_1 : [num_users=1] = get_attr[target=const_inp] + # %add : [num_users=1] = call_function[target=operator.add](args = (%inp_1, %inp_1), kwargs = {}) + # return add + root_const_gm = torch.fx.GraphModule(split, const_gm.graph) + + # The order of placeholders in the const_gm graph should match the order of + # args in the outer module, so we can simply use an index for the + # placeholder mapping + ph_idx = 0 + for node in root_const_gm.graph.nodes: + if node.op == "output": + multiple_outputs = isinstance(node.args[0], tuple) + continue + if node.op != "placeholder": + continue + assert ph_idx < len(call_const_gm_args) + in_node = call_const_gm_args[ph_idx] + ph_idx += 1 + assert in_node.op == "get_attr" + with root_const_gm.graph.inserting_before(node): + new_node = root_const_gm.graph.get_attr(in_node.target) + new_node.meta = node.meta.copy() + node.replace_all_uses_with(new_node) + root_const_gm.graph.erase_node(node) + assert "multiple_outputs" in locals() + + # Now find the call to const_gm inside split, and replace it with a getattr to the + # folded tensor(s) that result from constant folding. Note that we don't need to + # worry about whether this is one or more tensors because the original graph + # correctly uses getitem to extract individual tensors if there are multiple folded. + fx_const_folded_attrs_name = get_unique_attr_name_in_module( + mod_traced, "_FX_CONST_FOLDED_ATTRS" + ) + setattr( + split, + fx_const_folded_attrs_name, + torch.nn.ParameterList() if multiple_outputs else torch.nn.Parameter(), # type: ignore[possibly-undefined] + ) + for node in split.graph.nodes: + if node.op == "call_module" and node.target == const_mod_name: + with node.graph.inserting_before(node): + folded_attrs = node.graph.get_attr(fx_const_folded_attrs_name) + folded_attrs.meta = node.meta.copy() + node.replace_all_uses_with(folded_attrs) + break + + # Finally, inline the non-constant submod (if it exists) into the split submod. + # This is so that the original caller who may have passed in a graph module will + # get back out a graph module whose graph is traced to the same granularity. + if hasattr(split, non_const_mod_name): + _inline_module(split, non_const_mod_name) + + split.graph.eliminate_dead_code() + + return FoldedGraphModule( + split, + split.graph, + root_const_gm.graph, + fx_const_folded_attrs_name, + device_for_folded_attrs, + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/debug.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/debug.py new file mode 100644 index 0000000000000000000000000000000000000000..b87dee9db9c73f0b4ea1a0a27682a167e125a71d --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/debug.py @@ -0,0 +1,33 @@ +from collections.abc import Sequence + +import torch.fx as fx + + +__all__ = ["set_trace"] + + +def set_trace(gm: fx.GraphModule) -> fx.GraphModule: + """ + Sets a breakpoint in `gm`'s generated python code. It drops into pdb when + `gm` gets run. + + Args: + gm: graph module to insert breakpoint. It is then recompiled for it to + take effect. + + Returns: + the `gm` with breakpoint inserted. + """ + + def insert_pdb(body: Sequence[str]) -> list[str]: + return ["import pdb; pdb.set_trace()\n", *body] + + with gm.graph.on_generate_code( + make_transformer=lambda cur_transform: ( + # new code transformer to register + lambda body: (insert_pdb(cur_transform(body) if cur_transform else body)) + ) + ): + gm.recompile() + + return gm diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/graph_gradual_typechecker.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/graph_gradual_typechecker.py new file mode 100644 index 0000000000000000000000000000000000000000..58a62aee314607320bb5f7eb922192888fa172a5 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/graph_gradual_typechecker.py @@ -0,0 +1,1011 @@ +# mypy: allow-untyped-defs +import itertools +import operator +from collections.abc import Callable +from functools import reduce +from typing import TypeVar +from typing_extensions import ParamSpec + +import sympy + +import torch +from torch.fx.experimental.refinement_types import Equality +from torch.fx.experimental.unification import Var # type: ignore[attr-defined] +from torch.fx.node import Node, Target +from torch.fx.tensor_type import Dyn, is_consistent, is_more_precise, TensorType +from torch.nn.modules.batchnorm import BatchNorm2d +from torch.nn.modules.conv import Conv2d + + +_T = TypeVar("_T") +_P = ParamSpec("_P") + +_INFERENCE_RULES: dict[Target, Callable] = {} +_REFINEMENT_RULES: dict[Target, Callable] = {} +_RULES: dict[Target, Callable] = {} + +__all__ = [ + "GraphTypeChecker", + "Refine", + "adaptiveavgpool2d_check", + "adaptiveavgpool2d_inference_rule", + "add_inference_rule", + "all_eq", + "bn2d_inference_rule", + "broadcast_types", + "calculate_out_dimension", + "conv2d_inference_rule", + "conv_refinement_rule", + "conv_rule", + "element_wise_eq", + "expand_to_tensor_dim", + "first_two_eq", + "flatten_check", + "flatten_inference_rule", + "flatten_refinement_rule", + "get_attr_inference_rule", + "get_greatest_upper_bound", + "get_parameter", + "linear_check", + "linear_inference_rule", + "linear_refinement_rule", + "maxpool2d_check", + "maxpool2d_inference_rule", + "register_algebraic_expressions_inference_rule", + "register_inference_rule", + "register_refinement_rule", + "relu_inference_rule", + "reshape_inference_rule", + "transpose_inference_rule", +] + + +def expand_to_tensor_dim(t, n): + """ + Expand a type to the desired tensor dimension if possible + Raise an error otherwise. + - t is the given type + - n is a number of dimensions to expand to + """ + if t == Dyn: + dims = [Dyn] * n + return TensorType(tuple(dims)) + elif isinstance(t, TensorType): + if len(t.__args__) != n: + raise TypeError( + f"Cannot extend tensor. Tensor {t} has rank {len(t.__args__)}. It should have rank {n}" + ) + return t + else: + raise TypeError(f"Cannot match the type {t}") + + +def broadcast_types(t1, t2): + """ + Applies broadcasting to both given types such that they + become consistent with each other and returns two new + resulting types + """ + + # if either type is Dyn, do nothing since the types are already consistent + if t1 == Dyn or t2 == Dyn or isinstance(t1, Var) or isinstance(t2, Var): + return t1, t2 + + if isinstance(t1, TensorType) and isinstance(t2, TensorType): + s1 = len(t1.__args__) + s2 = len(t2.__args__) + + new_t1 = list(t1.__args__) + new_t2 = list(t2.__args__) + + # We make the types the same length which is the first requirement + # for consistency + if s1 > s2: + for _ in range(s1 - s2): + new_t2.insert(0, 1) + + elif s2 > s1: + for _ in range(s2 - s1): + new_t1.insert(0, 1) + + # we replace occurrences of "1" with each tensor with + # the corresponding type from the other tensor + for i, (x, y) in enumerate(zip(new_t1, new_t2)): + if x == 1: + new_t1[i] = y + elif y == 1: + new_t2[i] = x + + # at this point our tensors should be consistent + # and we can apply the element-wise operation and find the right dimension + # for the output of the operation + (t1, t2) = TensorType(tuple(new_t1)), TensorType(tuple(new_t2)) + return (t1, t2) + else: + raise TypeError(f"Cannot broadcast types {t1} and {t2}") + + +def register_inference_rule( + call_target: Target, +) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]: + def register(fn: Callable[_P, _T]) -> Callable[_P, _T]: + if call_target in _INFERENCE_RULES: + raise RuntimeError(f"Inference rule already registered for {call_target}!") + _INFERENCE_RULES[call_target] = fn + return fn + + return register + + +def register_refinement_rule( + call_target: Target, +) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]: + def register(fn: Callable[_P, _T]) -> Callable[_P, _T]: + if call_target in _REFINEMENT_RULES: + raise RuntimeError(f"Refinement rule already registered for {call_target}!") + _REFINEMENT_RULES[call_target] = fn + return fn + + return register + + +def register_algebraic_expressions_inference_rule( + call_target: Target, +) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]: + def register(fn: Callable[_P, _T]) -> Callable[_P, _T]: + if call_target in _RULES: + raise RuntimeError(f"Rule already registered for {call_target}!") + _RULES[call_target] = fn + return fn + + return register + + +@register_inference_rule(torch.add) +@register_inference_rule(operator.add) +def add_inference_rule(n: Node): + """ + Apply the addition inference rule. This includes: + - scalar addition + - broadcasting semantics + + Note that we always return the least precise type between + the operands (after applying broadcasting) to be the final type of the operation + + Note that we do not modify the operand types themselves after applying broadcasting + to them. We only use them to calculate the final type + """ + assert isinstance(n.args[0], Node) + assert isinstance(n.args[1], Node) + t1 = n.args[0].type + t2 = n.args[1].type + + # handle scalar addition + if t1 is int and isinstance(t2, TensorType): + n.type = t2 + return n.type + + # handle scalar addition + elif t2 is int and isinstance(t1, TensorType): + n.type = t1 + return n.type + + # we bring the new types to the point where + # we can check for consistency + # any inconsistency would not have been caused + # by broadcasting at this point + (new_t1, new_t2) = broadcast_types(t1, t2) + + if new_t1 != t1 or new_t2 != t2: + n.meta["broadcast"] = True + n.meta[str(n.args[0])] = new_t1 + n.meta[str(n.args[1])] = new_t2 + + else: + n.meta["broadcast"] = False + + new_t1 = t1 if not n.meta["broadcast"] else new_t1 + new_t2 = t2 if not n.meta["broadcast"] else new_t2 + + # we check for consistency between the new types + if is_consistent(new_t1, new_t2): + # we return the less precise type because + # broadcasting may have happened + # for operands with shape [1,2,Dyn] and [1,2,1] + # we have to assign the node [1,2,Dyn] + if is_more_precise(new_t1, new_t2): + n.type = new_t2 + else: + n.type = new_t1 + return n.type + else: + raise TypeError( + f"Cannot add arguments {n.args[0]} ({n.args[0].type}) and {n.args[1]} ({n.args[1].type}) in node {n}." + f" Types should match " + ) + + +@register_inference_rule(getattr) +def get_attr_inference_rule(n: Node, traced): + """ + The current getattr rule only handles the shape attribute + Can be extended to other attributes + The most representitive type we have is "Dyn" but the system + can be extended with more types, such as a type to represent shapes + """ + attr_name = n.args[1] + + if attr_name == "shape": + n.type = Dyn + else: + raise TypeError("Not yet implemented") + + # TODO. We leave it like this till we add a type to represent tensor sizes + return n.type + + +@register_inference_rule(torch.transpose) +def transpose_inference_rule(n: Node): + """ + We check that dimensions for the transpose operations + are within range of the tensor type of the node + """ + if n.target is torch.transpose: + assert isinstance(n.args[0], Node) + t = n.args[0].type + + assert isinstance(n.args[1], int) + assert isinstance(n.args[2], int) + dim1, dim2 = n.args[1], n.args[2] + + if t == Dyn: + n.type = Dyn + return n.type + + elif isinstance(t, TensorType): + if 0 <= dim1 < len(t.__args__) and 0 <= dim2 < len(t.__args__): + new_type = list(t.__args__) + new_type[dim1], new_type[dim2] = new_type[dim2], new_type[dim1] + final = TensorType(new_type) + n.type = get_greatest_upper_bound(n.type, final) + return n.type + else: + raise TypeError( + f"Cannot transpose {dim1} and {dim2} in type {t} for node {n}" + ) + else: + raise TypeError( + f"Cannot transpose {dim1} and {dim2} in type {t} for node {n}" + ) + + +@register_inference_rule(torch.reshape) +def reshape_inference_rule(n: Node): + """ + Without dynamism, the rule checks that the + product of the elements of the argument tensor + type is equal to the product of the elements + of the required shape. We gradualize this rule + by adding a case to handle fully dynamic input + as well as input where some of the tensor dimensions + are unknown. In this case we check for divisibility + """ + assert isinstance(n.args[0], Node) + t1 = n.args[0].type + + assert isinstance(n.args[1], list) + t2 = n.args[1] + t2_type = TensorType([Dyn if elem == -1 else elem for elem in t2]) + + # if we do not know the original tensor dimension, + # we return the required dimension + if t1 == Dyn: + n.type = t2_type + return t2_type + + # if any of the dimensions are unknown, + # we check for divisibility + elif isinstance(t1, TensorType): + assert isinstance(t1, TensorType) + a = [e if e != Dyn else 1 for e in t1.__args__] + p1 = reduce(operator.mul, a) + p2 = reduce(operator.mul, t2) + if p1 % p2 == 0 or p2 % p1 == 0: + n.type = t2_type + return t2_type + else: + raise TypeError(f"Cannot reshape in node {n} from {t1} to {t2_type}") + else: + raise TypeError(f"Cannot reshape in node {n} from {t1} to {t2_type}") + + +@register_inference_rule(BatchNorm2d) +def bn2d_inference_rule(n: Node, module_instance): + """ + Given a BatchNorm2D instance and a node check the following conditions: + - the input type can be expanded to a size 4 tensor: t = (x_1, x_2, x_3, x_4) + - the current node type can be expanded to a size 4 tensor: t' = (x_1', x_2', x_3', x_4') + - t is consistent with t' + - x_2 is consistent with the module's num_features + - x_2' is consistent with the module's num_features + output type: the more precise type of t and t' + """ + assert isinstance(n.args[0], Node) + n.args[0].type = expand_to_tensor_dim(n.args[0].type, 4) + arg_type = n.args[0].type + n.type = expand_to_tensor_dim(n.type, 4) + + # we check the conditions on the incoming argument + # and any existing annotation + # we also check for consistency between both annotations + if ( + is_consistent(arg_type.__args__[1], module_instance.num_features) + and is_consistent(n.type.__args__[1], module_instance.num_features) + and is_consistent(arg_type, n.type) + ): + # we choose the more precise type + # to be the node type + # so if an incoming argument has more type information + # we set this node's type to be the argument type + n.type = get_greatest_upper_bound(arg_type, n.type) + return n.type + else: + raise TypeError( + f"Cannot apply {module_instance} with input type {arg_type} and existing type {n.type} on {n}" + ) + + +def calculate_out_dimension(d_in, module_instance, index): + """ + For calculating h_in and w_out according to the conv2D documentation + """ + padding = ( + (module_instance.padding, module_instance.padding) + if isinstance(module_instance.padding, int) + else module_instance.padding + ) + kernel_size = ( + (module_instance.kernel_size, module_instance.kernel_size) + if isinstance(module_instance.kernel_size, int) + else module_instance.kernel_size + ) + stride = ( + (module_instance.stride, module_instance.stride) + if isinstance(module_instance.stride, int) + else module_instance.stride + ) + dilation = ( + (module_instance.dilation, module_instance.dilation) + if isinstance(module_instance.dilation, int) + else module_instance.dilation + ) + + DIMENSION_TYPES = (int, sympy.Symbol) + + if d_in == Dyn: + return Dyn + + elif isinstance(d_in, DIMENSION_TYPES): + n = d_in + 2 * padding[index] - dilation[index] * (kernel_size[index] - 1) - 1 + + return (n // stride[0]) + 1 + + else: + raise TypeError( + f"{d_in} in {module_instance} must be a number or Dyn. Received {type(d_in)}" + ) + + +def get_greatest_upper_bound(type1, type2): + """ + Get the most precise type that's consistent with the given types + """ + if type1 == Dyn: + return type2 + elif type2 == Dyn: + return type1 + elif isinstance(type1, TensorType) and isinstance(type2, TensorType): + if not is_consistent(type1, type2): + raise TypeError(f"Inconsistent types {type1}, {type2}") + gub = [ + t1 if is_more_precise(t1, t2) else t2 + for (t1, t2) in zip(type1.__args__, type2.__args__) + ] + return TensorType(tuple(gub)) + + +@register_inference_rule(Conv2d) +def conv2d_inference_rule(n: Node, module_instance): + """ + Given a Conv2D instance and a node check the following conditions: + - the input type can be expanded to a size 4 tensor: t = (x_1, x_2, H, W) + - the current node type can be expanded to a size 4 tensor: t' = (x_1', x_2', x_3', x_4') + - x_2 is consistent with the module's in_channels + - let o = (x_1, out_channels, H_out, W_out) + then the output is the greatest upper bound of o and the existing node type t'. + """ + assert isinstance(n.args[0], Node) + n.args[0].type = expand_to_tensor_dim(n.args[0].type, 4) + arg_type = n.args[0].type + curr_node_type = expand_to_tensor_dim(n.type, 4) + + if is_consistent(arg_type.__args__[1], module_instance.in_channels): + w_in = arg_type.__args__[3] + h_in = arg_type.__args__[2] + h_out = calculate_out_dimension(h_in, module_instance, 0) + w_out = calculate_out_dimension(w_in, module_instance, 1) + new_type = TensorType( + (arg_type.__args__[0], module_instance.out_channels, h_out, w_out) + ) + gub = get_greatest_upper_bound(new_type, curr_node_type) + n.type = gub + return n.type + else: + raise TypeError( + f"Cannot apply {module_instance} with input type {arg_type} and existing type {n.type} on {n}" + ) + + +@register_inference_rule(torch.nn.ReLU) +def relu_inference_rule(n: Node, module_instance): + """ + Input and output shapes should be equal. + """ + assert isinstance(n.args[0], Node) + + if n.args[0].type == Dyn and isinstance(n.type, TensorType): + n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__)) + + if isinstance(n.args[0].type, TensorType): + n.type = get_greatest_upper_bound(n.args[0].type, n.type) + return n.type + + +def maxpool2d_check(typ, module_instance): + """ + Applies the maxpool2d shape information to the input + this affects the last two dimensions + """ + new_type_list = list(typ.__args__) + if len(new_type_list) == 4 or len(new_type_list) == 3: + w_in = new_type_list[-1] + h_in = new_type_list[-2] + + h_out = calculate_out_dimension(h_in, module_instance, 0) + w_out = calculate_out_dimension(w_in, module_instance, 1) + + new_type_list[-1] = w_out + new_type_list[-2] = h_out + return TensorType(tuple(new_type_list)) + + else: + raise TypeError(f"Wrong size {typ} for {module_instance}") + + +@register_inference_rule(torch.nn.MaxPool2d) +def maxpool2d_inference_rule(n: Node, module_instance): + """ + Given a MaxPool2D instance and a node check the following conditions: + - Input size matches size 3 or 4 + - Current node type is consistent with the output type we will calculate + - Input size matches output size and the last two dimensions of the output + are w_out and h_out. The remaining dimensions are the same as the input + - Our final result is the greatest upper bound of the output we calculate + and the current node type. + """ + assert isinstance(n.args[0], Node) + + if n.args[0].type == Dyn and isinstance(n.type, TensorType): + n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__)) + if isinstance(n.args[0].type, TensorType): + output = maxpool2d_check(n.args[0].type, module_instance) + n.type = get_greatest_upper_bound(output, n.type) + return n.type + + +def linear_check(tensor_type, module_instance): + """ + Checks that an input tensor type satisfies the conditions for linear operation + and returns the output type based on in and out features given by module_instance + """ + if len(tensor_type.__args__) >= 2: + if is_consistent(module_instance.in_features, tensor_type.__args__[-1]): + new_type_args = list(tensor_type.__args__) + new_type_args[-1] = module_instance.out_features + return TensorType(tuple(new_type_args)) + else: + raise TypeError( + f"Inconsistent {module_instance.in_features} and {tensor_type.__args__[-1]} in {module_instance}" + ) + else: + raise TypeError(f"Type {tensor_type} must have rank 2 or more.") + + +@register_inference_rule(torch.nn.Linear) +def linear_inference_rule(n: Node, module_instance): + """ + Applies the shape information to the input then gets the greatest upper bound + of the resulting type and the existing type + """ + assert isinstance(n.args[0], Node) + if n.args[0].type == Dyn and isinstance(n.type, TensorType): + n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__)) + if isinstance(n.args[0].type, TensorType): + output_type = linear_check(n.args[0].type, module_instance) + n.type = get_greatest_upper_bound(output_type, n.type) + return n.type + + +def adaptiveavgpool2d_check(tensor_type, module_instance): + output_size = module_instance.output_size + if isinstance(output_size, int): + output_size = [output_size, output_size] + elif isinstance(output_size, tuple): + output_size = list(output_size) + if output_size[0] is None: + output_size[0] = output_size[1] + if output_size[1] is None: + output_size[1] = output_size[0] + + new_type_list = list(tensor_type.__args__) + + if len(tensor_type.__args__) == 4 or len(tensor_type.__args__) == 3: + new_type_list[-1] = output_size[1] + new_type_list[-2] = output_size[0] + + return TensorType(tuple(new_type_list)) + + else: + raise TypeError(f"Tensor ranks must be 3 or 4. Got {tensor_type}") + + +@register_inference_rule(torch.nn.AdaptiveAvgPool2d) +def adaptiveavgpool2d_inference_rule(n: Node, module_instance): + """ + The input and output sizes should be the same except for the last + two dimensions taken from the input, which represent width and height + """ + assert isinstance(n.args[0], Node) + if n.args[0].type == Dyn and isinstance(n.type, TensorType): + n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__)) + if isinstance(n.args[0].type, TensorType): + output_type = adaptiveavgpool2d_check(n.args[0].type, module_instance) + n.type = get_greatest_upper_bound(n.type, output_type) + return n.type + + +def flatten_check(tensor_type, start_dim, end_dim): + l = len(tensor_type.__args__) + + start_dim = l if start_dim == -1 else abs(start_dim) + end_dim = l + end_dim + 1 if end_dim < 0 else end_dim + 1 + + if 0 <= start_dim <= (l - 1) and 0 <= end_dim <= l and start_dim < end_dim: + my_args = list(tensor_type.__args__) + lhs = my_args[0:start_dim] + rhs = my_args[end_dim:] + mid = my_args[start_dim:end_dim] + if Dyn in mid: + mid = [Dyn] + else: + mid = [reduce(operator.mul, my_args[start_dim:end_dim])] + new_type_list = lhs + mid + rhs + return TensorType(tuple(new_type_list)) + else: + raise TypeError( + f"Incompatible dimensions {start_dim}, {end_dim - 1} in type {tensor_type}" + ) + + +@register_inference_rule(torch.flatten) +def flatten_inference_rule(n: Node): + """ + Applies the flatten shape information to the input then gets the + greatest upper bound of the resulting type and the existing type + """ + assert isinstance(n.args[0], Node) + + # set the default start and end dims + start_dim = 1 + end_dim = -1 + + if len(n.args) > 1: + assert isinstance(n.args[1], int) + start_dim = n.args[1] + + if len(n.args) > 2: + assert isinstance(n.args[2], int) + end_dim = n.args[2] + + if n.args[0].type == Dyn and isinstance(n.type, TensorType): + n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__)) + + if isinstance(n.args[0].type, TensorType): + output_type = flatten_check(n.args[0].type, start_dim, end_dim) + n.type = get_greatest_upper_bound(output_type, n.type) + + return n.type + + +class GraphTypeChecker: + def __init__(self, env, traced): + self.env = env + self.traced = traced + + def type_check(self): + """ + A gradual type checker for graphs + Effect: every node's field type will be + populated with a type after type-checking is done + """ + graph = self.traced.graph + + # type check every node with gradual type rules + # if any node does not type check return false + for n in graph.nodes: + self.type_check_node(n) + return True + + def type_check_node(self, n: Node): + """ + Type check a given fx node. + Current operations: + - Reshape + - Transpose + - Add + - Relu + - conv2d + - batchnorm2d + - flatten + - maxpool2d + - adaptiveavgpool2d + - linear + """ + if n.type is None: + n.type = Dyn + + if n.op == "placeholder": + return n.type + + elif n.op == "get_attr": + t = get_parameter(self.traced, n.target) # type: ignore[arg-type] + if isinstance(t.data, torch.Tensor): + n.type = TensorType(t.data.shape) + return n.type + + elif n.op == "call_function": + if n.target is getattr: + assert getattr in _INFERENCE_RULES + return _INFERENCE_RULES[n.target](n, self.traced) + + elif n.target in _INFERENCE_RULES: + return _INFERENCE_RULES[n.target](n) + else: + raise RuntimeError( + f"No inference rule registered for target {n.target}!" + ) + + elif n.op == "call_module": + module_instance = self.traced.get_submodule(n.target) + if type(module_instance) in _INFERENCE_RULES: + return _INFERENCE_RULES[type(module_instance)](n, module_instance) + else: + raise RuntimeError( + f"No inference rule registered for class {type(module_instance)}!" + ) + + elif n.op == "output": + + def get_node_type(a): + return a.type + + n.type = torch.fx.node.map_arg(n.args[0], get_node_type) + return n.type + + else: + raise NotImplementedError(f"Method {n.op} not yet implemented") + + +@register_refinement_rule(Conv2d) +def conv_refinement_rule(n: Node): + """ + The equality constraints are between the first dimension of + the input and output + """ + res = [] + assert isinstance(n.args[0], Node) + arg_type = n.args[0].type + if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType): + res = [Equality(arg_type.__args__[0], n.type.__args__[0])] + return res + + +@register_refinement_rule(torch.nn.Linear) +def linear_refinement_rule(n: Node): + """ + The equality constraints are between the first dimension of + the input and output + """ + res = [] + assert isinstance(n.args[0], Node) + arg_type = n.args[0].type + if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType): + res = [Equality(arg_type.__args__[0], n.type.__args__[0])] + return res + + +@register_refinement_rule(BatchNorm2d) +@register_refinement_rule(torch.nn.ReLU) +def all_eq(n: Node): + """ + For operations where the input shape is equal to the output shape + """ + res = [] + assert isinstance(n.args[0], Node) + arg_type = n.args[0].type + if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType): + args1 = arg_type.__args__ + args2 = n.type.__args__ + res = [Equality(args1[i], args2[i]) for i in range(len(args1))] + return res + + +@register_refinement_rule(torch.nn.AdaptiveAvgPool2d) +@register_refinement_rule(torch.nn.MaxPool2d) +def first_two_eq(n: Node): + """ + For operations where the first two dimensions of the input and output shape + are equal + """ + res = [] + assert isinstance(n.args[0], Node) + arg_type = n.args[0].type + if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType): + args1 = arg_type.__args__ + args2 = n.type.__args__ + res = [Equality(args1[0], args2[0]), Equality(args1[1], args2[1])] + return res + + +@register_refinement_rule(torch.add) +@register_refinement_rule(operator.add) +def element_wise_eq(n: Node): + """ + For element-wise operations and handles broadcasting. + Note that after applying broadcasting to the arguments + we are able to determine if certain dimensions have not been broadcast + if they are symbolicallu equal. + + in this case, we can establish equality between those dimensions and the + corresponding output dimensions. + + Note that it takes two iterations for this result. One iteration to establish + equality between certain dimensions of the operands (requiring the whole solver + including unification) and another iteration to establish equality between the operands + and the resulting type, requiring another round of constraint generation and unificaiton. + """ + res = [] + if isinstance(n.args[0], Node) and isinstance(n.args[1], Node): + arg_type1 = n.args[0].type + arg_type2 = n.args[1].type + if ( + isinstance(arg_type1, TensorType) + and isinstance(arg_type2, TensorType) + and isinstance(n.type, TensorType) + ): + args1, args2 = broadcast_types(arg_type1, arg_type2) + # by this point, we know that args1 and args2 are the same size. + a1 = args1.__args__ + a2 = args2.__args__ + a3 = n.type.__args__ + + # we would be here in the second iteration where we establish equality + # between operand type dimensions and the resulting type dimensions + r = [] + for x, y, z in zip(a1, a2, a3): + if x == y: + r.append(Equality(x, z)) + res = r + return res + + +@register_refinement_rule(torch.flatten) +def flatten_refinement_rule(n: Node): + """ + Generates equality constraints between the dimensions of the input and output + that will not be involved in the flatten operation + """ + assert isinstance(n.args[0], Node) + + eq_const = [] + + start_dim = 1 + end_dim = -1 + + if len(n.args) > 1: + assert isinstance(n.args[1], int) + start_dim = n.args[1] + + if len(n.args) > 2: + assert isinstance(n.args[2], int) + end_dim = n.args[2] + + if isinstance(n.type, TensorType) and isinstance(n.args[0].type, TensorType): + l = len(n.type.__args__) + arg_type = n.args[0].type + start_dim = l if start_dim == -1 else start_dim + end_dim = l + end_dim + 1 if end_dim < 0 else end_dim + 1 + + for t1, t2 in zip(n.type.__args__[0:start_dim], arg_type.__args__[0:start_dim]): + eq_const.append(Equality(t1, t2)) + + for t1, t2 in zip(n.type.__args__[end_dim:], arg_type.__args__[end_dim:]): + eq_const.append(Equality(t1, t2)) + return eq_const + + +@register_algebraic_expressions_inference_rule(Conv2d) +def conv_rule(n: Node, module_instance): + """ + Represents the output in terms of an algrbraic expression w.r.t + the input when possible + """ + assert isinstance(n.args[0], Node) + arg_type = n.args[0].type + if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType): + w_in = arg_type.__args__[3] + h_in = arg_type.__args__[2] + h_out = calculate_out_dimension(h_in, module_instance, 0) + w_out = calculate_out_dimension(w_in, module_instance, 1) + new_type = TensorType((n.type.__args__[0], n.type.__args__[1], h_out, w_out)) + n.type = new_type + return new_type + + +class Refine: + """ + Symbolic shape inference. + Generates constraints over type variables. + Currently all constraints are equality constraints. + """ + + def __init__(self, traced): + self.constraints = [] + self.traced = traced + self.symbol_iter = itertools.count(start=0, step=1) + + def refine(self): + """ + Generates constraints for + every node in the graph based on + the operation. + """ + graph = self.traced.graph + for n in graph.nodes: + self.refine_node(n) + return True + + def symbolic_relations(self): + """ + Infers algebraic relations + """ + graph = self.traced.graph + for n in graph.nodes: + self.infer_symbolic_relations(n) + return True + + def replace_dyn_with_fresh_var(self, typ): + """ + Replace all unknown types with fresh type variables. + """ + if typ == Dyn: + new_symbol = Var(next(self.symbol_iter)) + return new_symbol + elif isinstance(typ, TensorType): + new_args = [self.replace_dyn_with_fresh_var(a) for a in typ.__args__] + return TensorType(tuple(new_args)) + elif isinstance(typ, list): + return [self.replace_dyn_with_fresh_var(t) for t in typ] + elif isinstance(typ, tuple): + return (self.replace_dyn_with_fresh_var(t) for t in typ) + else: + return typ + + def convert_to_sympy_symbols(self, typ): + """ + Replace all unknown types with fresh type variables. + """ + if isinstance(typ, Var): + return sympy.symbols(str(typ)) + elif isinstance(typ, TensorType): + new_args = [self.convert_to_sympy_symbols(a) for a in typ.__args__] + return TensorType(tuple(new_args)) + elif isinstance(typ, list): + return [self.convert_to_sympy_symbols(t) for t in typ] + elif isinstance(typ, tuple): + return (self.convert_to_sympy_symbols(t) for t in typ) + else: + return typ + + def refine_node(self, n: Node): + """ + Returns a list of equality constraints for + call_module and call_function nodes. + Models the relation between input and output dimensions + using constraints in case they are both tensors. + All operations used in resnet50 are defined. + """ + if n.type is None: + n.type = Dyn + + n.type = self.replace_dyn_with_fresh_var(n.type) + + if n.op == "call_function": + if n.target in _REFINEMENT_RULES: + self.constraints += _REFINEMENT_RULES[n.target](n) + + if n.op == "call_module": + module_instance = self.traced.get_submodule(n.target) + if type(module_instance) in _REFINEMENT_RULES: + self.constraints += _REFINEMENT_RULES[type(module_instance)](n) + + if n.op == "output": + + def get_node_type(a): + return a.type + + n.type = torch.fx.node.map_arg(n.args[0], get_node_type) + return n.type + + def infer_symbolic_relations(self, n: Node): + n.type = self.convert_to_sympy_symbols(n.type) + if n.op == "call_function": + if n.target in _RULES: + return _RULES[n.target](n) + + if n.op == "call_module": + module_instance = self.traced.get_submodule(n.target) + if type(module_instance) in _RULES: + return _RULES[type(module_instance)](n, module_instance) + + if n.op == "output": + + def get_node_type(a): + return a.type + + n.type = torch.fx.node.map_arg(n.args[0], get_node_type) + return n.type + + +def get_parameter(traced, target: str): + """ + Returns the parameter given by ``target`` if it exists, + otherwise throws an error. + + See the docstring for ``get_submodule`` for a more detailed + explanation of this method's functionality as well as how to + correctly specify ``target``. + + Args: + target: The fully-qualified string name of the Parameter + to look for. (See ``get_submodule`` for how to specify a + fully-qualified string.) + + Returns: + torch.nn.Parameter: The Parameter referenced by ``target`` + + Raises: + AttributeError: If the target string references an invalid + path or resolves to something that is not an + ``nn.Parameter`` + """ + module_path, _, param_name = target.rpartition(".") + + mod: torch.nn.Module = traced.get_submodule(module_path) + + if not hasattr(mod, param_name): + raise AttributeError(mod._get_name() + " has no attribute `" + param_name + "`") + + param: torch.nn.Parameter = getattr(mod, param_name) + + return param diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/merge_matmul.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/merge_matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..bd239d78842dd8ba3cbfbf2d03e259a19427489b --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/merge_matmul.py @@ -0,0 +1,178 @@ +# mypy: allow-untyped-defs +import itertools +import operator + +import torch +from torch.fx._symbolic_trace import symbolic_trace +from torch.fx.node import Node +from torch.fx.passes.tools_common import legalize_graph + + +def split_result_tensors( + result: torch.Tensor, inputs: list[torch.Tensor] +) -> tuple[torch.Tensor, ...]: + """ + A free function for use in the merge_matmul graph transformation below that + splits the output from a merged matmul into the individual results for each + input tensor. + + Arguments: + result: The merged matmul result tensor. + inputs: The list of inputs that were merged into one for the matmul. + + Returns: + List of matmul results for each input tensor. + """ + # When fx tracer is running, x.shape[0] will be torch.fx.Attribute but we + # need an int even when tracing + if isinstance(result, torch.fx.Proxy): + splits = [0] * len(inputs) + else: + splits = [x.shape[0] for x in inputs] + + # pyrefly: ignore [bad-argument-type] + return torch.split(result, splits) + + +def may_depend_on(a: Node, b: Node, search_depth: int = 6): + """ + Determine if one node depends on another in a torch.fx.Graph. + + Arguments: + a: The node that may have a dependency on b. + b: The node that a may have a dependency on. + search_depth: In the case of an indirect dependency, this function + searches upto this many nodes away in search of a + data dependency. If none is found, the function + makes the conservative assumption that there is a + dependency. + + Returns: + True if a may depend on b, False if it definitely does not. + """ + # Equivalence is defined as dependence. + if a == b: + return True + + # If a has no inputs, it cannot depend on b. + if len(a.all_input_nodes) == 0: + return False + + # If the search depth has been exhausted and no conclusion has been + # reached, assume that there is a data dependency. + if search_depth == 0: + return True + + # Recursively check all inputs of a. + for inp in a.all_input_nodes: + if may_depend_on(inp, b, search_depth - 1): + return True + + return False + + +def are_nodes_independent(nodes: list[Node]): + """ + Check if all of the given nodes are pairwise-data independent. + + Arguments: + nodes: The nodes to check for data dependencies. + + Returns: + True if any pair in nodes has a data dependency. + """ + # For each pair in nodes: + for i, j in itertools.combinations(nodes, 2): + if may_depend_on(i, j) or may_depend_on(j, i): + return False + + return True + + +def merge_matmul(in_mod: torch.nn.Module): + """ + A graph transformation that merges matrix multiplication operations that share the same right-hand + side operand into one large matrix multiplication. + ____ _________ _________ + ---- | | | | M| A * C | + M| A | T| B | * K| C | = |---------| + ---- , | | | | T| B * C | + K ---- --------- --------- + K R R + """ + gm = symbolic_trace(in_mod) + + rhs_users: dict[Node, list[Node]] = {} + lhs_users: dict[Node, list[Node]] = {} + + # Populate rhs_users and lhs_users - maps from LHS/RHS matrix multiply operands to + # the matmul of which they are the LHS/RHS. + for node in gm.graph.nodes: + if node.op != "call_function" or node.target is not torch.matmul: + continue + + lhs, rhs = node.args + + # TODO: Properly handle aliasing caused by get_attr. For now, + # use the attribute name as the operand if the node is a + # get_attr. + lhs = lhs.target if lhs.op == "get_attr" else lhs + rhs = rhs.target if rhs.op == "get_attr" else rhs + + lhs_users.setdefault(lhs, []).append(node) + rhs_users.setdefault(rhs, []).append(node) + + for rhs, mms in rhs_users.items(): + # There must be at least matmuls for a merge to make sense. + if len(mms) < 2: + continue + + # All matmuls must not depend on each other directly or indirectly + # in order for the merge to be possible. + if not are_nodes_independent(mms): + continue + + lhs_vals = [mm.args[0] for mm in mms] + + # Merge the matmul. + # Collect a list of LHS operands and the single RHS operand. + lhs = [gm.graph.get_attr(l) if isinstance(l, str) else l for l in lhs_vals] + rhs = gm.graph.get_attr(rhs) if isinstance(rhs, str) else rhs + + # Concatenate all the LHS operands. + merge_mm_cat = gm.graph.call_function(torch.cat, (lhs,), {}) + + # Multiply the concatenated LHS operands with the one RHS. This will produce + # the same results as all the individual matmuls involving rhs in the original graph, + # but they will all be concatenated together. + merge_mm = gm.graph.call_function( + torch.matmul, + ( + merge_mm_cat, + rhs, + ), + {}, + ) + + # Split the result of the merged matmul using the shapes of the LHS operands + # to ascertain how large each chunk should be. + merge_mm_split = gm.graph.call_function( + split_result_tensors, (merge_mm, lhs), {} + ) + merge_mm_res = [ + gm.graph.call_function(operator.getitem, (merge_mm_split, out), {}) + for out in range(len(lhs)) + ] + + # Replace all uses of the original, unmerged matmuls with the equivalent split chunk from the merged matmul. + for old, new in zip(mms, merge_mm_res): + old.replace_all_uses_with(new) + gm.graph.erase_node(old) + + # All of the new nodes created above were inserted at the end, so we need to sort + # the nodes topologically to make sure all definitions precede uses. + legalize_graph(gm) + + gm.recompile() + gm.graph.lint() + return gm diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/meta_tracer.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/meta_tracer.py new file mode 100644 index 0000000000000000000000000000000000000000..cb3adfba8d412a12012cb3148732e0fab42a7b66 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/meta_tracer.py @@ -0,0 +1,320 @@ +# mypy: allow-untyped-defs +import builtins +import functools +import warnings +from collections.abc import Callable +from typing import Any, Optional, Union + +import torch +import torch.fx + + +def embedding_override(self, input): + return torch.empty(*input.shape, self.weight.shape[-1], device="meta") + + +def nn_layernorm_override(self, input): + return input + + +def torch_relu_override(x): + return x + + +def torch_nn_relu_override(self, x): + return x + + +def functional_relu_override(x, inplace=False): + assert not inplace, "dont support inplace functional.relu for metatensor analysis" + return x + + +def torch_where_override(condition, x, y): + # torch.where returns the broadcasted tensor of condition, x, and y, + # so hack it by using addition + return condition.to(device="meta") + x.to(device="meta") + y.to(device="meta") + + +def torch_abs_override(input, *, out=None): + assert out is None, "Dont support in-place abs for MetaTensor analysis" + return input + + +manual_meta_overrides: dict[Callable, Callable] = { + torch.nn.Embedding: embedding_override, + torch.nn.LayerNorm: nn_layernorm_override, + torch.relu: torch_relu_override, + torch.nn.functional.relu: functional_relu_override, + torch.nn.ReLU: torch_nn_relu_override, + torch.where: torch_where_override, + torch.abs: torch_abs_override, +} + + +def gen_constructor_wrapper(target): + @functools.wraps(target) + def wrapper(*args, **kwargs): + proxy = None + + def check_has_proxy(v): + if isinstance(v, torch.fx.Proxy): + nonlocal proxy + proxy = v + + torch.fx.node.map_aggregate(args, check_has_proxy) + torch.fx.node.map_aggregate(kwargs, check_has_proxy) + + if proxy is not None: + return proxy.tracer.create_proxy("call_function", target, args, kwargs) + else: + return target(*args, **kwargs) + + return wrapper, target + + +class MetaProxy(torch.fx.Proxy): + def install_tensor_meta(self, tensor_meta): + self._tensor_meta = tensor_meta + + def size(self, dim=None): + if hasattr(self, "_tensor_meta") and self._tensor_meta is not None: + return self._tensor_meta.size(*[dim] if dim else []) + return self.tracer.create_proxy( + "call_method", "size", (self, dim) if dim else (self,), {} + ) + + def dim(self): + if hasattr(self, "_tensor_meta") and self._tensor_meta is not None: + return self._tensor_meta.dim() + return self.tracer.create_proxy("call_method", "dim", (self,), {}) + + @property + def shape(self): + if hasattr(self, "_tensor_meta") and self._tensor_meta is not None: + return self._tensor_meta.shape + return self.tracer.create_proxy( + "call_function", builtins.getattr, (self, "shape"), {} + ) + + @property + def dtype(self): + if hasattr(self, "_tensor_meta") and self._tensor_meta is not None: + return self._tensor_meta.dtype + return self.tracer.create_proxy( + "call_function", builtins.getattr, (self, "dtype"), {} + ) + + @property + def device(self): + # Hack so we can track when devices are used. During meta-tensor propagation, + # replace these values with a constant 'meta' + return MetaDeviceAttribute(self, "device") + + def __getattr__(self, k): + if k == "_tensor_meta": + return self.__getattribute__(k) + # note: not added to the graph yet, if this is a method call + # we peephole optimize to the method invocation + return MetaAttribute(self, k) + + +class MetaAttribute(MetaProxy): + def __init__(self, root, attr: str): + self.root = root + self.attr = attr + self.tracer = root.tracer + self._node = None + + @property + def node(self): # type: ignore[override] + # the node for attributes is added lazily, since most will just be method calls + # which do not rely on the getitem call + if self._node is None: + self._node = self.tracer.create_proxy( + "call_function", getattr, (self.root, self.attr), {} + ).node + return self._node + + def __call__(self, *args, **kwargs): + return self.tracer.create_proxy( + "call_method", self.attr, (self.root,) + args, kwargs + ) + + +class MetaDeviceAttribute(MetaAttribute): + pass + + +def proxys_to_metas(v): + if isinstance(v, MetaDeviceAttribute): + return "meta" + if isinstance(v, torch.fx.Proxy): + assert isinstance(v, MetaProxy), f"Expected MetaProxy but got {type(v)}" + assert hasattr(v, "_tensor_meta"), "MetaProxy does not have an associated meta" + return v._tensor_meta + return v + + +class MetaTracer(torch.fx.Tracer): + allow_insert_stateless_mods: bool = True + + _TORCH_METHODS_TO_PATCH = ["arange", "zeros", "ones", "full_like", "eye"] + + def create_proxy( + self, + kind, + target, + args, + kwargs, + name=None, + type_expr=None, + proxy_factory_fn=None, + ): + rv = super().create_proxy( + kind, + target, + args, + kwargs, + name, + type_expr, + # pyrefly: ignore [bad-argument-type] + proxy_factory_fn, + ) + + if kind == "placeholder" and target in self.meta_args: + rv.install_tensor_meta(self.meta_args[target]) + return rv + + if target in self.orig_fns: + # NOTE: tensor constructors in PyTorch define the `device` argument as + # *kwargs-only*. That is why this works. If you add methods to + # _TORCH_METHODS_TO_PATCH that do not define `device` as kwarg-only, + # this will break and you will likely see issues where we cannot infer + # the size of the output. + if "device" in kwargs: + kwargs["device"] = "meta" + + try: + args_metas = torch.fx.node.map_aggregate(args, proxys_to_metas) + kwargs_metas = torch.fx.node.map_aggregate(kwargs, proxys_to_metas) + + if kind == "call_function": + meta_target = manual_meta_overrides.get(target, target) + # pyrefly: ignore [not-callable] + meta_out = meta_target(*args_metas, **kwargs_metas) + elif kind == "call_method": + meta_target = getattr(args_metas[0], target) # type: ignore[index] + meta_out = meta_target(*args_metas[1:], **kwargs_metas) # type: ignore[index] + elif kind == "call_module": + assert hasattr(self, "orig_forward") + self._disable_module_getattr = True + try: + mod = self.root.get_submodule(target) + mod_type = type(mod) + if mod_type in manual_meta_overrides: + meta_out = manual_meta_overrides[mod_type]( + mod, *args_metas, **kwargs_metas + ) # type: ignore[misc, arg-type] + else: + meta_out = self.orig_forward(*args_metas, **kwargs_metas) + finally: + self._disable_module_getattr = False + elif kind == "get_attr": + self._disable_module_getattr = True + try: + attr_itr = self.root + atoms = target.split(".") + for atom in atoms: + attr_itr = getattr(attr_itr, atom) + assert isinstance(attr_itr, torch.Tensor) + meta_out = attr_itr.to(device="meta") + finally: + self._disable_module_getattr = False + else: + return rv + + # TODO + assert isinstance(rv, torch.fx.Proxy), "Dont support composite output yet" + rv.install_tensor_meta(meta_out) + except Exception as e: + warnings.warn(f"Could not compute metadata for {kind} target {target}: {e}") + + return rv + + def getattr(self, attr, attr_val, parameter_proxy_cache): + if getattr(self, "_disable_module_getattr", False): + return attr_val + else: + return super().getattr(attr, attr_val, parameter_proxy_cache) + + def call_module(self, m, forward, args, kwargs): + self.orig_forward = forward + return super().call_module(m, forward, args, kwargs) + + def _insert_module_as_submodule(self, mod: torch.nn.Module) -> str: + """ + Helper method which tries to insert a module that was not declared as submodule. + """ + idx = 0 + mod_name = mod.__class__.__name__.lower() + path = f"{mod_name}_{idx}" + while hasattr(self.root, path): + path = f"{mod_name}_{idx}" + idx += 1 + + self.root.add_module(path, mod) + return path + + def path_of_module(self, mod: torch.nn.Module) -> str: + try: + return super().path_of_module(mod) + except NameError: + if ( + self.allow_insert_stateless_mods + and len(list(mod.parameters())) == 0 + and len(list(mod.buffers())) == 0 + ): + path = self._insert_module_as_submodule(mod) + self.prev_module = path + return path + raise + + def proxy(self, node): + return MetaProxy(node, self) + + def trace(self, root, meta_args: dict[str, torch.Tensor], concrete_args=None): # type: ignore[override] + assert isinstance(meta_args, dict) + self.meta_args = meta_args + + self.patched_torch_methods = { + target: gen_constructor_wrapper(getattr(torch, target)) + for target in self._TORCH_METHODS_TO_PATCH + } + self.orig_fns = set() + + for name, (wrapper, orig) in self.patched_torch_methods.items(): + setattr(torch, name, wrapper) + self.orig_fns.add(orig) + + try: + graph = super().trace(root, concrete_args) + graph._tracer_extras = {"meta_args": meta_args} + return graph + finally: + for name, (_, orig) in self.patched_torch_methods.items(): + setattr(torch, name, orig) + + +def symbolic_trace( + root: Union[torch.nn.Module, Callable[..., Any]], + meta_args: Optional[dict[str, torch.Tensor]] = None, + concrete_args: Optional[dict[str, Any]] = None, +) -> torch.fx.GraphModule: + tracer = MetaTracer() + graph = tracer.trace(root, meta_args, concrete_args) # type: ignore[arg-type] + name = ( + root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__ + ) + gm = torch.fx.GraphModule(tracer.root, graph, name) + return gm diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint.py new file mode 100644 index 0000000000000000000000000000000000000000..e46b3a607044a47774db97ec14c0ed40bea3d23d --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint.py @@ -0,0 +1,637 @@ +# mypy: allow-untyped-defs +from torch.fx.experimental.migrate_gradual_types.operation import ( + op_add, + op_div, + op_eq, + op_gt, + op_lt, + op_mod, + op_mul, + op_neq, + op_sub, +) +from torch.fx.tensor_type import Dyn, TensorType + + +class Constraint: + pass + + +class Conj(Constraint): + def __init__(self, conjuncts): + """ + :param conjuncts: Conjunction of constraints + """ + self.conjucts = conjuncts + + def __eq__(self, other): + if isinstance(other, Conj): + return self.conjucts == other.conjucts and self.conjucts == other.conjucts + else: + return False + + def __repr__(self): + return f"And({self.conjucts})" + + +class Disj(Constraint): + def __init__(self, disjuncts): + """ + :param disjuncts: Disjunction of constraints + """ + self.disjuncts = disjuncts + + def __eq__(self, other): + if isinstance(other, Disj): + return ( + self.disjuncts == other.disjuncts and self.disjuncts == other.disjuncts + ) + else: + return False + + def __repr__(self): + return f"Or({self.disjuncts})" + + +class Prod(Constraint): + def __init__(self, products): + """ + :param products: lists of dimensions to multiply + """ + self.products = products + + def __eq__(self, other): + if isinstance(other, Prod): + return self.products == other.products and self.products == other.products + else: + return False + + def __repr__(self): + return f"Product({self.products})" + + +class T(Constraint): + """ + True + """ + + def __init__(self) -> None: + pass + + def __eq__(self, other): + return isinstance(other, T) + + def __repr__(self): + return "True" + + +class F(Constraint): + """ + False + """ + + def __init__(self) -> None: + pass + + def __eq__(self, other): + return isinstance(other, F) + + def __repr__(self): + return "False" + + +class BinaryConstraint(Constraint): + """ + Represents all binary operations + """ + + def __init__(self, lhs, rhs, op): + """ + :param lhs: lhs of the constraint + :param rhs: rhs of the constraint + :param op: string representing the operation + """ + self.lhs = lhs + self.rhs = rhs + self.op = op + + def __eq__(self, other): + if isinstance(other, BinaryConstraint): + return ( + self.lhs == other.lhs and self.rhs == other.rhs and self.op == other.op + ) + else: + return False + + def __repr__(self): + return f"({self.lhs} {self.op} {self.rhs})" + + +class BinConstraintT(BinaryConstraint): + """ + Binary constraints about tensors + """ + + def __init__(self, lhs, rhs, op): + assert (isinstance(lhs, (TVar, TensorType, int)) or lhs == Dyn) and ( + isinstance(rhs, (TVar, TensorType, int)) or rhs == Dyn + ) + super().__init__(lhs, rhs, op) + + +class BinConstraintD(BinaryConstraint): + """ + Binary constraints about dimensions + """ + + def __init__(self, lhs, rhs, op): + assert is_algebraic_expression(lhs) or is_dim(lhs) or is_bool_expr(lhs) + assert is_algebraic_expression(rhs) or is_dim(rhs) or is_bool_expr(rhs) + + super().__init__(lhs, rhs, op) + + +class TGreatestUpperBound(Constraint): + """ + Greatest Upper bound for tensors with dynamic type + """ + + def __init__(self, res, rhs1, rhs2): + """ + :param res: tensor variable that stores the result of the output + :param rhs1: tensor or tensor variable + :param rhs2: tensor or tensor variabke + """ + self.res = res + self.rhs1 = rhs1 + self.rhs2 = rhs2 + + def __repr__(self): + return f"{self.res} = {self.rhs1}\u2294*{self.rhs2}" + + def __eq__(self, other): + if isinstance(other, TGreatestUpperBound): + return ( + self.res == other.res + and self.rhs1 == other.rhs1 + and self.rhs2 == other.rhs2 + ) + else: + return False + + +class DGreatestUpperBound(Constraint): + """ + Greatest Upper bound for dimensions + """ + + def __init__(self, res, rhs1, rhs2): + """ + :param res: Dimension variable to store the result + :param rhs1: dimension variable 1 + :param rhs2: dimension variable 2 + """ + assert is_dim(res) + assert is_dim(rhs1) + assert is_dim(rhs2) + + self.res = res + self.rhs1 = rhs1 + self.rhs2 = rhs2 + + def __repr__(self): + return f"{self.res} = {self.rhs1}\u2294{self.rhs2}" + + def __eq__(self, other): + if isinstance(other, DGreatestUpperBound): + return ( + self.res == other.res + and self.rhs1 == other.rhs1 + and self.rhs2 == other.rhs2 + ) + else: + return False + + +class CanReshape(Constraint): + """ + can_reshape constraint + """ + + def __init__(self, src, target): + """ + :param src: tensor variable + :param target: tensor + """ + self.src = src + self.target = target + + def __repr__(self): + return f"can-reshape({self.src}, {self.target})" + + def __eq__(self, other): + if isinstance(other, CanReshape): + return self.src == other.src and self.target == other.target + else: + return False + + +class IndexSelect(Constraint): + def __init__(self, tensor_size, input_var, dim_replace, index, output): + """ + Args: + input_var: input to index_select + tensor_size: tensor size we are considering + dim_replace: the dimension of the output at "index" + index: location of the dimensions to replace in the input + output: variable to store the result + """ + assert isinstance(input_var, TVar) + assert isinstance(output, TVar) + assert isinstance(dim_replace, DVar) or dim_replace == Dyn + assert isinstance(index, int) + + self.input_var = input_var + self.tensor_size = tensor_size + self.dim_replace = dim_replace + self.index = index + self.output = output + + def __repr__(self): + return ( + f" {self.output} = " + f"IndexSelect({self.input_var}, " + f"tensor_size: {self.tensor_size}, " + f"{self.dim_replace}, " + f"{self.index})" + ) + + def __eq__(self, other): + if isinstance(other, IndexSelect): + return ( + self.tensor_size == other.tensor_size + and self.dim_replace == other.dim_replace + and self.index == other.index + and self.output == other.output + and self.input_var == other.input_var + ) + else: + return False + + +class Transpose(Constraint): + def __init__(self, tensor_size, input_var, index1, index2, output): + """ + Args: + tensor_size: current tensor size + input_var: variable to hold input + index1: dimension 1 + index2: dimension 2 + output: output that stores result + """ + assert isinstance(input_var, TVar) + assert isinstance(output, TVar) + assert isinstance(index1, int) + assert isinstance(index2, int) + + self.input_var = input_var + self.tensor_size = tensor_size + self.index1 = index1 + self.index2 = index2 + self.output = output + + def __repr__(self): + return ( + f" {self.output} = " + f"Transpose({self.input_var}, " + f"tensor_size: {self.tensor_size}, " + f"{self.index1}, " + f"{self.index2})" + ) + + def __eq__(self, other): + if isinstance(other, Transpose): + return ( + self.tensor_size == other.tensor_size + and self.index1 == other.index1 + and self.index2 == other.index2 + and self.output == other.output + and self.input_var == other.input_var + ) + else: + return False + + +class GetItem(Constraint): + def __init__(self, tensor_size, index, res, input_var): + """ + Constraint for getting item given a tensor size + :param tensor_size: actual number + :param index: actual number representing the index + :param res: dimension variable to carry the item we get + :param input_var: a tensor variable from which we will get item + """ + assert isinstance(res, DVar) + + self.res = res + self.tensor_size = tensor_size + self.index = index + self.input_var = input_var + + def __repr__(self): + return f" {self.res} = GetItem({self.input_var}, tensor_size: {self.tensor_size}, {self.index})" + + def __eq__(self, other): + if isinstance(other, GetItem): + return ( + self.res == other.res + and self.tensor_size == other.tensor_size + and self.index == other.index + and self.input_var == other.input_var + ) + else: + return False + + +class GetItemTensor(Constraint): + def __init__(self, tensor_size, index_tuple, res, input_var): + """ + Constraint for getting item given a tensor size + However, when the argument is a tuple, we will + expect a tensor + :param tensor_size: actual number representing the rank + :param index_tuple: tuple for indexing + :param res: tensor variable to carry the item we get + :param input_var: a tensor variable from which we will get item + """ + assert isinstance(res, TVar) + + self.res = res + self.tensor_size = tensor_size + self.index_tuple = index_tuple + self.input_var = input_var + + def __repr__(self): + return f" {self.res} = GetItemT({self.input_var}, tensor_size: {self.tensor_size}, {self.index_tuple})" + + def __eq__(self, other): + if isinstance(other, GetItemTensor): + return ( + self.res == other.res + and self.tensor_size == other.tensor_size + and self.index_tuple == other.index_tuple + and self.input_var == other.input_var + ) + else: + return False + + +class CalcConv(Constraint): + def __init__( + self, + conv_result, + input_var, + c_out, + kernel, + padding, + stride, + dilation, + matching_constraint_vars, + ): + """ + :param conv_result: the convolution result + :param input_var: input to convolution + :param c_out: output channel type + :param kernel: kernel tuple + """ + self.conv_result = conv_result + self.input_var = input_var + self.c_out = c_out + self.kernel = kernel + self.padding = padding + self.stride = stride + self.dilation = dilation + self.matching_constraint = matching_constraint_vars + + def __repr__(self): + return ( + f"{self.conv_result} =" + f" calc-conv({self.input_var}," + f" {self.c_out}, {self.kernel}, " + f"{self.padding}, {self.stride}," + f" {self.dilation})" + ) + + def __eq__(self, other): + if isinstance(other, CalcConv): + return ( + self.conv_result == other.conv_result + and self.input_var == other.input_var + and self.c_out == other.c_out + and self.kernel == other.kernel + and self.padding == other.padding + and self.stride == other.stride + and self.dilation == other.dilation + and self.matching_constraint == other.matching_constraint + ) + else: + return False + + +class CalcMaxPool(Constraint): + def __init__( + self, + maxpool_result, + input_var, + kernel, + padding, + stride, + dilation, + matching_constraint_vars, + ): + """ + :param maxpool_result: the result of maxpool + :param input_var: input to convolution + :param kernel: kernel tuple + """ + self.maxpool_result = maxpool_result + self.input_var = input_var + self.kernel = kernel + self.padding = padding + self.stride = stride + self.dilation = dilation + self.matching_constraint = matching_constraint_vars + + def __repr__(self): + return ( + f"{self.maxpool_result} =" + f" calc-maxpool({self.input_var}," + f" {self.kernel}, " + f"{self.padding}, {self.stride}," + f" {self.dilation})" + ) + + def __eq__(self, other): + if isinstance(other, CalcMaxPool): + return ( + self.maxpool_result == other.maxpool_result + and self.input_var == other.input_var + and self.kernel == other.kernel + and self.padding == other.padding + and self.stride == other.stride + and self.dilation == other.dilation + and self.matching_constraint == other.matching_constraint + ) + else: + return False + + +class ApplyBroadcasting(Constraint): + def __init__(self, res1, res2, input1, input2): + """ + :param res1: resulting tensor 1 + :param res2: resulting tensor 2 + :param input1: tensor variable 1 + :param input2: tensor variable 2 + """ + self.res1 = res1 + self.res2 = res2 + self.input1 = input1 + self.input2 = input2 + + def __eq__(self, other): + if isinstance(other, ApplyBroadcasting): + return ( + self.res1 == other.res1 + and self.res2 == other.res2 + and self.input1 == other.input1 + and self.input2 == other.input2 + ) + else: + return False + + def __repr__(self): + return ( + f"{self.res1}, {self.res2} =" + f" apply-broadcasting({self.input1}," + f" {self.input2})" + ) + + +class CalcProduct(Constraint): + """ + Given correct dimensions, calculate the product for flatten accounting for Dyn + """ + + def __init__(self, start, end, flattened, dims_to_flatten): + """ + :param start: start index + :param end: end index + :param flattened: variable to store the product + :param dims_to_flatten: the type which we will flatten + """ + assert isinstance(dims_to_flatten, list) + assert isinstance(flattened, TVar) + assert isinstance(start, int) + assert isinstance(end, int) + + self.start = start + self.end = end + self.dims_to_flatten = dims_to_flatten + self.flattened = flattened + + def __eq__(self, other): + if isinstance(other, CalcProduct): + return ( + self.start == other.start + and self.end == other.end + and self.dims_to_flatten == other.dims_to_flatten + and self.flattened == other.flattened + ) + + else: + return False + + def __repr__(self): + return f"{self.flattened} = CalcProduct({self.start}, {self.end}, {self.dims_to_flatten})" + + +class TVar: + """ + Tensor variable with no tensor constructor + """ + + def __init__(self, tvar): + """ + :param tvar: tensor variable + """ + self.tvar = tvar + + def __repr__(self): + return f"TV({self.tvar})" + + def __eq__(self, other): + if isinstance(other, TVar): + return self.tvar == other.tvar + else: + return False + + +class DVar: + """ + Dimension variable + """ + + def __init__(self, c): + """ + :param c: character or number + """ + self.c = c + + def __repr__(self): + return f"DV({self.c})" + + def __eq__(self, other): + if isinstance(other, DVar): + return self.c == other.c + else: + return False + + +class BVar: + """ + Boolean variable + """ + + def __init__(self, c): + """ + :param c: character or number + """ + self.c = c + + def __repr__(self): + return f"BV({self.c})" + + def __eq__(self, other): + if isinstance(other, BVar): + return self.c == other.c + else: + return False + + +def is_algebraic_expression(constraint): + if isinstance(constraint, BinConstraintD): + return constraint.op in [op_add, op_sub, op_div, op_mul, op_mod] + else: + return isinstance(constraint, Prod) + + +def is_bool_expr(constraint): + if isinstance(constraint, BinConstraintD): + return constraint.op in [op_gt, op_lt, op_neq, op_eq] + else: + return isinstance(constraint, (BVar, Conj, Disj)) + + +def is_dim(d): + return isinstance(d, (DVar, int)) or d == Dyn diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_generator.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..28e5c7c215e64f0ee61a840f37482c56f988c445 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_generator.py @@ -0,0 +1,1565 @@ +# mypy: allow-untyped-defs +import operator +import warnings +from collections.abc import Callable, Iterable +from typing import TypeVar +from typing_extensions import ParamSpec + +import torch +from torch.fx._symbolic_trace import _assert_is_none +from torch.fx.experimental.migrate_gradual_types.constraint import ( + ApplyBroadcasting, + BinConstraintD, + BinConstraintT, + CalcConv, + CalcMaxPool, + CalcProduct, + CanReshape, + Conj, + DGreatestUpperBound, + Disj, + DVar, + F, + GetItem, + GetItemTensor, + IndexSelect, + T, + TGreatestUpperBound, + Transpose, + TVar, +) +from torch.fx.experimental.migrate_gradual_types.operation import ( + op_add, + op_consistency, + op_div, + op_eq, + op_gt, + op_leq, + op_lt, + op_matching, + op_mul, + op_neq, + op_precision, + op_sub, +) +from torch.fx.experimental.migrate_gradual_types.util import ( + gen_bvar, + gen_dvar, + gen_nat_constraints, + gen_tensor_dims, + gen_tvar, +) +from torch.fx.node import Node, Target +from torch.fx.tensor_type import Dyn, TensorType +from torch.nn.modules.batchnorm import BatchNorm2d +from torch.nn.modules.conv import Conv2d + + +_T = TypeVar("_T") +_P = ParamSpec("_P") + +_INFERENCE_RULES: dict[Target, Callable] = {} + +MAX_TENSOR_RANK = 4 + +__all__ = [ + "ConstraintGenerator", + "adaptive_inference_rule", + "add_layer_norm_constraints", + "add_linear_constraints", + "arange_inference_rule", + "assert_inference_rule", + "batchnorm_inference_rule", + "bmm_inference_rule", + "broadcasting_inference_rule", + "conv2d_inference_rule", + "cumsum_inference_rule", + "embedding_inference_rule", + "embedding_inference_rule_functional", + "eq_inference_rule", + "equality_inference_rule", + "expand_inference_rule", + "flatten_inference_rule", + "full_inference_rule", + "gen_broadcasting_constraints", + "gen_embedding_rules", + "gen_layer_norm_constraints", + "generate_flatten_constraints", + "get_attr_inference_rule", + "getitem_inference_rule", + "gt_inference_rule", + "index_select_inference_rule", + "layer_norm_functional", + "layer_norm_inference_rule", + "linear_constraints", + "linear_inference_rule", + "lt_inference_rule", + "masked_fill_inference_rule", + "maxpool_inference_rule", + "neq_inference_rule", + "range_check", + "register_inference_rule", + "relu_inference_rule", + "reshape_inference_rule", + "size_inference_rule", + "tensor_inference_rule", + "torch_dim_inference_rule", + "torch_linear_inference_rule", + "transpose_inference_rule", + "type_inference_rule", + "view_inference_rule", +] + + +def register_inference_rule( + call_target: Target, +) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]: + def register(fn: Callable[_P, _T]) -> Callable[_P, _T]: + if call_target in _INFERENCE_RULES: + raise RuntimeError(f"Inference rule already registered for {call_target}!") + _INFERENCE_RULES[call_target] = fn + return fn + + return register + + +def generate_flatten_constraints(start_dim, end_dim, input, flattened, n, counter): + d, counter = gen_tensor_dims(n, counter) + c1 = BinConstraintT(input, TensorType(d), op_eq) + start_dim = n if start_dim == -1 else abs(start_dim) + end_dim = n + end_dim + 1 if end_dim < 0 else end_dim + 1 + c2 = CalcProduct(start_dim, end_dim, flattened, d) + nat_constraints = gen_nat_constraints(d) + return Conj([c1, c2, *nat_constraints]), counter + + +@register_inference_rule(getattr) +def get_attr_inference_rule(n: Node, symbols, constraints, counter): + """ + If the attribute is "device" then the tensor shape is preserved + """ + assert isinstance(n.args[0], Node) + assert isinstance(n.args[1], str) + output, counter = gen_tvar(counter) + symbols[n] = output + + input = symbols[n.args[0]] + attr = n.args[1] + + if attr == "device": + return [BinConstraintT(input, output, op_eq)], counter + else: + raise NotImplementedError("Not yet implemented") + + +@register_inference_rule(torch.bmm) +def bmm_inference_rule(n: Node, symbols, constraints, counter): + """ + Constraints that match the input to a size 3 tensor + and switch the dimensions according to the rules + of batch multiplication + """ + assert isinstance(n.args[0], Node) + assert isinstance(n.args[1], Node) + + bmm_output, counter = gen_tvar(counter) + symbols[n] = bmm_output + + bmm_input1 = symbols[n.args[0]] + bmm_input2 = symbols[n.args[1]] + + dims_input1, counter = gen_tensor_dims(3, counter) + dims_input2, counter = gen_tensor_dims(3, counter) + + inputs_dyn = Conj( + [ + BinConstraintT(bmm_input1, Dyn, op_eq), + BinConstraintT(bmm_input2, Dyn, op_eq), + BinConstraintT(bmm_output, Dyn, op_eq), + ] + ) + + input1_dyn = Conj( + [ + BinConstraintT(bmm_input1, Dyn, op_eq), + BinConstraintT(bmm_input2, TensorType(dims_input2), op_eq), + BinConstraintT( + bmm_output, TensorType([dims_input2[0], Dyn, dims_input2[2]]), op_eq + ), + ] + ) + + input2_dyn = Conj( + [ + BinConstraintT(bmm_input2, Dyn, op_eq), + BinConstraintT(bmm_input1, TensorType(dims_input1), op_eq), + BinConstraintT( + bmm_output, TensorType([dims_input1[0], dims_input1[1], Dyn]), op_eq + ), + ] + ) + + consistency_constraints = [ + BinConstraintD(dims_input1[0], dims_input2[0], op_consistency) + ] + + batch_size, counter = gen_dvar(counter) + + inputs_are_tensors = Conj( + [ + BinConstraintT(bmm_input1, TensorType(dims_input1), op_eq), + BinConstraintT(bmm_input2, TensorType(dims_input2), op_eq), + BinConstraintT( + bmm_output, + TensorType([batch_size, dims_input1[1], dims_input2[2]]), + op_eq, + ), + *consistency_constraints, + DGreatestUpperBound(batch_size, dims_input1[0], dims_input2[0]), + ] + ) + + return [Disj([inputs_dyn, input1_dyn, input2_dyn, inputs_are_tensors])], counter + + +@register_inference_rule("index_select") +def index_select_inference_rule(n: Node, symbols, constraints, counter): + """ + We constrain the second argument to a vector or Dyn. + The output replaces the input with the shape of the vector + at the position given by the index (first argument) + """ + # print(n.args) + assert isinstance(n.args[0], Node) + assert isinstance(n.args[1], int) + assert isinstance(n.args[2], Node) + + index_select, counter = gen_tvar(counter) + symbols[n] = index_select + + dims, counter = gen_tensor_dims(1, counter) + + # equality constraint + is_size_1 = BinConstraintT(symbols[n.args[2]], TensorType(dims), op_eq) + is_dyn = BinConstraintT(symbols[n.args[2]], Dyn, op_eq) + + c2 = Conj( + [ + is_size_1, + Disj( + [ + IndexSelect( + i + 1, symbols[n.args[0]], dims[0], n.args[1], index_select + ) + for i in range(MAX_TENSOR_RANK) + ] + ), + ] + ) + c3 = Conj( + [ + is_dyn, + Disj( + [ + IndexSelect(i + 1, symbols[n.args[0]], Dyn, n.args[1], index_select) + for i in range(MAX_TENSOR_RANK) + ] + ), + ] + ) + + return [Disj([c2, c3])], counter + + +@register_inference_rule("expand") +def expand_inference_rule(n: Node, symbols, constraints, counter): + """ + We generate the exact constraints as we do for tensor additions but we constraint + the rank of this expression to be equal to len(n.args[1:]) so that only + those cases get considered for the output + """ + assert isinstance(n.args[0], Node) + + # define the output for expand + expand, counter = gen_tvar(counter) + symbols[n] = expand + + # since we do not have two nodes here, we will construct an argument variable + e1 = symbols[n.args[0]] + e2, counter = gen_tvar(counter) + + e2_nat_constraints = [] + for arg in n.args[1:]: + assert isinstance(arg, (Node, int)) + if isinstance(arg, Node): + assert isinstance(symbols[arg], DVar) + e2_nat_constraints.append(BinConstraintD(0, symbols[arg], op_leq)) + + e2_constraint = BinConstraintT( + e2, + TensorType( + [arg if isinstance(arg, int) else symbols[arg] for arg in n.args[1:]] + ), + op_eq, + ) + + constraints, counter = gen_broadcasting_constraints( + e1, e2, symbols, counter, expand + ) + + # constraint the output size + dims, counter = gen_tensor_dims(len(n.args[1:]), counter) + nat_constraints = gen_nat_constraints(dims) + c = [ + BinConstraintT(expand, TensorType(dims), op_eq), + *nat_constraints, + e2_constraint, + *e2_nat_constraints, + ] + constraints += c + + return constraints, counter + + +@register_inference_rule(torch.nn.functional.gelu) +@register_inference_rule(torch.nn.functional.dropout) +@register_inference_rule(torch.nn.functional.softmax) +@register_inference_rule("detach") +@register_inference_rule("to") +@register_inference_rule("int") +@register_inference_rule("long") +@register_inference_rule("contiguous") +@register_inference_rule(torch.ones) +@register_inference_rule(torch.zeros) +def equality_inference_rule(n: Node, symbols, constraints, counter): + """ + We generate the constraint: input = output + """ + output, counter = gen_tvar(counter) + symbols[n] = output + + if isinstance(n.args[0], Node): + input = symbols[n.args[0]] + if isinstance(input, TVar): + return [BinConstraintT(input, output, op_eq)], counter + + # then we have dimension variables + else: + for arg in n.args: + assert isinstance(symbols[arg], DVar) + my_size = [symbols[arg] for arg in n.args] + return [BinConstraintT(output, TensorType(my_size), op_eq)], counter + + elif isinstance(n.args[0], tuple): + # then the tuple is the size + assert len(n.args[0]) <= 4 + my_size = [symbols[arg] for arg in n.args[0]] + return [BinConstraintT(output, TensorType(my_size), op_eq)], counter + else: + raise NotImplementedError("Method not yet implemented") + + +@register_inference_rule("transpose") +def transpose_inference_rule(n: Node, symbols, constraints, counter): + """ + Can be considered as a sequence of two index selects, so we generate constraints accordingly + """ + assert isinstance(n.args[0], Node) + assert isinstance(n.args[1], int) + assert isinstance(n.args[2], int) + + output, counter = gen_tvar(counter) + symbols[n] = output + + from_arg = symbols[n.args[0]] + assert isinstance(from_arg, TVar) + + # input and output are dyn + is_dyn = Conj( + [BinConstraintT(from_arg, Dyn, op_eq), BinConstraintT(output, Dyn, op_eq)] + ) + + # or input is a tensor and we actually do the replacement + c3 = Disj( + [ + Transpose(i + 1, from_arg, n.args[1], n.args[2], output) + for i in range(MAX_TENSOR_RANK) + ] + ) + + return [Disj([is_dyn, c3])], counter + + +@register_inference_rule("type_as") +def type_inference_rule(n: Node, symbols, constraints, counter): + """ + We generate the constraint: input = output + """ + assert isinstance(n.args[0], Node) + assert isinstance(n.args[1], Node) + + output, counter = gen_tvar(counter) + symbols[n] = output + + from_arg = symbols[n.args[0]] + to_arg = symbols[n.args[1]] + + assert isinstance(from_arg, TVar) + assert isinstance(to_arg, TVar) + + return [ + BinConstraintT(from_arg, to_arg, op_consistency), + BinConstraintT(output, to_arg, op_eq), + ], counter + + +@register_inference_rule("masked_fill_") +def masked_fill_inference_rule(n: Node, symbols, constraints, counter): + """ + Similar to addition. For now we implement the constraints when + the argument is a boolean tensor. There is also a case for when + it is a condition. We will leave this out for now. + """ + + assert isinstance(n.args[0], Node) + assert isinstance(n.args[1], Node) + + # We will retrieve the type variables from the symbol table + # and confirm they are tensor variables + + e1 = symbols[n.args[0]] + e2 = symbols[n.args[1]] + + if isinstance(e1, TVar) and isinstance(e2, TVar): + masked_fill_tensor, counter = gen_tvar(counter) + symbols[n] = masked_fill_tensor + return gen_broadcasting_constraints( + e1, e2, symbols, counter, masked_fill_tensor + ) + else: + raise NotImplementedError("Not yet implemented") + + +@register_inference_rule(torch.nn.functional.embedding) +def embedding_inference_rule_functional(n: Node, symbols, constraints, counter): + assert isinstance(n.args[0], Node) + + embedding_dim_weights = symbols[n.args[1]] + + # will treat this as a static shape. So we will not use matching. + weight_dims, counter = gen_tensor_dims(2, counter) + equality_constraint = BinConstraintT( + embedding_dim_weights, TensorType(weight_dims), op_eq + ) + embedding_dim = weight_dims[1] + constraints, counter = gen_embedding_rules(n, symbols, embedding_dim, counter) + return [equality_constraint] + constraints, counter + + +@register_inference_rule(torch.nn.modules.sparse.Embedding) +def embedding_inference_rule(n: Node, module_instance, symbols, constraints, counter): + """ + The output shape differs from the input shape in the last dimension + """ + assert isinstance(n.args[0], Node) + return gen_embedding_rules(n, symbols, module_instance.embedding_dim, counter) + + +def gen_embedding_rules(n: Node, symbols, embedding_dim, counter): + embedding_output, counter = gen_tvar(counter) + symbols[n] = embedding_output + embedding_input = symbols[n.args[0]] + + input_dyn = BinConstraintT(embedding_input, Dyn, op_eq) + output_dyn = BinConstraintT(embedding_output, Dyn, op_eq) + + c1 = Conj([input_dyn, output_dyn]) + c2 = [] + + for i in range(1, MAX_TENSOR_RANK): + new_dims, counter = gen_tensor_dims(i, counter) + nat_constraints = gen_nat_constraints(new_dims) + + # we consider all tensor sizes and append embedding_dim to the end of the output dimension in all cases + c_tensor_i = Conj( + [ + BinConstraintT(embedding_input, TensorType(new_dims), op_eq), + BinConstraintT( + embedding_output, TensorType(new_dims + [embedding_dim]), op_eq + ), + ] + + nat_constraints + ) + c2.append(c_tensor_i) + + return [Disj([c1, Disj(c2)])], counter + + +@register_inference_rule(torch.tensor) +def tensor_inference_rule(n: Node, symbols, constraints, counter): + """ + If the tensor is a scalar, we will skip it since we + do not support scalars yet. We will add support in the future + if it's needed. For our examples so far, scalars are not needed. + """ + return [], counter + + +@register_inference_rule("reshape") +@register_inference_rule("view") +def view_inference_rule(n: Node, symbols, constraints, counter): + """ + Similar to reshape but with an extra condition on the strides + """ + assert isinstance(n.args[0], Node) + + # generate the new variable + my_view, counter = gen_tvar(counter) + symbols[n] = my_view + + src_var = symbols[n.args[0]] + t2 = [ + symbols[elem] if isinstance(elem, Node) else elem for elem in n.args[1:] + ] # target shape + t2_type = [] + num_constraints = [] + + for t in t2: + if t == -1: + var, counter = gen_dvar(counter) + t2_type.append(var) + # pyrefly: ignore [bad-argument-type] + num_constraints.append(BinConstraintD(var, Dyn, op_neq)) + + else: + # pyrefly: ignore [bad-argument-type] + num_constraints.append(BinConstraintD(t, Dyn, op_neq)) + t2_type.append(t) # type: ignore[arg-type] + + t2_type = TensorType(t2_type) # type: ignore[assignment] + + c1 = BinConstraintT(my_view, t2_type, op_eq) + c2 = CanReshape(src_var, t2_type) + + # TODO: add the extra check mentioned here: + # https://pytorch.org/docs/stable/generated/torch.Tensor.view.html#torch.Tensor.view + + return [c1, c2] + num_constraints, counter # type: ignore[operator] + + +@register_inference_rule("size") +def size_inference_rule(n: Node, symbols, constraints, counter): + """ + The constraint is just lhs = rhs. + Ex: size = input_ids.size() + """ + + if len(n.args) == 1: + # generate the new variable + size, counter = gen_tvar(counter) + symbols[n] = size + input = symbols[n.args[0]] + c = BinConstraintT(input, size, op_eq) + return [c], counter + + elif len(n.args) == 2: + # TODO: review this rule; should input = dyn; output = dyn be included here? + if isinstance(n.args[1], int): + # generate the new variable + size_index, counter = gen_dvar(counter) + symbols[n] = size_index + input = symbols[n.args[0]] + c2 = [ + GetItem(i + 1, n.args[1], size_index, input) + for i in range(MAX_TENSOR_RANK) + ] + c3 = BinConstraintD(0, size_index, op_leq) + + input_dyn = BinConstraintT(input, Dyn, op_eq) + output_dyn = BinConstraintD(size_index, Dyn, op_eq) + c1 = Conj([input_dyn, output_dyn]) + + return [Disj([c1, Conj([Disj(c2), c3])])], counter + + else: + raise NotImplementedError + + else: + raise NotImplementedError + + +def range_check(i, n): + """ + Checks if an index i is within range of a size n list + Args: + i: index + n: list size + + Returns: Boolean + """ + if i >= 0: + return T() if i < n else F() + else: + return T() if i >= n else F() + + +@register_inference_rule(torch.cumsum) +def cumsum_inference_rule(n: Node, symbols, constraints, counter): + """ + Input and output shapes should be equal + We should verify that the index is valid + """ + assert isinstance(n.args[0], Node) + arg_1 = n.args[1] if len(n.args) > 1 else n.kwargs["dim"] + assert isinstance(arg_1, int) + + output, counter = gen_tvar(counter) + symbols[n] = output + input = symbols[n.args[0]] + + input_dyn = BinConstraintT(input, Dyn, op_eq) + output_dyn = BinConstraintT(output, Dyn, op_eq) + c1 = Conj([input_dyn, output_dyn]) + c2 = [] + for i in range(1, MAX_TENSOR_RANK + 1): + new_dims, counter = gen_tensor_dims(i, counter) + + nat_constraints = gen_nat_constraints(new_dims) + + c_tensor_i = Conj( + [ + BinConstraintT(input, TensorType(new_dims), op_eq), + BinConstraintT(output, TensorType(new_dims), op_eq), + ] + + [range_check(arg_1, i)] + + nat_constraints + ) + + c2.append(c_tensor_i) + dyn_or_tensor = Disj([c1, Disj(c2)]) + return [dyn_or_tensor], counter + + +@register_inference_rule(_assert_is_none) +def assert_inference_rule(n: Node, symbols, constraints, counter): + assert len(n.users) == 0 + return [], counter + + +@register_inference_rule(operator.getitem) +def getitem_inference_rule(n: Node, symbols, constraints, counter): + assert isinstance(n.args[0], Node) + + # dimension output case + if isinstance(n.args[1], int): + # create and store the new dimension variable + get_item_output, counter = gen_dvar(counter) + symbols[n] = get_item_output + + # retrieve arg variables + get_item_arg = symbols[n.args[0]] + assert isinstance(get_item_arg, TVar) + + # if the input is dynamic, we accept any index and return + # a dynamic dimension as output + input_dyn = BinConstraintT(get_item_arg, Dyn, op_eq) + output_dyn = BinConstraintD(get_item_output, Dyn, op_eq) + c1 = Conj([input_dyn, output_dyn]) + + # if the input is a tensor, + # generate a getItem constraint which will be expanded based on the + # tensor dimension. + + c2 = [ + GetItem(i + 1, n.args[1], get_item_output, get_item_arg) + for i in range(MAX_TENSOR_RANK) + ] + + # since the output is a dimension, we make sure it's a natural number + # added as a conjunction to the disjunction of c2 + c3 = BinConstraintD(0, get_item_output, op_leq) + return [Disj([c1, Conj([Disj(c2), c3])])], counter + + # tensor output case + elif isinstance(n.args[1], tuple): + # create and store the new tensor variable + get_item_output, counter = gen_tvar(counter) # type: ignore[arg-type,assignment] + symbols[n] = get_item_output + + # retrieve arg variables + if n.args[0] in symbols: + get_item_arg = symbols[n.args[0]] + assert isinstance(get_item_arg, TVar) + + input_dyn = BinConstraintT(get_item_arg, Dyn, op_eq) + output_dyn = BinConstraintT(get_item_output, Dyn, op_eq) # type: ignore[assignment] + c1 = Conj([input_dyn, output_dyn]) + + c2 = [ + GetItemTensor(i + 1, n.args[1], get_item_output, get_item_arg) # type: ignore[misc] + for i in range(MAX_TENSOR_RANK) + ] + else: + # TODO: we should figure out why there is a key-error here. + return [], counter + + return [Disj([c1, *c2])], counter + + else: + raise RuntimeError("Method not yet implemented") + + +@register_inference_rule(operator.gt) +def gt_inference_rule(n: Node, symbols, constraints, counter): + assert isinstance(n.args[0], (Node, int)) + assert isinstance(n.args[1], (Node, int)) + + # We make sure this node will not be used again. We do not + # generate a constraint about that node. Only about the operands. + + e1 = symbols[n.args[0]] if isinstance(n.args[0], Node) else n.args[0] + e2 = symbols[n.args[1]] if isinstance(n.args[1], Node) else n.args[1] + + if isinstance(n.args[0], Node) and isinstance(n.args[1], Node): + if isinstance(e1, TVar) and isinstance(e2, TVar): + gt_tensor, counter = gen_tvar(counter) + symbols[n] = gt_tensor + return gen_broadcasting_constraints(e1, e2, symbols, counter, gt_tensor) + + elif isinstance(e1, DVar) and isinstance(e2, DVar): + # This is meant to be used for flow analysis only + gt_constraint = BinConstraintD(e1, e2, op_gt) + + my_gt, counter = gen_bvar(counter) + equality_constraint = BinConstraintD(my_gt, gt_constraint, op_eq) + return [equality_constraint], counter + + else: + raise RuntimeError("Sort Mismatch") + + elif isinstance(n.args[0], Node) and not isinstance(n.args[1], Node): + if isinstance(e1, DVar): + # This is meant to be used for flow analysis only + gt_constraint = BinConstraintD(e1, e2, op_gt) + + my_gt, counter = gen_bvar(counter) + equality_constraint = BinConstraintD(my_gt, gt_constraint, op_eq) + return [equality_constraint], counter + + elif isinstance(e1, TVar) and isinstance(e2, int): + # then we made the wrong assumption about the argument being a tensor + # so we should fix the assumption + warnings.warn( + f"Made the wrong assumption for node {n}. Correctness not guaranteed." + ) + + new_e1, counter = gen_dvar(counter) + symbols[n.args[0]] = new_e1 + symbols[n.args[0]] + + gt_constraint = BinConstraintD(new_e1, e2, op_gt) + + my_gt, counter = gen_bvar(counter) + equality_constraint = BinConstraintD(my_gt, gt_constraint, op_eq) + return [equality_constraint], counter + + else: + raise NotImplementedError("Method not yet implemented") + + else: + raise NotImplementedError("Method not yet implemented") + + +@register_inference_rule(operator.eq) +def eq_inference_rule(n: Node, symbols, constraints, counter): + assert isinstance(n.args[0], (Node, int)) + assert isinstance(n.args[1], (Node, int)) + + e1 = symbols[n.args[0]] if isinstance(n.args[0], Node) else n.args[0] + e2 = symbols[n.args[1]] if isinstance(n.args[1], Node) else n.args[1] + + if isinstance(n.args[0], Node) and isinstance(n.args[1], Node): + if isinstance(e1, TVar) and isinstance(e2, TVar): + eq_tensor, counter = gen_tvar(counter) + symbols[n] = eq_tensor + return gen_broadcasting_constraints(e1, e2, symbols, counter, eq_tensor) + + elif isinstance(e1, DVar) and isinstance(e2, DVar): + # This is meant to be used for flow analysis only + eq_constraint = BinConstraintD(e1, e2, op_eq) + + my_eq, counter = gen_bvar(counter) + equality_constraint = BinConstraintD(my_eq, eq_constraint, op_eq) + return [equality_constraint], counter + + else: + raise RuntimeError("Sort Mismatch") + + elif isinstance(n.args[0], Node) and not isinstance(n.args[1], Node): + if isinstance(e1, DVar): + # This is meant to be used for flow analysis only + eq_constraint = BinConstraintD(e1, e2, op_eq) + + my_eq, counter = gen_bvar(counter) + equality_constraint = BinConstraintD(my_eq, eq_constraint, op_eq) + return [equality_constraint], counter + else: + raise NotImplementedError("Method not yet implemented") + else: + raise NotImplementedError("Method not yet implemented") + + +@register_inference_rule(operator.ne) +def neq_inference_rule(n: Node, symbols, constraints, counter): + """ + Translates to inconsistent in gradual types. + To prove inequality, we should prove that + tensors are either different sizes or + disagree on at least one dimension + + This is a WIP (works when the condition + is false. We are working on making this operation work + when the condition is true as well) + """ + assert isinstance(n.args[0], Node) + assert isinstance(n.args[1], tuple) + + # implementing for size 3 and 4 + if len(n.args[1]) == 3: + assert isinstance(n.args[1][0], (Node, int)) + assert isinstance(n.args[1][1], (Node, int)) + assert isinstance(n.args[1][2], (Node, int)) + + lhs = symbols[n.args[0]] + + b, counter = gen_tensor_dims(4, counter) + input_is_size3 = BinConstraintT(lhs, TensorType([b[0], b[1], b[2]]), op_eq) + + d1 = n.args[1][0] if isinstance(n.args[1][0], int) else symbols[n.args[1][0]] + d2 = n.args[1][1] if isinstance(n.args[1][1], int) else symbols[n.args[1][1]] + d3 = n.args[1][2] if isinstance(n.args[1][2], int) else symbols[n.args[1][2]] + + # dimensions not equal + my_ne, counter = gen_bvar(counter) + neq_1 = BinConstraintD(d1, b[0], op_neq) + neq_2 = BinConstraintD(d2, b[1], op_neq) + neq_3 = BinConstraintD(d3, b[2], op_neq) + + # dimensions inconsistent + dims_inconsistent1 = Conj( + [BinConstraintD(d1, Dyn, op_neq), BinConstraintD(b[0], Dyn, op_neq), neq_1] + ) + dims_inconsistent2 = Conj( + [BinConstraintD(d2, Dyn, op_neq), BinConstraintD(b[1], Dyn, op_neq), neq_2] + ) + dims_inconsistent3 = Conj( + [BinConstraintD(d3, Dyn, op_neq), BinConstraintD(b[2], Dyn, op_neq), neq_3] + ) + + dims_inconsistent = Disj( + [dims_inconsistent1, dims_inconsistent2, dims_inconsistent3] + ) + + # we are covering size 3 and 4 only for now + ne_constraint = Conj([input_is_size3, dims_inconsistent]) + + my_ne, counter = gen_bvar(counter) + equality_constraint = BinConstraintD(my_ne, ne_constraint, op_eq) + + elif len(n.args[1]) == 4: + assert isinstance(n.args[1][0], (Node, int)) + assert isinstance(n.args[1][1], (Node, int)) + assert isinstance(n.args[1][2], (Node, int)) + assert isinstance(n.args[1][3], (Node, int)) + + lhs = symbols[n.args[0]] + + b1, counter = gen_dvar(counter) + b2, counter = gen_dvar(counter) + b3, counter = gen_dvar(counter) + b4, counter = gen_dvar(counter) + + input_is_size4 = BinConstraintT(lhs, TensorType([b1, b2, b3, b4]), op_eq) + + d1 = n.args[1][0] if isinstance(n.args[1][0], int) else symbols[n.args[1][0]] + d2 = n.args[1][1] if isinstance(n.args[1][1], int) else symbols[n.args[1][1]] + d3 = n.args[1][2] if isinstance(n.args[1][2], int) else symbols[n.args[1][2]] + d4 = n.args[1][3] if isinstance(n.args[1][3], int) else symbols[n.args[1][3]] + + # dimensions not equal + my_ne, counter = gen_bvar(counter) + neq_1 = BinConstraintD(d1, b1, op_neq) + neq_2 = BinConstraintD(d2, b2, op_neq) + neq_3 = BinConstraintD(d3, b3, op_neq) + neq_4 = BinConstraintD(d4, b4, op_neq) + + # dimensions to inconsistent + dims_inconsistent1 = Conj( + [BinConstraintD(d1, Dyn, op_neq), BinConstraintD(b1, Dyn, op_neq), neq_1] + ) + dims_inconsistent2 = Conj( + [BinConstraintD(d2, Dyn, op_neq), BinConstraintD(b2, Dyn, op_neq), neq_2] + ) + dims_inconsistent3 = Conj( + [BinConstraintD(d3, Dyn, op_neq), BinConstraintD(b3, Dyn, op_neq), neq_3] + ) + dims_inconsistent4 = Conj( + [BinConstraintD(d4, Dyn, op_neq), BinConstraintD(b3, Dyn, op_neq), neq_4] + ) + + dims_inconsistent = Disj( + [ + dims_inconsistent1, + dims_inconsistent2, + dims_inconsistent3, + dims_inconsistent4, + ] + ) + + ne_constraint = Conj([input_is_size4, dims_inconsistent]) + + my_ne, counter = gen_bvar(counter) + + equality_constraint = BinConstraintD(my_ne, ne_constraint, op_eq) + + else: + raise NotImplementedError("Method not yet implemented") + + return [equality_constraint], counter + + +@register_inference_rule(operator.lt) +def lt_inference_rule(n: Node, symbols, constraints, counter): + assert isinstance(n.args[0], (Node, int)) + assert isinstance(n.args[1], (Node, int)) + + # We make sure this node will not be used again. We do not + # generate a constraint about that node. Only about the operands. + + e1 = symbols[n.args[0]] if isinstance(n.args[0], Node) else n.args[0] + e2 = symbols[n.args[1]] if isinstance(n.args[1], Node) else n.args[1] + + if isinstance(n.args[0], Node) and isinstance(n.args[1], Node): + if isinstance(e1, TVar) and isinstance(e2, TVar): + lt_tensor, counter = gen_tvar(counter) + symbols[n] = lt_tensor + return gen_broadcasting_constraints(e1, e2, symbols, counter, lt_tensor) + + elif isinstance(e1, DVar) and isinstance(e2, DVar): + # This is meant to be used for flow analysis only + lt_constraint = BinConstraintD(e1, e2, op_lt) + + my_lt, counter = gen_bvar(counter) + equality_constraint = BinConstraintD(my_lt, lt_constraint, op_eq) + return [equality_constraint], counter + + else: + raise RuntimeError("Sort Mismatch") + + elif isinstance(n.args[0], Node) and not isinstance(n.args[1], Node): + if isinstance(e1, DVar): + # This is meant to be used for flow analysis only + lt_constraint = BinConstraintD(e1, e2, op_lt) + + my_lt, counter = gen_bvar(counter) + equality_constraint = BinConstraintD(my_lt, lt_constraint, op_eq) + return [equality_constraint], counter + else: + raise NotImplementedError("Method not yet implemented") + + else: + raise NotImplementedError("Method not yet implemented") + + +@register_inference_rule(torch.full) +def full_inference_rule(n: Node, symbols, constraints, counter): + full, counter = gen_tvar(counter) + symbols[n] = full + res = [] + + assert isinstance(n.args[0], Iterable) + for arg in n.args[0]: + dim = arg if isinstance(arg, int) else symbols[arg] + res.append(dim) + c = BinConstraintT(full, TensorType(list(res)), op_eq) # type: ignore[arg-type] + return [c], counter + + +# TODO normalize index +@register_inference_rule(torch.arange) +def arange_inference_rule(n: Node, symbols, constraints, counter): + start = 0 + step = 1 + + if len(n.args) == 1: + end = symbols[n.args[0]] + else: + raise NotImplementedError("Not yet implemented") + + # int((end - start) / step) + d1, counter = gen_dvar(counter) + size_constraint = BinConstraintD( + d1, BinConstraintD(BinConstraintD(end, start, op_sub), step, op_div), op_eq + ) + arange, counter = gen_tvar(counter) + symbols[n] = arange + + # either the a parameter is a number or it is Dyn + c1 = Disj( + [ + BinConstraintD(end, Dyn, op_eq), + BinConstraintD(start, Dyn, op_eq), + BinConstraintD(step, Dyn, op_eq), + ] + ) + c2 = BinConstraintD(d1, Dyn, op_eq) + both_dyn = Conj([c1, c2]) + + c11 = Conj( + [ + BinConstraintD(end, Dyn, op_neq), + BinConstraintD(start, Dyn, op_neq), + BinConstraintD(step, Dyn, op_neq), + ] + ) + c22 = BinConstraintD(d1, Dyn, op_neq) + both_numbers = Conj([c11, c22, size_constraint]) + + return [ + BinConstraintT(arange, TensorType([d1]), op_eq), + Disj([both_dyn, both_numbers]), + ], counter + + +def gen_broadcasting_constraints(e1, e2, symbols, counter, output_var): + # additional vars that don't correspond to expressions + e11, counter = gen_tvar(counter) + e22, counter = gen_tvar(counter) + + # generate constraints + c1 = TGreatestUpperBound(output_var, e11, e22) + c2 = ApplyBroadcasting(e11, e22, e1, e2) + c3 = BinConstraintT(e11, e22, op_consistency) + return [c1, c2, c3], counter + + +@register_inference_rule(operator.mul) +@register_inference_rule(torch.ne) +@register_inference_rule("ne") +@register_inference_rule(torch.add) +@register_inference_rule(operator.add) +def broadcasting_inference_rule(n: Node, symbols, constraints, counter): + op_code = None + if n.target is operator.add or n.target is torch.add: + op_code = op_add + elif n.target is operator.mul: + op_code = op_mul + + if isinstance(n.args[0], Node) and isinstance(n.args[1], Node): + if isinstance(symbols[n.args[0]], TVar) and isinstance( + symbols[n.args[1]], TVar + ): + my_output, counter = gen_tvar(counter) + symbols[n] = my_output + e1 = symbols[n.args[0]] + e2 = symbols[n.args[1]] + + return gen_broadcasting_constraints(e1, e2, symbols, counter, my_output) + else: + raise NotImplementedError("Method not yet implemented") + + elif isinstance(n.args[0], Node) and isinstance(n.args[1], (int, float)): + if isinstance(symbols[n.args[0]], TVar): + my_output, counter = gen_tvar(counter) + symbols[n] = my_output + e1 = symbols[n.args[0]] + return [BinConstraintT(my_output, e1, op_eq)], counter + elif isinstance(symbols[n.args[0]], DVar): + my_output, counter = gen_dvar(counter) # type: ignore[arg-type,assignment] + symbols[n] = my_output + e1 = symbols[n.args[0]] + + # we will propagate the runtime value here since this is regular addition + c = Conj( + [ + BinConstraintD( + my_output, BinConstraintD(e1, n.args[1], op_code), op_eq + ), + BinConstraintD(0, my_output, op_leq), + ] + ) + return [c], counter + + elif isinstance(n.args[1], Node) and isinstance(n.args[0], (int, float)): + if isinstance(symbols[n.args[1]], TVar): + my_output, counter = gen_tvar(counter) + symbols[n] = my_output + e2 = symbols[n.args[1]] + return [BinConstraintT(my_output, e2, op_eq)], counter + elif isinstance(symbols[n.args[1]], DVar): + my_output, counter = gen_dvar(counter) # type: ignore[arg-type,assignment] + symbols[n] = my_output + e2 = symbols[n.args[1]] + + # we will propagate the runtime value here since this is regular addition + c = Conj( + [ + BinConstraintD( + my_output, BinConstraintD(e2, n.args[0], op_code), op_eq + ), + BinConstraintD(0, my_output, op_leq), + ] + ) + return [c], counter + + else: + raise NotImplementedError("Method not yet implemented") + + else: + # TODO generate add constraints for scalar addition + raise NotImplementedError("Addition not yet implemented") + + +@register_inference_rule(torch.flatten) +def flatten_inference_rule(n: Node, symbols, constraints, counter): + assert isinstance(n.args[0], Node) + + # generate the new variable + flattened, counter = gen_tvar(counter) + symbols[n] = flattened + + input = symbols[n.args[0]] + + # set the default start and end dims + start_dim = 1 + end_dim = -1 + + if len(n.args) > 1: + assert isinstance(n.args[1], int) + start_dim = n.args[1] + + if len(n.args) > 2: + assert isinstance(n.args[2], int) + end_dim = n.args[2] + + c1 = BinConstraintT(input, Dyn, op_eq) + c2 = BinConstraintT(flattened, Dyn, op_eq) + both_dyn = Conj([c1, c2]) + + const = [] + for i in range(1, MAX_TENSOR_RANK + 1): + c, counter = generate_flatten_constraints( + start_dim, end_dim, input, flattened, i, counter + ) + const.append(c) + + return [Disj([both_dyn, *const])], counter + + +@register_inference_rule(torch.nn.functional.layer_norm) +def layer_norm_functional(n: Node, symbols, constraints, counter): + """ + We generate the constraint: input = output + """ + assert isinstance(n.args[0], Node) + return gen_layer_norm_constraints(n, n.args[1], symbols, counter) + + +@register_inference_rule(torch.nn.LayerNorm) +def layer_norm_inference_rule(n: Node, module_instance, symbols, constraints, counter): + """ + Input and output shapes should be equal. + Input should be consistent with the normalized_shape + """ + assert isinstance(n.args[0], Node) + return gen_layer_norm_constraints( + n, module_instance.normalized_shape, symbols, counter + ) + + +def gen_layer_norm_constraints(n: Node, normalized_shape, symbols, counter): + output, counter = gen_tvar(counter) + symbols[n] = output + input = symbols[n.args[0]] + + input_dyn = BinConstraintT(input, Dyn, op_eq) + output_dyn = BinConstraintT(output, Dyn, op_eq) + + c1 = Conj([input_dyn, output_dyn]) + + c2 = [] + for i in range(1, MAX_TENSOR_RANK + 1): + new_dims_rhs, counter = gen_tensor_dims(i, counter) + nat_constraints = gen_nat_constraints(new_dims_rhs) + + c_tensor_i = Conj( + [ + BinConstraintT(input, TensorType(new_dims_rhs), op_eq), + BinConstraintT(output, TensorType(new_dims_rhs), op_eq), + ] + + add_layer_norm_constraints(new_dims_rhs, list(normalized_shape)) + + nat_constraints + ) + c2.append(c_tensor_i) + return [Disj([c1, Disj(c2)])], counter + + +@register_inference_rule(torch.nn.Dropout) +@register_inference_rule(torch.nn.ReLU) +def relu_inference_rule(n: Node, module_instance, symbols, constraints, counter): + """ + Input and output shapes should be equal. + """ + assert isinstance(n.args[0], Node) + output, counter = gen_tvar(counter) + symbols[n] = output + input = symbols[n.args[0]] + assert isinstance(input, TVar) + return [BinConstraintT(input, output, op_eq)], counter + + +@register_inference_rule(torch.nn.Linear) +def linear_inference_rule(n: Node, module_instance, symbols, constraints, counter): + """ + Input and output sizes should be the same except for the last dimension + If the input is Dyn, then so should the output + """ + assert isinstance(n.args[0], Node) + return linear_constraints( + n, module_instance.in_features, module_instance.out_features, symbols, counter + ) + + +@register_inference_rule("dim") +def torch_dim_inference_rule(n: Node, symbols, constraints, counter): + assert isinstance(n.args[0], Node) + my_dim, counter = gen_dvar(counter) + symbols[n] = my_dim + input = symbols[n.args[0]] + + input_dyn = BinConstraintT(input, Dyn, op_eq) + output_dyn = BinConstraintD(my_dim, Dyn, op_eq) + + c1 = [] + + for i in range(1, MAX_TENSOR_RANK + 1): + new_dims_rhs_1, counter = gen_tensor_dims(i, counter) + + c_tensor_i = Conj( + [ + BinConstraintT(input, TensorType(new_dims_rhs_1), op_eq), + BinConstraintD(my_dim, i, op_eq), + ] + ) + c1.append(c_tensor_i) + + return [Disj([Conj([input_dyn, output_dyn]), Disj(c1)])], counter + + +@register_inference_rule(torch._C._nn.linear) +def torch_linear_inference_rule(n: Node, symbols, constraints, counter): + assert isinstance(n.args[0], Node) + weight_dims, counter = gen_tensor_dims(2, counter) + equality_constraint = BinConstraintT( + symbols[n.args[1]], TensorType(weight_dims), op_eq + ) + constraints, counter = linear_constraints( + n, weight_dims[1], weight_dims[0], symbols, counter + ) + return [equality_constraint] + constraints, counter + + +def linear_constraints(n: Node, in_features, out_features, symbols, counter): + linear_output, counter = gen_tvar(counter) + symbols[n] = linear_output + linear_input = symbols[n.args[0]] + + input_dyn = BinConstraintT(linear_input, Dyn, op_eq) + output_dyn = BinConstraintT(linear_output, Dyn, op_eq) + + c1 = Conj([input_dyn, output_dyn]) + + c2 = [] + for i in range(1, MAX_TENSOR_RANK + 1): + new_dims_rhs_1, counter = gen_tensor_dims(i, counter) + new_dims_rhs_2, counter = gen_tensor_dims(i, counter) + + nat_constraints = gen_nat_constraints(new_dims_rhs_1 + new_dims_rhs_2) + + c_tensor_i = Conj( + [ + BinConstraintT(linear_input, TensorType(new_dims_rhs_1), op_eq), + BinConstraintT(linear_output, TensorType(new_dims_rhs_2), op_eq), + ] + + add_linear_constraints( + new_dims_rhs_1, new_dims_rhs_2, in_features, out_features + ) + + nat_constraints + ) + c2.append(c_tensor_i) + return [Disj([c1, Disj(c2)])], counter + + +def add_layer_norm_constraints(input_dim, normalized_dim): + """ + The constraints say that the type has te form: [*, 1024, 1024] + while the normalized_dim have the form [1024, 1024] + Args: + input_dim: Input shape of layer norm + normalized_dim: normalized_dim parameter of the module instance + + """ + + # in this case we return false since there's a pattern mismatch + if len(normalized_dim) > len(input_dim): + return [F()] + + else: + constraints = [] + for i, n in zip(reversed(input_dim), reversed(normalized_dim)): + constraints.append(BinConstraintD(i, n, op_consistency)) + return constraints + + +def add_linear_constraints(dims1, dims2, in_features, out_features): + assert len(dims1) == len(dims2) + constraints = [] + for i in range(len(dims1)): + if i == len(dims1) - 1: + constraints.append(BinConstraintD(dims1[i], in_features, op_consistency)) + constraints.append(BinConstraintD(dims2[i], out_features, op_eq)) + else: + constraints.append(BinConstraintD(dims1[i], dims2[i], op_eq)) + + return constraints + + +@register_inference_rule(torch.reshape) +def reshape_inference_rule(n: Node, symbols, constraints, counter): + assert isinstance(n.args[0], Node) + + # generate the new variable + my_reshape, counter = gen_tvar(counter) + symbols[n] = my_reshape + + src_var = symbols[n.args[0]] + t2 = n.args[1] + t2_type = TensorType([Dyn if elem == -1 else elem for elem in t2]) # type: ignore[union-attr] + c1 = BinConstraintT(my_reshape, t2_type, op_eq) # type: ignore[union-attr] + c2 = CanReshape(src_var, t2_type) + + return [c1, c2], counter + + +@register_inference_rule(BatchNorm2d) +def batchnorm_inference_rule(n: Node, module_instance, symbols, constraints, counter): + assert isinstance(n.args[0], Node) + + # generate the new variable + batchnorm_output, counter = gen_tvar(counter) + symbols[n] = batchnorm_output + batchnorm_input = symbols[n.args[0]] + + # dim vars + d1, counter = gen_dvar(counter) + d2, counter = gen_dvar(counter) + d3, counter = gen_dvar(counter) + d4, counter = gen_dvar(counter) + + nat_constraints = gen_nat_constraints([d1, d2, d3, d4]) + + c1 = BinConstraintT(batchnorm_input, TensorType([d1, d2, d3, d4]), op_matching) + c2 = BinConstraintT(batchnorm_input, batchnorm_output, op_eq) + return [c1, c2, *nat_constraints], counter + + +@register_inference_rule(torch.nn.AdaptiveAvgPool2d) +def adaptive_inference_rule(n: Node, module_instance, symbols, constraints, counter): + assert isinstance(n.args[0], Node) + + avg_pool, counter = gen_tvar(counter) + + symbols[n] = avg_pool + input_var = symbols[n.args[0]] + + # dim vars + d1, counter = gen_dvar(counter) + d2, counter = gen_dvar(counter) + d3, counter = gen_dvar(counter) + d4, counter = gen_dvar(counter) + nat_constraints = gen_nat_constraints([d1, d2, d3, d4]) + c1 = BinConstraintT(input_var, TensorType([d1, d2, d3, d4]), op_matching) + c2 = BinConstraintT( + avg_pool, + TensorType( + [d1, d2, module_instance.output_size[0], module_instance.output_size[1]] + ), + op_eq, + ) + + return [c1, c2, *nat_constraints], counter + + +@register_inference_rule(Conv2d) +def conv2d_inference_rule(n: Node, module_instance, symbols, constraints, counter): + assert isinstance(n.args[0], Node) + + my_conv, counter = gen_tvar(counter) + symbols[n] = my_conv + input_var = symbols[n.args[0]] + + # dim vars + [d1, d2, d3, d4], counter = gen_tensor_dims(MAX_TENSOR_RANK, counter) + + # c1 = Matching(input_var, TensorType([d1, d2, d3, d4])) + c1 = BinConstraintT(input_var, TensorType([d1, d2, d3, d4]), op_matching) + + # c2 = DConsistency(module_instance.in_channels, d2) + c2 = BinConstraintD(module_instance.in_channels, d2, op_consistency) + + c3 = CalcConv( + my_conv, + input_var, + module_instance.out_channels, + module_instance.kernel_size, + module_instance.padding, + module_instance.stride, + module_instance.dilation, + [d1, d2, d3, d4], + ) + + nat_constraints = gen_nat_constraints([d1, d2, d3, d4]) + + return [c1, c2, c3, *nat_constraints], counter + + +@register_inference_rule(torch.nn.MaxPool2d) +def maxpool_inference_rule(n: Node, module_instance, symbols, constraints, counter): + assert isinstance(n.args[0], Node) + maxpool, counter = gen_tvar(counter) + symbols[n] = maxpool + input_var = symbols[n.args[0]] + + # dim vars + [d1, d2, d3, d4], counter = gen_tensor_dims(MAX_TENSOR_RANK, counter) + + c1 = BinConstraintT(input_var, TensorType([d1, d2, d3, d4]), op_matching) + + c2 = CalcMaxPool( + maxpool, + input_var, + module_instance.kernel_size, + module_instance.padding, + module_instance.stride, + module_instance.dilation, + [d1, d2, d3, d4], + ) + + nat_constraints = gen_nat_constraints([d1, d2, d3, d4]) + + return [c1, c2, *nat_constraints], counter + + +class ConstraintGenerator: + def __init__(self, traced, graph=None): + self.traced = traced # traced or tracer.root + self.traced_params = dict(self.traced.named_parameters()) + self.constraints = [] + self.symbol_dict = {} + self.graph = traced.graph if hasattr(traced, "graph") else graph + + def generate_constraints(self, counter=0): + """ + Iterate through every node and generate constraints + Effect: self.constraints will be populated with the final constraints + """ + graph = self.graph + + all_constraints = [] + + # pyrefly: ignore [missing-attribute] + for n in graph.nodes: + (constraints, counter) = self.generate_constraints_node(n, counter) + all_constraints += constraints + + return Conj(all_constraints), counter + + def generate_constraints_node(self, n: Node, counter): + """ + Generate constraints the given node: + Currently supported operations: + - Reshape + - Add + - conv2d + """ + + if n.op == "placeholder": + x, counter = gen_tvar(counter) + self.symbol_dict[n] = x + + my_type = n.type + + if n.type != Dyn and (not isinstance(n.type, TensorType)): + if n.type == torch.nn.parameter.Parameter: + # since we have a parameter, the shape must be static + assert "example_value" in n.meta + my_type = TensorType(n.meta["example_value"].size()) + else: + my_type = Dyn + + c1 = BinConstraintT(my_type, x, op_precision) + c2 = BinConstraintT(x, MAX_TENSOR_RANK, op_leq) + return [c1, c2], counter + + elif n.op == "call_function": + if n.target in _INFERENCE_RULES: + return _INFERENCE_RULES[n.target]( + n, self.symbol_dict, self.constraints, counter + ) + else: + raise RuntimeError( + f"No inference rule registered for target {n.target}!" + ) + + elif n.op == "call_module": + module_instance = self.traced.get_submodule(n.target) + if type(module_instance) in _INFERENCE_RULES: + return _INFERENCE_RULES[type(module_instance)]( + n, module_instance, self.symbol_dict, self.constraints, counter + ) + else: + raise RuntimeError( + f"No inference rule registered for class {type(module_instance)}!" + ) + + elif n.op == "call_method": + if n.target in _INFERENCE_RULES: + return _INFERENCE_RULES[n.target]( + n, self.symbol_dict, self.constraints, counter + ) + else: + raise RuntimeError( + f"No inference rule registered for target {n.target}!" + ) + + elif n.op == "get_attr": + t = self.traced_params.get(n.target, None) + + if isinstance(t, torch.Tensor): + if len(t.shape) > 0: + res = list(t.shape) + attr_type = TensorType(res) + output, counter = gen_tvar(counter) + self.symbol_dict[n] = output + return [BinConstraintT(output, attr_type, op_eq)], counter + else: + # scalar? + return [], counter + else: + return [], counter + + elif n.op == "output": + return [], counter + + else: + raise NotImplementedError(f"Method {n.op} not yet implemented") diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_transformation.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_transformation.py new file mode 100644 index 0000000000000000000000000000000000000000..0782ba5affc9cbbe6b55fbba131066a35f331f5a --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/constraint_transformation.py @@ -0,0 +1,1322 @@ +# mypy: ignore-errors +import copy +import itertools +from collections.abc import Callable + +from torch.fx.experimental.migrate_gradual_types.constraint import ( + ApplyBroadcasting, + BinConstraintD, + CalcConv, + CalcMaxPool, + CalcProduct, + CanReshape, + Conj, + Constraint, + DGreatestUpperBound, + Disj, + DVar, + F, + GetItem, + GetItemTensor, + IndexSelect, + Prod, + T, + TGreatestUpperBound, + Transpose, + TVar, +) +from torch.fx.experimental.migrate_gradual_types.constraint_generator import ( + BinConstraintT, + MAX_TENSOR_RANK, +) +from torch.fx.experimental.migrate_gradual_types.operation import ( + op_add, + op_consistency, + op_div, + op_eq, + op_leq, + op_matching, + op_mod, + op_mul, + op_neq, + op_precision, + op_sub, +) +from torch.fx.experimental.migrate_gradual_types.util import ( + gen_dvar, + gen_nat_constraints, + gen_tensor_dims, +) +from torch.fx.tensor_type import Dyn, TensorType + + +_TRANSFORMATION_RULES: dict[Constraint, Callable] = {} + + +def register_transformation_rule(call_target): + def register(fn): + if call_target in _TRANSFORMATION_RULES: + raise RuntimeError( + f"Transformation rule already registered for {call_target}!" + ) + _TRANSFORMATION_RULES[call_target] = fn + return fn + + return register + + +def valid_index(index, dims): + """ + Given a list of dimensions, checks if an index is valid in the list + """ + try: + dims[index] + return T() + except IndexError: + return F() + + +@register_transformation_rule(Transpose) +def transform_transpose(constraint, counter): + """ + Similar to a sequence of two index-selects + """ + dims, counter = gen_tensor_dims(constraint.tensor_size, counter) + is_valid_index1 = valid_index(constraint.index1, dims) + is_valid_index2 = valid_index(constraint.index2, dims) + new_dims = copy.deepcopy(dims) + nat_constraints = gen_nat_constraints(dims) + + if is_valid_index1 == T() and is_valid_index2 == T(): + new_dims[constraint.index1] = dims[constraint.index2] + new_dims[constraint.index2] = dims[constraint.index1] + + transformed_constraint = Conj( + [ + BinConstraintT(constraint.input_var, TensorType(dims), op_eq), + *nat_constraints, + is_valid_index1, + is_valid_index2, + BinConstraintT(constraint.output, TensorType(new_dims), op_eq), + ] + ) + return transformed_constraint, counter + + +@register_transformation_rule(IndexSelect) +def transform_index_select(constraint, counter): + """ + The constraints consider the given tensor size, checks if the index is valid + and if so, generates a constraint for replacing the input dimension + with the required dimension + """ + dims, counter = gen_tensor_dims(constraint.tensor_size, counter) + is_valid_index = valid_index(constraint.index, dims) + nat_constraints = gen_nat_constraints(dims) + + # if the index is valid then replace the input dimension with the new dimension + # otherwise the dimension will not be replaced and the clause will contain False + if is_valid_index == T(): + new_dims = copy.deepcopy(dims) + new_dims[constraint.index] = constraint.dim_replace + + transformed_constraint = Conj( + [ + BinConstraintT(constraint.input_var, TensorType(dims), op_eq), + *nat_constraints, + is_valid_index, + BinConstraintT(constraint.output, TensorType(new_dims), op_eq), + ] + ) + + # print(constraints) + return transformed_constraint, counter + + +@register_transformation_rule(GetItem) +def transform_get_item(constraint, counter): + """ + generate an equality of the form: + t = [a1, ..., an] + then generate constraints that check if the given index is valid + given this particular tensor size. + If the index is valid, generate a constraint to get the item + Note that we already handled the Dyn input case in the previous + step. + Args: + constraint: GetItem which assumes we are getting an item from a tensor (not Dyn) + counter: variable tracking + Returns: simplified constraints for GetItem + + """ + dims, counter = gen_tensor_dims(constraint.tensor_size, counter) + nat_constraints = gen_nat_constraints(dims) + + is_valid_index = valid_index(constraint.index, dims) + + all_constraints = [ + BinConstraintT(constraint.input_var, TensorType(dims), op_eq), + *nat_constraints, + is_valid_index, + ] + + # if the index is valid, we generate a constraint for getting an item + # otherwise this clause will have been UNSAT due to the wrong index + if is_valid_index == T(): + all_constraints.append( + BinConstraintD(constraint.res, dims[constraint.index], op_eq) + ) + + return Conj(all_constraints), counter + + +def valid_index_tensor(index, dims): + """ + if the slice instances exceed the length of the dimensions + then this is a type error so we return False + """ + slice_count = 0 + for s in index: + if isinstance(s, slice): + slice_count += 1 + if slice_count > len(dims): + return F() + else: + return T() + + +@register_transformation_rule(GetItemTensor) +def transform_get_item_tensor(constraint, counter): + """ + When the index is a tuple, then the output will be a tensor + TODO: we have to check if this is the case for all HF models + + The cases we are covering here are a tuple with one of: + - slice with default argument + - None + + None appends 1 to the input tensor dimensions + so each occurrence of 'None' increases the rank by 1 + + slice with default arguments does not change the rank + """ + assert isinstance(constraint.index_tuple, tuple) + + # generate a result tensor of the expected size + dims, counter = gen_tensor_dims(constraint.tensor_size, counter) + nat_constraints = gen_nat_constraints(dims) + + # generate a place-holder list of the right rank + # where "slice" does not contribute to the rank and "None" does + none_c = constraint.index_tuple.count(None) + resulting_tensor_dims = (none_c + len(dims)) * [None] + + dim_index = 0 + for i in range(len(constraint.index_tuple)): + # append 1 to the right location of the resulting tensor + if constraint.index_tuple[i] is None: + resulting_tensor_dims[i] = 1 + + elif constraint.index_tuple[i] == slice(None, None, None): + pass + + else: + raise NotImplementedError("Method not yet implemented") + + # append the remaining dimensions to the right location + dim_index = 0 + for i in range(len(resulting_tensor_dims)): + if resulting_tensor_dims[i] is None: + resulting_tensor_dims[i] = dims[dim_index] + dim_index += 1 + + # check if the index is valid + is_valid_index = valid_index_tensor(constraint.index_tuple, dims) + + # check if the resulting tensor is within bounds + if len(resulting_tensor_dims) > 4: + return F(), counter + + else: + constraints = [ + BinConstraintT(constraint.input_var, TensorType(dims), op_eq), + BinConstraintT(constraint.res, TensorType(resulting_tensor_dims), op_eq), + *nat_constraints, + is_valid_index, + ] + return Conj(constraints), counter + + +@register_transformation_rule(BinConstraintT) +def generate_binconstraint_t(constraint, counter): + """ + Transform binary constraints for tensors + """ + + # precision constraints + if constraint.op == op_precision: + if constraint.lhs == Dyn: + return T(), counter + elif isinstance(constraint.lhs, TensorType): + is_fully_static = all(d != Dyn for d in constraint.lhs.__args__) + if is_fully_static: + return BinConstraintT(constraint.lhs, constraint.rhs, op_eq), counter + else: + new_dims = [] + + for _ in range(len(constraint.lhs.__args__)): + dim, counter = gen_dvar(counter) + new_dims.append(dim) + + new_dim_constraints = ( + [ + BinConstraintD(old_dim, new_dim, op_precision) + for new_dim, old_dim in zip(new_dims, constraint.lhs.__args__) + ] + + [BinConstraintT(constraint.rhs, TensorType(new_dims), op_eq)] + + [BinConstraintD(1, new_dim, op_leq) for new_dim in new_dims] + ) + return Conj(new_dim_constraints), counter + + # matching + elif constraint.op == op_matching: + assert isinstance(constraint.rhs, TensorType) + d1 = constraint.rhs.__args__[0] + d2 = constraint.rhs.__args__[1] + d3 = constraint.rhs.__args__[2] + d4 = constraint.rhs.__args__[3] + + conj = [ + BinConstraintT(constraint.lhs, Dyn, op_eq), + BinConstraintD(d1, Dyn, op_eq), + BinConstraintD(d2, Dyn, op_eq), + BinConstraintD(d3, Dyn, op_eq), + BinConstraintD(d4, Dyn, op_eq), + ] + return ( + Disj( + [ + Conj(conj), + BinConstraintT(constraint.lhs, TensorType([d1, d2, d3, d4]), op_eq), + ] + ), + counter, + ) + + elif constraint.op == op_consistency: + c_dyn = Disj( + [ + BinConstraintT(constraint.lhs, Dyn, op_eq), + BinConstraintT(constraint.rhs, Dyn, op_eq), + ] + ) + ( + ( + c_tensor_1, + c_tensor_2, + c_tensor_3, + c_tensor_4, + ), + counter, + ) = gen_consistency_constraints(constraint, counter) + + return Disj([c_dyn, c_tensor_1, c_tensor_2, c_tensor_3, c_tensor_4]), counter + + elif constraint.op == op_leq: + assert isinstance(constraint.rhs, int) + disj = [BinConstraintT(constraint.lhs, Dyn, op_eq)] + for i in range(1, constraint.rhs + 1): + dims = [] + for _ in range(1, i + 1): + dim_var, counter = gen_dvar(counter) + dims.append(dim_var) + disj.append(BinConstraintT(constraint.lhs, TensorType(dims), op_eq)) + return Disj(disj), counter + else: + return constraint, counter + + +@register_transformation_rule(BinConstraintD) +def generate_binconstraint_d(constraint, counter): + """ + Transform binary constraints for dimensions + """ + if constraint.op == op_precision: + if isinstance(constraint.lhs, int): + return BinConstraintD(constraint.lhs, constraint.rhs, op_eq), counter + elif constraint.lhs == Dyn: + return T(), counter + + elif constraint.op == op_consistency: + return ( + Disj( + [ + BinConstraintD(constraint.lhs, constraint.rhs, op_eq), + BinConstraintD(constraint.rhs, Dyn, op_eq), + BinConstraintD(constraint.lhs, Dyn, op_eq), + ] + ), + counter, + ) + + else: + return constraint, counter + + +@register_transformation_rule(Conj) +def generate_conj(constraint, counter): + """ + Transform conjunctions + """ + new = [] + for c in constraint.conjucts: + new_c, counter = transform_constraint(c, counter) + new.append(new_c) + return Conj(new), counter + + +@register_transformation_rule(Disj) +def generate_disj(constraint, counter): + """ + Transform disjunctions + """ + new = [] + for c in constraint.disjuncts: + new_c, counter = transform_constraint(c, counter) + new.append(new_c) + return Disj(new), counter + + +@register_transformation_rule(TGreatestUpperBound) +def generate_gub(constraint, counter): + """ + Transform greatest upper bound for tensors. Results in equality and Greatest Upper Bound + on dimensions + """ + c1 = Conj( + [ + Disj( + [ + BinConstraintT(constraint.rhs1, Dyn, op_eq), + BinConstraintT(constraint.rhs2, Dyn, op_eq), + ] + ), + BinConstraintT(constraint.res, Dyn, op_eq), + ] + ) + + [c2, c3, c4, c5], counter = gen_greatest_upper_bound(constraint, counter) + + return Disj([c1, c2, c3, c4, c5]), counter + + +@register_transformation_rule(DGreatestUpperBound) +def generate_d_gub(constraint, counter): + """ + Transform greatest upper bound for dimensions into equality constraints + """ + c1 = Conj( + [ + BinConstraintD(constraint.rhs1, Dyn, op_eq), + BinConstraintD(constraint.res, constraint.rhs2, op_eq), + ] + ) + c2 = Conj( + [ + BinConstraintD(constraint.rhs2, Dyn, op_eq), + BinConstraintD(constraint.res, constraint.rhs1, op_eq), + ] + ) + c3 = Conj( + [ + BinConstraintD(constraint.rhs2, constraint.rhs1, op_eq), + BinConstraintD(constraint.res, constraint.rhs1, op_eq), + ] + ) + return Disj([c1, c2, c3]), counter + + +@register_transformation_rule(CalcConv) +def generate_calc_conv(constraint, counter): + d, counter = gen_tensor_dims(4, counter) + conv_result = TensorType([d[0], d[1], d[2], d[3]]) + + # the convolution result is a tensor of size 4 + c1 = BinConstraintT(constraint.conv_result, conv_result, op_eq) + + # the second dimension of the output is equal to the output channels + c2 = Conj( + [ + BinConstraintD(d[1], constraint.c_out, op_eq), + BinConstraintD(d[1], Dyn, op_neq), + ] + ) + + # the input corresponds to the output in the first dimension of the convolution + c3 = BinConstraintD(constraint.matching_constraint[0], d[0], op_eq) + + c4, c5 = calc_last_two_dims(constraint, d) + + leq_constraints = Conj( + [ + BinConstraintD(0, d[0], op_leq), + BinConstraintD(0, d[1], op_leq), + BinConstraintD(0, d[2], op_leq), + BinConstraintD(0, d[3], op_leq), + ] + ) + + return Conj([c1, c2, c3, c4, c5, leq_constraints]), counter + + +@register_transformation_rule(CalcMaxPool) +def generate_calc_maxpool(constraint, counter): + """ + Transform maxpool constraints + """ + d, counter = gen_tensor_dims(4, counter) + maxpool_result = TensorType([d[0], d[1], d[2], d[3]]) + + # the maxpool result is a tensor of size 4 + c1 = BinConstraintT(constraint.maxpool_result, maxpool_result, op_eq) + + # the input corresponds to the output in the first and second dimension of maxpool + c2 = BinConstraintD(constraint.matching_constraint[1], d[1], op_eq) + c3 = BinConstraintD(constraint.matching_constraint[0], d[0], op_eq) + c4, c5 = calc_last_two_dims(constraint, d) + + leq_constraints = Conj( + [ + BinConstraintD(0, d[0], op_leq), + BinConstraintD(0, d[1], op_leq), + BinConstraintD(0, d[2], op_leq), + BinConstraintD(0, d[3], op_leq), + ] + ) + + return Conj([c1, c2, c3, c4, c5, leq_constraints]), counter + + +@register_transformation_rule(CalcProduct) +def generate_calc_product(constraint, counter): + """ + Transform flatten constraints + """ + start = constraint.start + end = constraint.end + dims = constraint.dims_to_flatten + flattened = constraint.flattened + n = len(constraint.dims_to_flatten) + + # this will be evaluated right here + boundary_check = 0 <= start and start < end and end <= n + + c_boundary = T() if boundary_check else F() + + lhs = dims[0:start] + rhs = dims[end:] + mid = dims[start:end] + + all_possibilities = generate_all_int_dyn_dim_possibilities(mid) + + all_constraints = [] + + for p in all_possibilities: + p = list(p) + # this tells us there is a dynamic variable + contains_dyn = not all(constraint.op == op_neq for constraint in p) + if contains_dyn: + mid_var = [Dyn] + total_constraints = lhs + mid_var + rhs + if len(total_constraints) > 4: + all_constraints.append(F()) + else: + all_constraints.append( + Conj( + [ + BinConstraintT( + flattened, TensorType(lhs + mid_var + rhs), op_eq + ) + ] + + p + ) + ) + else: + new_var, counter = gen_dvar(counter) + mid_eq_prod = Conj( + [ + BinConstraintD(new_var, Prod(mid), op_eq), + BinConstraintD(new_var, Dyn, op_neq), + ] + ) + mid_var = [new_var] + total_constraints = lhs + mid_var + rhs + if len(total_constraints) > 4: + all_constraints.append(F()) + else: + all_constraints.append( + Conj( + [ + BinConstraintT( + flattened, TensorType(lhs + mid_var + rhs), op_eq + ), + mid_eq_prod, + ] + + p + ) + ) + + return Conj([Disj(all_constraints), c_boundary]), counter + + +@register_transformation_rule(CanReshape) +def generate_reshape(constraint, counter): + """ + Transform reshape constraints + """ + d, counter = gen_tensor_dims(4, counter) + + d1 = d[0] + d2 = d[1] + d3 = d[2] + d4 = d[3] + + target = constraint.target.__args__ + + is_fully_static = all(d != Dyn for d in target) + + # dynamic tensor + c1_dyn = BinConstraintT(constraint.src, Dyn, op_eq) + c2_tensor1 = BinConstraintT(constraint.src, TensorType([d1]), op_eq) + c2_tensor2 = BinConstraintT(constraint.src, TensorType([d1, d2]), op_eq) + c2_tensor3 = BinConstraintT(constraint.src, TensorType([d1, d2, d3]), op_eq) + c2_tensor4 = BinConstraintT(constraint.src, TensorType([d1, d2, d3, d4]), op_eq) + + d1_eq_dyn = BinConstraintD(d1, Dyn, op_eq) + d1_neq_dyn = BinConstraintD(d1, Dyn, op_neq) + + d2_eq_dyn = BinConstraintD(d2, Dyn, op_eq) + d2_neq_dyn = BinConstraintD(d2, Dyn, op_neq) + + d3_eq_dyn = BinConstraintD(d3, Dyn, op_eq) + d3_neq_dyn = BinConstraintD(d3, Dyn, op_neq) + + d4_eq_dyn = BinConstraintD(d3, Dyn, op_eq) + d4_neq_dyn = BinConstraintD(d3, Dyn, op_neq) + + nat_d1 = BinConstraintD(0, d1, op_leq) + nat_d2 = BinConstraintD(0, d2, op_leq) + nat_d3 = BinConstraintD(0, d3, op_leq) + nat_d4 = BinConstraintD(0, d4, op_leq) + + if is_fully_static: + # size 1 tensor + c3_tensor1 = Disj( + [d1_eq_dyn, (Conj([d1_neq_dyn, BinConstraintD(d1, Prod(target), op_eq)]))] + ) + all_tensor_1 = Conj([c2_tensor1, c3_tensor1]) + + # size 2 tensor + all_tensor_2 = Conj( + [c2_tensor2, gen_all_reshape_possibilities([d1, d2], target)] + ) + + # size 3 tensor + all_tensor_3 = Conj( + [c2_tensor3, gen_all_reshape_possibilities([d1, d2, d3], target)] + ) + + # size 4 tensor + all_tensor_4 = Conj( + [c2_tensor4, gen_all_reshape_possibilities([d1, d2, d3, d4], target)] + ) + + return ( + Conj( + [ + Disj( + [c1_dyn, all_tensor_1, all_tensor_2, all_tensor_3, all_tensor_4] + ), + nat_d1, + nat_d2, + nat_d3, + nat_d4, + ] + ), + counter, + ) + + # then there must be exactly one occurrence of dyn + else: + new_target = [n for n in target if n != Dyn] + + # tensor 1 + c3_tensor1 = Disj( + [d1_eq_dyn, (Conj([d1_neq_dyn, is_dim_div_by_target(new_target, d1)]))] + ) + all_tensor_1 = Conj([c2_tensor1, c3_tensor1]) + + # tensor 2 + c21 = Disj([d1_eq_dyn, d2_eq_dyn]) + c22 = Conj( + [d1_neq_dyn, d2_neq_dyn, is_dim_div_by_target(new_target, Prod([d1, d2]))] + ) + all_tensor_2 = Conj([c2_tensor2, Disj([c21, c22])]) + + # tensor 3 + c31 = Disj([d1_eq_dyn, d2_eq_dyn, d3_eq_dyn]) + c32 = Conj( + [ + d1_neq_dyn, + d2_neq_dyn, + d3_neq_dyn, + is_dim_div_by_target(new_target, Prod([d1, d2, d3])), + ] + ) + all_tensor_3 = Conj([c2_tensor3, Disj([c31, c32])]) + + # tensor 4 + c41 = Disj([d1_eq_dyn, d2_eq_dyn, d3_eq_dyn, d4_eq_dyn]) + c42 = Conj( + [ + d1_neq_dyn, + d2_neq_dyn, + d3_neq_dyn, + d4_neq_dyn, + is_dim_div_by_target(new_target, Prod([d1, d2, d3, d4])), + ] + ) + all_tensor_4 = Conj([c2_tensor4, Disj([c41, c42])]) + + return ( + Conj( + [ + Disj( + [c1_dyn, all_tensor_1, all_tensor_2, all_tensor_3, all_tensor_4] + ), + nat_d1, + nat_d2, + nat_d3, + nat_d4, + ] + ), + counter, + ) + + +@register_transformation_rule(ApplyBroadcasting) +def generate_broadcasting(constraint, counter): + """ + Transform broadcasting constraints + """ + e11, e12 = constraint.res1, constraint.res2 + e1, e2 = constraint.input1, constraint.input2 + + e1_dyn = BinConstraintT(e1, Dyn, op_eq) + e2_dyn = BinConstraintT(e2, Dyn, op_eq) + + # Introduce dimensions + e1_equal_e11 = BinConstraintT(e1, e11, op_eq) + e2_equal_e12 = BinConstraintT(e2, e12, op_eq) + + # dyn possibility + e1_dyn_constraint = Conj([e1_dyn, e1_equal_e11, e2_equal_e12]) + e2_dyn_constraint = Conj([e2_dyn, e1_equal_e11, e2_equal_e12]) + + # tensor possibility + # generate dimensions to create tensors of size 1 + final_tensor_1_constraint, _, _, nat_dims_1, counter = gen_broadcasting_constraints( + e1, e2, e11, e12, 1, counter + ) + + # generate dimensions to create tensors of size 2 + ( + final_tensor_2_constraint_no_padding, + final_tensor_2_constraint_padding_arg1, + final_tensor_2_constraint_padding_arg2, + nat_dims_2, + counter, + ) = gen_broadcasting_constraints(e1, e2, e11, e12, 2, counter) + + # generate dimensions to create tensors of size 3 + ( + final_tensor_3_constraint_no_padding, + final_tensor_3_constraint_padding_arg1, + final_tensor_3_constraint_padding_arg2, + nat_dims_3, + counter, + ) = gen_broadcasting_constraints(e1, e2, e11, e12, 3, counter) + + # generate dimensions to create tensors of size 4 + ( + final_tensor_4_constraint_no_padding, + final_tensor_4_constraint_padding_arg1, + final_tensor_4_constraint_padding_arg2, + nat_dims_4, + counter, + ) = gen_broadcasting_constraints(e1, e2, e11, e12, 4, counter) + + final_result = Disj( + [ + e1_dyn_constraint, + e2_dyn_constraint, + final_tensor_1_constraint, + final_tensor_2_constraint_no_padding, + final_tensor_2_constraint_padding_arg1, + final_tensor_2_constraint_padding_arg2, + final_tensor_3_constraint_no_padding, + final_tensor_3_constraint_padding_arg1, + final_tensor_3_constraint_padding_arg2, + final_tensor_4_constraint_no_padding, + final_tensor_4_constraint_padding_arg1, + final_tensor_4_constraint_padding_arg2, + ] + ) + + return ( + Conj([final_result, *nat_dims_1, *nat_dims_2, *nat_dims_3, *nat_dims_4]), + counter, + ) + + +def transform_constraint(constraint: Constraint, counter: int): + """ + Transforms a constraint into a simpler constraint. + Ex: precision and consistency are transformed to equality + Args: + constraint: constraint to be transformed + counter: for variable tracking + + Returns: Constraint + + """ + if type(constraint) in _TRANSFORMATION_RULES: + return _TRANSFORMATION_RULES[type(constraint)](constraint, counter) + + else: + return constraint, counter + + +def calc_last_two_dims(constraint, d: list[DVar]): + """ + Generates constraints for the last two dimensions of a convolution or a maxpool output + Args: + constraint: CalcConv or CalcMaxPool + d: The list of output dimensions + + Returns: Constraints for calculating the last two dimensions of the output + + """ + + assert isinstance(constraint, (CalcConv, CalcMaxPool)) + + b3 = constraint.matching_constraint[2] + b4 = constraint.matching_constraint[3] + + b3_dyn = Conj([BinConstraintD(d[2], Dyn, op_eq), BinConstraintD(b3, Dyn, op_eq)]) + b4_dyn = Conj([BinConstraintD(d[3], Dyn, op_eq), BinConstraintD(b4, Dyn, op_eq)]) + + d3_not_dyn = Conj( + [BinConstraintD(d[2], Dyn, op_neq), BinConstraintD(b3, Dyn, op_neq)] + ) + d4_not_dyn = Conj( + [BinConstraintD(d[3], Dyn, op_neq), BinConstraintD(b4, Dyn, op_neq)] + ) + + # transform parameters into tuples in case they are not already + padding = ( + (constraint.padding, constraint.padding) + if isinstance(constraint.padding, int) + else constraint.padding + ) + kernel = ( + (constraint.kernel, constraint.kernel) + if isinstance(constraint.kernel, int) + else constraint.kernel + ) + stride = ( + (constraint.stride, constraint.stride) + if isinstance(constraint.stride, int) + else constraint.stride + ) + dilation = ( + (constraint.dilation, constraint.dilation) + if isinstance(constraint.dilation, int) + else constraint.dilation + ) + + f1 = BinConstraintD(b3, BinConstraintD(2, padding[0], op_mul), op_add) + f2 = BinConstraintD(dilation[0], BinConstraintD(kernel[0], 1, op_sub), op_mul) + f3 = BinConstraintD( + BinConstraintD(BinConstraintD(f1, f2, op_sub), 1, op_sub), stride[0], op_div + ) + f4 = BinConstraintD(f3, 1, op_add) + + c4 = Disj([b3_dyn, Conj([d3_not_dyn, BinConstraintD(d[2], f4, op_eq)])]) + + f11 = BinConstraintD(b4, BinConstraintD(2, padding[1], op_mul), op_add) + f22 = BinConstraintD(dilation[1], BinConstraintD(kernel[1], 1, op_sub), op_mul) + f33 = BinConstraintD( + BinConstraintD(BinConstraintD(f11, f22, op_sub), 1, op_sub), stride[1], op_div + ) + f44 = BinConstraintD(f33, 1, op_add) + + c5 = Disj([b4_dyn, Conj([d4_not_dyn, BinConstraintD(d[3], f44, op_eq)])]) + + return c4, c5 + + +def generate_all_int_dyn_dim_possibilities(my_list: list[DVar]): + """ + Generate all possibilities of being equal or not equal to dyn for my_list + Args: + my_list: List of tensor dimensions + + Returns: A list of a list of constraints. Each list of constraints corresponds to + one possibility about the values of the dimension variables + """ + # generate all possibilities of being equal or not equal to dyn for my_list + eq_possibilities = [ + BinConstraintD(my_list[i], Dyn, op_eq) for i in range(len(my_list)) + ] + neq_possibilities = [ + BinConstraintD(my_list[i], Dyn, op_neq) for i in range(len(my_list)) + ] + + d_possibilities = [list(i) for i in zip(eq_possibilities, neq_possibilities)] + all_possibilities = list(itertools.product(*d_possibilities)) + return all_possibilities + + +def is_target_div_by_dim(target: list[int], dim: list[DVar]): + """ + Generate constraints to check if the target dimensions are divisible by the input dimensions + Args: + target: Target dimensions + dim: Input dimensions + + Returns: Constraints to check divisibility + + """ + return BinConstraintD(BinConstraintD(Prod(target), dim, op_mod), 0, op_eq) + + +def is_dim_div_by_target(target: list[int], dim: list[DVar]): + """ + Generate constraints to check if the input dimensions is divisible by the target dimensions + Args: + target: Target dimensions + dim: Input dimensions + + Returns: Constraints to check divisibility + + """ + return BinConstraintD(BinConstraintD(dim, Prod(target), op_mod), 0, op_eq) + + +def gen_all_reshape_possibilities(list_of_dims, target): + """ + Consider all possibilities what the input dimensions could be (number or dynamic) + Then generate the appropriate constraints using multiplication or mod depending on the possibility + The possibilities we consider here are the cross product of being equal to dyn or not equal to dyn + for the input. Target is fixed because at most one dimension could be dyn. + We have different cases for this. + + Args: + list_of_dims: The input list of dimensions + target: The tensor we want to reshape to + + Returns: A disjunction of transformed reshape constraints + + """ + all_possibilities = generate_all_int_dyn_dim_possibilities(list_of_dims) + + all_constraints = [] + + for p in all_possibilities: + to_multiply = [] + + p = list(p) + + for constraint in p: + assert isinstance(constraint, BinConstraintD) + if constraint.op == op_neq: + to_multiply.append(constraint.lhs) + + if not to_multiply: + all_constraints.append(Conj(p)) + + elif len(to_multiply) < len(list_of_dims): + all_constraints.append( + Conj(p + [is_target_div_by_dim(target, Prod(to_multiply))]) + ) + else: + all_constraints.append( + Conj(p + [BinConstraintD(Prod(list_of_dims), Prod(target), op_eq)]) + ) + + return Disj(all_constraints) + + +def broadcast_dim(tensor_input1, tensor_input2, res1, res2, index, padding=False): + """ + Apply broadcasting to the 'index' dimension of tensor_input1. + Args: + tensor_input1: should represent [d1, ..., d_index, ...] where d_index = 1 + tensor_input2: represents the second input + res1: broadcasted result 1 + res2: broadcasted result 2 + index: the index to broadcast + padding: If padding was used, then tensor_input1[index] does not exist + + Returns: + + """ + if tensor_input1[index] is None: + assert padding + + if not padding: + # then the inputs are the same length so they all have dimensions at "index" + return Conj( + [ + BinConstraintD(tensor_input1[index], 1, op_eq), + BinConstraintD(res1[index], res2[index], op_eq), + BinConstraintD(res2[index], tensor_input2[index], op_eq), + ] + ) + + else: + # we don't set the input dimension to 1, since it doesn't exist. + return Conj( + [ + BinConstraintD(res1[index], res2[index], op_eq), + BinConstraintD(res2[index], tensor_input2[index], op_eq), + ] + ) + + +def apply_padding( + e1_var: TVar, + e11: BinConstraintT, + e2: BinConstraintT, + e12: BinConstraintT, + d2: list[DVar], + d11: list[DVar], + d12: list[DVar], + counter: int, +): + """ + We are considering the possibility where one input has less dimensions than + another input, so we apply padding to the broadcasted results + + Args: + e1_var: Variable representing the first input where padding will be + e11: constraint of the form e11 = Tensortype[d1, ..., dn] + e2: constraint of the form e2 = Tensortype[d1, ..., dn] + e12: constraint of the form e11 = Tensortype[d1, ..., dn] + d2: Tensor variables for the second input + d11: Tensor variables for the broadcasted first input + d12: Tensor variables for the broadcasted second input + counter: variable tracking + + Returns: A new constraint whose goal is to apply padding to the broadcasted result + + """ + + res = [] + + # pad the shorter input with None so we can pass it to the broadcasting helper function + for i in range(1, len(d2)): + d1, counter = gen_tensor_dims(i, counter) + + nat_constraints = gen_nat_constraints(d1 + d2 + d11 + d12) + + e1 = BinConstraintT(e1_var, TensorType(d1), op_eq) + + simulate_padding = [None] * (len(d2) - i) + + assert len(simulate_padding + d1) == len(d2) + + # for every padding size, we also consider broadcasting + broadcast_padding = [ + broadcast_dim(simulate_padding, d2, d11, d12, j, True) + for j in range(len(d2) - i) + ] + + # we consider the possibilities for broadcasting for every dimension. Since we already + # padded d1, we do not consider it while broadcasting + all_broadcasting_possibilities = ( + generate_all_broadcasting_possibilities_no_padding( + d1, d2[(len(d2) - i) :], d11[(len(d2) - i) :], d12[(len(d2) - i) :] + ) + ) + # combine all constraints into a conjunction + c = Conj( + [ + e1, + e11, + e2, + e12, + *broadcast_padding, + all_broadcasting_possibilities, + *nat_constraints, + ] + ) + res.append(c) + + return Disj(res), counter + + +def no_broadcast_dim_with_index( + d1: list[DVar], d2: list[DVar], d3: list[DVar], d4: list[DVar], i: int +): + """ + Args: + d1: input 1 + d2: input 2 + d3: simulated broadcasting for input 1 + d4: simulated broadcasting for input 2 + i: the rank of the resulting tensor addition + + Returns: Constraints for when no broadcasting occurs + """ + return Conj( + [ + Disj( + [ + Conj( + [ + BinConstraintD(d1[i], 1, op_eq), + BinConstraintD(d2[i], 1, op_eq), + ] + ), + Conj( + [ + BinConstraintD(d1[i], 1, op_neq), + BinConstraintD(d2[i], 1, op_neq), + ] + ), + ] + ), + BinConstraintD(d1[i], d3[i], op_eq), + BinConstraintD(d2[i], d4[i], op_eq), + ] + ) + + +def gen_lists_of_dims(num_tensors: int, dim_size: int, counter: int): + """ + Generate lists of DVar to represent tensor dimensions + Args: + num_tensors: the required number of tensors + dim_size: the number of dimensions for each tensor + counter: variable tracking + + Returns: A list of a list of tensor dimensions + + """ + res = [] + + for _ in range(num_tensors): + dims, counter = gen_tensor_dims(dim_size, counter) + res.append(dims) + + return res, counter + + +def create_equality_constraints_for_broadcasting( + e1: TVar, + e2: TVar, + e11: TVar, + e12: TVar, + d1: list[DVar], + d2: list[DVar], + d11: list[DVar], + d12: list[DVar], +): + """ + Create equality constraints for when no broadcasting occurs + Args: + e1: Input 1 + e2: Input 2 + e11: Broadcasted input 1 + e12: Broadcasted input 2 + d1: Variables that store dimensions for e1 + d2: Variables that store dimensions for e2 + d11: Variables that store dimensions for e11 + d12: Variables that store dimensions for e22 + + Returns: Four equality constraints + + """ + + e1_tensor = BinConstraintT(e1, TensorType(d1), op_eq) + e11_tensor = BinConstraintT(e11, TensorType(d11), op_eq) + e2_tensor = BinConstraintT(e2, TensorType(d2), op_eq) + e12_tensor = BinConstraintT(e12, TensorType(d12), op_eq) + return [e1_tensor, e11_tensor, e2_tensor, e12_tensor] + + +def gen_consistency_constraints(constraint: Constraint, counter: int): + """ + Args: + constraint: Consistency constraint on tensors + counter: for variable tracking + + Returns: Equality and consistency constraints on dimensions + + """ + + all_constraints = [] + + for i in range(1, MAX_TENSOR_RANK + 1): + new_dims_rhs_1, counter = gen_tensor_dims(i, counter) + new_dims_rhs_2, counter = gen_tensor_dims(i, counter) + + nat_constraints = gen_nat_constraints(new_dims_rhs_1 + new_dims_rhs_2) + + c_tensor_i = Conj( + [ + BinConstraintT(constraint.lhs, TensorType(new_dims_rhs_1), op_eq), + BinConstraintT(constraint.rhs, TensorType(new_dims_rhs_2), op_eq), + ] + + [ + BinConstraintD(d1, d2, op_consistency) + for d1, d2 in zip(new_dims_rhs_1, new_dims_rhs_2) + ] + + nat_constraints + ) + + all_constraints.append(c_tensor_i) + + return all_constraints, counter + + +def gen_greatest_upper_bound(constraint: TGreatestUpperBound, counter: int): + """ + Args: + constraint: Greatest upper bound on tensors + counter: variable tracking + + Returns: A set of equality constraints and DGreatestUpperBound constraints + + """ + + all_constraints = [] + + for i in range(1, MAX_TENSOR_RANK + 1): + c = [] + dims1, counter = gen_tensor_dims(i, counter) + c1tensor = TensorType(dims1) + + dims2, counter = gen_tensor_dims(i, counter) + c2tensor = TensorType(dims2) + + dims3, counter = gen_tensor_dims(i, counter) + c3tensor = TensorType(dims3) + + c += [ + BinConstraintT(constraint.rhs1, c1tensor, op_eq), + BinConstraintT(constraint.rhs2, c2tensor, op_eq), + BinConstraintT(constraint.res, c3tensor, op_eq), + ] + gen_nat_constraints(dims1 + dims2 + dims3) + + assert ( + len(c3tensor.__args__) == len(c1tensor.__args__) == len(c2tensor.__args__) + ) + for i in range(len(c3tensor.__args__)): + c.append( + DGreatestUpperBound( + c3tensor.__args__[i], c1tensor.__args__[i], c2tensor.__args__[i] + ) + ) + + all_constraints.append(Conj(c)) + return all_constraints, counter + + +def generate_all_broadcasting_possibilities_no_padding( + d1: list[DVar], d2: list[DVar], d11: list[DVar], d12: list[DVar] +): + """ + Generate broadcasting constraints assuming no padding. Broadcasting can happen at any dimension. + We look at all combinations for all dimensions in d1 and d2 + Args: + d1: input1 dimensions + d2: input2 dimensions + d11: broadcasted input1 dimensions + d12: broadcasted input2 dimensions + + Returns: broadcasting constraints relating the input dimensions to the broadcasted dimensions + + """ + + size = len(d1) + + res2 = [] + + for i in range(size): + t1 = broadcast_dim(d1, d2, d11, d12, i) + t2 = broadcast_dim(d2, d1, d12, d11, i) + t3 = no_broadcast_dim_with_index(d1, d2, d11, d12, i) + + res2.append(Disj([t1, t2, t3])) + + return Conj(res2) + + +def gen_broadcasting_constraints( + e1: TVar, e2: TVar, e11: TVar, e12: TVar, i: int, counter: int +): + """ + Simulates broadcasting on e1 and e2 and returns the results + respectively in e11 and e12. Because of gradual types, + e1 and e2 may not be equal. Similarly, e11 and e12 may not + be equal. e11 and e12 should be guaranteed to be consistent + as they represent the shapes of the tensors to be added after + broadcasting. + Args: + e1: TVar representing the type of input 1 + e2: TVar representing the type of input 2 + e11: TVar representing the representing broadcasted input 1 + e12: TVar representing the representing broadcasted input 2 + i: The rank of the resulting type of addition + counter: for variable tracking + + Returns: Simplified broadcasting constraints + + """ + dims, counter = gen_lists_of_dims(4, i, counter) + [d1, d2, d3, d4] = dims + nat_dims_i = gen_nat_constraints(list(itertools.chain.from_iterable(dims))) + + initialize_tensors_constraints = create_equality_constraints_for_broadcasting( + e1, e2, e11, e12, d1, d2, d3, d4 + ) + + [e1_tensor, e11_tensor, e2_tensor, e12_tensor] = initialize_tensors_constraints + + # without padding, broadcast all possibilities for tensors of size i + final_tensor_constraint_no_padding = Conj( + [ + *initialize_tensors_constraints, + generate_all_broadcasting_possibilities_no_padding(d1, d2, d3, d4), + ] + ) + + # with padding, broadcast all possibilities for tensors of size i + final_tensor_constraint_padding_arg1, counter = apply_padding( + e1, e11_tensor, e2_tensor, e12_tensor, d2, d3, d4, counter + ) + + final_tensor_constraint_padding_arg2, counter = apply_padding( + e2, e12_tensor, e1_tensor, e11_tensor, d1, d4, d3, counter + ) + + return ( + final_tensor_constraint_no_padding, + final_tensor_constraint_padding_arg1, + final_tensor_constraint_padding_arg2, + nat_dims_i, + counter, + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/operation.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/operation.py new file mode 100644 index 0000000000000000000000000000000000000000..267100c8545c8b2310299337ecf64211f633f6ce --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/operation.py @@ -0,0 +1,14 @@ +op_add = "+" +op_sub = "-" +op_mul = "*" +op_div = "/" +op_eq = "=" +op_neq = "!=" +op_imp = "=>" +op_matching = "\u22b3" # (contains) +op_consistency = "~" +op_precision = "\u2291" # (square image of or equal to) +op_leq = "\u2264" # less-than or equal to +op_lt = "<" +op_gt = ">" +op_mod = "%" diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/transform_to_z3.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/transform_to_z3.py new file mode 100644 index 0000000000000000000000000000000000000000..d1f9f33965e07551c651fa560a80c5e263dd5b85 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/transform_to_z3.py @@ -0,0 +1,446 @@ +# mypy: allow-untyped-defs +from torch.fx.experimental.migrate_gradual_types.constraint import ( + BinConstraintD, + BinConstraintT, + BVar, + Conj, + Disj, + DVar, + F, + is_algebraic_expression, + is_bool_expr, + is_dim, + Prod, + T, + TVar, +) +from torch.fx.experimental.migrate_gradual_types.constraint_generator import ( + ConstraintGenerator, +) +from torch.fx.experimental.migrate_gradual_types.constraint_transformation import ( + transform_constraint, +) +from torch.fx.experimental.migrate_gradual_types.operation import ( + op_add, + op_div, + op_eq, + op_gt, + op_leq, + op_lt, + op_mod, + op_mul, + op_neq, + op_sub, +) +from torch.fx.tensor_type import Dyn, TensorType + + +try: + import z3 # type: ignore[import] + + from torch.fx.experimental.migrate_gradual_types.z3_types import ( + D, + tensor_type, + z3_dyn, + ) + + HAS_Z3 = True + + def transform_to_z3(constraint, counter, dimension_dict): + if isinstance(constraint, Conj): + conjuncts = [] + for c in constraint.conjucts: + new_c, counter = transform_to_z3(c, counter, dimension_dict) + conjuncts.append(new_c) + return z3.And(conjuncts), counter + + elif isinstance(constraint, Disj): + disjuncts = [] + for c in constraint.disjuncts: + new_c, counter = transform_to_z3(c, counter, dimension_dict) + disjuncts.append(new_c) + return z3.Or(disjuncts), counter + + elif isinstance(constraint, T): + return True, counter + + elif isinstance(constraint, F): + return False, counter + + elif isinstance(constraint, BinConstraintT): + if constraint.op == op_eq: + lhs, counter = transform_var(constraint.lhs, counter, dimension_dict) + rhs, counter = transform_var(constraint.rhs, counter, dimension_dict) + return (lhs == rhs), counter + + else: + raise NotImplementedError("Method not yet implemented") + + elif isinstance(constraint, BinConstraintD): + if constraint.op == op_eq: + if isinstance(constraint.lhs, BVar) and is_bool_expr(constraint.rhs): + transformed_rhs, counter = transform_to_z3( + constraint.rhs, counter, dimension_dict + ) + transformed_lhs = z3.Bool(constraint.lhs.c) + return transformed_lhs == transformed_rhs, counter + + elif is_dim(constraint.lhs) and is_dim(constraint.rhs): + # with dimension transformations we consider the encoding + lhs, counter = transform_dimension( + constraint.lhs, counter, dimension_dict + ) + rhs, counter = transform_dimension( + constraint.rhs, counter, dimension_dict + ) + return lhs == rhs, counter + + else: + # then we have an algebraic expression which means that we disregard the + # first element of the encoding + lhs, counter = transform_algebraic_expression( + constraint.lhs, counter, dimension_dict + ) + rhs, counter = transform_algebraic_expression( + constraint.rhs, counter, dimension_dict + ) + return lhs == rhs, counter + + # The assumption here is that the LHS and RHS must be dimensions + elif constraint.op == op_neq: + assert is_dim(constraint.lhs) + assert is_dim(constraint.rhs) + lhs, counter = transform_dimension( + constraint.lhs, counter, dimension_dict + ) + rhs, counter = transform_dimension( + constraint.rhs, counter, dimension_dict + ) + if constraint.rhs == Dyn or constraint.lhs == Dyn: + if constraint.rhs == Dyn: + return lhs.arg(0) == 1, counter + elif constraint.lhs == Dyn: + return rhs.arg(0) == 1, counter + + # if one of the instances is a number + elif isinstance(constraint.lhs, int) or isinstance(constraint.rhs, int): + if isinstance(constraint.lhs, int): + return ( + z3.Or( + [ + rhs.arg(0) == 0, + z3.And([rhs.arg(0) == 1, lhs.arg(1) != rhs.arg(1)]), + ] + ), + counter, + ) + + elif isinstance(constraint.rhs, int): + return ( + z3.Or( + [ + lhs.arg(0) == 0, + z3.And([lhs.arg(0) == 1, lhs.arg(1) != rhs.arg(1)]), + ] + ), + counter, + ) + + else: + return ( + z3.Or( + [ + z3.And([lhs.arg(0) == 0, rhs.arg(0) != 0]), + z3.And([lhs.arg(0) != 0, rhs.arg(0) == 0]), + z3.And( + [ + lhs.arg(0) != 0, + rhs.arg(0) != 0, + lhs.arg(1) != rhs.arg(1), + ] + ), + ] + ), + counter, + ) + + elif constraint.op == op_leq: + # if the dimensions are not dyn, this will come into effect + # there would have been another constraint specifying if a given dimension + # is dyn or not + assert is_dim(constraint.lhs) and is_dim(constraint.rhs) + lhs, counter = transform_algebraic_expression( + constraint.lhs, counter, dimension_dict + ) + rhs, counter = transform_algebraic_expression( + constraint.rhs, counter, dimension_dict + ) + return lhs <= rhs, counter + + elif constraint.op == op_gt: + assert is_dim(constraint.lhs) and is_dim(constraint.rhs) + lhs, counter = transform_algebraic_expression( + constraint.lhs, counter, dimension_dict + ) + rhs, counter = transform_algebraic_expression( + constraint.rhs, counter, dimension_dict + ) + return lhs > rhs, counter + + elif constraint.op == op_lt: + assert is_dim(constraint.lhs) and is_dim(constraint.rhs) + lhs, counter = transform_algebraic_expression( + constraint.lhs, counter, dimension_dict + ) + rhs, counter = transform_algebraic_expression( + constraint.rhs, counter, dimension_dict + ) + return lhs < rhs, counter + + else: + raise NotImplementedError("operation not yet implemented") + + else: + raise NotImplementedError("Operation not yet implemented") + + def transform_var(tensor, counter, dimension_dict): + """ + Transforms tensor variables to a format understood by z3 + Args: + tensor: Tensor variable or a tensor type potentially with variable dimensions + Returns: Transformed variable to a z3 format + + """ + if isinstance(tensor, TensorType): + res = [] + for t in tensor.__args__: + transformed, counter = transform_dimension(t, counter, dimension_dict) + res.append(transformed) + + assert len(res) <= 4 + if len(tensor.__args__) == 1: + return tensor_type.tensor1(res[0]), counter + elif len(tensor.__args__) == 2: + return tensor_type.tensor2(res[0], res[1]), counter + elif len(tensor.__args__) == 3: + return tensor_type.tensor3(res[0], res[1], res[2]), counter + elif len(tensor.__args__) == 4: + return tensor_type.tensor4(res[0], res[1], res[2], res[3]), counter + + elif tensor == Dyn: + return z3_dyn, counter + + elif isinstance(tensor, TVar): + return z3.Const(tensor.tvar, tensor_type), counter + + def transform_dimension(dimension, counter, dimension_dict): + """ + Takes a dimension variable or a number and transforms it to a tuple + according to our scheme + Args: + dimension: The dimension to be transformed + counter: variable tracking + + Returns: tuple and the current counter + + """ + if dimension == Dyn: + counter += 1 + return D(0, z3.Int(counter)), counter + elif isinstance(dimension, int): + return D(1, dimension), counter + elif isinstance(dimension, DVar): + if dimension.c in dimension_dict: + return ( + D(z3.Int(dimension_dict[dimension.c]), z3.Int(dimension.c)), + counter, + ) + else: + counter += 1 + dimension_dict[dimension.c] = counter + return D(z3.Int(counter), z3.Int(dimension.c)), counter + + def transform_algebraic_expression(expr, counter, dimension_dict): + """ + Transforms an algebraic expression to z3 format + Args: + expr: An expression is either a dimension variable or an algebraic-expression + + + Returns: the transformed expression + + """ + assert is_algebraic_expression(expr) or is_dim(expr) + + if is_dim(expr): + transformed, counter = transform_dimension(expr, counter, dimension_dict) + return transformed.arg(1), counter + + elif isinstance(expr, Prod): + dims = [] + for dim in expr.products: + assert is_dim(dim) + d, counter = transform_dimension(dim, counter, dimension_dict) + dims.append(d.arg(1)) + return z3.Product(dims), counter + + elif is_algebraic_expression(expr): + lhs, counter = transform_algebraic_expression( + expr.lhs, counter, dimension_dict + ) + rhs, counter = transform_algebraic_expression( + expr.rhs, counter, dimension_dict + ) + + if expr.op == op_sub: + c = lhs - rhs + + elif expr.op == op_add: + c = lhs + rhs + + elif expr.op == op_div: + c = lhs / rhs + + elif expr.op == op_mul: + c = lhs * rhs + + elif expr.op == op_mod: + c = lhs % rhs + + else: + raise NotImplementedError("operation not yet implemented") + + return c, counter + + else: + raise RuntimeError + + def transform_all_constraints(traced, counter=0): + """ + Given a trace, generates constraints and transforms them to z3 format + + """ + dimension_dict = {} # type: ignore[var-annotated] + + generator = ConstraintGenerator(traced) + new_constraints, counter = generator.generate_constraints(counter) + + # print(new_constraints.conjucts[0]) + # print(*new_constraints.conjucts, sep='\n') + + # transform precision, matching, consistency till obtaining a fixed point + new_constraints, counter = iterate_till_fixed_point(new_constraints, counter) + # print(new_constraints) + # print(new_constraints.conjucts) + # new_constraints.conjucts = new_constraints.conjucts[:-1] + # print(*new_constraints.conjucts, sep='\n') + + transformed, counter = transform_to_z3(new_constraints, counter, dimension_dict) + # print(transformed) + return transformed + + def iterate_till_fixed_point(constraints, counter): + """ + Transform constraints till reaching a fixed point + """ + old_c = None + while old_c != constraints: + old_c = constraints + constraints, counter = transform_constraint(constraints, counter) + return constraints, counter + + def transform_all_constraints_trace_time(tracer_root, graph, node, counter=0): + """ + Takes a node and a graph and generates two sets of constraints. + One set constraints the node's constraints and another set + constraints the negation of the node's constraints + Args: + tracer_root: the root for getting the module instances + graph: the graph so far in the tracing process + node: node that represents a conditional + counter: variable tracking + + Returns: Two sets of constraints. One with a conjunction with the + the conditional constraint and the other with a conjunction with + its negation. + + """ + dimension_dict = {} # type: ignore[var-annotated] + + generator = ConstraintGenerator(tracer_root, graph) + new_constraints, counter = generator.generate_constraints(counter) + + condition_constraint = new_constraints.conjucts[-1] + + # we know the constraint is a conjunction where the last constraint is about the conditional + # so remove the last constraint + new_constraints.conjucts = new_constraints.conjucts[:-1] + + # transform precision, matching, consistency till obtaining a fixed point + new_constraints, counter = iterate_till_fixed_point(new_constraints, counter) + + # since the function returns a list of one element, we get the first element + # we are only interested in the RHS in this case because the LHS just stores + # the result + + # we make sure the constraint is of the form: + # c = b where b is a boolean expression + # and we consider b (constraint.rhs) for transformation + assert isinstance(condition_constraint.lhs, BVar) + assert is_bool_expr(condition_constraint.rhs) + condition_constraint_rhs = condition_constraint.rhs + + # transform the condition constraint + condition_constraint_rhs, counter = iterate_till_fixed_point( + condition_constraint_rhs, counter + ) + + transformed, counter = transform_to_z3(new_constraints, counter, dimension_dict) + + transformed_condition_constraint, counter = transform_to_z3( + condition_constraint_rhs, counter, dimension_dict + ) + + negation_transformed_condition_constraint = z3.Not( + transformed_condition_constraint + ) + + return z3.And([transformed, transformed_condition_constraint]), z3.And( + [transformed, negation_transformed_condition_constraint] + ) + + def evaluate_conditional_with_constraints( + tracer_root, graph, node, counter=0, user_constraints=None + ): + """ + Given an IR and a node representing a conditional, evaluate the conditional + and its negation + Args: + tracer_root: Tracer root for module instances + node: The node to be evaluated + + Returns: the results of evaluating the condition and the negation with + the rest of the constraints + + """ + + ( + transformed_positive, + transformed_negative, + ) = transform_all_constraints_trace_time(tracer_root, graph, node, counter) + + s = z3.Solver() + s.add(transformed_positive) + if user_constraints is not None: + s.add(user_constraints) + condition = s.check() + + s = z3.Solver() + s.add(transformed_negative) + if user_constraints is not None: + s.add(user_constraints) + negation = s.check() + return condition, negation + +except ImportError: + HAS_Z3 = False diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/util.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/util.py new file mode 100644 index 0000000000000000000000000000000000000000..b160ec8de70f950db66cbe51d3657fbaf6b3aaf1 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/util.py @@ -0,0 +1,58 @@ +from torch.fx.experimental.migrate_gradual_types.constraint import ( + BinConstraintD, + BVar, + DVar, + TVar, +) +from torch.fx.experimental.migrate_gradual_types.operation import op_leq + + +def gen_tvar(curr: int) -> tuple[TVar, int]: + """ + Generate a tensor variable + :param curr: The current counter + :return: a tensor variable and the updated counter + """ + curr += 1 + return TVar(curr), curr + + +def gen_dvar(curr: int) -> tuple[DVar, int]: + """ + Generate a dimension variable + :param curr: the current counter + :return: a dimension variable and an updated counter + """ + curr += 1 + return DVar(curr), curr + + +def gen_bvar(curr: int) -> tuple[BVar, int]: + """ + Generate a boolean variable + :param curr: the current counter + :return: a boolean variable and an updated counter + """ + curr += 1 + return BVar(curr), curr + + +def gen_tensor_dims(n: int, curr: int) -> tuple[list[DVar], int]: + """ + Generate a list of tensor dimensions + :param n: the number of dimensions + :param curr: the current counter + :return: a list of dimension variables and an updated counter + """ + dims = [] + for _ in range(n): + dvar, curr = gen_dvar(curr) + dims.append(dvar) + return dims, curr + + +def gen_nat_constraints(list_of_dims: list[DVar]) -> list[BinConstraintD]: + """ + Generate natural number constraints for dimensions + """ + return [BinConstraintD(0, d, op_leq) for d in list_of_dims] diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/z3_types.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/z3_types.py new file mode 100644 index 0000000000000000000000000000000000000000..939f4865ab7d982289303093db2024eda6603521 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/migrate_gradual_types/z3_types.py @@ -0,0 +1,30 @@ +try: + import z3 # type: ignore[import] + + HAS_Z3 = True + # dynamic type + dyn = z3.DeclareSort("Dyn") + dyn_type = z3.Const("dyn", dyn) + + # dimension + dim = z3.Datatype("dim") + dim.declare("dim", ("0", z3.IntSort()), ("1", z3.IntSort())) + dim = dim.create() + + # tensors + tensor_type = z3.Datatype("TensorType") + tensor_type.declare("Dyn", ("dyn", dyn)) + tensor_type.declare("tensor1", ("0", dim)) + tensor_type.declare("tensor2", ("0", dim), ("1", dim)) + tensor_type.declare("tensor3", ("0", dim), ("1", dim), ("2", dim)) + tensor_type.declare("tensor4", ("0", dim), ("1", dim), ("2", dim), ("3", dim)) + tensor_type = tensor_type.create() + + # create dimension + D = dim.dim + + z3_dyn = tensor_type.Dyn(dyn_type) + + +except ImportError: + HAS_Z3 = False diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/normalize.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/normalize.py new file mode 100644 index 0000000000000000000000000000000000000000..e2dd3c962bbe4d274284d8db26bac70a1a170bed --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/normalize.py @@ -0,0 +1,164 @@ +# mypy: allow-untyped-defs +import operator +from collections.abc import Callable +from typing import Any, Optional + +import torch +import torch.fx +import torch.fx as fx +from torch.fx import Proxy, Transformer +from torch.fx.node import Argument, map_aggregate, Node, Target +from torch.fx.operator_schemas import ( + create_type_hint, + normalize_function, + normalize_module, +) + +from .schema_type_annotation import AnnotateTypesWithSchema + + +class NormalizeArgs(Transformer): + """ + Normalize arguments to Python targets. This means that + `args/kwargs` will be matched up to the module/functional's + signature and rewritten to exclusively kwargs in positional order + if `normalize_to_only_use_kwargs` is true. Also populates default + values. Does not support positional-only parameters or varargs + parameters (*args, **kwargs). + + If the nodes have 'type' metadata, it will use it to disambiguate + overloads. Otherwise, it will throw an error. + + Example usage: + m = torchvision.models.resnet18() + traced = torch.fx.symbolic_trace(m) + traced = NormalizeArgs(traced).transform() + """ + + def __init__( + self, module: torch.fx.GraphModule, normalize_to_only_use_kwargs: bool = True + ): + super().__init__(module) + self.node_map: dict[Proxy, Node] = {} + self.normalize_to_only_use_kwargs = normalize_to_only_use_kwargs + + def run_node(self, n: Node) -> Any: + args, kwargs = self.fetch_args_kwargs_from_env(n) + + def get_type(arg): + if isinstance(arg, fx.Node): + return n.meta.get("type") + return type(arg) + + arg_types = map_aggregate(n.args, get_type) + assert isinstance(arg_types, tuple) + arg_types = tuple(create_type_hint(i) for i in arg_types) + kwarg_types = {k: get_type(v) for k, v in kwargs.items()} + if n.op == "call_function": + out = self.call_function(n.target, args, kwargs, arg_types, kwarg_types) + else: + out = super().run_node(n) + if n.op != "output": + self.node_map[out] = n + out.node.meta = n.meta + out.node.type = n.type + return out + + def call_function( + self, + target: Target, + args: tuple[Argument, ...], + kwargs: dict[str, Any], + arg_types: Optional[tuple[Any, ...]] = None, + kwarg_types: Optional[dict[str, Any]] = None, + ): + assert callable(target) + new_args_and_kwargs = normalize_function( + target, + args, # type: ignore[arg-type] + kwargs, + arg_types, # type: ignore[arg-type] + kwarg_types, + self.normalize_to_only_use_kwargs, + ) + if new_args_and_kwargs: + new_args, new_kwargs = new_args_and_kwargs + return self.tracer.create_proxy( + "call_function", target, new_args, new_kwargs + ) + else: + return super().call_function(target, args, kwargs) + + def call_module( + self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any] + ): + assert isinstance(target, str) + new_args_and_kwargs = normalize_module( + self.module, + target, + args, # type: ignore[arg-type] + kwargs, + self.normalize_to_only_use_kwargs, + ) + if new_args_and_kwargs: + new_args, new_kwargs = new_args_and_kwargs + return super().call_module(target, new_args, new_kwargs) + else: + return super().call_module(target, args, kwargs) + + +class NormalizeOperators(AnnotateTypesWithSchema): + """ + Normalize callsites that are different ways of "spelling" the same + invocation into a single, canonical call. Currently supports: + + 1. Normalize operators (e.g. operator.add) to the `torch` ops they + ultimately invoke (e.g. torch.add) when it is possible to statically + reason that + + Example usage: + + m = torchvision.models.resnet18() + + traced = torch.fx.symbolic_trace(m) + + traced = NormalizeOperators(traced).transform() + """ + + binary_magic_method_remap: dict[ + Callable[[Any, Any], Any], Callable[[Any, Any], Any] + ] = { + torch.add: operator.add, + torch.mul: operator.mul, + torch.sub: operator.sub, + torch.div: operator.truediv, + torch.floor_divide: operator.floordiv, + torch.remainder: operator.mod, + torch.eq: operator.eq, + torch.ne: operator.ne, + torch.lt: operator.lt, + torch.le: operator.le, + torch.gt: operator.gt, + torch.ge: operator.ge, + } + + def call_function( + self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any] + ): + # Normalize operators according to the magic methods implemented on tensors here: + # https://github.com/pytorch/pytorch/blob/28c5d90b679c6b38bf4183ec99f16d933c2f1bcd/tools/autograd/templates/python_variable_methods.cpp#L1137 # noqa: B950 + + assert callable(target) + + if target in self.binary_magic_method_remap: + if len(args) != 2: + return super().call_function(target, args, kwargs) + lhs, rhs = args + + return super().call_function( + target=self.binary_magic_method_remap[target], + args=(lhs, rhs), + kwargs={}, + ) + + return super().call_function(target, args, kwargs) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/optimization.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..219e6f66c7bf52d8f4bf6384b871dee4a9a494d1 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/optimization.py @@ -0,0 +1,490 @@ +# mypy: allow-untyped-defs +import copy +import logging +import operator +import time +from collections import defaultdict +from collections.abc import Iterable +from enum import Enum +from typing import Any, cast, Optional + +import torch +import torch.fx as fx +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.mkldnn as th_mkldnn +from torch.fx.node import Argument, Target +from torch.fx.passes.shape_prop import ShapeProp +from torch.nn.utils.fusion import fuse_conv_bn_eval, fuse_linear_bn_eval + + +__all__ = [ + "matches_module_pattern", + "replace_node_module", + "fuse", + "remove_dropout", + "extract_subgraph", + "modules_to_mkldnn", + "reset_modules", + "MklSubgraph", + "gen_mkl_autotuner", + "use_mkl_length", + "UnionFind", + "optimize_for_inference", +] + + +def _parent_name(target: str) -> tuple[str, str]: + """ + Splits a qualname into parent path and last atom. + For example, `foo.bar.baz` -> (`foo.bar`, `baz`) + """ + *parent, name = target.rsplit(".", 1) + return parent[0] if parent else "", name + + +# Works for length 2 patterns with 2 modules +def matches_module_pattern( + pattern: Iterable[type], node: fx.Node, modules: dict[str, Any] +): + if len(node.args) == 0: + return False + nodes: tuple[Any, fx.Node] = (node.args[0], node) + for expected_type, current_node in zip(pattern, nodes): + if not isinstance(current_node, fx.Node): + return False + if current_node.op != "call_module": + return False + if not isinstance(current_node.target, str): + return False + if current_node.target not in modules: + return False + if type(modules[current_node.target]) is not expected_type: + return False + return True + + +def replace_node_module( + node: fx.Node, modules: dict[str, Any], new_module: torch.nn.Module +): + assert isinstance(node.target, str) + parent_name, name = _parent_name(node.target) + modules[node.target] = new_module + setattr(modules[parent_name], name, new_module) + + +def fuse(model: torch.nn.Module, inplace=False, no_trace=False) -> torch.nn.Module: + """ + Fuses convolution/BN and linear/BN layers for inference purposes. + Will deepcopy your model by default, but can modify the model inplace as well. + """ + patterns = [ + (nn.Conv1d, nn.BatchNorm1d), + (nn.Conv2d, nn.BatchNorm2d), + (nn.Conv3d, nn.BatchNorm3d), + (nn.Linear, nn.BatchNorm1d), + ] + if not inplace: + model = copy.deepcopy(model) + if not no_trace or not isinstance(model, torch.fx.GraphModule): + fx_model = fx.symbolic_trace(model) + else: + fx_model = model + modules = dict(fx_model.named_modules()) + new_graph = copy.deepcopy(fx_model.graph) + + for pattern in patterns: + for node in new_graph.nodes: + if matches_module_pattern(pattern, node, modules): + if len(node.args[0].users) > 1: + # Output of conv/linear is used by other nodes + continue + first_layer = modules[node.args[0].target] + bn = modules[node.target] + if not bn.track_running_stats: + continue + if pattern[0] in [nn.Conv1d, nn.Conv2d, nn.Conv3d]: + fused_layer = fuse_conv_bn_eval(first_layer, bn) + else: # nn.Linear + fused_layer = fuse_linear_bn_eval(first_layer, bn) + replace_node_module(node.args[0], modules, fused_layer) + node.replace_all_uses_with(node.args[0]) + new_graph.erase_node(node) + return fx.GraphModule(fx_model, new_graph) + + +def remove_dropout(model: nn.Module) -> nn.Module: + """ + Removes all dropout layers from the module. + """ + fx_model = fx.symbolic_trace(model) + + class DropoutRemover(torch.fx.Transformer): + def call_module( + self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + if isinstance(self.submodules[target], nn.Dropout): + assert len(args) == 1 + return args[0] + else: + return super().call_module(target, args, kwargs) + + return DropoutRemover(fx_model).transform() + + +def extract_subgraph( + orig_module: nn.Module, + nodes: list[fx.Node], + inputs: list[fx.Node], + outputs: list[fx.Node], +): + """ + Given lists of nodes from an existing graph that represent a subgraph, returns a submodule that executes that subgraph. + """ + new_graph = fx.Graph() + env: dict[fx.Node, fx.Node] = {} + for input in inputs: + new_node = new_graph.placeholder(input.name) + env[input] = new_node + for node in nodes: + new_node = new_graph.node_copy(node, lambda x: env[x]) + env[node] = new_node + new_graph.output([env[output] for output in outputs]) + new_graph.lint() + return fx.GraphModule(orig_module, new_graph) + + +mkldnn_supported = [ + nn.Conv2d, + nn.Linear, + nn.BatchNorm2d, + nn.ReLU, + nn.MaxPool2d, + nn.AvgPool2d, + nn.AdaptiveAvgPool2d, + torch.relu, + torch.transpose, + torch.sigmoid, + F.relu, + F.avg_pool2d, + F.adaptive_avg_pool2d, +] +# These are operators that may not be convertible into MKLDNN ops (e.g. the +# args are scalar values). Thus, we only include them in the subgraph if their +# arguments are already in MKLDNN. +# TODO: Determine whether this can be removed after type inference. +mkldnn_supported_unknown = [operator.add, operator.mul] +mkldnn_map = { + nn.Conv2d: th_mkldnn.MkldnnConv2d, + nn.Linear: th_mkldnn.MkldnnLinear, + nn.BatchNorm2d: lambda a, _: th_mkldnn.MkldnnBatchNorm(a), +} + + +def modules_to_mkldnn(nodes: list[fx.Node], modules: dict[str, nn.Module]): + """ + For each node, if it's a module that can be preconverted into MKLDNN, + then we do so and create a mapping to allow us to convert from the MKLDNN + version of the module to the original. + """ + old_modules: dict[nn.Module, nn.Module] = {} + for node in nodes: + if node.op == "call_module": + assert isinstance(node.target, str) + cur_module = modules[node.target] + if type(cur_module) in mkldnn_map: + # pyrefly: ignore [index-error] + new_module = mkldnn_map[type(cur_module)](cur_module, torch.float) + assert isinstance(new_module, nn.Module) + old_modules[new_module] = copy.deepcopy(cur_module) + replace_node_module(node, modules, new_module) + return old_modules + + +def reset_modules( + nodes: list[fx.Node], + modules: dict[str, nn.Module], + old_modules: dict[nn.Module, nn.Module], +): + """ + Maps each module that's been changed with `modules_to_mkldnn` back to its + original. + """ + for node in nodes: + if node.op == "call_module": + assert isinstance(node.target, str) + cur_module = modules[node.target] + if cur_module in old_modules: + replace_node_module(node, modules, old_modules[cur_module]) + + +class MklSubgraph: + def __init__(self, fx_graph: fx.Graph): + self.fx_graph = fx_graph + self.nodes: list[fx.Node] = [] + self.start_nodes: list[fx.Node] = [] + self.end_nodes: list[fx.Node] = [] + + +def gen_mkl_autotuner(example_inputs, iters=10, warmup=1): + """ + This generates a heuristic that can be passed into `optimize_for_inference` that + determines whether a subgraph should be run in MKL by running it with the example_inputs. + + Example usage: + heuristic = gen_mkl_autotuner(example_inputs, iters=10) + fast_model = optimization.optimize_for_inference(model, heuristic) + """ + fx_model = None + old_modules = None + + def use_mkl_heuristic(graph: MklSubgraph) -> bool: + nonlocal fx_model, old_modules + input_nodes = graph.start_nodes + if fx_model is None: + fx_model = graph.fx_graph.owning_module + old_modules = graph.fx_graph.old_modules # type: ignore[attr-defined] + ShapeProp(fx_model).propagate(example_inputs) + sample_inputs = [torch.randn(node.shape) for node in input_nodes] # type: ignore[attr-defined] + output_args = cast(list[fx.Node], [node.args[0] for node in graph.end_nodes]) + submodule = extract_subgraph(fx_model, graph.nodes, input_nodes, output_args) + + def benchmark(f): + for _ in range(warmup): + f() + begin = time.time() + for _ in range(iters): + f() + return time.time() - begin + + mkl_time = benchmark( + lambda: [ + i.to_dense() for i in submodule(*[i.to_mkldnn() for i in sample_inputs]) + ] + ) + + reset_modules( + submodule.graph.nodes, + dict(submodule.named_modules()), + # pyrefly: ignore [bad-argument-type] + old_modules, + ) + no_mkl_time = benchmark(lambda: submodule(*sample_inputs)) + return mkl_time < no_mkl_time + + return use_mkl_heuristic + + +def use_mkl_length(graph: MklSubgraph) -> bool: + """ + This is a heuristic that can be passed into `optimize_for_inference` that + determines whether a subgraph should be run in MKL by checking if there + are more than 2 nodes in it + """ + return len(graph.nodes) > 2 + + +class UnionFind: + def __init__(self, n): + self.parent: list[Optional[int]] = [None] * n + self.size: list[int] = [0] * n + + def make_set(self, v: int): + self.parent[v] = v + self.size[v] = 1 + + def find(self, v: int) -> int: + par = self.parent[v] + if v == par: + return v + assert par is not None + self.parent[v] = self.find(par) + return cast(int, self.parent[v]) + + def join(self, a: int, b: int): + a, b = self.find(a), self.find(b) + if a == b: + return a + if self.size[a] < self.size[b]: + a, b = b, a + self.parent[b] = a + self.size[a] += self.size[b] + + +def optimize_for_inference( + model: torch.nn.Module, + pass_config: Optional[dict[str, Any]] = None, + tracer: type[fx.Tracer] = fx.Tracer, +) -> torch.nn.Module: + """ + Performs a set of optimization passes to optimize a model for the + purposes of inference. Specifically, the passes that are run are: + 1. Conv/BN fusion + 2. Dropout removal + 3. MKL layout optimizations + + The third optimization takes a function `use_mkl_heuristic` that's used + to determine whether a subgraph should be explicitly run in MKL layout. + + Note: As FX does not currently handle aliasing, this pass currently + assumes nothing aliases. If that isn't true, use at your own risk. + """ + default_pass_config = { + "conv_bn_fuse": True, + "remove_dropout": True, + "mkldnn_layout_optimize": {"heuristic": use_mkl_length}, + } + if pass_config is None: + pass_config = {} + default_pass_config.update(pass_config) + + if default_pass_config["conv_bn_fuse"]: + model = fuse(model) + if default_pass_config["remove_dropout"]: + model = remove_dropout(model) + if default_pass_config["mkldnn_layout_optimize"] is False: + return model + if not isinstance(default_pass_config["mkldnn_layout_optimize"], dict): + raise RuntimeError("mkldnn_layout_optimize config is not a dict") + if "heuristic" not in default_pass_config["mkldnn_layout_optimize"]: + raise RuntimeError("Heuristic not found in mkldnn_layout_optimize config") + use_mkl_heuristic = default_pass_config["mkldnn_layout_optimize"]["heuristic"] + + cur_tracer = tracer() + fx_graph = cur_tracer.trace(copy.deepcopy(model)) + fx.GraphModule(cur_tracer.root, fx_graph) + modules: dict[str, nn.Module] = dict(model.named_modules()) + + class MklSupport(Enum): + NO = 1 + YES = 2 + UNKNOWN = 3 + + # Inserts to_mkldnn and to_dense around every node we want to be a MKLDNN node. + # If the op is in `mkldnn_supported` then we always treat it as a MKLDNN node. + # However, if it's in `mkldnn_supported_unknown`, then we only treat it as + # a MKLDNN node if its inputs are MKLDNN nodes. + for node in list(fx_graph.nodes): + supports_mkldnn = MklSupport.NO + if node.op == "call_module": + cur_module = modules[node.target] + if type(cur_module) in mkldnn_supported: + supports_mkldnn = MklSupport.YES + sample_parameter = next(cur_module.parameters(), None) + if sample_parameter is not None: + assert sample_parameter.dtype == torch.float, ( + "this pass is only for torch.float modules" + ) + assert sample_parameter.device == torch.device("cpu"), ( + "this pass is only for CPU modules" + ) + elif node.op == "call_function": + if node.target in mkldnn_supported: + supports_mkldnn = MklSupport.YES + elif node.target in mkldnn_supported_unknown: + supports_mkldnn = MklSupport.UNKNOWN + + if supports_mkldnn != MklSupport.NO: + if supports_mkldnn == MklSupport.UNKNOWN: + if not any(arg.target == "to_dense" for arg in node.args): + continue + with fx_graph.inserting_before(node): + mkldnn_args = fx.map_arg( + node.args, lambda n: fx_graph.call_method("to_mkldnn", (n,)) + ) + + node.args = cast(tuple[fx.node.Argument], mkldnn_args) + + with fx_graph.inserting_after(node): + dense_x = fx_graph.create_node("call_method", "to_dense", (node,)) + node.replace_all_uses_with(dense_x) + dense_x.args = (node,) + + # Does pre-conversion of all modules into MKLDNN (when possible) + old_modules = modules_to_mkldnn(list(fx_graph.nodes), modules) + fx_graph.old_modules = old_modules # type: ignore[attr-defined] + + # optimizes all a -> to_dense -> to_mkldnn -> b patterns into a -> b + for node in fx_graph.nodes: + if node.op == "call_method" and node.target == "to_dense": + prv_node = node.args[0] + users = list(node.users) + for user in users: + if user.op == "call_method" and user.target == "to_mkldnn": + user.replace_all_uses_with(prv_node) + fx_graph.erase_node(user) + if len(node.users) == 0: + fx_graph.erase_node(node) + + num_nodes = len(fx_graph.nodes) + uf = UnionFind(num_nodes) + + def get_color(n): + if hasattr(n, "color"): # Current node is part of a MKL subgraph + return uf.find(n.color) + if hasattr(n, "start_color"): # Current node is input to MKL subgraph + return uf.find(n.start_color) + return None + + # This code is to find each MKLDNN subgraph. Each MKLDNN subgraph consists + # of input nodes (which are only `to_mkldnn` calls), output nodes + # (`to_dense` calls), and intermediate nodes, which are run entirely on + # MKLDNN layout tensors. + # + # Specifically, this code does a flood fill on a directed acyclic graph + # (DAG), starting from each possible "start node" (i.e: `to_mkldnn` nodes). + # If every node only had one input, this would be sufficient. However, in + # the case that a node has multiple inputs coming from different start + # nodes (i.e. colors), we need to join these 2 colors into 1. That's done + # using a Disjoint Set Union. + for cur_idx, node in enumerate(fx_graph.nodes): + if node.op == "call_method" and node.target == "to_mkldnn": + node.start_color = cur_idx + uf.make_set(cur_idx) + elif node.op == "call_method" and node.target == "to_dense": + assert get_color(node.args[0]) is not None + node.end_color = get_color(node.args[0]) + else: + cur_colors = [ + get_color(i) + for i in node.all_input_nodes + if isinstance(i, fx.Node) + if get_color(i) is not None + ] + + if len(cur_colors) == 0: + continue + assert not any(i is None for i in cur_colors) + cur_colors = sorted(cur_colors) + node.color = cur_colors[0] + for other_color in cur_colors[1:]: + uf.join(cur_colors[0], other_color) + + mkldnn_graphs: dict[int, MklSubgraph] = defaultdict(lambda: MklSubgraph(fx_graph)) + for node in fx_graph.nodes: + if hasattr(node, "color"): + mkldnn_graphs[uf.find(node.color)].nodes.append(node) + if hasattr(node, "start_color"): + mkldnn_graphs[uf.find(node.start_color)].start_nodes.append(node) + if hasattr(node, "end_color"): + mkldnn_graphs[uf.find(node.end_color)].end_nodes.append(node) + + # Now that we have all the subgraphs, we need to decide which MKLDNN + # subgraphs we actually want to keep in MKLDNN. + for graph in mkldnn_graphs.values(): + if not use_mkl_heuristic(graph): + for node in graph.start_nodes + graph.end_nodes: + prv = node.args[0] + node.replace_all_uses_with(prv) # type: ignore[arg-type] + fx_graph.erase_node(node) + reset_modules(graph.nodes, modules, old_modules) + + mkldnn_conversions = 0 + for node in fx_graph.nodes: + if node.target == "to_mkldnn" or node.target == "to_dense": + mkldnn_conversions += 1 + + logging.getLogger(__name__).info("mkldnn conversions: %s", mkldnn_conversions) + fx_graph.lint() + result = fx.GraphModule(model, fx_graph) + return result diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/partitioner_utils.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/partitioner_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3658dd1a9ce96aff26adbc5f47818e9e57e13d35 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/partitioner_utils.py @@ -0,0 +1,317 @@ +# mypy: allow-untyped-defs +from enum import Enum +from typing import NamedTuple + +from torch.fx.node import map_arg, Node + + +class Partition: + """Partition class contains all the information about an individual partition. + It also provides necessary methods for manipulation the partition. + """ + + def __init__(self, partition_id: int) -> None: + self.nodes: set[Node] = set() + self.partition_id = partition_id + self.parents: set[Partition] = set() + self.children: set[Partition] = set() + self.bfs_level: int = -1 + self.used_mem_bytes: int = 0 + self.logical_device_ids: list[int] = [] + + def __str__(self): + return str(self.partition_id) + + def recalculate_mem_size(self): + self.used_mem_bytes = 0 + for node in self.nodes: + self.used_mem_bytes += get_extra_size_of(node, self.nodes) + + def add_node(self, node): + input_nodes: dict[Node, None] = {} + map_arg(node.args, input_nodes.setdefault) + map_arg(node.kwargs, input_nodes.setdefault) + # Add current node's input nodes if they are placeholder or constants + for n in input_nodes: + if n.op in {"placeholder", "get_attr"}: + self.nodes.add(n) + self.nodes.add(node) + self.recalculate_mem_size() + + def remove_node(self, node): + # Remove a node only if the node is in the partition + if node in self.nodes: + self.nodes.remove(node) + # Collect the node's input nodes + input_nodes: dict[Node, None] = {} + map_arg(node.args, input_nodes.setdefault) + map_arg(node.kwargs, input_nodes.setdefault) + # Check if an input node is a placeholder or get_attr, + # and this input node is not used by some other nodes in this partition, + # the remove this input node + for input_node in input_nodes: + if all( + n not in self.nodes for n in input_node.users + ) and input_node.op in {"placeholder", "get_attr"}: + self.nodes.remove(input_node) + self.recalculate_mem_size() + + +class Device(NamedTuple): + name: str + available_mem_bytes: int + logical_id: int + + +class NodeLatency(NamedTuple): + # Latency due to the memory bandwidth + mem_latency_sec: float + # Latency due to the computation + computer_latency_sec: float + + +class PartitionLatency(NamedTuple): + # Sum of all nodes' memory latency on the critical path + mem_latency_sec: float + # Sum of all nodes' compute latency on the critical path + computer_latency_sec: float + # Latency of the critical path + overall_latency_sec: float + + +class PartitionMode(Enum): + size_based = 0 + sparse_nn = 1 + cost_aware = 2 + kl_based = 3 + aot_based = 4 + + +class PartitionerConfig(NamedTuple): + devices: list[Device] + mode: PartitionMode = PartitionMode.size_based + transfer_rate_bytes_per_sec: float = 0.0 + node_to_latency_mapping: dict[Node, NodeLatency] = {} + node_to_partition_mapping: dict[Node, int] = {} + partition_to_logical_device_mapping: dict[int, list[int]] = {} + # Saturate host by replicating partitions to the remaining idle devices. + saturate_host: bool = False + + +def get_extra_size_of(node: Node, nodes: set[Node]) -> int: + """Given a node and a set of nodes, + this function return the extra size that needed + if this node is included in this set. + """ + # Find all its input nodes + input_nodes: dict[Node, None] = {} + map_arg(node.args, input_nodes.setdefault) + map_arg(node.kwargs, input_nodes.setdefault) + # Calculate total size of related nodes + total_size_of_input_nodes = 0 + for n in input_nodes: + # Make sure this node hasn't been in this set yet + if n not in nodes: + size_bytes = getattr(n, "size_bytes", None) + if size_bytes: + total_size_of_input_nodes += size_bytes.output_size + else: + raise RuntimeError("node has no size_bytes attr") + # Don't forget the op node itself + size_bytes = getattr(node, "size_bytes", None) + if size_bytes: + total_size_of_input_nodes += size_bytes.total_size + else: + raise RuntimeError("node has no size_bytes attr") + return total_size_of_input_nodes + + +def get_latency_of_one_partition( + partition: Partition, node_to_latency_mapping: dict[Node, NodeLatency] +) -> PartitionLatency: + """Given a partition and its nodes' latency, return a PartitionLatency for this partition""" + + def get_top_nodes(partition: Partition) -> list[Node]: + """Given a partition, return a list of nodes on the top bfs level""" + top_nodes: list[Node] = [] + for node in partition.nodes: + # Skip placeholder and get_attr nodes + if node.op in {"placeholder", "get_attr"}: + continue + input_nodes: dict[Node, None] = {} + map_arg(node.args, input_nodes.setdefault) + map_arg(node.kwargs, input_nodes.setdefault) + # If a node has no input nodes in this partition, + # or its input nodes in this partition are placeholders and get_attrs + # this node is on the top bfs level in this partition + if not any( + n in partition.nodes and n.op not in {"placeholder", "get_attr"} + for n in input_nodes + ): + top_nodes.append(node) + return top_nodes + + def dfs_helper(node: Node, partition_latency) -> PartitionLatency: + """Given a top node of a partition, this function returns + the latency of the critical path in the partition + """ + node_latency = node_to_latency_mapping[node] + # Calculate the current overall latency of the partition + overall_latency_sec = partition_latency.overall_latency_sec + max( + node_latency.computer_latency_sec, node_latency.mem_latency_sec + ) + # Update the mem latency of this path + mem_latency_sec = ( + partition_latency.mem_latency_sec + node_latency.mem_latency_sec + ) + # Update the compute latency of this path + computer_latency_sec = ( + partition_latency.computer_latency_sec + node_latency.computer_latency_sec + ) + # Get all users of this node that are in this partition + users = set(node.users).intersection(partition.nodes) + if users: + max_latency = PartitionLatency( + mem_latency_sec=0.0, computer_latency_sec=0.0, overall_latency_sec=0.0 + ) + for n in users: + # Get new partition latency recursively + new_partition_latency = dfs_helper( + n, + PartitionLatency( + mem_latency_sec, computer_latency_sec, overall_latency_sec + ), + ) + if ( + new_partition_latency.overall_latency_sec + > max_latency.overall_latency_sec + ): + max_latency = new_partition_latency + return max_latency + # If there is no user, the node is at bottom of the partition + return PartitionLatency( + mem_latency_sec, computer_latency_sec, overall_latency_sec + ) + + # Main part starts + # Get all top level nodes of this partition + top_nodes = get_top_nodes(partition) + critical_path_latency = PartitionLatency( + mem_latency_sec=0.0, computer_latency_sec=0.0, overall_latency_sec=0.0 + ) + # Go through all top nodes and find the largest latency (critical pass latency) + for node in top_nodes: + partition_latency = dfs_helper( + node, + PartitionLatency( + mem_latency_sec=0.0, computer_latency_sec=0.0, overall_latency_sec=0.0 + ), + ) + if ( + partition_latency.overall_latency_sec + > critical_path_latency.overall_latency_sec + ): + critical_path_latency = partition_latency + return critical_path_latency + + +def get_partition_to_latency_mapping( + partitions: list[Partition], node_to_latency_mapping: dict[Node, NodeLatency] +) -> dict[Partition, PartitionLatency]: + """Given all the partitions and node_to_latency_mapping dictionary, + return a mapping dictionary of each partition to its overall latency + """ + partition_to_latency_mapping: dict[Partition, PartitionLatency] = {} + # Go through each partition and get its latency + for partition in partitions: + partition_latency = get_latency_of_one_partition( + partition, node_to_latency_mapping + ) + partition_to_latency_mapping[partition] = partition_latency + return partition_to_latency_mapping + + +def get_comm_latency_between( + parent_partition: Partition, + child_partition: Partition, + transfer_rate_bytes_per_sec: float, +): + """Given two partitions (parent and child), + calculate the communication latency between the two. + """ + # If two partitions are on the same device, the comm latency is 0. + if ( + parent_partition.logical_device_ids != [] + and child_partition.logical_device_ids != [] + and parent_partition.logical_device_ids == child_partition.logical_device_ids + ): + return 0.0 + # Keep tracking the communication size between parent and child + comm_size = 0 + # Keep tracking all the counted node + visited_nodes = set() + # Go through all nodes in the child partition + # If a node has input nodes from the parent partition, + # the output size of those input nodes will be counted + # and added to comm_size + for node in child_partition.nodes: + input_nodes: dict[Node, None] = {} + map_arg(node.args, input_nodes.setdefault) + map_arg(node.kwargs, input_nodes.setdefault) + for n in input_nodes: + if n in parent_partition.nodes and n not in visited_nodes: + size_bytes = getattr(n, "size_bytes", None) + if size_bytes is not None: + comm_size += size_bytes.output_size + visited_nodes.add(n) + return comm_size / transfer_rate_bytes_per_sec + + +def get_latency_of_partitioned_graph( + partitions: list[Partition], + partition_to_latency_mapping: dict[Partition, PartitionLatency], + transfer_rate_bytes_per_sec: float, +): + """Given all partitions in a graph, find the critical path among all partitions + and return its latency as the latency of the whole graph + """ + + def dfs_helper(partition: Partition, latency_so_far_sec: float) -> float: + """This function helps to recursively get the latency of a path of partitions""" + # Update latency by adding current partition's latency + latency_so_far_sec += partition_to_latency_mapping[ + partition + ].overall_latency_sec + + if partition.children: + max_latency_sec = 0.0 + for child in partition.children: + # Calculate latency between + comm_latency_sec = get_comm_latency_between( + partition, child, transfer_rate_bytes_per_sec + ) + new_latency_sec = dfs_helper( + child, latency_so_far_sec + comm_latency_sec + ) + if new_latency_sec > max_latency_sec: + max_latency_sec = new_latency_sec + return max_latency_sec + return latency_so_far_sec + + def get_top_partitions(partitions: list[Partition]) -> list[Partition]: + """This function is to return all the partitions without parents + as the starting points of all the paths + """ + # If a partition has no parents, then it is a top partition + top_partitions = [ + partition for partition in partitions if len(partition.parents) == 0 + ] + return top_partitions + + top_partitions = get_top_partitions(partitions) + critical_path_latency_sec = 0.0 + for partition in top_partitions: + latency_sec = dfs_helper(partition, 0.0) + if latency_sec > critical_path_latency_sec: + critical_path_latency_sec = latency_sec + return critical_path_latency_sec diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..f763ad2ee2cfc1e3bd500f1da9877144aca1a3b2 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py @@ -0,0 +1,2817 @@ +# mypy: allow-untyped-decorators +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from __future__ import annotations + +import functools +import inspect +import logging +import operator +import threading +import typing +import typing_extensions +import weakref +from collections import defaultdict, OrderedDict +from collections.abc import Callable, Generator, Mapping, Sequence +from contextlib import _GeneratorContextManager, contextmanager, ExitStack, nullcontext +from dataclasses import dataclass +from typing import ( + Any, + Concatenate, + Optional, + overload, + Protocol, + TYPE_CHECKING, + TypeVar, + Union, +) +from typing_extensions import ParamSpec, Self, TypeVarTuple, Unpack +from weakref import WeakKeyDictionary + +import torch +import torch._ops +import torch.fx as fx +import torch.fx.traceback as fx_traceback +import torch.utils._pytree as pytree +from torch import SymBool, SymInt, Tensor +from torch._dispatch.python import enable_python_dispatcher +from torch._library.fake_class_registry import FakeScriptObject +from torch._library.opaque_object import is_opaque_type +from torch._logging import trace_structured +from torch._ops import HigherOrderOperator +from torch._subclasses.fake_impls import fast_detach +from torch._subclasses.fake_tensor import ( + FakeTensor, + FakeTensorMode, + is_fake, + unset_fake_temporarily, +) +from torch._subclasses.meta_utils import is_sparse_any +from torch.fx import GraphModule, Proxy, Tracer +from torch.fx.graph_module import _assign_attr +from torch.fx.node import ( + _side_effectful_need_to_be_preserved_pre_dispatch, + Argument, + Target, +) +from torch.fx.passes.shape_prop import _extract_tensor_metadata +from torch.nn import Module +from torch.overrides import TorchFunctionMode +from torch.utils._python_dispatch import ( + _disable_infra_mode, + _push_mode, + _unset_infra_mode, + autograd_would_have_decomposed, + TorchDispatchMode, +) +from torch.utils._stats import count +from torch.utils._thunk import Thunk +from torch.utils.weak import _WeakHashRef, WeakIdKeyDictionary, WeakTensorKeyDictionary + +from ._backward_state import BackwardState +from .sym_node import SymNode + + +if TYPE_CHECKING: + import types + from collections.abc import MutableMapping + + import sympy + + from torch._ops import OpOverload + from torch.fx._symbolic_trace import PHBase + from torch.types import BoolLikeType, FloatLikeType, IntLikeType + +__all__ = [ + "PythonKeyTracer", + "dispatch_trace", + "make_fx", + "DecompositionInterpreter", + "selective_decompose", + "py_sym_types", + "get_innermost_proxy_mode", + "get_proxy_mode", + "handle_sym_dispatch", + "maybe_enable_thunkify", + "maybe_disable_thunkify", +] + +_ProxyTracer = Union["PythonKeyTracer", "_GraphAppendingTracerEx"] + +_AnyScriptObject = (torch.ScriptObject, FakeScriptObject) +_AnyScriptObjectType = Union[torch.ScriptObject, FakeScriptObject] + +aten = torch.ops.aten +prim = torch.ops.prim + +log = logging.getLogger(__name__) +not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented") + +CURRENT_DECOMPOSITION_TABLE: Mapping[OpOverload, Callable] = {} + +CONSTANT_NUMEL_LIMIT = 1 + +T = TypeVar("T") +U = TypeVar("U") +_P = ParamSpec("_P") +R = TypeVar("R") +_Ts = TypeVarTuple("_Ts") + +null_ctx_type = type(nullcontext) +# We currently convert all SymInt to proxies before we use them. +# This could plausibly be handled at the Dynamo level. +pytree.register_pytree_node( + torch.Size, + lambda xs: (list(xs), None), + lambda xs, _: tuple(xs), + # pyrefly: ignore [bad-argument-type] + flatten_with_keys_fn=lambda xs: ( + [(pytree.SequenceKey(i), x) for i, x in enumerate(xs)], + None, + ), + serialized_type_name="torch.Size", +) +# Ideally unflattening should not lose info, but we unflatten +# torch.Size to tuple (see above). This is necessary because the +# torch.Size constructor only accepts ints whereas our infra often +# transforms them to non-ints, e.g. symint proxies. Anyway, losing +# such info can cause pytree mapping or spec matching to fail, so +# work around this problem using the following dict as needed. +_pytree_subclasses_that_lose_info = {torch.Size: tuple} + + +def fake_signature(fn: Callable[_P, R], nargs: int) -> Callable[_P, R]: + """FX gets confused by varargs, de-confuse it""" + argnames = ",".join(f"arg{i}" for i in range(nargs)) + return eval(f"lambda {argnames}: fn({argnames})", {"fn": fn}) + + +@contextmanager +def decompose( + decomposition_table: Optional[Mapping[OpOverload, Callable]], +) -> Generator[Mapping[OpOverload, Callable], None, None]: + global CURRENT_DECOMPOSITION_TABLE + old_decomposition_table = CURRENT_DECOMPOSITION_TABLE + CURRENT_DECOMPOSITION_TABLE = decomposition_table or {} + try: + yield CURRENT_DECOMPOSITION_TABLE + finally: + CURRENT_DECOMPOSITION_TABLE = old_decomposition_table + + +# ensure we cannot collide with other properties +proxy_slot = object() + + +class _NoDefault: + pass + + +no_default = _NoDefault() + +from torch.types import py_sym_types, PySymType + + +class _HasMeta(Protocol): + meta: dict[str, PySymType] + + +def is_sym_node(node: _HasMeta) -> bool: + assert hasattr(node, "meta"), "All nodes traced with proxy_tensor should have meta" + return "val" in node.meta and isinstance(node.meta["val"], py_sym_types) + + +@overload # type: ignore[no-overload-impl] +def set_proxy_slot(obj: Tensor, tracer: _ProxyTracer, proxy: _ProxyTensor) -> None: ... + + +@overload +def set_proxy_slot( + obj: _AnyScriptObjectType, tracer: _ProxyTracer, proxy: Proxy +) -> None: ... + + +@overload +def set_proxy_slot( + obj: PySymType, tracer: _ProxyTracer, proxy: _PySymProxyType +) -> None: ... + + +class _DisableUpdateTensorTracker(threading.local): + value: bool = False + + +_disable_update_tensor_tracker_tls = _DisableUpdateTensorTracker() + + +_FAKE_TENSOR_ID_TO_PROXY_MAP_FOR_EXPORT: dict[int, torch.fx.Node] = {} + + +def _is_proxy_tensor_update_tensor_tracker_disabled() -> bool: + """ + Returns current state of disabling update tensor tracker. + """ + return _disable_update_tensor_tracker_tls.value + + +@contextmanager +def _proxy_tensor_disable_update_tensor_tracker() -> Generator[None, None, None]: + """ + NOTE "Do not clobber inplace ops" + By default tensor_tracker is updated every time. + This leads to chaining every operation by the FakeTensor. + For example for mutable ops if we have several consecutive mutable operations: + + def f(x, y, z): + x.copy_(y) + x.copy_(z) + return x + + Default graph result: + def f_graph(x, y, z) + x_1 = x.copy_(y) + x_2 = x_1.copy_(z) + return x_2 + + This chaining simplifies the fx passes and helps to prevent the reordering. + But in some cases, we want those nodes to be disconnected. + E.g. in case of splitting joint graph into forward and backward. + If first inplace op happened in forward, second in backward, + we want them after split to be properly placed. + + Enabling this context manager for copy_ will result in: + def f_graph_2(x, y, z): + x_1 = x.copy_(y) + x_2 = x.copy_(z) + return x + + Results of copy_ x1 and x2 will have empty users in the graph. + The reason why this behavior is not enabled for all inplace ops is that + some fx passes (e.g. fx quantization) rely on chaining inplace ops like add_ + in their fusions passes. + We could revisit enabling this logic for all inplace ops in future. + """ + orig_value = _disable_update_tensor_tracker_tls.value + _disable_update_tensor_tracker_tls.value = True + try: + yield + finally: + _disable_update_tensor_tracker_tls.value = orig_value + + +def set_proxy_slot( # type: ignore[no-redef] + obj: Union[PySymType, _AnyScriptObjectType, Tensor], + tracer: _ProxyTracer, + proxy: object, +) -> None: + log.debug("set_proxy_slot %s (%s) %s", obj, id(obj), proxy) + if isinstance(obj, Tensor): + # We DO want to clobber proxies whenever we run an inplace operation + # on a tensor, and it affects the metadata on the proxy. + assert isinstance(proxy, _ProxyTensor) + # see NOTE [Do not clobber inplace ops] + if not _is_proxy_tensor_update_tensor_tracker_disabled(): + tracer.tensor_tracker[obj] = proxy + elif isinstance(obj, (_AnyScriptObject)): + # We DO want to clobber proxies, with a similar rationale as for tensors. + assert isinstance(proxy, Proxy) + tracer.script_object_tracker[obj] = proxy + else: + # NB: Never clobber pre-existing proxy. Although the proxies + # are in principle equivalent, when we do graph partitioning + # we need there not to be spurious dependencies on tangent inputs. + # This works because primals get their SymInts set first, and + # THEN later we allocate tangent inputs. Make sure if a SymInt + # is derivable from a primal that we use that. + assert isinstance(obj, py_sym_types), type(obj) + if obj not in tracer.symnode_tracker: + proxy = typing.cast(_PySymProxyType, proxy) + tracer.symnode_tracker[obj] = proxy + + # WAR: python test/dynamo/test_subclasses.py + # TestNestedTensor.test_basic_autograd + # + # AOTAutograd doesn't pass the "outer sizes" as an actual argument + # to make_fx, but it is made use of internally in AOTAutograd's + # call to tensor unflatten. Because the outer sizes isn't passed + # as an argument, it is therefore untracked. However, it turns + # out you luck out, because *Dynamo* will manually add the outer + # sizes as an argument so you can fix up the proxy'ness. + # + # This is probably fixed in + # https://github.com/pytorch/pytorch/pull/125941/ + import sympy + + if isinstance(obj.node.expr, sympy.Symbol): + tracer.sympy_expr_tracker[obj.node.expr] = _SympyExprTrackerValue( + proxy, obj + ) + + +def has_proxy_slot(obj: Tensor, tracer: _ProxyTracer) -> bool: + assert isinstance(obj, (Tensor, SymNode)), type(obj) + # pyrefly: ignore [no-matching-overload] + return bool(get_proxy_slot(obj, tracer, False, lambda _: True)) + + +_PySymProxyType = Thunk[Proxy] + + +@overload +def get_proxy_slot( + obj: Tensor, + tracer: _ProxyTracer, +) -> _ProxyTensor: ... + + +@overload +def get_proxy_slot( + obj: Tensor, + tracer: _ProxyTracer, + default: U, +) -> Union[_ProxyTensor, U]: ... + + +@overload +def get_proxy_slot( + obj: Tensor, + tracer: _ProxyTracer, + default: U, + transform: Callable[[_ProxyTensor], R], +) -> Union[R, U]: ... + + +@overload +def get_proxy_slot( + obj: _AnyScriptObjectType, + tracer: _ProxyTracer, +) -> Proxy: ... + + +@overload +def get_proxy_slot( + obj: _AnyScriptObjectType, + tracer: _ProxyTracer, + default: U, +) -> Union[Proxy, U]: ... + + +@overload +def get_proxy_slot( + obj: _AnyScriptObjectType, + tracer: _ProxyTracer, + default: U, + transform: Callable[[Proxy], R], +) -> Union[R, U]: ... + + +@overload +def get_proxy_slot( + obj: PySymType, + tracer: _ProxyTracer, +) -> _PySymProxyType: ... + + +@overload +def get_proxy_slot( + obj: PySymType, + tracer: _ProxyTracer, + default: T, +) -> Union[T, _PySymProxyType]: ... + + +@overload +def get_proxy_slot( + obj: PySymType, + tracer: _ProxyTracer, + default: U, + transform: Callable[[_PySymProxyType], R], +) -> Union[R, U]: ... + + +# the default argument is what to return if the slot is not set. +# the transform argument is handy if you need to extract a subfield from +# the successfully looked up result (but NOT the default.) +def get_proxy_slot( + obj: Union[Tensor, _AnyScriptObjectType, PySymType], + tracer: _ProxyTracer, + default: object = no_default, + transform: Callable = lambda x: x, +) -> object: + tracker: Any + if isinstance(obj, Tensor): + tracker = tracer.tensor_tracker + elif isinstance(obj, _AnyScriptObject): + tracker = tracer.script_object_tracker + else: + assert isinstance(obj, py_sym_types), type(obj) + tracker = tracer.symnode_tracker + + # pyrefly: ignore [index-error] + # pyrefly: ignore [no-matching-overload, bad-argument-type] + value = tracker.get(obj) + + if value is None and isinstance(obj, py_sym_types): + if obj.node.is_symbolic(): + # Last ditch - we found a SymInt (SymBool, etc) we don't know + # about. + if (tmp := tracer.sympy_expr_tracker.get(obj.node.expr)) is not None: + value = tmp.proxy + + else: + # Attempt to build it from first principles. + _build_proxy_for_sym_expr(tracer, obj.node.expr, obj) + # pyrefly: ignore [no-matching-overload] + value = tracker.get(obj) + + if value is None: + # We don't know this value - return the default. + if isinstance(default, _NoDefault): + raise RuntimeError( + f"{obj} ({type(obj)}, {id(obj)})is not tracked with proxy for {tracer}" + ) + return default + + res = transform(value) + return res + + +@functools.cache +def _sympy_handlers() -> dict[type[sympy.Expr], Callable[..., Any]]: + """ + Returns a dict converting sympy functions to python operators + (i.e. `sympy.Mul` -> `operator.mul`) + """ + import torch.utils._sympy.interp + + handlers = {} + for k, v in torch.utils._sympy.interp.handlers().items(): + op = getattr(operator, v, None) + if op is not None: + handlers[k] = op + return handlers + + +def _build_proxy_for_sym_expr( + tracer: _ProxyTracer, expr: sympy.Expr, out: PySymType | None = None +) -> IntLikeType | FloatLikeType | BoolLikeType | None: + """ + Decompose `expr` and look for the pieces as inputs. If `out` is provided + then that will be the resulting SymNode (and `out.expr` must be the same as + `expr`). + + This function is used when the ProxyTorchDispatchMode sees a SymNode + that it hasn't seen before to try to associate it with traced inputs. + + How can this happen? + + First thing to remember is that although sympy.Exprs are interned (so + `sympy.Expr("s3*s4")` will always have the same `id` and will always compare + equal) SymNode does not (so doing `SymNode("s3")*SymNode("s4")` twice in a + row will give two unique SymNodes). + + - On way for this to happen is if we turn off tracing to compute an + intermediate value and then USE that value with tracing turned on - for + example if we turn off tracing to do some FakeTensor propagation to + compute a size (dtensor does this) but then turn tracing back on and use + that computed size. + + - Another way is if we compute a size in one graph and stash it somewhere + hidden (such as in some meta-data) and later use it in a different graph + (dtensor does this too). Since the size was computed in the first graph + and it's not an official input to the second graph it's not tracked + properly. This is often going to show up as it usually works in fullgraph + but a graph break causes a failure. + + To handle this we decompose the sympy.Expr and look for the pieces as + inputs. But there are problems with this approach: + + - We lose operation provanance: We end up figuring out where to get the + inputs - but those may not actually be correct. If we have "s1" coming in + from both tensor1 and tensor2 and we pick the wrong one we could end up + keeping a tensor alive longer than intended. + + - There's no guarantee that those values are inputs to the graph: If we have + "s1*s2" computed in a graph #1 and used in graph #2 there's no guarantee + that the input that holds "s1" is actually an input on graph #2. + + - The decomposition isn't guaranteed to be the same: Sympy can "simplify" + expressions so it's possible that our inputs are "s1*s2" and "s3" but we + decompose it into "s1" and "s2*s3" - which wouldn't be found. + + Other ways we could handle this: + + - Don't: Just require that all inputs are tracked properly. This is the + "correct" solution but harder because you need to track down each + potential problem one by one and fix them. And when it fails it's a lot of + work to figure out both why it's failing and the right way to fix it. This + is complicated by the fact that a stashed value could be incorrect but + work fine until we happen to get an graph break in the wrong place - so it + may be a while before the bug is found. (Maybe we need a "dynamo abuse + mode" where we run tests with as many graph breaks inserted as possible?) + + - Track SymNode ops separately from proxy tracing: Right now SymNode + operations are tracked as part of the proxy tracing - so when we disable + proxy tracing we also disable SymNode tracing. But we don't have to do + that - we could instead always have SymNodes track where they came from + and just use that when needed. This solves the problem of tracing being + temporarily turned off but doesn't help if an input isn't present after a + graph break. + + - Better decomposition: Right now the decomposition is pretty simple. We do + have a sat-solver available to us so we could theoretically do a better + job figuring out a "correct" decomposition. But that still relies on + having the inputs available at all - which isn't a guarantee. + """ + + if (value := tracer.sympy_expr_tracker.get(expr)) is not None: + assert not out + return value.value + + if isinstance(expr, (int, float, bool)): + return expr + if expr.is_Integer: + return int(expr) + if expr.is_Float: + return float(expr) + + args = [] + for arg in expr.args: + if (arg_value := _build_proxy_for_sym_expr(tracer, arg)) is None: + return None + args.append(arg_value) + args = tuple(args) + + func: OpOverload | None = _sympy_handlers().get(expr.func) # type: ignore[assignment] + if not func: + # Handler not found + return None + + if out is None: + out = func(*args) + else: + _sym_register(tracer, func, args, out) + return out + + +def snapshot_fake(val: Tensor, include_real: bool = False) -> Optional[Tensor]: + # val.detach() will also eventually call fast_detach(), + # but this saves us a full trip into __torch_dispatch__ + # (snapshot_fake is called a lot) + if isinstance(val, FakeTensor): + return fast_detach(val.fake_mode, val, include_real) + else: + return val.detach() + + +_ExtractValType = Optional[ + Union[ + PySymType, + _AnyScriptObjectType, + BackwardState, + list["_ExtractValType"], + tuple["_ExtractValType", ...], + dict[str, "_ExtractValType"], + Tensor, + int, + float, + bool, + ] +] + + +def extract_val(val: _ExtractValType, include_real: bool = False) -> _ExtractValType: + if is_fake(val): + return snapshot_fake(val, include_real=include_real) + elif isinstance(val, py_sym_types): + return val + elif isinstance(val, _AnyScriptObject): + return val + elif isinstance(val, BackwardState): + return val + elif isinstance(val, (list, tuple)): + return val.__class__([extract_val(x) for x in val]) + elif isinstance(val, dict): + return {k: extract_val(v) for k, v in val.items()} + elif isinstance(val, Tensor): + if not val.is_sparse: + # NB: Kinda hacky, but we should try to get val as the metadata + # everywhere + # TODO: This doesn't properly track storages. A more robust + # approach would be to maintain a per-trace FakeTensorMode and + # from_real_tensor to create fake values (don't forget to + # snapshot_fake) + from torch._guards import detect_fake_mode + + fake_tensor_mode = detect_fake_mode(val) + if not fake_tensor_mode: + fake_tensor_mode = FakeTensorMode(allow_fallback_kernels=True) + with fake_tensor_mode: + return torch.empty_strided( + val.shape, val.stride(), device=val.device, dtype=val.dtype + ) + else: + return None + elif isinstance(val, (int, float, bool)): + return val + elif val is None: + return None + + typing_extensions.assert_never(val) + + +@contextmanager +def _enable_thunkify( + tracer: _ProxyTracer, *, enable: bool = True +) -> Generator[None, None, None]: + """ + Enable thunkification inside the context manager. Thunkification prevents + SymNode computation from directly being traced into an FX graph; instead, + the compute is only added to the graph if it is actually used. This helps + us track SymNode compute when it is computed (since we need /something/ + to put in the tracker) even if it is unlikely to be used. + """ + old = tracer.enable_thunkify + tracer.enable_thunkify = enable + try: + yield + finally: + tracer.enable_thunkify = old + + +@contextmanager +def maybe_disable_thunkify() -> Generator[None, None, None]: + """Within a context, disable thunkification. See :func:`maybe_enable_thunkify` + for more details. This is helpful if you have a wrapper function which + you want to enable thunkification on, but in some segment on the inside (say, + the original user function), you want to disable thunkification as you know + it is not needed there. + """ + proxy_mode = get_proxy_mode() + if proxy_mode is not None: + with _enable_thunkify(proxy_mode.tracer, enable=False): + yield + else: + yield + + +@contextmanager +def maybe_enable_thunkify() -> Generator[None, None, None]: + """Within this context manager, if you are doing make_fx tracing, we will thunkify + all SymNode compute and avoid tracing it into the graph unless it is actually needed. + You should prefer to avoid using this as much as possible, as lazy evaluation of + SymNode tracing can lead to long chains of thunks which will stack overflow + if you evaluate them. However, this is currently sometimes necessary as there + are buggy parts of PT2 which will fail with "s0 is not tracked with proxy" error + due to insufficient tracing of SymNode computation. + """ + proxy_mode = get_proxy_mode() + if proxy_mode is not None: + with _enable_thunkify(proxy_mode.tracer): + yield + else: + yield + + +# Note [invariants for node meta 'val'] +# What invariants do we have for the 'val' set on the FX node? It has accurate +# metadata... but only for metadata that exists "below" all other subsystems +# (most notably autograd, but also vmap, functorch transforms, etc). This means +# you can get the dtype, shape, stride, storage, but you CANNOT get requires_grad, +# grad_fn, _base (_base actually may be set due to recursive call to +# ADInplaceOrView, but you shouldn't rely on it.) +def set_meta(proxy: Proxy, val: _ExtractValType) -> Proxy: + proxy.node.meta["val"] = extract_val( + val, include_real=(proxy.node.op == "placeholder") + ) + + with _enable_thunkify(proxy.tracer): # type: ignore[arg-type] + # Best effort tensor_meta setting; prefer using val! + if is_fake(val): + proxy.node.meta["tensor_meta"] = _extract_tensor_metadata(val) + elif isinstance(val, Tensor) and not val.is_sparse: + proxy.node.meta["tensor_meta"] = _extract_tensor_metadata(val) + return proxy + + +def thunkify( + tracer: _ProxyTracer, f: Callable[_P, R], *args: _P.args, **kwargs: _P.kwargs +) -> Thunk[R]: + """ + Delays computation of f until it's called again + Also caches the result + """ + if tracer.enable_thunkify: + return Thunk(functools.partial(f, *args, **kwargs)) + else: + r = f(*args, **kwargs) + return Thunk(lambda: r) + + +def track_tensor( + tensor: Tensor, proxy: Proxy, *, constant: Optional[Tensor], tracer: _ProxyTracer +) -> None: + def try_set_proxy_slot( + outer_s: IntLikeType, + proxy_callable: Callable[Concatenate[PySymType, _P], Proxy], + *args: _P.args, + **kwargs: _P.kwargs, + ) -> None: + assert callable(proxy_callable) + if isinstance(outer_s, SymInt): + with _enable_thunkify(tracer): + set_proxy_slot( + outer_s, + tracer, + thunkify(tracer, proxy_callable, outer_s, *args, **kwargs), + ) + + # The basic idea is that we need to associate each tensor/SymInt + # with a Proxy. How do we setup this association? We just store + # the proxy on the proxy slot of the object, keyed on the tracer + # (so that if we have multiple tracers at the same time, they + # don't clobber each other.) + for i, s in enumerate(tensor.shape): + try_set_proxy_slot( + s, + lambda x, i: set_meta( + tracer.create_proxy( + "call_function", torch.ops.aten.sym_size.int, (proxy, i), {} + ), + x, + ), + i, + ) + + if not is_sparse_any(tensor): + for i, s in enumerate(tensor.stride()): + try_set_proxy_slot( + s, + lambda x, i: set_meta( + tracer.create_proxy( + "call_function", torch.ops.aten.sym_stride.int, (proxy, i), {} + ), + x, + ), + i, + ) + + try_set_proxy_slot( + tensor.numel(), + lambda x: set_meta( + tracer.create_proxy( + "call_function", torch.ops.aten.sym_numel.default, (proxy,), {} + ), + x, + ), + ) + if not is_sparse_any(tensor): + try_set_proxy_slot( + tensor.storage_offset(), + lambda x: set_meta( + tracer.create_proxy( + "call_function", + torch.ops.aten.sym_storage_offset.default, + (proxy,), + {}, + ), + x, + ), + ) + set_proxy_slot(tensor, tracer, _ProxyTensor(proxy, constant)) + + +_NestedProxys = Union[ + Proxy, Sequence["_NestedProxys"], Mapping[object, "_NestedProxys"] +] +_NestedTensors = Union[ + Tensor, Sequence["_NestedTensors"], Mapping[object, "_NestedTensors"] +] + + +def track_tensor_tree( + inner_res: T, + proxy_res: _NestedProxys, + *, + constant: Optional[_NestedTensors], + tracer: _ProxyTracer, +) -> T: + # NB: We call set_unbacked_bindings only on the *topmost* call to + # track_tensor_tree, not recursive calls. This is because there must + # be only ONE unbacked_binding proxy call, and it should be the one + # where all of the unbacked SymInts actually first come into existence. + # If you call this again on the inner proxies for the tuple projections, + # you will have multiple unbacked_bindings for the same symbol, but + # they're not going to show up anywhere. + # + # I was briefly deceived into setting unbacked bindings recursively when + # working on https://github.com/pytorch/pytorch/pull/133585 because I + # observed that some extra unbacked bindings were needed to handle some + # higher order operator code. But actually it looks like this was + # just an unrelated bug that needed to be fixed separately. + _set_unbacked_bindings(inner_res, proxy_res) + + def wrap_with_proxy( + e: object, proxy: _NestedProxys, constant: Optional[_NestedTensors] + ) -> None: + if isinstance(e, Tensor): + assert isinstance(proxy, Proxy) + assert constant is None or isinstance(constant, Tensor) + track_tensor(e, proxy, tracer=tracer, constant=constant) + set_meta(proxy, e) + elif isinstance(e, py_sym_types): + assert isinstance(proxy, Proxy) + # NB: eagerly set meta here, so that the numbering is in order + set_meta(proxy, e) + set_proxy_slot(e, tracer, thunkify(tracer, lambda: proxy)) + elif isinstance(e, _AnyScriptObject): + assert isinstance(proxy, Proxy) + set_proxy_slot(e, tracer, proxy) + set_meta(proxy, e) + elif isinstance(e, (tuple, list)): + # example use case: allreduce_ returns ([tensor], work) + if isinstance(proxy, fx.Proxy): + set_meta(proxy, e) + + def get_constant( + c: Optional[_NestedTensors], idx: int + ) -> Optional[_NestedTensors]: + if c is None: + return None + else: + assert isinstance(c, (list, tuple)) + return c[idx] + + for idx, ee in enumerate(e): + # Use an indexer here - if proxy is a List then it will unwrap + # it. If it's a Proxy then it will proxy the getelem. + wrap_with_proxy(ee, proxy[idx], get_constant(constant, idx)) # type: ignore[index] + + elif isinstance(e, dict): + # example use case: triton_kernel_wrapper takes arguments as kwargs + + # In theory we could support const-prop when proxy-tensor-tracing + # operators that returns dicts of tensors, but we have no use case + # for it today (since the only op we currently trace that can + # return a dict is triton_kernel_wrapper_functional/mutation, + # which does not participate in const-prop) + assert constant is None + + if isinstance(proxy, fx.Proxy): + set_meta(proxy, e) + + for key, val in e.items(): + wrap_with_proxy(val, proxy[key], None) # type: ignore[index] + + elif isinstance(e, BackwardState): + assert isinstance(proxy, Proxy) + set_meta(proxy, e) + e.proxy = proxy + else: + # intentionally pass on primitives + pass + + wrap_with_proxy(inner_res, proxy_res, constant) + + return inner_res + + +@dataclass +class _ProxyTensor: + proxy: Proxy + constant: Optional[Tensor] + + +def fetch_sym_proxy( + tracer: _ProxyTracer, +) -> Callable[[PySymType], Union[bool, int, float, Proxy]]: + def inner(e: PySymType) -> Union[int, bool, float, Proxy]: + n = e.node + if n.constant is not None: + return n.constant + if e.node.expr.is_number: + if isinstance(e, SymBool): + return bool(e.node.expr) + elif isinstance(e, SymInt): + return int(e.node.expr) + return float(e.node.expr) + else: + assert isinstance(e, py_sym_types) + # NB: we REQUIRE all symints to be tracked + return get_proxy_slot(e, tracer).force() + + return inner + + +@overload +def fetch_object_proxy( + tracer: _ProxyTracer, t: Tensor +) -> Union[_ProxyTensor, Tensor]: ... + + +@overload +def fetch_object_proxy( + tracer: _ProxyTracer, t: _AnyScriptObjectType +) -> Union[Proxy, _AnyScriptObjectType]: ... + + +@overload +def fetch_object_proxy( + tracer: _ProxyTracer, t: PySymType +) -> Union[_PySymProxyType, PySymType]: ... + + +def fetch_object_proxy( + tracer: _ProxyTracer, t: Union[Tensor, _AnyScriptObjectType, PySymType] +) -> object: + return get_proxy_slot(t, tracer, t) + + +HANDLED_TYPES = (Tensor, torch.nn.Parameter, FakeTensor) + + +def _maybe_record_pointwise_barrier( + func: object, proxy_mode: ProxyTorchDispatchMode +) -> None: + """ + Records operators whose tensor outputs or inputs are fp16/bf16 so downstream pointwise code can + emulate eager's rounding behavior when emulate_precision_casts is enabled. + """ + if proxy_mode.decomp_layers or not proxy_mode.emulate_precision_casts: + return + + if not isinstance(func, torch._ops.OpOverload): + return + + last_node = next(iter(reversed(proxy_mode.tracer.graph.nodes))) + t = last_node.meta.get("val") + low_pr_fp = (torch.bfloat16, torch.float16) + + output_low_precision = isinstance(t, torch.Tensor) and t.dtype in low_pr_fp + + if not output_low_precision: + for input_node in last_node.all_input_nodes: + val = input_node.meta.get("val") if hasattr(input_node, "meta") else None + if isinstance(val, torch.Tensor) and val.dtype in low_pr_fp: + output_low_precision = True + break + + if not output_low_precision: + return + + last_node.meta["low_precision_pointwise_barrier"] = True + + +def _fetch_proxies_and_all_constant_flag( + flat_args_kwargs: Union[list[object], tuple[object, ...]], tracer: _ProxyTracer +) -> tuple[list[object], tuple[object, ...], bool]: + """ + Given flat arguments, fetch the proxies and whether they are all constants. + This is later used in proxy_call or when someone is trying to stitch together + graph node in tf or td modes. + """ + f_flat_args_kwargs = [ + ( + fetch_object_proxy(tracer, x) + if isinstance(x, (Tensor, _AnyScriptObject)) + else x + ) + for x in flat_args_kwargs + ] + + # If there are SymInts, we also should not consider this constant. + # However, fake tensor handling of SymInts is sufficiently broken that + # I couldn't write a test for this case + all_constant = ( + not any( + t.constant is None + for t in f_flat_args_kwargs + if isinstance(t, _ProxyTensor) + ) + # TODO: maybe constant SymInts should also be allowed? Not sure if + # this can happen + and not any(isinstance(x, py_sym_types) for x in flat_args_kwargs) + ) + + proxy_flat_args_kwargs = [ + e.proxy if isinstance(e, _ProxyTensor) else e for e in f_flat_args_kwargs + ] + + proxy_flat_args_kwargs = [ + (fetch_sym_proxy(tracer)(e) if isinstance(e, py_sym_types) else e) + for e in proxy_flat_args_kwargs + ] + + return f_flat_args_kwargs, tuple(proxy_flat_args_kwargs), all_constant + + +def proxy_call( + proxy_mode: ProxyTorchDispatchMode, + func: OpOverload, + pre_dispatch: bool, + args: tuple[object, ...], + kwargs: dict[str, object], +) -> object: + unrecognized_types: list[type] = [] + flat_args_kwargs, spec = pytree.tree_flatten((args, kwargs)) + + def can_handle_tensor(x: Tensor) -> bool: + r = type(x) in HANDLED_TYPES or has_proxy_slot(x, proxy_mode.tracer) + if proxy_mode._allow_fake_constant: + r = r or type(x) is torch._subclasses.FakeTensor + if not r: + unrecognized_types.append(type(x)) + return r + + # If there are any tensor subclasses, we need to handle those tensor subclasses first + # TODO: we could use types to test this + if not all(can_handle_tensor(x) for x in flat_args_kwargs if isinstance(x, Tensor)): + not_implemented_log.debug( + "ProxyTensorMode tensors without proxy had unrecognized subclasses: %s", + unrecognized_types, + ) + return NotImplemented + + r = maybe_handle_decomp(proxy_mode, func, args, kwargs) + if r is not NotImplemented: + _maybe_record_pointwise_barrier(func, proxy_mode) + return r + + # For pre-autograd tracing, we do not want to run CompositeImplicit decomps. + if ( + not pre_dispatch + and func + not in [ + torch.ops.aten.size.default, + torch.ops.aten.stride.default, + torch.ops.aten.storage_offset.default, + ] + and autograd_would_have_decomposed(func, flat_args_kwargs) + ): + with proxy_mode: + r = func.decompose(*args, **kwargs) + if r is not NotImplemented: + return r + + if func is torch.ops.aten.is_nonzero.default: + with proxy_mode: + torch._check( + args[0].numel() == 1, # type: ignore[attr-defined] + lambda: "Boolean value of Tensor with more than one value is ambiguous", + ) + return (args[0] != 0).item() # type: ignore[attr-defined] + + tracer = proxy_mode.tracer + f_flat_args_kwargs, proxy_flat_args_kwargs, all_constant = ( + _fetch_proxies_and_all_constant_flag(flat_args_kwargs, tracer) + ) + + if torch.Tag.data_dependent_output in func.tags: + # Check if all of the Tensor inputs are constants + if all_constant: + const_flat_args_kwargs = [ + t.constant if isinstance(t, _ProxyTensor) else t + for t in f_flat_args_kwargs + ] + const_args, const_kwargs = pytree.tree_unflatten( + const_flat_args_kwargs, spec + ) + with unset_fake_temporarily(): + return func(*const_args, **const_kwargs) + # If any of the Tensor inputs are "real" (not FakeTensor), we may + # incorrectly burn in constants by allowing this access. Raise + # an error in this case + if proxy_mode._error_on_data_dependent_ops and pytree.tree_all_only( + Tensor, lambda t: not is_fake(t), (args, kwargs) + ): + raise RuntimeError( + f"It appears that you're trying to get value out of a tracing tensor with {func} - erroring out! " + "It's likely that this is caused by data-dependent control flow or similar. " + "It may be possible to trace this with dynamic shapes; try setting tracing_mode='symbolic' " + "in your make_fx call." + ) + + proxy_args, proxy_kwargs = pytree.tree_unflatten(proxy_flat_args_kwargs, spec) + + # When we trace through a torch.tensor invocation, you never actually + # see a torch.ops.aten.tensor call. Instead, the way this function is + # implemented internally is that we allocate a plain tensor (this is + # *guaranteed* to be a plain tensor, we disable all modes when doing + # so), and then call at::lift_fresh on it (to give modes a chance to do + # their stuff). Furthermore, the tensor argument to lift_fresh is guaranteed + # to be freshly allocated, so we want lift_fresh to be a no-op (directly + # returning the input argument). + # + # Here is the basic problem: when we trace this sequence of executions + # into an FX graph, what happens to this call sequence? Traditionally, + # tensor constants get interned as buffers on the FX GraphModule. But + # this is dangerous. Consider: + # + # x = torch.tensor(1) + # x.add_(2) + # + # Naively, this traces into: + # + # t = self._tensor_constant0 # initialized to torch.tensor(1) + # x = torch.ops.aten.lift_fresh(t) + # x.add_(2) + # + # If lift_fresh returns t directly, the subsequent add_ call will + # modify the tensor constant. Really, the problem is we've violated + # the invariant the argument to lift is fresh. So what we should + # preserve the invariant by replacing lift_fresh with lift_fresh_copy: + # + # t = self._tensor_constant0 # initialized to torch.tensor(1) + # x = torch.ops.aten.lift_fresh_copy(t) + # x.add_(2) + # + # This is what the overload modification does. + if func is torch.ops.aten.lift_fresh.default: + func = torch.ops.aten.lift_fresh_copy.default + + proxy_out = proxy_mode.tracer.create_proxy( + "call_function", + func, + proxy_args, + proxy_kwargs, + name=proxy_mode.tracer.graph._target_to_str(func.overloadpacket.__name__), + ) + + with _enable_thunkify(proxy_mode.tracer): + out = func(*args, **kwargs) + + # In some circumstances, we will be tracing in a situation where a tensor + # is *statically* known to be a constant (currently, this only happens if + # you run torch.tensor; deterministic factory functions like torch.arange + # don't get this treatment). When the tensor in question is small, it's + # helpful to due constant propagation in case we call item() (in which + # case we can return the constant value that is known, rather than give + # an error.) The logic here tests if constant propagation is possible + # (because all of the inputs are constant). If so, we disable fake tensor + # mode (if it is on) and do true compute on the constant. + # + # It's worth highlighting that we're making a policy decision here. + # There is a potential that the tensor is actually quite large, and we + # don't actually want to run the compute. The tensor being quite large + # is one of the reasons why factory functions don't get this treatment + # (since they can be quite large; if a parameter is initialized to a + # constant value it will be!) Similarly, there is also a potential + # to run an operator that blows up the size of a small tensor; we don't + # protect against this case, but we could force, e.g., only single + # element constant computation by testing the numel of the result before + # propagating const-ness. Similarly, we don't require the constant to + # live on CPU, but we could. + any_constant = any( + t.constant is not None + for t in f_flat_args_kwargs + if isinstance(t, _ProxyTensor) + ) + + constant = None + + def tensor_numel_in_limit(t: Tensor) -> bool: + return t.numel() <= CONSTANT_NUMEL_LIMIT + + # If this is a lift, the input tensor is guaranteed to be a + # constant, so we keep a copy of the original argument along so + # we can query it if we're asked to item() it at some later point + if ( + func is torch.ops.aten.lift_fresh_copy.default + and out.numel() <= CONSTANT_NUMEL_LIMIT + ): + with unset_fake_temporarily(): + assert isinstance(args[0], (Proxy, Tensor)), type(args[0]) + constant = args[0].clone() + elif ( + torch.Tag.nondeterministic_seeded not in func.tags + and all_constant + and any_constant + and pytree.tree_all_only(Tensor, tensor_numel_in_limit, out) + ): + # NB: do NOT include factories as constants + with unset_fake_temporarily(): + const_flat_args_kwargs = [ + t.constant if isinstance(t, _ProxyTensor) else t + for t in f_flat_args_kwargs + ] + const_args, const_kwargs = pytree.tree_unflatten( + const_flat_args_kwargs, spec + ) + constant = func(*const_args, **const_kwargs) + else: + constant = None + + track_tensor_tree(out, proxy_out, constant=constant, tracer=tracer) + _maybe_record_pointwise_barrier(func, proxy_mode) + return out + + +class _SymNodeDict: + """ + Wrapper around a dictionary that will hash SymInts with their nodes + """ + + def __init__(self) -> None: + self.sym_node_dict: dict[PySymType, _PySymProxyType] = {} + + def __setitem__(self, key: PySymType, value: _PySymProxyType) -> None: + self.sym_node_dict[key.node] = value + + def __getitem__(self, key: PySymType) -> _PySymProxyType: + return self.sym_node_dict[key.node] + + def __contains__(self, key: PySymType) -> bool: + return key.node in self.sym_node_dict + + def get( + self, key: PySymType, default: Optional[_PySymProxyType] = None + ) -> _PySymProxyType: + # dict.get()'s annotation doesn't accept `None` when the value type + # isn't Optional. + return self.sym_node_dict.get(key.node, default) # type: ignore[arg-type, return-value] + + def __iter__(self) -> Any: + raise NotImplementedError + + def __len__(self) -> int: + return len(self.sym_node_dict) + + +@dataclass +class _SympyExprTrackerValue: + proxy: _PySymProxyType + value: PySymType + + +class PythonKeyTracer(Tracer): + script_object_tracker: MutableMapping[_AnyScriptObjectType, Proxy] + symnode_tracker: _SymNodeDict + sympy_expr_tracker: dict[sympy.Symbol, _SympyExprTrackerValue] + tensor_tracker: MutableMapping[Tensor, _ProxyTensor] + torch_fn_counts: dict[OpOverload, int] + enable_thunkify: bool = False + + def __init__(self) -> None: + super().__init__(autowrap_modules=()) # type: ignore[arg-type] + self.tensor_tracker = WeakTensorKeyDictionary() + self.symnode_tracker = _SymNodeDict() + self.script_object_tracker = WeakIdKeyDictionary( + dict=None, ref_type=_WeakHashRef + ) + self.sympy_expr_tracker = {} + + # Stores the torch function that was called during tracing + self.torch_fn_metadata = None + # Stores the counts for every torch function called. This is to help + # distinguish between different calls to the same torch function. + self.torch_fn_counts = {} + self.enable_thunkify = False + + # In general, we don't want to make modules leaves. In principle, users of + # this tracer might want to override this in order to turn a couple specific + # modules into leaves in the traced graph. + def call_module( + self, + m: Module, + forward: Callable[..., Any], + args: tuple[Any, ...], + kwargs: dict[str, Any], + ) -> Any: + return forward(*args, **kwargs) + + # We don't want to turn getattr calls into proxies. So we just return the actual value. + def getattr( + self, attr: str, attr_val: object, parameter_proxy_cache: dict[str, Proxy] + ) -> object: + return attr_val + + def create_arg(self, a: object) -> fx.node.Node: + if isinstance(a, torch.nn.Parameter): + for n, p in self.root.named_parameters(): + if a is p: + return self.create_node("get_attr", n, (), {}) + + qualname = self.get_fresh_qualname("_param_constant") + setattr(self.root, qualname, a) + + return self.create_node("get_attr", qualname, (), {}) + elif isinstance(a, py_sym_types): + assert a.node.constant is not None + return a.node.constant + return super().create_arg(a) # type: ignore[return-value] + + @overload + def unwrap_proxy(self, e: Tensor) -> Union[Proxy, Tensor]: ... + + @overload + def unwrap_proxy(self, e: PySymType) -> Union[Proxy, PySymType]: ... + + @overload + def unwrap_proxy( + self, e: _AnyScriptObjectType + ) -> Union[Proxy, _AnyScriptObjectType]: ... + + def unwrap_proxy(self, e: T) -> object: + if isinstance(e, Tensor): + return get_proxy_slot(e, self, e, lambda x: x.proxy) # type: ignore[attr-defined] + elif isinstance(e, py_sym_types): + return get_proxy_slot(e, self, e, lambda e: e.force()) + elif isinstance(e, _AnyScriptObject): + return get_proxy_slot(e, self, e) + else: + return e + + def create_node( + self, + kind: str, + target: Target, + args: tuple[Argument, ...], + kwargs: dict[str, Argument], + name: Optional[str] = None, + type_expr: Optional[Any] = None, + ) -> torch.fx.Node: + node = super().create_node(kind, target, args, kwargs, name, type_expr) # type: ignore[arg-type] + + if node.op in ["placeholder", "output"] and "stack_trace" in node.meta: + del node.meta["stack_trace"] + + if kind == "get_attr": + assert isinstance(target, str) + attr = getattr(self.root, target) + if isinstance(attr, torch.Tensor): + with disable_proxy_modes_tracing(): + node.meta["val"] = extract_val(attr) + + def map_fn(v: Any) -> Optional[_ExtractValType]: + if not isinstance(v, torch.fx.Node) or "val" not in v.meta: + return None + val = v.meta["val"] + # other subclasses like FunctionalTensor error on `extract_val` + # "Attempting to use FunctionalTensor on its own." just store FakeTensors for now + if isinstance(val, torch.Tensor) and not isinstance(val, FakeTensor): + return None + return extract_val(v.meta["val"]) + + if _should_save_eager_input_vals(target, (args, kwargs)): + # NOTE "eager_input_vals" + # We save the original (args, kwargs) FakeTensor values for nodes + # that have exact stride requirements. This is useful downstream. + # We use this information inside Inductor to ensure that inputs to + # stride-sensitive operators have the correct strides. + arg_inp, kwarg_inp = torch.fx.node.map_aggregate((args, kwargs), map_fn) # type: ignore[misc, arg-type] + node.meta["eager_input_vals"] = (arg_inp, kwarg_inp) + + return node + + +def _should_save_eager_input_vals( + target: Any, + args_kwargs: Optional[tuple[tuple[Argument, ...], dict[str, Argument]]] = None, +) -> bool: + from torch._higher_order_ops.invoke_subgraph import InvokeSubgraphHOP + + if not callable(target): + return False + if isinstance( + target, + ( + torch._higher_order_ops.triton_kernel_wrap.TritonKernelWrapperFunctional, + torch._higher_order_ops.triton_kernel_wrap.TritonKernelWrapperMutation, + InvokeSubgraphHOP, + ), + ): + return True + if args_kwargs is not None and ( + target is torch.ops.higher_order.auto_functionalized + or target is torch.ops.higher_order.auto_functionalized_v2 + ): + args = args_kwargs[0] + assert isinstance( + args[0], (torch._ops.OpOverload, torch._ops.HigherOrderOperator) + ) + return _should_save_eager_input_vals(args[0], None) + if target is torch.ops.higher_order.with_effects: + # TODO: inductor lowering for with_effects needs to be updated to propagate + # the arg_kwarg_vals + return False + if isinstance(target, torch._ops.HigherOrderOperator): + if pytree.tree_any(_should_save_eager_input_vals, args_kwargs): + raise RuntimeError( + f"NYI: The HOP {target} has an input that is an OpOverload that " + f"needs exact strides. We probably need special logic to " + f"propagate the FakeTensor vals. Please file an issue." + ) + if isinstance(target, torch._ops.OpOverload): + from torch._library.utils import get_layout_constraint_tag + + return get_layout_constraint_tag(target) == torch._C.Tag.needs_exact_strides + return False + + +def _make_temp_remove_mode_context_manager( + mode_ty: type[TorchFunctionMode], +) -> Callable[[], _GeneratorContextManager[Optional[TorchFunctionMode]]]: + @contextmanager + def context_manager_fn() -> Generator[Optional[TorchFunctionMode], None, None]: + from torch.overrides import _len_torch_function_stack, _pop_mode, _push_mode + + temp_elements = [] + removed_mode = None + + while _len_torch_function_stack() > 0: + mode = _pop_mode() + if isinstance(mode, mode_ty): + removed_mode = mode + break + else: + temp_elements.append(mode) + + for mode in reversed(temp_elements): + _push_mode(mode) + + try: + yield removed_mode + + finally: + if removed_mode is not None: + count = len(temp_elements) + while count > 0: + mode = _pop_mode() + count -= 1 + + temp_elements.append(removed_mode) + + for mode in reversed(temp_elements): + _push_mode(mode) + + return context_manager_fn + + +@torch._disable_dynamo +def dispatch_trace( + root: Union[Module, Callable], + tracer: Tracer, + concrete_args: Optional[tuple[Any, ...]] = None, +) -> GraphModule: + graph = tracer.trace(root, concrete_args) # type: ignore[arg-type] + + # NB: be careful not to DCE .item() calls + def impure_pred(n: fx.Node) -> bool: + from .symbolic_shapes import is_accessor_node + + # Always defer to the built-in notion of impure + if n.is_impure(): + return True + + # Accessors always OK to DCE + if is_accessor_node(n): + return False + + # If the operator in question takes SymInt args to SymInt output, + # we assume it's pure and OK to DCE + if ( + isinstance(n.meta.get("val"), py_sym_types) + and + # NB: constant args ok + all( + isinstance(a.meta.get("val"), py_sym_types) + for a in n.args + if isinstance(a, fx.Node) + ) + ): + return False + + # No idea, just assume it's not OK + return True + + graph.eliminate_dead_code(impure_pred) + from torch._inductor.fx_passes.dedupe_symint_uses import dedupe_symints + + dedupe_symints(graph) + name = root.__class__.__name__ if isinstance(root, Module) else root.__name__ + return fx._lazy_graph_module._make_graph_module(tracer.root, graph, name) + + +def wrap_key( + f: Callable[[Unpack[_Ts]], R], + tensors: tuple[Unpack[_Ts]], + tracer: _ProxyTracer, + pre_dispatch: bool, +) -> Callable[_P, R]: + flat_tensors, _tensors_spec = pytree.tree_flatten(tensors) + + @functools.wraps(f) + def wrapped(*proxies: _P.args, **_unused: _P.kwargs) -> R: + nonlocal tensors + + flat_proxies, _proxies_spec = pytree.tree_flatten(proxies) + assert len(flat_proxies) == len(flat_tensors) + with disable_proxy_modes_tracing() as m: + assert isinstance(m, ProxyTorchDispatchMode) + track_tensor_tree(flat_tensors, flat_proxies, constant=None, tracer=tracer) + + if getattr(tracer, "proxy_module_inputs", False): + tensors = [ # type: ignore[assignment, var-annotated] + p if isinstance(t, torch.nn.Module) else t + for t, p in zip(tensors, proxies) # type: ignore[arg-type] + ] + + def get_tensor_proxy_slot(t: Tensor) -> Union[Tensor, Proxy]: + return get_proxy_slot(t, tracer, t, lambda x: x.proxy) # type: ignore[attr-defined] + + out = f(*tensors) # type:ignore[call-arg] + out = pytree.tree_map_only(Tensor, get_tensor_proxy_slot, out) + out = pytree.tree_map_only( + _AnyScriptObject, lambda t: get_proxy_slot(t, tracer, t, lambda x: x), out + ) + + def get_sym_proxy_slot(t: PySymType) -> Proxy: + return get_proxy_slot(t, tracer).force() + + out = pytree.tree_map_only(py_sym_types, get_sym_proxy_slot, out) + return out + + return wrapped + + +# TODO: Make downstream users of this work with OperatorBase +ORIGINAL_ATEN: Optional[object] = None + + +@contextmanager +def set_original_aten_op( + func: OpOverload | torch._ops.HigherOrderOperator, +) -> Generator[None, None, None]: + global ORIGINAL_ATEN + if ORIGINAL_ATEN is None and fx_traceback.has_preserved_node_meta(): + ORIGINAL_ATEN = func + fx_traceback.current_meta["original_aten"] = func + try: + yield + finally: + ORIGINAL_ATEN = None + fx_traceback.current_meta["original_aten"] = None + else: + yield + + +class TorchFunctionMetadataMode(TorchFunctionMode): + def __init__(self, tracer: _ProxyTracer) -> None: + self.tracer = tracer + + def __torch_function__( + self, + func: OpOverload, + types: tuple[torch._C._TensorMeta, ...], + args: tuple[object, ...] = (), + kwargs: Optional[dict[str, object]] = None, + ) -> object: + kwargs = kwargs or {} + # pyrefly: ignore [bad-assignment] + self.tracer.torch_fn_metadata = func + self.tracer.torch_fn_counts[func] = self.tracer.torch_fn_counts.get(func, 0) + 1 + return func(*args, **kwargs) + + +_temp_remove_metadata_torch_function_mode = _make_temp_remove_mode_context_manager( + TorchFunctionMetadataMode +) + + +# This mode is **only** used for pre_dispatch tracing. +# In particular, we need to make sure that autograd/autocast API's +# that do not desugar into dispatcher operators stay in the graph. +class PreDispatchTorchFunctionMode(TorchFunctionMode): + def __init__(self, tracer: _ProxyTracer) -> None: + self.tracer = tracer + # The input to torch.amp.autocast_mode._exit_autocast graph node should be the + # enter_autocast node. So we have to save the enter autocast node here, and assign it + # to the exit_autocast call_function node. + self.enter_autocast_nodes: list[torch.fx.Node] = [] + + def __torch_function__( + self, + func: Union[OpOverload, Callable], + types: tuple[torch._C._TensorMeta, ...], + args: tuple[object, ...] = (), + kwargs: Optional[dict[str, object]] = None, + ) -> object: + kwargs = kwargs or {} + if func in _side_effectful_need_to_be_preserved_pre_dispatch: + # It's for passing the export verifier which needs to verify the meta['val'] + # TODO(tmanlaibaatar): we should systematically couple it with export verifier, + # instead of hardcoding it here. + # T203648563 + if func is torch.amp.autocast_mode._exit_autocast: + enter_node = self.enter_autocast_nodes.pop() + args = (enter_node,) + node = self.tracer.create_node("call_function", func, args, {}) # type: ignore[arg-type] + if func is torch.amp.autocast_mode._enter_autocast: + self.enter_autocast_nodes.append(node) + if func in [ + torch._C._set_grad_enabled, + torch.amp.autocast_mode._enter_autocast, + torch.amp.autocast_mode._exit_autocast, + ]: + node.meta["val"] = None + # For autocast, the python APIs run so we don't have to run them again + # here. + if func is torch._C._set_grad_enabled: + # pyrefly: ignore [bad-argument-type] + func(*args, **kwargs) + return node + + # We need more complicated handling here because the inputs + # to these functions are sometimes tensors or symints where + # we need to fetch the proxies properly. + if func in [ + torch._functorch.predispatch._add_batch_dim, + torch._functorch.predispatch._remove_batch_dim, + torch._functorch.predispatch._vmap_increment_nesting, + torch._functorch.predispatch._vmap_decrement_nesting, + torch._functorch.vmap.lazy_load_decompositions, + ]: + _, proxies, _ = _fetch_proxies_and_all_constant_flag(args, self.tracer) + out_proxy = self.tracer.create_proxy( + "call_function", + func, + proxies, + {}, + ) + res = func(*args, **kwargs) + track_tensor_tree(res, out_proxy, constant=None, tracer=self.tracer) + return res + return func(*args, **kwargs) + + +_temp_remove_pre_dispatch_torch_function_mode = _make_temp_remove_mode_context_manager( + PreDispatchTorchFunctionMode +) + + +class ProxyTorchDispatchMode(TorchDispatchMode): + # Ensure this is read-only; this exists only for legacy reasons + @property + def enable_tracing(self) -> bool: + return True + + def __init__( + self, + tracer: _ProxyTracer, + tracing_mode: str, + pre_dispatch: bool = False, + _allow_fake_constant: bool = False, + _error_on_data_dependent_ops: bool = True, + ) -> None: + dk = torch._C.DispatchKey.PreDispatch if pre_dispatch else None + super().__init__(dk) + self.tracer = tracer + self.tracing_mode = tracing_mode + self.pre_dispatch = pre_dispatch + self._allow_fake_constant = _allow_fake_constant + self._error_on_data_dependent_ops = _error_on_data_dependent_ops + # Indicates to our torch_dispatch dispatching infra that + # this is an "infra" mode with lower dispatching precedence. + self._mode_key = torch._C._TorchDispatchModeKey.PROXY + # Every time we enter a mode, we maintain a stack telling us what the previous + # ProxyTorchDispatchMode state was (if there was any). + # This lets us properly reset the state on exit. + self.enter_stack: list[Optional[ProxyTorchDispatchMode]] = [] + self.decomp_layers: int = 0 + from torch._inductor import config + + self.emulate_precision_casts: bool = config.emulate_precision_casts + + @count + def __torch_dispatch__( + self, + func: OpOverload, + types: tuple[torch._C._TensorMeta, ...], + args: tuple[object, ...] = (), + kwargs: Optional[dict[str, object]] = None, + ) -> object: + with set_original_aten_op(func): + kwargs = kwargs or {} + + if func == prim.device.default: + return func(*args, **kwargs) + + return proxy_call(self, func, self.pre_dispatch, args, kwargs) + + def __enter__(self) -> Self: + # Stash and store the previous proxy mode (there may or may not be one) + maybe_prev_proxy_mode = _unset_infra_mode(torch._C._TorchDispatchModeKey.PROXY) + self.enter_stack.append(maybe_prev_proxy_mode) + return super().__enter__() + + def __exit__( + self, + exc_type: Optional[type[BaseException]], + exc_value: Optional[BaseException], + traceback: Optional[types.TracebackType], + ) -> Optional[bool]: + b = super().__exit__(exc_type, exc_value, traceback) + + # Re-enable the previous proxy mode, if there was one. + mb_previous_proxy_mode = self.enter_stack.pop() + if mb_previous_proxy_mode is not None: + _push_mode(mb_previous_proxy_mode) + + return b + + @classmethod + def is_infra_mode(cls) -> bool: + return True + + def __sym_dispatch__( + self, + func: OpOverload, + types: tuple[torch._C._TensorMeta, ...], + args: tuple[object, ...], + kwargs: dict[str, object], + ) -> object: + # Peephole optimize multiply by one + # NB: be careful not to trigger guards here! + if func is operator.mul: + if isinstance(args[1], int) and args[1] == 1: + return args[0] + elif isinstance(args[0], int) and args[0] == 1: + return args[1] + + # For speed, we assume there are no nested data structures + # (otherwise we could use tree_map) + # We also assume there are no keyword arguments. + assert not kwargs + out = func(*args, **kwargs) + _sym_register(self.tracer, func, args, out) + return out + + +def _sym_register( + tracer: _ProxyTracer, func: OpOverload, args: tuple[object, ...], out: object +) -> None: + # If func returned a constant, we don't need to trace; we have + # determined that the result is constant (no matter if the inputs + # were symbolic) and it is no longer necessary to trace the + # computation. This could occur if func triggered some guards. + if isinstance(out, py_sym_types): + p_out_thunk = thunkify( + tracer, _compute_proxy, tracer, func=func, args=args, out=out + ) + set_proxy_slot(out, tracer, p_out_thunk) + + +def _compute_proxy( + tracer: _ProxyTracer, func: OpOverload, args: tuple[object, ...], out: PySymType +) -> Proxy: + # Handle torch.sym_sum + n_args: tuple[object, ...] + if len(args) == 1 and isinstance(args[0], (list, tuple)): + n_args = ( + tuple( + ( + get_proxy_slot(a, tracer).force().node + if isinstance(a, py_sym_types) + else a + ) + for a in args[0] + ), + ) + else: + n_args = tuple( + ( + get_proxy_slot(a, tracer).force().node + if isinstance(a, py_sym_types) + else a + ) + for a in args + ) + + # func doesn't have a __torch_function__ that Proxy can interpose, so + # we gotta do it manually + n_out = tracer.create_node("call_function", func, n_args, {}) # type: ignore[arg-type] + p_out = fx.Proxy(n_out, tracer) + set_meta(p_out, out) + return p_out + + +class _GraphAppendingTracerEx(fx.proxy.GraphAppendingTracer): + script_object_tracker: MutableMapping[_AnyScriptObjectType, Proxy] + symnode_tracker: MutableMapping[PySymType, _PySymProxyType] + tensor_tracker: MutableMapping[Tensor, _ProxyTensor] + sympy_expr_tracker: dict[sympy.Symbol, _SympyExprTrackerValue] + torch_fn_metadata: Optional[OpOverload] + torch_fn_counts: dict[OpOverload, int] + enable_thunkify: bool = False + + def __init__(self, graph: fx.graph.Graph) -> None: + super().__init__(graph) + self.symnode_tracker = weakref.WeakKeyDictionary() + self.tensor_tracker = WeakTensorKeyDictionary() + self.sympy_expr_tracker = {} + self.script_object_tracker = WeakIdKeyDictionary( + dict=None, ref_type=_WeakHashRef + ) + # Stores the torch function that was called during tracing + self.torch_fn_metadata = None + # Stores the counts for every torch function called. This is to help + # distinguish between different calls to the same torch function. + self.torch_fn_counts = {} + + +# TODO: I'm not sure what the point of this class is; you can just +# make_fx through a regular Interpreter +class DecompositionInterpreter(fx.Interpreter): + def __init__( + self, + module: fx.GraphModule, + new_graph: fx.Graph, + decomposition_table: Optional[Mapping[OpOverload, Callable]] = None, + **kwargs: object, + ) -> None: + super().__init__(module, **kwargs) # type: ignore[arg-type] + self.new_graph = new_graph + self.tracer = _GraphAppendingTracerEx(self.new_graph) + # Blegh + self.decomposition_table = decomposition_table or {} + self.mode = ProxyTorchDispatchMode(self.tracer, tracing_mode="real") + + # pyrefly: ignore [bad-override] + def placeholder( + self, + target: str, # type: ignore[override] + args: tuple[object, ...], + kwargs: dict[str, object], + ) -> object: + out = super().placeholder(target, args, kwargs) # type: ignore[arg-type] + proxy = fx.Proxy(self.new_graph.placeholder(target), self.tracer) + track_tensor_tree(out, proxy, constant=None, tracer=self.tracer) + # TODO handle case where the first character of target is '*' + return out + + # pyrefly: ignore [bad-override] + def get_attr( + self, + target: str, # type: ignore[override] + args: tuple[object, ...], + kwargs: dict[str, object], + ) -> object: + out = super().get_attr(target, args, kwargs) # type: ignore[arg-type] + proxy = fx.Proxy(self.new_graph.get_attr(target), self.tracer) + track_tensor_tree(out, proxy, constant=None, tracer=self.tracer) + return out + + # call_function, call_method, call_module get traced automatically by the outer mode. + + # pyrefly: ignore [bad-override] + def output( + self, + target: str, # type: ignore[override] + args: tuple[object, ...], + kwargs: dict[str, object], + ) -> object: + out = super().output(target, args, kwargs) # type: ignore[arg-type] + + def get_proxy_node(x: _ProxyTensor) -> fx.node.Node: + return x.proxy.node + + def unwrap(e: Tensor) -> Union[Tensor, fx.Node]: + return get_proxy_slot(e, self.tracer, e, get_proxy_node) + + self.new_graph.output(pytree.tree_map(unwrap, out)) + return out + + def run(self, *args: object, **kwargs: object) -> object: + # Should enter the mode at least once for being able to restore it later + # See: https://github.com/pytorch/pytorch/pull/82549#discussion_r934782025 + with decompose(self.decomposition_table), self.mode: + return super().run(*args, **kwargs) # type: ignore[arg-type] + + +class _SelectiveDecomposeInterpreter(fx.Interpreter): + def __init__( + self, + module: fx.GraphModule, + should_decompose: Callable[[fx.Node], bool], + decomposition_table: Mapping[OpOverload, Callable], + **kwargs: object, + ) -> None: + """ + For all nodes in `module`, selectively decompose if is `should_decompose`, + following the given `decomposition_table`. + """ + super().__init__(module, **kwargs) # type: ignore[arg-type] + self.should_decompose = should_decompose + self.decomposition_table = decomposition_table + + @staticmethod + def recursive_wrap( + gm: fx.GraphModule, + should_decompose: Callable[[fx.Node], bool], + decomposition_table: Mapping[OpOverload, Callable], + **kwargs: object, + ) -> _SelectiveDecomposeInterpreter: + """ + Recursively wrap gm and its sub graph modules. Specifically, HOP takes + sub graph module as args. We may not want to decompose all nodes within + these sub graph modules. So we also need to wrap these sub graph modules. + As a result: + - if should_decompose(hop) is True, we decompose all nodes within the hop. + - if should_decompose(hop) is False, we check each node within the hop + and decide whether decompose or not. + """ + for node in gm.graph.nodes: + if node.op == "call_function" and isinstance( + node.target, HigherOrderOperator + ): + new_args = [] + for arg in node.args: + if isinstance(arg, fx.GraphModule): + new_arg = _SelectiveDecomposeInterpreter.recursive_wrap( + arg, should_decompose, decomposition_table, **kwargs + ) + else: + new_arg = arg + new_args.append(new_arg) + node.args = tuple(new_args) + + return _SelectiveDecomposeInterpreter( + gm, should_decompose, decomposition_table, **kwargs + ) + + def run_node(self, n): + if self.should_decompose(n): + with decompose(self.decomposition_table): + result = super().run_node(n) + else: + result = super().run_node(n) + return result + + +def selective_decompose( + joint_gm: fx.GraphModule, + *args, + decomposition, + should_decompose, + trace_joint_graph: bool, +) -> fx.GraphModule: + """Retrace a joint graph module and selectively apply decomposition.""" + + if trace_joint_graph: + # the arg name, primals and tangents, are important. + # make_fx keeps the name in the traced graph and partitioner later relies + # on the name to partition joint graph correctly. + def wrap_fn(primals: list[Any], tangents: list[Any]): + return _SelectiveDecomposeInterpreter.recursive_wrap( + joint_gm, should_decompose, decomposition + ).run(*args) + else: + + def wrap_fn(*args): + return _SelectiveDecomposeInterpreter.recursive_wrap( + joint_gm, should_decompose, decomposition + ).run(*args) + + return make_fx(wrap_fn, decomposition_table={})(*args) + + +def wrapper_and_args_for_make_fx( + func: Callable[..., R], args: tuple[object, ...], kwargs: dict[str, object] +) -> tuple[Callable[[list[object]], R], list[object]]: + # make_fx doesn't support kwargs, so we need to do this flattening + # and then unflatten the args before calling func + flat_args, spec = pytree.tree_flatten((args, kwargs)) + + def wrapped(flat_args: list[object]) -> R: + fn_args, fn_kwargs = pytree.tree_unflatten(flat_args, spec) + return func(*fn_args, **fn_kwargs) + + return wrapped, flat_args + + +@contextmanager +def disable_autocast_cache() -> Generator[None, None, None]: + old_value = torch.is_autocast_cache_enabled() + torch.set_autocast_cache_enabled(False) + try: + yield + finally: + torch.set_autocast_cache_enabled(old_value) + + +class _ModuleNotInstalledAsSubmoduleError(NameError): + pass + + +# Base class for inline _ModuleStackTracer.__init__.AttrProxy +class _AttrProxy: + def reset_proxy_mapping(self, base: Module, path: str) -> None: + pass + + +class _ModuleStackTracer(PythonKeyTracer): + r"""Customized version of PythonKeyTracer that retains module stack + information in node.meta["nn_module_stack"]. + + FX symbolic trace actually does this already, but it relies on `self.root` + being the actual module being traced. Since make_fx traces a lambda of our + creation, things don't work properly. + + So for this version we hold onto a reference to the original module + (scope_root) and use that to match the path. Also when we see, + A + / \ + B C + \ / + D + we want to record the path as A.B.D by recording only one path. + See Note [Preserving the nn module stack metadata during export non-strict mode] # noqa: W605 + """ + + def __init__(self, scope_root: GraphModule) -> None: + super().__init__() + self.record_stack_traces = True + self._record_forward_stack_traces_only = True + self.scope_root = scope_root + self.enable_attr_proxy = False + self.submodule_paths = {} + for name, m in self.scope_root.named_modules(remove_duplicate=False): + if m in self.submodule_paths: + log.info( + "Shared module found between %s and %s, AttrProxy is enabled.", + self.submodule_paths[m], + name, + ) + self.enable_attr_proxy = True + else: + self.submodule_paths[m] = name + + self.proxy_paths: WeakKeyDictionary[_AttrProxy, str] = WeakKeyDictionary() + self.attr_proxy_map: WeakKeyDictionary[Module, _AttrProxy] = WeakKeyDictionary() + self.proxy_modules: WeakKeyDictionary[_AttrProxy, Module] = WeakKeyDictionary() + self.counter = 0 + + self.module_id_cache = defaultdict(list) + for name, mod in self.scope_root.named_modules(remove_duplicate=False): + self.module_id_cache[id(mod)].append(name) + + # Build a wrapper around _AttrProxy to provide the tracer. We can't + # store it on _AttrProxy itself beceause we mimic the underlying class + # (including its attributes). + tracer = self + + class AttrProxy(_AttrProxy): + def __init__(self, base: Union[Module, _AttrProxy], path: str) -> None: + if isinstance(base, _AttrProxy): + base = base.get_base() # type: ignore[attr-defined] + + assert isinstance(base, Module) + # Class is modified to be a subclass of torch.nn.Module + # Warning: We blow away our own attributes here to mimic the base class + # - so don't expect `self.x` to do anything useful. + # pyrefly: ignore [no-matching-overload] + # pyrefly: ignore [bad-override] + self.__class__ = type( + base.__class__.__name__, + (self.__class__, base.__class__), + {}, + ) + self.__dict__ = base.__dict__ + self.__class__.__module__ = base.__class__.__module__ + self.__class__.__qualname__ = base.__class__.__qualname__ + + # This overwrites any existing paths if `base` is an AttrProxy + tracer.proxy_paths[self] = path + tracer.proxy_modules[self] = base + + def __getattr__(self, name: str) -> AttrProxy: + assert isinstance(self, Module) + # Calling into torch.nn.Module.__getattr__ with super(), + # That __getattr__ is patched to be module_getattr_wrapper in _symbolic_trace.py. + # which then calls into _ModuleStackTracer.getattr + attr_val = super().__getattr__(name) # type: ignore[misc] + if not isinstance(attr_val, Module): + return attr_val + + # pyrefly: ignore [index-error] + return AttrProxy(attr_val, tracer.proxy_paths[self] + "." + name) + + def get_base(self) -> Module: + return tracer.proxy_modules[self] + + def __getitem__(self, idx: Union[int, slice]) -> AttrProxy: + if isinstance(idx, slice): + if isinstance(self, torch.nn.Sequential): + # Copied from nn/modules/container.py + res = torch.nn.Sequential( + OrderedDict(list(self._modules.items())[idx]) + ) + # pyrefly: ignore [index-error] + return AttrProxy(res, f"{tracer.proxy_paths[self]}.{idx}") + elif isinstance(self, torch.nn.ModuleList): + # Copied from nn/modules/container.py + res = torch.nn.ModuleList(list(self._modules.values())[idx]) + # pyrefly: ignore [index-error] + return AttrProxy(res, f"{tracer.proxy_paths[self]}.{idx}") + + return super().__getitem__(idx) # type: ignore[misc] + + @property + def _modules(self) -> dict[str, AttrProxy]: + assert "_modules" in self.__dict__ + submodules = self.__dict__["_modules"] + assert isinstance(submodules, dict) + return { + key: ( + AttrProxy(value, tracer.proxy_paths[self] + "." + str(key)) # type: ignore[misc] + if value is not None + else value + ) + for key, value in submodules.items() + } + + self.proxy_type = AttrProxy + + def path_of_module(self, mod: Module) -> str: + """ + Use tracked access path during tracing instead of the default BFS behavior. + Still use all the possible module paths to verify the result. + """ + if mod is self.scope_root: + return "" + + if isinstance(mod, _AttrProxy): + return self.proxy_paths[mod] + + try: + return Tracer.path_of_module(self, mod) + except NameError as e: + raise _ModuleNotInstalledAsSubmoduleError from e + + def getattr( + self, attr: str, attr_val: object, parameter_proxy_cache: dict[str, Proxy] + ) -> object: + if ( + not isinstance(attr_val, Module) + or isinstance(attr_val, fx.GraphModule) + or not self.enable_attr_proxy + ): + return super().getattr(attr, attr_val, parameter_proxy_cache) + if isinstance(attr_val, _AttrProxy): + return attr_val + + # See NOTE [caching AttrProxy]. + if attr_val not in self.attr_proxy_map: + self.attr_proxy_map[attr_val] = self.proxy_type(attr_val, attr) + else: + self.attr_proxy_map[attr_val].reset_proxy_mapping(attr_val, attr) + return self.attr_proxy_map[attr_val] + + def trace( # type: ignore[override] + self, root: Union[Module, Callable], concrete_args: Optional[dict[str, object]] + ) -> fx.Graph: + res = super().trace(root, concrete_args) + + # NOTE [export non-strict fake tensor leak detection] + # In non-strict export, we don't have dynamo's side effect + # tracking logic which makes some cases hard to detect. + # In general, our detecting strategy is: + # (1) We instrument fake tensor creation to log all the fake tensors created during export. + # (2) We dump the proxy to fake tensor map from make_fx tracer (_FAKE_TENSOR_ID_TO_PROXY_MAP_FOR_EXPORT)) + # (3) Filter out fake tensors that are logged during (1): + # (1) Associated with TrackedFake (input tracking thing in symbolic_shapes) + # (2) Associated with gm.meta + # (4) Do ID match with the proxies + + global _FAKE_TENSOR_ID_TO_PROXY_MAP_FOR_EXPORT + _FAKE_TENSOR_ID_TO_PROXY_MAP_FOR_EXPORT.clear() + + for key, val in self.tensor_tracker.items(): + _FAKE_TENSOR_ID_TO_PROXY_MAP_FOR_EXPORT[id(key)] = val.proxy.node + + # Since we are making _AttrProxy mimic the original + # submodule, when someone registers a module directly + # to the tracer while tracing, the proxy object gets registered + # first. So we need to replace the proxy modules with the real ones + # This can happen during HOO tracing + proxy_module_names_to_be_replaced: list[tuple[str, _AttrProxy]] = [] + for name, module in self.root.named_modules(): + if module in self.proxy_modules: + proxy_module_names_to_be_replaced.append((name, module)) + + def _delete_proxy_attr(obj: Module, target: str) -> bool: + # Copied from fx/graph_module.py + # Customized it for proxy type + atoms = target.split(".") + path, target_submod = atoms[:-1], atoms[-1] + assert isinstance(obj, Module) + mod = obj + + # Get the parent module + for item in path: + if not hasattr(mod, item): + return False + + mod = getattr(mod, item) + + if not isinstance(mod, (_AttrProxy, Module)): + return False + + if not hasattr(mod, target_submod): + return False + + # At least the leaf module should be proxy type. + if not isinstance(getattr(mod, target_submod), _AttrProxy): + return False + + delattr(mod, target_submod) + return True + + for proxy_module_name, proxy_module in proxy_module_names_to_be_replaced: + _delete_proxy_attr(self.root, proxy_module_name) + actual_module = self.proxy_modules[proxy_module] + _assign_attr(actual_module, self.root, proxy_module_name) + + return res + + def call_module( + self, + m: Module, + forward: Callable, + args: tuple[object, ...], + kwargs: dict[str, object], + ) -> None: + """PythonKeyTracer overrides call_module to avoid the scope handling, + but we actually want it. + """ + from torch._dynamo import OptimizedModule + + # FIXME (tmanlaibaatar) + # When we call torch.compile inside HOO, we will end up + # invoking a module that is not registered on the root. For + # now, we just inline them. But once we start supporting + # mark_strict in export, we do need to properly handle this. + # Right now, it doesn't matter because current non-strict + # use cases don't need to work with HOO. + if isinstance(m, (OptimizedModule, GraphModule)): + return forward(*args, **kwargs) + + try: + return Tracer.call_module(self, m, forward, args, kwargs) + except _ModuleNotInstalledAsSubmoduleError: + log.debug( + "Unable to find the path of the module %s. " + "This might be because the module was not properly registered " + "as a submodule, which is not good practice. We will trace " + "through the module without recording stack information.", + str(m), + ) + return forward(*args, **kwargs) + + def is_leaf_module(self, m: Module, module_qualified_name: str) -> bool: + return False + + def create_node(self, *args: object, **kwargs: object) -> fx.node.Node: + """ + Create node and add on metadata. + Add nn_module_stack here instead of TracerBase, + since calls to make_fx() might not want to record module stack metadata. + Add torch_fn by looking at torch_fn_metadata and torch_fn_counts. + Add stack_trace by filtering out forward() stack frames. + """ + node = super().create_node(*args, **kwargs) # type: ignore[arg-type] + + # nn_module_stack + if node.op not in ["placeholder", "output"]: + if node.meta.get("nn_module_stack") is None: + node.meta["nn_module_stack"] = self.module_stack.copy() + # convert nn_module_stack from Dict[key, (FQN, class)] -> Dict[str, Tuple[str, str]] + for key, (fqn, mod_cls) in node.meta["nn_module_stack"].items(): + if isinstance(mod_cls, type): + node.meta["nn_module_stack"][key] = ( + fqn, + mod_cls.__module__ + "." + mod_cls.__qualname__, + ) + + # torch_fn + if ( + node.op == "call_function" + and self.torch_fn_metadata is not None + and "torch_fn" not in node.meta + ): + node.meta["torch_fn"] = ( + f"{self.torch_fn_metadata.__name__}_{self.torch_fn_counts[self.torch_fn_metadata]}", + f"{self.torch_fn_metadata.__class__.__name__}.{self.torch_fn_metadata.__name__}", + ) + + return node + + +class _MakefxTracer: + def __init__( + self, + decomposition_table: Optional[Mapping[OpOverload, Callable]], + tracing_mode: str, + _allow_non_fake_inputs: bool, + pre_dispatch: bool, + record_module_stack: bool, + _allow_fake_constant: bool, + _error_on_data_dependent_ops: bool, + record_stack_traces: bool = False, + parent_tracer: Optional[_MakefxTracer] = None, + proxy_module_inputs: bool = False, + ) -> None: + # Configurations that are used to initialize the context managers and their states. + # Should not modify them during tracing. + self.decomposition_table: dict[OpOverload, Callable] = dict( + decomposition_table or {} + ) + self.decomposition_table.setdefault( + torch.ops.aten.sym_numel.default, torch._decomp.decompositions.sym_numel + ) + self.tracing_mode: str = tracing_mode + self._allow_non_fake_inputs: bool = _allow_non_fake_inputs + self.pre_dispatch: bool = pre_dispatch + self.record_module_stack: bool = record_module_stack + self._allow_fake_constant: bool = _allow_fake_constant + self._error_on_data_dependent_ops: bool = _error_on_data_dependent_ops + + # All context managers and their states should be initialized before tracing based on the inputs + # and configurations. After tracing, their states should be cleaned except for shape_env. + # Remember to specify how to initialize it from user inputs and from parent tracer whenever + # adding new modes in _MakefxTracer. + self.fake_tensor_mode: Optional[FakeTensorMode] = None + self.proxy_mode: Union[nullcontext, ProxyTorchDispatchMode] = nullcontext() + self.proxy_function_mode: Union[nullcontext, PreDispatchTorchFunctionMode] = ( + nullcontext() + ) + self.fx_tracer: Optional[PythonKeyTracer] = None + self.python_dispatcher_mode: Union[nullcontext, Any] = nullcontext() + self.torch_fn_metadata_mode: Union[nullcontext, TorchFunctionMetadataMode] = ( + nullcontext() + ) + self.record_stack_traces = record_stack_traces + self.parent_tracer: Optional[_MakefxTracer] = parent_tracer + self.proxy_module_inputs = proxy_module_inputs + + def _checkpoint_modes(self) -> list[Any]: + return [ + self.fake_tensor_mode, + self.proxy_mode, + self.proxy_function_mode, + self.fx_tracer, + self.python_dispatcher_mode, + self.torch_fn_metadata_mode, + ] + + def _restore_modes( + self, + prev_fake_tensor_mode: Optional[FakeTensorMode], + prev_proxy_mode: Union[nullcontext, ProxyTorchDispatchMode], + prev_proxy_function_mode: Union[nullcontext, PreDispatchTorchFunctionMode], + prev_fx_tracer: Optional[PythonKeyTracer], + prev_python_dispatcher_mode: Union[nullcontext, Any], + prev_torch_fn_metadata_mode: Union[nullcontext, TorchFunctionMetadataMode], + ) -> None: + self.fake_tensor_mode = prev_fake_tensor_mode + self.proxy_mode = prev_proxy_mode + self.proxy_function_mode = prev_proxy_function_mode + self.fx_tracer = prev_fx_tracer + self.python_dispatcher_mode = prev_python_dispatcher_mode + self.torch_fn_metadata_mode = prev_torch_fn_metadata_mode + + @contextmanager + def _init_modes_from_inputs( + self, f: Callable, args: tuple[object, ...] + ) -> Generator[None, None, None]: + prev_modes = self._checkpoint_modes() + try: + # Avoid importing sympy at a module level + from .symbolic_shapes import ShapeEnv + + if hasattr(f, "_orig_mod") and self.record_module_stack: + scope_root = f._orig_mod + # _ModuleStackTracer always try to preserve stack trace + # in forward functions + self.fx_tracer = _ModuleStackTracer(scope_root) + else: + self.fx_tracer = PythonKeyTracer() + self.fx_tracer.record_stack_traces = self.record_stack_traces + if self.record_stack_traces: + self.fx_tracer._record_forward_stack_traces_only = True + + if self.tracing_mode == "fake": + import torch._dynamo + + fake_tensor_mode = torch._dynamo.utils.detect_fake_mode(args) + if fake_tensor_mode is None: + import torch._functorch.config as _config + + with _config.patch(fake_tensor_allow_unsafe_data_ptr_access=False): + fake_tensor_mode = FakeTensorMode( + allow_fallback_kernels=True, + allow_non_fake_inputs=self._allow_non_fake_inputs, + shape_env=ShapeEnv(), + static_shapes=True, + ) + self.fake_tensor_mode = fake_tensor_mode + elif self.tracing_mode == "symbolic": + import torch._dynamo + + fake_tensor_mode = torch._dynamo.utils.detect_fake_mode(args) + if fake_tensor_mode is None: + shape_env = ShapeEnv() + import torch._functorch.config as _config + + with _config.patch(fake_tensor_allow_unsafe_data_ptr_access=False): + fake_tensor_mode = FakeTensorMode( + allow_fallback_kernels=False, + allow_non_fake_inputs=self._allow_non_fake_inputs, + shape_env=shape_env, + ) + assert fake_tensor_mode.shape_env is not None, ( + "shape_env should be set if tracing with 'symbolic'" + ) + self.fake_tensor_mode = fake_tensor_mode + else: + if not self.tracing_mode == "real": + raise AssertionError( + f"Unexpected tracing type: {self.tracing_mode}" + ) + + self._construct_modes_with_fx_tracer(self.fx_tracer) + yield + finally: + self._restore_modes(*prev_modes) + + def _construct_modes_with_fx_tracer(self, fx_tracer: _ProxyTracer) -> None: + self.proxy_mode = ProxyTorchDispatchMode( + fx_tracer, + self.tracing_mode, + pre_dispatch=self.pre_dispatch, + _allow_fake_constant=self._allow_fake_constant, + _error_on_data_dependent_ops=self._error_on_data_dependent_ops, + ) + + if self.pre_dispatch: + self.proxy_function_mode = PreDispatchTorchFunctionMode(fx_tracer) + + # pre-autograd tracing uses per-dispatch-key modes, + # which requires the python dispatcher + if self.tracing_mode == "symbolic" or self.pre_dispatch: + self.python_dispatcher_mode = enable_python_dispatcher() + + self.torch_fn_metadata_mode = TorchFunctionMetadataMode(fx_tracer) + fx_tracer.proxy_module_inputs = self.proxy_module_inputs # type: ignore[union-attr] + + @contextmanager + def _init_modes_from_parent( + self, parent_tracer: _MakefxTracer + ) -> Generator[None, None, None]: + # By default, subtracer creates new modes based on parent tracer's config. + # However, there are cases where we want to share the same modes with parent tracer + # For example, fake_tensor_mode, we want the example value's fake_mode of parent graph and subgraphs to be the same. + prev_modes = self._checkpoint_modes() + try: + self.fake_tensor_mode = parent_tracer.fake_tensor_mode + + def _create_sub_fx_tracer(parent_tracer: _ProxyTracer) -> PythonKeyTracer: + if type(parent_tracer) is PythonKeyTracer: + return PythonKeyTracer() + elif type(parent_tracer) is _ModuleStackTracer: + return _ModuleStackTracer(parent_tracer.scope_root) + else: + raise RuntimeError( + f"Unexpected tracer type: {type(parent_tracer)}." + ) + + assert parent_tracer.fx_tracer is not None + self.fx_tracer = _create_sub_fx_tracer(parent_tracer.fx_tracer) + self._construct_modes_with_fx_tracer(self.fx_tracer) + yield + finally: + self._restore_modes(*prev_modes) + + def _trace_inner(self, f: Callable, *args: object) -> GraphModule: + # TODO: We need to explicitly import torch._dynamo before calling dispatch_trace, + # because dispatch_trace will introduce the lazy import of torch._dynamo, + # and some contexts set before calling dispatch_trace will cause problems with the import of torch._dynamo, + # such as some torch API(torch.ones and so on) in populate_builtin_to_tensor_fn_map() will be affected + # by the context set before dispatch_trace. + import torch._dynamo + + phs = pytree.tree_map(lambda _: torch.fx._symbolic_trace.PH, args) + + def _wrap_fake(args: T) -> T: + arg_count = 0 + + def inner_wrap_fake(x: object) -> object: + nonlocal arg_count + # TODO: it would be nice to line these up with the names + # FX will choose for the placeholders, but we don't + # actually know what the names will be at this point yet + # NB: the Source here is actually meaningless + from torch._dynamo.source import ConstantSource + + assert self.fake_tensor_mode is not None + source = ConstantSource(f"input{arg_count}") + if isinstance(x, Tensor): + arg_count += 1 + return self.fake_tensor_mode.from_tensor(x, source=source) + # NB: don't match on bools + elif type(x) is int and self.tracing_mode == "symbolic": + assert self.fake_tensor_mode.shape_env is not None, ( + "shape_env should be set if tracing with 'symbolic'" + ) + return self.fake_tensor_mode.shape_env.create_symintnode( + self.fake_tensor_mode.shape_env.create_symbol( + x, source, positive=None + ), + hint=x, + source=source, + ) + elif isinstance(x, torch.ScriptObject) or is_opaque_type(type(x)): + return torch._library.fake_class_registry.maybe_to_fake_obj( + self.fake_tensor_mode, x + ) + + assert not isinstance(x, FakeScriptObject), ( + f"ScriptObject {x} has been fakified. Cannot wrap_fake it again." + ) + return x + + wrap_fn_map = { + "real": lambda x: x, + "fake": inner_wrap_fake, + "symbolic": inner_wrap_fake, + } + return pytree.tree_map(wrap_fn_map[self.tracing_mode], args) + + def _wrap_func(f: Callable[_P, R], phs: Sequence[PHBase]) -> Callable[_P, R]: + if ( + not hasattr(inspect.unwrap(f), "__code__") + or inspect.unwrap(f).__code__.co_flags & inspect.CO_VARARGS + ): + # FX doesn't support varargs, so we gotta fake up a wrapper + # TODO: Would be nice to fix this at the source... + return fake_signature(f, len(phs)) + return f + + args = _wrap_fake(args) + func = _wrap_func(f, phs) + # We disable the autocast cache as the autocast cache causes type conversions on parameters to + # check a cache, which introduces untracked tensors into the graph + # + # We also disable tracing by any other tensor proxy-based tracers except the current. The + # purpose of `make_fx` is to produce graphmodules as a side effect; its internal execution is + # thus irrelevant to any external functional trace. + proxy_mode: ProxyTorchDispatchMode = typing.cast( + ProxyTorchDispatchMode, self.proxy_mode + ) + with ExitStack() as stack: + stack.enter_context(decompose(self.decomposition_table)) + if self.fake_tensor_mode: + stack.enter_context(self.fake_tensor_mode) + stack.enter_context(self.python_dispatcher_mode) + stack.enter_context(self.proxy_function_mode) + stack.enter_context(self.torch_fn_metadata_mode) + stack.enter_context(proxy_mode) + stack.enter_context(disable_autocast_cache()) + stack.enter_context(_set_make_fx_tracer(self)) + + assert self.fx_tracer is not None + try: + t = dispatch_trace( + wrap_key(func, args, self.fx_tracer, self.pre_dispatch), + tracer=self.fx_tracer, + concrete_args=tuple(phs), + ) + except Exception: + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "make_fx_fail_partial", + "encoding": "string", + }, + payload_fn=lambda: self.fx_tracer.graph.python_code( # type: ignore[union-attr] + root_module="self", + verbose=True, + include_stride=True, + include_device=True, + ).src, + ) + raise + + if ( + self.is_hop_subgraph_tracer() + and (fake_mode := torch._guards.detect_fake_mode(args)) + and fake_mode.shape_env is not None + ): + from torch.fx.passes.runtime_assert import insert_deferred_runtime_asserts + + insert_deferred_runtime_asserts(t, fake_mode.shape_env, "reenter_make_fx") + t.recompile() + # TODO: kind of a bad way to do it, should maybe figure out a better way + if self.tracing_mode == "symbolic": + assert self.fake_tensor_mode is not None + t.shape_env = self.fake_tensor_mode.shape_env # type: ignore[assignment] + return t + + def trace(self, f: Callable, *args: object) -> fx.GraphModule: + with self._init_modes_from_inputs(f, args): + return self._trace_inner(f, *args) + + def is_hop_subgraph_tracer(self) -> bool: + return self.parent_tracer is not None + + def trace_subgraph(self, f: Callable, *args: object) -> GraphModule: + # Create a new tracer based on parent's config + sub_tracer = _MakefxTracer( + self.decomposition_table, + "real", + self._allow_non_fake_inputs, + self.pre_dispatch, + self.record_module_stack, + self._allow_fake_constant, + self._error_on_data_dependent_ops, + parent_tracer=self, + ) + with sub_tracer._init_modes_from_parent(self): + return sub_tracer._trace_inner(f, *args) + + +_CURRENT_MAKE_FX_TRACER: Optional[_MakefxTracer] = None + + +@contextmanager +def _set_make_fx_tracer(tracer: _MakefxTracer) -> Generator[None, None, None]: + global _CURRENT_MAKE_FX_TRACER + prev_tracer = _CURRENT_MAKE_FX_TRACER + try: + _CURRENT_MAKE_FX_TRACER = tracer + yield + finally: + _CURRENT_MAKE_FX_TRACER = prev_tracer + + +def make_fx( + f: Callable, + decomposition_table: Optional[Mapping[OpOverload, Callable]] = None, + tracing_mode: str = "real", + _allow_non_fake_inputs: bool = False, + *, + pre_dispatch: bool = False, + record_module_stack: bool = False, + _allow_fake_constant: bool = False, + _error_on_data_dependent_ops: bool = True, + record_stack_traces: bool = False, + proxy_module_inputs: bool = False, +) -> Callable[..., GraphModule]: + """ + Given a function f, return a new function which when executed with valid + arguments to f, returns an FX GraphModule representing the set of operations that + were executed during the course of execution. + + If record_stack_traces is True, the stack trace will be preserved on node.meta["stack_trace"] + """ + + assert tracing_mode in ["real", "fake", "symbolic"] + + from torch._inductor import config + + make_fx_tracer = _MakefxTracer( + decomposition_table, + tracing_mode, + _allow_non_fake_inputs, + pre_dispatch, + record_module_stack, + _allow_fake_constant, + _error_on_data_dependent_ops, + record_stack_traces=record_stack_traces + or config.trace.provenance_tracking_level == 1, + proxy_module_inputs=proxy_module_inputs, + ) + + @functools.wraps(f) + def wrapped(*args: object) -> GraphModule: + return make_fx_tracer.trace(f, *args) + + return wrapped + + +def get_torch_dispatch_modes() -> list[TorchDispatchMode]: + return torch.utils._python_dispatch._get_current_dispatch_mode_stack() + + +# TODO: this is a legacy name, there is only ever one proxy mode as it's an +# infra mode +def get_innermost_proxy_mode() -> Optional[ProxyTorchDispatchMode]: + return get_proxy_mode() + + +def get_proxy_mode() -> Optional[ProxyTorchDispatchMode]: + """ + Current the currently active proxy tracing mode, or None if + we are not currently tracing. This includes pre-dispatch proxy + tracing. + """ + pre_dispatch_mode = torch._ops._get_dispatch_mode_pre_dispatch( + torch._C._TorchDispatchModeKey.PROXY + ) + mode = torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.PROXY) + assert pre_dispatch_mode is None or mode is None, ( + f"pre_dispatch_mode={pre_dispatch_mode}, mode={mode}" + ) + return pre_dispatch_mode or mode + + +def handle_sym_dispatch( + func: Callable[_P, R], + args: _P.args, # type: ignore[valid-type] # not allowed to use _P.args here + kwargs: _P.kwargs, # type: ignore[valid-type] # not allowed to use _P.kwargs here +) -> R: + """ + Call into the currently active proxy tracing mode to do a + SymInt/SymFloat/SymBool dispatch trace on a function that operates on + these arguments. + """ + mode = get_proxy_mode() + assert mode + # Have to do it manually, because we're not doing the normal torch + # dispatch machinery which disables it for us + with disable_proxy_modes_tracing(): + # TODO: properly compute types + types: list[type] = [] + return mode.__sym_dispatch__(func, types, args, kwargs) # type: ignore[arg-type, return-value] + + +@contextmanager +def disable_proxy_modes_tracing() -> Generator[ProxyTorchDispatchMode, None, None]: + return _disable_infra_mode(torch._C._TorchDispatchModeKey.PROXY) + + +def maybe_handle_decomp( + proxy_mode: ProxyTorchDispatchMode, + op: OpOverload, + args: tuple[object, ...], + kwargs: dict[str, object], +) -> object: + from torch._inductor.compiler_bisector import CompilerBisector + + if op in CURRENT_DECOMPOSITION_TABLE: + if CompilerBisector.disable_subsystem( + "aot_eager_decomp_partition", "decomposition", lambda: repr(op) + ): + return NotImplemented + + with proxy_mode: + proxy_mode.decomp_layers += 1 + out = CURRENT_DECOMPOSITION_TABLE[op](*args, **kwargs) + proxy_mode.decomp_layers -= 1 + return out + + return NotImplemented + + +def get_isolated_graphmodule( + func: Callable, + args: tuple[object, ...], + kwargs: dict[str, object], + tracing_mode: str = "real", + decomposition_table: Optional[Mapping[OpOverload, Callable]] = None, +) -> GraphModule: + """A helper function used to get the GraphModule for the given func. + + It's expected to be used in the ProxyTensor tracing context. + It detaches the args and kwargs from the current tracer so that the trace of + the current graph module can be created without any side-effects. + """ + wrapped, all_args = wrapper_and_args_for_make_fx(func, args, kwargs) + + with disable_proxy_modes_tracing(): + gm = make_fx( + wrapped, decomposition_table=decomposition_table, tracing_mode=tracing_mode + )(all_args) + return gm + + +def _set_unbacked_bindings(out: object, out_proxy: _NestedProxys) -> None: + """A helper function for setting up unbacked_bindings on the destination FX graph.""" + from .symbolic_shapes import compute_unbacked_bindings + + # Can't use detect_fake_mode here, + # + # python test/distributed/_tensor/test_dtensor_compile.py -k + # test_tp_compile_fullgraph_is_seq_parallel_False + # + # will fail. Very strange, it probably isn't right for them to be using + # two fake modes there... + fake_mode = torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE) + if fake_mode and fake_mode.shape_env: + if symbol_to_path := compute_unbacked_bindings(fake_mode.shape_env, out): + assert isinstance(out_proxy, Proxy), out_proxy + out_proxy.node.meta["unbacked_bindings"] = symbol_to_path diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/recording.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/recording.py new file mode 100644 index 0000000000000000000000000000000000000000..4ec092898cd69d74362acbe57a029b09d9b23bee --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/recording.py @@ -0,0 +1,530 @@ +# mypy: allow-untyped-defs +import functools +import inspect +import itertools +import logging +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, Optional, Union + +import torch +import torch.utils._pytree as pytree + + +log = logging.getLogger(__name__) +trace_shape_events_log = torch._logging.getArtifactLogger( + __name__, "trace_shape_events" +) + + +__all__ = [ + "ShapeEnvEvent", + "record_shapeenv_event", + "replay_shape_env_events", + "FakeTensorMeta", + "shape_env_check_state_equal", + "NotEqualError", +] + +# [Note: Recording ShapeEnv Events] +# ================================= +# +# What is a ShapeEnv event? +# ------------------------- +# We consider a ShapeEnv event every function call (ShapeEnv method or +# independent function) that modifies the state of the ShapeEnv instance. +# Such calls are recorded alongside their positional and keyword arguments, +# so that it may be replayed over a different ShapeEnv instance. +# +# See [Note: ShapeEnv State Equality] for what is considered the state +# of a ShapeEnv instance. +# +# What is it for? +# --------------- +# ShapeEnv events recording is used for reconstructing the ShapeEnv in an +# arbitrary state in time. +# +# Being able to arbitrarily replay events like so is useful, mainly for +# translation validation bisection. i.e. if a ValidationException has been +# raised, find the earliest point in time where the translation validation +# fails. +# +# Besides that, it also allows us to inspect the given instance and, +# for example, check the guards that would actually be issued at that point. +# +# What kind of arguments can be stored in an event? +# ------------------------------------------------- +# There's no specific rule for what cannot be used as an argument. +# That said, pay special attention to the following cases: +# +# 1. Tensor inputs: there are some tests that check whether the inputs +# were garbage collected after execution. These will fail if there's +# an event that is holding a reference to those inputs. +# +# 2. ShapeEnv arguments: if there is an argument of ShapeEnv type, that +# will be automatically replaced by the new given ShapeEnv instance. +# +# 3. SymTypes arguments: they also hold references to ShapeEnv. So, +# whenever we see them, we create a new instance, replacing the +# ShapeEnv reference. +# +# 4. FX nodes: specifically, FX nodes from the FX graph for symbolic +# shapes. That argument must be replaced when replaying the event at +# ShapeEnvEvent.run, since it has to reference a node from the given +# instance, and not from the recorded instance. + + +# Event class for reconstructing ShapeEnv at arbitrary time. +# +# Represents a method call that mutates ShapeEnv in a way that affects the +# issued guards, when ShapeEnv.produce_guards is called. +@dataclass +class ShapeEnvEvent: + # ShapeEnv method. + f: Callable + + # Arguments and keyword arguments called with. + args: Optional[list[Any]] = None + kwargs: Optional[dict[str, Any]] = None + + # List of tracked_fakes at the time the method was called. + tracked_fakes: Optional[list[Any]] = None + + # Name of the captured event. + # Used for special handling of particular methods. + name: Optional[str] = None + + # Replay itself, but using shape_env as self. + def run(self, shape_env=None) -> Any: + from torch.fx.experimental.symbolic_shapes import ( + is_symbolic, + ShapeEnv, + SymTypes, + ) + + # Special handling for the constructor event. + if self.f is ShapeEnv: + assert shape_env is None and self.args is None and self.kwargs is not None + return ShapeEnv(**self.kwargs) + + assert shape_env is not None + args = list(self.args or []) + kwargs = dict(self.kwargs or {}) + + # Replace any argument of type ShapeEnv by the given one. + args, kwargs = pytree.tree_map_only( + ShapeEnv, lambda _: shape_env, (args, kwargs) + ) + + # Replace any argument of type SymTypes by a new instance, + # replacing its ShapeEnv reference. + args, kwargs = pytree.tree_map_only( + lambda x: isinstance(x, SymTypes) and is_symbolic(x), + lambda a: type(a)(a.node.with_shape_env(shape_env)), + (args, kwargs), + ) + + # Converts FX nodes using the mapping argument. + def maybe_convert_node(x: Any) -> Any: + if not isinstance(x, torch.fx.Node): + # Don't do anything to x if it's not an FX node. + return x + + # If, at some point, we created an FX node, it means that translation validation is on. + # It also means we are building an FX graph for symbolic shapes at shape_env.graph, and + # we are tracking node names at shape_env.name_to_node. + assert hasattr(shape_env, "name_to_node") + name_to_node = shape_env.name_to_node # type: ignore[attr-defined] + assert x.name in name_to_node + return name_to_node[x.name] + + # Replaces the value of an specific argument by the result of fn. + def replacearg(index: int, key: str, fn: Callable): + if index < len(args): + args[index] = fn(args[index]) + if key in kwargs: + kwargs[key] = fn(kwargs[key]) + + if self.is_create_fx_call_function(): + # ShapeEnv.create_fx_call_function: + # "args" parameter is a tuple of FX nodes from the FX graph of the old ShapeEnv. + # They must be replaced, since a "call_function" FX node with this tuple as argument + # will be added to the FX graph of the new shape_env. + replacearg( + index=2, + key="args", + fn=lambda args: tuple(maybe_convert_node(a) for a in args), + ) + if self.is_evaluate_expr() or self.is_defer_runtime_assert(): + # ShapeEnv.evaluate_expr and ShapeEnv.guard_or_defer_runtime_assert: + # "fx_node" parameter is an (optional) FX node that represents the evaluate expression. + # They must be replaced, since it will be part of a "call_function" FX node for + # torch._assert, which will be added to the FX graph of the new shape_env. + replacearg(index=3, key="fx_node", fn=maybe_convert_node) + + # Actually call the method with the converted arguments. + return self.f(*args, **kwargs) + + def __str__(self) -> str: + name = self.name if self.name is not None else self.f.__name__ + return f"event: {name} ({self.args}, {self.kwargs})" + + def is_create_fx_call_function(self) -> bool: + return self.name == "_create_fx_call_function" + + def is_evaluate_expr(self) -> bool: + return self.name == "evaluate_expr" + + def is_defer_runtime_assert(self) -> bool: + return self.name == "guard_or_defer_runtime_assert" + + +NEST = 0 + + +# Extracts a ShapeEnv instance inside args and kwargs. +# Specifically, it looks for: +# 1. ShapeEnv arguments +# 2. SymInt, SymFloat, or SymBool arguments +# If we find more than one object of any of the above types, we +# also check that the ShapeEnv instance is the same for all of them. +def _extract_shape_env_and_assert_equal(args, kwargs): + from torch.fx.experimental.symbolic_shapes import is_symbolic, ShapeEnv, SymTypes + + def assert_equal(old: Optional[ShapeEnv], new: ShapeEnv) -> ShapeEnv: + if old is not None: + assert old is new, "call with different ShapeEnv" + return new + + shape_env = None + for val in itertools.chain(args, kwargs.values()): + if isinstance(val, ShapeEnv): + shape_env = assert_equal(shape_env, val) + if isinstance(val, SymTypes) and is_symbolic(val): + shape_env = assert_equal(shape_env, val.node.shape_env) + + return shape_env + + +# Decorator for recording the given function as a replayable event. +# +# This decorator should be used at every function that mutates the state of +# ShapeEnv in some way that affects the resulting issued guards (i.e. when +# ShapeEnv.produce_guards is called). +# +# save_tracked_fakes: saves a snapshot of the TrackedFake list. +# This is used when calling ShapeEnv.produce_guards at arbitrary points in time. +# +# name: the name of the function being recorded. Normally (and by default) this +# is taken from the decorated function but can be set if you need to override +# it. +# +# When to save the list of TrackedFake? +# ===================================== +# We should save the list of TrackedFake whenever the translation validation +# bisection may actually stop and call the produce_guards method at the moment +# right after the recorded function was played. In other words, since the +# bisection bisects through torch._assert calls, we should save in all methods +# that adds a torch._assert call to the symbolic shapes FX graph. +# +# At the moment, there are 2 methods that save the list: +# - ShapeEnv.evaluate_expr +# - ShapeEnv.guard_or_defer_runtime_assert +def record_shapeenv_event( + *, save_tracked_fakes: bool = False, name: Optional[str] = None +) -> Callable: + def decorator(fn: Callable) -> Callable: + assert callable(fn) + args = inspect.getfullargspec(fn).args + assert args and args[0] == "self", ( + "record_shapeenv_event should only wrap methods on ShapeEnv; refactor your " + "code so that it calls into a method on ShapeEnv" + ) + nonlocal name + if name is None: + name = fn.__name__ + + @functools.wraps(fn) + def wrapper(*args, **kwargs): + from torch.fx.experimental.symbolic_shapes import ShapeEnv + + assert isinstance(args[0], ShapeEnv) + + global NEST + + trace_shape_events_log.debug( + "%scall %s(*%r, **%r)", " " * NEST, name, args[1:], kwargs + ) + NEST += 1 + + def retlog(r): + trace_shape_events_log.debug("%s-> %s", " " * (NEST - 1), r) + return r + + shape_env = args[0] + + try: + if not shape_env.should_record_events or shape_env.is_recording: # type: ignore[has-type] + # If ShapeEnv is already recording an event, call the wrapped + # function directly. + # + # NB: here, we skip the check of whether all ShapeEnv instances + # are equal, in favor of a faster dispatch. + return retlog(fn(*args, **kwargs)) + + # Retrieve an instance of ShapeEnv. + # Assumption: the collection of args and kwargs may not reference + # different ShapeEnv instances. + self = _extract_shape_env_and_assert_equal(args, kwargs) + + # If we are calling this function without any ShapeEnv instance + # alive in its arguments, we don't record and call the original. + if self is None: + return retlog(fn(*args, **kwargs)) + + # Otherwise, start recording and call the function. + with self._recording(): + # Take a snapshot of the current tracked_fakes. + tracked_fakes = ( + self._snapshot_tracked_fakes() if save_tracked_fakes else None + ) + # Record the event for 'fn'. + event = ShapeEnvEvent( + fn, + list(args), + kwargs, + tracked_fakes, + name=name, + ) + # Play the event on this ShapeEnv. + # NB: It's important to put the event first, because running + # the event can trigger internal events that must be ordered + # after this event. However, if an exception happens, we do + # NOT want to have the event in the list, so pop it off from + # the record if an error happened + self.events.append(event) + try: + return retlog(event.run(self)) + except Exception: + self.events.pop() + raise + + except Exception: + if not shape_env.should_record_events or shape_env.is_recording: + # If ShapeEnv is disabled or already recording an event, re-raise the exception without logging. + raise + log.error( # noqa: G201 + "failed while running %s(*%s, **%s)", + name, + args[1:], + kwargs, + exc_info=log.isEnabledFor(logging.INFO), + ) + raise + + finally: + NEST -= 1 + + return wrapper + + return decorator + + +# Replays the ShapeEnvEvents list. +# It assumes the first event is the constructor call. +# +# fn: transforms an old FX node into one corresponding to the newly created ShapeEnv. +def replay_shape_env_events(events): + from torch.fx.experimental.symbolic_shapes import ShapeEnv + + constructor_event = events[0] + assert constructor_event.f == ShapeEnv + + # Constructs the new ShapeEnv. + shape_env = constructor_event.run() + + for event in events[1:]: + try: + # Actually replays each event. + # We need to call create_mapping_fn every time, since the node list might + # change after each event is replayed. + event.run(shape_env) + except Exception: + log.error("failed when running event: %s", event) + raise + + return shape_env + + +# FakeTensor metadata. +# This is to be used in place of FakeTensor placeholders when calling +# ShapeEnv.produce_guards. +@dataclass +class FakeTensorMeta: + tensor_size: tuple[Union[int, torch.SymInt], ...] + tensor_stride: tuple[Union[int, torch.SymInt], ...] + tensor_storage_offset: Union[int, torch.SymInt] + is_nested: bool + + def size(self) -> tuple[Union[int, torch.SymInt], ...]: + return self.tensor_size + + def stride(self) -> tuple[Union[int, torch.SymInt], ...]: + return self.tensor_stride + + def storage_offset(self) -> Union[int, torch.SymInt]: + return self.tensor_storage_offset + + def dim(self) -> int: + return len(self.tensor_size) + + @staticmethod + def from_fake(fake) -> "FakeTensorMeta": + return FakeTensorMeta( + fake.size(), fake.stride(), fake.storage_offset(), fake.is_nested + ) + + +# [Note: ShapeEnv State Equality] +# =============================== +# +# What is considered ShapeEnv state? +# ---------------------------------- +# We consider to be the state of a ShapeEnv instance everything that +# is not in the inline tuple inside remove_nonstate_variables function. +# That is: the fields within ShapeEnv that modify the flow of execution +# of the program. +# +# So, for example: the replacements field might influence on how an +# expression is simplified. That, in turn, may result in a guard being +# statically known (i.e. not added). +# +# On the other hand, var_to_stack serves only changes what is printed +# in the screen, i.e. used only for debugging purposes. Therefore, we +# should not consider it when comparing states. +# +# What to do on NotEqualError? +# ---------------------------- +# Here are a few possible causes for getting a NotEqualError raised: +# +# 1. New field that does not belong in the ShapeEnv state. +# For example: log field of type ShapeEnvLoggerAdapter. Different +# ShapeEnv instances will always have different ShapeEnvLoggerAdapter +# instances, i.e. equality comparison would fail. +# Solution: add it to the inlined tuple inside remove_nonstate_variables +# function inside check_equal method. +# +# 2. New field that is not directly comparable across instances. +# For example: guards field of type List[ShapeGuard]. More specifically, +# the ShapeGuard type holds an expression and a stack information +# for debugging purposes. When replaying the even on a new ShapeEnv +# instance, the stack would be different, which would trigger this error. +# Solution: add a special case to the map_value function inside +# check_equal function. +# +# 3. Mutation of ShapeEnv on some not recorded function. +# If a mutation of the state of ShapeEnv happens inside a function +# that is not recorded (or that no caller in the stack is recorded), +# then, the replayed ShapeEnv won't catch that. +# Solution: decorate the function with record_shape_env_event. + + +# Checks whether the state of two ShapeEnv are equal w.r.t. the guards +# returned by ShapeEnv.produce_guards. +def shape_env_check_state_equal(env1, env2, non_state_variable_names, map_value): + # Collect and remove variables that don't necessarily represent the state + # of a ShapeEnv. Note: we copy the dictionary so that we don't modify the + # instance itself. + env1_vars = vars(env1).copy() + env2_vars = vars(env2).copy() + + for v in non_state_variable_names: + if v in env1_vars: + env1_vars.pop(v) + if v in env2_vars: + env2_vars.pop(v) + + # Function for transforming the mismatched values into string. + # Needed, since dict and set entries order might not be the same every time. + def value_to_str(value: Any) -> str: + if isinstance(value, dict): + return ( + "{" + + ", ".join(f"{k}: {value[k]}" for k in sorted(value.keys(), key=str)) + + "}" + ) + if isinstance(value, set): + return "{" + ", ".join(f"{v}" for v in sorted(value)) + "}" + return str(value) + + # Compares env1_vars with env2_vars. + # Here, we allow the value of each field to be mapped, so that we appropriately + # compare the two values. + def compare_vars( + map_value: Callable[[str, Any], Any], + ) -> list[tuple[str, str, str]]: + env1_set, env2_set = set(env1_vars), set(env2_vars) + + # First, compare the set of keys in each vars dictionary. + if env1_set != env2_set: + raise NotEqualError( + "field set mismatch:", + [ + ( + "found unique fields:", + str(sorted(env1_set - env2_set)), + str(sorted(env2_set - env1_set)), + ), + ], + ) + + # Then, sort the keys, and compare the mapped values of each key. + sorted_keys = list(env1_set) + sorted_keys.sort() + + mapped_dict = [ + (k, map_value(k, env1_vars[k]), map_value(k, env2_vars[k])) + for k in sorted_keys + ] + + # Return a list of tuples representing the fields that did not match + # alongside their respective mapped values. + return [ + (f"{k}: values don't match.", value_to_str(val1), value_to_str(val2)) + for k, val1, val2 in mapped_dict + if val1 != val2 + ] + + # Accumulate the mismatching fields. + errors = compare_vars(map_value) + + if len(errors) > 0: + raise NotEqualError("field values don't match:", errors) + + +class NotEqualError(Exception): + def __init__( + self, + msg: str, + mismatched: list[tuple[str, str, str]], + ) -> None: + details = "\n".join( + [ + "\n".join( + [ + f"==> {inner_msg}", + f" > Left: {str1}", + f" > Right: {str2}", + ] + ) + for inner_msg, str1, str2 in mismatched + ] + ) + + super().__init__( + f"""\ +ShapeEnv not equal: {msg} + +{details} +""" + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/refinement_types.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/refinement_types.py new file mode 100644 index 0000000000000000000000000000000000000000..8e92163a2139caab2fd2a690d810f52073e75644 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/refinement_types.py @@ -0,0 +1,16 @@ +class Equality: + def __init__(self, lhs: object, rhs: object): + self.lhs = lhs + self.rhs = rhs + + def __str__(self) -> str: + return f"{self.lhs} = {self.rhs}" + + def __repr__(self) -> str: + return f"{self.lhs} = {self.rhs}" + + def __eq__(self, other: object) -> bool: + if isinstance(other, Equality): + return self.lhs == other.lhs and self.rhs == other.rhs + else: + return False diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/rewriter.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/rewriter.py new file mode 100644 index 0000000000000000000000000000000000000000..2cc902599aeb0a36d8253b0cf8cbece3f6e5ac68 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/rewriter.py @@ -0,0 +1,144 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import ast +import copy +import functools +import inspect +import textwrap +from collections.abc import Callable +from types import FunctionType +from typing import Any, cast, Optional, Union + +import torch +from torch._sources import normalize_source_lines +from torch.fx._symbolic_trace import Tracer +from torch.fx.graph import Graph + + +class AST_Rewriter(ast.NodeTransformer): + """ + Take a FunctionType object representing a `forward` method, then + perform an AST rewrite to swap out nodes that are not symbolically + traceable with a callsite to the FX alternative. + + To support swapping out an AST node, define a new `visit` method on + that node. For more details, see: + https://docs.python.org/3/library/ast.html#ast.NodeTransformer + """ + + # This function checks for new keys added in the globals dict. TorchDynamo + # can insert new keys in the global dict and upset the check. Therefore, put + # a disable here. This function is an optimization pass and not really + # suitable for dynamo tracing anyways. + @torch._dynamo.disable + def rewrite(self, fn: FunctionType): + # Normalize the source lines + sourcelines, _ = inspect.getsourcelines(fn) + sourcelines = normalize_source_lines(sourcelines) + source = "".join(sourcelines) + normalized_str = textwrap.dedent(source) + + # Rewrite the original AST + source_ast = ast.parse(normalized_str) + dest_ast = ast.fix_missing_locations(self.visit(source_ast)) + + # Pull out the compiled function from the newly-created Module + code = compile(dest_ast, "", "exec") + globals_dict = copy.copy(fn.__globals__) + keys_before = set(globals_dict.keys()) + exec(code, globals_dict) + new_keys = list(set(globals_dict.keys()) - keys_before) + assert len(new_keys) == 1 + fn_compiled = globals_dict[new_keys[0]] + + # return the compiled function with the original globals + def change_func_globals(f, globals): + """Based on https://stackoverflow.com/a/13503277/2988730 (@unutbu)""" + # __globals__ is a private member of the function class + # so we have to copy the function, f, all of its member, except f.__globals__ + g = FunctionType( + f.__code__, + globals, + name=f.__name__, + argdefs=f.__defaults__, + closure=f.__closure__, + ) + g = functools.update_wrapper(g, f) + g.__kwdefaults__ = copy.copy(f.__kwdefaults__) # type:ignore[attr-defined] + return g + + # Return the correct FunctionType object + return change_func_globals(fn_compiled, globals=fn.__globals__) + + def visit_Assert(self, node): + """ + Swap out the Assert node (Python's `assert`) with a callsite to the + symbolically-traceable torch._assert function + """ + # Create the Call node + n = ast.parse("torch._assert()", mode="eval") + assert isinstance(n, ast.Expression) + call_node = n.body + assert isinstance(call_node, ast.Call) + msg = node.msg if node.msg else ast.Constant(value="", kind=None) + call_node.args = [node.test, msg] + + # Ensure that the new node conforms to the Python AST grammar + expr_wrapper = ast.Expr(value=call_node) + + # Return the new Call node to signify that we want to use it as + # a replacement for the original _assert node + return ast.copy_location(expr_wrapper, node) + + def visit_AnnAssign(self, node): + """ + Swap out Python's AnnAssign with an Assign node where the annotation function is called. + Example: + Original: + y: Tensor_Type(1,2,3, Dyn) = f2(x) + Output: + y = annotate(f2(x),Tensor_Type((1,2,3,Dyn))) + """ + return ast.Assign( + targets=[node.target], + value=ast.Call( + func=ast.Name(id="annotate", ctx=ast.Load()), + args=[node.value, node.annotation], + keywords=[], + ), + ) + + +class RewritingTracer(Tracer): + def trace( + self, + root: Union[torch.nn.Module, Callable], + concrete_args: Optional[dict[str, Any]] = None, + ) -> Graph: + return super().trace(_rewrite(root), concrete_args) + + +def _rewrite(fn: Union[torch.nn.Module, Callable]) -> Union[torch.nn.Module, Callable]: + if isinstance(fn, torch.nn.Module): + # Rewrite this module's `forward` as well as the `forward`s of + # all of this module's recursive descendents. Return the new, + # rewritten module hierarchy. + def rewrite_module(m: torch.nn.Module): + class RewrittenModule(torch.nn.Module): + def __init__(self, orig): + super().__init__() + for k, v in orig.__dict__.items(): + if isinstance(v, torch.nn.Module): + self.__dict__[k] = copy.copy(rewrite_module(v)) + else: + self.__dict__[k] = copy.copy(v) + + RewrittenModule.forward = AST_Rewriter().rewrite( + cast(FunctionType, m.forward) + ) + return RewrittenModule(m) + + return rewrite_module(fn) + else: + # Rewrite this single free function + return AST_Rewriter().rewrite(cast(FunctionType, fn)) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/schema_type_annotation.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/schema_type_annotation.py new file mode 100644 index 0000000000000000000000000000000000000000..b1b2f1680d64a1ff928a8519dd4d93d61a861a54 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/schema_type_annotation.py @@ -0,0 +1,145 @@ +# mypy: allow-untyped-defs +import inspect +from typing import Any, Optional + +import torch +import torch.fx +from torch._jit_internal import boolean_dispatched +from torch.fx import Transformer +from torch.fx.node import Argument, Target +from torch.fx.operator_schemas import _torchscript_type_to_python_type + + +class AnnotateTypesWithSchema(Transformer): + """ + Use Python function signatures to annotate types for `Nodes` within an FX graph. + This pulls out Python function signatures for: + + 1. Standard `torch.nn` Module calls + 2. `torch.nn.functional` calls + 3. Attribute fetches via `get_attr` + + Example usage: + + m = torchvision.models.resnet18() + + traced = torch.fx.symbolic_trace(m) + + traced = AnnotateTypesWithSchema(traced).transform() + + """ + + def __init__( + self, + module: torch.nn.Module, + annotate_functionals: bool = True, + annotate_modules: bool = True, + annotate_get_attrs: bool = True, + ): + super().__init__(module) + self.annotate_functionals = annotate_functionals + self.annotate_modules = annotate_modules + self.annotate_get_attrs = annotate_get_attrs + + def call_function( + self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any] + ): + python_ret_type = None + if self.annotate_functionals and target.__module__ == "torch.nn.functional": + target_for_analysis = target + if target in boolean_dispatched: + # HACK: `boolean_dispatch` as used in `torch.nn.functional` makes it so that we have + # a 2-way dispatch based on a boolean value. Here we check that the `true` and `false` + # branches of the dispatch have exactly the same signature. If they do, use the `true` + # branch signature for analysis. Otherwise, leave this un-normalized + assert not isinstance(target, str) + dispatched = boolean_dispatched[target] + if_true, if_false = dispatched["if_true"], dispatched["if_false"] + # TODO: can we emit the union of these? What are the implications on TorchScript + # compilation? + if ( + inspect.signature(if_true).return_annotation + != inspect.signature(if_false).return_annotation + ): + return super().call_function(target, args, kwargs) + target_for_analysis = if_true + + python_ret_type = self._extract_python_return_type(target_for_analysis) + + return_proxy = super().call_function(target, args, kwargs) + return_proxy.node.type = ( + return_proxy.node.type if return_proxy.node.type else python_ret_type + ) + return return_proxy + + def call_module( + self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any] + ): + python_ret_type = None + assert isinstance(target, str) + submod = self.fetch_attr(target) + if self.annotate_modules and hasattr(submod.__class__, "__name__"): + classname = submod.__class__.__name__ + if getattr(torch.nn, classname, None) == submod.__class__: + python_ret_type = self._extract_python_return_type(submod.forward) + return_proxy = super().call_module(target, args, kwargs) + return_proxy.node.type = ( + return_proxy.node.type if return_proxy.node.type else python_ret_type + ) + return return_proxy + + def get_attr( + self, + target: torch.fx.node.Target, + args: tuple[Argument, ...], + kwargs: dict[str, Any], + ): + attr_proxy = super().get_attr(target, args, kwargs) + + if self.annotate_get_attrs: + module_itr = self.module + assert isinstance(target, str) + atoms = target.split(".") + for i, atom in enumerate(atoms): + if not hasattr(module_itr, atom): + raise RuntimeError( + f"Node referenced nonextent target {'.'.join(atoms[:i])}!" + ) + module_itr = getattr(module_itr, atom) + + maybe_inferred_ts_type = torch._C._jit_try_infer_type(module_itr) + if maybe_inferred_ts_type.success(): + python_type = _torchscript_type_to_python_type( + maybe_inferred_ts_type.type() + ) + attr_proxy.node.type = ( + python_type if not attr_proxy.node.type else attr_proxy.node.type + ) + + return attr_proxy + + def _extract_python_return_type(self, target: Target) -> Optional[Any]: + """ + Given a Python call target, try to extract the Python return annotation + if it is available, otherwise return None + + Args: + + target (Callable): Python callable to get return annotation for + + Returns: + + Optional[Any]: Return annotation from the `target`, or None if it was + not available. + """ + assert callable(target) + try: + sig = inspect.signature(target) + except (ValueError, TypeError): + return None + + return ( + sig.return_annotation + if sig.return_annotation is not inspect.Signature.empty + else None + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/sym_node.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/sym_node.py new file mode 100644 index 0000000000000000000000000000000000000000..96b44b0aebd4d34eb9dc00fa3bfc0e133fe609bc --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/sym_node.py @@ -0,0 +1,1896 @@ +# mypy: allow-untyped-defs + +from __future__ import annotations + + +""" +This file does three things: +- Contains the definition of SymNode +- Installs all the magic methods into SymBool, SymFloat, SymFloat at import time +- Does not depend on sympy at import time + +As this file is imported from within torch/__init__.py we do not want it to depend on SymPy +to avoid having to load SymPy at import time, as doing so is *very* slow. +""" + + +import builtins +import functools +import inspect +import itertools +import logging +import math +import operator +import sys +from functools import lru_cache, update_wrapper +from typing import Optional, TYPE_CHECKING, Union + +import torch +import torch._logging.structured as structured + +# NB: The sym_* functions are used via getattr() and must be imported here. +from torch import ( # noqa: F401 + sym_float, + sym_ite, + sym_max, + sym_min, + sym_not, + SymBool, + SymFloat, + SymInt, +) +from torch._logging import dtrace_structured + + +if TYPE_CHECKING: + from torch.fx.experimental.symbolic_shapes import ShapeEnv + +log = logging.getLogger(__name__) +sym_node_log = torch._logging.getArtifactLogger(__name__, "sym_node") + + +__all__ = ["SymNode", "method_to_operator", "magic_methods", "DynamicInt"] + + +from torch.types import py_sym_types as SymTypes + + +def _to_symtype(t): + if t is bool: + return SymBool + if t is int: + return SymInt + if t is float: + return SymFloat + return t + + +# TODO: An incomplete list +# 1. Set variables to be equal when we do equality +# 2. Specialize on 0/1 when we do subtraction +class SymNode: + """ + This is a type erased SymInt/SymFloat which we use to do actual operations. + End users don't touch this. Magic methods are NOT defined on this object. + """ + + # Note [optimized_summation]: indicates that SymNode is an Add expression of the form + # a + b + c + d... etc where all terms are unique symbols. This allows us to do some optimizations + # for common patterns see _optimized_add. + + # The unfortunate reason we have this here is because sympy sets __slots__ = () for add expression, + # so we cannot add the attribute directly to the sympy expression. Furthermore, we cannot use it as + # a weak dictionary key either! So instead, we attach the attribute here to the SymNode. + _optimized_summation: bool = False + + def __init__( + self, + expr, + shape_env, + pytype, + hint: Optional[Union[int, float, bool]], + constant=None, + fx_node=None, + optimized_summation=False, + ): + self._expr = expr + self.shape_env = shape_env + self.pytype = pytype + self._optimized_summation = optimized_summation + + # What's the difference between hint and constant? + # + # - A constant is known to be invariant across invocations of the model; + # it will always be this value. We only really know this when we + # encounter an honest-to-goodness literal (when wrapping it into + # a SymNode, we set constant.) Most of the time, constant is None + # + # - A hint is a *particular* value from the particular run we are + # tracing, but it may vary the next time around. It's useful to + # keep this around, as if we need a concrete value from a SymNode, + # we will return the hint and guard on the expression that produced + # it giving the same hint next time around. The hint is not + # guaranteed to be set either: if you have an unbacked SymNode, + # there won't be any hint; it was the result of some tensor-dependent + # computation, but we don't know what it actually is because we + # haven't actually run the tensor computation. + # + # If _hint is None, we will query maybe_evaluate_static(compute_hint=True) + # in hopes that we've learned enough about the unbacked symints to + # discharge the hint; otherwise, you're likely to just error out. + # + # (A previous version of this system had some optimizations to only + # recompute when it was possible we had learned enough about the + # unbacked symint that a hint was now possible, but as we added more + # potential refinements to unbacked symints this got harder to keep + # in sync, so we've deleted it for now.) + + def compute_hint(): + from torch.fx.experimental.symbolic_shapes import has_free_unbacked_symbols + + # This occasionally gets exercised by, e.g., + # convert_shape_to_symint. It's just a nicety so you don't HAVE + # to have a correct hint on hand when making a SymNode. + # Don't attempt to compute for unbacked, this can be quite + # expensive. + if has_free_unbacked_symbols(self.expr): + return None + hint = self.shape_env._maybe_evaluate_static(self.expr, compute_hint=True) + if hint is not None: + hint = self.pytype(hint) if not isinstance(hint, SymTypes) else hint + return hint + + if hint is not None: + assert type(hint) is pytype or type(hint) is _to_symtype(pytype), ( + "Cannot create SymNode of type " + f"{pytype} with incompatible hint of type {type(hint)}" + ) + if self.shape_env and self.shape_env._translation_validation_enabled: + # This is technically not TV, but this assert is expensive so + # let's only do it when we're already doing expensive things + computed_hint = compute_hint() + assert hint == computed_hint, ( + f"{hint} != {computed_hint} (for {self.expr})" + ) + else: + hint = compute_hint() + self._hint = hint + self.constant: Optional[Union[int, float, bool]] = constant + + # Record the FX node of the current node if we are doing translation + # validation. They will be used for building the input assertions for + # the translation validation problem. + tx_validation_en = ( + self.shape_env and self.shape_env._translation_validation_enabled + ) + self.fx_node = tx_validation_en and fx_node + + def with_shape_env(self, shape_env: ShapeEnv) -> SymNode: + return SymNode( + self._expr, shape_env, self.pytype, self._hint, self.constant, self.fx_node + ) + + def _value_eq(self, other: SymNode) -> bool: + # Purposely don't include the shape_env in the eq. + return ( + self._expr == other._expr + and self.pytype == other.pytype + and self._hint == other._hint + and self.constant == other.constant + and self.fx_node == other.fx_node + ) + + def _value_hash(self) -> int: + # Purposely don't include the shape_env in the hash. + return hash((self._expr, self.pytype, self._hint, self.constant, self.fx_node)) + + @property + def expr(self): + return self.shape_env.replace(self._expr) + + @property + def hint(self): + return self._hint + + def has_hint(self): + return self._hint is not None + + def require_hint(self, fallback=None): + from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols + + if self._hint is None: + if fallback is not None: + # Say we have some expr like 2*u0 + s0 + # The hint will be None, since the expr contains at least 1 unbacked. + # We will: + # - replace every backed free symbol with its corresponding hint + # - replace every unbacked free symbol with the fallback + # - regenerate the expression with those symbol replacements + # Note: this is not really complete either, since right now + # this logic does not take into account any value ranges + # for the unbacked symints, we may need to beef it up at some point. + unbacked_symbols = free_unbacked_symbols(self.expr) + replacements = { + s: 4096 if s in unbacked_symbols else self.shape_env.var_to_val[s] + for s in self.expr.free_symbols + } + return self.expr.xreplace(replacements) + # NB: we expect this to raise + return self.shape_env.size_hint(self.expr) + return self._hint + + def maybe_as_int(self): + if self.expr.is_number: + return int(self.expr) + else: + return None + + # NB: This does conversions, not sure if this is good or not + def maybe_as_float(self): + import sympy + + if isinstance(self.expr, sympy.Float): + return float(self.expr) + else: + return None + + def maybe_as_bool(self): + import sympy + + if self.expr is sympy.true: + return True + elif self.expr is sympy.false: + return False + else: + return None + + def is_int(self): + return self.pytype is int + + def is_float(self): + return self.pytype is float + + def is_bool(self): + return self.pytype is bool + + def is_nested_int(self): + # Unbacked SymInts cannot be nested int today + return ( + self._hint is not None + and isinstance(self._hint, SymInt) + and self._hint.node.is_nested_int() + ) + + def wrap_int(self, num): + assert type(num) is int + import sympy + + return SymNode( + sympy.Integer(num), self.shape_env, int, num, constant=num, fx_node=num + ) + + def wrap_float(self, num): + assert type(num) is float + import sympy + + return SymNode( + sympy.Float(num), self.shape_env, float, num, constant=num, fx_node=num + ) + + def wrap_bool(self, num): + assert type(num) is bool + import sympy + + return SymNode( + sympy.true if num else sympy.false, + self.shape_env, + bool, + num, + constant=num, + fx_node=num, + ) + + def clone(self): + return self + + def str(self): + return f"{self.expr}" + + def __str__(self): + return self.str() + + def __repr__(self): + rep = [ + f"SymNode({self._expr}, shape_env={self.shape_env}, pytype={self.pytype}", + ] + if self._hint is not None: + rep.append(f"hint={self._hint}") + if self.constant is not None: + rep.append(f"constant={self.constant}") + if self.fx_node is not None: + rep.append(f"fx_node={self.fx_node}") + return ", ".join(rep) + ")" + + def _graph_repr(self) -> builtins.str: + # Representation used by GraphModule to create a pythonic version of a graph + return self.str() + + # These methods call the metaprogrammed methods, they're hand written + # here so we get good stack traces + def abs(self) -> SymNode: + return self._abs() # type: ignore[attr-defined] + + def pos(self) -> SymNode: + return self._pos() # type: ignore[attr-defined] + + def round(self, ndigits=None) -> SymNode: + return self._round(ndigits) # type: ignore[attr-defined] + + def trunc(self) -> SymNode: + return self._trunc() # type: ignore[attr-defined] + + def add(self, other) -> SymNode: + return self._add(other) # type: ignore[attr-defined] + + def sub(self, other) -> SymNode: + return self._sub(other) # type: ignore[attr-defined] + + def mul(self, other) -> SymNode: + return self._mul(other) # type: ignore[attr-defined] + + def mod(self, other) -> SymNode: + return self._mod(other) # type: ignore[attr-defined] + + def float_pow(self, other) -> SymNode: + return self._float_pow(other) # type: ignore[attr-defined] + + def pow_by_natural(self, other) -> SymNode: + return self._pow_by_natural(other) # type: ignore[attr-defined] + + def and_(self, other) -> SymNode: + return self._and_(other) # type: ignore[attr-defined] + + def or_(self, other) -> SymNode: + return self._or_(other) # type: ignore[attr-defined] + + def float_truediv(self, other) -> SymNode: + return self._float_truediv(other) # type: ignore[attr-defined] + + def int_truediv(self, other) -> SymNode: + return self._int_truediv(other) # type: ignore[attr-defined] + + def int_floordiv(self, other) -> SymNode: + return self._int_floordiv(other) # type: ignore[attr-defined] + + def lshift(self, other) -> SymNode: + return self._lshift(other) # type: ignore[attr-defined] + + def rshift(self, other) -> SymNode: + return self._rshift(other) # type: ignore[attr-defined] + + def sym_not(self) -> SymNode: # noqa: F811 + return self._sym_not() # type: ignore[attr-defined] + + def eq(self, other) -> SymNode: + return self._eq(other) # type: ignore[attr-defined] + + def ne(self, other) -> SymNode: + return self._ne(other) # type: ignore[attr-defined] + + def gt(self, other) -> SymNode: + return self._gt(other) # type: ignore[attr-defined] + + def lt(self, other) -> SymNode: + return self._lt(other) # type: ignore[attr-defined] + + def le(self, other) -> SymNode: + return self._le(other) # type: ignore[attr-defined] + + def ge(self, other) -> SymNode: + return self._ge(other) # type: ignore[attr-defined] + + def floor(self) -> SymNode: + return self._floor() # type: ignore[attr-defined] + + def is_integer(self) -> SymNode: + return self._is_integer() # type: ignore[attr-defined] + + def sym_float(self) -> SymNode: # noqa: F811 + return self._sym_float() # type: ignore[attr-defined] + + def sym_int(self) -> SymNode: + return self._sym_int() # type: ignore[attr-defined] + + def ceil(self) -> SymNode: + return self._ceil() # type: ignore[attr-defined] + + def neg(self) -> SymNode: + return self._neg() # type: ignore[attr-defined] + + def sym_min(self, other) -> SymNode: # noqa: F811 + return self._sym_min(other) # type: ignore[attr-defined] + + def sym_max(self, other) -> SymNode: # noqa: F811 + return self._sym_max(other) # type: ignore[attr-defined] + + def sym_ite(self, then_val, else_val) -> SymNode: + return self._sym_ite(then_val, else_val) # type: ignore[attr-defined] + + def is_contiguous(self, sizes, strides) -> SymNode: + return self._is_contiguous(sizes, strides) # type: ignore[attr-defined] + + def is_channels_last_contiguous_2d(self, sizes, strides) -> SymNode: + return self._is_channels_last_contiguous_2d(sizes, strides) # type: ignore[attr-defined] + + def is_channels_last_contiguous_3d(self, sizes, strides) -> SymNode: + return self._is_channels_last_contiguous_3d(sizes, strides) # type: ignore[attr-defined] + + def is_channels_last_strides_2d(self, sizes, strides) -> SymNode: + return self._is_channels_last_strides_2d(sizes, strides) # type: ignore[attr-defined] + + def is_channels_last_strides_3d(self, sizes, strides) -> SymNode: + return self._is_channels_last_strides_3d(sizes, strides) # type: ignore[attr-defined] + + def is_non_overlapping_and_dense_indicator(self, sizes, strides) -> SymNode: + return self._is_non_overlapping_and_dense_indicator(sizes, strides) # type: ignore[attr-defined] + + # Make C++ happy + def sym_or(self, other): + return self.or_(other) + + def sym_and(self, other): + return self.and_(other) + + # Integer bitwise ops + def bitwise_and(self, other): + return self._bitwise_and(other) # type: ignore[attr-defined] + + def bitwise_or(self, other): + return self._bitwise_or(other) # type: ignore[attr-defined] + + def bitwise_xor(self, other): + return self._bitwise_xor(other) # type: ignore[attr-defined] + + # There is no int_truediv available from C++ + def truediv(self, other): + return self.float_truediv(other) + + def floordiv(self, other) -> SymNode: + return self.int_floordiv(other) + + # We didn't bind integer pow in C++ + def pow(self, other): + return self.float_pow(other) + + def is_non_overlapping_and_dense(self, sizes, strides): + return self.is_non_overlapping_and_dense_indicator(sizes, strides).eq( + to_node(self, 1) + ) # type: ignore[attr-defined] + + def int_(self): + return self.guard_int("", 0) # NB: uses Python backtrace + + # This one is currently done by hand, but if we add other variadic + # functions consider factoring it out to be metaprogrammed too. Note that + # some load bearing logic is directly in torch.sym_sum + + def sym_sum(self, args) -> SymNode: + import sympy + + # Inner impl + from torch.fx.experimental.proxy_tensor import ( + get_proxy_mode, + handle_sym_dispatch, + ) + + if get_proxy_mode(): + return to_node( + self, + handle_sym_dispatch( + torch.sym_sum, + (tuple(wrap_node(a) for a in args),), + {}, + ), + ) + exprs = [a.expr for a in args] + out = sympy.Add(*exprs) + + size_hints = [] + out_hint = None + for a in args: + if a.hint is None: + break + size_hints.append(a.hint) + else: + out_hint = sum(size_hints) + + fx_node, _ = self.shape_env._create_fx_call_function( + torch.sym_sum, (tuple(a.fx_node for a in args),) + ) + + # NB: Only for integers! + return SymNode(out, self.shape_env, int, out_hint, fx_node=fx_node) + + def evaluate(self, size_oblivious=False): + return self.shape_env.evaluate_sym_node(self, size_oblivious) + + # You can manually trigger a guard with this function + def guard_int(self, file, line): + # TODO: use the file/line for some useful diagnostic on why a + # guard occurred + r = self.evaluate() + try: + return int(r) + except Exception: + log.warning("Failed to convert to int: %s", r) + raise + + def guard_float(self, file, line): + # TODO: use the file/line for some useful diagnostic on why a + # guard occurred + r = self.evaluate() + try: + return float(r) + except Exception: + log.warning("Failed to convert to float: %s", r) + raise + + def guard_bool(self, file, line): + # TODO: use the file/line for some useful diagnostic on why a + # guard occurred + r = self.evaluate() + try: + return bool(r) + except Exception: + log.warning("Failed to convert to bool: %s", r) + raise + + def expect_true(self, file, line): + from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols + + if ( + self.has_hint() + and not free_unbacked_symbols(self.expr) + and not self.shape_env.prefer_deferred_runtime_asserts_over_guards + ): + # OK to generate guards + return self.guard_bool(file, line) + # Generate a deferred runtime assert (this might actually end up doing + # a regular guard if we can!) + # TODO: file/line here is very important, because the assert has been + # deferred so you can't backtrace easily + return self.shape_env.guard_or_defer_runtime_assert( + self.expr, f"{file}:{line}", fx_node=self.fx_node + ) + + def statically_known_true(self, file, line): + from torch.fx.experimental.symbolic_shapes import statically_known_true + + assert self.is_bool() + return statically_known_true(SymBool(self)) + + def guard_size_oblivious(self, file, line): + """ + Like guard_bool, but if we encounter unbacked symbols, if those symbols + are size-like, we will treat them as >= 2 for the purposes of the analysis. + + This CHANGES the runtime semantics, but all size-oblivious sites have been + audited to ensure that the runtime semantics don't change in a material way. + Acceptable runtime semantic changes are, e.g., squeeze() no longer dropping + an unbacked one size, or a tensor reporting as non-contiguous even if it's + contiguous if it would have been reported contiguous due to being empty. + """ + # TODO: use the file/line for some useful diagnostic on why a + # guard occurred + r = self.evaluate(size_oblivious=True) + try: + return bool(r) + except Exception: + log.warning("Failed to convert to bool: %s", r) + raise + + def guard_or_false(self, file, line): + from torch.fx.experimental.symbolic_shapes import guard_or_false + + assert self.is_bool() + return guard_or_false(SymBool(self)) + + def guard_or_true(self, file, line): + from torch.fx.experimental.symbolic_shapes import guard_or_true + + assert self.is_bool() + return guard_or_true(SymBool(self)) + + def bool_(self): + return self.guard_bool("", 0) + + def is_symbolic(self): + return True + + def nested_int(self): + return None + + def is_constant(self): + return False + + +class _DynamicScalar: + def __new__(cls, *args): + if cls is _DynamicScalar: + raise TypeError("_DynamicScalar is an abstract base class, use DynamicInt.") + return super().__new__(cls, *args) + + +class DynamicInt(_DynamicScalar, int): + """ + User API for marking dynamic integers in `torch.compile`. + Intended to be compatible with both compile and eager mode. + + Example usage:: + + fn = torch.compile(f) + x = DynamicInt(4) + fn(x) # compiles x as a dynamic integer input; returns f(4) + """ + + def __new__(cls, val): + assert isinstance(val, int) + obj = super().__new__(cls, int(val)) + return obj + + def __repr__(self): + return f"DynamicInt({self.real})" + + def __floordiv__(self, other): # // was casting to int without these overrides? + return DynamicInt(self.real // other) + + def __rfloordiv__(self, other): + return DynamicInt(other // self.real) + + +# TODO: this probably needs the sizes-strides eval functions +METHOD_TO_OPERATOR = { + "pos": operator.pos, + "abs": operator.abs, + "add": operator.add, + "and": operator.and_, + "bitwise_and": operator.and_, + "ceil": math.ceil, + "eq": operator.eq, + "floor": math.floor, + "trunc": math.trunc, + "int_floordiv": operator.floordiv, + "ge": operator.ge, + "gt": operator.gt, + "is_integer": lambda x: x.is_integer(), + "le": operator.le, + "lshift": operator.lshift, + "lt": operator.lt, + "mod": operator.mod, + "mul": operator.mul, + "ne": operator.ne, + "neg": operator.neg, + "or": operator.or_, + "bitwise_or": operator.or_, + "bitwise_xor": operator.xor, + "float_pow": operator.pow, + "pow_by_natural": operator.pow, + "round": builtins.round, + "rshift": operator.rshift, + "sub": operator.sub, + "sym_float": sym_float, + "sym_ite": sym_ite, + "sym_max": sym_max, + "sym_min": sym_min, + "sym_not": sym_not, + "float_truediv": operator.truediv, + "int_truediv": operator.truediv, +} + +unary_magic_methods = { + "abs", + "sym_float", + "sym_int", + "ceil", + "floor", + "neg", + "sym_not", + "pos", + "trunc", +} + + +# Adding math ops: sqrt, cos, sin, ... +def _get_sym_node_fn(name): + def fn(self): + return getattr(self, f"_sym_{name}")() + + return fn + + +math_op_names = ( + "sqrt", + "cos", + "cosh", + "sin", + "sinh", + "tan", + "tanh", + "asin", + "acos", + "atan", + "log2", +) +for name in math_op_names: + sym_name = f"sym_{name}" + priv_sym_name = f"_{sym_name}" + setattr(SymNode, sym_name, _get_sym_node_fn(name)) + METHOD_TO_OPERATOR[sym_name] = getattr(torch, priv_sym_name) + unary_magic_methods.add(sym_name) + __all__.append(sym_name) + + +# Unary methods that are not magic methods +unary_nonmagic_methods = { + "is_integer", +} + +unary_methods = unary_magic_methods | unary_nonmagic_methods + +# Most methods are only registered on SymInt and SymFloat +# Some methods are only be registered on SymBool +only_bool_magic_methods = {"and", "or", "sym_not", "sym_ite"} +# Methods that implicitly convert SymBool into SymInt +bool_becomes_int_magic_methods = {"add", "sub", "mul"} +# Methods that are also on SymBool, in addition to on SymInt and SymFloat +also_bool_magic_methods = {"eq"} +bool_magic_methods = only_bool_magic_methods | also_bool_magic_methods + +# Methods that are only for float +only_float_magic_methods = {"is_integer", "round", "sym_int", "sym_log2"} + + +magic_methods_on_operator_with_trailing_underscore = {"and", "or"} +# remap necessary because an op name can have a bitwise and boolean implementation +bitwise_ops = {"bitwise_and": "and", "bitwise_or": "or", "bitwise_xor": "xor"} + + +always_float_magic_methods = {"int_truediv", "float_truediv", "sym_float", "float_pow"} + +for name in math_op_names: + sym_name = f"sym_{name}" + always_float_magic_methods.add(sym_name) + + +always_int_magic_methods = {"ceil", "floor", "trunc", "pow_by_natural"} +always_bool_magic_methods = { + "eq", + "ne", + "gt", + "lt", + "le", + "ge", + "and", + "or", + "sym_not", + "is_non_overlapping_and_dense", + "is_integer", +} + +# Methods that have a `__foo__` as well as `__rfoo__` + + +def _sympy_float_truediv(a, b): + from torch.utils._sympy.functions import FloatTrueDiv + + return FloatTrueDiv(a, b) + + +def _sympy_int_truediv(a, b): + from torch.utils._sympy.functions import IntTrueDiv + + return IntTrueDiv(a, b) + + +def _sympy_floordiv(a, b): + from torch.utils._sympy.functions import FloorDiv + + return FloorDiv(a, b) + + +def _sympy_mod(a, b): + from torch.utils._sympy.functions import Mod, PythonMod + + if a.is_nonnegative and b.is_nonnegative: + return Mod(a, b) + else: + return PythonMod(a, b) + + +def _sympy_pow_by_natural(a, b): + from torch.utils._sympy.functions import PowByNatural + + return PowByNatural(a, b) + + +def _sympy_float_pow(a, b): + from torch.utils._sympy.functions import FloatPow + + return FloatPow(a, b) + + +def _sympy_and(a, b): + import sympy + + return sympy.And(a, b) + + +def _sympy_or(a, b): + import sympy + + return sympy.Or(a, b) + + +def _sympy_lshift(a, b): + from torch.utils._sympy.functions import LShift + + return LShift(a, b) + + +def _sympy_rshift(a, b): + from torch.utils._sympy.functions import RShift + + return RShift(a, b) + + +def _binary_search_insert_arg(ordered_args, new_arg): + """ + If new_arg is found in ordered_args None is returned, else the new + ordered_args with new_arg inserted + """ + if len(ordered_args) == 0: + return [new_arg] + + from sympy.core.basic import _args_sortkey as sort_key, Basic + + # Fast path when new_arg > ordered_args[-1]. + if sort_key(ordered_args[-1]) < sort_key(new_arg): + return ordered_args + [new_arg] + + # Fast path when new_arg < ordered_args[0]. + if sort_key(ordered_args[0]) > sort_key(new_arg): + return [new_arg] + ordered_args + + low, high = 0, len(ordered_args) - 1 + + while low <= high: + mid = (low + high) // 2 + compare_result = Basic.compare(ordered_args[mid], new_arg) + if compare_result == 0: + return None + elif compare_result < 0: + low = mid + 1 + else: + high = mid - 1 + + ordered_args.insert(low, new_arg) + return ordered_args + + +def _optimized_add( + lhs, rhs, lhs_is_optimized_summation=False, rhs_is_optimized_summation=False +): + """ + Custom optimization for Add used to optimize incremental binary summations of certain properties. The idea + is when we know the expression is a summation of unique symbols all we need to know is the correct order of symbols, + and no other optimizations are needed. We pass evaluate=false, with the correct order of args and save the following. + 1. Avoid running other optimizations when the Add is constructed. + 2. Manually figure out the order of the args for the new expression in log(n) comparisons instead of nLog(n) + (comparing terms is expensive and shows in the profiles). + The function returns a tuple of (1) a boolean that indicates whether the output is a summation of unique symbols, + (2) the result sympy expression. + """ + import sympy + from sympy.core.basic import _args_sortkey as sortkey + + def make_optimized(ordered_args): + assert ordered_args is not None + result = sympy.Add(*ordered_args, evaluate=False) + return (True, result) + + from torch.utils._sympy.functions import _is_symbols_binary_summation + + lhs_is_optimized_summation |= _is_symbols_binary_summation(lhs) + rhs_is_optimized_summation |= _is_symbols_binary_summation(rhs) + + if lhs_is_optimized_summation and rhs_is_optimized_summation: + # (a0+a1..) + (a2+a3..) => (a0+a1+a2+a3) + if sortkey(lhs._args[-1]) < sortkey(rhs._args[0]): + return make_optimized(lhs._args + rhs._args) + # (a2+a3..) + (a0+a1..) => (a0+a1+a2+a3) + if sortkey(lhs._args[0]) > sortkey(rhs._args[-1]): + return make_optimized(rhs._args + lhs._args) + + # (a1+a3) + (a0+a2) => (a0+a1+a2+a3) + if len(lhs._args) <= 2 and len(rhs._args) <= 2: + new_args = list(lhs._args) + for a in rhs._args: + new_args = _binary_search_insert_arg(new_args, a) + if new_args is None: + break + # None means an element already exists. + if new_args is not None: + return make_optimized(new_args) + + # (a0+a2) + a1 => (a0+a1+a2) + if lhs_is_optimized_summation and rhs.is_symbol: + new_args = _binary_search_insert_arg(list(lhs._args), rhs) + # None means an element already exists. + if new_args is not None: + return make_optimized(new_args) + + # a1 + (a0+a2)=> (a0+a1+a2) + if rhs_is_optimized_summation and lhs.is_symbol: + new_args = _binary_search_insert_arg(list(rhs._args), lhs) + # None means an element already exists. + if new_args is not None: + return make_optimized(new_args) + + result = sympy.Add(lhs, rhs) + return (_is_symbols_binary_summation(result), result) + + +def _bitwise_and(a, b): + from torch.utils._sympy.functions import BitwiseFn_bitwise_and + + return BitwiseFn_bitwise_and(a, b) + + +def _bitwise_or(a, b): + from torch.utils._sympy.functions import BitwiseFn_bitwise_or + + return BitwiseFn_bitwise_or(a, b) + + +def _bitwise_xor(a, b): + from torch.utils._sympy.functions import BitwiseFn_bitwise_xor + + return BitwiseFn_bitwise_xor(a, b) + + +reflectable_magic_methods = { + "add": operator.add, + "sub": operator.sub, + "mul": operator.mul, + "mod": _sympy_mod, + "pow_by_natural": _sympy_pow_by_natural, + "float_pow": _sympy_float_pow, + "and": _sympy_and, + "bitwise_and": _bitwise_and, + "or": _sympy_or, + "bitwise_or": _bitwise_or, + "bitwise_xor": _bitwise_xor, + "float_truediv": _sympy_float_truediv, + "int_truediv": _sympy_int_truediv, + "int_floordiv": _sympy_floordiv, + "lshift": _sympy_lshift, + "rshift": _sympy_rshift, +} + + +def _floor_ceil_helper(a, fn): + import sympy + + if isinstance(a, sympy.Mul): + aa = a.args + if len(aa) == 2 and isinstance(aa[0], sympy.Float) and aa[1].is_integer: + coef = sympy.Integer(aa[0]) + if aa[0] == coef: # structural equality test + return coef * aa[1] + if ( + isinstance(a, sympy.Float) + and a == sympy.Integer(a) + or isinstance(a, sympy.Integer) + ): + return sympy.Integer(a) + return fn(a) + + +def _sympy_floor(a): + from torch.utils._sympy.functions import FloorToInt + + return FloorToInt(a) + + +# NB: this is Python trunc semantics which returns an int. Do NOT use this to +# represent torch.trunc (which is float to float) +def _sympy_trunc(a): + from torch.utils._sympy.functions import TruncToInt + + return TruncToInt(a) + + +def _sympy_ceil(a): + from torch.utils._sympy.functions import CeilToInt + + return CeilToInt(a) + + +def _sympy_eq(a, b): + import sympy + + return sympy.Eq(a, b) + + +def _sympy_ne(a, b): + import sympy + + return sympy.Ne(a, b) + + +def _sympy_gt(a, b): + import sympy + + return sympy.Gt(a, b) + + +def _sympy_lt(a, b): + import sympy + + return sympy.Lt(a, b) + + +def _sympy_le(a, b): + import sympy + + return sympy.Le(a, b) + + +def _sympy_ge(a, b): + import sympy + + return sympy.Ge(a, b) + + +def _sympy_min(a, b): + from torch.utils._sympy.functions import Min + + return Min(a, b) + + +def _sympy_max(a, b): + from torch.utils._sympy.functions import Max + + return Max(a, b) + + +def _sympy_ite(a, t, f): + import sympy + + return sympy.Piecewise((t, a), (f, True)) + + +current_module = sys.modules[__name__] + + +def _get_sym_math_fn(name): + def fn(a): + import torch.utils._sympy.functions + + return getattr(torch.utils._sympy.functions, f"OpaqueUnaryFn_{name}")(a) + + return fn + + +for name in math_op_names: + priv_sympy_name = f"_sympy_{name}" + fn = _get_sym_math_fn(name) + fn.__qualname__ = fn.__name__ = priv_sympy_name + setattr(current_module, priv_sympy_name, fn) + +del fn, name, priv_sympy_name # type: ignore[possibly-undefined] + + +def _sympy_abs(a): + import sympy + + return sympy.Abs(a) + + +def _sympy_round(number, ndigits=None): + from torch.utils._sympy.functions import RoundDecimal, RoundToInt + + if ndigits is None: + return RoundToInt(number) + else: + return RoundDecimal(number, ndigits) + + +def _sympy_sym_float(a): + from torch.utils._sympy.functions import ToFloat + + # NB: Cannot use a * 1.0 here, because 0 * 1.0 is 0 which incorrectly + # reports that it is an integer + return ToFloat(a) + + +def _sympy_is_integer(a): + import sympy + + from torch.utils._sympy.functions import ToFloat + + return sympy.Eq(ToFloat(sympy.floor(a)), a) + + +magic_methods = { + **reflectable_magic_methods, + "sym_not": operator.invert, + "pos": operator.pos, + "eq": _sympy_eq, + "ne": _sympy_ne, + "gt": _sympy_gt, + "lt": _sympy_lt, + "le": _sympy_le, + "ge": _sympy_ge, + "floor": _sympy_floor, + "trunc": _sympy_trunc, + "sym_float": _sympy_sym_float, + "ceil": _sympy_ceil, + "neg": operator.neg, + "sym_min": _sympy_min, + "sym_max": _sympy_max, + "sym_ite": _sympy_ite, + "abs": _sympy_abs, + "round": _sympy_round, + "is_integer": _sympy_is_integer, +} + + +for name in math_op_names: + sym_name = f"sym_{name}" + magic_methods[sym_name] = getattr(current_module, f"_sympy_{name}") + +del name, sym_name, math_op_names, current_module # type: ignore[possibly-undefined] + + +def sympy_is_contiguous(sizes, strides): + dim = len(sizes) + return sympy_is_contiguous_generic(sizes, strides, list(range(dim - 1, -1, -1))) + + +def sympy_is_contiguous_generic(sizes, strides, dim_order): + import sympy + + dim = len(sizes) + + if len(dim_order) != dim: + return sympy.false + + is_contiguous = sympy.true + z = sympy.S.One + # Contiguous if the strides make sense (or the dim is size 1) + for d in dim_order: + is_contiguous &= sympy.Eq(sizes[d], sympy.S.One) | sympy.Eq(strides[d], z) + z *= sizes[d] + # OR if any size is zero + for d in range(dim): + is_contiguous |= sympy.Eq(sizes[d], sympy.S.Zero) + return is_contiguous + + +# NB: There is a TODO in C++ to allow omitting the batch dim. If that +# happens you will need to refactor this + + +def sympy_is_channels_last_contiguous_2d(sizes, strides): + return sympy_is_contiguous_generic(sizes, strides, [1, 3, 2, 0]) + + +def sympy_is_channels_last_contiguous_3d(sizes, strides): + return sympy_is_contiguous_generic(sizes, strides, [1, 4, 3, 2, 0]) + + +def sympy_is_channels_last_strides_generic(sizes, strides, dim_order): + import sympy + + from torch.utils._sympy.functions import Max + + dim = len(sizes) + + if dim != len(dim_order): + return sympy.false + + m = sympy.S.Zero + r = sympy.true + + # special case for trivial C dimension. default to NCHW + r &= sympy.Ne(strides[1], 0) + + for d in dim_order: + r &= sympy.Ne(sizes[d], 0) & (strides[d] >= m) + # Fallback to NCHW as default layout for ambiguous cases + # This is the flaw of implicit memory_format from strides. + # N111 tensor with identical strides for size 1 dimension; + # Two cases could lead us here: + # a. N111 contiguous Tensor ([N,1,1,1]@[1,1,1,1]) + # b. N11W contiguous Tensor sliced on the W-dimension. + # ([N,1,1,1]@[W,W,W,W]) + if d == 0: + r &= sympy.Ne(m, strides[1]) + # This is necessary to: + # 1. distinguish the memory_format of N1H1; + # [H, 1, 1, 1] channels_last stride + # [H, H, 1, 1] contiguous stride + # 2. permutation of 1C1W: + # [1, C, 1, H]@[HC, H, H, 1] transpose(1, 3) + # [1, H, 1, C]@[HC, 1, H, H] shouldn't be identified as + # channels_last + m = strides[d] * Max(sizes[d], 1) + + return r + + +def sympy_is_channels_last_strides_2d(sizes, strides): + return sympy_is_channels_last_strides_generic(sizes, strides, [1, 3, 2, 0]) + + +def sympy_is_channels_last_strides_3d(sizes, strides): + return sympy_is_channels_last_strides_generic(sizes, strides, [1, 4, 3, 2, 0]) + + +def _sympy_is_non_overlapping_and_dense_indicator(sizes, strides): + from torch.utils._sympy.functions import IsNonOverlappingAndDenseIndicator + + return IsNonOverlappingAndDenseIndicator(*sizes, *strides) + + +sizes_strides_methods = { + # TODO: These could also be done with indicators, maybe it is better + # for reasoning to do it that way + "is_contiguous": sympy_is_contiguous, + "is_channels_last_contiguous_2d": sympy_is_channels_last_contiguous_2d, + "is_channels_last_contiguous_3d": sympy_is_channels_last_contiguous_3d, + "is_channels_last_strides_2d": sympy_is_channels_last_strides_2d, + "is_channels_last_strides_3d": sympy_is_channels_last_strides_3d, + "is_non_overlapping_and_dense_indicator": _sympy_is_non_overlapping_and_dense_indicator, +} + + +def to_node(self, num): + if isinstance(num, SymTypes): + return num.node + elif type(num) is bool: + return self.wrap_bool(num) + elif type(num) is int: + return self.wrap_int(num) + elif type(num) is float: + return self.wrap_float(num) + else: + # NotImplemented is important so that Python tries the + # other magic method + return NotImplemented + + +def wrap_node(x): + # TODO: let C++ also take advantage of this + if isinstance(x, SymNode) and x.constant is not None: + return x.constant + if x.is_int(): + return SymInt(x) + elif x.is_float(): + return SymFloat(x) + elif x.is_bool(): + return SymBool(x) + else: + raise AssertionError(f"unrecognized return type {x}") + + +def method_to_operator(method): + return METHOD_TO_OPERATOR[method] + + +def _make_node_magic(method, func): + func = lru_cache(256)(func) + + if method in magic_methods_on_operator_with_trailing_underscore: + method_attr = f"{method}_" + else: + method_attr = method + + def uninteresting_files() -> set[str]: + import torch + + mods = [ + torch._dynamo.eval_frame, + torch._dynamo.utils, + torch.fx.experimental.sym_node, + torch, + ] + import torch._dynamo.guards + + return ( + {inspect.getfile(m) for m in mods} + | torch._dynamo.guards.uninteresting_files() + | {""} + ) + + def capture_provenance(fn): + @functools.wraps(fn) + def wrapper(self, other=None): + if other is None: + result = fn(self) + else: + result = fn(self, other) + if torch._logging._internal.GET_DTRACE_STRUCTURED: + if other is not None: + arguments = [self, other] + else: + arguments = [self] + + def get_id(sym_node) -> Optional[int]: + # We don't want to return an ID if the input is a constant + import sympy + + if sym_node.constant is not None: + return None + elif id(sym_node) == id(result): + return None + elif isinstance(sym_node.expr, (sympy.Integer, sympy.Float)): + return None + elif sym_node.expr in (sympy.true, sympy.false): + return None + return id(sym_node) + + dtrace_structured( + "expression_created", + metadata_fn=lambda: { + "method": method, + "result": str(result), + "result_id": id(result), + "arguments": [str(a) for a in arguments], + "argument_ids": [ + get_id(i) for i in arguments if get_id(i) is not None + ], + "user_stack": structured.get_user_stack(3), + "stack": structured.get_framework_stack(3), + }, + ) + + return result + + return wrapper + + @capture_provenance + def binary_magic_impl(self, other): + from torch.fx.experimental.proxy_tensor import ( + get_proxy_mode, + handle_sym_dispatch, + ) + + op = method_to_operator(method) + + out_hint = None + if self.hint is not None and other.hint is not None: + out_hint = op(self.hint, other.hint) + + if get_proxy_mode(): + return to_node( + self, handle_sym_dispatch(op, (wrap_node(self), wrap_node(other)), {}) + ) + assert isinstance(other, SymNode) + optimized_summation = False + try: + if method == "mod": + from torch.utils._sympy.functions import Mod, PythonMod + + # Special handling for mod that requires access to the value + # ranges + shape_env = self.shape_env + if ( + self.expr.is_nonnegative + or shape_env.bound_sympy(self.expr).lower >= 0 + ) and ( + other.expr.is_nonnegative + or shape_env.bound_sympy(other.expr).lower >= 0 + ): + out = Mod(self.expr, other.expr) + else: + out = PythonMod(self.expr, other.expr) + elif method == "add": + # see Note [optimized_summation] + (optimized_summation, out) = _optimized_add( + self.expr, + other.expr, + self._optimized_summation, + other._optimized_summation, + ) + else: + # TODO: consider constant prop here + out = func(self.expr, other.expr) + except Exception: + log.warning("failed to eval %s(%s, %s)", method, self.expr, other.expr) + raise + sym_node_log.debug("%s %s %s -> %s", method, self.expr, other.expr, out) + pytype: type + # This is not strictly correct. In Python, a**b may return complex when + # a < 0 and b is a float: (-1)**2.1. Same for sympy.sqrt(-3.14). This + # returns a float while both arguments are ints: 2**(-1). Also, max and + # min do not type promote. To avoid having data-dependent control flow + # here, we just set the type to float if one of the args is a float. In + # case of a type mismatch, we assume that it will be detected during + # evaluation. + if method in always_float_magic_methods: + pytype = float + elif method in always_bool_magic_methods: + pytype = bool + elif self.pytype is float or other.pytype is float: + pytype = float + else: + pytype = self.pytype + + if ( + pytype is not None + and out_hint is not None + and not isinstance(out_hint, SymTypes) + ): + out_hint = pytype(out_hint) + + # Create a FX node that corresponds to the operation being applied to + # this node. + fx_node, _ = self.shape_env._create_fx_call_function( + op, (self.fx_node, other.fx_node) + ) + + result = SymNode( + out, + self.shape_env, + pytype, + out_hint, # type: ignore[arg-type] + fx_node=fx_node, + optimized_summation=optimized_summation, # see Note [optimized_summation] + ) + return result + + @capture_provenance + def unary_magic_impl(self): + from torch.fx.experimental.proxy_tensor import ( + get_proxy_mode, + handle_sym_dispatch, + ) + + op = method_to_operator(method) + if get_proxy_mode(): + return to_node(self, handle_sym_dispatch(op, (wrap_node(self),), {})) + # TODO: consider constant prop here + expr = self.expr + if method == "floor" or method == "ceiling": + expr = self.shape_env._simplify_floor_div(expr) + + try: + out = func(expr) + except Exception: + log.warning("failed to eval %s(%s)", method, expr) + raise + sym_node_log.debug("%s %s -> %s", func, expr, out) + out_hint = None + if self.hint is not None: + out_hint = op(self.hint) + pytype: type + if method in always_int_magic_methods: + pytype = int + elif method in always_bool_magic_methods: + pytype = bool + elif method in always_float_magic_methods: + pytype = float + else: + pytype = self.pytype + + fx_node, _ = self.shape_env._create_fx_call_function(op, (self.fx_node,)) + return SymNode(out, self.shape_env, pytype, out_hint, fx_node=fx_node) + + if method in unary_methods: + setattr(SymNode, f"_{method_attr}", unary_magic_impl) + elif method == "sym_ite": + + def sym_ite_impl(pred_node, then_node, else_node): + from torch.fx.experimental.proxy_tensor import ( + get_proxy_mode, + handle_sym_dispatch, + ) + + out_hint = then_node.hint if pred_node.hint else else_node.hint + if get_proxy_mode(): + return to_node( + pred_node, + handle_sym_dispatch( + sym_ite, + ( + wrap_node(pred_node), + wrap_node(then_node), + wrap_node(else_node), + ), + {}, + ), + ) + + try: + out = func(pred_node.expr, then_node.expr, else_node.expr) + except Exception: + log.warning( + "failed to eval %s(%s, %s, %s)", + method, + pred_node.expr, + then_node.expr, + else_node.expr, + ) + raise + + fx_node, _ = pred_node.shape_env._create_fx_call_function( + sym_ite, (pred_node.fx_node, then_node.fx_node, else_node.fx_node) + ) + return SymNode( + out, pred_node.shape_env, then_node.pytype, out_hint, fx_node=fx_node + ) + + setattr(SymNode, f"_{method_attr}", sym_ite_impl) + elif method == "round": + + def round_impl(self, ndigits=None): + from torch.fx.experimental.proxy_tensor import ( + get_proxy_mode, + handle_sym_dispatch, + ) + + op = builtins.round + if get_proxy_mode(): + return to_node( + self, handle_sym_dispatch(op, (wrap_node(self), ndigits), {}) + ) + + expr = self.expr + try: + out = func(expr, ndigits) + except Exception: + log.warning("failed to eval %s(%s, ndigits=%s)", method, expr, ndigits) + raise + + if ndigits is None: + pytype = int + else: + pytype = self.pytype + + out_hint = None + if self.hint is not None: + out_hint = op(self.hint, ndigits) + + # Internally, None is used as sentinel to indicate that a something is not a node on an FX graph. At the + # same time, there is no way to wrap a plain None into an FX node. Thus, there is no way to pass None here + # without triggering some asserts that check whether we are mixing FX nodes with untracked arguments. The + # hack down below works, because all round function down the line all take ndigits=None as default in their + # signature. + # TODO: Remove the args construction below if a different sentinel is used by FX. + # ezyang(May 2024): LOL + args = [self.fx_node] + if ndigits is not None: + args.append(ndigits) + fx_node, _ = self.shape_env._create_fx_call_function(op, tuple(args)) + return SymNode(out, self.shape_env, pytype, out_hint, fx_node=fx_node) + + setattr(SymNode, f"_{method_attr}", round_impl) + else: + setattr(SymNode, f"_{method_attr}", binary_magic_impl) + + +def _make_node_sizes_strides(method, func): + # NB: don't LRU cache, lots of arguments + + def sizes_strides_impl(self, sizes, strides): + from torch.fx.experimental.proxy_tensor import ( + get_proxy_mode, + handle_sym_dispatch, + ) + + op = getattr(sys.modules[__name__], method) + if get_proxy_mode(): + return to_node( + self, + handle_sym_dispatch( + op, + ([wrap_node(s) for s in sizes], [wrap_node(s) for s in strides]), + {}, + ), + ) + size_exprs = [s.expr for s in sizes] + stride_exprs = [s.expr for s in strides] + try: + out = func(size_exprs, stride_exprs) + except Exception: + log.warning("failed to eval %s(%s, %s)", method, size_exprs, stride_exprs) + raise + # bool is never expandable + + size_hints = [] + out_hint = None + for s in sizes: + if s.hint is None: + break + size_hints.append(s.hint) + else: + stride_hints = [] + for s in strides: + if s.hint is None: + break + stride_hints.append(s.hint) + else: + out_hint = op(size_hints, stride_hints) + + # NB: This is the indicator function, not the actual bool! + pytype: type + if method.endswith("_indicator"): + pytype = int + else: + pytype = bool + return SymNode(out, self.shape_env, pytype, out_hint) + + setattr(SymNode, f"_{method}", sizes_strides_impl) + + # TODO: This is technically hotpath, but in the ideal end state + # guards on this will resolve at a higher level so you never + # spend time in this code + def sizes_strides_user(sizes, strides): + import sympy + + from torch.fx.experimental.symbolic_shapes import ( + eval_is_non_overlapping_and_dense, + ) + + for a in itertools.chain(sizes, strides): + if isinstance(a, SymInt): + return wrap_node( + getattr(a.node, method)( + [to_node(a.node, b) for b in sizes], + [to_node(a.node, b) for b in strides], + ) + ) + if method == "is_non_overlapping_and_dense_indicator": + return eval_is_non_overlapping_and_dense(sizes, strides) + else: + # TODO: this is an awful implementation + return bool( + func( + [sympy.sympify(a) for a in sizes], + [sympy.sympify(a) for a in strides], + ) + ) + + # Skip for is_non_overlapping_and_dense_indicator + if not hasattr(sys.modules[__name__], method): + setattr(sys.modules[__name__], method, sizes_strides_user) + + +for method, func in magic_methods.items(): + _make_node_magic(method, func) + +for method, func in sizes_strides_methods.items(): + _make_node_sizes_strides(method, func) + + +def _make_user_magic(method, user_type): + # User magic takes care of wrapping the other operand into a node, + # so that our internal logic can assume everything is nodes + if method in magic_methods_on_operator_with_trailing_underscore: + method_attr = f"sym_{method}" + else: + method_attr = method + + def get_constant(x: Union[SymInt, int, SymFloat, float, SymBool, bool]): + if isinstance(x, (int, float, bool)): + return x + if isinstance(x, SymInt): + return x.node.guard_int("", 0) + if isinstance(x, SymBool): + return x.node.guard_bool("", 0) + raise AssertionError("expect to be called with constant SymBools") + + def is_constant(x): + if isinstance(x, (int, float, bool)): + return True + if isinstance(x, (SymInt, SymFloat, SymBool)): + return x.node.is_constant() + return False + + # Promotion rules for binary operations. NB: we preserve PYTHON semantics + # - if args are same type, do nothing + # - if one arg is float, promote other arg to float + # - nb: this applies to floordiv, even though output is integral + # (it's still float) + # - pow is funny business + # - if both ints + # - trigger a guard on exponent >= 0 + # - if non-negative, output is int + # - otherwise, output is float + # - otherwise, promote other arg to float + # - nb: complex is impossible to handle correctly lol, with + # negative base and integral float need to diverge semantics and + # just always return complex. Neener neener pretend this problem + # doesn't exist + # - equality is pain: Python does the fancy thing where it unpacks the + # mantissa from the float and then compares that against the int. + # Which means it is able to tell that + # 9007199254740993 != 9007199254740992. (rather than if the LHS was + # promoted to float, in which case it would have truncated to the RHS + # and subsequently been equal). We'll model this exactly by having + # special mixed type equality operations. Unfortunately, we need to + # do this for all comparison operations (maybe I'll only implement + # compare) + # - sym_ite mumble mumble really shouldn't allow mixed but whatever + + if method in bool_becomes_int_magic_methods: + + def promote(x): + """Implements True+True=2, which works in python but not sympy""" + if isinstance(x, SymBool): + return SymInt(x.node.wrap_int(int(x))) + return x + + else: + + def promote(x): + return x + + def promote2(self, other): + # TODO: Remove eq and other relations from this list. + # CPython has fancy implementations for these to get as much precision + # as possible instead of just promoting to float64 and praying, so we + # need to handle them specially too. + # Also, note that int_truediv doesn't go through this path: both + # arguments are "int" so there isn't any promotion + if method not in [ + "add", + "sub", + "mul", + "mod", + "float_pow", + "float_truediv", + "int_floordiv", + "sym_min", + "sym_max", + # TODO: remove these + "eq", + "ne", + "gt", + "lt", + "le", + "ge", + ]: + return self, other + f_self = isinstance(self, (float, torch.SymFloat)) + f_other = isinstance(other, (float, torch.SymFloat)) + if f_self or f_other: + if not f_self: + self = torch.sym_float(self) + if not f_other: + other = torch.sym_float(other) + return self, other + + # Before and after performing the operation, check if any operands are constant. + # If so, extract out the constant values first. If `self` itself is a + # constant, then "redispatch" by calling back into the operator. Sometimes + # this means that operations involving SymBool return plain bools. + # Alternatively, we could also rewrap into constant Symbool (i.e. by + # implementing wrap_bool in ConstantSymNodeImpl), but we're not doing that + # today for no particular reason. + def unary_magic_impl(self): + self = promote(self) + if is_constant(self): + return (method_to_operator(method))(get_constant(self)) + return wrap_node(getattr(self.node, method_attr)()) + + def binary_magic_impl(self, other): + if not isinstance(other, (int, float, bool, SymInt, SymFloat, SymBool)): + return NotImplemented + sym_node_log.debug("MAGIC %s %s %s", method, self, other) + self = promote(self) + other = promote(other) + self, other = promote2(self, other) + if is_constant(self): + return (method_to_operator(method))(get_constant(self), other) + if is_constant(other): + other = get_constant(other) + other_node = to_node(self.node, other) + if other_node is NotImplemented: + return NotImplemented + ret = wrap_node(getattr(self.node, method_attr)(other_node)) + return get_constant(ret) if is_constant(ret) else ret + + def rbinary_magic_impl(self, other): + if not isinstance(other, (int, float, bool, SymInt, SymFloat, SymBool)): + return NotImplemented + self = promote(self) + other = promote(other) + self, other = promote2(self, other) + if is_constant(self): + return (method_to_operator(method))(other, get_constant(self)) + if is_constant(other): + other = get_constant(other) + other_node = to_node(self.node, other) + if other_node is NotImplemented: + return NotImplemented + ret = wrap_node(getattr(other_node, method_attr)(self.node)) + return get_constant(ret) if is_constant(ret) else ret + + def setattrs(user_type, attr, symnode_impl): + """ + Registers the SymNode magic method on SymInt/Float/Bool, + and optionally registers a corresponding wrapped method on DynamicInt. + """ + + # SymInt/Float/Bool + setattr(user_type, attr, symnode_impl) + + # DynamicInt impl + def dynamic_int_impl(*args): + args = [x.real if isinstance(x, DynamicInt) else x for x in args] + out = getattr(int, attr)(*args) + if isinstance(out, int) and not isinstance(out, bool): + return DynamicInt(out) + return out + + if user_type is SymInt: + setattr(DynamicInt, attr, dynamic_int_impl) + + if method in unary_magic_methods: + setattrs(user_type, f"__{method}__", unary_magic_impl) + elif method in unary_nonmagic_methods: + orig = getattr(user_type, method) + setattrs(user_type, method, update_wrapper(unary_magic_impl, orig)) + elif method == "sym_ite": + + def sym_ite_magic_impl(pred, then_val, else_val): + pred_node = pred.node + then_node = to_node(pred_node, then_val) + else_node = to_node(pred_node, else_val) + if then_node is NotImplemented or else_node is NotImplemented: + return NotImplemented + assert ( + isinstance(then_node, SymNode) + and isinstance(else_node, SymNode) + and then_node.pytype == else_node.pytype + ) + ret = wrap_node(getattr(pred.node, method_attr)(then_node, else_node)) + return get_constant(ret) if ret.node.is_constant() else ret + + setattrs(user_type, f"__{method}__", sym_ite_magic_impl) + elif method == "round": + + def round_magic_impl(self, ndigits=None): + if is_constant(self): + return builtins.round(get_constant(self), ndigits) + + return wrap_node(getattr(self.node, method)(ndigits)) + + setattrs(user_type, f"__{method}__", round_magic_impl) + else: + method_name = method + if method in bitwise_ops: + method_name = bitwise_ops[method] + setattrs(user_type, f"__{method_name}__", binary_magic_impl) + if method in reflectable_magic_methods: + setattrs(user_type, f"__r{method_name}__", rbinary_magic_impl) + + +for method in magic_methods: # type: ignore[assignment] + if method in only_bool_magic_methods: + _make_user_magic(method, SymBool) + continue + if method in only_float_magic_methods: + _make_user_magic(method, SymFloat) + continue + if method in also_bool_magic_methods or method in bool_becomes_int_magic_methods: + _make_user_magic(method, SymBool) + _make_user_magic(method, SymInt) + if method not in bitwise_ops: + _make_user_magic(method, SymFloat) + +del method +del func diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py new file mode 100644 index 0000000000000000000000000000000000000000..56ffc77c23b08e0c35860783658c2c84f3ce0397 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py @@ -0,0 +1,8121 @@ +from __future__ import annotations + +import sympy +from sympy import S + +from torch._prims_common import BoolLike, FloatLike, IntLike + + +""" +``torch.fx.experimental.symbolic_shapes`` provides interfaces for interacting with +our symbolic shapes reasoning system that is used heavily in torch.compile. Although +this is not generally considered public API, when writing framework code in PyTorch +as well as extensions to PyTorch (e.g., in custom operator implementations), you may +need to make use of these APIs to setup dynamic shapes support appropriately. +""" + +import abc +import atexit +import collections +import dis +import functools +import hashlib +import inspect +import itertools +import logging +import math +import operator +import os +import re +import sys +import threading +import traceback +from collections import Counter, defaultdict +from collections.abc import Callable, Generator, Iterator, Mapping, Sequence +from contextlib import _GeneratorContextManager, contextmanager +from dataclasses import asdict, dataclass, field +from enum import Enum +from typing import ( + Any, + cast, + Generic, + NamedTuple, + NoReturn, + Optional, + TYPE_CHECKING, + TypeAlias, + TypeGuard, + TypeVar, + Union, +) +from typing_extensions import deprecated, ParamSpec + +import torch +import torch.fx +import torch.fx.traceback as fx_traceback +import torch.utils._pytree as pytree + +# NB: The sym_* functions are used via getattr() and must be imported here. +from torch import SymBool, SymFloat, SymInt +from torch._C._functorch import get_unwrapped, is_batchedtensor +from torch._guards import ShapeGuard, SLoc, Source, TracingContext +from torch._logging import dtrace_structured, LazyString, structured, trace_structured +from torch._subclasses.meta_utils import is_sparse_any +from torch._utils_internal import signpost_event +from torch.fx.experimental import _config as config +from torch.fx.experimental.recording import ( + FakeTensorMeta, + record_shapeenv_event, + replay_shape_env_events, + shape_env_check_state_equal, + ShapeEnvEvent, +) +from torch.fx.experimental.sym_node import SymNode, SymTypes +from torch.types import py_sym_types +from torch.utils._ordered_set import OrderedSet +from torch.utils._python_dispatch import is_traceable_wrapper_subclass +from torch.utils._sympy.functions import ( + Application, + CeilToInt, + CleanDiv, + FloorDiv, + FloorToInt, + IntTrueDiv, + IsNonOverlappingAndDenseIndicator, + Max, + Mod, + PythonMod, + TruncToInt, +) +from torch.utils._sympy.numbers import int_oo +from torch.utils._sympy.printers import CppPrinter, PythonPrinter +from torch.utils._sympy.singleton_int import SingletonInt +from torch.utils._sympy.solve import try_solve +from torch.utils._sympy.symbol import make_symbol, symbol_is_type, SymT +from torch.utils._sympy.value_ranges import ( + bound_sympy, + SymPyValueRangeAnalysis, + ValueRangeError, + ValueRanges, +) +from torch.utils._traceback import CapturedTraceback, format_frame + + +if TYPE_CHECKING: + import types + + from torch import Tensor + from torch._dynamo.source import TensorPropertySource + from torch._subclasses.fake_tensor import FakeTensor + from torch.types import BoolLikeType, FloatLikeType, IntLikeType + + +InputList = list +DimList = list + +log = logging.getLogger(__name__) + + +class GuardOnDataDependentSymNode(RuntimeError): + cond: sympy.Basic + + def __init__(self, cond: sympy.Basic, *args: Any) -> None: + super().__init__(*args) + self.cond = cond + + +class PendingUnbackedSymbolNotFound(RuntimeError): + pass + + +aten = torch._ops.ops.aten # type: ignore[has-type] + +__all__ = [ + "size_hint", + "guard_or_false", + "guard_or_true", + "has_symbolic_sizes_strides", + "create_contiguous", + "ShapeEnv", + "is_concrete_int", + "is_concrete_float", + "is_concrete_bool", + "has_static_value", + "guard_int", + "guard_float", + "guard_scalar", + "canonicalize_bool_expr", + "hint_int", + "SYMPY_INTERP", + "free_symbols", + "is_symbol_binding_fx_node", + "is_nested_int", + "SHAPEENV_EVENT_KEY", + "CURRENT_NODE_KEY", + "has_free_symbols", + "has_free_unbacked_symbols", + "sym_and", + "sym_eq", + "sym_or", + "SymbolicContext", + "StatelessSymbolicContext", + "StatefulSymbolicContext", + "SubclassSymbolicContext", + "SymIntSymbolicContext", + "TrackedFake", + "statically_known_true", + "statically_known_false", + "guard_size_oblivious", + "check_consistent", + "compute_unbacked_bindings", + "ConvertIntKey", + "rebind_unbacked", + "resolve_unbacked_bindings", + "is_accessor_node", + "ValueRangesSLoc", + "SymIntEqByExpr", + "Specialization", +] + +# FX node metadata keys for symbolic shape FX graph. +SHAPEENV_EVENT_KEY = "shapeenv_event" +CURRENT_NODE_KEY = "current_node" + + +def log_lru_cache_stats(wrapped_f: functools._lru_cache_wrapper[object]) -> None: + log.debug( + "lru_cache_stats %s: %s", + wrapped_f.__name__, # type: ignore[attr-defined] + wrapped_f.cumulative_cache_info(), # type: ignore[attr-defined] + ) + + +# Note about Sympy Expr/SympyBoolean/Basic typing: the Sympy hierarchy is +# +# Basic +# Expr +# SympyBoolean +# Relational +# +# Notably, Expr and SympyBoolean are not related. So use Basic when the +# expression could denote int, float OR bool, and otherwise use the more +# specific Expr for int/float and SympyBoolean for bool. +# +# In obscure Meta only situations, sympy.logic.boolalg doesn't exist at runtime. +# So make sure only type checker evaluates this alias. +# Xref: https://www.internalfb.com/diff/D53324783 +SympyBoolean: TypeAlias = "sympy.logic.boolalg.Boolean" + + +_T = TypeVar("_T") +_SympyT = TypeVar("_SympyT", sympy.Expr, SympyBoolean, sympy.Basic) + + +class SymIntEqByExpr: + """ + This is a wrapper around SymInt which has alternative semantics for + equality and pickling. Specifically, instead of erroring or guarding, we + instead will hash/compare equality based on the underlying sympy + expression; e.g., s0 and s1 will always compare as False. + + NB: This does NOT do fancy analysis that maybe_evaluate_static does; + we can only reason through equalities that occur because to expressions + canonicalize to the same expression via regular simplification. + """ + + @staticmethod + def _extract(val: Union[torch.SymInt, int]) -> sympy.Expr: + if isinstance(val, torch.SymInt): + return val.node.expr + else: + return sympy.Integer(val) + + def __init__(self, val: Union[torch.SymInt, int]) -> None: + self.val: sympy.Expr = SymIntEqByExpr._extract(val) + + def __repr__(self) -> str: + return repr(self.val) + + def __eq__(self, other: object) -> bool: + assert isinstance(other, SymIntEqByExpr) + return self.val == other.val + + def __hash__(self) -> int: + return hash(self.val) + + +def _nested_int_aware_sort( + tup: tuple[IntLikeType, int], +) -> tuple[int, IntLikeType, int]: + return ( + # Order nested ints by their coefficients. + # 1 here to order nested ints after non-nested-ints. + (1, tup[0].node.nested_int_coeff(), tup[1]) + if is_nested_int(tup[0]) + else (0, *tup) + ) + + +def size_hint(x: int | torch.SymInt, *, allow_none: bool = False) -> int | None: + """Gets a size hint for a given expression from the underlying shapes we had. + Does not introduce a guard, so only use this when you can guarantee that + your code is still valid for arbitrary shapes (such as optimization decisions) + """ + if isinstance(x, int): + return x + assert isinstance(x, torch.SymInt) + return x.node.shape_env.size_hint(x.node.expr, allow_none=allow_none) + + +# Wrapper on lru_cache that reports statistics at process end +def lru_cache( + maxsize: Optional[int], +) -> Callable[[Callable[..., _T]], functools._lru_cache_wrapper[_T]]: + def inner(f: Callable[..., _T]) -> functools._lru_cache_wrapper[_T]: + wrapped_f = functools.lru_cache(maxsize)(f) + old_cache_clear = wrapped_f.cache_clear + prev_hits = 0 + prev_misses = 0 + + # TODO: There's a ref-cycle here (wrapped_f -> cumulative_cache_info + # -> wrapped_f) but cannot be solved with weakref as wrapped_f is not + # weakref'able on some versions of Python + + def cumulative_cache_info() -> functools._CacheInfo: + cur = wrapped_f.cache_info() + return functools._CacheInfo( + prev_hits + cur.hits, + prev_misses + cur.misses, + cur.maxsize, + cur.currsize, + ) + + def new_cache_clear() -> None: + nonlocal prev_hits, prev_misses + cur = wrapped_f.cache_info() + prev_hits += cur.hits + prev_misses += cur.misses + old_cache_clear() + + wrapped_f.cache_clear = new_cache_clear # type: ignore[attr-defined, method-assign] + wrapped_f.cumulative_cache_info = cumulative_cache_info # type: ignore[attr-defined, method-assign] + if log.isEnabledFor(logging.DEBUG): + atexit.register(log_lru_cache_stats, wrapped_f) # type: ignore[arg-type] + return wrapped_f + + return inner + + +# These are modules that contain generic code for interacting with ShapeEnv +# which are unlikely to identify a particular interesting guard statement +@lru_cache(None) +def uninteresting_files() -> set[str]: + import torch._compile + import torch._dynamo.eval_frame + import torch._inductor.sizevars + import torch._library.custom_ops + import torch._library.fake_impl + import torch._logging + import torch._subclasses.fake_tensor + import torch._subclasses.meta_utils + import torch.export._trace + + mods = [ + sys.modules[__name__], + torch.export._trace, + torch.fx.experimental.recording, + torch.fx.experimental.sym_node, + torch.fx.interpreter, + torch.fx._symbolic_trace, + torch, + torch._compile, + torch._dynamo.eval_frame, + torch._inductor.sizevars, + torch._library.custom_ops, + torch._library.fake_impl, + torch._subclasses.meta_utils, + torch._subclasses.fake_tensor, + torch._logging._internal, + torch._logging.structured, + ] + import torch._dynamo.guards + + return ( + {inspect.getfile(m) for m in mods} + | torch._dynamo.guards.uninteresting_files() + | {""} + ) + + +class ConstraintViolationError(RuntimeError): + pass + + +def has_symbolic_sizes_strides(elem: torch.Tensor) -> bool: + return elem._has_symbolic_sizes_strides + + +Int: TypeAlias = Union[torch.SymInt, int] + + +def create_contiguous(shape: Sequence[Int]) -> list[Int]: + strides: list[Int] = [1] + for dim in reversed(shape[:-1]): + strides.append(dim * strides[-1]) # type: ignore[operator] + return list(reversed(strides)) + + +def hint_int(a: Union[torch.SymInt, int], fallback: Optional[int] = None) -> int: + """ + Retrieve the hint for an int (based on the underlying real values as observed + at runtime). If no hint is available (e.g., because data dependent shapes), + if fallback is not None, use that instead (otherwise raise an error). + """ + if isinstance(a, torch.SymInt): + return a.node.require_hint(fallback) + assert type(a) is int, a + return a + + +Scalar: TypeAlias = Union[torch.SymInt, torch.SymFloat, torch.SymBool, int, float, bool] + + +def has_hint(a: Scalar) -> bool: + if isinstance(a, SymTypes): + return a.node.has_hint() + return True + + +def is_concrete_int(a: IntLikeType) -> bool: + """ + Utility to check if underlying object + in SymInt is concrete value. Also returns + true if integer is passed in. + + Args: + a (SymInt or int): Object to test if it int + """ + assert isinstance(a, (SymInt, int)) + + if isinstance(a, int): + return True + + if isinstance(a.node.expr, sympy.core.numbers.Integer): + return True + + return False + + +def is_concrete_float(a: FloatLikeType) -> bool: + r"""Utility to check if underlying object + in SymInt is concrete value. Also returns + true if integer is passed in. + + Args: + a (SymInt or float): Object to test if it float + """ + assert isinstance(a, (SymFloat, float)) + + if isinstance(a, float): + return True + + if isinstance(a.node.expr, sympy.core.numbers.Float): + return True + + return False + + +def is_concrete_bool(a: BoolLikeType) -> bool: + """ + Utility to check if underlying object + in SymBool is concrete value. Also returns + true if integer is passed in. + + Args: + a (SymBool or bool): Object to test if it bool + """ + assert isinstance(a, (SymBool, bool)) + + if isinstance(a, bool): + return True + + if isinstance( + a.node.expr, (sympy.logic.boolalg.BooleanTrue, sympy.logic.boolalg.BooleanFalse) + ): + return True + + return False + + +def has_static_value(a: Union[SymBool, SymFloat, SymInt, bool, float, int]) -> bool: + """ + User-code friendly utility to check if a value is static or dynamic. + Returns true if given a constant, or a symbolic expression with a fixed value. + + Args: + a (Union[SymBool, SymFloat, SymInt, bool, float, int]): Object to test + """ + assert isinstance(a, BoolLike + FloatLike + IntLike) + if ( + isinstance(a, BoolLike) + and is_concrete_bool(a) # type: ignore[arg-type] + or isinstance(a, FloatLike) + and is_concrete_float(a) # type: ignore[arg-type] + or isinstance(a, IntLike) + and is_concrete_int(a) # type: ignore[arg-type] + ): + return True + + assert isinstance(a, py_sym_types) + return a.node.shape_env.bound_sympy(a.node.expr).is_singleton() # type: ignore[union-attr] + + +def guard_size_oblivious(expr: Union[torch.SymBool, bool]) -> bool: + """ + Perform a guard on a symbolic boolean expression in a size oblivious way. + This is typically used when a non-oblivious test would result in a guard + on a data dependent value of which we don't know the value of at compile time. + When a guard is tested this way, we may diverge in behavior from how regular + PyTorch semantics would treat it. For more information, see + https://github.com/pytorch/pytorch/pull/118579 + """ + if isinstance(expr, torch.SymBool): + return expr.node.guard_size_oblivious("", 0) + else: + assert isinstance(expr, bool), expr + return expr + + +def check_consistent(new: _T, old: _T) -> None: + """ + Test that two "meta" values (typically either Tensor or SymInt) have + the same values, e.g., after retracing. If we don't understand the + quantities in question, we'll just skip the consistency check. + """ + # TODO: do boolean equality test too, see + # https://github.com/pytorch/pytorch/issues/124110 + scalar_types = (torch.SymInt, torch.SymFloat, int, float) + + if isinstance(new, torch.Tensor): + assert isinstance(old, torch.Tensor) + torch._check( + old.dim() == new.dim(), lambda: f"{old.shape} != {new.shape} (old != new)" + ) + # Do this manually so that each individual test is irrefutable + # (TODO: should be a helper for this, maybe sym_eq? That + # gives us a compound expression and I'm not sure it + # simplifies right now) + for i, j in zip(old.shape, new.shape): + torch._check(i == j, lambda: f"{old.shape} != {new.shape} (old != new)") + # NB: bool is subclass of int + elif isinstance(new, scalar_types) and not isinstance(new, bool): + assert isinstance(old, scalar_types) and not isinstance(old, bool), ( + f"{old} != {new}" + ) + torch._check(old == new, lambda: f"{old} != {new} (old != new)") + + +def resolve_unbacked_bindings( + shape_env: Optional[ShapeEnv], + bindings: Optional[dict[sympy.Symbol, pytree.KeyPath]], +) -> Optional[dict[sympy.Symbol, pytree.KeyPath]]: + """ + When we do fake tensor prop, we oftentimes will allocate new unbacked symints. + We then run proxy tensor mode, which populates node.meta["unbacked_bindings"] + with these new symints. To ensure consistency we use PropagateUnbackedSymInts + to rename unbacked bindings to their old ones. But all of the node metas are + still using the old bindings from before the renaming. This function helps to + post facto apply any renamings discovered in the PropogateUnbackedSymInts pass. + """ + if bindings is None: + return None + assert shape_env is not None + return {shape_env.unbacked_renamings.get(k, k): v for k, v in bindings.items()} + + +Result: TypeAlias = Union[torch.Tensor, tuple[torch.Tensor, ...]] + + +def rebind_unbacked( + shape_env: Optional[ShapeEnv], n: torch.fx.Node, result: Result +) -> None: + """ + Suppose we are retracing a pre-existing FX graph that previously had + fake tensor propagation (and therefore unbacked SymInts). When we retrace, + we re-propagate fake tensors, which results in new unbacked SymInts. + When this happens, we need to tell the shape environment about the equivalence + of the old and new unbacked SymInts. Pass us the old torch.fx.Node (which + has the old binding information) and the new result (which we can extract the + new unbacked SymInts out from). + """ + + # Inputs never need rebinding + if n.op == "placeholder": + return + + if bindings := resolve_unbacked_bindings( + shape_env, n.meta.get("unbacked_bindings") + ): + assert shape_env is not None + for raw_u0, path in bindings.items(): + u1 = pytree.key_get(result, path) + + # Sometimes, things were previously unbacked bindings become constants. + # There are two situations this can happen. + # + # First, you might have a runtime assert that causes the + # constant-ification. In this case, the /binding/ itself will + # still be an unbacked symbol (because we will only force it + # to be a constant later in fake tensor propagation). In this + # case, u1 is a SymInt and we still do all our work as normal. + # + # But second, it might be that fake tensor propagation DIRECTLY + # converted the unbacked SymInt into a constant. This happens + # more rarely, but we have identified two situations it can + # validly occur: + # + # - If you have a tensor_version operator, these are initially + # allocated as unbacked SymInts, but after AOTAutograd they + # get forced specialized to specific values. In this case, + # there is no reason to do runtime asserts on them, this is + # just a hack to properly keep track of them to start. + # + # - If you have an item() call on a constant tensor, the result + # of the item() call is constant and we do not need runtime + # asserts on this symbol. In + # https://github.com/pytorch/pytorch/issues/140625 we have a + # case where in the initial trace of the program we are unable + # to determine that torch.tensor is constant, but then + # subsequent passes cause torch.tensor to become a constant and + # then the unbacked symbol goes poof. + # + # In all of these cases, it is no longer necessary to generate + # deferred runtime asserts, since other subsystems (e.g., the + # constant-ification pass) ensure that the quantity is now truly + # static and cannot change at runtime. So it's OK to discard + # in these situations. + # + # There is one more hazard (re + # https://github.com/pytorch/pytorch/issues/141248), the problem + # is that you can end up with "dangling" unbacked symbols that + # exist in the ShapeEnv but are never bound anywhere. You might + # like an invariant that unbacked symbols never get lost. But + # we do not have this invariant, so do not try to enforce it. + if isinstance(u1, (int, float)): + log.info( + "rebind_unbacked: discard %s %s %s -> %s", + n.target, + raw_u0, + path, + u1, + ) + continue + + # We only care about rebinding unbacked things + if u1.node.hint is not None: + continue + + # unbacked symbols bindings might be replaced to other backed or + # unbacked replacements. + # + # Example: + # u = x.item() + # torch._check(u == 5) + # + # The safest approach is to retrieve raw_u1 from u1.node._expr + # and perform the rebinding on the original unbacked symbol, + # even if it’s no longer directly referenced. + # + # In other words, we should always rebind the original symbol + # before any replacements are applied. + # u0 -> u0 == s1 + raw_u1 = u1.node._expr + + # TODO Do we still need this logic below? + # Simplify SymBool binding + if ( + isinstance(raw_u1, sympy.Piecewise) + and len(raw_u1.args) == 2 + and ( + raw_u1_args0 := cast( + tuple[sympy.Basic, sympy.Basic], raw_u1.args[0] + ) + ) + and raw_u1_args0[0] == 1 + and isinstance(eq := raw_u1_args0[1], sympy.Eq) + and isinstance(new_raw_u1 := eq.lhs, sympy.Symbol) + and shape_env.var_to_range[new_raw_u1].issubset(ValueRanges(0, 1)) + and eq.rhs == 1 + and cast(tuple[sympy.Basic, sympy.Basic], raw_u1.args[1]) == (0, True) + ): + # This is what the pattern match above is testing + repacked = _sympy_cast_symbool_to_symint_guardless( + sympy.Eq(new_raw_u1, 1) + ) + assert repacked == raw_u1, f"{repacked} != {raw_u1}" + # Cancel the to_int(to_bool(x)). This is sound because x in + # [0, 1] + + raw_u1 = new_raw_u1 + + if not isinstance(raw_u1, sympy.Symbol): + assert not raw_u1.free_symbols, ( + f"should have been constant, but got {raw_u1}" + ) + continue + + # The old and new could be the same if you improperly hit the memo + # while retracing. Make sure you updated FakeTensorMode.epoch + assert raw_u0 != raw_u1, f"{raw_u0} possible memo disaster" + # Reuse the OLD symbol name + shape_env._rename_unbacked_to(raw_u1, raw_u0) + + +# NB: You could try to expand this to cover more cases by simply +# detecting whenever you have an int output, but this is a bit +# dangerous in case someone adds a function that returns an int but is +# mutating. So manually whitelist for now. +def is_accessor_node(node: torch.fx.Node) -> bool: + """ + Helper function to determine if a node is trying to access + a symbolic integer such as size, stride, offset or item. Currently + primarily only used in a DCE pass to figure out purity. + """ + + # Dynamo only exercised condition + if ( + node.op == "call_method" + and isinstance(node.args[0], torch.fx.Node) + and isinstance(node.args[0].meta.get("example_value"), torch.Tensor) + and node.target in ["size", "stride", "storage_offset", "item"] + ): + return True + + if node.op == "call_function" and node.target in [ + torch.ops.aten.sym_size, + torch.ops.aten.sym_size.default, + torch.ops.aten.sym_size.int, + torch.ops.aten.sym_stride, + torch.ops.aten.sym_stride.default, + torch.ops.aten.sym_stride.int, + torch.ops.aten.sym_storage_offset, + torch.ops.aten.sym_storage_offset.default, + torch.ops.aten.sym_numel.default, + ]: + return True + + return False + + +def canonicalize_bool_expr(expr: _T) -> _T: + """ + Canonicalize a boolean expression by transforming it into a lt / le + inequality and moving all the non-constant terms to the rhs. + We canonicalize And / Ors / Not via cnf and then canonicalize their subexpr + recursively + nb. sympy.Rel.canonical is not good enough https://github.com/sympy/sympy/issues/25924 + + Args: + expr (sympy.Expr): Expression to canonicalize + """ + # Canonicalise an inequality by transforming it into a lt / le + # inequality and moving all the non-constant terms to the rhs + # We canonicalise And / Ors / Not via cnf + # nb. Relational.canonical in sympy is broken + # https://github.com/sympy/sympy/issues/25924 + + if not isinstance( + expr, (sympy.Rel, sympy.And, sympy.Or, sympy.Not, sympy.Eq, sympy.Ne) + ): + return expr + + if isinstance(expr, (sympy.And, sympy.Or, sympy.Not)): + expr = sympy.logic.boolalg.to_cnf(expr) + return _canonicalize_bool_expr_impl(expr) # type: ignore[arg-type, return-value] + + +def _sympy_from_args( + cls: type[Union[sympy.Add, sympy.Mul]], + args: list[sympy.Expr], + sort: bool = True, + is_commutative: Optional[bool] = None, +) -> sympy.Expr: + """ + Create a sympy expression from a list of arguments, optimizing for performance. + + This function creates a sympy Add or Mul expression from a list of arguments + while avoiding expensive operations like flattening. It handles sorting the + arguments appropriately based on the expression type. + + Args: + cls: The sympy class to create (Add or Mul) + args: List of sympy expressions to combine + sort: Whether to sort the arguments (default: True) + is_commutative: Whether the operation is commutative (default: None) + + Returns: + A sympy expression of type cls combining all arguments + + Raises: + ValueError: If cls is not sympy.Add or sympy.Mul + """ + + if not args: + return cls.identity # type: ignore[union-attr] + + # These args are already in canonical form, so we avoid calling + # Add(*args) to avoid expensive Add.flatten operation + if sort: + if cls is sympy.Add: + sort_fn = sympy.core.add._addsort + elif cls is sympy.Mul: + sort_fn = sympy.core.mul._mulsort + else: + raise ValueError(f"Unknown cls: {cls}") + + # we don't support non commutative with sort + assert is_commutative is True + if args[0].is_Number: + rest = args[1:] + sort_fn(rest) + return cls._from_args([args[0]] + rest, is_commutative=is_commutative) # type: ignore[attr-defined] + else: + args = args.copy() + sort_fn(args) + return cls._from_args(args, is_commutative=is_commutative) # type: ignore[attr-defined] + else: + # if the args are already sorted, we create directly + return cls._from_args(args, is_commutative=is_commutative) # type: ignore[attr-defined] + + +def _canonicalize_bool_expr_impl(expr: SympyBoolean) -> SympyBoolean: + """ + After canonicalization, we are guaranteed to have eliminated Ge/Gt relations + (rewriting them to Le/Lt, respectively). + """ + if isinstance(expr, (sympy.And, sympy.Or)): + return type(expr)(*map(canonicalize_bool_expr, expr.args)) + + opposite = {sympy.Gt: sympy.Lt, sympy.Ge: sympy.Le} + t: Union[type[Any]] + if isinstance(expr, tuple(opposite.keys())): + rhs = expr.lhs - expr.rhs # type: ignore[attr-defined] + t = opposite[type(expr)] # type: ignore[index] + else: + assert isinstance(expr, (sympy.Lt, sympy.Le, sympy.Eq, sympy.Ne)) + rhs = expr.rhs - expr.lhs + t = type(expr) + + def is_neg(t: sympy.Expr) -> bool: + return (t.is_Number and t.is_negative) or ( + isinstance(t, sympy.Mul) and t.args[0].is_Number and t.args[0].is_negative + ) + + lhs = S.Zero + rhs = _reduce_to_lowest_terms(rhs) + if isinstance(rhs, sympy.Add): + pos = [] + neg = [] + for term in rhs.args: + if is_neg(term): + neg.append(-term) + else: + pos.append(term) + # these are already sorted + rhs = _sympy_from_args(sympy.Add, pos, sort=False, is_commutative=True) + # the terms were changed, so needs a sorting + lhs = _sympy_from_args(sympy.Add, neg, sort=True, is_commutative=True) + elif is_neg(rhs): + # lhs == 0 + lhs, rhs = -rhs, S.Zero + # We don't have to evaluate here because lhs, rhs came from a Boolean + # and it was already simplified + return t(lhs, rhs, evaluate=False) + + +def _reduce_to_lowest_terms(expr: sympy.Expr) -> sympy.Expr: + """ + Eliminates any integer factor from a given expression. + E.g., 6x + 4y reduces to 3x + 2y. + + Useful when an expression is == or != to 0. + """ + + def integer_coefficient(x: sympy.Expr) -> int: + if x.is_Integer: + return abs(int(x)) + elif x.is_Mul: + # If one of the args of a Mul is an Integer, it is the + # first arg. eg: args(2*x*3*y) == (6, x, y) + return abs(int(x.args[0])) if x.args[0].is_Integer else 1 # type: ignore[call-overload] + else: + return 1 + + def div_by_factor(x: sympy.Expr, factor: int) -> sympy.Expr: + if x.is_Integer: + return x / factor + elif x.is_Mul: + if x.args[0] != factor: + args = [x.args[0] / sympy.Integer(factor), *x.args[1:]] + else: + # Mul._from_args require a canonical list of args + # so we remove the first arg (x.args[0] / factor) if it was 1 + args = list(x.args[1:]) + return _sympy_from_args(sympy.Mul, args, is_commutative=x.is_commutative) + else: + raise AssertionError(f"illegal arg to div_by_factor: {x}") + + if expr.is_Add: + atoms = cast(Sequence[sympy.Expr], expr.args) + factor = functools.reduce(math.gcd, map(integer_coefficient, atoms)) + if factor == 1: + return expr + # pyrefly: ignore [bad-argument-type] + atoms = [div_by_factor(x, factor) for x in atoms] + return _sympy_from_args( + sympy.Add, atoms, sort=True, is_commutative=expr.is_commutative + ) + elif expr.is_Integer: + return S.One + elif expr.is_Mul: + return div_by_factor(expr, integer_coefficient(expr)) + return expr + + +def is_nested_int(s: IntLikeType) -> TypeGuard[SymInt]: + return isinstance(s, torch.SymInt) and s.node.is_nested_int() + + +IterateExprsAtom: TypeAlias = Union[ + SymInt, SymFloat, SymBool, int, float, bool, sympy.Basic, torch.Tensor +] +IterateExprs: TypeAlias = Union[IterateExprsAtom, Sequence[IterateExprsAtom]] + + +def _iterate_exprs(val: IterateExprs) -> Iterator[sympy.Basic]: + """ + Recursively iterate through a value and yield all sympy expressions contained within it. + + This function traverses various data structures (tensors, lists, tuples, etc.) and extracts + any symbolic expressions they contain. It's used for operations like finding free symbols + in complex nested structures. + + Args: + val: The value to extract sympy expressions from. Can be a symbolic type (SymInt, SymFloat, SymBool), + a sympy expression, a primitive type (int, float, bool), a container (tuple, list), + a sparse tensor, a regular tensor, None, or a torch.Generator. + + Yields: + sympy.Basic: Each sympy expression found in the value. + + Raises: + AssertionError: If the value is of an unsupported type. + """ + # This is almost close enough to implement in terms of _iterate_nodes() + # except that it needs to handle `list[sympy.Basic]` which _iterate_nodes() + # can't handle. + if isinstance(val, SymTypes): + # This allow applies to the jagged layout NestedTensor case as + # nested ints are not symbolic + if is_symbolic(val): + yield val.node.expr + elif isinstance(val, SymNode): + yield val.expr + elif isinstance(val, sympy.Basic): + yield val + elif isinstance(val, (int, float, bool)): + pass + elif isinstance(val, (tuple, list)): + for s in val: + yield from _iterate_exprs(s) + elif is_sparse_any(val): + yield from _iterate_exprs(val.size()) + elif isinstance(val, torch.Tensor): + yield from _iterate_exprs(val.size()) + yield from _iterate_exprs(val.stride()) + yield from _iterate_exprs(val.storage_offset()) + elif val is None: + pass + # see Note: [Generator arguments in AOTDispatcher] + elif isinstance(val, torch.Generator): + pass + else: + raise AssertionError(f"cannot extract sympy expressions from {val} {type(val)}") + + +def _iterate_nodes(val: Any) -> Iterator[SymNode]: + """ + Recursively iterate through a value and yield all SymNodes contained + within it. + """ + if isinstance(val, SymNode): + yield val + elif isinstance(val, py_sym_types): + # This allow applies to the jagged layout NestedTensor case as + # nested ints are not symbolic + if is_symbolic(val): + yield val.node + elif isinstance(val, (tuple, list, torch.Size)): + for s in val: + yield from _iterate_nodes(s) + elif isinstance(val, torch.Tensor): + yield from _iterate_nodes(val.size()) + if not is_sparse_any(val): + yield from _iterate_nodes(val.stride()) + yield from _iterate_nodes(val.storage_offset()) + + +def free_symbols(val: IterateExprs) -> OrderedSet[sympy.Symbol]: + """ + Recursively collect all free symbols from a value. + + This function traverses various data structures (tensors, lists, tuples, etc.) and extracts + all sympy symbols contained within them. It's useful for finding all symbolic variables + that a complex nested structure depends on. + + Args: + val: The value to extract symbols from. Can be a symbolic type (SymInt, SymFloat, SymBool), + a container (tuple, list), a tensor, or None. + + Returns: + OrderedSet[sympy.Symbol]: An ordered set of all free symbols found in the value. + """ + if val is None: + return OrderedSet() + + itr = _iterate_exprs(val) + + # we need at least 1 to call union, so we hand code the identity + try: + first_expr = next(itr) + except StopIteration: + return OrderedSet() + + # TODO: Apparently, returning an OrderedSet here breaks + # python test/distributed/tensor/test_dtensor_compile.py TestDTensorCompile.test_dtensor_dynamic + return first_expr.free_symbols.union(*(e.free_symbols for e in itr)) # type: ignore[return-value] + + +def has_free_symbols(val: IterateExprs) -> bool: + """Faster version of bool(free_symbols(val))""" + return not all((e.is_number or e.is_Boolean) for e in _iterate_exprs(val)) + + +def has_free_unbacked_symbols(x: IterateExprs) -> bool: + """Faster version of bool(free_unbacked_symbols(val))""" + from sympy.core.traversal import iterargs + + for s in _iterate_exprs(x): + for arg in iterargs(s): + if arg.is_Symbol and symbol_is_type( + arg, (SymT.UNBACKED_INT, SymT.UNBACKED_FLOAT) + ): + return True + return False + + +def free_unbacked_symbols(x: IterateExprs) -> OrderedSet[sympy.Symbol]: + """Like free_symbols, but filtered to only report unbacked symbols""" + + # NB: keep synced with is_unbacked_symint + return OrderedSet( + s + for s in free_symbols(x) + if symbol_is_type(s, (SymT.UNBACKED_INT, SymT.UNBACKED_FLOAT)) + ) + + +def _free_non_source_unbacked_symbols( + x: IterateExprs, unbacked_inputs: OrderedSet[sympy.Symbol] +) -> OrderedSet[sympy.Symbol]: + """Unbacked symbols that are not inputs to the graph. These are symbols that originated from + data-dependent operations as opposed to mark_unbacked calls.""" + unbacked_symbols = free_unbacked_symbols(x) + non_source_symbols = unbacked_symbols - unbacked_inputs + return non_source_symbols + + +# WARNING: Don't use this on Dynamo produced graphs, they don't have meta +# setup! +def is_symbol_binding_fx_node(node: torch.fx.Node) -> Optional[sympy.Symbol]: + """ + Check if a given FX node is a symbol binding node. + + A symbol binding node is one that has a SymInt value in its meta that contains + a sympy Symbol expression, and is either a placeholder node or contains unbacked symbols. + + Args: + node (torch.fx.Node): The FX node to check + + Returns: + Optional[sympy.Symbol]: The sympy Symbol if the node is a symbol binding node, None otherwise + """ + if ( + "val" in node.meta + and isinstance(node.meta["val"], torch.SymInt) + and isinstance(node.meta["val"].node.expr, sympy.Symbol) + and ( + node.op == "placeholder" + or free_unbacked_symbols(node.meta["val"].node.expr) + ) + ): + return node.meta["val"].node.expr + return None + + +def find_symbol_binding_fx_nodes( + graph: torch.fx.Graph, +) -> dict[sympy.Symbol, torch.fx.Node]: + """ + Find all nodes in an FX graph that bind sympy Symbols. + + This function scans through all nodes in the given FX graph and identifies + nodes that bind sympy Symbols (typically placeholder nodes with SymInt values). + When multiple nodes bind the same symbol, only the first occurrence is kept. + + Args: + graph: The FX graph to search for symbol binding nodes + + Returns: + A dictionary mapping from sympy Symbols to their binding FX nodes + """ + r = {} + # NB: Prefer first occurrence of symbol + for node in graph.nodes: + if (s := is_symbol_binding_fx_node(node)) is not None and s not in r: + r[s] = node + return r + + +@dataclass(frozen=True) +class Specialization: + """ + This class is used in multi-graph compilation contexts where we generate + multiple specialized graphs and dispatch to the appropriate one at runtime. + This allows us to optimize the trade-off between performance and generality + by creating specialized versions for common patterns (e.g., x.shape[0] % 16 == 0) + while maintaining a general fallback. + """ + + source: TensorPropertySource + check_fn: Callable + + +# Analogous to ConvertIntSource +@dataclass(frozen=True) +class ConvertIntKey: + def __str__(self) -> str: + return ".cast_symbool_to_symint_guardless()" + + def get(self, b: bool) -> IntLikeType: + """Get the int value from bool""" + return cast_symbool_to_symint_guardless(b) + + +@dataclass(frozen=True) +class CallMethodKey: + name: str + + def __str__(self) -> str: + return f".{self.name}()" + + def get(self, o: Any) -> Any: + """Call the method on object""" + return getattr(o, self.name)() + + +@dataclass(frozen=True) +class InnerTensorKey: + inner_name: str + + def __str__(self) -> str: + return f".{self.inner_name}" + + def get(self, o: Any) -> Any: + """Get the inner tensor attribute""" + return getattr(o, self.inner_name) + + +@dataclass(frozen=True) +class DivideByKey: + divisor: IntLikeType + + def __str__(self) -> str: + return f".__floordiv__({self.divisor})" + + def get(self, o: int) -> int: + """Divide object by divisor""" + return o // self.divisor + + +def _free_unbacked_symbols_with_path( + a: object, + path: pytree.KeyPath, + real: Optional[object] = None, + shape_env: Optional[ShapeEnv] = None, + pending: Optional[set[sympy.Symbol]] = None, + simplify: bool = False, +) -> dict[sympy.Symbol, pytree.KeyPath]: + """ + Recursively traverses a structure to find unbacked symbols and their access paths. + + This function walks through tensors, lists, tuples, and symbolic values to locate + unbacked symbols that are in the pending set, and returns a mapping from those + symbols to their access paths in the structure. + + Args: + a: The object to traverse (tensor, list, tuple, SymInt, etc.) + path: The current path in the object tree + real: Optional real tensor corresponding to the fake tensor being traversed + shape_env: Optional ShapeEnv to register unbacked values with + pending: Set of unbacked symbols to look for (will be modified in-place) + simplify: Whether to use simplified expressions + + Returns: + A dictionary mapping unbacked symbols to their access paths + """ + go = functools.partial( + _free_unbacked_symbols_with_path, + shape_env=shape_env, + pending=pending, + simplify=simplify, + ) + + def expr(s: Union[SymInt, SymFloat, SymBool]) -> sympy.Expr: + if simplify: + return s.node.expr + # (When called from compute_unbacked_bindings) + # NB: Intentionally access _expr, not expr, do not want + # simplification! + return s.node._expr + + if pending is None: + pending = set() + r = {} + + def match_tensor(a: torch.Tensor, real_tensor: Optional[torch.Tensor] = None): + r.update( + go( + a.size(), + path + (CallMethodKey("size"),), + real=real_tensor.size() if real_tensor is not None else None, + ) + ) + if a.layout not in [ + torch.sparse_csr, + torch.sparse_csc, + torch.sparse_bsr, + torch.sparse_bsc, + ]: + r.update( + go( + a.stride(), + path + (CallMethodKey("stride"),), + real=real_tensor.stride() if real_tensor is not None else None, + ) + ) + r.update( + go( + a.storage_offset(), + path + (CallMethodKey("storage_offset"),), + real=( + real_tensor.storage_offset() if real_tensor is not None else None + ), + ) + ) + + if isinstance(a, (tuple, list)): + # NB: real is apparently not always a tuple/list here + # python test/inductor/test_torchinductor.py CpuTests.test_index_propagation_nested_indirect_indexing_cpu + for i in range(len(a)): + r.update( + go( + a[i], + path + (pytree.SequenceKey(i),), + real=real[i] if real is not None else None, # type: ignore[index] + ) + ) + elif is_traceable_wrapper_subclass(a): + # TODO: Determine if this is correct + attrs, _ = a.__tensor_flatten__() + for attr in attrs: + sub = getattr(a, attr) + r.update(go(sub, path + (InnerTensorKey(attr),))) + + # match DTensor outer shapes + if torch.distributed.is_available() and isinstance( + a, torch.distributed.tensor.DTensor + ): + match_tensor(a) + elif isinstance(a, torch.Tensor) and is_batchedtensor(a): + unwrapped_tensor = get_unwrapped(a) + r.update(go(unwrapped_tensor, path)) + elif isinstance(a, torch.Tensor) and not is_batchedtensor(a): + from torch._subclasses.fake_tensor import FakeTensor + + assert isinstance(a, FakeTensor) + match_tensor(a, a.real_tensor) + elif ( + isinstance(a, (torch.SymInt, torch.SymFloat)) + and isinstance(s := expr(a), sympy.Symbol) + and s in pending + ): + r[s] = path + if shape_env and real is not None: + assert isinstance(real, (int, float)) + + shape_env.set_unbacked_var_to_val(s, real) + + pending.remove(s) + # When an unbacked SymInt is perfectly divisible by an integer + # constant, we replace it with the integer constant to improve + # reasoning capabilities. However, in synthetic examples, it is + # then possible that the factor never is explicitly allocated. + # Fortunately, we can compute it by division. + elif ( + isinstance(a, torch.SymInt) + and isinstance(s := expr(a), sympy.Mul) + and len(s.args) == 2 + and isinstance(lhs := s.args[0], (sympy.Integer, sympy.Symbol)) + and isinstance(rhs := s.args[1], sympy.Symbol) + # support exactly one unbacked for now + and ((rhs in pending) ^ (lhs in pending)) + # support constant coefficient or backed symbolic coefficient + and ( + isinstance(coeff := lhs if lhs not in pending else rhs, sympy.Integer) + or shape_env + and coeff in shape_env.var_to_val + ) + ): + + def _symint_wrap(s: sympy.Symbol) -> SymInt: + return shape_env.create_symintnode( # type: ignore[union-attr] + s, + hint=int(shape_env.var_to_val[s]), # type: ignore[union-attr] + source=shape_env.var_to_sources.get(s, [None])[0], # type: ignore[union-attr] + ) + + unbacked = lhs if lhs in pending else rhs + divisor: IntLikeType = ( + int(coeff) + if shape_env and isinstance(coeff, sympy.Integer) + else _symint_wrap(coeff) + ) + # TODO: DivideByKey needs to test divisibility at runtime! + + r[unbacked] = path + (DivideByKey(divisor),) + if real is not None: + assert isinstance(real, int) + val = ( + real // int(coeff) + if isinstance(coeff, sympy.Integer) + else CleanDiv(real, coeff) + ) + if shape_env: + shape_env.set_unbacked_var_to_val(unbacked, val) + pending.remove(unbacked) + # The annoyance here arises from the fact that SymBool is + # allocated by allocating a SymInt and then testing if it's equal + # to one. So you have a complicated binding site logic for this. + elif ( + isinstance(a, torch.SymBool) + and isinstance(s := expr(a), sympy.Eq) + # This must match create_unbacked_symbool EXACTLY + and isinstance(s.lhs, sympy.Symbol) + and s.rhs == 1 + and s.lhs in pending + ): + r[s.lhs] = path + (ConvertIntKey(),) + if real is not None: + assert type(real) is bool + if shape_env: + shape_env.set_unbacked_var_to_val(s, int(real)) + + pending.remove(s.lhs) + + return r + + +def compute_unbacked_bindings( + shape_env: Optional[ShapeEnv], + example_value: object, + old_example_value: Optional[object] = None, + peek: bool = False, +) -> Optional[dict[sympy.Symbol, pytree.KeyPath]]: + """ + After having run fake tensor propagation and producing example_value + result, traverse example_value looking for freshly bound unbacked + symbols and record their paths for later. It is an error if + we have allocated an unbacked SymInt but it cannot be found in + example_value. (NB: this means if you have a multi-output + function, you must call this on the tuple of tensor output, you + cannot wait!) + + The peek parameter lets you check out what the bindings are without + changing the affected list. This is primarily useful for ensuring + unbacked_var_to_val is promptly populated when propagate_real_tensors is on. + """ + if shape_env is None: + return None + + fs = shape_env.pending_fresh_unbacked_symbols + + pending = set(fs) + if not pending: + return None + + if not peek: + log.info("compute_unbacked_bindings %s", fs) + fs.clear() + + symbol_to_path = _free_unbacked_symbols_with_path( + example_value, (), shape_env=shape_env, pending=pending, simplify=False + ) + if not peek and pending: + extra = ( + repr((example_value.stride(), example_value.storage_offset())) + if isinstance(example_value, torch.Tensor) + else "" + ) + raise PendingUnbackedSymbolNotFound( + f"Pending unbacked symbols {pending} not in returned outputs {example_value} {extra}.\n" + "Did you accidentally call new_dynamic_size() or item() more times " + "than you needed to in your fake implementation?\n" + "For more help, see https://docs.google.com/document/d/1RWrH-3wLEpzR9kCS6gGBNen_-Fs-8PVbWWFE5AcgeWE/edit" + ) + + # Why do we have to do some rebinding here? If the original FX node + # wasn't a binding site because you had a memo hit, but post + # translation you aren't a memo hit anymore, there's now a new binding + # site... but we know (because it's the same FX node) that the value + # is actually the same, they're just not obviously equal anymore. + # + # The logic here is written carefully, because unlike the + # bind_unbacked case, we are not guaranteed to have a symbol for + # old_sym. If we have a symbol, do regular rename unbacked to; but if + # we don't, we need to specially eliminate the fresh unbacked symbol + # (NB: we are /trusting/ that the memoization is correct, and that we + # don't need to generate a new runtime assert. This is load bearing, + # as repropagation can happen after we've frozen runtime asserts.) + if old_example_value is not None: + for keypath in symbol_to_path.values(): + old_sym = pytree.key_get(old_example_value, keypath) + new_sym = pytree.key_get(example_value, keypath) + if isinstance(new_sym, SymTypes) and isinstance( + new_s := new_sym.node.expr, sympy.Symbol + ): + if ( + isinstance(old_sym, SymTypes) + and (old_s := old_sym.node.expr) != new_s + ): + # If old_s is not an unbacked_symbol, + # we assume that the original unbacked symbol is replaced + # by a backed symbol (old_s). This can happen + # when this node reuses the original symbol (due to memoi) + # and the original symbol gets replaced by the backed symbol. + # When this happens we just replace new_s by the old_s + # because we know the value is the same. + + if isinstance(old_s, sympy.Symbol) and free_unbacked_symbols(old_s): + shape_env._rename_unbacked_to(new_s, old_s) + else: + shape_env._eliminate_unbacked(new_s, old_s) + elif not isinstance(old_sym, SymTypes): + shape_env._eliminate_unbacked(new_s, sympy.sympify(old_sym)) + + return symbol_to_path + + +# Note [guard_or_] +# The following two functions are common utilities used while defining unbacked semantics +# of various framework code. Those would be used in situations you prefer to guard and know +# the result of the expression over not guarding, but in case you hit a data dependent error +# you are ok with just returning true or false. +# +# When to use this? +# (1) If you can use a higher level combinator prefer using those instead, they are definitely safe (modulo short-circuiting). +# +# (2) It can be used if the program would behave equivalently if _guard_or returned true or false. +# Many inductor optimizations fall in this bracket for example. +# +# (3) Finally, it's even be OK if the program wouldn't behave equivalently, so long as the +# change is semantics preserving. It can be semantics preserving if the program errors in more +# cases than it did previously (but otherwise behaves identically), or if it changes some quantity +# in a way that doesn't matter (e.g., strides often fall in this bucket.) +# +# (4) Specialize for the general case and add a runtime assertion that would fail during +# runtime if the conditions for the general case are not satisfied. Examples for this are; +# assuming expand/reshape inputs are not -1. or assuming the non-broadcasting path. +# +def _guard_or(a: BoolLikeType, default: bool) -> bool: + """ + Try to guard a, if data dependent error encountered just return default. + """ + if not isinstance(a, SymBool): + assert isinstance(a, bool) + return a + + # if backed_size_oblivious is True we treat backed as unbacked here. + if torch.fx.experimental._config.backed_size_oblivious: + result = _static_eval_sym_bool(a) + return result if result is not None else default + + shape_env = getattr(a.node, "shape_env", None) + + # xla symnode path. + if shape_env is None: + return guard_bool(a) + + sym_node = a.node + r = sym_node.shape_env.evaluate_sym_node( + sym_node, size_oblivious=False, fallback_value=default + ) + return bool(r) + + +def guard_or_false(a: BoolLikeType) -> bool: + """ + Try to guard a, if data dependent error encountered just return false. + """ + return _guard_or(a, False) + + +def guard_or_true(a: BoolLikeType) -> bool: + """ + Try to guard a, if data dependent error encountered just return true. + """ + return _guard_or(a, True) + + +def _static_eval_sym_bool(x: SymBool) -> Optional[bool]: + assert isinstance(x, SymBool) + expr = x.node.expr + + try: + # Shape env access is inside the try on purpose. xla symnode does not + # have it on its attributes. + shape_env = x.node.shape_env + simplified = shape_env._maybe_evaluate_static(expr) + if simplified is not None: + return bool(simplified) + else: + return None + except Exception: + log.debug("Could not simplify %s", expr) + return None + + +def statically_known_false(x: BoolLikeType) -> bool: + """ + Returns True if x can be simplified to a constant and is False. + If x cannot be evaluated from static, we return False + + .. note:: + This function doesn't introduce new guards, so the expression may end + up evaluating to False at runtime even if this function returns False. + + Args: + x (bool, SymBool): The expression to try statically evaluating + """ + if not isinstance(x, SymBool): + assert isinstance(x, bool) + return not x + + result = _static_eval_sym_bool(x) + if result is None: + return False + + return not result + + +def statically_known_true(x: BoolLikeType) -> bool: + """ + Returns True if x can be simplified to a constant and is true. + + .. note:: + This function doesn't introduce new guards, so the expression may end + up evaluating to true at runtime even if this function returns False. + + Args: + x (bool, SymBool): The expression to try statically evaluating + """ + if not isinstance(x, SymBool): + assert isinstance(x, bool) + return x + result = _static_eval_sym_bool(x) + if result is None: + return False + + return result + + +def sym_and(x: BoolLikeType, *others: BoolLikeType) -> BoolLikeType: + """ + and, but for symbolic expressions, without bool casting. + """ + if len(others) == 0: + return x + for y in others: + x = operator.and_(x, y) + return x + + +def sym_eq(x: _T, y: _T) -> BoolLikeType: + """ + Like ==, but when run on list/tuple, it will recursively test equality + and use sym_and to join the results together, without guarding. + """ + if isinstance(x, (tuple, list)) and isinstance(y, (list, tuple)): + if len(x) != len(y): + return False + return functools.reduce(operator.and_, map(sym_eq, x, y), True) + elif isinstance(x, (int, torch.SymInt)) and isinstance(y, (int, torch.SymInt)): + return x == y + else: + raise AssertionError(f"unexpected sym_eq between {type(x)} {type(y)}") + + +def sym_or(x: BoolLikeType, *others: BoolLikeType) -> BoolLikeType: + """ + or, but for symbolic expressions, without bool casting. + """ + if len(others) == 0: + return x + for y in others: + x = operator.or_(x, y) + return x + + +def guard_scalar( + a: Union[SymBool, SymInt, SymFloat, int, bool, float], +) -> Union[bool, int, float]: + """ + Guard a scalar value, which can be a symbolic or concrete boolean, integer, or float. + + This function dispatches to the appropriate guard function based on the type of the input. + + Args: + a: A symbolic or concrete scalar value (bool, int, or float) + + Returns: + The concrete value after guarding + + Raises: + AssertionError: If the input is not a recognized scalar type + """ + if isinstance(a, (SymBool, bool)): + return guard_bool(a) + elif isinstance(a, (SymInt, int)): + return guard_int(a) + elif isinstance(a, (SymFloat, float)): + return guard_float(a) + else: + raise AssertionError(f"unrecognized scalar {a}") + + +def _advise_is_size(a: SymInt) -> None: + """ + Don't use this directly; use torch._check_is_size instead. + + This is a softer version of _constrain_range_for_size (with min=0, + max=Inf). Instead of forcibly constraining a variable (and erroring if we + failed to constrain it), it will simply advise us that a size is + constrained in some way. We will always defer a runtime assert for this + constraint if we cannot prove it at compile-time, but we we only + *sometimes* learn useful extra information at compile-time with this + information. This is in contrast to constrain_range_for_size, where if + you don't call that on a fresh unbacked symint, chances are we will choke. + + TODO: Make Dynamo handle this appropriately if this is seen in Dynamo-ed + code. Right now this is only really used in code with AOTAutograd trace + through, so it is not a big problem that this isn't supported, but in + principle all of this code should be Dynamo'able too. + + TODO: I didn't support min/max because I didn't have a use case where this + actually helped. In principle we can support it, it just makes the + implementation below more complicated. + """ + + # This must always succeed, because the sole allowed caller _check_is_size + # was responsible for expect_true'ing this + # This assert triggers expensive sym compute, do not do it until its cheap. + # assert a >= 0 + + # NB: it's important not to constrain range for size for *hinted* SymInts, + # because it is not only unsound, it will immediately trip our asserts + # that hints have to be consistent with static analysis! If you somehow + # have an unbounded SymInt that later constrains to 1, this will be + # inconsistent with the range + if ( + isinstance(a, SymInt) + and isinstance(a.node, SymNode) + and isinstance(a.node.expr, sympy.Symbol) + and a.node.shape_env.is_unbacked_symint(a.node.expr) + ): + _constrain_range_for_size(a) + + +def _advise_is_bounded(a: SymInt, upper_bound: IntLikeType) -> None: + if ( + isinstance(a, SymInt) + and isinstance(a.node, SymNode) + and isinstance(a.node.expr, sympy.Symbol) + and a.node.shape_env.is_unbacked_symint(a.node.expr) + and isinstance(upper_bound, int) # TODO: relax + ): + a.node.shape_env._constrain_is_bounded(a.node.expr, upper_bound) + + +def _constrain_range_for_size( + a: SymInt, min: Optional[int] = None, max: Optional[int] = None +) -> None: + """ + This function is NOT INTENDED to be used by itself. + """ + + if isinstance(a, (SymFloat, SymBool)): + raise ValueError("Constraining SymFloat/SymBool is nyi") + + assert isinstance(a, SymInt), "can only constrain range for SymInt" + assert isinstance(a.node.expr, sympy.Symbol), f"constraining non-Symbols NYI: {a}" + + a.node.shape_env._constrain_range_for_size(a.node.expr, min, max) + + +# inclusive both ways +def constrain_range( + a: SymInt, *, min: Optional[int], max: Optional[int] = None +) -> None: + """ + Applies a constraint that the passed in SymInt must lie between min-max + inclusive-inclusive, WITHOUT introducing a guard on the SymInt (meaning + that it can be used on unbacked SymInts). If min/max are None, we assume + that the dimension is unbounded in that direction. Repeated application + of constrain_range intersects the ranges. This is a fairly low level API + that doesn't have a lot of safety guarantees (TODO: provide higher level + APIs). + + Currently, we use this API in the following circumstance: when we allocate + an unbacked SymInt, denoting an integer quantity which is data dependent, + we ordinarily do not know anything about what values it may take. This + means that any sort of guard on it will immediately fail. However, in + many cases, we know something about the unbacked SymInt: for example, we + know that nonzero(x).size(0) must be >= 0. We use constrain_range to + narrow the possible range, declaring that negative symbols are impossible. + This permits to definitely answer True to queries like 'nnz >= 0', even if + we don't know what the actual (hinted) value of 'nnz' is. In fact, we + actually use constrain_range to unsoundly discharge common guards: for an + unbacked SymInt produced by nonzero, we will also assume that it is not + equal to 0/1 (even though these are perfectly possible values at runtime), + because we generally expect graphs that are valid for N=2 to also be valid + for N=1. + """ + if min is None: + min = -int_oo + if max is None: + max = int_oo + + if max < min: + raise ValueError( + "Maximum value to constrain_as_size can't be less than the specified min value, " + f"received min={min} and max={max}" + ) + + if isinstance(a, int): + if not (min <= a <= max): + raise ValueError(f"Invalid value {a} for range [{min}:{max}]") + return + + a.node.shape_env._constrain_range(a.node.expr, min, max) + + +def constrain_unify(a: torch.SymInt, b: torch.SymInt) -> None: + """ + Given two SymInts, constrain them so that they must be equal. NB: + this will not work with SymInts that represent nontrivial expressions + (yet!) + """ + if not isinstance(a, SymInt): + if not isinstance(b, SymInt): + assert a == b + return + else: + shape_env = b.node.shape_env + else: + shape_env = a.node.shape_env + + shape_env._constrain_unify(a, b) + + +# Assume that a boolean is true for the purposes of subsequent symbolic +# reasoning. This will keep track of corresponding runtime checks to verify +# that the result is upheld: either as a regular guard, or as a special set +# of asserts which are triggered when an unbacked SymInt is allocated. +# +# DO NOT use this function for these cases: +# +# - This is inappropriate for "branching" conditions (where both +# true and false result in valid programs). We will always assume +# the condition evaluates true, and so it will never be possible +# to trace the false condition when you use it. For true branching +# on unbacked SymInts, you must use torch.cond; if you incorrectly +# use expect_true in this case, you will make the false branch +# unreachable (as we will simply assume that only the true branch +# is ever exercised). +# +# - This is inappropriate for situations where you know some other system +# invariant guarantees that this property holds, since you don't +# really need to insert a runtime check in that case. Use something +# like constrain_range in that case. +# +# This API has a hitch. To avoid having to reimplement error reporting +# capabilities, this function CAN return False. The invariant is that +# the surrounding code must raise an error when this function returns +# False. This is quite low level, so we recommend using other functions +# like check() which enforce this in a more intuitive way. +# +# By the way, this name is a nod to the __builtin_expect macro, +# which is used similarly (but unlike __builtin_expect, you MUST fail +# in the unlikely branch.) (I think expect is a good name; in recent +# versions of C++, this is replaced with [[likely]], which is weaker +# and not accurate for this function!) +def expect_true(a: BoolLikeType, skip: int = 0) -> bool: + if isinstance(a, SymBool): + # TODO: check perf implications of this + frame = inspect.currentframe() + for _ in range(skip + 1): # always run this loop at least once + if frame is None: + break + frame = frame.f_back + return a.node.expect_true( + frame.f_code.co_filename if frame else "", frame.f_lineno if frame else 0 + ) + assert type(a) is bool, a + return a + + +def guard_bool(a: BoolLikeType) -> bool: + if isinstance(a, SymBool): + return a.node.guard_bool("", 0) # NB: uses Python backtrace + assert type(a) is bool, a + return a + + +def guard_int(a: IntLikeType) -> int: + if isinstance(a, SymInt): + return a.node.guard_int("", 0) # NB: uses Python backtrace + assert type(a) is int, a + return a + + +def guard_float(a: FloatLikeType) -> float: + if isinstance(a, SymFloat): + return a.node.guard_float("", 0) # NB: uses Python backtrace + assert isinstance(a, float), a + return a + + +# Given a GraphModule, return all the FakeTensors for all the placeholders +def fx_placeholder_vals(gm: torch.fx.GraphModule) -> list[object]: + return [n.meta["val"] for n in gm.graph.nodes if n.op == "placeholder"] + + +def fx_placeholder_targets(gm: torch.fx.GraphModule) -> list[str]: + return [n.target for n in gm.graph.nodes if n.op == "placeholder"] + + +# Given a GraphModule and arguments to run it with, evaluate that the guards +# for its associated ShapeEnv are satisfied by the passed arguments. This +# WILL check for duck sizing. +def eval_guards( + gm: torch.fx.GraphModule, *args: Tensor, ignore_static: bool = True +) -> bool: + assert gm.shape_env is not None + return gm.shape_env.evaluate_guards_for_args( # type: ignore[operator, union-attr] + fx_placeholder_vals(gm), args, ignore_static=ignore_static + ) + + +def bind_symbols(gm: torch.fx.GraphModule, *args: Tensor) -> dict[sympy.Symbol, int]: + assert gm.shape_env is not None + return gm.shape_env.bind_symbols(fx_placeholder_vals(gm), args) # type: ignore[operator, union-attr] + + +class DimDynamic(Enum): + """ + Controls how to perform symbol allocation for a dimension. It is always + sound to default this to DYNAMIC, but the policies DUCK and STATIC can + result in better trace-time and compile-time performance, as they reduce + the number of allocated symbols and generally make your graph more static. + + NB: If we notice you've applied a constraint to the dimension, we will + force it to DYNAMIC for simplicity. + + DimDynamic is controlled by a variety of higher level UX features. + Currently: + + - In eager mode, the default policy is DUCK. + - The default is changed to STATIC with assume_static_by_default. + - An individual dim is marked DYNAMIC if you mark_dynamic_dim. + - In export mode, the default policy is STATIC. + - An individual dim is marked DYNAMIC if you specify it in + dynamic_shapes passed to export. + """ + + # Treat the dimension symbolically + DYNAMIC = 0 + # Treat the dimension symbolically, but if its hint matches another + # dynamic dimension, unify the two symbols ("duck sizing") + DUCK = 1 + # Treat the dimension statically based on its hint + STATIC = 2 + # Treat the dimension as a size-like unbacked + SIZE_LIKE_UNBACKED = 3 + # Infer the strides from stride. If size is static, strides will be static as well. + INFER_STRIDE = 4 + # Like SIZE_LIKE_UNBACKED, but there's a hint + OBLIVIOUS_SIZE = 5 + + +# NB: These constraints affect both clients and backends: given some +# constraint C, the client must pass inputs that satisfy the constraint, +# while a backend must not introduce guards BEYOND this constraint. +# For clarity, we document the implications on both sides for both the client +# and the backend. +# +# NB: These constraints are on a *single* dimension. In principle, we could +# also have multi-dimension constraints, but our guess is that this is not +# actually useful and so we are not supporting it right now. +# +# NB: Strict constraints are typically only suitable for export, as in eager +# a backend like inductor may validly introduce extra, discretionary guards +# to improve performance of code. A StrictMinMaxConstraint would be brittle +# under future optimizations performed by inductor; we don't guarantee +# eager code with StrictMinMaxConstraint will keep working in the future! + + +@dataclass(frozen=True) +class Constraint: + warn_only: bool + + +@dataclass(frozen=True) +class StrictMinMaxConstraint(Constraint): + """ + For clients: the size at this dimension must be within 'vr' (which + specifies a lower and upper bound, inclusive-inclusive) AND it + must be non-negative and should not be 0 or 1 (but see NB below). + + For backends: there must not be any guards on this dimension which + are not implied by the given lower and upper bound. Regardless of + the lower bound, the backend can assume the size is non-negative + and that it is not 0 or 1. + + An unbounded StrictMinMaxConstraint can be thought of as a strict version + of "RelaxedUnspecConstraint". + + NB: Export will often unsoundly assume that a graph works for 0/1, even + though at trace time we assumed size is not 0 or 1. The idea is that + if we produce a graph that works for a range of values, it will be OK + for N=0/1 too. + """ + + vr: ValueRanges + + def render(self, source: Source) -> str: + """Format the constrain equation""" + # TODO: better printing for -oo and oo + return f"{self.vr.lower} <= {source.name} <= {self.vr.upper}" + + +@dataclass(frozen=True) +class RelaxedUnspecConstraint(Constraint): + """ + For clients: no explicit constraint; constraint is whatever is implicitly + inferred by guards from tracing. + + For backends: there must exist at least TWO possible values for the + size at this dimension which satisfy the guards for this dimension. + + In other words, this constraint helps us distinguish between "we don't + care if this dimension specializes or not" versus "this dimension must be + unspecialized." However, this constraint doesn't say very much about what + specialization is permitted; for example, if we guard on a size being + even, this would still be acceptable under an unspec constraint. This + makes RelaxedUnspecConstraint useful for eager mode, where your backend compiler + may add constraints to otherwise dynamic dimensions; we can't assert that + there are NO guards as this is brittle because compilers should be able to + add extra constraints. If you want to assert that there are no guards, + use StrictMinMaxConstraint with an unbounded ValueRanges. + """ + + def render(self, source: Source) -> str: + return f"RelaxedUnspecConstraint({source.name})" + + +# NB: None here indicates the client constraint is whatever is implicitly +# inferred by guards from tracing, and that a backend can add whatever guards +# it wants (including fully specializing the value). +DimConstraint = Union[StrictMinMaxConstraint, RelaxedUnspecConstraint, None] + + +@dataclass(frozen=True) +class EqualityConstraint(Constraint): + """ + Represent and decide various kinds of equality constraints between input sources. + + A "source pair" is a pair of input sources for dynamic dimensions that + are specified equal. We represent `source_pairs` in a union-find forest + so that we can efficiently check whether two such sources are transitively equal. + + A "derived equality" relates an input source to an expression over a root. + The root can be another input source, corresponding to some dynamic dimension, + or a phantom symbol that does not directly represent any dynamic dimension. We + represent `derived_equalities` involving input sources in a transitively-closed map + so that we can efficiently check whether an input source is transitively equal to + a given expression over another input source. + (NOTE: In contrast, it is easy to decide whether an input source is transitively equal + to a given expression over a phantom symbol; such expressions are already in canonical + form and so the problem reduces to symbolic expression equality.) + """ + + source_pairs: list[tuple[Source, Source]] + derived_equalities: list[ + tuple[Source, Union[Source, sympy.Symbol], Callable[[sympy.Expr], sympy.Expr]] + ] + phantom_symbols: list[sympy.Symbol] + relaxed_sources: set[Source] + + _parents: dict[Source, Source] = field(init=False) + _defs: dict[Source, sympy.Expr] = field(init=False) + + def __post_init__(self) -> None: + """ + Pre-processing to answer queries `is_equal` and `is_derived` below. + + Example: Suppose we are given: + source_pairs [a = b, b = c] + derived_equalities [d = c + 1, e = d - 1] + We first construct a union find with source_pairs: + _parents = {a: a, b: a, c: a} + Then we compute canonical symbolic expressions, recursively applying derived_equalities + until we bottom out: + _defs = {d: c + 1, e: (c + 1) - 1 aka c} + """ + + # self._parents is a map from input sources to input sources where, conceptually, + # these are directed edges in a union-find forest + _parents: dict[Source, Source] = {} + object.__setattr__(self, "_parents", _parents) + # self._defs is a map from input sources to "canonical" symbolic expressions, + # i.e., unary expressions with symbols that corresponds to regular Dims (i.e., + # not derived Dims) + _defs: dict[Source, sympy.Expr] = {} + object.__setattr__(self, "_defs", _defs) + + for source1, source2 in self.source_pairs: + # preprocess into a union-find forest + self._union(self._find(source1), self._find(source2)) + for source, root, fn in self.derived_equalities: + # preprocess into a transitively-closed map + # NOTE(avik): we reuse the union-find forest for canonicalizing input sources + if isinstance(root, (sympy.Symbol, sympy.Integer)): + self._defs[self._find(source)] = fn(root) + else: + self._defs[self._find(source)] = fn(self._rewrite(root)) + + def _find(self, source: Source) -> Source: + # chase edges to find the root of this equivalence class + if source in self._parents: + return self._find(self._parents[source]) + else: + return source + + def _union(self, root1: Source, root2: Source) -> None: + # merge two equivalence classes by adding an edge from one root to the other + if root1 != root2: + self._parents[root1] = root2 + + def _rewrite(self, src: Source) -> sympy.Expr: + # always represent the given source by the root of its equivalence class + src = self._find(src) + if src in self._defs: + # simply look up the definition if it exists + # NOTE(avik): This works because definitions are always transitively-closed; + # otherwise we would have to do recursive rewriting. + return self._defs[src] + else: + # otherwise, create a symbol representing the source + return sympy.Symbol(src.name) + + def is_equal(self, source1: Source, source2: Source) -> bool: + return ( + # check whether source1 and source2 have the same root + # or are relaxed + (src1 := self._find(source1)) in self.relaxed_sources + or (src2 := self._find(source2)) in self.relaxed_sources + or src1 == src2 + # check whether source1 is derived equal to source2 + or self.is_derived(source1, source2, lambda x: x) + ) + + def is_derived( + self, src: Source, symbol_src: Source, fn: Callable[[sympy.Expr], sympy.Expr] + ) -> bool: + # check whether both src and symbol_src have the same definition + return self._rewrite(src) == fn(self._rewrite(symbol_src)) + + +def _assert_symbol_context(symbolic_context: object) -> TypeGuard[SymbolicContext]: + assert isinstance(symbolic_context, SymbolicContext), ( + "Invalid symbolic_context object" + ) + assert type(symbolic_context) is not SymbolicContext, ( + "Illegal usage of symbolic_context ABC" + ) + return True + + +def _is_supported_equivalence(expr: sympy.Expr) -> bool: + # Currently supported Dim ops are linear expressions with integer coefficients. + # So check that expr only contains +, *, ints, and a single occurrence of a symbol. + # (See also documentation of dynamic_shapes._DerivedDim.) + if isinstance(expr, (sympy.Add, sympy.Mul)): + if len(expr.args) > 2: + return False + lhs, rhs = expr.args + return (_is_supported_equivalence(lhs) and isinstance(rhs, sympy.Integer)) or ( + isinstance(lhs, sympy.Integer) and _is_supported_equivalence(rhs) + ) + return isinstance(expr, sympy.Symbol) + + +def _has_uninterpretable_sympy_function(expr: sympy.Basic) -> bool: + """ + Add functions that our sympy interpreter can't reify into FX nodes + """ + return expr.has( + torch.utils._sympy.functions.ToFloat, + torch.utils._sympy.functions.TruncToInt, + torch.utils._sympy.functions.CeilToInt, + ) + + +@dataclass(frozen=True) +class SymbolicContext: + """ + Data structure specifying how we should create symbols in + ``create_symbolic_sizes_strides_storage_offset``; e.g., should + they be static or dynamic. + + This is an abstract base class because we are probably going to add + another version of this that says "use exactly these SymInts, don't + allocate fresh symbols." + """ + + +@dataclass(frozen=True) +class SymIntSymbolicContext(SymbolicContext): + """ + Data structure specifying any constraints on a SymInt input + """ + + constraint: DimConstraint + + +_P1 = ParamSpec("_P1") +_T1 = TypeVar("_T1") + + +@dataclass(frozen=True) +class StatelessSymbolicContext(SymbolicContext, Generic[_P1, _T1]): + """ + Create symbols in ``create_symbolic_sizes_strides_storage_offset`` via + a symbolic_context determination as given by ``DimDynamic`` and ``DimConstraint``. + This will cause fresh symbols to be allocated + """ + + dynamic_sizes: DimList[DimDynamic] + dynamic_strides: DimList[DimDynamic] = None # type: ignore[assignment] + constraint_sizes: DimList[DimConstraint] = None # type: ignore[assignment] + constraint_strides: DimList[DimConstraint] = None # type: ignore[assignment] + specialize_on: Optional[list[list[Callable[_P1, _T1]]]] = None + # If the tensor is a view, this should be populated for the base. It contains + # information on how to allocate symbols when recursively fakeifying the base + # during view fake-ification. + view_base_context: Optional[SymbolicContext] = None + # TODO: add storage offset and stride symbolic_context + + def __post_init__(self) -> None: + if self.specialize_on is None: + object.__setattr__( + self, + "specialize_on", + [[]] * len(self.dynamic_sizes), + ) + if self.dynamic_strides is None: + object.__setattr__( + self, + "dynamic_strides", + [DimDynamic.INFER_STRIDE] * len(self.dynamic_sizes), + ) + if self.constraint_sizes is None: + object.__setattr__( + self, "constraint_sizes", [None] * len(self.dynamic_sizes) + ) + if self.constraint_strides is None: + object.__setattr__( + self, "constraint_strides", [None] * len(self.dynamic_sizes) + ) + assert all( + stride in (DimDynamic.INFER_STRIDE, DimDynamic.DYNAMIC, DimDynamic.DUCK) + for stride in self.dynamic_strides + ) + + +# note [Tensor Fakification and Symbol Caching] +# +# As of the time of this note, dynamo creates a fresh fake tensor mode for backends. +# The reason we do this is because there are certain classes of operations, namely, +# metadata mutations, that change tensor size, stride, etc. This means that the fake tensor +# state at the end of a dynamo trace is different than the fake tensor state at the beginning +# of a trace. Backends like aot_autograd need a fresh fake tensor to correctly track metadata mutation, +# view relationships, etc. +# +# As we create a new fake mode, we also lose the memoization that comes with it. Rather than +# transfer the memoization cache, we instead transfer the shape env. However, with this +# comes nuance - as dynamo is selective in how it makes symbolic shapes. Due to strategies in +# automatic dynamic and constraints, the policy for which dims are dynamic is nuanced and varies across +# recompilations. +# +# In order to preserve the symbolic decisions made during dynamo tensor fakification, we pass +# a StatefulSymbolicContext at creation time. This object is tracked, per tensor, on the TracingContext. +# The lifecycle of this object should match the lifecycle of the original dynamo tracked tensor, and it is +# safe to reuse this object as many times as necessary to create a fake tensor. Fake tensors +# created with new fake modes should produce the same exact symbols as the original, providing the same shape_env +# is used. +# TODO(voz): Shape env validation +@dataclass(frozen=True) +class StatefulSymbolicContext(StatelessSymbolicContext): + """ + Create symbols in ``create_symbolic_sizes_strides_storage_offset`` via + a symbolic_context determination as given by a cache of Source:Symbol. A cache hit + will reuse a stored symbol, and a cache miss will write to this cache. + + This behaves like StatelessSymbolicContext, except the cache supersedes the + other values - dynamic_sizes and constraint_sizes will not be read if we cache + hit. + + It is the cache owner's responsibility to maintain the lifecycle of the cache + with respect to different shape_envs, clearing, etc. + """ + + tensor_source: Source = None # type: ignore[assignment] + # Why is this keyed on int first? + # That integer is actually the id of the shape_env. This cache short-circuits symbol + # creation, and we must store it per shape env. Now, while tracing invariants are a single + # shape env per tracing context, and every new frame gets a new shape_env. So where would we have + # multiple shape envs? The answer lies in recording. When we are replaying, replay_shape_env_events + # is invoked, and creates a new shape_env. Replaying events against this new shape_env will + # cause it to fail with unknown symbols, as the symbols cached here will skip creation, and never + # get recorded in var_to_val, etc. + # TODO(voz): consider a weakref to the shape_env here + shape_env_to_source_to_symbol_cache: dict[int, dict[str, sympy.Expr]] = None # type: ignore[assignment] + + def __post_init__(self) -> None: + super().__post_init__() + # The None default is annoying, but required because of dataclass limitations + assert self.tensor_source is not None + if not self.shape_env_to_source_to_symbol_cache: + object.__setattr__(self, "shape_env_to_source_to_symbol_cache", {}) + + +@dataclass(frozen=True) +class SubclassSymbolicContext(StatefulSymbolicContext): + """ + The correct symbolic context for a given inner tensor of a traceable tensor subclass + may differ from that of the outer symbolic context. This structure allows for this + flexibility, with inner symbolic contexts mapped via attr -> symbolic context. + """ + + inner_contexts: dict[str, SymbolicContext] = None # type: ignore[assignment] + + def __post_init__(self) -> None: + super().__post_init__() + if self.inner_contexts is None: + # pyrefly: ignore [bad-assignment] + self.inner_contexts = {} + + +@dataclass +class TrackedFake: + """ + Tracks the sources of all fake tensors we wrap in Dynamo. + Used by shape guard computation. + """ + + fake: Union[FakeTensor, SymInt] + source: Source + symbolic_context: Optional[SymbolicContext] + + def __hash__(self) -> int: + return hash((self.fake, self.source.name)) + + def __eq__(self, other: object) -> bool: + if isinstance(other, TrackedFake): + return self.fake is other.fake and self.source.name == other.source.name + return False + + +def is_symbolic( + val: Union[int, SymInt, float, SymFloat, bool, SymBool], +) -> TypeGuard[Union[SymInt, SymFloat, SymBool]]: + if isinstance(val, (int, float, bool)): + return False + return val.node.is_symbolic() + + +IndicatorTypes = (IsNonOverlappingAndDenseIndicator,) + + +def _expandsums(args: list[sympy.Expr]) -> tuple[sympy.Expr, bool]: + """ + Expand products of sums into sums of products. + + This function takes a list of sympy expressions and separates them into + additive expressions (those with is_Add=True) and other expressions. + It then computes the distributive product, expanding (a+b)*(c+d) into a*c + a*d + b*c + b*d. + + Args: + args: A list of sympy expressions to expand + + Returns: + A tuple containing: + - The expanded expression as a sympy.Expr + - A boolean indicating whether expansion occurred (True if multiple additive + expressions were present or if there was at least one additive and one other expression) + """ + adds, other = [], [] + for arg in args: + if arg.is_Add: + adds.append(arg) + else: + other.append(arg) + + result = [sympy.Mul(*other)] + for add in adds: + result = [a * b for a, b in itertools.product(result, add.args)] + + result = sympy.Add(*result) + return result, len(adds) > 1 or (len(adds) > 0 and len(other) > 0) + + +def _fast_expand(expr: _SympyT) -> _SympyT: + """ + A faster implementation of sympy's expand function for common cases. + + This function expands expressions like (a+b)^n or (a+b)*(c+d) into sums of products, + but avoids the expensive checks and features of sympy's full expand implementation. + It only recreates objects when necessary to avoid expensive operations. + + Args: + expr: A sympy expression to expand + + Returns: + The expanded expression + """ + + # The expand algorithm in sympy is slow due to all the features is supports + # For eg: e^(-x)*(x-1)/(x+1) is expanded to (x-1)/(e^x + e^x*x) if x is + # positive and (e^(-x)*x-e^(-x))/(x+1) if x is negative. We do not implement + # such features here to avoid expensive checks. We also make sure that we + # only re-create the objects if any of the args changed to avoid expensive + # checks when re-creating objects. + new_args = [_fast_expand(arg) for arg in expr.args] # type: ignore[arg-type] + # pyrefly: ignore [missing-attribute] + if any(arg is not new_arg for arg, new_arg in zip(expr.args, new_args)): + # pyrefly: ignore [missing-attribute] + return _fast_expand(expr.func(*new_args)) + + # pyrefly: ignore [missing-attribute] + if expr.is_Pow: + base: sympy.Expr + exp: sympy.Expr + base, exp = expr.args # type: ignore[assignment] + if exp.is_Integer and base.is_Add: + if exp > 1: + return sympy.expand_multinomial(expr, deep=False) + elif exp < 0: + return S.One / sympy.expand_multinomial(S.One / expr, deep=False) + # pyrefly: ignore [missing-attribute] + elif expr.is_Mul: + num: list[sympy.Expr] = [] + den: list[sympy.Expr] = [] + # pyrefly: ignore [missing-attribute] + for arg in expr.args: + if arg.is_Pow and arg.args[1] == -1: + den.append(S.One / arg) # type: ignore[operator, arg-type] + else: + num.append(arg) # type: ignore[arg-type] + + num, num_changed = _expandsums(num) + den, den_changed = _expandsums(den) + if num_changed or den_changed: + return num / den + + return expr + + +@lru_cache(256) +def safe_expand(r: _SympyT) -> _SympyT: + """ + Expand the given symbolic expression by recursively rewriting product of + sums into sum of products (with the product being either a multiplication or + exponentiation). + + NOTE: using this on an intermediate expression may prevent simplification + down the line, e.g., if we eagerly expand `(a + b)^2` into `a^2 + 2ab + b^2`, + we won't be able to simplify `(a^2 + 2ab + b^2) / (a + b)` as easily. + """ + if hasattr(r, "expand"): + try: + return _fast_expand(r) + except RecursionError: + log.warning("RecursionError in _fast_expand(%s)", r) + return r + else: + return r + + +class _SymbolInfo(NamedTuple): + k: sympy.Symbol + vr: Optional[ValueRanges] + val: Optional[sympy.Integer] + is_size_like: bool + + +@lru_cache(None) +def _maybe_evaluate_static_worker( + expr: _SympyT, + # NB: this is a tuple to ensure it can be LRU cached + symbol_info: tuple[_SymbolInfo, ...], + unbacked_only: bool, + size_oblivious: bool, +) -> Optional[_SympyT]: + """ + This variant of ShapeEnv._maybe_evaluate_static has no dependence on + ShapeEnv and thus can be cached indefinitely. It does the "heavy" lifting + for static evaluation, including nontrivial reliance on Sympy simplification + that occurs when we reallocate the symbols + """ + + # Simplify making use of value range lower bound + new_shape_env = {} + new_range_env = {} + for idx, sinfo in enumerate(symbol_info): + k, vr, val, is_size_like = sinfo + if isinstance(val, SingletonInt): + # Skip var_ranges logic for SingletonInt which is only used + # for jagged layout NestedTensors today + continue + assert vr is not None + if size_oblivious and is_size_like: + lower = max(2, vr.lower) + # Clamping size-oblivious to some quantity below sys.maxsize + # helps us determine that f(u0) != sys.maxsize, which is a + # test that is looking for sys.maxsize as a sentinel, but you + # don't really want to worry about it for unbacked SymInts. + # This is similar to the flavor where size oblivious omits + # 0/1, it changes semantics but in a benign way. + upper = min(2**48, vr.upper) + # Excluding the very upper bound can be helpful + if upper > lower: + upper = upper - 1 + # This is a bit dodgy: what this means is that there was a + # size-like unbacked symbol whose upper bound < 2. This + # causes... problems. + if lower <= upper: + vr = ValueRanges(lower, upper) + else: + lower = vr.lower + # Don't do anything if we don't have a nontrivial lower bound + # Also don't do anything if we asked only to simplify unbacked + # SymInt + if lower is -int_oo or (unbacked_only and val is not None) or not vr.is_int: + new_range_env[k] = vr + continue + # The goal is to take our symbols which have various lower bounds + # and reallocate them into new symbols which are exactly positive; + # e.g., if we have s0 in [2, inf], we want to turn it into ess0 in + # [1, inf], where s0 = ess0 + 1. This gives the most information + # to sympy for subsequent simplifications. + # + # Positive means >= 1 + # Positive - 1 means >= 0 + # Positive + lower - 1 means >= lower + # The new symbol 's' is "too low", so when we substitute it in + # we have to increase it by offset (and conversely, the new + # variables have to have their value range bounds adjusted as + # well) + s = sympy.Symbol(f"evaluate_static_shape_{idx}", positive=True, integer=True) + + # Note: + # Offset might be a fraction(e.g. aten.split.Tensor), but shapes are always integers. + # Sympy might give unexpected results when comparing an integer with a non-integer + # Therefore, we cast offset to int here. + # For example: + # shape_0 = sympy.Symbol("shape_0", positive=True, integer=True) + # expr = sympy.Eq(shape_0 - 1/3, 4) + # expr.xreplace({}) # False + offset = int(lower - 1) + new_shape_env[k] = s + offset + new_range_env[s] = SymPyValueRangeAnalysis.add(vr, -offset) + + # TODO: remove this try catch (esp for unbacked_only) + try: + # pyrefly: ignore [missing-attribute] + new_expr = expr.xreplace(new_shape_env) + except RecursionError: + log.warning("RecursionError in sympy.xreplace(%s, %s)", expr, new_shape_env) + return None + + # We need to canonicalize, as after expand we may have something like `a + b = a` and + # sympy will not simplify the a. The two appeareances of the a will then make value ranges + # analysis give lose bounds + new_expr = canonicalize_bool_expr(safe_expand(new_expr)) + if new_expr.is_number: + return new_expr + + # Check if the range can solve it statically + out = bound_sympy(new_expr, new_range_env) + if out.is_singleton(): + return out.lower + + return new_expr if unbacked_only else None + + +def error() -> NoReturn: + raise AssertionError("shouldn't be hit") + + +# TODO: Deduplicate this with torch/_prims_common/__init__.py +def eval_is_non_overlapping_and_dense( + sizes: Sequence[int], strides: Sequence[int] +) -> int: + return int(guard_bool(_eval_is_non_overlapping_and_dense(sizes, strides))) + + +def _eval_is_non_overlapping_and_dense( + sizes: Sequence[int], strides: Sequence[int] +) -> bool: + """ + Evaluates whether a tensor with the given sizes and strides is non-overlapping and dense. + + A tensor is non-overlapping if there's no memory location that belongs to more than one element. + A tensor is dense if all elements are stored in memory without gaps. + + Args: + sizes: Sequence of dimension sizes for the tensor + strides: Sequence of strides for the tensor + + Returns: + True if the tensor is non-overlapping and dense, False otherwise + """ + dim = len(sizes) + + # Short-circuits for tensors of rank one, which are + # non-overlapping and "dense" if their stride is one + # or it is a 0/1 element tensor + if dim == 1: + return strides[0] == 1 or sizes[0] < 2 + + # Checks that there exists a permutation of the strides s.t. the tensor would be contiguous + # Sorts (length, stride) pairs by stride + lengths_and_strides = sorted(zip(sizes, strides), key=operator.itemgetter(1)) + + # Unlike the C++ code, we don't move the 0/1 size dimensions to the + # end. So we have to keep going for this code. + expected_stride = 1 + for length, stride in lengths_and_strides: + if length == 1: + continue + + if stride != expected_stride: + return False + + expected_stride *= length + + return True + + +def _sympy_cast_symbool_to_symint_guardless(x: SympyBoolean) -> sympy.Expr: + return sympy.Piecewise((1, x), (0, True)) + + +def cast_symbool_to_symint_guardless( + symbool: Union[bool, torch.SymBool], +) -> Union[int, torch.SymInt]: + """ + Converts a SymBool or bool to a SymInt or int without introducing guards. + + This function maps True to 1 and False to 0, preserving the symbolic nature + of the input when it's a SymBool. Unlike regular casting which might introduce + guards, this function performs the conversion without adding any guards. + + Args: + symbool: A boolean value, either a concrete bool or symbolic SymBool + + Returns: + The corresponding integer value (1 for True, 0 for False) as either + a concrete int or symbolic SymInt + """ + if isinstance(symbool, bool): + return 1 if symbool else 0 + int_sym = _sympy_cast_symbool_to_symint_guardless(symbool.node.expr) + return symbool.node.shape_env.create_symintnode( + int_sym, hint=int(symbool.node.require_hint()) if has_hint(symbool) else None + ) + + +SYMPY_INTERP = { + "IsNonOverlappingAndDenseIndicator": eval_is_non_overlapping_and_dense, + "cast_symbool_to_symint_guardless": cast_symbool_to_symint_guardless, + "math": math, + "torch": torch, +} + + +def _lru_cache( + fn: Callable[..., _T], maxsize: Optional[int] = None +) -> functools._lru_cache_wrapper[_T]: + """ + Wrapper around lru_cache that clears when new info about shapes has been + updated. + + Use lru_cache if the output is always the same, regardless of the + constraints we know now (i.e. evaluate_expr) + + Use _lru_cache otherwise. + + Also note that this depends on _update_version_counter being called on the + shape environment whenever the constraints are updated, otherwise the cache + will not be cleared. + """ + fn_cache = lru_cache(maxsize)(fn) + prior_version = 0 + + if config.validate_shape_env_version_key: + prior_key = None + + @functools.wraps(fn) + def wrapper(self: ShapeEnv, *args: Any, **kwargs: Any) -> _T: + nonlocal prior_version, prior_key + if prior_key is None: + prior_key = self._get_key() + + if prior_version != self._version_counter: + fn_cache.cache_clear() + prior_version = self._version_counter + prior_key = self._get_key() + else: + assert prior_key == self._get_key(), ( + "ShapeEnv cache key changed without version being updated!" + ) + + return fn_cache(self, *args, **kwargs) + + else: + + @functools.wraps(fn) + def wrapper(self: ShapeEnv, *args: Any, **kwargs: Any) -> _T: # type: ignore[misc] + nonlocal prior_version + if prior_version != self._version_counter: + fn_cache.cache_clear() + prior_version = self._version_counter + + return fn_cache(self, *args, **kwargs) + + wrapper.cache_clear = fn_cache.cache_clear # type: ignore[attr-defined] + wrapper.cache_info = fn_cache.cache_info # type: ignore[attr-defined] + return wrapper # type: ignore[return-value] + + +@dataclass(frozen=True) +class RuntimeAssert: + """ + This is pretty similar to ShapeGuard but it also comes with a message, + and is exclusively used for things that MUST be true (unlike guards, + which can evaluate False, in which case you just choose not to use + a particular specialization) + """ + + expr: SympyBoolean + msg: str = field(repr=False) + stack: CapturedTraceback = field(repr=False) + + +# Used for printing SymExprs in compile_fx +class SymExprPrinter(PythonPrinter): + def _print_Float(self, expr: sympy.Float) -> str: + return str(float(expr)) + + +class _ShapeGuardPrinter(abc.ABC): + """ + Abstract base class for printers that convert symbolic expressions to string representations. + + This class provides common functionality for printing symbolic expressions with + special handling for symbols that represent tensor shapes, strides, etc. + Subclasses implement specific formatting for different output languages. + + Args: + symbol_to_source: Mapping from sympy symbols to their source objects + source_ref: Function to convert a source to its string representation + var_to_sources: Mapping from sympy symbols to their source objects (for error reporting) + """ + + def __init__( + self, + symbol_to_source: Mapping[sympy.Symbol, list[Source]], + source_ref: Callable[[Source], str], + var_to_sources: Mapping[sympy.Symbol, list[Source]], + ) -> None: + self.symbol_to_source = symbol_to_source + self.source_ref = source_ref + self.var_to_sources = var_to_sources + super().__init__() + + def _print_Float(self, expr: sympy.Float) -> str: + """Convert a sympy Float to a Python float string representation.""" + return str(float(expr)) + + def _print_Symbol(self, expr: sympy.Symbol) -> str: + """ + Convert a sympy Symbol to its source representation. + + This method looks up the symbol in symbol_to_source mapping and returns + the string representation of its first source. If the symbol is not in + symbol_to_source (which can happen when symbols appear in guard expressions + through simplification or substitution), it falls back to var_to_sources. + + Args: + expr: The sympy Symbol to convert + + Returns: + String representation of the symbol's source + + Raises: + AssertionError: If the symbol is not found in either mapping + """ + assert isinstance(expr, sympy.Symbol), str(type(expr)) + + # Try symbol_to_source first, fall back to var_to_sources if not found + if source := self.symbol_to_source.get(expr): + return self.print_source(source[0]) + elif source := self.var_to_sources.get(expr): + return self.print_source(source[0]) + else: + + def repr_sources(src: Mapping[sympy.Symbol, list[Source]]) -> str: + return repr( + { + symbol: [s.name for s in sources] + for symbol, sources in src.items() + } + ) + + raise RuntimeError( + f"{expr} not in {repr_sources(self.symbol_to_source)} or " + f"{repr_sources(self.var_to_sources)}. This could be due to " + "the issue described in https://github.com/pytorch/pytorch/pull/90665" + ) + + @abc.abstractmethod + def print_source(self, source: Source) -> str: + """ + Convert a source object to its string representation. + + Args: + source: The source object to convert + + Returns: + String representation of the source + """ + ... + + @abc.abstractmethod + def doprint(self, expr: sympy.Expr) -> str: + """ + Convert a sympy expression to its string representation. + + Args: + expr: The sympy expression to convert + + Returns: + String representation of the expression + """ + ... + + +class ShapeGuardPythonPrinter(_ShapeGuardPrinter, PythonPrinter): + """ + Python printer for shape guards that extends the base ShapeGuardPrinter. + + This class provides functionality to print symbolic expressions as Python code, + with caching to improve performance when printing the same expressions multiple times. + It handles printing of sources and expressions according to Python syntax. + + Args: + *args: Arguments passed to the parent classes. + """ + + def __init__(self, *args: Any) -> None: + super().__init__(*args) + self._print_cache: dict[sympy.Expr, str] = {} + + def print_source(self, source: Source) -> str: + """ + Convert a source object to its string representation using the source_ref function. + + Args: + source: The source object to convert + + Returns: + String representation of the source + """ + return self.source_ref(source) + + def doprint(self, expr: sympy.Expr) -> str: + """ + Convert a sympy expression to its Python string representation with caching. + + This method first checks if the expression is already in the cache. + If found, it returns the cached result; otherwise, it delegates to + PythonPrinter's doprint method and caches the result. + + Args: + expr: The sympy expression to convert + + Returns: + String representation of the expression in Python syntax + """ + val = self._print_cache.get(expr, None) + if val is not None: + return val + else: + res = PythonPrinter.doprint(self, expr) + self._print_cache[expr] = res + return res + + +@deprecated( + "`torch.fx.experimental.symbolic_shapes.ShapeGuardPrinter` is deprecated, " + "please use `torch.fx.experimental.symbolic_shapes.ShapeGuardPythonPrinter` instead.", + category=FutureWarning, +) +class ShapeGuardPrinter(ShapeGuardPythonPrinter): + pass + + +class _ShapeGuardCppPrinter(_ShapeGuardPrinter, CppPrinter): + def __init__(self, *args: Any) -> None: + self.all_symbols: set[str] = set() + self.source_to_symbol: dict[Source, sympy.Symbol] = {} + super().__init__(*args) + + def print_source(self, source: Source) -> str: + if source in self.source_to_symbol: + return self.source_to_symbol[source].name + + source_name = source.name + mangled_name = re.sub("[^0-9a-zA-Z_]+", "_", source_name) + old_mangled_name = mangled_name + count = 0 + while mangled_name in self.all_symbols: + mangled_name = f"{old_mangled_name}_{count}" + count += 1 + self.source_to_symbol[source] = sympy.Symbol(mangled_name) + self.all_symbols.add(mangled_name) + return mangled_name + + def doprint(self, expr: sympy.Expr) -> str: + return CppPrinter.doprint(self, expr) + + +# A dataclass for storing shape guards +@dataclass(frozen=True) +class _ShapeGuardsHelper: + exprs: list[str] + + +# A dataclass for storing C++ expressions and helper variables +@dataclass(frozen=True) +class _CppShapeGuardsHelper(_ShapeGuardsHelper): + source_to_symbol: dict[Source, sympy.Symbol] + + +class LoggingShapeGuardPrinter(ShapeGuardPythonPrinter): + def __init__(self, var_to_sources: Mapping[sympy.Symbol, list[Source]]): + super().__init__(var_to_sources, lambda n: n.name, var_to_sources) + + +class DynamicDimConstraintPrinter(PythonPrinter): + """ + Printer for dynamic dim constraints. + - Instead of symbol s_k it prints its source t.size()[i] + - Instead of Eq(_, _), Mod(_, _), etc. it prints _ == _, _ % _, etc. + + We use this to suggest code for specifying dynamic dim constraints. + """ + + def __init__( + self, + symbol_to_source: dict[sympy.Symbol, list[Source]], + source_name_to_debug_name: Mapping[str, str], + ): + super().__init__() + self.symbol_to_source = symbol_to_source + self.source_name_to_debug_name = source_name_to_debug_name + + def _print_Symbol(self, expr: sympy.Symbol) -> str: + assert isinstance(expr, sympy.Symbol), str(type(expr)) + assert self.symbol_to_source.get(expr), ( + f"Unknown symbol {expr} created by constraints solver" + ) + return self.symbol_to_source[expr][0].name + + +class DimConstraints: + """ + Custom solver for a system of constraints on symbolic dimensions. + Solutions are "static" values or simplified "dynamic" constraints. + """ + + def __init__( + self, + symbol_to_source: dict[sympy.Symbol, list[Source]], + var_to_val: Mapping[sympy.Symbol, sympy.Integer], + marked_dynamic: set[sympy.Symbol], + source_name_to_debug_name: Mapping[str, str], + ) -> None: + # We try to solve systems of inequalities with 1 free variable. + self._univariate_inequalities: dict[sympy.Symbol, set[SympyBoolean]] = ( + defaultdict(set) + ) + # Among them, we prioritize solving for a free variable that has equalities. + # NOTE: _symbols_with_equalities is always a subset of _univariate_inequalities.keys() + # and removing a symbol from the former => removing it from the latter. + self._symbols_with_equalities: set[sympy.Symbol] = set() + # A solution of a free variable with equalities becomes a substitution. + # We use these substitutions to simplify other constraints. + # NOTE: removing a symbol from _symbols_with_equalities => adding it to _substitutions. + self._substitutions: dict[sympy.Symbol, sympy.Integer] = {} + + # In general, constraints may have // and % operations. + # Of course, // can be expressed in terms of / and %. + # Our inequality solver can handle / but not %. So we need to transform them away. + # We do so by using the values of variables as hints to evaluate %. + # For soundness we record additional congruence guards and solve them separately. + self._var_to_val: Mapping[sympy.Symbol, sympy.Integer] = var_to_val + self._congruences: defaultdict[sympy.Symbol, set[sympy.Expr]] = defaultdict(set) + + # We do not try to (directly) solve inequalities with > 1 free variables. + # NOTE: free variables in these inequalities cannot also be in _substitutions. + self._multivariate_inequalities: set[SympyBoolean] = set() + + # We park external equalities between free variables here. + self._symbolic_equivalences: list[tuple[Source, sympy.Expr]] = [] + + # Solutions come in two forms: + # - (static) specializations + # - (dynamic) inequalities / congruences + self._static_results: set[str] = set() + self._dynamic_results: set[str] = set() + + # printer for solutions + self._dcp = DynamicDimConstraintPrinter( + symbol_to_source, source_name_to_debug_name + ) + + # inconsistencies found on substituting with concrete values / static solutions + self._inconsistencies: list[str] = [] + + # symbols that are marked dynamic + self._marked_dynamic = marked_dynamic + + # track supported sympy functions and subtract from list of all sympy functions + self._supported_sympy_functions: set[sympy.Function] = { + Application, + Mod, + PythonMod, + FloorDiv, + } + self._enumerate_sympy_functions() + + def rewrite_with_congruences(self, s: sympy.Symbol, expr: _SympyT) -> _SympyT: + """ + Eliminate expressions of the form b // d and b % d while adding congruences of the form b % d == k. + This leaves rational operators (in particular of the form b / d) that our inequality solver can handle. + We solve the added congruences separately (using our congruence solver, see below). + """ + + def mod_handler(*args: sympy.Expr) -> sympy.Expr: + # Suppose that we have an expression of the form b % d with free variable s. + # Using the value of s as a "hint," we can evaluate b % d to a value k. + # Then we can rewrite b % d to k while adding the guard b % d == k. + + # NOTE(avik): This abstraction is provably sound but, in general, incomplete. It is complete IFF + # the original expression always evaluates to a constant value (i.e., it does not vary with s). + # In other words, + # - solutions of s with the rewritten expression are guaranteed to also be solutions of s with + # the original expression; + # - while it may be possible to find solutions of s with the original expression that are not + # solutions with the rewritten expression, in that case the original expression cannot evaluate + # to the same value for all solutions of s. + # + # Should we be worried about this incompleteness? No, because of the following reasons: + # 1. It unblocks dramatic simplification that would not be otherwise possible with current tech + # (i.e., "don't let perfect be the enemy of the good"). + # 2. We already have a tradition of using hints to add guards in the compiler for making progress. + # 3. We have not yet seen a counterexample arise in practice! In particular, any congruence guards + # we generate (or simplify to) seem to be of the form b % d == k where k is a constant. + # + # Here's a theoretical counterexample: 3*s % (s + 1) == s - 2, that is satisfied by all s >= 2. + # With any hint (say) s = k, we'd rewrite this to: 3*s % (s + 1) == k - 2. But, substituting, we + # would then get k - 2 == s - 2, and thus s = k as the (only, constant) solution! + base, divisor = args + base, divisor = ( + self.rewrite_with_congruences(s, base), + self.rewrite_with_congruences(s, divisor), + ) + mod_reduced = base.xreplace(self._var_to_val) % divisor.xreplace( + self._var_to_val + ) + congruence = (base - mod_reduced) % divisor + if congruence != 0: + self._congruences[s].add(congruence) + return mod_reduced + + def floor_div_handler(*args: sympy.Expr) -> sympy.Expr: + # Suppose that we have an expression of the form b // d with free variable s. + # Using the value of s, we can evaluate b % d to a value k. + # Then we can rewrite b // d to (b - k) / d, while adding the guard b % d == k. + + # NOTE(avik): This is exactly equivalent to rewriting b // d as (b - (b % d)) / d + # and eliminating b % d as above. + base, divisor = args + base, divisor = ( + self.rewrite_with_congruences(s, base), + self.rewrite_with_congruences(s, divisor), + ) + mod_reduced = base.xreplace(self._var_to_val) % divisor.xreplace( + self._var_to_val + ) + congruence = (base - mod_reduced) % divisor + if congruence != 0: + self._congruences[s].add(congruence) + # NB: Must not be CleanDiv, it needs to be regular sympy division + # so inequality solver works. This is sort of problematic for + # is_integer tests though haha + return (base - mod_reduced) / divisor + + # pyrefly: ignore [missing-attribute] + if expr.has(Mod): + # pyrefly: ignore [missing-attribute] + expr = expr.replace(Mod, mod_handler) + # 7 // -3 is -3, 7 % -3 is -2, and 7 - (-2) / -3 is -3.0 so negative + # arguments should be OK. + # pyrefly: ignore [missing-attribute] + if expr.has(PythonMod): + # pyrefly: ignore [missing-attribute] + expr = expr.replace(PythonMod, mod_handler) + # pyrefly: ignore [missing-attribute] + if expr.has(FloorDiv): + # pyrefly: ignore [missing-attribute] + expr = expr.replace(FloorDiv, floor_div_handler) + return expr + + def _enumerate_sympy_functions(self) -> None: + module = torch.utils._sympy.functions + all_functions = set() + for attr in dir(module): + if isinstance(func := getattr(module, attr), sympy.FunctionClass): + all_functions.add(func) + self._unsupported_sympy_functions = all_functions.difference( + self._supported_sympy_functions + ) + + def _has_unsupported_sympy_function(self, expr: sympy.Basic) -> bool: + """ + Tracks list of sympy.Functions the export solver doesn't know how to handle. + """ + return expr.has(*self._unsupported_sympy_functions) + + def add(self, expr: SympyBoolean) -> bool: + """Add an expression to the set of constraints. + + Return whether the expression is a trivial constraint (i.e., an obvious tautology). + """ + if expr == sympy.true: + return True + orig_expr = expr + orig_reduced = orig_expr.xreplace(self._var_to_val) + # TODO(avik): https://github.com/pytorch/pytorch/issues/101093 + # It is possible that `expr` will fail the consistency check because of + # precision errors. Specifically, on substituting its free symbols with + # their concrete values, we might end up comparing floats. Until we have + # a fix for this issue, we delay raising such failures. See solve(). + if orig_reduced == sympy.false: + self._inconsistencies.append(f"{orig_expr} is inconsistent!") + if isinstance( + expr, (sympy.Ne, sympy.Or, sympy.And) + ) or self._has_unsupported_sympy_function(expr): + # we're not going to do anything useful with these, so drop them + return False + free_symbols = expr.free_symbols + assert free_symbols, f"Did not expect constraint with no free variables: {expr}" + if len(free_symbols) > 1: + # multivariate: record and move on + self._multivariate_inequalities.add(expr) + else: + # univariate: can solve these immediately + s = next(iter(free_symbols)) + # eliminate // and % (see documentation of `rewrite_with_congruences` above) + old_n_congruences = len(self._congruences[s]) + expr = self.rewrite_with_congruences(s, expr) + new_n_congruences = len(self._congruences[s]) + if expr == sympy.true: + return old_n_congruences == new_n_congruences + reduced = expr.xreplace(self._var_to_val) + if reduced == sympy.false: + self._inconsistencies.append( + f"{expr}, obtained by rewriting {orig_expr} with congruences, " + "is inconsistent!" + ) + if isinstance(expr, sympy.Eq): + # special status for symbols that have equalities (see `solve` below) + self._symbols_with_equalities.add(s) + self._univariate_inequalities[s].add(expr) + return False + + def add_equality(self, source: Source, expr: sympy.Expr) -> None: + """Add an equality constraint""" + if expr.is_number: + # specialization, right here + self._static_results.add(f"{source.name} == {expr}") + else: + # these will resolve to either specializations or dynamic equality constraints + self._symbolic_equivalences.append((source, expr)) + + def _reduce_congruences(self) -> dict[sympy.Symbol, set[sympy.Expr]]: + reduced_congruences: dict[sympy.Symbol, set[sympy.Expr]] = {} + for s, congruences in self._congruences.items(): + remainder_modulus_pairs = [] + congruences_to_check = set() + for congruence in congruences: + base, divisor = congruence.args + # We are given a congruence of the form base % divisor == 0 with a free variable s. So: + # - we transform this into an equation of the form base = divisor * tmp; + # - we solve this equation for s to get a linear solution with free variable tmp. + tmp = sympy.Symbol("reduce_congruences_tmp", integer=True) + symbol, solution = sympy.solve_linear(base - divisor * tmp, symbols=[s]) + # See https://docs.sympy.org/latest/modules/solvers/solvers.html#sympy.solvers.solvers.solve_linear + # for how to interpret the results. + if s == symbol: + # This means the solution is of the form s = modulus*tmp + remainder. + modulus, remainder = sympy.polys.polytools.div(solution, tmp) + if isinstance(modulus, sympy.Integer) and isinstance( + remainder, sympy.Integer + ): + # Make sure 0 <= remainder <= modulus. + remainder = remainder % modulus + remainder_modulus_pairs.append((remainder, modulus)) + continue + # This means that we did not get a unique solution to the equation. + # No problem, we will check it. + congruences_to_check.add(congruence) + # Finally we solve for a congruence s such that s = r_i mod m_i for each (r_i, m_i). + # The solution will be a congruence of the form s = r mod m. + # NOTE(avik): Since the given m_i may not be pairwise coprime, we can't just use CRT. + if remainder_modulus_pairs: + remainder, modulus = sympy.ntheory.modular.solve_congruence( + *remainder_modulus_pairs + ) + reduced_congruences[s] = {(s - remainder) % modulus} + substitution = { + s: modulus * sympy.Symbol("tmp", integer=True) + remainder + } + reduced_congruences[s].update( + congruence + for congruence in congruences_to_check + if not sympy.checksol(congruence, substitution) + ) + else: + reduced_congruences[s] = congruences_to_check + + return reduced_congruences + + def _raise_inconsistencies(self) -> None: + if self._inconsistencies: + msg = "\n".join(self._inconsistencies) + self._inconsistencies.clear() + raise ValueError(f"The following inconsistencies were found:\n{msg}") + + def solve(self) -> None: + """Solve the system of constraint equations to find simplified constraints""" + self._raise_inconsistencies() + # as long as there are symbols with equalities, solve for them + # NOTE(avik): this is guaranteed to terminate (#iterations <= #symbols) + while self._symbols_with_equalities: + s = self._symbols_with_equalities.pop() + exprs = self._univariate_inequalities.pop(s) + solution = sympy.solvers.inequalities.reduce_inequalities(exprs, s) + if isinstance(solution, sympy.And): + solution = next( + (arg for arg in solution.args if isinstance(arg, sympy.Eq)), + solution, + ) + assert isinstance(solution, sympy.Eq), ( + f"Expected an equality constraint for {s}, got {solution}" + ) + symbol, val = solution.args + assert symbol == s, f"Expected a constraint on {s} instead of on {symbol}" + # because this is univariate, the solution is a specialization + self._static_results.add( + f"{self._dcp.symbol_to_source[s][0].name} == {val}" + ) + # add this as a substitution to simplify other constraints + self._substitutions[s] = val # type: ignore[assignment] + + # simplify multivariate inequalities: some of them will now become univariate! + multivariate_inequalities = self._multivariate_inequalities + self._multivariate_inequalities = set() + for expr in multivariate_inequalities: + self.add(expr.xreplace({s: self._substitutions[s]})) + self._raise_inconsistencies() + + # solve linear congruences + # NOTE(avik): We do not need to solve them for symbols that have already been specialized. + reduced_congruences = self._reduce_congruences() + for s, congruences in reduced_congruences.items(): + for congruence in congruences: + # any congruence that cannot be checked becomes a dynamic constraint as well + if s not in self._substitutions or not sympy.checksol( + congruence, {s: self._substitutions[s]} + ): + if self._is_supported_congruence(congruence): + base, divisor = congruence.args + tmp_name = "_" + str( + self._dcp.source_name_to_debug_name.get( + self._dcp.symbol_to_source[s][0].name, + self._dcp.symbol_to_source[s][0].name, + ) + ) + tmp = sympy.Symbol(tmp_name, integer=True) + from torch._dynamo.source import ConstantSource + + self._dcp.symbol_to_source[tmp] = [ConstantSource(tmp_name)] + r = try_solve(sympy.Eq(base, divisor * tmp), s) + assert r is not None + self._dynamic_results.add(self._dcp.doprint(sympy.Eq(s, r[1]))) + + # remaining symbols have only pure inequalities (no equalities) + for s, exprs in self._univariate_inequalities.items(): + try: + solution = sympy.solvers.inequalities.reduce_inequalities(exprs, s) + # because this is univariate, the solution is a dynamic (range) constraint + if isinstance(solution, sympy.Or): + solution = next( + iter( + arg + for arg in solution.args + if arg.xreplace(self._var_to_val) + ) + ) + if isinstance(solution, sympy.And): + for arg in solution.args: + self._dynamic_results.add(self._dcp.doprint(arg)) + else: + self._dynamic_results.add(self._dcp.doprint(solution)) + except (NotImplementedError, AssertionError): + log.warning("Failed to reduce inequalities", exc_info=True) + for expr2 in exprs: + self._dynamic_results.add(self._dcp.doprint(expr2)) + + # simplify symbolic equivalences: some of them will now become specializations! + symbolic_equivalences = self._symbolic_equivalences + self._symbolic_equivalences = [] + for source, expr3 in symbolic_equivalences: + self.add_equality(source, expr3.xreplace(self._substitutions)) + + # remaining symbolic equivalences become dynamic equality constraints + for source, expr3 in self._symbolic_equivalences: + self._dynamic_results.add(f"{source.name} == {self._dcp.doprint(expr3)}") + + @classmethod + def _is_supported_congruence(cls, congruence: sympy.Expr) -> bool: + base, divisor = congruence.args + # Congruences that can be currently expressed with supported Dim ops are + # of the form (x + a) % b == 0, where x is a Dim and a and b are constants. + # This allows us to derive x as b*y - a for some Dim y. + # (See also documentation of dynamic_shapes._DerivedDim.) + if isinstance(base, sympy.Add): + lhs, rhs = base.args + cond = ( + isinstance(lhs, sympy.Symbol) and isinstance(rhs, sympy.Integer) + ) or (isinstance(lhs, sympy.Integer) and isinstance(rhs, sympy.Symbol)) + else: + cond = isinstance(base, sympy.Symbol) + cond = cond and isinstance(divisor, sympy.Integer) + return cond + + def forced_specializations(self) -> dict[str, sympy.Expr]: + """Returns a dictionary of the names of symbols to their specialized value""" + + def debug_name(src: Source) -> str: + name = src.name + if self._dcp.source_name_to_debug_name: + return f"{self._dcp.source_name_to_debug_name[name]} = {name}" + else: + return name + + return { + debug_name(self._dcp.symbol_to_source[s][0]): val + for s, val in self._substitutions.items() + if s in self._marked_dynamic + } + + def _is_derived_dim( + self, dim: object + ) -> TypeGuard[torch.export.dynamic_shapes._DerivedDim]: + return isinstance(dim, torch.export.dynamic_shapes._DerivedDim) + + def _is_dim(self, dim: object) -> TypeGuard[torch.export.dynamic_shapes.Dim]: + return isinstance(dim, torch.export.dynamic_shapes.Dim) and not isinstance( + dim, torch.export.dynamic_shapes._DerivedDim + ) + + def _process_derived_dim_roots( + self, + results: dict[str, dict[str, Any]], + name_to_dim: dict[str, Any], + ) -> None: + """ + Here we resolve 2 concerns with derived dims suggested fixes: 1) newly introduced roots, + and 2) root swapping. + + 1) Newly introduced roots appear with modulo guards, e.g. Mod(dx, 2) = 0 suggests + dx is a derived dim equal to 2 * _dx, introducing a new root _dx. Currently the final + suggested fixes handle this correctly, but we can get intermediate results that look like + {"dy": {"eq": "dx + 1"}, "dx": {"eq": "2 * _dx + 1, "min": 3, "max": 15}} + and this routine prettifies this by unifying to a single root, and making each suggestion + either a derived dim or min/max range, not both. + + 2) With suggested fixes for derived dims, roots can be swapped, + e.g. dx, dx - 1 -> dy + 1, dy. Here we don't want to print out the attached name, + since this leads to messages like "dx - 1 = Dim("dx - 1", ...)". + Instead we evaluate the new root value, and remove results for its derivations. + + First we find all the original roots (specified in dynamic_shapes), that are found in the + values of results (i.e. used for computing suggesting fix values). These original roots + (suppose `dx`) are either specialized, unchanged, refined, or swapped + (expressed as a derived dim). If any of the first 3 cases happen, we suggest `dx`'s value + in results, and remove suggestions for derivations of `dx`, assuming the derived relation + is valid. If swapped, we find the new root, and use the fix to evaluate `dx`'s new value, + and then do the same with `dx`'s derivations. + + Assuming the originally specified derived relations are correct is valid, because: + 1) if the relations are plain wrong (e.g. input shape = (6, 4) with spec (dx, dx - 1)) + produce_guards() will catch this and crash before hand. + 2) if the relations are numerically correct but do not match the emitted guard, + for example: + + def forward(self, x, y): + return x.reshape([-1]) + y # guard: s0 * 2 = s1 + inputs = (torch.randn(6, 2), torch.randn(12)) + dx = Dim("dx", min=2, max=32) + dynamic_shapes={"x": (dx, 2), "y": (dx + 6, )} # this matches values but not op + + then this leads to 2 linear equations, and a) produce_guards() is able to solve for + the unique solution of dx = 6 and specialize, and b) the export constraint solver will + raise an issue due to range constraints (a unique solution means not all values in a + range satisfy a guard) and also force specializations. + """ + from torch.export.dynamic_shapes import Dim + + def _check_same_range(c: Mapping[str, int], dim: object) -> bool: + # returns True if c & dim are both min/max ranges with same values + return ( + self._is_dim(dim) + and ("min" in c or "max" in c) + and ( + (dim.min < 2 and c.get("min", 2) == 2) or dim.min == c.get("min", 2) # type: ignore[attr-defined] + ) # let pass if analysis min = 2 and specified min = 0/1 + and dim.max == c.get("max", int_oo) # type: ignore[attr-defined] + ) + + # 1) newly introduced roots + # this part we handle adding newly introduced roots + # these arise from guards like "x.shape[0] % 3 == 0" + # leading to suggested fixes like "dx = 3*_dx" + # extract _dx, and find appropriate min/max values + # + # before, we have something like: + # {"dx": {"eq": 3*_dx+1, "min": 4, "max": 10}, "dy": dx+1, "dz": dx+2} + # we want instead: + # {"_dx": {"min": 1, "max": 4}, "dx": 3*_dx+1, "dy": 3*_dx+2, "dz": 3*_dx+3} + introduced_roots: dict[str, str] = {} # map new root -> old root + for k, c in list(results.items()): + if "eq" in c and isinstance(c["eq"], sympy.Expr): # derived dim + root = next(iter(c["eq"].free_symbols)) + if str(root) not in name_to_dim: + introduced_roots[str(root)] = k + # calculate necessary min & max + modulus, remainder = sympy.polys.polytools.div(c["eq"], root) + c_min = c.get("min", 2) + min_ = math.ceil((c_min - remainder) / modulus) + c_max = c.get("max", int_oo) + max_ = math.floor((c_max - remainder) / modulus) + # create result & dim + results[str(root)] = {"min": min_, "max": max_} + name_to_dim[str(root)] = Dim(str(root), min=min_, max=max_) + # remove old root min/max bounds + c.pop("min", None) + c.pop("max", None) + + # alter derivations that depend on old root, to unify to new root + # e.g. dx=3*_dx+1, dy=dx+1 -> dy=3*_dx+2 + for old_root in introduced_roots.values(): + for c in results.values(): + if ( + "eq" in c + and isinstance(c["eq"], sympy.Expr) + and str(symbol := next(iter(c["eq"].free_symbols))) == old_root + ): # derived dim with root = old_root + new_root_expr = results[str(old_root)]["eq"] # dx=3*_dx+1 + + new_expr = c["eq"].subs({symbol: new_root_expr}) # dy=(3*_dx+1)+1 + c["eq"] = new_expr + + # 2) root swapping + # collect all the original roots that are used for calculating values of suggested fixes + # this consists of: + # 1) {"dx": {"min": ..., "max": ...}} -> dx: refined root dim + # 2) {"dy": "dx + 1"} -> dx: root for suggested fix + modified_roots: set[str] = set() + for k, c in results.items(): + if k not in name_to_dim: # _dynamo.export() may handle source directly + continue + if self._is_dim(name_to_dim[k]) and ("min" in c or "max" in c): # case 1) + modified_roots.add(k) + elif "eq" in c and isinstance(c["eq"], sympy.Expr): # case 2) + root = next(iter(c["eq"].free_symbols)) + assert root is not None + modified_roots.add(str(root)) + + # exclude newly introduced roots, we've already processed these + modified_roots = modified_roots.difference(introduced_roots) + + # evaluate the new value for each root + # this is now either 1) unchanged, 2) refined with a new range, + # or 3) specialized to a concrete value + modified_root_values: dict[str, dict[str, Any]] = {} + for mroot in modified_roots: + swapped_root = True + if mroot in results: + c = results[mroot] + if ("min" in c or "max" in c) or isinstance( # range + c["eq"], int + ): # specialized + # here, the original root is a root Dim or concrete value in results. + # if it is a derived dim, it is swapped, and we handle that below. + if not _check_same_range( + c, name_to_dim[mroot] + ): # ignore if unchanged + modified_root_values[mroot] = c + swapped_root = False + + if swapped_root: + # if the original root has been swapped in results, that means the new root + # is a range (if it had specialized, the original root would have too). + # find this new root, and solve for the original root's range. + for k, c in results.items(): + if k not in name_to_dim: + continue + dim = name_to_dim[k] + if ( + dim.__class__.__name__ == "_DerivedDim" + and dim.root.__name__ == mroot + ): + # only look for min/max root, otherwise root would have specialized + if "min" in c or "max" in c: + expr = sympy.sympify(k) + s = next(iter(expr.free_symbols)) + result = { + "min": try_solve(sympy.Eq(expr, c["min"]), s)[1], # type: ignore[arg-type, index] + "max": try_solve(sympy.Eq(expr, c["max"]), s)[1], # type: ignore[arg-type, index] + } + if not _check_same_range( + result, + name_to_dim[mroot], # type: ignore[index, arg-type] + ): # ignore if unchanged + modified_root_values[mroot] = result # type: ignore[index] + break + + # filter out results where the key is a derived dim (e.g. {"dx - 1" : 4}) + # we only want to suggest fixes for the root, to avoid derived names. + # also, remove anything in modified_roots, since we either add new modified values after this, + # or have decided they are unchanged. + for k in list(results.keys()): + if k not in name_to_dim: + continue + if self._is_derived_dim(name_to_dim[k]) or k in modified_roots: + del results[k] + + # update results with modified root values + # now results has the following properties: + # - only contains original roots as keys + # - each root is now either specialized, refined, or derived from another original root + results.update(modified_root_values) + + def prettify_results( + self, + original_signature: inspect.Signature, + dynamic_shapes: Union[dict[str, Any], tuple[Any], list[Any]], + constraint_violation_error: object, + forced_specializations: dict[str, str], + ) -> str: + """Format a message for constraint violation errors""" + from torch.export.dynamic_shapes import _get_dim_name_mapping + + if not self._dcp.source_name_to_debug_name: + # nothing to do + return "" + + def transform(s: str, inverse: bool = False) -> str: + for k, v in self._dcp.source_name_to_debug_name.items(): + s = s.replace(k, v) if not inverse else s.replace(v, k) + return s + + results: defaultdict[str, dict[str, Any]] = defaultdict(dict) + if dynamic_shapes is None: + dynamic_shapes = {} + + def flip(op: str) -> str: + if op == "<=": + return ">=" + if op == ">=": + return "<=" + if op == "<": + return ">" + if op == ">": + return "<" + assert op == "==" + return op + + def relation_with_digit(expr: str, op: str, digit: int) -> None: + if op == "<=": + results[expr]["max"] = digit + elif op == "<": + results[expr]["max"] = digit - 1 + elif op == ">=": + results[expr]["min"] = digit + elif op == ">": + results[expr]["min"] = digit + 1 + else: + assert op == "==" + results[expr]["eq"] = digit + + # retrieve dynamic shapes + name_to_dim = _get_dim_name_mapping(dynamic_shapes) + + for s in self._static_results.union(self._dynamic_results): + t = transform(s) + if t == s: + continue + left, op, right = re.split(r"( == | <= | >= | < | > )", t) + op = op.strip() + if op == "==" and left == right: + continue + if right.isdigit(): + relation_with_digit(left, op, int(right)) + elif left.isdigit(): + relation_with_digit(right, flip(op), int(left)) + else: + assert op == "==", t + try: + results[left]["eq"] = sympy.sympify(right) + except TypeError: # rhs source is not linked to Dim name + pass + + # order forced specializations based on name + forced_specializations = { + k: forced_specializations[k] + for k in sorted( + forced_specializations.keys(), + key=lambda x: x.split(" = ")[1], + ) + } + + buf = "" + if forced_specializations: + debug_names = set() + for k in forced_specializations: + dim = name_to_dim[k.split(" = ")[0]] + if self._is_derived_dim(dim): + debug_names.add(dim.root.__name__) # type: ignore[attr-defined] + else: + debug_names.add(dim.__name__) + + buf += ( + f"Specializations unexpectedly required ({', '.join(sorted(debug_names))})! " + 'For more information, run with TORCH_LOGS="+dynamic".\n' + ) + for s, val in forced_specializations.items(): + buf += f" - solving the guards generated for {s} resulted in a specialized value of {val}.\n" + + self._process_derived_dim_roots(results, name_to_dim) + + dims = [] + others = [] + + # order results by source name + results2 = { + k: results[k] + for k in sorted( + results.keys(), + key=lambda x: transform(x, inverse=True), + ) + } + for k, c in results2.items(): + if "eq" in c: + other = c["eq"] + if isinstance(other, int): + others.append(f"{k} = {other}") + elif _is_supported_equivalence(other): + others.append(f"{k} = {other}") + else: + min_ = c.get("min", None) + if min_ == 2: + min_ = None + max_ = c.get("max", None) + if min_ is not None and max_ is not None: + dims.append(f"{k} = Dim('{k}', min={min_}, max={max_})") + elif min_ is not None: + dims.append(f"{k} = Dim('{k}', min={min_})") + elif max_ is not None: + dims.append(f"{k} = Dim('{k}', max={max_})") + else: + dims.append(f"{k} = Dim('{k}')") + + # results2 will get filtered out if no new suggestions, + # this can happen if guards are too complex. + # in that case don't suggest fix + if dims or others: + buf += "\nSuggested fixes:\n " + buf += "\n ".join(dims + others) + + return buf + + +TLS = threading.local() + + +@dataclass(frozen=True) +class ShapeEnvSettings: + """ + Encapsulates all shape env settings that could potentially affect + FakeTensor dispatch. Used when creating dispatch cache keys. + """ + + allow_scalar_outputs: bool + allow_dynamic_output_shape_ops: bool + assume_static_by_default: bool + specialize_zero_one: bool + duck_shape: bool + prefer_deferred_runtime_asserts_over_guards: bool + trace_asserts: bool + + +@dataclass +class ValueRangesSLoc: + """ + Locations of the guards that triggered lower and upper bound. + """ + + lower: SLoc + upper: SLoc + + +@contextmanager +def _suppress_guards(shape_env: ShapeEnv) -> Iterator[None]: + shape_env._suppress_guards_enter() + try: + yield + finally: + shape_env._suppress_guards_exit() + + +@dataclass +class _FrameLocalResult: + loc: Optional[str] = None + locals: dict[str, Any] = field(default_factory=dict) + symbols: dict[str, str] = field(default_factory=dict) + + +class ShapeEnv: + # This is a wrapper over the actual __init__ function. + # + # Where to add a new constructor parameter to ShapeEnv? + # ===================================================== + # This __init__ function should be used only for parameters related to event recording. + # These are parameters that we don't wish to pass down the road to new ShapeEnv instances + # created from replaying events. + # + # If you wish to add a parameter to the constructor of ShapeEnv, unrelated to event + # recording, do so in the _init function. + def __init__( + self, + *, + should_record_events: Optional[bool] = None, + tracked_fakes: Optional[list[Any]] = None, + **kwargs: Any, + ) -> None: + self._init(**kwargs) + + # Disable event recording when replaying. + kwargs["should_record_events"] = False + + from torch.fx.experimental.validator import translation_validation_enabled + + self._translation_validation_enabled = translation_validation_enabled() + + # If not specified, enable event recording if both: + # - Translation validation is on + # - Translation validation bisection is not disabled + self.should_record_events = ( + should_record_events + if should_record_events is not None + else ( + self._translation_validation_enabled + and not config.translation_validation_no_bisect + ) + ) + + # Enable event recording check if both: + # - It should record events + # - The recording check is enabled + self.check_recorded_events = ( + self.should_record_events and config.check_shape_env_recorded_events + ) + + # This will make sure we only record the top-level function call. + self.is_recording = False + # Keep track of the list of tracked fakes. + self.tracked_fakes = tracked_fakes + # List of events for reconstructing ShapeEnv at arbitrary points in time. + self.events: list[ShapeEnvEvent] = ( + [ShapeEnvEvent(ShapeEnv, kwargs=kwargs)] + if self.should_record_events + else [] + ) + + # FakeTensor per-ShapeEnv operation cache. This is used for caching + # operations that contain symbolic shapes which have guards on the + # ShapeEnv (so are ShapeEnv-dependent). + # + # NOTE: It's important that SymNodes in this cache have their ShapeEnv + # stripped otherwise you end up with cycles which can only be cleaned + # with the GC. + self.fake_tensor_cache: dict[ + torch._subclasses.fake_tensor._DispatchCacheKey, + torch._subclasses.fake_tensor._DispatchCacheEntry, + ] = {} + + # Pro-tip: if you add new field to ShapeEnv, this affects some accept + # tests. Accept their output with: + # + # EXPECTTEST_ACCEPT=1 python test/dynamo/test_dynamic_shapes.py -k test_shape_env_equal + # + def _init( + self, + *, + allow_scalar_outputs: bool = True, + allow_dynamic_output_shape_ops: bool = True, + # NB: These are legacy configuration that help us make good choices + # when the constraint/dynamic dims are not explicitly passed to us. + # Ideally we will fix all call sites to be explicit and not have + # implicit choices, but this apparently was pretty involved. + assume_static_by_default: bool = False, + # Note - On 0/1 specialization + # + # The following options affect decisions we make about eager + # specialization. Disabling them will increase trace time (as we do + # more symbolic reasoning) and can also harm the quality of generated + # code (because inductor may not be able to specialize for bounds + # being equal--although if we later respecialize because of a guard, + # your code may be just as good as it was before.) + # + # When True, eagerly specialize input sizes which have 0/1. + specialize_zero_one: bool = True, + # When True, assume input sizes which have the same size are + # symbolically equal. + duck_shape: Optional[bool] = None, + # For debugging + co_fields: Optional[dict[str, str]] = None, + # When True, whenever safe, we will generate a deferred runtime assert + # instead of a guard whenever we know that an expression must be True, + # otherwise it would be an error, even for backed SymInts (where we + # could ostensibly unconditionally generate guards). This is useful + # for export, where preventing "error checking" sizes from showing up + # in guards is helpful, since these guards in some sense are overly + # pedantic. See also https://github.com/pytorch/pytorch/issues/121749 + prefer_deferred_runtime_asserts_over_guards: bool = False, + # XXX Add any new settings that could affect FakeTensor evaluation + # to: torch._subclasses.fake_tensor._ShapeEnvSettings + trace_asserts: bool = False, + ) -> None: + if duck_shape is None: + duck_shape = config.use_duck_shape + + self.settings = ShapeEnvSettings( + # Not directly used by ShapeEnv; indirectly used by FakeTensor + allow_scalar_outputs=allow_scalar_outputs, + allow_dynamic_output_shape_ops=allow_dynamic_output_shape_ops, + # End + assume_static_by_default=assume_static_by_default, + specialize_zero_one=specialize_zero_one, + duck_shape=duck_shape, + prefer_deferred_runtime_asserts_over_guards=prefer_deferred_runtime_asserts_over_guards, + trace_asserts=trace_asserts, + ) + + self.guards: list[ShapeGuard] = [] + self.axioms: dict[sympy.Expr, sympy.Expr] = {} + + # A set of ids that have already been allocated. This is used + # for when we allocate symbol ids using the hash of the source + # names to ensure we don't have collisions via linear probing + self.unique_ids: set[int] = set() + # Maps symbolic ints to their original concrete values + # Currently populated from tensors + self.var_to_val: dict[sympy.Symbol, sympy.Integer] = {} + # Like var_to_val, but only set when propagate_real_tensors is on. + # Used as last resort to avoid GuardOnDataDependent error + self.unbacked_var_to_val: dict[sympy.Symbol, sympy.Integer] = {} + # Like above, but used exclusively for OBLIVIOUS_SIZE. These + # potentially could be put together but I am not sure, writing out + # the logic individually before abstracting. + self.oblivious_var_to_val: dict[sympy.Symbol, sympy.Integer] = {} + # Maps symbolic ints to their min/max range. These ranges + # are conservative: the int MUST fall in the range, but the + # range may contain ints which may not actually appear in + # practice + self.var_to_range: dict[sympy.Symbol, ValueRanges] = {} + self.var_to_range_sloc: dict[sympy.Symbol, ValueRangesSLoc] = {} + self.source_name_to_debug_name: dict[str, str] = {} + self.var_to_sources: dict[sympy.Symbol, list[Source]] = {} + # A set of unbacked symbols that are inputs (i.e: not data dependent). + self.unbacked_inputs: OrderedSet[sympy.Symbol] = OrderedSet() + self.var_to_stack: dict[sympy.Symbol, CapturedTraceback] = {} + self.var_to_hint_override: dict[sympy.Symbol, int] = {} + # Maps a source to the *original* symbol that was assigned to it + self.source_to_var: dict[str, sympy.Symbol] = {} + # Maps from sympy ints to expressions representing them + # Populated from equality guards (i.e. a.shape[0] == b.shape[0]) + self.replacements: dict[sympy.Symbol, sympy.Expr] = {} + # The sloc of the guard that triggered this replacement to be added + self.replacements_slocs: dict[sympy.Symbol, SLoc] = {} + self.unbacked_renamings: dict[sympy.Symbol, sympy.Symbol] = {} + # Set holds a % b expressions that evaluate to 0. + self.divisible: set[sympy.Expr] = set() + # Set that holds "size-like" symbols. When we perform + # "size-oblivious" tests, these can be assumed to be >= 2. + self.size_like: set[sympy.Symbol] = set() + # Duck-shaping says that if two input tensors have the same size, + # they get assigned the same symbolic variable + self.val_to_var: dict[int, sympy.Symbol] = {} + self.unbacked_symfloat_counter = 0 + self.unbacked_symint_counter = 0 + # Similar to guards, but these MUST evaluate to true and can + # only be evaluated at runtime midway through (i.e., they always + # involve unbacked symints) + # + # For efficiency reasons, we index in the following way. Suppose you have + # a runtime assert i0 + i1 <= s1. We pick the most recently allocated + # symbol in the source expression and add the assert to the list for + # that symbol e.g., {i1: [i0 + i1 <= s1]}. + # + # We access the runtime asserts in two situations: + # + # - When we are guarding on an expression, we will attempt to + # statically evaluate it, in case the unbacked SymInts can + # simplify away. If we have a runtime assert, we may be able + # to discharge the guard entirely. We only need to attempt + # runtime asserts that mention freevars of the expression in + # question. + # + # - When we are performing codegen (in Inductor for eager, or + # when finalizing the export FX graph), we need to know what + # extra runtime asserts to insert. Whenever an unbacked + # SymInt comes into scope, all runtime asserts involving it + # become eligible for insertion (so long as all of their other + # free unbacked symbols are also in scope). We technically + # can handle any choice of key by kicking inexpressible asserts + # to the next unbacked symbol to wait on, but if we choose the + # latest key, an assert will only show up at the moment when + # we can actually codegen it. + self.deferred_runtime_asserts: dict[ + Optional[sympy.Symbol], list[RuntimeAssert] + ] = {} + # This exists so we can efficiently invalidate the cache (it's used as + # part of the cache key); otherwise we'd have to iterate through + # deferred_runtime_asserts to compute its length + self.num_deferred_runtime_asserts = 0 + self.log = log + self.log.info("create_env") + self.frozen = False + self.runtime_asserts_frozen = False + self.dim_constraints: Optional[DimConstraints] = None + self.counter: Counter[str] = collections.Counter() + # Mapping from sympy.Symbol to the number of guards which mention this + # symbol + self.symbol_guard_counter: Counter[sympy.Symbol] = collections.Counter() + # A selection of important fields on co_field; solely used for + # signpost_event + self.co_fields = co_fields if co_fields else {} + + # Whenever we allocate a fresh unbacked Symbol, we add it to this + # pending list. Unbacked symbol allocation can occur at unpredictable + # points during meta tensor propagation, but at some point, we + # have to know what the binding site for an unbacked symbol is, and + # this is computed when we actually place the node in the graph. The + # important thing is that we always actually handle every unaccounted + # for unbacked symbol, so this list helps us keep track of them and + # then make sure they are all accounted for. + # + # We could potentially give rise to errors earlier by lexically + # scoping when we do propagation, and only allowing unbacked symbols + # to be allocated at this point in time. However this is inconvenient + # to do in Dynamo, because fake tensor propagation is far from when we + # analyze binding sites (set_example_value), so we do it in a more + # mutatey way. + # + # NB: fresh unbacked symbols NEVER get substitutions applied to them, + # they are binding sites! + self.pending_fresh_unbacked_symbols: list[sympy.Symbol] = [] + + # Version counter used to invalidate cached values + self._prev_cache_key = self._get_key() + self._version_counter = 0 + + # Each time divisible is changed this should be set to True, this is set in _update_version_counter. + self._resimplify_floor_div_axioms = True + + # Cache for FX nodes. + # Maps an already built node a tuple of: + # 1. node's target + # 2. list of arguments + # This drastically reduces the size of the FX graph, avoiding + # duplicated nodes. + self.fx_node_cache: dict[tuple[Callable, tuple[Any, ...]], torch.fx.Node] = {} + self.source_to_symbol: dict[str, sympy.Symbol] = {} + + # Suppose you want to replace an unbacked symbol with another + # unbacked symbol. This is error prone because you can cause + # references to unbacked symbols to time travel backwards. E.g., + # + # u1 = x.item() + # ... use of u1 ... + # u2 = y.item() + # u3 = z.item() + # torch._check(u1 == u2 + u3) + # + # If you replace u1 with u2 + u3, then the use of u1 now + # references u2 and u3 prior to them actually being bound at + # runtime. + # + # To control for this, we track the order unbacked symbols + # were allocated, and only allow substitutions if they respect + # the dependency from this order; an unbacked symbol can only + # be substituted with unbacked symbols that come before it in the + # order. + # + # This also imposes an ordering on the unbacked symbol binding + # sites themselves: you are not allowed to reorder unbacked symbol + # bindings. At the moment, this is not tracked, but we potentially + # could track this at the IR level using a higher order operator + # with something like effect token tracking. + self.unbacked_alloc_order: dict[sympy.Symbol, int] = {} + + self.specialization_stacks: dict[Source, traceback.StackSummary] = {} + + self.trace_asserts = trace_asserts + + self.specializations: OrderedSet[Specialization] = OrderedSet() + + from torch.fx.experimental.validator import translation_validation_enabled + + self._translation_validation_enabled = translation_validation_enabled() + + if self._translation_validation_enabled: + from torch.fx.experimental.validator import TranslationValidator + + self.validator = TranslationValidator() + self.graph = torch.fx.Graph() + # Create an output graph and start inserting before that. + # This is needed when 'deepcopy'-ing this object. + self.graph.inserting_before(self.graph.output(None)) + + # Mapping of each node name to the node itself. + # + # This is useful for matching an FX node from a recorded ShapeEnv.graph + # to the FX node of the ShapeEnv we are running the event on. + # + # Whenever you add a node to self.graph, you must add a mapping to this + # variable. Otherwise, the built FX graph on the replayed ShapeEnv will + # not be valid. + self.name_to_node: dict[str, torch.fx.Node] = {} + + @property + def allow_scalar_outputs(self) -> bool: + return self.settings.allow_scalar_outputs + + @property + def allow_dynamic_output_shape_ops(self) -> bool: + return self.settings.allow_dynamic_output_shape_ops + + @property + def assume_static_by_default(self) -> bool: + return self.settings.assume_static_by_default + + @property + def specialize_zero_one(self) -> bool: + return self.settings.specialize_zero_one + + @property + def duck_shape(self) -> bool: + return self.settings.duck_shape + + @property + def prefer_deferred_runtime_asserts_over_guards(self) -> bool: + return self.settings.prefer_deferred_runtime_asserts_over_guards + + @contextmanager + def patch_source_specialization( + self, source: Source, check_fn: Callable[[sympy.Symbol], sympy.Expr] + ) -> Iterator[None]: + """ + Temporarily add symbol-level axioms to the ShapeEnv. This is useful when you want to "fork" + and have parallel universes of ShapeEnvs. For example, we use this when doing multi-graph + compile so we can support various graphs with varying levels of specializations. + + This context manager allows for temporarily adding constraints to the shape environment + based on a specialization function applied to a symbol associated with a source. + + Args: + source: The source of the symbol to specialize + check_fn: A function that takes a sympy Symbol and returns a sympy expression + representing a constraint/specialization to be applied + """ + name = source.name + sym = self.source_to_var[name] + expr = check_fn(SymInt(SymNode(sym, self, int, None))).node._expr + new_axioms = dict(self.get_implications(self.simplify(expr))) + added_replacements = {} + + for axiom in new_axioms: + if ( + isinstance(axiom, sympy.Eq) + and isinstance(axiom.lhs, sympy.Symbol) + and isinstance(axiom.rhs, sympy.Integer) + and axiom.lhs not in self.replacements + ): + self.replacements[axiom.lhs] = axiom.rhs + added_replacements[axiom.lhs] = axiom.rhs + self.axioms.update(new_axioms) + + # We need to freeze the ShapeEnv because any additional modification of + # the ShapeEnv will cause unsoundness for subsequent specialization calls. + self.frozen = True + try: + yield + finally: + for k in new_axioms: + self.axioms.pop(k, None) + for k in added_replacements: + self.replacements.pop(k, None) + self.frozen = False + + def check_equal(self, other: ShapeEnv) -> None: + """Compare another ShapeEnv for equivalence""" + # ShapeEnv fields that are not relevant for the outcome of + # ShapeEnv.produce_guards call: + # - Debugging variables + # - Translation validation related variables + # - Events recording related variables + non_state_variable_names = ( + "counter", + "log", + "var_to_stack", + "fx_node_cache", + "graph", + "validator", + "check_recorded_events", + "should_record_events", + "is_recording", + "tracked_fakes", + "events", + "source_name_to_debug_name", + "_prev_cache_key", + "_version_counter", + "dim_constraints", + # source locations are OK to diverge + "var_to_range_sloc", + "replacements_slocs", + "_resimplify_floor_div_axioms", + "_expr_sym_node_id", + "specialization_stacks", + ) + + # Mapping of the value of each to-be-compared field into the values that + # should actually be compared. + # + # You should modify this if, for example, the field that holds state and + # debugging information. e.g. ShapeGuard holds the actual guard (sympy.Expr) + # and the stack when it was added to the set of guards. In order to compare + # it, we throw away the stack information. + def map_value(key: str, value: Any) -> Any: + if key == "guards": + # Transform the list of ShapeGuard into a list of expressions. + return [g.expr for g in value] + elif key == "deferred_runtime_asserts": + # Transform the list of RuntimeAsserts into a list of expressions. + return {s: [ra.expr for ra in ras] for s, ras in value.items()} + elif key == "name_to_node": + # Compare just the set of keys is the same. + return set(value.keys()) + elif key in ( + "symbol_guard_counter", + "pending_fresh_unbacked_symbols", + "fake_tensor_cache", + ): + # Skip this for comparisons + return None + return value + + shape_env_check_state_equal(self, other, non_state_variable_names, map_value) + + def _snapshot_tracked_fakes(self) -> Optional[list[Any]]: + if self.tracked_fakes is None: + return None + + from torch._dynamo.variables.builder import TrackedFake + + def maybe_transform_fake(fake: TrackedFake) -> TrackedFake: + inner_fake = ( + fake.fake + if isinstance(fake.fake, (torch.SymInt, torch.SymFloat)) + else FakeTensorMeta.from_fake(fake.fake) + ) + # Even though TrackedFake accepts either a Union[SymInt, FakeTensor], here we give it a + # FakeTensorMeta for two reasons: + # 1. this is all the information we need when recording ShapeEnvEvents. + # 2. it works even if each TrackedFake changes its metadata. + return TrackedFake(inner_fake, fake.source, fake.symbolic_context) # type: ignore[arg-type] + + return [maybe_transform_fake(fake) for fake in self.tracked_fakes] + + def _last_event_index(self) -> int: + return len(self.events) - 1 + + @contextmanager + def _recording(self) -> Iterator[None]: + self.is_recording = True + try: + yield + finally: + self.is_recording = False + + @record_shapeenv_event() + def _eliminate_unbacked(self, orig_s: sympy.Symbol, new_s: sympy.Expr) -> None: + self._set_replacement(orig_s, new_s, "eliminate_unbacked") + + @record_shapeenv_event() + def set_unbacked_var_to_val(self, k: sympy.Symbol, v: int) -> None: + """Used only when propagate_real_tensors; registers a value for an + unbacked symbol, which can be used last resort to resolve hints.""" + log.info("set_unbacked_var_to_val %s = %s", k, v) + self.unbacked_var_to_val[k] = sympy.sympify(v) + + # Unlike set_replacement, this records a shapeenv event + @record_shapeenv_event() + def _rename_unbacked_to(self, orig_s: sympy.Symbol, new_s: sympy.Symbol) -> None: + assert isinstance(orig_s, sympy.Symbol), orig_s + assert isinstance(new_s, sympy.Symbol), new_s + assert free_unbacked_symbols(new_s), new_s + assert free_unbacked_symbols(orig_s), orig_s + dest = self.replacements.get(orig_s) + if dest is not None: + assert not free_unbacked_symbols(dest), f"{orig_s} -> {dest}" + self._set_replacement(orig_s, new_s, "rename_unbacked_to") + self.unbacked_renamings[orig_s] = new_s + if dest is not None: + self._set_replacement(new_s, dest, "rename_unbacked_to_dest") + + @record_shapeenv_event() + def _constrain_is_bounded(self, a: sympy.Symbol, upper_bound: int) -> None: + # TODO: Do something nontrivial when upper_bound is expression + pass + + @record_shapeenv_event() + def _constrain_range_for_size( + self, a: sympy.Symbol, min: Optional[int] = None, max: Optional[int] = None + ) -> None: + if min is None: + min = 0 + if max is None: + max = int_oo + + if max < min: + raise ValueError( + "Maximum value to constrain_as_size can't be less than the specified min value, " + f"received min={min} and max={max}" + ) + + self.constrain_symbol_range( + a, + compiler_min=min, + compiler_max=max, + ) + self.size_like.add(a) + + @record_shapeenv_event() + def _constrain_range(self, a: sympy.Expr, min: int, max: int) -> None: + if isinstance(a, sympy.Integer): + if not (min <= int(a) <= max): + raise ValueRangeError(f"Invalid value {int(a)} for range [{min}:{max}]") + return + + # TODO: Shouldn't we install a guard if the symbol is backed? Or is the + # semantics that this is an "unchecked" assert (but it this actually + # something useful? Might be better to restrict only for unbacked + # SymInt). + if isinstance(a, sympy.Symbol): + self.constrain_symbol_range( + a, + compiler_min=min, + compiler_max=max, + ) + + @record_shapeenv_event() + def _constrain_unify(self, a: SymInt, b: SymInt) -> None: + """ + Given two SymInts, constrain them so that they must be equal. NB: + this will not work with SymInts that represent nontrivial expressions + (yet!) + """ + # TODO: this does not install a deferred runtime assert yet + + # TODO: Maybe dedupe this with _maybe_guard_rel? + # Update Feb 2024: this is extra important to do, this doesn't handle + # unbacked replacements properly nor does it generate deferred runtime + # asserts + if not isinstance(a, SymInt): + if not isinstance(b, SymInt): + assert a == b + else: + assert isinstance(b.node.expr, sympy.Symbol), ( + "constraining non-Symbols NYI" + ) + assert b.node.shape_env is self + self.replacements[b.node.expr] = sympy.Integer(a) + else: + # TODO: Actually, we can support this as long as one of them is a symbol. + # NB: We can't actually do "unification" as our operators are not + # injective + assert isinstance(a.node.expr, sympy.Symbol), "constraining non-Symbols NYI" + assert a.node.shape_env is self + if not isinstance(b, SymInt): + self.replacements[a.node.expr] = sympy.Integer(b) + else: + assert a.node.shape_env is b.node.shape_env + assert isinstance(b.node.expr, sympy.Symbol), ( + "constraining non-Symbols NYI" + ) + new_var = self._find(a.node.expr) + self.replacements[b.node.expr] = new_var + + def _ignore_fresh_unbacked_symbols_tls(self) -> bool: + return getattr(TLS, "ignore_fresh_unbacked_symbols", False) + + @record_shapeenv_event() + def _ignore_fresh_unbacked_symbols_set(self, b: bool) -> bool: + prev = self._ignore_fresh_unbacked_symbols_tls() + TLS.ignore_fresh_unbacked_symbols = b + return prev + + @contextmanager + def ignore_fresh_unbacked_symbols(self) -> Iterator[None]: + """ + Indicates that the newly allocated unbacked SymInts are being + discarded + """ + prev = self._ignore_fresh_unbacked_symbols_set(True) + try: + yield + finally: + self._ignore_fresh_unbacked_symbols_set(prev) + + @record_shapeenv_event() + def freeze(self) -> None: + """Freeze this ShapeEnv to stop accumulating guards + + A frozen ShapeEnv will ignore any further guards generated on it and + only emit a warning which may lead to accuracy problems. + """ + self.frozen = True + + @record_shapeenv_event() + def freeze_runtime_asserts(self) -> None: + """Freeze this ShapeEnv to stop adding deferred runtime asserts. + + We will error if you try to install a new runtime assert when it is + frozen. This would indicate a lowering violation, or perhaps something + we know statically is already True but we are checking it again in a way + that is not clearly dischargeable. + """ + # self.prefer_deferred_runtime_asserts_over_guards = False + self.runtime_asserts_frozen = True + + def _create_symbol_for_source(self, source: Source) -> Optional[sympy.Symbol]: + if not self._translation_validation_enabled: + return None + srcname = source.name + if source not in self.source_to_symbol: + self.source_to_symbol[srcname] = sympy.Symbol(srcname, integer=True) + return self.source_to_symbol[srcname] + + def _add_z3var(self, symbol: sympy.Symbol, type: type) -> None: + if self._translation_validation_enabled: + self.validator.add_var(symbol, type) + + def _add_target_expr(self, expr: SympyBoolean) -> None: + if self._translation_validation_enabled: + self.validator.add_target_expr(expr) + + def _add_assertion(self, expr: SympyBoolean) -> None: + if self._translation_validation_enabled: + self.validator.add_assertion(expr) + + def _check_translation_validate(self) -> None: + if self._translation_validation_enabled: + self.validator.validate() + + @record_shapeenv_event() + def _create_fx_call_function( + self, + op: Callable, + args: tuple, + ) -> tuple[Optional[torch.fx.Node], bool]: + # Cache this tuple in order to avoid duplicated nodes. + node_key = (op, args) + # Flags whether the returned node was cached or not. + fresh = False + + if self._translation_validation_enabled and node_key not in self.fx_node_cache: + # Presence of None in the arguments implies that we should ignore this operation. + if any(a is None for a in args): + # We check if we are not mixing SymNode that should not be ignored + # (fx_node is not None) with those that should (fx_node is None). + assert all(not isinstance(a, torch.fx.Node) for a in args) + return None, fresh + + fresh = True + + # If translation validation is enabled, all arguments must have its + # own FX node. + assert all(a is not None for a in args), ( + f"missing arg in FX graph ({op.__name__}): {args}" + ) + node = self.fx_node_cache[node_key] = self.graph.call_function(op, args) + self.name_to_node[node.name] = node + + return self.fx_node_cache.get(node_key, None), fresh + + def _create_fx_placeholder_and_z3var( + self, + symbol: sympy.Symbol, + type: type, + ) -> Optional[torch.fx.Node]: + if not self._translation_validation_enabled: + return None + + node_key = (self.graph.placeholder, (symbol,)) + + # Check if we haven't added this symbol already. + # If so, skip the placeholder creation, as it + # generates invalid Python code. + if node_key not in self.fx_node_cache: + # Add a Z3 variable according to 'type'. + self._add_z3var(symbol, type) + # Create the FX placeholder out of a mangled name. + mangled_name = re.sub( + r"[^a-zA-Z0-9]", "_", re.sub(r"[()]", "", symbol.name) + ) + node = self.fx_node_cache[node_key] = self.graph.placeholder(mangled_name) + self.name_to_node[node.name] = node + # Attach the 'symbol' to the placeholder so that we can retrieve + # the Z3 variable later. + node.meta["symbol"] = symbol + + return self.fx_node_cache[node_key] + + def _remove_fx_node(self, node: Optional[torch.fx.Node]) -> None: + if self._translation_validation_enabled and node is not None: + self.name_to_node.pop(node.name) + self.graph.erase_node(node) + + def _add_fx_node_metadata(self, node: torch.fx.Node) -> None: + from torch._dynamo.utils import get_current_node + + if self.should_record_events: + node.meta[SHAPEENV_EVENT_KEY] = self._last_event_index() + node.meta[CURRENT_NODE_KEY] = get_current_node() + + @staticmethod + def _suppress_guards_tls() -> bool: + return getattr(TLS, "suppress_guards", False) + + @record_shapeenv_event() + def _suppress_guards_enter(self) -> None: + if not hasattr(TLS, "suppress_guards_stack"): + TLS.suppress_guards_stack = [] + old = self._suppress_guards_tls() + TLS.suppress_guards_stack.append(old) + TLS.suppress_guards = True + + @record_shapeenv_event() + def _suppress_guards_exit(self) -> None: + old = ( + TLS.suppress_guards_stack.pop() + if len(TLS.suppress_guards_stack) > 0 + else False + ) + TLS.suppress_guards = old + + def suppress_guards(self) -> _GeneratorContextManager[None]: + """Context manager to ignore all guards generated inside""" + return _suppress_guards(self) + + def _get_key(self) -> tuple[int, int, int, int]: + """ + Defines the current "state" of the guards we've accumulated in this ShapeEnv. + Determines when we need to invalidate our cache + """ + return ( + len(self.replacements), + len(self.divisible), + self.num_deferred_runtime_asserts, + len(self.unbacked_var_to_val), + ) + + def _update_version_counter(self) -> None: + # if the change to shape env effects self.divisible set + # _resimplify_floor_div_axioms. + # This is used to trigger a resimplication of FloorDiv to CleanDivs + # in implication inside the function resimplify_floor_div. + if len(self.divisible) != self._prev_cache_key[1]: + self._resimplify_floor_div_axioms = True + + # The shape environment is queried orders of magnitude more often than + # it is changed, so we summarise the cache key into a linearly + # increasing version counter which is cheaper to check in _lru_cache + + # Only update version counter if the state actually changed + cur_key = self._get_key() + + if self._prev_cache_key != cur_key: + self._prev_cache_key = cur_key + self._version_counter += 1 + + def _produce_dyn_sizes( + self, + ex_size: Sequence[IntLikeType], + source: Source, + symbolic_context: SymbolicContext, + ) -> list[sympy.Expr]: + return self._produce_dyn_sizes_from_int_tuple( + tuple(ex_size), source, symbolic_context + ) + + def _produce_dyn_sizes_from_int_tuple( + self, + tensor_size: Sequence[IntLikeType], + source: Source, + symbolic_context: SymbolicContext, + hint_overrides: Optional[dict[int, int]] = None, + ) -> list[sympy.Expr]: + assert all(not is_symbolic(val) for val in tensor_size), ( + f"Expect size to be a plain tuple of ints but got {tensor_size}" + ) + from torch._dynamo.source import TensorProperty, TensorPropertySource + + if not hint_overrides: + hint_overrides = {} + + _assert_symbol_context(symbolic_context) + dynamic_dims = symbolic_context.dynamic_sizes # type: ignore[attr-defined] + constraint_dims = symbolic_context.constraint_sizes # type: ignore[attr-defined] + size = [] + for i, val in enumerate(tensor_size): + sym = self.create_symbol( + hint_overrides.get(i, val), + TensorPropertySource(source, TensorProperty.SIZE, i), + dynamic_dims[i], + constraint_dims[i], + do_not_specialize_zero_one=config.backed_size_oblivious, + symbolic_context=symbolic_context, + ) + if ( + isinstance(symbolic_context, StatelessSymbolicContext) + and symbolic_context.specialize_on + ): + for specialization in symbolic_context.specialize_on[i]: + self.specializations.add( + Specialization( + TensorPropertySource(source, TensorProperty.SIZE, i), + specialization, + ) + ) + if ( + config.backed_size_oblivious + and isinstance(sym, sympy.Symbol) # could be static + and symbol_is_type(sym, SymT.SIZE) + ): + self.size_like.add(sym) + size.append(sym) + return size + + def create_symbolic_sizes_strides_storage_offset( + self, + ex: torch.Tensor, + source: Source, + *, + symbolic_context: Optional[SymbolicContext] = None, + ) -> tuple[ + tuple[IntLikeType, ...], + tuple[IntLikeType, ...], + IntLikeType, + ]: + """ + Returns a list of symbolic sizes and strides for the given tensor. + We try our best to express stride in terms of the sizes, so as to not + introduce new symbolic variables. + """ + + ex_size = tuple( + self._maybe_specialize_sym_int_with_hint(sz) for sz in ex.size() + ) + ex_stride = tuple( + self._maybe_specialize_sym_int_with_hint(sd) for sd in ex.stride() + ) + ex_storage_offset = self._maybe_specialize_sym_int_with_hint( + ex.storage_offset() + ) + + return self._create_symbolic_sizes_strides_storage_offset( + ex_size, + ex_stride, + ex_storage_offset, + [_is_dim_dynamic(ex, i) for i in range(ex.dim())], + source, + symbolic_context=symbolic_context, + ) + + # Dynamo may want to wrap FakeTensors with SymInt sizes up e.g. make_fx(opt_f(), tracing_mode="symbolic"). + # We create symbols in shape_env using the backed hints behind SymInt. + + # Case 1: when SymInt is backed, dynamo can proceed with FakeTensors that have concrete shape. + # produce_guards will trigger specializations on the outer stuff + + # Case 2: when the SymInt is unbacked, we will throw an data dependent error in require_hint(). + # + # It's probably good for now but it's important to note that this approach has implications for + # the original shape_env when checking guards in different order. + + # Example: + # --------- + # Consider a function "opt_f" as shown below: + + # @torch.compile() + # def opt_f(x: bool, y: Tensor): + # if x == True: + # return y + torch.randn([4]) + # else: + # return y + # Depending on the sequence of calls, we might install two different sets of guards: + + # 1. opt_f(False, y): + # - "x == False" (always works for any size y) + + # 2. opt_f(True, y): + # - Triggers recompilation and results in guards like: + # - "x == True and y.size(0) == 4" + # - (or "y.size(0) == 4 and x == True") + + # The order of checking the guards matters. In this specific example: + # If True branch guard check precedes False branch and for True branch, y.size(0) check precedes x == True, + # we may have an unnecessary shape specialization for y. + def _maybe_specialize_sym_int_with_hint( + self, maybe_sym: IntLikeType + ) -> IntLikeType: + assert isinstance(maybe_sym, (int, torch.SymInt)) + if is_symbolic(maybe_sym): + assert maybe_sym.node.shape_env is not self, ( + "expect the symbol is created from an shape env other than current one." + ) + return maybe_sym.node.require_hint() + return maybe_sym + + @record_shapeenv_event() + def _create_symbolic_sizes_strides_storage_offset( + self, + # NB: SymInt is allowed here due to nested int, normally you don't + # actually pass true symbolic sizes to this function + ex_size: Sequence[IntLikeType], + ex_stride: Sequence[IntLikeType], + ex_storage_offset: IntLikeType, + is_dim_dynamic: Sequence[bool], + source: Source, + *, + symbolic_context: Optional[SymbolicContext] = None, + hint_overrides: Optional[dict[int, int]] = None, + ) -> tuple[ + tuple[IntLikeType, ...], + tuple[IntLikeType, ...], + IntLikeType, + ]: + dim = len(ex_size) + + if not hint_overrides: + hint_overrides = {} + + # Reimplement the legacy behavior + if symbolic_context is None: + constraint_sizes: list[DimConstraint] = [None] * dim + constraint_strides: list[DimConstraint] = [None] * dim + dynamic_dims = [] + dynamic_strides = [] + for i in range(dim): + # NB: This is encapsulation breaking! Legacy behavior was + # bad. + if is_dim_dynamic[i]: + r = DimDynamic.DYNAMIC + elif self.assume_static_by_default: + r = DimDynamic.STATIC + else: + r = DimDynamic.DUCK + dynamic_dims.append(r) + dynamic_strides.append(r) + dynamic_dims = [DimDynamic.DUCK] * dim + dynamic_strides = [DimDynamic.INFER_STRIDE] * dim + # symbolic_context is None - set one + symbolic_context = StatelessSymbolicContext( + dynamic_sizes=dynamic_dims, + dynamic_strides=dynamic_strides, + constraint_sizes=constraint_sizes, + constraint_strides=constraint_strides, + ) + # We got a StatelessSymbolicContext + _assert_symbol_context(symbolic_context) + constraint_sizes = symbolic_context.constraint_sizes # type: ignore[attr-defined] + constraint_strides = symbolic_context.constraint_strides # type: ignore[attr-defined] + dynamic_sizes = symbolic_context.dynamic_sizes # type: ignore[attr-defined] + dynamic_strides = symbolic_context.dynamic_strides # type: ignore[attr-defined] + + # TODO: make this configurable from outside symbolic_context; we made a symbolic_context + # decision here where if all sizes are static, we are going to + # specialize all of the inner strides/offset too. We don't have to + # do this, and arguably we should ALWAYS allow for dynamic offset, + # this is cheap. + # TODO: This should be DYNAMIC, using DUCK for BC + dynamic_offset = ( + DimDynamic.STATIC + if all(r == DimDynamic.STATIC for r in dynamic_sizes) + else DimDynamic.DUCK + ) + are_sizes_static = all(r == DimDynamic.STATIC for r in dynamic_sizes) + + assert len(dynamic_sizes) == dim, f"{len(dynamic_sizes)} != {dim}" + assert len(dynamic_strides) == dim, f"{len(dynamic_sizes)} != {dim}" + assert len(constraint_sizes) == dim + assert len(constraint_strides) == dim + + from torch._dynamo.source import TensorProperty, TensorPropertySource + + size: list[sympy.Expr] = self._produce_dyn_sizes_from_int_tuple( + ex_size, source, symbolic_context, hint_overrides=hint_overrides + ) + stride = self._compute_symbolic_stride( + source, + size, + ex_size, + ex_stride, + dynamic_strides, + constraint_strides, + are_sizes_static, + symbolic_context, + ) + + sym_sizes = [ + self.create_symintnode( + sym, + hint=hint_overrides.get(i, hint), + source=TensorPropertySource(source, TensorProperty.SIZE, i), + ) + for i, (sym, hint) in enumerate(zip(size, ex_size)) + ] + + for i, sym in enumerate(sym_sizes): + if isinstance(sym, torch.SymInt) and i in hint_overrides: + self.var_to_hint_override[sym.node.expr] = hint_overrides[i] + + sym_stride = [] + for i, stride_expr in enumerate(stride): + # NB: Don't duck size the stride; instead use the expression + # we computed + assert stride_expr is not None + sym_stride.append( + self.create_symintnode( + stride_expr, + hint=ex_stride[i], + source=TensorPropertySource(source, TensorProperty.STRIDE, i), + ) + ) + sym_storage_offset = self.create_symintnode( + self.create_symbol( + ex_storage_offset, + TensorPropertySource(source, TensorProperty.STORAGE_OFFSET), + dynamic_dim=dynamic_offset, + constraint_dim=None, + symbolic_context=symbolic_context, + ), + hint=ex_storage_offset, + source=TensorPropertySource(source, TensorProperty.STORAGE_OFFSET), + ) + return tuple(sym_sizes), tuple(sym_stride), sym_storage_offset + + def _compute_symbolic_stride( + self, + source: Source, + size: Sequence[sympy.Expr], + ex_size: Sequence[IntLikeType], + ex_stride: Sequence[IntLikeType], + dynamic_strides: Sequence[DimDynamic], + constraint_strides: Sequence[ + Optional[Union[StrictMinMaxConstraint, RelaxedUnspecConstraint]] + ], + are_sizes_static: bool, + symbolic_context: SymbolicContext, + ) -> list[sympy.Expr]: + from torch._dynamo.source import TensorProperty, TensorPropertySource + + stride: list[Optional[sympy.Expr]] = [None] * len(size) + candidates: dict[IntLikeType, sympy.Expr] = {} + + # iterate over unbound strides in val ascending order with + # index descending as a tie breaker since for cases like + # [(1, 1), (1, 0)], we want to fill in the right most + # stride first. + val_list = [(val, -i) for i, val in enumerate(ex_stride)] + val_list.sort(key=_nested_int_aware_sort) + + for val, neg_i in val_list: + i = -neg_i + contiguous_stride = ( + i != len(ex_stride) - 1 + and ex_stride[i] == ex_size[i + 1] * ex_stride[i + 1] + ) + if val in (0, 1) and not contiguous_stride: + out_stride = sympy.Integer(val) + else: + dynamic_stride = dynamic_strides[i] + if dynamic_stride == DimDynamic.INFER_STRIDE and val in candidates: + # Set stride to a candidate only for DimDynamic.INFER_STRIDE + out_stride = candidates[val] + else: + # Set INFER_STRIDE to STATIC or DUCK depending on sizes + dyn_stride = dynamic_stride + if dynamic_stride == DimDynamic.INFER_STRIDE: + dyn_stride = ( + DimDynamic.STATIC if are_sizes_static else DimDynamic.DUCK + ) + out_stride = self.create_symbol( + val, + TensorPropertySource(source, TensorProperty.STRIDE, i), + dynamic_dim=dyn_stride, + constraint_dim=constraint_strides[i], + symbolic_context=symbolic_context, + ) + stride[i] = out_stride + candidates[ex_size[i] * val] = size[i] * out_stride + + assert all(x is not None for x in stride) + return stride + + @record_shapeenv_event() + def create_symintnode( + self, + sym: sympy.Expr, + *, + hint: Optional[int], + source: Optional[Source] = None, + ) -> IntLikeType: + """Create a SymInt value from a symbolic expression + + If you know what the current hint value of the SymInt to be created + is, pass it into hint. Otherwise, pass None and we will make our best + guess + + """ + if self._translation_validation_enabled and source is not None: + # Create a new symbol for this source. + symbol = self._create_symbol_for_source(source) + assert symbol is not None + + # Create a new FX placeholder and Z3 variable for 'symbol'. + fx_node = self._create_fx_placeholder_and_z3var(symbol, int) + + # Add an equality assertion for the newly created symbol and 'sym'. + self._add_assertion(sympy.Eq(symbol, sym)) + else: + fx_node = None + + out: IntLikeType + if isinstance(sym, sympy.Integer): + if hint is not None: + assert int(sym) == hint + out = int(sym) + else: + # How can this occur? When we mark_unbacked, we end up with a real + # tensor that has hints for all sizes, but we MUST NOT create a + # SymNode with a hint, because we're hiding the hint from our eyes + # with the unbacked Symbol. And in fact, the hint compute may be + # inconsistent with size oblivious tests. + if free_unbacked_symbols(sym): + hint = None + out = SymInt(SymNode(sym, self, int, hint, fx_node=fx_node)) + return out + + @record_shapeenv_event() + def create_symfloatnode( + self, + sym: sympy.Expr, + *, + hint: Optional[int | float | bool], + source: Optional[Source] = None, + ) -> FloatLikeType: + """Create a SymFloat value from a symbolic expression""" + if self._translation_validation_enabled and source is not None: + # Create a new symbol for this source. + symbol = self._create_symbol_for_source(source) + assert symbol is not None + + # Create a new FX placeholder and Z3 variable for 'symbol'. + fx_node = self._create_fx_placeholder_and_z3var(symbol, float) + + # Add an equality assertion for the newly created symbol and 'sym'. + self._add_assertion(sympy.Eq(symbol, sym)) + else: + fx_node = None + + out: FloatLikeType + if isinstance(sym, sympy.Float): + if hint is not None: + assert float(sym) == hint + out = float(sym) + else: + # You could give this the same treatment as SymInt above if + # you supported mark_unbacked on a float, but it's a kind of + # strange thing to do though because floats don't get 0/1 + # specialization anyway + if free_unbacked_symbols(sym): + assert hint is None, sym + out = SymFloat(SymNode(sym, self, float, hint, fx_node=fx_node)) + return out + + @record_shapeenv_event() + def create_unspecified_symint_and_symbol( + self, value: int, source: Source, dynamic_dim: DimDynamic + ) -> IntLikeType: + """Create a SymInt wrapping a new unspecified symbol""" + return self.create_symintnode( + self.create_unspecified_symbol( + value, + source=source, + dynamic_dim=dynamic_dim, + ), + hint=value, + source=source, + ) + + def create_symboolnode(self, sym: sympy.Expr) -> SymBool: + """Create a SymBool object from a sympy boolean expression""" + # This function is only being used in serialization, so we do not track it + # for validation. + return SymBool(SymNode(sym, self, bool, None)) + + def _log_create_unbacked_symbol( + self, + prefix: str, + symbol: sympy.Symbol, + vr: ValueRanges, + source: Optional[Source] = None, + sym_node: Optional[SymNode] = None, + ) -> None: + is_debug = config.extended_debug_create_symbol is not None and str( + symbol + ) in config.extended_debug_create_symbol.split(",") + sloc: Union[str, SLoc] + if source is None: + sloc, maybe_extra_debug = self._get_stack_summary(is_debug) + else: + sloc, maybe_extra_debug = source.name, "" + log.info( + "%s %s [%s, %s] %s%s", + prefix, + symbol, + vr.lower, + vr.upper, + sloc, + maybe_extra_debug, + stack_info=is_debug, + ) + trace_structured( + "create_unbacked_symbol", + metadata_fn=lambda: { + "symbol": str(symbol), + "node_id": id(sym_node), + "vr": f"[{vr.lower}, {vr.upper}]", + "user_stack": structured.get_user_stack(3), + "stack": structured.get_framework_stack(), + }, + ) + + @record_shapeenv_event() + def create_unbacked_symfloat(self) -> SymFloat: + """Create a symbolic float without a hint value""" + symbol: sympy.Symbol = make_symbol( + SymT.UNBACKED_FLOAT, self.unbacked_symfloat_counter + ) + self.unbacked_symfloat_counter += 1 + self.counter["create_unbacked_symbol"] += 1 + if not self._ignore_fresh_unbacked_symbols_tls(): + self.pending_fresh_unbacked_symbols.append(symbol) + self.var_to_stack[symbol] = CapturedTraceback.extract(skip=1) + vr = self.var_to_range[symbol] = ValueRanges.unknown() + assert vr.is_float + sloc = self._get_sloc() + self.var_to_range_sloc[symbol] = ValueRangesSLoc(sloc, sloc) + + # Create a new FX placeholder and Z3 variable for 'symbol'. + fx_node = self._create_fx_placeholder_and_z3var(symbol, float) + + sym_node = SymNode(symbol, self, float, None, fx_node=fx_node) + self._log_create_unbacked_symbol( + "create_unbacked_symfloat", symbol, vr, sym_node=sym_node + ) + + return SymFloat(sym_node) + + @record_shapeenv_event() + def create_unbacked_symint(self, source: Optional[Source] = None) -> SymInt: + """Create a symbolic integer without a hint value""" + symbol: sympy.Symbol = make_symbol( + SymT.UNBACKED_INT, self.unbacked_symint_counter, integer=True + ) + self.unbacked_symint_counter += 1 + if not self._ignore_fresh_unbacked_symbols_tls(): + self.pending_fresh_unbacked_symbols.append(symbol) + self.counter["create_unbacked_symbol"] += 1 + self.var_to_stack[symbol] = CapturedTraceback.extract(skip=1) + vr = self.var_to_range[symbol] = self._default_unspecified_value_range() + assert vr.is_int + sloc = self._get_sloc() + self.var_to_range_sloc[symbol] = ValueRangesSLoc(sloc, sloc) + + # Create a new FX placeholder and Z3 variable for 'symbol'. + fx_node = self._create_fx_placeholder_and_z3var(symbol, int) + + sym_node = SymNode(symbol, self, int, None, fx_node=fx_node) + self._log_create_unbacked_symbol( + "create_unbacked_symint", symbol, vr, source, sym_node=sym_node + ) + return SymInt(sym_node) + + def is_unbacked_symint(self, symbol: sympy.Symbol) -> bool: + """Check if a sympy symbol matches the naming convention for unbacked symbols""" + return symbol_is_type(symbol, SymT.UNBACKED_INT) + + @record_shapeenv_event() + def create_unbacked_symbool(self) -> SymBool: + """Create a symbolic boolean without a hint value""" + symbol: sympy.Symbol = make_symbol( + SymT.UNBACKED_INT, self.unbacked_symint_counter, integer=True + ) + self.unbacked_symint_counter += 1 + if not self._ignore_fresh_unbacked_symbols_tls(): + self.pending_fresh_unbacked_symbols.append(symbol) + self.counter["create_unbacked_symbol"] += 1 + self.var_to_stack[symbol] = CapturedTraceback.extract(skip=1) + vr = self.var_to_range[symbol] = ValueRanges(0, 1) + assert vr.is_int + sloc = self._get_sloc("default value range for unbacked SymBool") + self.var_to_range_sloc[symbol] = ValueRangesSLoc(sloc, sloc) + + # Create a new FX placeholder and Z3 variable for 'symbol'. + fx_node = self._create_fx_placeholder_and_z3var(symbol, bool) + + sym_node = SymNode(sympy.Eq(symbol, 1), self, bool, None, fx_node=fx_node) + self._log_create_unbacked_symbol( + "create_unbacked_symbool", symbol, vr, sym_node=sym_node + ) + + return SymBool(sym_node) + + @record_shapeenv_event() + def create_unspecified_symbol( + self, + val: Union[int, SymInt, float, SymFloat], + source: Source, + dynamic_dim: DimDynamic = DimDynamic.DUCK, + constraint_dim: DimConstraint = None, # NB: includes None + symbolic_context: Optional[StatelessSymbolicContext] = None, + ) -> sympy.Expr: + """ + Create a symbol with an unspecified value + + Compared to standard symbols we do not assume the value is positive, + nor do we specialze on zero or one values. + """ + # 'positive' is None for unspecified symbols, since we can't + # assume that it will be neither positive nor negative. + + # We don't want to specialize zero one val for unspecified symbol + # so that we can always get a new symbol despite val. + return self.create_symbol( + val, + source, + dynamic_dim, + constraint_dim, + positive=None, + do_not_specialize_zero_one=True, + symbolic_context=symbolic_context, + ) + + @record_shapeenv_event() + def create_symbol( + self, + val: int, + source: Source, + dynamic_dim: DimDynamic = DimDynamic.DUCK, + constraint_dim: DimConstraint = None, # NB: includes None + positive: Optional[bool] = True, + do_not_specialize_zero_one: bool = False, + symbolic_context: Optional[StatelessSymbolicContext] = None, + ) -> sympy.Expr: + """Create a new symbol which is tracked by this ShapeEnv""" + # check if constraint_dim is actually static integer + if ( + isinstance(constraint_dim, StrictMinMaxConstraint) + and constraint_dim.vr.lower == constraint_dim.vr.upper + ): + dynamic_dim = DimDynamic.STATIC + if constraint_dim.vr.lower != val: + raise ConstraintViolationError( + f"Static shape constraint of {constraint_dim.vr.lower} does not match input size of {val}, " + f"for {source.name}" + ) + if symbolic_context: + from torch._dynamo.source import TensorPropertySource + + assert isinstance(source, TensorPropertySource) + # TODO: storage_offset handling? + assert source.idx is not None + symbolic_context.dynamic_sizes[source.idx] = dynamic_dim + symbolic_context.constraint_sizes[source.idx] = None + constraint_dim = None + + # see note [Tensor Fakification and Symbol Caching] + source_name = source.name + if ( + isinstance(symbolic_context, StatefulSymbolicContext) + and id(self) not in symbolic_context.shape_env_to_source_to_symbol_cache + ): + symbolic_context.shape_env_to_source_to_symbol_cache[id(self)] = {} + + if ( + isinstance(symbolic_context, StatefulSymbolicContext) + and source_name + and ( + source_name + in symbolic_context.shape_env_to_source_to_symbol_cache[id(self)] + ) + ): + return symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][ + source_name + ] + + if dynamic_dim in (DimDynamic.SIZE_LIKE_UNBACKED, DimDynamic.OBLIVIOUS_SIZE): + out = self.create_unbacked_symint(source).node.expr + self._constrain_range_for_size(out) + + self.unbacked_inputs.add(out) + + if isinstance(symbolic_context, StatefulSymbolicContext) and source_name: + symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][ + source_name + ] = out + if dynamic_dim is DimDynamic.OBLIVIOUS_SIZE: + self.oblivious_var_to_val[out] = val + return out + + if do_not_specialize_zero_one: + specialize_zero_one = False + else: + specialize_zero_one = self.specialize_zero_one + + assert isinstance(source, Source), f"{type(source)} {source}" + assert not (positive and val < 0), f"positive set for negative value: {val}" + # It's always sound to allocate a symbol as DYNAMIC. If the user + # constrained the symbol, force the symbolic_context to DYNAMIC, because our + # constraint code will do weird stuff if, e.g., it's duck shaped + if constraint_dim is not None: + dynamic_dim = DimDynamic.DYNAMIC + + if dynamic_dim is DimDynamic.STATIC: + out = sympy.Integer(val) + if isinstance(symbolic_context, StatefulSymbolicContext) and source_name: + symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][ + source_name + ] = out + return out + + elif dynamic_dim is DimDynamic.DUCK: + # duck_shape can be used to globally turn off duck shaping, even + # if it was requested + duck = self.duck_shape + elif dynamic_dim is DimDynamic.DYNAMIC: + duck = False + else: + raise AssertionError(f"unhandled dynamic_dim {dynamic_dim}") + + sloc = self._get_sloc() + + if val in (0, 1) and specialize_zero_one: + if val == 0: + return sympy.S.Zero + else: + return sympy.S.One + elif not duck or val not in self.val_to_var: + # If we're not duck shaping, we always create a new symbol + # Even if we're duck shaping, if we haven't seen this particular + # value before, we also create a new symbol + symbol_id = self._generate_unique_id(source.name) + if type(val) is int or is_nested_int(val): + sympy_expr = make_symbol( + SymT.SIZE, symbol_id, positive=positive, integer=True + ) + else: + sympy_expr = make_symbol( + SymT.FLOAT, symbol_id, positive=positive, real=True + ) + self.source_to_var[source_name] = sympy_expr + # We always associate vars to vals + if isinstance(val, int): + self.var_to_val[sympy_expr] = sympy.Integer(val) + elif isinstance(val, float): + self.var_to_val[sympy_expr] = sympy.Float(val) + else: + # Only used for jagged layout nested tensors + self.var_to_val[sympy_expr] = SingletonInt( + val.node.nested_int(), coeff=val.node.nested_int_coeff() + ) + + # Do the appending later, because we always want to populate this + self.var_to_sources[sympy_expr] = [] + # Create a Z3 variable for the new symbol. + self._add_z3var(sympy_expr, int) + + if duck: + # Make sure to reuse this symbol for subsequent duck shaping + # pyrefly: ignore [unsupported-operation] + self.val_to_var[val] = sympy_expr + + if isinstance(val, int): + if positive: + # Add assertions for the newly created symbols + self._add_assertion(sympy_expr > 1) + + # Apply default range, which assumes not zero-one + self.var_to_range[sympy_expr] = self._default_value_range( + do_not_specialize_zero_one + ) + self.var_to_range_sloc[sympy_expr] = ValueRangesSLoc( + self._get_sloc( + "user code shown is first use of this value--the guard itself is not " + "due user code but due to 0/1 specialization in the framework; to " + "avoid specialization try torch._dynamo.decorators.mark_unbacked(tensor, dim)" + if self.specialize_zero_one + else None + ), + sloc, + ) + else: + self.var_to_range[sympy_expr] = ( + self._default_unspecified_value_range() + ) + self.var_to_range_sloc[sympy_expr] = ValueRangesSLoc(sloc, sloc) + + # Small performance optimization: if we have a min-max constraint, + # we can proactively narrow to that range + if isinstance(constraint_dim, StrictMinMaxConstraint): + assert not duck + self._update_var_to_range( + sympy_expr, constraint_dim.vr, is_constraint=True + ) + + vr = self.var_to_range[sympy_expr] + assert vr.is_int + + if val not in vr: + raise ConstraintViolationError( + f"{val} not in range [{vr.lower}, {vr.upper}]" + ) + + range_str = f"[{vr.lower}, {vr.upper}]" + elif isinstance(val, float): + self.var_to_range[sympy_expr] = vr = ValueRanges(-sympy.oo, sympy.oo) + self.var_to_range_sloc[sympy_expr] = ValueRangesSLoc(sloc, sloc) + range_str = f"[{vr.lower}, {vr.upper}]" + assert vr.is_float + else: + # Skip var_range logic for SingletonInt + # Only used for jagged layout nested tensors + range_str = "" + + r = sympy_expr + + is_debug = config.extended_debug_create_symbol is not None and str( + sympy_expr + ) in config.extended_debug_create_symbol.split(",") + maybe_more_info = "" + if not is_debug and os.getenv("TORCHDYNAMO_EXTENDED_ADVICE", "1") not in ( + "0", + "", + ): + maybe_more_info = ( + ", for more info run with " + f'TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="{sympy_expr}" ' + "or to suppress this message run with " + 'TORCHDYNAMO_EXTENDED_ADVICE="0"' + ) + sloc, maybe_extra_debug = self._get_stack_summary(is_debug) + self.log.info( + "create_symbol %s = %s for %s %s %s%s%s", + sympy_expr, + val, + source.name, + range_str, + sloc, + maybe_more_info, + maybe_extra_debug, + stack_info=is_debug, + ) + trace_structured( + "create_symbol", + metadata_fn=lambda: { + "symbol": str(sympy_expr), + "val": repr(val), + "vr": range_str, + "source": source.name, + "user_stack": structured.from_traceback( + TracingContext.extract_stack() + ), + "stack": structured.from_traceback( + CapturedTraceback.extract(skip=1).summary() + ), + }, + ) + + self.counter["create_symbol"] += 1 + else: + # This implements duck-shaping: input sizes that match are assigned + # the same symint + r = self.val_to_var[val] + self.source_to_var[source_name] = r + self.log.debug("create_symbol %s duck sized %s", r, source.name) + + if isinstance(r, sympy.Symbol): + r_sources = self.var_to_sources[r] + r_sources.append(source) + if not source.is_ephemeral() and r_sources[0].is_ephemeral(): + # prefer non-ephemeral source first since it may be guarded on later + r_sources[0], r_sources[-1] = r_sources[-1], r_sources[0] + + # This ensures we get zeros in symbol_guard_counts, which makes + # some queries simpler (since we will accumulate mass on 0 this + # way) + self.symbol_guard_counter[r] = 0 + + if isinstance(symbolic_context, StatefulSymbolicContext) and source_name: + symbolic_context.shape_env_to_source_to_symbol_cache[id(self)][ + source_name + ] = r + return r + + def add_var_to_val(self, expr: sympy.Symbol, val: int) -> None: + """Adds a new symbol to the symbolic environment.""" + log.debug("add_var_to_val %s %s", expr, val, stack_info=True) + assert expr not in self.var_to_val, f"{expr} already exists" + self.var_to_val[expr] = sympy.Integer(val) + + def _debug_name(self, source: Source) -> str: + src_name = source.name + return self.source_name_to_debug_name.get(src_name, src_name) + + def _render_range_for_constraint_violation( + self, source: Source, c: Union[StrictMinMaxConstraint, RelaxedUnspecConstraint] + ) -> str: + if isinstance(c, StrictMinMaxConstraint): + lower, upper = c.vr.lower, c.vr.upper + default = self._default_value_range() + if lower <= default.lower: + lower = None + if upper >= default.upper: + upper = None + c_render = ( + f"{self._debug_name(source)} = {source.name} in the specified range" + ) + if lower is not None and upper is not None: + c_render += f" {lower} <= {self._debug_name(source)} <= {upper}" + elif lower is None and upper is not None: + c_render += f" {self._debug_name(source)} <= {upper}" + elif lower is not None and upper is None: + c_render += f" {lower} <= {self._debug_name(source)}" + return c_render + return c.render(source) + + def produce_guards(self, *args: Any, **kwargs: Any) -> list[str]: + """ + Like produce_guards_verbose, but only returns the non-verbose python guard expressions + (no verbose guards produced.) + """ + return self.produce_guards_verbose(*args, **kwargs, langs=("python",))[0].exprs + + def produce_guards_verbose( + self, + placeholders: Sequence[FakeTensor], + sources: Sequence[Source], + source_ref: Callable[[Source], str] = lambda n: n.name, + *, + guards: Optional[list[ShapeGuard]] = None, + input_contexts: Optional[DimList[SymbolicContext]] = None, + # Encodes user-specified input shape equations of the form s = s' and s = fn(s'). + # (See docs on EqualityConstraint for details of the encoding.) + equalities_inputs: Optional[EqualityConstraint] = None, + _simplified: bool = False, + # Indicates if we should produce guards for known static values. + ignore_static: bool = True, + langs: tuple[str, ...] = ("python", "verbose_python"), + ) -> list[_ShapeGuardsHelper]: + """ + Generates a list of guards strings which, when evaluated in a context that + defines tensors for all the sources, returns True or False depending + on if the guards in the list evaluated to True or not. Primarily used by Dynamo, + but this is also helpful for manual testing of guards (see + evaluate_guards_for_args) + + For convenience in testing, a source is allowed to be a str, + in which case we will assume it is a LocalSource + + simplified lets you omit duck sizing, equality and 0/1 guards. + This is useful for testing when you don't care about the boilerplate + guards, and it may be helpful for user output too (be careful though; + some equality guards are nontrivial! It would be nice to get simplified + output to print them too). It's private because it's not + intended for normal use + + Returns guards in python and python with verbose comments (verbose) by + default. + """ + self.log.info("produce_guards") + + # Check if we get to the same ShapeEnv state by replaying the recorded events. + # This will create a new ShapeEnv instance, and call all recorded function + # calls on this new instance. Finally, it will check whether this new instance + # has equal state. + # + # It's important that we do it in the beginning of this function, since it modifies + # self.dim_constraints through its execution. Changes that happen in this method + # aren't interesting, since this is the function call we wish to reproduce at the + # end. If we wish to simply reproduce ShapeEnv instances even after this call, + # this method should also be recorded. + if self.check_recorded_events: + shape_env = replay_shape_env_events(self.events) + self.check_equal(shape_env) + + assert len(placeholders) == len(sources), ( + f"len({placeholders}) != len({sources})" + ) + Tensorlike = (torch.Tensor, FakeTensorMeta) + + def _create_no_constraints_context(t: Tensor) -> StatelessSymbolicContext: + return StatelessSymbolicContext( + # Ignored; only the constraints part is relevant below. + dynamic_sizes=[DimDynamic.DYNAMIC] * t.dim(), + dynamic_strides=[DimDynamic.INFER_STRIDE] * t.dim(), + constraint_sizes=[None] * t.dim(), + constraint_strides=[None] * t.dim(), + ) + + # Expand optional inputs, or verify invariants are upheld + if input_contexts is None: + # pyrefly: ignore [bad-assignment] + input_contexts = [ + # pyrefly: ignore [bad-argument-type] + _create_no_constraints_context(t) if isinstance(t, Tensorlike) else None + for t in placeholders + ] + else: + assert len(input_contexts) == len(placeholders) + + for i, (t, context) in enumerate(zip(placeholders, input_contexts)): + if isinstance(t, Tensorlike): + if context is None: + # pyrefly: ignore [bad-argument-type] + input_contexts[i] = _create_no_constraints_context(t) + else: + assert isinstance(t, (SymInt, int, SymFloat, float)) + assert not isinstance(context, list) + + # It took a lot of sweat to figure out the algorithm here. Let's + # explain how it works. + # + # The ShapeEnv lifecycle looks something like this: + # + # - For each input, you either generate a fresh Sympy symbol (s0) to + # represent its value (a binding site), or you reuse some + # preexisting symbol or expression, skipping the symbol allocation + # (e.g., duck sizing to a preexisting symbol, or expressing a + # stride as a multiplication of a separate stride and size.) + # Naively, you might expect to bind a fresh Sympy symbol for + # every input, but this is fairly wasteful as most of these + # symbols immediately simplify away, and if you don't eagerly + # specialize, e.g., 0/1 symbols, you end up with very complicated + # expressions that are not optimizable in practice. + # + # - You perform some compute on these symbols, occasionally + # introducing guards on boolean expressions on these symbols. + # In particular, whenever we guard on equality (_maybe_guard_rel), + # we can simplify shapes; e.g., when s0 == s1 * 2, we can now + # replace all occurrences of s0 with s1 * 2. Sometimes, a + # boolean expression evaluation doesn't introduce a guard, as + # the guard is already entailed by the simplifications we have + # applied. + # + # - In the end, you have a bunch of replacements (saying how to + # simplify shapes) and a bunch of guards (all the equality guards + # are trivial, because they're covered by the replacements). + # + # From the ShapeEnv, we must generate a Python expression that, when + # evaluated on a set of inputs, tells us whether or not these boolean + # expressions would have evaluated in the same way. However, + # we cannot easily compute this, as we elide recording boolean + # expressions when we think they are vacuously true. Thus, we seek + # an approximation: we must generate an expression, if true, would have + # produced an "equivalent" ShapeEnv, which would answer guard + # expressions in the same way. + # + # Our notion of equivalence is a bit subtle. For example, consider + # the ShapeEnv created from an input of size (5, 4) versus (4, 4) + # (no other guards.) Duck sizing would generate (s0, s1) in the first + # case but (s0, s0) in the second. We do NOT assume that size + # variables are disjoint; so in fact a graph that assumes the input + # could be (s0, s1) subsumes (s0, s0) (setting s0 == s1), but not + # vice versa. However, consider an analogous case (1,) versus (2,). + # Duck sizing generates (1,) and (s0,); the (s0,) graph does NOT + # subsume the (1,) graph because we assume that any size variables + # is NOT 0/1 (and make simplifications according to this; e.g., if + # we queried s0 == 0, we would immediately return False without + # returning a guard.) + # + # So, it is perhaps easier to flip things on their head: the guard + # expressions we generate here say what simplifications are valid, + # and what are not. Below, we explain each of the guard expressions + # we generate + + # TODO: Make this more efficient by binding all the size/stride/offsets + # to locals before performing tests on them. + + from torch._dynamo.source import TensorProperty, TensorPropertySource + + # Actual codegen must be delayed as we don't necessarily know what + # the symbol mapping is + input_guards = [] + + symbol_to_source: dict[sympy.Symbol, list[Source]] = collections.defaultdict( + list + ) + symbol_to_constraints: defaultdict[sympy.Symbol, set[Constraint]] = ( + collections.defaultdict(set) + ) + constraint_violations: list[tuple[bool, str, Callable[[], str]]] = [] + + printers: list[_ShapeGuardPrinter] = [] + py_printer = ShapeGuardPythonPrinter( + symbol_to_source, source_ref, self.var_to_sources + ) + for lang in langs: + if lang in ["python", "verbose_python"]: + printers.append(py_printer) + elif lang == "cpp": + printers.append( + _ShapeGuardCppPrinter( + symbol_to_source, source_ref, self.var_to_sources + ) + ) + else: + raise NotImplementedError(f"Unknown lang: {lang}") + + def record_constraint_violation( + warn_only: bool, + debug_name: str, + msg: str, + hint: Optional[Callable[[], str]] = None, + ) -> None: + constraint_violations.append( + (warn_only, debug_name, lambda: f"{msg}{hint()}" if hint else msg) + ) + + def is_dim(src: object) -> TypeGuard[TensorPropertySource]: + return ( + isinstance(src, TensorPropertySource) + and src.prop is TensorProperty.SIZE + ) + + if equalities_inputs: + source_index = {} + for i, src in enumerate(sources): + source_index[src.name] = i + + def get_expression(tensor_dim_src: Source) -> sympy.Expr: + fake = placeholders[source_index[tensor_dim_src.base.name]] # type: ignore[attr-defined] + assert tensor_dim_src.idx is not None # type: ignore[attr-defined] + symint = fake.shape[tensor_dim_src.idx] # type: ignore[attr-defined] + if isinstance(symint, torch.SymInt): + return symint.node.expr + else: + assert type(symint) is int, f"Expected int, got {type(symint)}" + return sympy.Integer(symint) + + for src1, src2 in equalities_inputs.source_pairs: + expr1, expr2 = get_expression(src1), get_expression(src2) # type: ignore[] + # Check whether given input shape values satisfy a specified equation s = s'. + # - Raise when the equation was violated by the given input shape values. + # - Otherwise issue a guard to constrain them. + concrete_val = self.evaluate_expr(sympy.Eq(expr1, expr2)) + if not concrete_val: + raise ConstraintViolationError( + f"{src1.name} = {expr1 if isinstance(expr1, int) else expr1.xreplace(self.var_to_val)}" + " is not equal to " + f"{src2.name} = {expr2 if isinstance(expr2, int) else expr2.xreplace(self.var_to_val)}" + ) + + for srcEq, root, fn in equalities_inputs.derived_equalities: + expr1 = get_expression(srcEq) + # recall that root is either a phantom symbol or an input source + if isinstance(root, sympy.Symbol): + expr2, debug_name = root, self.var_to_sources[root][0].name + elif isinstance(root, sympy.Integer): + expr2, debug_name = root, str(root) + else: + expr2, debug_name = get_expression(root), self._debug_name(root) + expr2_ = fn(expr2) + # Check whether given input shape values satisfy a specified equation s = fn(s'). + # - Raise when the equation was violated by the given input shape values. + # - Otherwise issue a guard to constrain them. + concrete_val = self.evaluate_expr(sympy.Eq(expr1, expr2_)) + if not concrete_val: + raise ConstraintViolationError( + f"Expected input {srcEq.name} to be equal to " + f"{fn(sympy.Symbol(debug_name))}, " + f"where {debug_name} = {expr2.xreplace(self.var_to_val)}, " + f"but got {expr1.xreplace(self.var_to_val)}" + ) + + for phantom_symbol in equalities_inputs.phantom_symbols: + if isinstance(phantom_symbol, sympy.Symbol): + # we created additional phantom symbols that are not input shape dimensions + symbol_to_source[phantom_symbol].extend( + self.var_to_sources[phantom_symbol] + ) + + # How do we know what the value of s0 is? Fresh variables can only be + # bound by inputs, so there MUST be some other input which binds the + # variable. If there is no such input, this is an error in our + # system. We record where all symbols come from, to help you diagnose + # why those symbols didn't occur. + # + # In fact, generally speaking it is only possible for the "outermost" + # user of a ShapeEnv to evaluate the guards, because some inputs may + # not be available to inner levels. For example, Dynamo can guard on + # tensors that never actually become graph arguments (they are + # pruned). In this case, only Dynamo knows about these arguments. + def track_symint( + source: Source, val: IntLikeType, constraint: DimConstraint = None + ) -> None: + log.debug( + "track_symint %s %s %s", + LazyString(lambda: source.name), + val, + constraint, + ) + assert not isinstance(val, SymInt) or is_symbolic(val) + + if isinstance(val, SymInt) and val.node.maybe_as_int() is not None: + val = val.node.maybe_as_int() + + if isinstance(val, SymInt): + s = val.node.expr + if isinstance(s, sympy.Symbol): + symbol_to_source[s].append(source) + if constraint is not None and not isinstance( + constraint, RelaxedUnspecConstraint + ): + symbol_to_constraints[s].add(constraint) + else: + constraint_violated = False + if isinstance(constraint, StrictMinMaxConstraint): + # try inferring the ranges of the expr s + sym_vrs = { + x: self.var_to_range.get(x, None) for x in s.free_symbols + } + if any(vr is None for vr in sym_vrs.values()): + # some of the free symbols in s don't have ranges + constraint_violated = True + elif isinstance(constraint, RelaxedUnspecConstraint): + if s.is_number: + i = int(s) + # Don't complain about 0/1 specialization, we + # expect to have to compile in this case anyway + if i not in (0, 1): + constraint_violated = True + if constraint_violated: + assert constraint is not None + + def hint(s: sympy.Expr) -> str: + sexpr = py_printer.doprint(s) + return f"{sexpr}." + + var_with_range = self._render_range_for_constraint_violation( + source, constraint + ) + msg = ( + f"Not all values of {var_with_range} are valid because " + f"{self._debug_name(source)} was inferred to be equal to " + ) + record_constraint_violation( + constraint.warn_only, + self._debug_name(source), + msg, + hint=functools.partial(hint, s), + ) + + input_guards.append((source, s)) + else: + s = sympy.Integer(val) + input_guards.append((source, s)) + constraint_violated = False + if isinstance(constraint, StrictMinMaxConstraint): + if not ( + s == constraint.vr.lower == constraint.vr.upper + ): # allow static constraints + constraint_violated = True + elif isinstance(constraint, RelaxedUnspecConstraint): + # Don't complain about 0/1 specialization, we + # expect to have to compile in this case anyway + if val not in (0, 1): + constraint_violated = True + if constraint_violated: + assert constraint is not None + var_with_range = self._render_range_for_constraint_violation( + source, constraint + ) + user_stack = self.specialization_stacks.get(source, None) + msg = ( + f"You marked {self._debug_name(source)} as dynamic but your code " + f"specialized it to be a constant ({val}). If you're using mark_dynamic, " + f"either remove it or use maybe_mark_dynamic. If you're using Dim.DYNAMIC, " + f"replace it with either Dim.STATIC or Dim.AUTO." + + ( + "\n\nUser stack:\n" + "".join(user_stack.format()) + if user_stack + else "" + ) + ) + record_constraint_violation( + constraint.warn_only, self._debug_name(source), msg + ) + + def track_symfloat(source: Source, val: FloatLikeType) -> None: + log.debug("track_symfloat %s %s", LazyString(lambda: source.name), val) + assert not isinstance(val, SymFloat) or is_symbolic(val) + + if isinstance(val, SymFloat) and val.node.maybe_as_float() is not None: + val = val.node.maybe_as_float() + + if isinstance(val, SymFloat): + s = val.node.expr + if isinstance(s, sympy.Symbol): + symbol_to_source[s].append(source) + input_guards.append((source, s)) + else: + s = sympy.Float(val) + input_guards.append((source, s)) + + # pyrefly: ignore [no-matching-overload] + for t, source, context in zip(placeholders, sources, input_contexts): + if isinstance(source, str): + from torch._dynamo.source import LocalSource + + source = LocalSource(source) + assert isinstance(source, Source) + if t is None: + continue + if isinstance(t, (SymInt, int)): + constraint = ( + None if context is None else getattr(context, "constraint", None) + ) + track_symint(source, t, constraint) + continue + elif isinstance(t, (SymFloat, float)): + track_symfloat(source, t) + continue + assert isinstance(t, Tensorlike) + if is_traceable_wrapper_subclass(t): + from torch._dynamo.source import AttrSource + + assert isinstance(context, SubclassSymbolicContext) + + # For subclasses, we need to track symints on BOTH the outer + # and inner tensors. + # TODO: type this better + sources_tensors_constraints: list[tuple[Source, Any, Any, Any]] = [ + (source, t, context.constraint_sizes, context.constraint_strides) + ] + attrs, _ = t.__tensor_flatten__() + for attr in attrs: + inner_t = getattr(t, attr) + inner_context = context.inner_contexts[attr] + sources_tensors_constraints.append( + ( + AttrSource(source, attr), + inner_t, + inner_context.constraint_sizes, # type: ignore[attr-defined] + inner_context.constraint_strides, # type: ignore[attr-defined] + ) + ) + else: + sources_tensors_constraints = [ + (source, t, context.constraint_sizes, context.constraint_strides) # type: ignore[attr-defined] + ] + + for ( + src, + curr_t, + constraint_size, + constraint_stride, + ) in sources_tensors_constraints: + if is_sparse_any(curr_t): + for i, ss in enumerate(curr_t.size()): + property_source = TensorPropertySource( + src, TensorProperty.SIZE, i + ) + track_symint(property_source, ss, constraint_size[i]) + else: + for i, ss in enumerate(curr_t.size()): + property_source = TensorPropertySource( + src, TensorProperty.SIZE, i + ) + track_symint(property_source, ss, constraint_size[i]) + + for i, ss in enumerate(curr_t.stride()): + property_source = TensorPropertySource( + src, TensorProperty.STRIDE, i + ) + track_symint(property_source, ss, constraint_stride[i]) + track_symint( + TensorPropertySource(src, TensorProperty.STORAGE_OFFSET), + curr_t.storage_offset(), + ) + + # 1. Every input must equal the final simplified symbolic expression + # stored on the placeholder. Given a placeholder (s0*2, s1), + # if we have an input (2, 3), we must show s0*2 == 2 and s1 == 3. + # This does a lot of work: it covers duck sizing and equality guards. + all_exprs: list[list[str]] = [[] for _ in langs] + + self.dim_constraints = DimConstraints( + symbol_to_source, + self.var_to_val, + set(symbol_to_constraints.keys()), + self.source_name_to_debug_name, + ) + + if not _simplified: + for source, expr in input_guards: + srcname = source.name + if self._translation_validation_enabled: + # Ignore sources that were not turned into SymInts. + if srcname in self.source_to_symbol: + self._add_target_expr( + sympy.Eq(self.source_to_symbol[srcname], expr) + ) + + # Small optimization + if ( + isinstance(expr, sympy.Symbol) + and symbol_to_source.get(expr) + and source == symbol_to_source[expr][0] + ): + continue + + # This logic excludes static values found on tensors from guarding, because + # dynamo's check_tensor_fn does that (see guards.cpp). + # However, for non tensor sources, we still need to guard here. + if ignore_static and isinstance(source, TensorPropertySource): + if expr.is_number: + self.log.debug( + "Skipping guard %s", f"{source_ref(source)} == {expr}" + ) + continue + + if is_dim(source): + self.dim_constraints.add_equality(source, expr) + + for exprs, printer, lang in zip(all_exprs, printers, langs): + res = f"{printer.print_source(source)} == {printer.doprint(expr)}" + + if lang == "verbose_python": + if (s0 := self.source_to_var.get(srcname)) is not None: + if source != self.var_to_sources[s0][0]: + res = ( + f"{res} # duck sizing added this equality because these " + f"variables had the same size {self.var_to_val[s0]} " + "(to avoid this specialization, set torch.fx.experimental._config.use_duck_shape = False)" + ) + elif (sloc := self.replacements_slocs.get(s0)) is not None: + res = f"{res} # {sloc}" + else: + res = f"{res} # (unknown var {s0}, please file a bug)" + else: + res = f"{res} # (unknown source {srcname}, please file a bug)" + exprs.append(res) + + if ( + isinstance(source, TensorPropertySource) + and source.prop is TensorProperty.SIZE + and equalities_inputs + and len(expr.free_symbols) == 1 + ): + symbol = next(iter(expr.free_symbols)) + if ( + isinstance(expr, sympy.Symbol) + and expr in symbol_to_constraints + and not equalities_inputs.is_equal( + source, symbol_to_source[expr][0] + ) + ): + msg = ( + f"The values of {self._debug_name(source)} = {source.name} and " + f"{self._debug_name(symbol_to_source[expr][0])} = {symbol_to_source[expr][0].name} " + "must always be equal." + ) + record_constraint_violation( + equalities_inputs.warn_only, self._debug_name(source), msg + ) + + if ( + not isinstance(expr, sympy.Symbol) + and symbol in symbol_to_constraints + and not equalities_inputs.is_derived( + source, + symbol_to_source[symbol][0], + lambda x: expr.xreplace({symbol: x}), + ) + ): + src = symbol_to_source[symbol][0] + msg = ( + f"The values of {self._debug_name(source)} = {source.name} must always be related to " + f"the values of {self._debug_name(src)} = {src.name} by " + f"{self._debug_name(source)} = {expr.xreplace({symbol: sympy.sympify(self._debug_name(src))})}." + ) + record_constraint_violation( + equalities_inputs.warn_only, self._debug_name(source), msg + ) + + # NB: Not necessary to report constraint violations here: + # constraints are guaranteed to be on symbols (we've already + # caught constants and non-atomic expressions), so we only + # have relational constraints, but we don't support those + # at the moment + + # 2. Every guard must evaluate to True (but remember many guards + # like s0 == s1*2 because trivial due to simplification) + issued = set() + + def issue_guard(guard: ShapeGuard) -> None: + expr = self.simplify(guard.expr) + + # Avoid re-issuing the same guard. + if expr in issued: + return + + issued.add(expr) + + try: + is_trivial = False + if any( + is_dim(source) + for s in expr.free_symbols + for source in symbol_to_source[s] + ): + assert self.dim_constraints is not None + is_trivial = self.dim_constraints.add(expr) + + for exprs, printer, lang in zip(all_exprs, printers, langs): + guard_expr = printer.doprint(expr) + if lang == "verbose_python": + guard_expr = f"{guard_expr} # {guard.sloc}" + exprs.append(guard_expr) + + self._add_target_expr(expr) + # A non-relational constraint on a single sizevar can violate + # a constraint + if not is_trivial and len(expr.free_symbols) == 1: + symbol = next(iter(expr.free_symbols)) + source = symbol_to_source[symbol][0] + constraints = symbol_to_constraints[symbol] + for c in constraints: + if isinstance(c, StrictMinMaxConstraint): + var_with_range = ( + self._render_range_for_constraint_violation(source, c) + ) + msg = ( + f"Not all values of {var_with_range} " + f"satisfy the generated guard {py_printer.doprint(expr)}." + ) + record_constraint_violation( + c.warn_only, self._debug_name(source), msg + ) + elif isinstance(c, RelaxedUnspecConstraint): + # This is fine, we allow guards here as long as it + # didn't constrain it to one value (we don't + # actually know this; this depends on our + # ValueRanges reasoning capability) + pass + else: + raise AssertionError(f"unrecognized constraint {c}") + except Exception: + self.log.warning("Failing guard allocated at %s", guard.sloc) + raise + + # First, issue all guards. + # This removes all the checks that follow from bounds + # We could simply emit those and also the bounds 2 <= size when necessary + for guard in guards if guards is not None else self.guards: + if ( + self._maybe_evaluate_static( + guard.expr, axioms=(), size_oblivious=guard.size_oblivious + ) + is not None + ): + continue + + issue_guard(guard) + + # Because there are guards that export's constraint solver can suggest good fixes for, that we may have + # deferred as runtime asserts, and that produce_guards() alone won't do anything with (e.g. divisiblity guards), + # we want to send runtime asserts to export's constraint solver too. These will still stay in the graph as asserts, + # but export's constraint solver can decide whether to do anything with them (i.e. raise an error and provide + # suggested fixes, or decide it's out of scope and leave as a runtime assert in the graph). + for ra in self.deferred_runtime_asserts.get(None, []): + if self._maybe_evaluate_static(ra.expr, axioms=()) is not None: + continue + expr = self.simplify(ra.expr) + + self.dim_constraints.add(expr) + + # 3. Every symbol must be within its value range (this handles 0/1 + # specialization too). + for symbol, sources in symbol_to_source.items(): + r = self.var_to_range.get(symbol) + if r is None: + continue + vr_sloc = self.var_to_range_sloc[symbol] + + assert sources + bounds = [] + rf = source_ref(sources[0]) + verbose_expr = "" + if r.lower not in (-sympy.oo, -int_oo): + if any(is_dim(source) for source in sources): + self.dim_constraints.add(sympy.Ge(symbol, r.lower)) + # Only print lower bound in simplified mode if it is not the + # default + if not _simplified or r.lower != self._default_value_range().lower: + bounds.append(sympy.Le(r.lower, symbol, evaluate=False)) + verbose_expr = f"{r.lower} <= {rf} # {vr_sloc.lower}" + if r.upper not in (sympy.oo, int_oo): + if any(is_dim(source) for source in sources): + self.dim_constraints.add(sympy.Le(symbol, r.upper)) + # nontrivial upper bound is always interesting + bounds.append(sympy.Le(symbol, r.upper, evaluate=False)) + if verbose_expr: + verbose_expr = f"{r.lower} <= {rf} <= {r.upper} # {vr_sloc.lower} and {vr_sloc.upper}" + else: + verbose_expr = f"{rf} <= {r.upper} # {vr_sloc.upper}" + if bounds: + bound = sympy.And(*bounds, evaluate=False) + + for exprs, printer, lang in zip(all_exprs, printers, langs): + if lang == "verbose_python": + exprs.append(verbose_expr) + else: + exprs.append(printer.doprint(bound)) + # NB: verbose_exprs are done above + + # Check constraints + constraints = symbol_to_constraints[symbol] + for c in constraints: + if isinstance(c, StrictMinMaxConstraint): + # TODO: With int_oo, I think this condition is a noop + # now + if not (c.vr & self._default_value_range()).issubset(r): + source = sources[0] + + expr = sympy.And( + sympy.Le(r.lower, symbol), sympy.Le(symbol, r.upper) + ) + guard_expr = py_printer.doprint(expr) + var_with_range = ( + self._render_range_for_constraint_violation(source, c) + ) + msg = f"Not all values of {var_with_range} satisfy the generated guard {guard_expr}" + record_constraint_violation( + c.warn_only, + self._debug_name(source), + msg, + ) + # We NaN specialize, which means similar to 0/1 specialization we + # should assume that the float is NOT nan. This is load bearing + # if you have something like an equality guard, nan will play + # merry hell with the reasoning. + if symbol_is_type(symbol, SymT.FLOAT): + res = f"not math.isnan({py_printer.print_source(sources[0])})" + for exprs, printer, lang in zip(all_exprs, printers, langs): + if lang == "verbose_python": + exprs.append( + f"{res} # implicit guard for float input due to NaN specialization in the framework" + ) + elif lang == "python": + exprs.append(res) + elif lang == "cpp": + exprs.append(f"~std::isnan({printer.print_source(sources[0])})") + else: + raise NotImplementedError(f"Unimplemented for lang: {lang}") + + if constraint_violations: + warn_msgs: list[str] = [] + error_msgs: list[str] = [] + debug_names = set() + for warn_only, debug_name, msg_cb in constraint_violations: + if warn_only: + str_msg = f" {len(warn_msgs) + 1}. {msg_cb()}" + warn_msgs.append(str_msg) + else: + str_msg = f" - {msg_cb()}" + error_msgs.append(str_msg) + # pyrefly: ignore [bad-argument-type] + debug_names.add(debug_name) + if len(error_msgs) > 0: + debug_names_str = ", ".join(sorted(debug_names)) + err = "\n".join(error_msgs) + raise ConstraintViolationError( + f"Constraints violated ({debug_names_str})! " + 'For more information, run with TORCH_LOGS="+dynamic".\n' + f"{err}" + ) + elif len(warn_msgs) > 0: + log.debug("%s Warning only constraints violated", len(warn_msgs)) + + signpost_event( + "dynamic", + "produce_guards", + { + **self.co_fields, + **self.counter, + "num_guards": len(all_exprs[0]), + "free_symbols": sum(1 for v in symbol_to_source.values() if v), + # The keys are meaningless from an aggregate perspective, so + # don't include them. Biggest first. + "symbol_guard_counts": sorted( + self.symbol_guard_counter.values(), reverse=True + ), + }, + ) + + if self._translation_validation_enabled: + from torch.fx.experimental.validator import PopulateValidator + + # Add all deferred runtime assertions; these are not technically + # handled by produce_guards but we need to put them in the target + # set + for ras in self.deferred_runtime_asserts.values(): + for ra in ras: + self._add_target_expr(ra.expr) + + # Add value range bound guards for all symbols with no trivial bounds. + # Reason: '_maybe_evaluate_static' may eliminate guards based on the + # refined value ranges. + for sym, vr in self.var_to_range.items(): + if vr.lower not in (-sympy.oo, -int_oo): + self._add_target_expr(sympy.Le(vr.lower, sym)) + if vr.upper not in (sympy.oo, int_oo): + self._add_target_expr(sympy.Le(sym, vr.upper)) + + # Before validating, populate the input of the validator with the + # built FX graph. + with fx_traceback.preserve_node_meta(): + PopulateValidator(self.graph, self.validator).run() + + # Only run translation validation when we are not passing custom guards + if guards is None: + self._check_translation_validate() + + helpers: list[_ShapeGuardsHelper] = [] + for exprs, printer, lang in zip(all_exprs, printers, langs): + if lang == "cpp": + assert isinstance(printer, _ShapeGuardCppPrinter) + helpers.append(_CppShapeGuardsHelper(exprs, printer.source_to_symbol)) + else: + helpers.append(_ShapeGuardsHelper(exprs)) + return helpers + + def produce_guards_expression( + self, + placeholders: Sequence[Union[SymInt, FakeTensor]], + *, + guards: Optional[list[ShapeGuard]] = None, + ignore_static: bool = True, + ) -> Optional[str]: + """ + Expected to be used with evaluate_guards_expression(). Produces the guards + for the given placeholders and returns a string expression to be evaluated + by evaluate_guards_expression given concrete values for the placeholders. + """ + from torch._dynamo.source import LocalSource + + arg_names = [f"t{i}" for i in range(len(placeholders))] + produced_guards = self.produce_guards( + placeholders, + [LocalSource(a) for a in arg_names], + guards=guards, + ignore_static=ignore_static, + ) + if produced_guards: + return " and ".join(produced_guards) + return None + + def evaluate_symexpr(self, code: str) -> Union[int, float, bool]: + """ + To be used by compile_fx to evaluate symexprs + """ + args = {str(e): val for e, val in self.var_to_val.items()} + return eval(code, SYMPY_INTERP, args) + + def deserialize_symexpr(self, code: str) -> Union[SymInt, SymFloat, SymBool]: + """ + To be used by compile_fx to deserialize symexprs + """ + args = { + str(e): SymInt(SymNode(e, self, int, int(val), fx_node=None)) + for e, val in self.var_to_val.items() + } + return eval(code, SYMPY_INTERP, args) + + def evaluate_guards_expression(self, code: str, args: Sequence[object]) -> bool: + """ + Expected to be used with produce_guards_expression(). Evaluates an expression + generated by produce_guards_expression for the given concrete args. + """ + arg_names = [f"t{i}" for i in range(len(args))] + return eval(code, SYMPY_INTERP, {"L": dict(zip(arg_names, args))}) + + def evaluate_guards_for_args( + self, + placeholders: Sequence[FakeTensor], + args: Sequence[Tensor], + *, + ignore_static: bool = True, + ) -> bool: + """Generate guards for a graph's placeholder values and evaluate the guards with args""" + code = self.produce_guards_expression(placeholders, ignore_static=ignore_static) + if code: + return self.evaluate_guards_expression(code, args) + return True + + def get_pruned_guards(self, symints: Sequence[torch.SymInt]) -> list[ShapeGuard]: + """ + Get a list of guards, but pruned so it only provides guards that + reference symints from the passed in input + """ + # pyrefly: ignore [bad-assignment] + symints = { + s.node.expr for s in symints if isinstance(s.node.expr, sympy.Symbol) + } + guards = [ + g for g in self.guards if all(s in symints for s in g.expr.free_symbols) + ] + return guards + + def bind_symbols( + self, placeholders: Sequence[FakeTensor], args: Sequence[Tensor] + ) -> dict[sympy.Symbol, int]: + """ + Given a paired list of placeholders (fake tensors with + symbolic sizes) and concrete arguments (regular tensors + with real sizes), returns a dictionary mapping each + symbol to its real value. So for example, if you + have a placeholder with size (s0, s1), binding + (2, 4) to it will give you {s0: 2, s1: 4}. This is + not guaranteed to bind ALL symbols in the ShapeEnv; + we can't bind a symbol if it doesn't occur in any placeholder, + and symbols that already have replacements won't get bindings. + + This is a little duplicative with evaluate_guards but + it's different enough that it seemed cleanest to make + another copy. This assumes the guards are already checked, + though if it's cheap we'll check for shenanigans + """ + bindings: dict[sympy.Symbol, int] = {} + + def bind_symint(arg: object, val: object) -> None: + if isinstance(val, SymInt): + assert isinstance(arg, int) + s = val.node.expr + + if isinstance(s, sympy.Symbol): + if s in bindings: + assert bindings[s] == arg, f"{bindings[s]} != {arg}" + else: + bindings[s] = arg + elif isinstance(-s, sympy.Symbol): + if -s in bindings: + assert bindings[-s] == -arg, f"{bindings[-s]} != {-arg}" + else: + bindings[-s] = -arg + + for t, arg in zip(placeholders, args): + if t is None: + continue + if isinstance(t, SymInt): + bind_symint(arg, t) + continue + assert isinstance(t, torch.Tensor) + for i, s in enumerate(t.size()): + bind_symint(arg.size(i), s) + for i, s in enumerate(t.stride()): + bind_symint(arg.stride(i), s) + bind_symint(arg.storage_offset(), t.storage_offset()) + + return bindings + + def get_nontrivial_guards(self) -> list[SympyBoolean]: + """Returns a list of guard expressions that aren't statically known (i.e. not trivial)""" + return [ + self.simplify(guard.expr) + for guard in self.guards + if self._maybe_evaluate_static( + guard.expr, axioms=(), size_oblivious=guard.size_oblivious + ) + is None + ] + + def format_guards(self, verbose: bool = False) -> str: + """Format this shape env's guard expressions with optional traceback info if verbose""" + + return "\n".join( + f" - {guard.expr}{' ' + str(guard.sloc) if verbose else ''}" + for guard in self.guards + ) + + def bound_sympy( + self, expr: sympy.Expr, size_oblivious: bool = False + ) -> ValueRanges: + """Given a sympy expression, computes a ValueRanges bound for what values it can be""" + # TODO: maybe it's guaranteed x in is var_to_range? + var_to_range = {x: self.var_to_range.get(x, None) for x in expr.free_symbols} + if size_oblivious: + # Clamp values of size-like variables + # NB: discarding the old upper bound in intentional, per + # https://github.com/pytorch/pytorch/pull/123675 + for x in self.size_like & var_to_range.keys(): + if var_to_range[x] is not None: + # NB: do NOT set upper to 2 ** 48, we're using this solely + # to determine if we can do size-like replacement, the + # upper bound is irrelevant here + var_to_range[x] = ValueRanges(2, int_oo) + return bound_sympy(expr, var_to_range) # type: ignore[arg-type] + + @_lru_cache + def get_axioms( + self, + symbols: Optional[tuple[sympy.Symbol]] = None, + compute_hint: bool = False, + ) -> tuple[SympyBoolean, ...]: + """ + Given the symbols in an expression, it returns all the runtime asserts that have those symbols + concatenated with all the guards. + If symbols is None, it returns all the runtime asserts (and all the guards) + """ + if symbols is None: + runtime_asserts = ( + r.expr for rs in self.deferred_runtime_asserts.values() for r in rs + ) + else: + runtime_asserts = ( + r.expr + for s in symbols + if s not in self.var_to_val + for r in self.deferred_runtime_asserts.get(s, ()) + ) + guards: Iterator[SympyBoolean] = (g.expr for g in self.guards) + axioms: Iterator[SympyBoolean] = itertools.chain(guards, runtime_asserts) + if compute_hint: + axioms = ( + canonicalize_bool_expr(a.xreplace(self.var_to_val)) for a in axioms + ) + return tuple(dict.fromkeys(axioms).keys()) + + @lru_cache(None) + def get_implications( + self, e: SympyBoolean + ) -> tuple[tuple[SympyBoolean, sympy.logic.boolalg.BooleanAtom], ...]: + """Given a expression, it returns a list of predicates that follow from it""" + equiv: dict[SympyBoolean, sympy.logic.boolalg.BooleanAtom] = {} + + def add_expr(expr: SympyBoolean) -> None: + expr = canonicalize_bool_expr(expr) + if isinstance(expr, (sympy.Eq, sympy.Ne)): + # No need to canonicalize + # TODO We could further canonicalize Eq ordering the lhs and rhs somehow + # With this, we could remove the need for the commutativity part + opposite = sympy.Eq if isinstance(expr, sympy.Ne) else sympy.Ne + # Commutativity of == and != + equiv[type(expr)(expr.lhs, expr.rhs, evaluate=False)] = sympy.true + equiv[type(expr)(expr.rhs, expr.lhs, evaluate=False)] = sympy.true + equiv[opposite(expr.lhs, expr.rhs, evaluate=False)] = sympy.false + equiv[opposite(expr.rhs, expr.lhs, evaluate=False)] = sympy.false + else: + # Expr and negation + equiv[expr] = sympy.true + # we do not pass evaluate=False like others on purpose here! + # we want not(a=b and not ~(a Optional[sympy.Basic]: + """ + Tries to evaluate expr without introducing guards + + If unbacked_only == True, then we only do substitutions on + unbacked SymInts (leaving regular hinted integers alone). This could + result in an expression that still contains backed SymInts, which you + could then potentially guard on. + + Use compute_hint == True if you are trying to compute a non-binding + hint for the particular hint values of backed and unbacked SymInts, + e.g., if s0 happens to be 3 this run, compute_hint will substitute s0 with 3. + """ + + # axioms with compute hint NYE + assert not compute_hint or not axioms + expr = self.simplify(expr, size_oblivious) + + if compute_hint: + expr = expr.xreplace(self.var_to_val).xreplace(self.unbacked_var_to_val) + + expr = canonicalize_bool_expr(expr) + + def resimplify_floor_div(axioms: dict[sympy.Expr, sympy.Expr]) -> None: + if not self._resimplify_floor_div_axioms: + return + self._resimplify_floor_div_axioms = False + new_items = {} + for k, v in list(axioms.items()): + # A FloorDiv in implications could have became CleanDiv at this point, due to new facts + # to the shapeEnv. This handles such issue but its not ideal. This is the only expression + # simplification that depends on the global state of shape env. + # TODO try to get rid of CleanDiv since it breaks the invariant that's simplifications of sympy + # expressions only depend on the expression itself. + if k.has(FloorDiv): + new_items.update({self.simplify(k): v}) + axioms.update(new_items) + + # Pattern matching + if axioms is None: + resimplify_floor_div(self.axioms) + subst = self.axioms + else: + subst = {} + for e in axioms: + if e.free_symbols.issubset(expr.free_symbols): + subst.update(dict(self.get_implications(self.simplify(e)))) + + resimplify_floor_div(subst) + + expr = expr.xreplace(subst) + # TODO: compute hint might have gotten broken here + + fs = expr.free_symbols + + if not fs and (expr.is_number or expr.is_Boolean): + return expr + + if var_to_range is None: + var_ranges = self.var_to_range + else: + var_ranges = dict(var_to_range) + + symbol_info = tuple( + _SymbolInfo( + s, + var_ranges.get(s), + self.var_to_val.get(s), + s in self.size_like, + ) + for s in sorted(fs, key=str) # TODO: speed up sort? + ) + + r = _maybe_evaluate_static_worker( + expr, symbol_info, unbacked_only, size_oblivious + ) + return r + + @_lru_cache + def replace(self, expr: _SympyT) -> _SympyT: + """ + Apply symbol replacements to any symbols in the given expression. + """ + replacements = {} + # pyrefly: ignore [missing-attribute] + for s in expr.free_symbols: + r = self._find(s) + + # Micro-optimization: only do replacements if r and s are different + # Otherwise, xreplace is not a no-op and will trigger expensive + # assumption queries if expr has a relational node. + if not r.is_Symbol or r != s: + replacements[s] = r + if replacements: + # pyrefly: ignore [missing-attribute] + return safe_expand(expr.xreplace(replacements)) + else: + return expr + + @_lru_cache + def _update_divisible(self) -> None: + new_divisible = set() + for k in self.divisible: + res = self.replace(k) + if not res.is_number: + new_divisible.add(k) + + self.divisible = new_divisible + self._update_version_counter() + + @_lru_cache + def simplify(self, expr: _SympyT, size_oblivious: bool = False) -> _SympyT: + """Use known constraints and replacements to simplify the given expr""" + expr = safe_expand(expr) + expr = self.replace(expr) + + # Simplify max(0/1, x) to x when x >= 0/1. max(1, x) is a commonly introduced + # expression when creating contiguous strides. + if not size_oblivious: + min_max_replacements = {} + for atom in expr.atoms(Max): # type: ignore[has-type] + if len(atom.args) > 2: + continue + a, b = atom.args + if b == 1 or b == 0: + a, b = b, a + + if a == 1 and self._maybe_evaluate_static(sympy.Ge(b, 1)): + min_max_replacements[atom] = b + if a == 0 and self._maybe_evaluate_static(sympy.Ge(b, 0)): + min_max_replacements[atom] = b + if min_max_replacements: + expr = expr.xreplace(min_max_replacements) + + if expr.has(TruncToInt): + trunc_replacements = {} + for atom in expr.atoms(TruncToInt): + if isinstance(atom.args[0], IntTrueDiv): + base, divisor = atom.args[0].args + if base % divisor == 0: + trunc_replacements[atom] = CleanDiv(base, divisor) + else: + # TruncToInt(IntTrueDiv(a,b)) == FloorDiv(a, b) + trunc_replacements[atom] = FloorDiv(base, divisor) + if trunc_replacements: + expr = expr.xreplace(trunc_replacements) + + # TODO it would seem that this pass is not necessary given the + # below replacement of // with /, but for nested FloorDivs + # the non-recursive replacement doesn't work, and + # recursive makes it hard to look up divisibility, + # because existing divisibility info has FloorDiv in it, not / + # for now just do a separate pass to catch common nested case + if expr.has(FloorDiv): + self._update_divisible() + div_replacements = {} + for atom in expr.atoms(FloorDiv): + base, divisor = atom.args + if isinstance(divisor, FloorDiv): + base1, divisor1 = divisor.args + if ( + self.replace(Mod(base, divisor)) in self.divisible + and base == base1 + and self.replace(Mod(base1, divisor1)) in self.divisible + ): + div_replacements[atom] = divisor1 + if div_replacements: + expr = expr.xreplace(div_replacements) + expr = safe_expand(expr) + if expr.has(FloorDiv): + div_replacements = {} + pows = expr.atoms(sympy.Pow) + rationals = expr.atoms(sympy.Rational).difference(expr.atoms(sympy.Integer)) + for fd in expr.atoms(FloorDiv): + base, divisor = fd.args + if self.replace(Mod(base, divisor)) in self.divisible: + div_replacements[fd] = CleanDiv(base, divisor) + if div_replacements: + new_expr = expr.xreplace(div_replacements) + new_expr = safe_expand(new_expr) + new_pows = new_expr.atoms(sympy.Pow) + new_rationals = new_expr.atoms(sympy.Rational).difference( + new_expr.atoms(sympy.Integer) + ) + # divisions simplified away + if new_pows.issubset(pows) and new_rationals.issubset(rationals): + expr = new_expr + return expr + + # TODO: overload for allow_none literal + @lru_cache(256) + def size_hint( + self, expr: sympy.Basic, *, allow_none: bool = False + ) -> Optional[sympy.Basic]: + """ + Gets a size hint for a given expression from the underlying shapes we had. + Does not introduce a guard, so only use this when you can guarantee that + your code is still valid for arbitrary shapes (such as optimization decisions) + """ + result_expr = safe_expand(expr).xreplace(self.var_to_val) + if not result_expr.is_number: + from torch.utils._sympy.singleton_int import SingletonInt + + if isinstance(result_expr, SingletonInt): + return None + r = self._maybe_evaluate_static(result_expr, compute_hint=True) + if r is not None: + return r + if allow_none: + return None + + if self.oblivious_var_to_val: + # See https://github.com/pytorch/pytorch/issues/137100#issuecomment-2495778113 + correct_hint = result_expr.xreplace(self.oblivious_var_to_val) + counterfactual_hint = result_expr.xreplace( + {k: max(v, 2) for k, v in self.oblivious_var_to_val.items()} + ) + if ( + not correct_hint.free_symbols + and not counterfactual_hint.free_symbols + ): + if correct_hint == counterfactual_hint: + log.info("oblivious_size hit %s -> %s", expr, correct_hint) + return correct_hint + else: + log.info( + "oblivious_size counterfactual failed %s -> %s != %s", + expr, + correct_hint, + counterfactual_hint, + ) + else: + log.info( + "oblivious_size miss %s -> %s (counterfactual: %s)", + expr, + correct_hint, + counterfactual_hint, + ) + + if self.unbacked_var_to_val: + unsound_expr = result_expr.xreplace(self.unbacked_var_to_val) + if not unsound_expr.free_symbols: + log.warning( + "propagate_real_tensors size_hint(%s) -> %s", expr, unsound_expr + ) + trace_structured( + "propagate_real_tensors", + metadata_fn=lambda: { + "expr": repr(expr), + "result": repr(unsound_expr), + "stack": structured.from_traceback( + CapturedTraceback.extract(skip=1).summary() + ), + }, + ) + self.guard_or_defer_runtime_assert( + sympy.Eq(result_expr, unsound_expr), + f"propagate_real_tensors: {result_expr} == {unsound_expr}", + ) + return unsound_expr + + raise self._make_data_dependent_error(result_expr, expr) + return result_expr + + # NB: keep in sync with size_hint + @lru_cache(256) + def has_hint(self, expr: sympy.Expr) -> bool: + result_expr = safe_expand(expr).xreplace(self.var_to_val) + return ( + result_expr.is_number + or self._maybe_evaluate_static(result_expr) is not None + ) + + def _make_data_dependent_error( + self, + expr: sympy.Basic, + unhinted_expr: sympy.Basic, + *, + expr_sym_node_id: Optional[int] = None, + ) -> GuardOnDataDependentSymNode: + # TODO: in a Dynamo context, having user code, and having the + # name of the local, will be much better + size_like_symbols = [] + for s in expr.free_symbols: + stacktrace = "".join(self.var_to_stack[s].format()) + self.log.debug( + "Data dependent variable '%s' allocated at:\n%s", s, stacktrace + ) + if s in self.size_like: + size_like_symbols.append(s) + size_oblivious_result_msg = "" + sloc, maybe_extra_debug = self._get_stack_summary(True) + if expr.is_integer: # type: ignore[attr-defined] + desc = ( + "Could not extract specialized integer from data-dependent expression" + ) + else: + desc = "Could not guard on data-dependent expression" + size_oblivious_result_msg = ( + "consider using data-dependent friendly APIs such as " + "guard_or_false, guard_or_true and statically_known_true." + ) + + msg = ( + f"{desc} {expr} (unhinted: {unhinted_expr}). " + f"(Size-like symbols: {', '.join(map(str, size_like_symbols)) or 'none'})\n\n" + f"{size_oblivious_result_msg}\n" + f"Caused by: {sloc}\n" + 'For more information, run with TORCH_LOGS="dynamic"\n' + "For extended logs when we create symbols, also add " + f'TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="{",".join(map(str, expr.free_symbols))}"\n' + "If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n" + "For more debugging help, see " + "https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n" + + maybe_extra_debug + # TODO: Help text about how to use our runtime tests to fix this + # problem + ) + + dtrace_structured( + "guard_on_data_dependent_error", + metadata_fn=lambda: { + "expr": repr(expr), + "unhinted_expr": repr(unhinted_expr), + "expr_id": self._expr_sym_node_id, + "stack": structured.from_traceback( + CapturedTraceback.extract(skip=1).summary() + ), + }, + ) + return GuardOnDataDependentSymNode(expr, msg) + + def _update_var_to_range( + self, + symbol: sympy.Symbol, + vr: ValueRanges, + vr_sloc: Optional[ValueRangesSLoc] = None, + *, + is_constraint: bool = False, + ) -> None: + lower, upper = vr.lower, vr.upper + + # If we have a size-like unbacked SymInt, refuse to refine the range to be + # less than two. This is because when we intersect this range + # with [2, inf] for size oblivious tests, the range would be + # unsatisfiable. In other words, once you have a size-like + # unbacked SymInt, we can never learn that it is exactly zero or one, + # because we would now give inconsistent results for all size + # oblivous tests! + if upper < 2 and symbol in self.size_like: + vr = ValueRanges(lower, 2) + + # Updates the range and the guards corresponding to each bound of the symbol. + if symbol not in self.var_to_range: + self.log.debug("_update_var_to_range %s = %s (new)", symbol, vr) + self.var_to_range[symbol] = vr + if vr_sloc is None: + sloc = self._get_sloc() + vr_sloc = ValueRangesSLoc(sloc, sloc) + self.var_to_range_sloc[symbol] = vr_sloc + else: + old = self.var_to_range[symbol] + new = old & vr + if new != old: + if vr_sloc is None: + sloc = self._get_sloc() + vr_sloc = ValueRangesSLoc(sloc, sloc) + if new.lower != old.lower: + self.var_to_range_sloc[symbol].lower = vr_sloc.lower + if new.upper != old.upper: + self.var_to_range_sloc[symbol].upper = vr_sloc.upper + self.var_to_range[symbol] = new + self.log.debug("_update_var_to_range %s = %s (update)", symbol, new) + + if (v := self.var_to_val.get(symbol)) is not None: + r = self.var_to_range[symbol] + if v not in r: + # For constraint failure, delay this for later + # TODO: Rework all of this, the constraint logic is very + # duplicative with regular reasoning + if not is_constraint: + assert v in r, f"{v} not in {r}" + + def _set_replacement(self, a: sympy.Symbol, tgt: sympy.Expr, msg: str) -> None: + """ + Adds or updates a replacement for a symbol. + Use this instead of `self.replacements[a] = tgt`. + """ + + if tgt == self.replacements.get(a, None): + return + + if a in tgt.free_symbols: + return + + # Precondition: a == tgt + assert isinstance(a, sympy.Symbol) + + if ( + self.prefer_deferred_runtime_asserts_over_guards + and not _is_supported_equivalence(tgt) + ): + return # continuing leads to placeholder shapes having complex expressions that we can't resolve + + # Handles nested tensor symbolic variables which don't have + # var_to_range bounds + tgt_bound = None + if a in self.var_to_range: + src_bound = self.var_to_range[a] + + # First, refine the value range of a based on the computed value range + # of tgt. This is always OK to do, even if we decide not to do the + # substitution in the end. This might be a no-op, if a already has + # a tighter bound + tgt_bound = self.bound_sympy(tgt) + self._update_var_to_range(a, tgt_bound) + + # Next, check if we can update the range of free symbols in tgt + # based on the range in a. But only do it if: + # - the source bound non-trivially improves over what we get out of + # the existing bounds. + # - the replacement is univariate and we can invert the tgt expression + if not tgt_bound.issubset(src_bound) and len(tgt.free_symbols) == 1: + b = next(iter(tgt.free_symbols)) + # Try to invert the equality + r = try_solve(sympy.Eq(a, tgt), b, floordiv_inequality=False) + if r is not None: + self.log.debug( + "set_replacement: solve for %s in %s == %s gives %s", + b, + a, + tgt, + r, + ) + # The solution here can be non-integral, for example, if + # we have s0 = 2*s1, then s1 = s0/2. What we would like + # to do is calculated the bounds in arbitrary precision, + # and then requantize the bound to integers when we are + # done. + rat_b_bound = self.bound_sympy(r[1]) + b_bound = ValueRanges( + CeilToInt(rat_b_bound.lower), FloorToInt(rat_b_bound.upper) + ) + self._update_var_to_range(b, b_bound, self.var_to_range_sloc[a]) + tgt_bound = self.bound_sympy(tgt) + assert tgt_bound.issubset(src_bound), ( + f"{tgt_bound=} not a subset of {src_bound=}" + ) + + # TODO: Should we propagate size-like-ness? + # + # Pros: if u0 is size-like, intuitively u0 == u1 should cause u1 + # to become size-like. + # + # Cons: if u0 is size-like, what about u0 - 1 == u1? You CAN'T + # propagate in this case, because what if u0 == 0, then u1 is negative + # and clearly isn't a size. So, at minimum, any f(x) whose value + # range isn't [0, inf] given x in [0, inf] cannot propagate + # size-like-ness. But there are many situations where you could + # imagine u1 is going to be size-like and actually you just didn't + # have a refined enough value range on u0. Since even innocuous + # looking arithmetic operations can destroy size-like-ness, it's + # best to not propagate it at all and force the user to annotate it + # as necessary. + # + # Compromise: we preserve size-like-ness only for exact equality + # and nothing else. + if a in self.size_like and isinstance(tgt, sympy.Symbol): + self.size_like.add(tgt) + elif isinstance(tgt, sympy.Symbol) and tgt in self.size_like: + self.size_like.add(a) + + # Now, decide if we will do the substitution. + # + # - If the source has a non-trivial range, only substitute if + # we preserve this range. Note that we may have propagated + # the src_range to free variables in tgt when tgt is univariate + # and we could find an inverse, which helps us achieve this. + # This ensures we never "forget" about user defined ranges, + # even if they end up being defined on composite formulas + # like s0 + s1. + # + # - If the variable is unbacked, only substitute if the substitution + # would preserve the bounds also under size-like-ness conditions. + + if not tgt_bound.issubset(src_bound): + self.log.debug( + "skipped set_replacement %s = %s (%s) [%s not subset of %s]", + a, + tgt, + msg, + tgt_bound, + src_bound, + ) + return + elif a in self.size_like: + tgt_bound_so = self.bound_sympy(tgt, size_oblivious=True) + src_bound_so = self.bound_sympy(a, size_oblivious=True) + if not tgt_bound_so.issubset(src_bound_so): + self.log.debug( + "skipped set_replacement %s = %s (%s) " + "[%s not subset of %s (size-oblivious conditions)]", + a, + tgt, + msg, + tgt_bound_so, + src_bound_so, + ) + return + + if isinstance(tgt, (sympy.Integer, sympy.Float)): + # specializing to a constant, which is likely unexpected (unless + # you specified dynamic=True) + + user_tb = TracingContext.extract_stack() + trace_structured( + "symbolic_shape_specialization", + metadata_fn=lambda: { + "symbol": repr(a), + "sources": [s.name for s in self.var_to_sources.get(a, [])], + "value": repr(tgt), + "reason": msg, + "stack": structured.from_traceback( + CapturedTraceback.extract(skip=1).summary() + ), + "user_stack": ( + structured.from_traceback(user_tb) if user_tb else None + ), + }, + ) + + for source in self.var_to_sources.get(a, []): + if user_tb: + self.specialization_stacks[source] = user_tb + + if config.print_specializations: + self.log.warning( + "Specializing %s to %s", self.var_to_sources[a][0].name, tgt + ) + self.log.debug("SPECIALIZATION", stack_info=True) + log.info("set_replacement %s = %s (%s) %s", a, tgt, msg, tgt_bound) + self.replacements[a] = tgt + # NB: the replacement may get refined, but the user will find the + # FIRST one most useful (TODO: Maybe we could consider tracking all of + # them) + if a not in self.replacements_slocs: + self.replacements_slocs[a] = self._get_sloc() + self._update_version_counter() + + # When specializing 'a == tgt', the equality should be also conveyed to + # Z3, in case an expression uses 'a'. + self._add_target_expr(sympy.Eq(a, tgt, evaluate=False)) + + def _add_divisible(self, expr: sympy.Expr) -> None: + self.divisible.add(expr) + self._update_version_counter() + + @_lru_cache + @record_shapeenv_event() + def _find(self, a: sympy.Symbol) -> sympy.Expr: + """ + Implements a DSU-like algorithm to find the variable that represents a + Also handles transitive non-identity replacements. + + a: b + c + c: d + """ + if a not in self.replacements: + return a + res = self.replacements[a] + cur_replace = {s: self._find(s) for s in res.free_symbols} + replaced, changed = self.replacements[a]._xreplace(cur_replace) + if changed: + self._set_replacement(a, replaced, "find") + return self.replacements[a] + + @lru_cache(256) + def _maybe_guard_rel(self, expr: sympy.Expr) -> None: + """ + The relational guard is guarded to be true. Use this information to + simplify shapes (i.e. a == b or a % 5 == 0) + """ + if isinstance(expr, sympy.And): + for arg in expr.args: + self._maybe_guard_rel(arg) + return + elif not isinstance(expr, sympy.Rel): + return + + # A good example of what goes wrong if you don't do this is + # python test/functorch/test_aotdispatch.py -k + # test_aot_autograd_symbolic_module_exhaustive_nn_LazyConv3d_cpu_float32 + if isinstance(expr, sympy.Ne): + return + + free = list(expr.free_symbols) + + assert len(free) > 0, ( + f"The expression should not be static by this point: {expr}" + ) + # In case of really gnarly expression, we don't blow up + if len(free) > 5: + return + + # Prioritize unbacked symints for solving by ordering them last. + # Prefer to simplify out lexicographically higher symbols (i.e. simplify out s4 over s3). + # (NB: this unfortunately isn't strictly equivalent to simplifying out newer symbols) + # Prefer to simplify out symbols with ephemeral sources. + def _smart_symbol_sort(x: sympy.Symbol) -> tuple[int, int, str]: + has_only_ephemeral_sources = x in self.var_to_sources and all( + s.is_ephemeral() for s in self.var_to_sources[x] + ) + # NB: size_hint is int, not sympy.Expr, do not use int_oo here + hint_size = self.size_hint(x, allow_none=True) + if hint_size is None: + size = sys.maxsize + elif symbol_is_type(x, SymT.SIZE): + assert isinstance(hint_size, sympy.Expr) + size = int(hint_size) + else: + size = sys.maxsize + name = x.name + # 1 puts ephemeral sourced symbols first when sorting in reverse + return (1 if has_only_ephemeral_sources else 0, size, name) + + free = sorted(free, key=_smart_symbol_sort, reverse=True) # type: ignore[attr-defined] + lhs = expr.lhs + rhs = expr.rhs + + self._refine_ranges(expr) + + # The rest of this stuff is for equality only + if not isinstance(expr, sympy.Eq): + return + + if not expr.has(Mod): + try: + floor_div_atoms = lhs.atoms(FloorDiv).union(rhs.atoms(FloorDiv)) + if len(floor_div_atoms) > 0 and any( + a.divisor != 1 for a in floor_div_atoms + ): + raise NotImplementedError + + # Never replace unbacked symbols with other unbacked symbols that are + # not function arguments. (ex:mark_unbacked symbols are fine to replace + # other unbacked, but not those coming from .item() calls). + + # This is error prone because you can cause references to + # unbacked symbols to time travel backwards. E.g., + # + # u1 = x.item() + # ... use of u1 ... + # u2 = y.item() + # u3 = z.item() + # torch._check(u1 == u2 + u3) + # + # If you replace u1 with u2 + u3, then the use of u1 now + # references u2 and u3 prior to them actually being bound at + # runtime. It's pretty inconvenient to setup control + # dependencies for substitutions, so ban it entirely. + def trivial_solve(lhs: sympy.Expr, rhs: sympy.Expr) -> bool: + if isinstance(lhs, sympy.Symbol): + if free_unbacked_symbols( + lhs + ) and not _free_non_source_unbacked_symbols( + rhs, self.unbacked_inputs + ): + return True + if symbol_is_type(lhs, SymT.FLOAT): + return True + # TODO: Maybe trivial solutions for int should also be + # done? + return False + + # short-circuit when no solving is needed + if trivial_solve(lhs, rhs): + self._set_replacement(lhs, self._find(rhs), "trivial_lhs") + elif trivial_solve(rhs, lhs): + self._set_replacement(rhs, self._find(lhs), "trivial_rhs") + else: + r = try_solve(expr, free[0], floordiv_inequality=False) + if r is not None and all( + t.is_integer for t in sympy.preorder_traversal(r[1]) + ): + new_var = self._find(r[1]) + ok = len(free_unbacked_symbols(new_var)) == 0 + if ok: + self._set_replacement(free[0], new_var, "solve") + + except NotImplementedError: + pass + else: + # expression has mod. + mod_expr = next(iter(expr.atoms(Mod))) + try: + r = try_solve(expr, mod_expr, floordiv_inequality=False) + if r is not None and r[1] == 0: + self._add_divisible(mod_expr) + except NotImplementedError: + pass + return + + # See: Note - On 0/1 specialization + def _default_value_range( + self, do_not_specialize_zero_one: bool = False + ) -> ValueRanges: + lower = 0 if (do_not_specialize_zero_one or not self.specialize_zero_one) else 2 + return ValueRanges(lower, int_oo) + + def _default_unspecified_value_range(self) -> ValueRanges: + return ValueRanges.unknown_int() + + @_lru_cache + def _simplify_floor_div(self, expr: sympy.Expr) -> sympy.Expr: + floor_divs = tuple(expr.atoms(FloorDiv)) + # we expect floor_divs to be exact, + # and thus add the guards for the exact floordivs, + # even if tracing doesn't require them otherwise + for fd in reversed(floor_divs): + base, divisor = fd.args + mod_expr = Mod(base, divisor) + eq_expr = sympy.Eq(mod_expr, 0) + # add necessary mod guards + self.evaluate_expr(eq_expr) + return self.simplify(expr) + + # We're about to add a guard/runtime assert, check if the ShapeEnv is frozen + # and if so issue a warning + def _check_frozen(self, expr: sympy.Basic, concrete_val: sympy.Basic) -> None: + if self.frozen: + self.counter["ignored_backward_guard"] += 1 + signpost_event( + "dynamic", + "evaluate_expr_frozen", + { + **self.co_fields, + "ignored_guard": f"{expr} == {concrete_val}", + # no version = original state (this signpost is expected) + # version 2 = dynamic backwards is eagerly compiled + "version": 2, + }, + ) + log.info( + "Ignored guard %s == %s, this could result in accuracy problems", + expr, + concrete_val, + # only print stack trace when debug mode is on (e.g. TORCH_LOGS="dynamic") + stack_info=log.getEffectiveLevel() < logging.WARNING, + ) + + def _get_user_frame(self) -> Optional[types.FrameType]: + frame = inspect.currentframe() + while frame is not None: + if frame.f_code.co_filename not in uninteresting_files(): + return frame + frame = frame.f_back + return frame + + def _get_stack_summary( + self, is_debug: bool = False, framework_loc: Optional[str] = None + ) -> tuple[SLoc, str]: + floc: Optional[Union[str, traceback.FrameSummary]] = framework_loc + if floc is None: + frame = self._get_user_frame() + try: + if frame is not None: + floc = traceback.FrameSummary( + frame.f_code.co_filename, + frame.f_lineno, + frame.f_code.co_name, + ) + finally: + del frame + + # NB: this stack is truncated, but it's fine because the main + # stack_info will give you the rest of the info you need + maybe_user_loc = None + user_tb = TracingContext.extract_stack() + if user_tb: + idx = len(user_tb) - 1 + while idx > 0 and user_tb[idx].filename in uninteresting_files(): + idx -= 1 + maybe_user_loc = format_frame(user_tb[idx], line=True) + + maybe_extra_debug = "" + if is_debug and user_tb: + maybe_extra_debug = ( + "\nUser Stack (most recent call last):\n" + + " (snipped, see stack below for prefix)\n" + + "".join(traceback.format_list(user_tb)) + ) + if is_debug and config.extended_debug_cpp: + cpp_stack = CapturedTraceback.extract(cpp=True) + maybe_extra_debug += "\nC++ stack trace:\n" + "".join(cpp_stack.format()) + elif is_debug: + maybe_extra_debug += ( + "\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1" + ) + + return SLoc(floc, maybe_user_loc), maybe_extra_debug + + # Pass in framework_loc to override the framework location info + def _get_sloc(self, framework_loc: Optional[str] = None) -> SLoc: + sloc, _ = self._get_stack_summary(framework_loc=framework_loc) + return sloc + + def _generate_unique_id(self, source_name: str) -> int: + attempt = int(hashlib.sha256(source_name.encode()).hexdigest(), 16) % 100 + while attempt in self.unique_ids: + attempt += 1 + self.unique_ids.add(attempt) + return attempt + + def _find_frame_locals(self) -> _FrameLocalResult: + """ + Given the current user code frame, finds the relevant lines of code, + values of symbolic locals, and free symbols involved. + """ + frame_locals: dict[str, Any] = {} + frame_symbols: dict[str, str] = {} + + if ( + frame := _find_user_code_frame() + ) is None or frame.f_code.co_filename == "": + return _FrameLocalResult() + + # find bytecode instructions relevant to the frame + instructions = list(dis.Bytecode(frame.f_code)) + co_lines, offset = inspect.getsourcelines(frame.f_code) + start, end, cur = None, None, None + # pyrefly: ignore [bad-assignment] + for i, instr in enumerate(instructions): + if instr.starts_line is not None: + cur = instr.starts_line + if cur != frame.f_lineno: + continue + if start is None: + start = end = i + else: + end = i + + if start is None or end is None: # no instructions found + return _FrameLocalResult() + + # track involved locals and free symbols + def go(x: Any) -> Optional[str]: + if isinstance(x, torch.Tensor): + for y in x.size(): + go(y) + for y in x.stride(): + go(y) + go(x.storage_offset()) + return ( + f"Tensor(shape: {x.size()}, " + f"stride: {x.stride()}, " + f"storage_offset: {x.storage_offset()})" + ) + elif isinstance(x, (SymBool, SymInt, SymFloat)): + for s in x.node.expr.free_symbols: + if str(s) in frame_symbols: # type: ignore[operator] + continue + if s in self.var_to_sources: + frame_symbols[str(s)] = self.var_to_sources[s][0].name # type: ignore[assignment] + return str(x) + return None + + # go through instructions, seeing linenos & involved locals + last_lineno = frame.f_lineno + for instr in instructions[start : end + 1]: + if (lineno := instr.starts_line) is not None: + last_lineno = max(last_lineno, lineno) + if isinstance(instr.argval, str) and instr.argval in frame.f_locals: + flat_locals = pytree.tree_flatten(frame.f_locals[instr.argval])[0] + frame_locals[instr.argval] = [ + go(flat_local) for flat_local in flat_locals + ] + + # store LOC + locs = co_lines[frame.f_lineno - offset : last_lineno + 1 - offset] + if not locs: + return _FrameLocalResult() + + indent = len(locs[0]) - len(locs[0].lstrip()) + frame_loc = "".join([loc[indent:] for loc in locs]).strip() # type: ignore[assignment] + return _FrameLocalResult( + loc=frame_loc, locals=frame_locals, symbols=frame_symbols + ) + + def _log_guard(self, prefix: str, g: SympyBoolean, forcing_spec: bool) -> None: + dtrace_structured( + "guard_added", + metadata_fn=lambda: { + "expr": str(g), + "prefix": prefix, + "expr_node_id": self._expr_sym_node_id, + "user_stack": structured.get_user_stack(3), + "stack": structured.get_framework_stack(3), + "symbol_to_sources": { + str(v): k + for k, v in self.source_to_var.items() + if v in g.free_symbols + }, + "frame_locals": asdict(self._find_frame_locals()), + }, + ) + trace_structured( + "guard_added_fast", + metadata_fn=lambda: { + "expr": str(g), + "user_stack": structured.from_traceback(TracingContext.extract_stack()), + "stack": structured.from_traceback( + CapturedTraceback.extract(skip=1).summary() + ), + }, + ) + if self.log.isEnabledFor(logging.INFO): + str_g = str(g) + is_debug = ( + config.extended_debug_guard_added is not None + and str_g == config.extended_debug_guard_added + ) + sloc, maybe_extra_debug = self._get_stack_summary(is_debug) + maybe_more_info = "" + if not is_debug: + maybe_more_info = ( + ", for more info run with " + f'TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED="{str_g}"' + ) + self.log.info( + "%s %s [guard added] %s%s%s", + prefix if not forcing_spec else f"{prefix} (forcing_spec)", + str_g, + sloc, + maybe_more_info, + maybe_extra_debug, + stack_info=is_debug, + ) + + # A local variable to evaluate_expr stored in the class to avoid + # using it for the lru_cache that is on top of it since it does + # not effect the results. When needed its read directly. + _expr_sym_node_id: Optional[int] = None + + def evaluate_sym_node( + self, + sym_node: SymNode, + size_oblivious: bool = False, + fallback_value: Optional[bool] = None, + ) -> sympy.Basic: + """ + Given a a SymNode, evaluates sym_node.expr, adding guards if necessary. + """ + + self._expr_sym_node_id = id(sym_node) + return self.evaluate_expr( + sym_node.expr, + sym_node.hint, + sym_node.fx_node, + size_oblivious, + fallback_value=fallback_value, + ) + + def _is_python_assert(self) -> bool: + # Check if this boolean is used in an assertion, bytecode pattern for + # assertions is pretty stable for Python 3.7--3.13, ported with minimal + # changes from torch/fx/proxy.py + # Bytecode pattern for `assert` statements: + # TO_BOOL / COMPARE_OP # Only for Python >= 3.13 + # POP_JUMP_IF_TRUE + # LOAD_ASSERTION_ERROR + # RAISE_VARARGS + frame = self._get_user_frame() + assert frame is not None + + insts = list(dis.get_instructions(frame.f_code)) + if sys.version_info >= (3, 11): + # For Python >= 3.11, instructions can be 2-4 bytes long. + from bisect import bisect_left + + cur = bisect_left(insts, frame.f_lasti, key=lambda x: x.offset) + else: + # For Python <= 3.10, instructions are always 2 bytes. + cur = frame.f_lasti // 2 + + if sys.version_info >= (3, 13): + if insts[cur].opname in ("TO_BOOL", "COMPARE_OP"): + # Peek 1 instruction further. + cur += 1 + + assert_insts = torch._dynamo.symbolic_convert.get_assert_bytecode_sequence( + False + ) + + cur_insts = insts[cur + 1 : cur + 1 + len(assert_insts)] + cur_insts = [inst.opname for inst in cur_insts] + return cur_insts == assert_insts + + def _log_real_tensor_propagation( + self, orig_expr: sympy.Basic, unsound_result: sympy.Basic + ) -> None: + log.warning( + "propagate_real_tensors evaluate_expr(%s) -> %s", + orig_expr, + unsound_result, + ) + trace_structured( + "propagate_real_tensors", + metadata_fn=lambda: { + "expr": repr(orig_expr), + "result": repr(unsound_result), + "stack": structured.from_traceback( + CapturedTraceback.extract(skip=1).summary() + ), + }, + ) + dtrace_structured( + "propagate_real_tensors_provenance", + metadata_fn=lambda: { + "expr": repr(orig_expr), + "result": repr(unsound_result), + "expr_node_id": self._expr_sym_node_id, + "user_stack": structured.get_user_stack(3), + "stack": structured.get_framework_stack(3), + "symbol_to_sources": { + str(v): k + for k, v in self.source_to_var.items() + if v in orig_expr.free_symbols + }, + "frame_locals": asdict(self._find_frame_locals()), + }, + ) + + def evaluate_expr( + self, + orig_expr: sympy.Basic, + hint: Optional[Union[int, bool, float]] = None, + fx_node: Optional[torch.fx.Node] = None, + size_oblivious: bool = False, + fallback_value: Optional[bool] = None, + *, + forcing_spec: bool = False, + ) -> sympy.Basic: + """ + Given an expression, evaluates it, adding guards if necessary + When fallback_value is not None the function return fallback_value instead of failing with data dependent error. + """ + + # Add extra state that evaluate_expr() depends on. + suppress_guards_tls = ShapeEnv._suppress_guards_tls() + return self._inner_evaluate_expr( + orig_expr, + hint, + fx_node, + size_oblivious, + forcing_spec, + suppress_guards_tls, + fallback_value, + ) + + @lru_cache(256) + @record_shapeenv_event(save_tracked_fakes=True, name="evaluate_expr") + def _inner_evaluate_expr( + self, + orig_expr: sympy.Basic, + hint: Optional[Union[int, bool, float]], + fx_node: Optional[torch.fx.Node], + size_oblivious: bool, + forcing_spec: bool, + _suppress_guards_tls: bool, + fallback_value: Optional[bool] = None, + ) -> sympy.Basic: + try: + return self._evaluate_expr( + orig_expr, + hint, + fx_node, + size_oblivious, + fallback_value, + forcing_spec=forcing_spec, + ) + except Exception as e: + if isinstance(e, GuardOnDataDependentSymNode): + pass + else: + self.log.warning( + "failed during evaluate_expr(%s, hint=%s, size_oblivious=%s, forcing_spec=%s", + orig_expr, + hint, + size_oblivious, + forcing_spec, + ) + raise + + def _log_suppressed_dde(self, a: SymBool, assumed_value: bool) -> None: + sloc, extra = self._get_stack_summary(True) + log.info( + "could not evaluate %s due to data dependency, it was assumed to be %s with no runtime assertions %s %s", + a, + assumed_value, + sloc, + extra, + ) + + def _evaluate_expr( + self, + orig_expr: sympy.Basic, + hint: Optional[Union[bool, int, float]] = None, + fx_node: Optional[torch.fx.Node] = None, + size_oblivious: bool = False, + fallback_value: Optional[bool] = None, + *, + forcing_spec: bool = False, + ) -> sympy.Basic: + # TODO: split conjunctions and evaluate them separately + if isinstance( + orig_expr, + (sympy.logic.boolalg.BooleanTrue, sympy.logic.boolalg.BooleanFalse), + ): + return orig_expr + + # Don't track this one. (Because this cache is inside this function the + # cache only lasts for the invocation of this function call) + @functools.cache + def compute_concrete_val() -> sympy.Basic: + if hint is None: + # This is only ever called for expressions WITHOUT unbacked + # symbols + r = self.size_hint(orig_expr) + assert r is not None + return r + else: + return sympy.sympify(hint) + + concrete_val: Optional[sympy.Basic] + + # Check if: + # 1. 'translation_validation' is set + # 2. the corresponding 'fx_node' is not 'None' + # 3. the guard should not be suppressed + # 4. the guard doesn't contain backed symfloat symbols + # since z3 can't handle floats + # 5. fallback_value is none. + # If all of the above check, we create an FX node representing the + # actual expression to be guarded. + node = None + fresh = False + if ( + self._translation_validation_enabled + and fx_node is not None + and not self._suppress_guards_tls() + and not size_oblivious + and not any(symbol_is_type(s, SymT.FLOAT) for s in orig_expr.free_symbols) + and fallback_value is None + ): + # TODO: does this even worked with unbacked :think: + concrete_val = compute_concrete_val() + if concrete_val is sympy.true: + node, fresh = self._create_fx_call_function(torch._assert, (fx_node,)) + elif concrete_val is sympy.false: + neg, _ = self._create_fx_call_function(operator.not_, (fx_node,)) + node, fresh = self._create_fx_call_function(torch._assert, (neg,)) + else: + eql, _ = self._create_fx_call_function( + operator.eq, (fx_node, concrete_val) + ) + node, fresh = self._create_fx_call_function(torch._assert, (eql,)) + + assert node is not None + # If this is a fresh node, we have to remember the event index that + # corresponds to this assertion node. + # Reason: so that, given an assertion node, we can replay the ShapeEnv + # events until the point where this assertion node was freshly created. + if fresh: + self._add_fx_node_metadata(node) + + # After creating the FX node corresponding to orig_expr, we must make sure that + # no error will be raised until the end of this function. + # + # Reason: the translation validation may become invalid otherwise. + # + # If an error is raised before the end of this function, we remove the FX node + # inserted, and re-raise the error. + guard = None + + try: + if orig_expr.is_number: + self.log.debug("eval %s [trivial]", orig_expr) + if hint is not None: + if isinstance(hint, bool): + assert orig_expr == hint, f"{orig_expr} != {hint}" + else: + assert sympy.Eq(orig_expr, hint), f"{orig_expr} != {hint}" + return orig_expr + + expr = orig_expr + + static_expr = self._maybe_evaluate_static( + expr, size_oblivious=size_oblivious + ) + if static_expr is not None: + self.log.debug( + "eval %s == %s [statically known]", + ( + f"size_oblivious({orig_expr})" + if size_oblivious + else size_oblivious + ), + static_expr, + ) + if ( + not size_oblivious + and config.backed_size_oblivious + and hint is not None + ): + # TODO: maybe reconcile this with use of counterfactual hints + # in unbacked case + assert static_expr == hint, f"{static_expr} != {hint}" + return static_expr + + transmute_into_runtime_assert = False + + concrete_val = None + if not (expr.free_symbols <= self.var_to_val.keys()): + # TODO: dedupe this with _maybe_evaluate_static + # Attempt to eliminate the unbacked SymInt + new_expr = self._maybe_evaluate_static(expr, unbacked_only=True) + assert new_expr is not None + if not (new_expr.free_symbols <= self.var_to_val.keys()): + ok = False + + # fallback_value is set when guard_or_true or guard_or_false are used. + if not ok and fallback_value is not None: + self._log_suppressed_dde(orig_expr, fallback_value) + return fallback_value + + # oblivious_var_to_val will be defined iff we have sizes with DimDynamic.OBLIVIOUS_SIZE type. + # See https://github.com/pytorch/pytorch/issues/137100#issuecomment-2495778113 + if ( + self.oblivious_var_to_val + and not ( + correct_hint := orig_expr.xreplace( + self.oblivious_var_to_val + ) + ).free_symbols + and not ( + counterfactual_hint := orig_expr.xreplace( + { + k: max(2, v) + for k, v in self.oblivious_var_to_val.items() + } + ) + ).free_symbols + and correct_hint == counterfactual_hint + ): + # TODO: better logging + log.info( + "oblivious_size %s -> %s (passed counterfactual)", + orig_expr, + correct_hint, + ) + + concrete_val = correct_hint + # NB: do NOT transmute into runtime assert + ok = True + + # unbacked_var_to_val is not None iff propagate_real_tensors is on. + # if propagate_real_tensors is on, we check the example values to generate (unsound_result) + # and if they pass we add a runtime assertions and continue. + if ( + not ok + and self.unbacked_var_to_val + and not ( + unsound_result := orig_expr.xreplace( + self.unbacked_var_to_val + ).xreplace(self.var_to_val) + ).free_symbols + ): + self._log_real_tensor_propagation(orig_expr, unsound_result) + transmute_into_runtime_assert = True + + concrete_val = unsound_result + ok = True + + # Check if this is coming from a python assert statement, if so, convert it to a runtime assertion + # instead of failing. + if not ok and self.trace_asserts and self._is_python_assert(): + concrete_val = sympy.true + transmute_into_runtime_assert = True + ok = True + + if not ok: + raise self._make_data_dependent_error( + expr.xreplace(self.var_to_val), + expr, + expr_sym_node_id=self._expr_sym_node_id, + ) + else: + expr = new_expr + + if concrete_val is None: + concrete_val = compute_concrete_val() + self._check_frozen(expr, concrete_val) + + if ( + config.inject_EVALUATE_EXPR_flip_equality_TESTING_ONLY + and isinstance(hint, bool) + and isinstance(expr, (sympy.Eq, sympy.Ne)) + ): + expr = sympy.Not(expr) + + # Turn this into a boolean expression, no longer need to consult + # concrete_val + if concrete_val is sympy.true: + g = cast(SympyBoolean, expr) + elif concrete_val is sympy.false: + g = sympy.Not(expr) + else: + g = sympy.Eq(expr, concrete_val) # type: ignore[arg-type] + + if transmute_into_runtime_assert: + self.guard_or_defer_runtime_assert( + g, f"propagate_real_tensors: {orig_expr} == {concrete_val}" + ) + return concrete_val + + if not self._suppress_guards_tls(): + self._log_guard("eval", g, forcing_spec=forcing_spec) + + # TODO: If we successfully eliminate a symbol via equality, it + # is not actually necessary to save a guard for the equality, + # as we will implicitly generate a guard when we match that + # input against the symbol. Probably the easiest way to + # implement this is to have maybe_guard_rel return a bool + # saying if it "subsumed" the guard (and therefore the guard + # is no longer necessary) + self._maybe_guard_rel(g) + + if ( + torch.compiler.is_exporting() + and self.prefer_deferred_runtime_asserts_over_guards + ): + # it's fine to defer simple guards here without checking, + # the _maybe_guard_rel() call above will set replacements if possible, + # and so the result here will be statically known + self.guard_or_defer_runtime_assert(g, f"evaluate_expr: {orig_expr}") + else: + # at this point, we've evaluated the concrete expr value, and have + # flipped/negated the guard if necessary. Now we know what to guard + # or defer to runtime assert on. + guard = ShapeGuard( + g, self._get_sloc(), size_oblivious=size_oblivious + ) + self.guards.append(guard) + self.axioms.update(dict(self.get_implications(self.simplify(g)))) + else: + self._log_guard("eval [guard suppressed]", g, forcing_spec=forcing_spec) + + except Exception: + if fresh: + self._remove_fx_node(node) + raise + + if not self._suppress_guards_tls(): + if guard is not None: # we might have deferred this to runtime assert + for s in g.free_symbols: + self.symbol_guard_counter[s] += 1 + # Forcing_spec to avoid infinite recursion + if ( + not forcing_spec + and config.symbol_guard_limit_before_specialize is not None + and self.symbol_guard_counter[s] + > config.symbol_guard_limit_before_specialize + ): + # Force specialization + self.log.info( + "symbol_guard_limit_before_specialize=%s exceeded on %s", + config.symbol_guard_limit_before_specialize, + s, + ) + self.evaluate_expr(s, forcing_spec=True) + + return concrete_val + + def cleanup(self) -> None: + """ + Break reference cycles. + + This destroys the stacks. If you really want to keep them, we + just need some way to break references on code objects. + """ + for s in self.var_to_stack.values(): + s.cleanup() + for ras in self.deferred_runtime_asserts.values(): + for ra in ras: + ra.stack.cleanup() + + @lru_cache(256) + @record_shapeenv_event(save_tracked_fakes=True) + def guard_or_defer_runtime_assert( + self, orig_expr: SympyBoolean, msg: str, fx_node: Optional[torch.fx.Node] = None + ) -> bool: + """ + Adds a guard that orig_expr is True if we can or fall back to adding an assert + that is checked at runtime. + + Args: + orig_expr (sympy.Expr): Boolean expression to assert is true + msg (str): Message to display on assertion failure + fx_node (Optional, torch.fx.Node): node in ``self.graph`` corresponding + to the expression, if applicable + """ + expr = orig_expr + + # TODO: split conjunctions and evaluate them separately + + static_expr = self._maybe_evaluate_static(expr) + if static_expr is not None: + self.log.debug( + "runtime_assert %s == %s [statically known]", orig_expr, static_expr + ) + # TODO: assert bool(static_expr) + return bool(static_expr) + + # Attempt to eliminate the unbacked SymInt + new_expr = self._maybe_evaluate_static(expr, unbacked_only=True) + assert new_expr is not None + if ( + not self.prefer_deferred_runtime_asserts_over_guards + and new_expr.free_symbols <= self.var_to_val.keys() + ): + # Do a normal guard + return self.evaluate_expr(new_expr, fx_node=fx_node) + # NB: Don't use new_expr as expr; it could contain gunk like shape0 + # which we don't want to guard on + + if ( + self._translation_validation_enabled + and fx_node is not None + and not self._suppress_guards_tls() + ): + node, fresh = self._create_fx_call_function(torch._assert, (fx_node,)) + assert node is not None + if fresh: + self._add_fx_node_metadata(node) + + if not self._suppress_guards_tls(): + self._log_guard("runtime_assert", orig_expr, forcing_spec=False) + # If you're here because of this assert, read Note [Backwards runtime asserts] + # in torch/_inductor/graph.py + if self.runtime_asserts_frozen: + log.debug("runtime_asserts_frozen but then got %s", expr) + self._check_frozen(expr, sympy.true) + # eliminate symbols on equality tests / refine ranges + self._maybe_guard_rel(expr) + + # canonicalise to remove equations that are trivially equal + orig_expr = expr + expr = canonicalize_bool_expr(expr) + stack = CapturedTraceback.extract(skip=1) + ra = RuntimeAssert(expr, msg, stack) + + # TODO: Do this in a way that is less janky than int(s.name[1:]) + cands = sorted( + (s for s in expr.free_symbols if symbol_is_type(s, SymT.UNBACKED_INT)), + key=lambda s: int(s.name[1:]), + ) + # Is None when prefer_deferred_runtime_asserts_over_guards=True + # and the guard in question has no unbacked SymInts in front + ix = cands[-1] if cands else None + self.deferred_runtime_asserts.setdefault(ix, []).append(ra) + self.axioms.update(dict(self.get_implications(self.simplify(expr)))) + self.num_deferred_runtime_asserts += 1 + self._update_version_counter() + else: + self._log_guard( + "runtime_assert [guard suppressed]", orig_expr, forcing_spec=False + ) + + return True + + # Refines the ranges of the variables present in 'guard'. + # + # This function tries to refine the range of the variables inside + # 'guard' by reasoning about it. Specifically, when 'guard' is a + # 'sympy.Relational' operation. + # + # It does mainly 3 things: + # 1. Tries to isolate a variable in the left-hand side + # 2. Compute the value range of the right-hand side + # 3. Update the value range of the variable, if better + def _refine_ranges(self, expr: SympyBoolean) -> None: + expr = self.simplify(expr) + + for symbol in expr.free_symbols: + assert isinstance(symbol, sympy.Symbol) + + if isinstance(self.var_to_val.get(symbol, None), SingletonInt): + # Skip var_to_range logic for SingletonInt which is only used + # for jagged layout NestedTensors today + continue + + r = try_solve(expr, symbol) + + if r is None or not (symbol.is_integer and r[1].is_integer): + # Range refinement only supports integer symbols for now. + # There are lots of SymPy bugs when it comes to comparing + # reals and integers, so we skip that for now. + continue + + r_expr, rhs = r + vr = self.var_to_range[symbol] + lower, upper = vr.lower, vr.upper + + rhs_vr = bound_sympy(rhs, self.var_to_range) + + # Let's suppose that we have a preexisting range for x [0, 100]. + # Now, we issue a guard x > y, where the range for y is [50, 150]. + # Then, lower = 0, rhs_vr.lower = 50 and therefore refinement can happen, + # refining x to [51, 100], since x must be greater than y, but the lowest + # y could be is 50. + # + # sympy.Eq may update both lower and upper bounds. + # sympy.G{t,e} may update the lower bound, only. + # sympy.L{t,e} may update the upper bound, only. + if lower <= rhs_vr.lower and isinstance( + r_expr, (sympy.Eq, sympy.Ge, sympy.Gt) + ): + # Strictly greater relations allow us to refine a bit more, since + # x < y implies that the lower bound for x is: y + 1. + lower = rhs_vr.lower + int(isinstance(r_expr, sympy.Gt)) + if upper >= rhs_vr.upper and isinstance( + r_expr, (sympy.Eq, sympy.Le, sympy.Lt) + ): + upper = rhs_vr.upper - int(isinstance(r_expr, sympy.Lt)) + + # Do nothing if the new value range is no better than what we already have. + if vr == ValueRanges(lower, upper): + continue + + # Updates the range and the guards corresponding to each bound of the symbol. + self._update_var_to_range(symbol, ValueRanges(lower, upper)) + # If the range is refined to singleton, set replacement + if self.var_to_range[symbol].is_singleton(): + self._set_replacement( + symbol, + self.var_to_range[symbol].lower, + "range_refined_to_singleton", + ) + + # Clears the cache, since this update can change the result. + self._maybe_evaluate_static.cache_clear() + + @lru_cache(maxsize=None) + @record_shapeenv_event() + def constrain_symbol_range( + self, s: sympy.Symbol, compiler_min: int, compiler_max: int + ) -> None: + upd_vr = ValueRanges(compiler_min, compiler_max) + old_vr = self.var_to_range.get(s, ValueRanges.unknown()) + self._update_var_to_range(s, upd_vr) + if (new_vr := self.var_to_range[s]) != old_vr: + log.info( + "constrain_symbol_range %s [%s, %s]", s, new_vr.lower, new_vr.upper + ) + + +def _is_int(expr: object) -> bool: + return isinstance(expr, SymInt) and expr.node.expr.is_number + + +# WARNING: This is legacy, DO NOT USE +def _is_dim_dynamic(t: torch.Tensor, d: int) -> bool: + return hasattr(t, "_dynamo_dynamic_indices") and d in t._dynamo_dynamic_indices + + +class PropagateUnbackedSymInts(torch.fx.Interpreter): + def run_node(self, n: torch.fx.Node) -> Result: + """ + Run an FX node, propagating unbacked Symbol bindings to the new fake tensor + """ + from torch._guards import detect_fake_mode + + result = super().run_node(n) + fake_mode = detect_fake_mode() + assert fake_mode is not None + rebind_unbacked(fake_mode.shape_env, n, result) + return result + + +def _find_user_code_frame() -> Optional[types.FrameType]: + frame = inspect.currentframe() + while frame is not None: + if not frame.f_code.co_filename.startswith( + os.path.dirname(inspect.getfile(torch)) + os.path.sep + ): + break + frame = frame.f_back + return frame + + +def _blame_user_code(e: Exception, frame: types.FrameType) -> None: + frame_summary = traceback.FrameSummary( + frame.f_code.co_filename, + frame.f_lineno, + frame.f_code.co_name, + ) + msg = e.args[0] + msg += "\n\nThe following call raised this error:\n" + "".join( + traceback.StackSummary.from_list([frame_summary]).format() + ) + e.args = (msg,) + + +class _PythonMsgPrinter(PythonPrinter): + """ + Util printer that replaces sympy symbols with their source-level names + and renders sympy relational operators (e.g., Eq, Ne, Ge, Le) inline + (i.e., as ==, !=, >, <). + """ + + def __init__(self, src_map: dict[str, list[str]]) -> None: + super().__init__() + self.src_map = src_map + + def _print_Symbol(self, sym: sympy.Symbol) -> str: + return self.src_map[sym.name][0] + + +def _suggest_torch_checks( + e: GuardOnDataDependentSymNode, src_map: defaultdict[str, list[str]] +) -> None: + """ + Enhances a GuardOnDataDependentSymNode error with suggested fixes using torch._check. + + This function analyzes the condition that caused the data-dependent error and generates + user-friendly suggestions for fixing it by adding appropriate torch._check calls. + It handles special cases like non-negative checks with specific recommendations. + + Args: + e: The GuardOnDataDependentSymNode error to enhance with suggestions + src_map: A mapping from symbol names to their corresponding source-level variable names + + Returns: + None. Modifies the error message in-place by updating e.args[0]. + """ + # extract the unresolved condition on unbacked symints in the error + cond = e.cond + diff = ", ".join(s.name for s in cond.free_symbols if s.name not in src_map) + if diff: + log.warning("Unable to find user code corresponding to {%s}", diff) + return + printer = _PythonMsgPrinter(src_map) + msg = e.args[0] + msg += "\nTo fix the error, insert one of the following checks before this call:" + + not_cond_str = printer.doprint(sympy.Not(cond)) + + # suggested fixes to resolve `cond` are to tell the compiler to assume + # either `cond` or its negation (the user will need to select which) + suggested_fixes = [ + f"torch._check({printer.doprint(cond)})", + f"torch._check({not_cond_str})", + ] + + for i, fix in enumerate(suggested_fixes): + msg += f"\n {i + 1}. {fix}" + src_mapped = ", ".join( + f"`{s}` with {' or '.join(src_map[s])}" + for s in sorted(s.name for s in cond.free_symbols) + ) + msg += f"\n\n(These suggested fixes were derived by replacing {src_mapped} in {cond} and its negation.)" + e.args = (msg,) + + +def _suggest_fixes_for_data_dependent_error_non_strict( + e: GuardOnDataDependentSymNode, +) -> None: + """ + Given a raised data-dependent error, add the following to the error message: + 1. the closest user code location that raised the error; + 2. suggested fixes for the error in terms of live variables at that location. + """ + + # walk the stack up from the data-dependent error until a non-torch frame is found + frame = _find_user_code_frame() + if frame is not None: + # add frame info to error message + _blame_user_code(e, frame) + + # map symbol names reachable via frame locals to their source-level names + src_map = defaultdict(list) + for var, val in frame.f_locals.items(): + try: + tree_leaves_with_path = pytree.tree_leaves_with_path(val) + except ValueError: + log.warning( + "pytree.tree_leaves_with_path failed for value of type {%s} in local variable {%s}", + type(val), + var, + ) + continue + # figure out how to access any symbol inside `val` through `var` + for path, leaf in tree_leaves_with_path: + name = var + pytree.keystr(path) + if isinstance(leaf, torch.SymInt): + src_map[str(leaf.node.expr)].append(name) + elif isinstance(leaf, torch.Tensor): + for i, dim in enumerate(leaf.shape): + if isinstance(dim, torch.SymInt): + src_map[str(dim.node.expr)].append(f"{name}.shape[{i}]") + + # add suggested torch.check()s based on `src_map` to the error message + # replacing unbacked symints in the unresolved condition in the error + if isinstance(e.cond, sympy.logic.boolalg.Boolean): + _suggest_torch_checks(e, src_map) + + +@contextmanager +def _remove_effect_token_unbacked_bindings( + node: torch.fx.Node, +) -> Generator[None, None, None]: + """ + Temporarily modifies unbacked_bindings in a node's metadata by removing the first element + of each path, which corresponds to an effect token. + + This is used when processing nodes that have effect tokens as the first element in their + unbacked_bindings paths. The context manager ensures that the original bindings are + restored after the operation is complete. + + Args: + node: The FX node whose unbacked_bindings will be temporarily modified + + Yields: + None + """ + old_bindings = node.meta.get("unbacked_bindings", {}) + + # Remove the extra layer for effect token + new_bindings = {k: path[1:] if path else path for k, path in old_bindings.items()} + + node.meta["unbacked_bindings"] = new_bindings + + try: + yield + finally: + node.meta["unbacked_bindings"] = old_bindings + + +# This helper function is used in passes that insert runtime assertions in the graph. +# When accessing expressions representing input placeholders, we do not apply replacements +# since those inputs should be seen by assertions that use them to be inserted. The only replacement +# that we apply is unbacked renaming. +def _get_placeholder_expr(sym_node: SymNode) -> sympy.Expr: + shape_env = sym_node.shape_env + result = sym_node._expr + if result in shape_env.unbacked_renamings: + return shape_env.unbacked_renamings[result] + return result diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7db0e29d1d4f75c770562c65013c03817643f6b7 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/__init__.py @@ -0,0 +1,4 @@ +# mypy: disable-error-code=attr-defined +from .core import reify, unify # noqa: F403 +from .more import unifiable # noqa: F403 +from .variable import isvar, Var, var, variables, vars # noqa: F403 diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/core.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/core.py new file mode 100644 index 0000000000000000000000000000000000000000..3d8071c847ae5da144d7ab57b5d24e7968b5daf6 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/core.py @@ -0,0 +1,141 @@ +# mypy: allow-untyped-defs +from collections.abc import Iterator # type: ignore[import] +from functools import partial + +from .dispatch import dispatch +from .unification_tools import assoc # type: ignore[import] +from .utils import transitive_get as walk +from .variable import isvar + + +__all__ = ["reify", "unify"] + +############### +# Reification # +############### + + +@dispatch(Iterator, dict) +def _reify(t, s): + return map(partial(reify, s=s), t) + # return (reify(arg, s) for arg in t) + + +_reify + + +@dispatch(tuple, dict) # type: ignore[no-redef] +def _reify(t, s): + return tuple(reify(iter(t), s)) + + +_reify + + +@dispatch(list, dict) # type: ignore[no-redef] +def _reify(t, s): + return list(reify(iter(t), s)) + + +_reify + + +@dispatch(dict, dict) # type: ignore[no-redef] +def _reify(d, s): + return {k: reify(v, s) for k, v in d.items()} + + +_reify + + +@dispatch(object, dict) # type: ignore[no-redef] +def _reify(o, s): + return o # catch all, just return the object + + +def reify(e, s): + """Replace variables of expression with substitution + >>> # xdoctest: +SKIP + >>> x, y = var(), var() + >>> e = (1, x, (3, y)) + >>> s = {x: 2, y: 4} + >>> reify(e, s) + (1, 2, (3, 4)) + >>> e = {1: x, 3: (y, 5)} + >>> reify(e, s) + {1: 2, 3: (4, 5)} + """ + if isvar(e): + return reify(s[e], s) if e in s else e + return _reify(e, s) + + +############### +# Unification # +############### + +seq = tuple, list, Iterator + + +@dispatch(seq, seq, dict) # type: ignore[arg-type] +def _unify(u, v, s): + if len(u) != len(v): + return False + for uu, vv in zip(u, v): # avoiding recursion + s = unify(uu, vv, s) + if s is False: + return False + return s + + +# +# @dispatch((set, frozenset), (set, frozenset), dict) +# def _unify(u, v, s): +# i = u & v +# u = u - i +# v = v - i +# return _unify(sorted(u), sorted(v), s) +# +# +# @dispatch(dict, dict, dict) +# def _unify(u, v, s): +# if len(u) != len(v): +# return False +# for key, uval in iteritems(u): +# if key not in v: +# return False +# s = unify(uval, v[key], s) +# if s is False: +# return False +# return s +# +# +# @dispatch(object, object, dict) +# def _unify(u, v, s): +# return False # catch all + + +@dispatch(object, object, dict) +def unify(u, v, s): # no check at the moment + """Find substitution so that u == v while satisfying s + >>> x = var("x") + >>> unify((1, x), (1, 2), {}) + {~x: 2} + """ + u = walk(u, s) + v = walk(v, s) + if u == v: + return s + if isvar(u): + return assoc(s, u, v) + if isvar(v): + return assoc(s, v, u) + return _unify(u, v, s) + + +unify + + +@dispatch(object, object) # type: ignore[no-redef] +def unify(u, v): + return unify(u, v, {}) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/dispatch.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/dispatch.py new file mode 100644 index 0000000000000000000000000000000000000000..72b950c5b36d67f34cca322ffbbf6851b151de36 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/dispatch.py @@ -0,0 +1,8 @@ +from functools import partial + +from .multipledispatch import dispatch as _dispatch # type: ignore[import] + + +namespace = {} # type: ignore[var-annotated] + +dispatch = partial(_dispatch, namespace=namespace) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/match.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/match.py new file mode 100644 index 0000000000000000000000000000000000000000..01861a086f64b6121aa9e174d16176533cd0e1a5 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/match.py @@ -0,0 +1,129 @@ +# mypy: allow-untyped-defs +from .core import reify, unify # type: ignore[attr-defined] +from .unification_tools import first, groupby # type: ignore[import] +from .utils import _toposort, freeze +from .variable import isvar + + +class Dispatcher: + def __init__(self, name): + self.name = name + self.funcs = {} + self.ordering = [] + + def add(self, signature, func): + self.funcs[freeze(signature)] = func + self.ordering = ordering(self.funcs) + + def __call__(self, *args, **kwargs): + func, _ = self.resolve(args) + return func(*args, **kwargs) + + def resolve(self, args): + n = len(args) + for signature in self.ordering: + if len(signature) != n: + continue + s = unify(freeze(args), signature) + if s is not False: + result = self.funcs[signature] + return result, s + raise NotImplementedError( + "No match found. \nKnown matches: " + + str(self.ordering) + + "\nInput: " + + str(args) + ) + + def register(self, *signature): + def _(func): + self.add(signature, func) + return self + + return _ + + +class VarDispatcher(Dispatcher): + """A dispatcher that calls functions with variable names + >>> # xdoctest: +SKIP + >>> d = VarDispatcher("d") + >>> x = var("x") + >>> @d.register("inc", x) + ... def f(x): + ... return x + 1 + >>> @d.register("double", x) + ... def f(x): + ... return x * 2 + >>> d("inc", 10) + 11 + >>> d("double", 10) + 20 + """ + + def __call__(self, *args, **kwargs): + func, s = self.resolve(args) + d = {k.token: v for k, v in s.items()} + return func(**d) + + +global_namespace = {} # type: ignore[var-annotated] + + +def match(*signature, **kwargs): + namespace = kwargs.get("namespace", global_namespace) + dispatcher = kwargs.get("Dispatcher", Dispatcher) + + def _(func): + name = func.__name__ + + if name not in namespace: + namespace[name] = dispatcher(name) + d = namespace[name] + + d.add(signature, func) + + return d + + return _ + + +def supercedes(a, b): + """``a`` is a more specific match than ``b``""" + if isvar(b) and not isvar(a): + return True + s = unify(a, b) + if s is False: + return False + s = {k: v for k, v in s.items() if not isvar(k) or not isvar(v)} + if reify(a, s) == a: + return True + if reify(b, s) == b: + return False + + +# Taken from multipledispatch +def edge(a, b, tie_breaker=hash): + """A should be checked before B + Tie broken by tie_breaker, defaults to ``hash`` + """ + if supercedes(a, b): + if supercedes(b, a): + return tie_breaker(a) > tie_breaker(b) + else: + return True + return False + + +# Taken from multipledispatch +def ordering(signatures): + """A sane ordering of signatures to check, first to last + Topological sort of edges as given by ``edge`` and ``supercedes`` + """ + signatures = list(map(tuple, signatures)) + edges = [(a, b) for a in signatures for b in signatures if edge(a, b)] + edges = groupby(first, edges) + for s in signatures: + if s not in edges: + edges[s] = [] + edges = {k: [b for a, b in v] for k, v in edges.items()} # type: ignore[attr-defined, assignment] + return _toposort(edges) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/more.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/more.py new file mode 100644 index 0000000000000000000000000000000000000000..42074a46a4202cface9799af4b81743c292e766d --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/more.py @@ -0,0 +1,131 @@ +# mypy: allow-untyped-defs +from .core import ( # type: ignore[attr-defined] + _reify as core_reify, + _unify as core_unify, + reify, + unify, +) +from .dispatch import dispatch + + +__all__ = ["unifiable", "reify_object", "unify_object"] + + +def unifiable(cls): + """Register standard unify and reify operations on class + This uses the type and __dict__ or __slots__ attributes to define the + nature of the term + See Also: + >>> # xdoctest: +SKIP + >>> class A(object): + ... def __init__(self, a, b): + ... self.a = a + ... self.b = b + >>> unifiable(A) + + >>> x = var("x") + >>> a = A(1, 2) + >>> b = A(1, x) + >>> unify(a, b, {}) + {~x: 2} + """ + core_unify.add((cls, cls, dict), unify_object) # type: ignore[attr-defined] + core_reify.add((cls, dict), reify_object) # type: ignore[attr-defined] + + return cls + + +######### +# Reify # +######### + + +def reify_object(o, s): + """Reify a Python object with a substitution + >>> # xdoctest: +SKIP + >>> class Foo(object): + ... def __init__(self, a, b): + ... self.a = a + ... self.b = b + ... + ... def __str__(self): + ... return "Foo(%s, %s)" % (str(self.a), str(self.b)) + >>> x = var("x") + >>> f = Foo(1, x) + >>> print(f) + Foo(1, ~x) + >>> print(reify_object(f, {x: 2})) + Foo(1, 2) + """ + if hasattr(o, "__slots__"): + return _reify_object_slots(o, s) + else: + return _reify_object_dict(o, s) + + +def _reify_object_dict(o, s): + obj = object.__new__(type(o)) + d = reify(o.__dict__, s) + if d == o.__dict__: + return o + obj.__dict__.update(d) + return obj + + +def _reify_object_slots(o, s): + attrs = [getattr(o, attr) for attr in o.__slots__] + new_attrs = reify(attrs, s) + if attrs == new_attrs: + return o + else: + newobj = object.__new__(type(o)) + for slot, attr in zip(o.__slots__, new_attrs): + setattr(newobj, slot, attr) + return newobj + + +@dispatch(slice, dict) +def _reify(o, s): + """Reify a Python ``slice`` object""" + # pyrefly: ignore [not-iterable] + return slice(*reify((o.start, o.stop, o.step), s)) + + +######### +# Unify # +######### + + +def unify_object(u, v, s): + """Unify two Python objects + Unifies their type and ``__dict__`` attributes + >>> # xdoctest: +SKIP + >>> class Foo(object): + ... def __init__(self, a, b): + ... self.a = a + ... self.b = b + ... + ... def __str__(self): + ... return "Foo(%s, %s)" % (str(self.a), str(self.b)) + >>> x = var("x") + >>> f = Foo(1, x) + >>> g = Foo(1, 2) + >>> unify_object(f, g, {}) + {~x: 2} + """ + if type(u) is not type(v): + return False + if hasattr(u, "__slots__"): + return unify( + [getattr(u, slot) for slot in u.__slots__], + [getattr(v, slot) for slot in v.__slots__], + s, + ) + else: + return unify(u.__dict__, v.__dict__, s) + + +@dispatch(slice, slice, dict) +def _unify(u, v, s): + """Unify a Python ``slice`` object""" + return unify((u.start, u.stop, u.step), (v.start, v.stop, v.step), s) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bb7304069243fb45604e165b06b377a5db233a7d --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/__init__.py @@ -0,0 +1,7 @@ +from .core import dispatch +from .dispatcher import ( + Dispatcher, + halt_ordering, + MDNotImplementedError, + restart_ordering, +) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/conflict.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/conflict.py new file mode 100644 index 0000000000000000000000000000000000000000..181e0e8dd167ac8b15d58f612308cdfeca1547e1 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/conflict.py @@ -0,0 +1,139 @@ +# mypy: allow-untyped-defs +import operator + +from .utils import _toposort, groupby +from .variadic import isvariadic + + +__all__ = [ + "AmbiguityWarning", + "supercedes", + "consistent", + "ambiguous", + "ambiguities", + "super_signature", + "edge", + "ordering", +] + + +class AmbiguityWarning(Warning): + pass + + +def supercedes(a, b): + """A is consistent and strictly more specific than B""" + if len(a) < len(b): + # only case is if a is empty and b is variadic + return not a and len(b) == 1 and isvariadic(b[-1]) + elif len(a) == len(b): + return all(map(issubclass, a, b)) + else: + # len(a) > len(b) + p1 = 0 + p2 = 0 + while p1 < len(a) and p2 < len(b): + cur_a = a[p1] + cur_b = b[p2] + if not (isvariadic(cur_a) or isvariadic(cur_b)): + if not issubclass(cur_a, cur_b): + return False + p1 += 1 + p2 += 1 + elif isvariadic(cur_a): + assert p1 == len(a) - 1 + return p2 == len(b) - 1 and issubclass(cur_a, cur_b) + elif isvariadic(cur_b): + assert p2 == len(b) - 1 + if not issubclass(cur_a, cur_b): + return False + p1 += 1 + return p2 == len(b) - 1 and p1 == len(a) + + +def consistent(a, b): + """It is possible for an argument list to satisfy both A and B""" + + # Need to check for empty args + if not a: + return not b or isvariadic(b[0]) + if not b: + return not a or isvariadic(a[0]) + + # Non-empty args check for mutual subclasses + if len(a) == len(b): + return all(issubclass(aa, bb) or issubclass(bb, aa) for aa, bb in zip(a, b)) + else: + p1 = 0 + p2 = 0 + while p1 < len(a) and p2 < len(b): + cur_a = a[p1] + cur_b = b[p2] + if not issubclass(cur_b, cur_a) and not issubclass(cur_a, cur_b): + return False + if not (isvariadic(cur_a) or isvariadic(cur_b)): + p1 += 1 + p2 += 1 + elif isvariadic(cur_a): + p2 += 1 + elif isvariadic(cur_b): + p1 += 1 + # We only need to check for variadic ends + # Variadic types are guaranteed to be the last element + return ( + isvariadic(cur_a) # type: ignore[possibly-undefined] + and p2 == len(b) + or isvariadic(cur_b) # type: ignore[possibly-undefined] + and p1 == len(a) + ) + + +def ambiguous(a, b): + """A is consistent with B but neither is strictly more specific""" + return consistent(a, b) and not (supercedes(a, b) or supercedes(b, a)) + + +def ambiguities(signatures): + """All signature pairs such that A is ambiguous with B""" + signatures = list(map(tuple, signatures)) + return { + (a, b) + for a in signatures + for b in signatures + if hash(a) < hash(b) + and ambiguous(a, b) + and not any(supercedes(c, a) and supercedes(c, b) for c in signatures) + } + + +def super_signature(signatures): + """A signature that would break ambiguities""" + n = len(signatures[0]) + assert all(len(s) == n for s in signatures) + + return [max((type.mro(sig[i]) for sig in signatures), key=len)[0] for i in range(n)] + + +def edge(a, b, tie_breaker=hash): + """A should be checked before B + Tie broken by tie_breaker, defaults to ``hash`` + """ + # A either supersedes B and B does not supersede A or if B does then call + # tie_breaker + return supercedes(a, b) and ( + not supercedes(b, a) or tie_breaker(a) > tie_breaker(b) + ) + + +def ordering(signatures): + """A sane ordering of signatures to check, first to last + Topological sort of edges as given by ``edge`` and ``supercedes`` + """ + signatures = list(map(tuple, signatures)) + edges = [(a, b) for a in signatures for b in signatures if edge(a, b)] + edges = groupby(operator.itemgetter(0), edges) + for s in signatures: + if s not in edges: + edges[s] = [] + edges = {k: [b for a, b in v] for k, v in edges.items()} # type: ignore[assignment, attr-defined] + return _toposort(edges) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/core.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/core.py new file mode 100644 index 0000000000000000000000000000000000000000..69b9f3b2b5a2cb8e9df9d502b4254abffff2dd18 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/core.py @@ -0,0 +1,92 @@ +# mypy: allow-untyped-defs +import inspect +from collections.abc import Callable +from typing import Any, TypeVar +from typing_extensions import TypeVarTuple, Unpack + +from .dispatcher import Dispatcher, MethodDispatcher + + +global_namespace = {} # type: ignore[var-annotated] + +__all__ = ["dispatch", "ismethod"] + +T = TypeVar("T") +Ts = TypeVarTuple("Ts") + + +def dispatch( + *types: Unpack[Ts], **kwargs: Any +) -> Callable[[Callable[..., T]], Callable[..., T]]: + """Dispatch function on the types of the inputs + Supports dispatch on all non-keyword arguments. + Collects implementations based on the function name. Ignores namespaces. + If ambiguous type signatures occur a warning is raised when the function is + defined suggesting the additional method to break the ambiguity. + + Example: + >>> # xdoctest: +SKIP + >>> @dispatch(int) + ... def f(x): + ... return x + 1 + >>> @dispatch(float) + ... def f(x): + ... return x - 1 + >>> # xdoctest: +SKIP + >>> f(3) + 4 + >>> f(3.0) + 2.0 + >>> # Specify an isolated namespace with the namespace keyword argument + >>> my_namespace = {} + >>> @dispatch(int, namespace=my_namespace) + ... def foo(x): + ... return x + 1 + >>> # Dispatch on instance methods within classes + >>> class MyClass(object): + ... @dispatch(list) + ... def __init__(self, data): + ... self.data = data + ... + ... @dispatch(int) + ... def __init__(self, datum): + ... self.data = [datum] + >>> MyClass([1, 2, 3]).data + [1, 2, 3] + >>> MyClass(3).data + [3] + """ + namespace = kwargs.get("namespace", global_namespace) + + types_tuple: tuple[type, ...] = tuple(types) # type: ignore[arg-type] + + def _df(func): + name = func.__name__ + + if ismethod(func): + dispatcher = inspect.currentframe().f_back.f_locals.get( # type: ignore[union-attr] + name, # type: ignore[union-attr] + MethodDispatcher(name), + ) + else: + if name not in namespace: + namespace[name] = Dispatcher(name) + dispatcher = namespace[name] + + dispatcher.add(types_tuple, func) + return dispatcher + + return _df + + +def ismethod(func): + """Is func a method? + Note that this has to work as the method is defined but before the class is + defined. At this stage methods look like functions. + """ + if hasattr(inspect, "signature"): + signature = inspect.signature(func) + return signature.parameters.get("self", None) is not None + else: + spec = inspect.getfullargspec(func) # type: ignore[union-attr, assignment] + return spec and spec.args and spec.args[0] == "self" diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py new file mode 100644 index 0000000000000000000000000000000000000000..e2459b82247bce59cd13ed040722c7278bf36ea0 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py @@ -0,0 +1,455 @@ +# mypy: allow-untyped-defs +import inspect +import itertools as itl +from typing_extensions import deprecated +from warnings import warn + +from .conflict import ambiguities, AmbiguityWarning, ordering, super_signature +from .utils import expand_tuples +from .variadic import isvariadic, Variadic + + +__all__ = [ + "MDNotImplementedError", + "ambiguity_warn", + "halt_ordering", + "restart_ordering", + "variadic_signature_matches_iter", + "variadic_signature_matches", + "Dispatcher", + "source", + "MethodDispatcher", + "str_signature", + "warning_text", +] + + +class MDNotImplementedError(NotImplementedError): + """A NotImplementedError for multiple dispatch""" + + +def ambiguity_warn(dispatcher, ambiguities): + """Raise warning when ambiguity is detected + Parameters + ---------- + dispatcher : Dispatcher + The dispatcher on which the ambiguity was detected + ambiguities : set + Set of type signature pairs that are ambiguous within this dispatcher + See Also: + Dispatcher.add + warning_text + """ + warn(warning_text(dispatcher.name, ambiguities), AmbiguityWarning) + + +@deprecated( + "`halt_ordering` is deprecated, you can safely remove this call.", + category=FutureWarning, +) +def halt_ordering(): + """Deprecated interface to temporarily disable ordering.""" + + +@deprecated( + "`restart_ordering` is deprecated, if you would like to eagerly order the dispatchers, " + "you should call the `reorder()` method on each dispatcher.", + category=FutureWarning, +) +def restart_ordering(on_ambiguity=ambiguity_warn): + """Deprecated interface to temporarily resume ordering.""" + + +def variadic_signature_matches_iter(types, full_signature): + """Check if a set of input types matches a variadic signature. + Notes + ----- + The algorithm is as follows: + Initialize the current signature to the first in the sequence + For each type in `types`: + If the current signature is variadic + If the type matches the signature + yield True + Else + Try to get the next signature + If no signatures are left we can't possibly have a match + so yield False + Else + yield True if the type matches the current signature + Get the next signature + """ + sigiter = iter(full_signature) + sig = next(sigiter) + for typ in types: + matches = issubclass(typ, sig) + yield matches + if not isvariadic(sig): + # we're not matching a variadic argument, so move to the next + # element in the signature + sig = next(sigiter) + else: + try: + sig = next(sigiter) + except StopIteration: + assert isvariadic(sig) + yield True + else: + # We have signature items left over, so all of our arguments + # haven't matched + yield False + + +def variadic_signature_matches(types, full_signature): + # No arguments always matches a variadic signature + assert full_signature + return all(variadic_signature_matches_iter(types, full_signature)) + + +class Dispatcher: + """Dispatch methods based on type signature + Use ``dispatch`` to add implementations + Examples + -------- + >>> # xdoctest: +SKIP("bad import name") + >>> from multipledispatch import dispatch + >>> @dispatch(int) + ... def f(x): + ... return x + 1 + >>> @dispatch(float) + ... def f(x): + ... return x - 1 + >>> f(3) + 4 + >>> f(3.0) + 2.0 + """ + + __slots__ = "__name__", "name", "funcs", "_ordering", "_cache", "doc" + + def __init__(self, name, doc=None): + self.name = self.__name__ = name + self.funcs = {} + self.doc = doc + + self._cache = {} + + def register(self, *types, **kwargs): + """register dispatcher with new implementation + >>> # xdoctest: +SKIP + >>> f = Dispatcher("f") + >>> @f.register(int) + ... def inc(x): + ... return x + 1 + >>> @f.register(float) + ... def dec(x): + ... return x - 1 + >>> @f.register(list) + ... @f.register(tuple) + ... def reverse(x): + ... return x[::-1] + >>> f(1) + 2 + >>> f(1.0) + 0.0 + >>> f([1, 2, 3]) + [3, 2, 1] + """ + + def _df(func): + self.add(types, func, **kwargs) # type: ignore[call-arg] + return func + + return _df + + @classmethod + def get_func_params(cls, func): + if hasattr(inspect, "signature"): + sig = inspect.signature(func) + return sig.parameters.values() + + @classmethod + def get_func_annotations(cls, func): + """get annotations of function positional parameters""" + params = cls.get_func_params(func) + if params: + Parameter = inspect.Parameter + + params = ( + param + for param in params + if param.kind + in (Parameter.POSITIONAL_ONLY, Parameter.POSITIONAL_OR_KEYWORD) + ) + + annotations = tuple(param.annotation for param in params) + + if all(ann is not Parameter.empty for ann in annotations): + return annotations + + def add(self, signature, func): + """Add new types/method pair to dispatcher + >>> # xdoctest: +SKIP + >>> D = Dispatcher("add") + >>> D.add((int, int), lambda x, y: x + y) + >>> D.add((float, float), lambda x, y: x + y) + >>> D(1, 2) + 3 + >>> D(1, 2.0) + Traceback (most recent call last): + ... + NotImplementedError: Could not find signature for add: + >>> # When ``add`` detects a warning it calls the ``on_ambiguity`` callback + >>> # with a dispatcher/itself, and a set of ambiguous type signature pairs + >>> # as inputs. See ``ambiguity_warn`` for an example. + """ + # Handle annotations + if not signature: + annotations = self.get_func_annotations(func) + if annotations: + signature = annotations + + # Handle union types + if any(isinstance(typ, tuple) for typ in signature): + for typs in expand_tuples(signature): + self.add(typs, func) + return + + new_signature = [] + + for index, typ in enumerate(signature, start=1): + if not isinstance(typ, (type, list)): + str_sig = ", ".join( + c.__name__ if isinstance(c, type) else str(c) for c in signature + ) + raise TypeError( + f"Tried to dispatch on non-type: {typ}\n" + f"In signature: <{str_sig}>\n" + f"In function: {self.name}" + ) + + # handle variadic signatures + if isinstance(typ, list): + if index != len(signature): + raise TypeError("Variadic signature must be the last element") + + if len(typ) != 1: + raise TypeError( + "Variadic signature must contain exactly one element. " + "To use a variadic union type place the desired types " + "inside of a tuple, e.g., [(int, str)]" + ) + # pyrefly: ignore [bad-specialization] + new_signature.append(Variadic[typ[0]]) + else: + new_signature.append(typ) + + self.funcs[tuple(new_signature)] = func + self._cache.clear() + + try: + del self._ordering + except AttributeError: + pass + + @property + def ordering(self): + try: + return self._ordering + except AttributeError: + return self.reorder() + + def reorder(self, on_ambiguity=ambiguity_warn): + self._ordering = od = ordering(self.funcs) + amb = ambiguities(self.funcs) + if amb: + on_ambiguity(self, amb) + return od + + def __call__(self, *args, **kwargs): + types = tuple(type(arg) for arg in args) + try: + func = self._cache[types] + except KeyError as e: + func = self.dispatch(*types) + if not func: + raise NotImplementedError( + f"Could not find signature for {self.name}: <{str_signature(types)}>" + ) from e + self._cache[types] = func + try: + return func(*args, **kwargs) + + except MDNotImplementedError as e: + funcs = self.dispatch_iter(*types) + next(funcs) # burn first + for func in funcs: + try: + return func(*args, **kwargs) + except MDNotImplementedError: + pass + + raise NotImplementedError( + "Matching functions for " + f"{self.name}: <{str_signature(types)}> found, but none completed successfully", + ) from e + + def __str__(self): + return f"" + + __repr__ = __str__ + + def dispatch(self, *types): + """Determine appropriate implementation for this type signature + This method is internal. Users should call this object as a function. + Implementation resolution occurs within the ``__call__`` method. + >>> # xdoctest: +SKIP + >>> from multipledispatch import dispatch + >>> @dispatch(int) + ... def inc(x): + ... return x + 1 + >>> implementation = inc.dispatch(int) + >>> implementation(3) + 4 + >>> print(inc.dispatch(float)) + None + See Also: + ``multipledispatch.conflict`` - module to determine resolution order + """ + + if types in self.funcs: + return self.funcs[types] + + try: + return next(self.dispatch_iter(*types)) + except StopIteration: + return None + + def dispatch_iter(self, *types): + n = len(types) + for signature in self.ordering: + if len(signature) == n and all(map(issubclass, types, signature)): + result = self.funcs[signature] + yield result + elif len(signature) and isvariadic(signature[-1]): + if variadic_signature_matches(types, signature): + result = self.funcs[signature] + yield result + + @deprecated( + "`resolve()` is deprecated, use `dispatch(*types)`", category=FutureWarning + ) + def resolve(self, types): + """Determine appropriate implementation for this type signature + .. deprecated:: 0.4.4 + Use ``dispatch(*types)`` instead + """ + return self.dispatch(*types) + + def __getstate__(self): + return {"name": self.name, "funcs": self.funcs} + + def __setstate__(self, d): + self.name = d["name"] + self.funcs = d["funcs"] + self._ordering = ordering(self.funcs) + self._cache = {} + + @property + def __doc__(self): # type: ignore[override] + docs = [f"Multiply dispatched method: {self.name}"] + + if self.doc: + docs.append(self.doc) + + other = [] + for sig in self.ordering[::-1]: + func = self.funcs[sig] + if func.__doc__: + s = f"Inputs: <{str_signature(sig)}>\n" + s += "-" * len(s) + "\n" + s += func.__doc__.strip() + docs.append(s) + else: + other.append(str_signature(sig)) + + if other: + docs.append("Other signatures:\n " + "\n ".join(other)) + + return "\n\n".join(docs) + + def _help(self, *args): + return self.dispatch(*map(type, args)).__doc__ + + def help(self, *args, **kwargs): + """Print docstring for the function corresponding to inputs""" + print(self._help(*args)) + + def _source(self, *args): + func = self.dispatch(*map(type, args)) + if not func: + raise TypeError("No function found") + return source(func) + + def source(self, *args, **kwargs): + """Print source code for the function corresponding to inputs""" + print(self._source(*args)) + + +def source(func): + s = f"File: {inspect.getsourcefile(func)}\n\n" + s = s + inspect.getsource(func) + return s + + +class MethodDispatcher(Dispatcher): + """Dispatch methods based on type signature + See Also: + Dispatcher + """ + + # pyrefly: ignore [bad-override] + __slots__ = ("obj", "cls") + + @classmethod + def get_func_params(cls, func): + if hasattr(inspect, "signature"): + sig = inspect.signature(func) + return itl.islice(sig.parameters.values(), 1, None) + + def __get__(self, instance, owner): + self.obj = instance + self.cls = owner + return self + + def __call__(self, *args, **kwargs): + types = tuple(type(arg) for arg in args) + func = self.dispatch(*types) + if not func: + raise NotImplementedError( + f"Could not find signature for {self.name}: <{str_signature(types)}>" + ) + return func(self.obj, *args, **kwargs) + + +def str_signature(sig): + """String representation of type signature + >>> str_signature((int, float)) + 'int, float' + """ + return ", ".join(cls.__name__ for cls in sig) + + +def warning_text(name, amb): + """The text for ambiguity warnings""" + text = f"\nAmbiguities exist in dispatched function {name}\n\n" + text += "The following signatures may result in ambiguous behavior:\n" + for pair in amb: + text += "\t" + ", ".join("[" + str_signature(s) + "]" for s in pair) + "\n" + text += "\n\nConsider making the following additions:\n\n" + text += "\n\n".join( + [ + "@dispatch(" + str_signature(super_signature(s)) + f")\ndef {name}(...)" + for s in amb + ] + ) + return text diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0b21183c40b97a0757fc5c332cb783f39fc85efe --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py @@ -0,0 +1,127 @@ +# mypy: allow-untyped-defs +from collections import OrderedDict + + +__all__ = ["raises", "expand_tuples", "reverse_dict", "groupby", "typename"] + + +def raises(err, lamda): # codespell:ignore lamda + try: + lamda() # codespell:ignore lamda + return False + except err: + return True + + +def expand_tuples(L): + """ + >>> expand_tuples([1, (2, 3)]) + [(1, 2), (1, 3)] + >>> expand_tuples([1, 2]) + [(1, 2)] + """ + if not L: + return [()] + elif not isinstance(L[0], tuple): + rest = expand_tuples(L[1:]) + return [(L[0],) + t for t in rest] + else: + rest = expand_tuples(L[1:]) + return [(item,) + t for t in rest for item in L[0]] + + +# Taken from theano/theano/gof/sched.py +# Avoids licensing issues because this was written by Matthew Rocklin +def _toposort(edges): + """Topological sort algorithm by Kahn [1] - O(nodes + vertices) + inputs: + edges - a dict of the form {a: {b, c}} where b and c depend on a + outputs: + L - an ordered list of nodes that satisfy the dependencies of edges + >>> _toposort({1: (2, 3), 2: (3,)}) + [1, 2, 3] + >>> # Closely follows the wikipedia page [2] + >>> # [1] Kahn, Arthur B. (1962), "Topological sorting of large networks", + >>> # Communications of the ACM + >>> # [2] http://en.wikipedia.org/wiki/Toposort#Algorithms + """ + incoming_edges = reverse_dict(edges) + incoming_edges = OrderedDict((k, set(val)) for k, val in incoming_edges.items()) + S = OrderedDict.fromkeys(v for v in edges if v not in incoming_edges) + L = [] + + while S: + n, _ = S.popitem() + L.append(n) + for m in edges.get(n, ()): + assert n in incoming_edges[m] + incoming_edges[m].remove(n) + if not incoming_edges[m]: + S[m] = None + if any(incoming_edges.get(v, None) for v in edges): + raise ValueError("Input has cycles") + return L + + +def reverse_dict(d): + """Reverses direction of dependence dict + >>> d = {"a": (1, 2), "b": (2, 3), "c": ()} + >>> reverse_dict(d) # doctest: +SKIP + {1: ('a',), 2: ('a', 'b'), 3: ('b',)} + :note: dict order are not deterministic. As we iterate on the + input dict, it make the output of this function depend on the + dict order. So this function output order should be considered + as undeterministic. + """ + result = OrderedDict() # type: ignore[var-annotated] + for key in d: + for val in d[key]: + result[val] = result.get(val, ()) + (key,) + return result + + +# Taken from toolz +# Avoids licensing issues because this version was authored by Matthew Rocklin +def groupby(func, seq): + """Group a collection by a key function + >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] + >>> groupby(len, names) # doctest: +SKIP + {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} + >>> iseven = lambda x: x % 2 == 0 + >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP + {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} + See Also: + ``countby`` + """ + + d = OrderedDict() # type: ignore[var-annotated] + for item in seq: + key = func(item) + if key not in d: + d[key] = [] + d[key].append(item) + return d + + +def typename(type): + """Get the name of `type`. + Parameters + ---------- + type : Union[Type, Tuple[Type]] + Returns + ------- + str + The name of `type` or a tuple of the names of the types in `type`. + Examples + -------- + >>> typename(int) + 'int' + >>> typename((int, float)) + '(int, float)' + """ + try: + return type.__name__ + except AttributeError: + if len(type) == 1: + return typename(*type) + return f"({', '.join(map(typename, type))})" diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py new file mode 100644 index 0000000000000000000000000000000000000000..1b5604a152480f83916108cb1b02de3bc9b9adb5 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py @@ -0,0 +1,96 @@ +# mypy: allow-untyped-defs +from .utils import typename + + +__all__ = ["VariadicSignatureType", "isvariadic", "VariadicSignatureMeta", "Variadic"] + + +class VariadicSignatureType(type): + # checking if subclass is a subclass of self + def __subclasscheck__(cls, subclass): + other_type = subclass.variadic_type if isvariadic(subclass) else (subclass,) + return subclass is cls or all( + issubclass(other, cls.variadic_type) # type: ignore[attr-defined] + for other in other_type + ) + + def __eq__(cls, other): + """ + Return True if other has the same variadic type + Parameters + ---------- + other : object (type) + The object (type) to check + Returns + ------- + bool + Whether or not `other` is equal to `self` + """ + return isvariadic(other) and set(cls.variadic_type) == set(other.variadic_type) # type: ignore[attr-defined] + + def __hash__(cls): + return hash((type(cls), frozenset(cls.variadic_type))) # type: ignore[attr-defined] + + +def isvariadic(obj): + """Check whether the type `obj` is variadic. + Parameters + ---------- + obj : type + The type to check + Returns + ------- + bool + Whether or not `obj` is variadic + Examples + -------- + >>> # xdoctest: +SKIP + >>> isvariadic(int) + False + >>> isvariadic(Variadic[int]) + True + """ + return isinstance(obj, VariadicSignatureType) + + +class VariadicSignatureMeta(type): + """A metaclass that overrides ``__getitem__`` on the class. This is used to + generate a new type for Variadic signatures. See the Variadic class for + examples of how this behaves. + """ + + def __getitem__(cls, variadic_type): + if not (isinstance(variadic_type, (type, tuple)) or type(variadic_type)): + raise ValueError( + "Variadic types must be type or tuple of types" + " (Variadic[int] or Variadic[(int, float)]" + ) + + if not isinstance(variadic_type, tuple): + variadic_type = (variadic_type,) + return VariadicSignatureType( + f"Variadic[{typename(variadic_type)}]", + (), + dict(variadic_type=variadic_type, __slots__=()), + ) + + +class Variadic(metaclass=VariadicSignatureMeta): + """A class whose getitem method can be used to generate a new type + representing a specific variadic signature. + Examples + -------- + >>> # xdoctest: +SKIP + >>> Variadic[int] # any number of int arguments + + >>> Variadic[(int, str)] # any number of one of int or str arguments + + >>> issubclass(int, Variadic[int]) + True + >>> issubclass(int, Variadic[(int, str)]) + True + >>> issubclass(str, Variadic[(int, str)]) + True + >>> issubclass(float, Variadic[(int, str)]) + False + """ diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py new file mode 100644 index 0000000000000000000000000000000000000000..8b4216a79ad0351cc6fedba64c06810fdc894426 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/unification_tools.py @@ -0,0 +1,420 @@ +# mypy: allow-untyped-defs +import collections +import operator +from collections.abc import Mapping +from functools import reduce + + +__all__ = [ + "merge", + "merge_with", + "valmap", + "keymap", + "itemmap", + "valfilter", + "keyfilter", + "itemfilter", + "assoc", + "dissoc", + "assoc_in", + "update_in", + "get_in", +] + + +def _get_factory(f, kwargs): + factory = kwargs.pop("factory", dict) + if kwargs: + raise TypeError( + f"{f.__name__}() got an unexpected keyword argument '{kwargs.popitem()[0]}'" + ) + return factory + + +def merge(*dicts, **kwargs): + """Merge a collection of dictionaries + + >>> merge({1: "one"}, {2: "two"}) + {1: 'one', 2: 'two'} + + Later dictionaries have precedence + + >>> merge({1: 2, 3: 4}, {3: 3, 4: 4}) + {1: 2, 3: 3, 4: 4} + + See Also: + merge_with + """ + if len(dicts) == 1 and not isinstance(dicts[0], Mapping): + dicts = dicts[0] + factory = _get_factory(merge, kwargs) + + rv = factory() + for d in dicts: + rv.update(d) + return rv + + +def merge_with(func, *dicts, **kwargs): + """Merge dictionaries and apply function to combined values + + A key may occur in more than one dict, and all values mapped from the key + will be passed to the function as a list, such as func([val1, val2, ...]). + + >>> merge_with(sum, {1: 1, 2: 2}, {1: 10, 2: 20}) + {1: 11, 2: 22} + + >>> merge_with(first, {1: 1, 2: 2}, {2: 20, 3: 30}) # doctest: +SKIP + {1: 1, 2: 2, 3: 30} + + See Also: + merge + """ + if len(dicts) == 1 and not isinstance(dicts[0], Mapping): + dicts = dicts[0] + factory = _get_factory(merge_with, kwargs) + + result = factory() + for d in dicts: + for k, v in d.items(): + if k not in result: + result[k] = [v] + else: + result[k].append(v) + return valmap(func, result, factory) + + +def valmap(func, d, factory=dict): + """Apply function to values of dictionary + + >>> bills = {"Alice": [20, 15, 30], "Bob": [10, 35]} + >>> valmap(sum, bills) # doctest: +SKIP + {'Alice': 65, 'Bob': 45} + + See Also: + keymap + itemmap + """ + rv = factory() + rv.update(zip(d.keys(), map(func, d.values()))) + return rv + + +def keymap(func, d, factory=dict): + """Apply function to keys of dictionary + + >>> bills = {"Alice": [20, 15, 30], "Bob": [10, 35]} + >>> keymap(str.lower, bills) # doctest: +SKIP + {'alice': [20, 15, 30], 'bob': [10, 35]} + + See Also: + valmap + itemmap + """ + rv = factory() + rv.update(zip(map(func, d.keys()), d.values())) + return rv + + +def itemmap(func, d, factory=dict): + """Apply function to items of dictionary + + >>> accountids = {"Alice": 10, "Bob": 20} + >>> itemmap(reversed, accountids) # doctest: +SKIP + {10: "Alice", 20: "Bob"} + + See Also: + keymap + valmap + """ + rv = factory() + rv.update(map(func, d.items())) + return rv + + +def valfilter(predicate, d, factory=dict): + """Filter items in dictionary by value + + >>> iseven = lambda x: x % 2 == 0 + >>> d = {1: 2, 2: 3, 3: 4, 4: 5} + >>> valfilter(iseven, d) + {1: 2, 3: 4} + + See Also: + keyfilter + itemfilter + valmap + """ + rv = factory() + for k, v in d.items(): + if predicate(v): + rv[k] = v + return rv + + +def keyfilter(predicate, d, factory=dict): + """Filter items in dictionary by key + + >>> iseven = lambda x: x % 2 == 0 + >>> d = {1: 2, 2: 3, 3: 4, 4: 5} + >>> keyfilter(iseven, d) + {2: 3, 4: 5} + + See Also: + valfilter + itemfilter + keymap + """ + rv = factory() + for k, v in d.items(): + if predicate(k): + rv[k] = v + return rv + + +def itemfilter(predicate, d, factory=dict): + """Filter items in dictionary by item + + >>> def isvalid(item): + ... k, v = item + ... return k % 2 == 0 and v < 4 + + >>> d = {1: 2, 2: 3, 3: 4, 4: 5} + >>> itemfilter(isvalid, d) + {2: 3} + + See Also: + keyfilter + valfilter + itemmap + """ + rv = factory() + for item in d.items(): + if predicate(item): + k, v = item + rv[k] = v + return rv + + +def assoc(d, key, value, factory=dict): + """Return a new dict with new key value pair + + New dict has d[key] set to value. Does not modify the initial dictionary. + + >>> assoc({"x": 1}, "x", 2) + {'x': 2} + >>> assoc({"x": 1}, "y", 3) # doctest: +SKIP + {'x': 1, 'y': 3} + """ + d2 = factory() + d2.update(d) + d2[key] = value + return d2 + + +def dissoc(d, *keys, **kwargs): + """Return a new dict with the given key(s) removed. + + New dict has d[key] deleted for each supplied key. + Does not modify the initial dictionary. + + >>> dissoc({"x": 1, "y": 2}, "y") + {'x': 1} + >>> dissoc({"x": 1, "y": 2}, "y", "x") + {} + >>> dissoc({"x": 1}, "y") # Ignores missing keys + {'x': 1} + """ + factory = _get_factory(dissoc, kwargs) + d2 = factory() + + if len(keys) < len(d) * 0.6: + d2.update(d) + for key in keys: + if key in d2: + del d2[key] + else: + remaining = set(d) + remaining.difference_update(keys) + for k in remaining: + d2[k] = d[k] + return d2 + + +def assoc_in(d, keys, value, factory=dict): + """Return a new dict with new, potentially nested, key value pair + + >>> purchase = { + ... "name": "Alice", + ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, + ... "credit card": "5555-1234-1234-1234", + ... } + >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP + {'credit card': '5555-1234-1234-1234', + 'name': 'Alice', + 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} + """ + return update_in(d, keys, lambda x: value, value, factory) + + +def update_in(d, keys, func, default=None, factory=dict): + """Update value in a (potentially) nested dictionary + + inputs: + d - dictionary on which to operate + keys - list or tuple giving the location of the value to be changed in d + func - function to operate on that value + + If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the + original dictionary with v replaced by func(v), but does not mutate the + original dictionary. + + If k0 is not a key in d, update_in creates nested dictionaries to the depth + specified by the keys, with the innermost value set to func(default). + + >>> inc = lambda x: x + 1 + >>> update_in({"a": 0}, ["a"], inc) + {'a': 1} + + >>> transaction = { + ... "name": "Alice", + ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, + ... "credit card": "5555-1234-1234-1234", + ... } + >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP + {'credit card': '5555-1234-1234-1234', + 'name': 'Alice', + 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} + + >>> # updating a value when k0 is not in d + >>> update_in({}, [1, 2, 3], str, default="bar") + {1: {2: {3: 'bar'}}} + >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) + {1: 'foo', 2: {3: {4: 1}}} + """ + ks = iter(keys) + k = next(ks) + + rv = inner = factory() + rv.update(d) + + # pyrefly: ignore [not-iterable] + for key in ks: + if k in d: + d = d[k] + dtemp = factory() + dtemp.update(d) + else: + d = dtemp = factory() + + inner[k] = inner = dtemp + k = key + + if k in d: + inner[k] = func(d[k]) + else: + inner[k] = func(default) + return rv + + +def get_in(keys, coll, default=None, no_default=False): + """Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. + + If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless + ``no_default`` is specified, then it raises KeyError or IndexError. + + ``get_in`` is a generalization of ``operator.getitem`` for nested data + structures such as dictionaries and lists. + + >>> transaction = { + ... "name": "Alice", + ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, + ... "credit card": "5555-1234-1234-1234", + ... } + >>> get_in(["purchase", "items", 0], transaction) + 'Apple' + >>> get_in(["name"], transaction) + 'Alice' + >>> get_in(["purchase", "total"], transaction) + >>> get_in(["purchase", "items", "apple"], transaction) + >>> get_in(["purchase", "items", 10], transaction) + >>> get_in(["purchase", "total"], transaction, 0) + 0 + >>> get_in(["y"], {}, no_default=True) + Traceback (most recent call last): + ... + KeyError: 'y' + + See Also: + itertoolz.get + operator.getitem + """ + try: + return reduce(operator.getitem, keys, coll) + except (KeyError, IndexError, TypeError): + if no_default: + raise + return default + + +def getter(index): + if isinstance(index, list): + if len(index) == 1: + index = index[0] + return lambda x: (x[index],) + elif index: + return operator.itemgetter(*index) + else: + return lambda x: () + else: + return operator.itemgetter(index) + + +def groupby(key, seq): + """Group a collection by a key function + + >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] + >>> groupby(len, names) # doctest: +SKIP + {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} + + >>> iseven = lambda x: x % 2 == 0 + >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP + {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} + + Non-callable keys imply grouping on a member. + + >>> groupby( + ... "gender", + ... [ + ... {"name": "Alice", "gender": "F"}, + ... {"name": "Bob", "gender": "M"}, + ... {"name": "Charlie", "gender": "M"}, + ... ], + ... ) # doctest:+SKIP + {'F': [{'gender': 'F', 'name': 'Alice'}], + 'M': [{'gender': 'M', 'name': 'Bob'}, + {'gender': 'M', 'name': 'Charlie'}]} + + Not to be confused with ``itertools.groupby`` + + See Also: + countby + """ + if not callable(key): + key = getter(key) + d = collections.defaultdict(lambda: [].append) # type: ignore[var-annotated] + for item in seq: + d[key(item)](item) + rv = {} + for k, v in d.items(): + rv[k] = v.__self__ # type: ignore[var-annotated, attr-defined] + return rv + + +def first(seq): + """The first element in a sequence + + >>> first("ABC") + 'A' + """ + return next(iter(seq)) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ab99ad1b4f0d495067cb33b8464c7c80777f7d8d --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/utils.py @@ -0,0 +1,108 @@ +# mypy: allow-untyped-defs +__all__ = ["hashable", "transitive_get", "raises", "reverse_dict", "xfail", "freeze"] + + +def hashable(x): + try: + hash(x) + return True + except TypeError: + return False + + +def transitive_get(key, d): + """Transitive dict.get + >>> d = {1: 2, 2: 3, 3: 4} + >>> d.get(1) + 2 + >>> transitive_get(1, d) + 4 + """ + while hashable(key) and key in d: + key = d[key] + return key + + +def raises(err, lamda): # codespell:ignore lamda + try: + lamda() # codespell:ignore lamda + return False + except err: + return True + + +# Taken from theano/theano/gof/sched.py +# Avoids licensing issues because this was written by Matthew Rocklin +def _toposort(edges): + """Topological sort algorithm by Kahn [1] - O(nodes + vertices) + inputs: + edges - a dict of the form {a: {b, c}} where b and c depend on a + outputs: + L - an ordered list of nodes that satisfy the dependencies of edges + >>> # xdoctest: +SKIP + >>> _toposort({1: (2, 3), 2: (3,)}) + [1, 2, 3] + Closely follows the wikipedia page [2] + [1] Kahn, Arthur B. (1962), "Topological sorting of large networks", + Communications of the ACM + [2] http://en.wikipedia.org/wiki/Toposort#Algorithms + """ + incoming_edges = reverse_dict(edges) + incoming_edges = {k: set(val) for k, val in incoming_edges.items()} + S = {v for v in edges if v not in incoming_edges} + L = [] + + while S: + n = S.pop() + L.append(n) + for m in edges.get(n, ()): + assert n in incoming_edges[m] + incoming_edges[m].remove(n) + if not incoming_edges[m]: + S.add(m) + if any(incoming_edges.get(v) for v in edges): + raise ValueError("Input has cycles") + return L + + +def reverse_dict(d): + """Reverses direction of dependence dict + >>> d = {"a": (1, 2), "b": (2, 3), "c": ()} + >>> reverse_dict(d) # doctest: +SKIP + {1: ('a',), 2: ('a', 'b'), 3: ('b',)} + :note: dict order are not deterministic. As we iterate on the + input dict, it make the output of this function depend on the + dict order. So this function output order should be considered + as undeterministic. + """ + result = {} # type: ignore[var-annotated] + for key in d: + for val in d[key]: + result[val] = result.get(val, ()) + (key,) + return result + + +def xfail(func): + try: + func() + raise Exception("XFailed test passed") # pragma:nocover # noqa: TRY002 + except Exception: + pass + + +def freeze(d): + """Freeze container to hashable form + >>> freeze(1) + 1 + >>> freeze([1, 2]) + (1, 2) + >>> freeze({1: 2}) # doctest: +SKIP + frozenset([(1, 2)]) + """ + if isinstance(d, dict): + return frozenset(map(freeze, d.items())) + if isinstance(d, set): + return frozenset(map(freeze, d)) + if isinstance(d, (tuple, list)): + return tuple(map(freeze, d)) + return d diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/variable.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/variable.py new file mode 100644 index 0000000000000000000000000000000000000000..1b5b51aaf99a5dc9864f5aa22fa9c50571f95797 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unification/variable.py @@ -0,0 +1,90 @@ +# mypy: allow-untyped-defs +from contextlib import contextmanager + +from .dispatch import dispatch +from .utils import hashable + + +_global_logic_variables = set() # type: ignore[var-annotated] +_glv = _global_logic_variables + + +class Var: + """Logic Variable""" + + _id = 1 + + def __new__(cls, *token): + if len(token) == 0: + token = f"_{Var._id}" # type: ignore[assignment] + Var._id += 1 + elif len(token) == 1: + token = token[0] + + obj = object.__new__(cls) + obj.token = token # type: ignore[attr-defined] + return obj + + def __str__(self): + return "~" + str(self.token) # type: ignore[attr-defined] + + __repr__ = __str__ + + def __eq__(self, other): + return type(self) is type(other) and self.token == other.token # type: ignore[attr-defined] + + def __hash__(self): + return hash((type(self), self.token)) # type: ignore[attr-defined] + + +def var(): + return lambda *args: Var(*args) + + +def vars(): + return lambda n: [var() for i in range(n)] + + +@dispatch(Var) +def isvar(v): + return True + + +isvar + + +@dispatch(object) # type: ignore[no-redef] +def isvar(o): + return _glv and hashable(o) and o in _glv + + +@contextmanager +def variables(*variables): + """ + Context manager for logic variables + + Example: + >>> # xdoctest: +SKIP("undefined vars") + >>> from __future__ import with_statement + >>> with variables(1): + ... print(isvar(1)) + True + >>> print(isvar(1)) + False + >>> # Normal approach + >>> from unification import unify + >>> x = var("x") + >>> unify(x, 1) + {~x: 1} + >>> # Context Manager approach + >>> with variables("x"): + ... print(unify("x", 1)) + {'x': 1} + """ + old_global_logic_variables = _global_logic_variables.copy() + _global_logic_variables.update(set(variables)) + try: + yield + finally: + _global_logic_variables.clear() + _global_logic_variables.update(old_global_logic_variables) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unify_refinements.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unify_refinements.py new file mode 100644 index 0000000000000000000000000000000000000000..efafb146179a6c35e0a5ccb9a29893aa3a379a87 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/unify_refinements.py @@ -0,0 +1,124 @@ +# mypy: allow-untyped-defs +from torch.fx.experimental.graph_gradual_typechecker import Refine +from torch.fx.experimental.unification import unify, Var # type: ignore[attr-defined] +from torch.fx.tensor_type import TensorType + + +def infer_symbolic_types_single_pass(traced): + """ + Calls our symbolic inferencer once. + """ + r = Refine(traced) + r.refine() + mgu = unify_eq(r.constraints) + substitute_all_types(traced.graph, mgu) + + +def infer_symbolic_types(traced): + """ + Calls our symbolic inferencer twice. + This is useful when one pass is not enough + to infer all the information such as the case + for braodcasting. + """ + r = Refine(traced) + r.refine() + mgu = unify_eq(r.constraints) + substitute_all_types(traced.graph, mgu) + + r = Refine(traced) + r.refine() + mgu = unify_eq(r.constraints) + substitute_all_types(traced.graph, mgu) + + r.symbolic_relations() + + +def convert_eq(list_of_eq): + """ + Convert equality constraints in the right format + to be used by unification library. + """ + lhs = [] + rhs = [] + for eq in list_of_eq: + lhs.append(eq.lhs) + rhs.append(eq.rhs) + return tuple(lhs), tuple(rhs) + + +def unify_eq(list_of_eq): + """ + Apply unification to a set of + equality constraints + """ + lhs, rhs = convert_eq(list_of_eq) + return unify(lhs, rhs) + + +def substitute_solution_one_type(mapping, t): + """ + Apply the most general unifier to a type + """ + if isinstance(t, Var): + if t in mapping: + return mapping[t] + else: + return t + + elif isinstance(t, TensorType): + new_type = [] + for typ in t.__args__: + if typ in mapping: + new_type.append(mapping[typ]) + else: + new_type.append(typ) + return TensorType(tuple(new_type)) + + elif isinstance(t, list): + new_type = [] + for typ in t: + new_type.append(substitute_solution_one_type(mapping, typ)) + return new_type + + elif isinstance(t, tuple): + new_type = [] + for typ in t: + new_type.append(substitute_solution_one_type(mapping, typ)) + return tuple(new_type) + + else: + return t + + +def substitute_all_types(graph, mapping): + """ + Apply the most general unifier to all types in a graph + till reaching a fixed point. If the input and output graph + are the same, we converge. + """ + flag = True + while flag: + flag = False + for k in mapping: + old_mapping_val = mapping[k] + if mapping[k] in mapping: + new_key = mapping[k] + mapping[k] = mapping[new_key] + if old_mapping_val != mapping[k]: + flag = True + + for n in graph.nodes: + n.type = substitute_solution_one_type(mapping, n.type) + + +def check_for_type_equality(g1, g2): + """ + A check equality to be used in fixed points. + We do not use graph equality but instead type + equality. + """ + for n, m in zip(g1.nodes, g2.nodes): + if n.type != m.type: + return False + return True diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/validator.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/validator.py new file mode 100644 index 0000000000000000000000000000000000000000..56b8b871626af81f23f4e88cc5f57161ed1287ad --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/experimental/validator.py @@ -0,0 +1,874 @@ +# mypy: allow-untyped-defs +import builtins +import functools +import logging +import math +import operator +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, Optional, Union + +import sympy + +import torch +import torch.fx +import torch.fx.traceback as fx_traceback +from torch._dynamo.exc import TorchDynamoException +from torch._dynamo.utils import dynamo_timed +from torch.fx.node import Argument, Target +from torch.utils._sympy.interp import sympy_interp + + +log = logging.getLogger(__name__) + +try: + import z3 # type: ignore[import] + + # Translation Validation for Dynamo guards + # ======================================== + # + # Checks whether optimizations applied to the collected guards are + # valid. In other words, whether the guard function we actually run + # does not have false positives (unsound). + # + # In order to do so, we build the guards using 2 different information + # attached to each 'SymNode': + # 1. SymPy expressions + # 2. FX nodes + # + # SymPy expressions have implicit optimizations baked within itself, + # which may have a few bugs. On the other hand, we build the FX graph + # manually, with no optimizations enabled. This gives us access to + # the "ground truth". + # + # We then convert into Z3 expressions both the SymPy expressions + # (see [Note: SympyToZ3]) that reach 'ShapeEnv.produce_guards' function + # and the FX nodes (see [Note: PopulateValidator]) that go through + # 'ShapeEnv.evaluate_expr' function. Finally, we run the validation. + # (see [Note: TranslationValidator]) + # Better Z3 to string implementation (for a small fraction of Z3). + # + # Here are the things we clean before showing the Z3 expression: + # - Rename a few ops (e.g. "Distinct" ==> "!=") + # + # - Ignore ToInt and ToReal operations: + # usually they don't really matter + # + # - Transform (ToInt (/ ...)) into (idiv ...): + # this is the pattern for floor division + # + # - Collect a chain of the same operations into one + def z3str(e: z3.ExprRef) -> str: + assert z3.is_expr(e), f"unsupported expression type: {e}" + + def get_args_str(e: z3.ExprRef) -> list[str]: + return [z3str(e.arg(i)) for i in range(e.num_args())] + + # First, we simplify the given expression. + # This is done using rewriting rules, so shouldn't take long. + e = z3.simplify(e) + + # Only support function applications. + # Even Z3 "variables" are, in fact, function applications. + if not z3.is_app(e): + raise ValueError(f"can't print Z3 expression: {e}") + + if z3.is_int_value(e) or z3.is_rational_value(e): + return e.as_string() # type: ignore[attr-defined] + + decl = e.decl() + kind = decl.kind() + op = str(decl) + args = get_args_str(e) + + if kind == z3.Z3_OP_POWER: + op = "pow" + + elif kind in (z3.Z3_OP_ADD, z3.Z3_OP_MUL): + # Collect the arguments of chains of ADD and MUL. + # This is safe, since they are associative. + + def collect_str_args(e): + if not (z3.is_app(e) and e.decl().kind() == kind): + return [z3str(e)] + else: + return [ + x + for i in range(e.num_args()) + for x in collect_str_args(e.arg(i)) + ] + + args = collect_str_args(e) + + elif kind == z3.Z3_OP_NOT: + # Revert some conversions that z3.simplify applies: + # - a != b ==> (Not (== a b)) ==> (!= a b) + # - a < b ==> (Not (<= b a)) ==> (> b a) + # - a > b ==> (Not (<= a b)) ==> (> a b) + + assert e.num_args() == 1 + arg = e.arg(0) + + assert z3.is_app(arg) + argkind = arg.decl().kind() + + logic_inverse = { + z3.Z3_OP_EQ: "!=", + z3.Z3_OP_LE: ">", + z3.Z3_OP_GE: "<", + } + + if argkind in logic_inverse: + op = logic_inverse[argkind] + args = get_args_str(arg) + + elif kind in (z3.Z3_OP_TO_INT, z3.Z3_OP_TO_REAL): + assert e.num_args() == 1 + argstr = z3str(e.arg(0)) + + # Check if it's the floor division pattern. + if argstr.startswith("(/"): + return "(idiv" + argstr[2:] + + # Otherwise, just ignore it. + return argstr + + elif kind == z3.Z3_OP_UNINTERPRETED: + assert e.num_args() == 0 + return str(decl) + + string = op + " " + " ".join(args) + return f"({string.rstrip()})" + + # We need to convert to/from BitVec in order to use z3 bitwise ops. + # We assume that integers are 64 bit. + # If all args are boolean, then use the boolean bitwise op implementation instead, if provided. + def _bitwise_op(bitwise_func, bool_func): + @functools.wraps(bitwise_func) + def wrapper(self, *args): + if bool_func is not None and all( + isinstance(arg, z3.BoolRef) for arg in args + ): + return bool_func(*args) + + wrapped_args = tuple(z3.Int2BV(a, 64) for a in args) + return z3.BV2Int(bitwise_func(*wrapped_args)) + + return wrapper + + # Implementation of Python semantics as Z3 expressions. + # + # Z3 Real-Int theory has operators with semantics that differ that of + # Python. Therefore, in order to get it right, we need to implement + # the (Python) semantics we are relying on in Z3. + @dataclass + class _Z3Ops: + # Validator used for adding assertions as needed. + # e.g. div(a, b) requires b != 0. + validator: "TranslationValidator" + + # The 2 functions below are used for conditionally casting between + # integer and reals. + # + # Returns a real expression from 'x'. + @staticmethod + def to_real(x: z3.ArithRef) -> z3.ArithRef: + return x if x.is_real() else z3.ToReal(x) + + # Returns an integer expression from 'x'. + @staticmethod + def to_int(x: z3.ArithRef) -> z3.ArithRef: + return x if x.is_int() else z3.ToInt(x) + + def sym_sum(self, args: z3.ArithRef) -> z3.ArithRef: + # pyrefly: ignore + return sum(args) + + # Implements Python division semantics. + def div(self, numerator: z3.ArithRef, denominator: z3.ArithRef) -> z3.ArithRef: + self.validator.add_assertion(denominator != 0) # type: ignore[arg-type] + return _Z3Ops.to_real(numerator) / _Z3Ops.to_real(denominator) + + def floor(self, number: z3.ArithRef) -> z3.ArithRef: + # Z3 ToInt function rounds a real number towards negative infinity. + return _Z3Ops.to_int(number) + + # Python semantics for 'FloorDiv' states that before applying the floor + # function, the operands are converted to their common type. + def floordiv( + self, numerator: z3.ArithRef, denominator: z3.ArithRef + ) -> z3.ArithRef: + cast_result_to_real = numerator.is_real() or denominator.is_real() + result = _Z3Ops.to_int(self.div(numerator, denominator)) + # Since the 'result' is already an integer, we just have to check + # whether we should cast it to real. + return _Z3Ops.to_real(result) if cast_result_to_real else result + + def ceil(self, number: z3.ArithRef) -> z3.ArithRef: + return z3.If(self.floor(number) < number, self.floor(number + 1), number) # type: ignore[return-value] + + def trunc(self, number: z3.ArithRef) -> z3.ArithRef: + return z3.If(number >= 0, self.floor(number), self.ceil(number)) # type: ignore[return-value] + + def max(self, a: z3.ArithRef, b: z3.ArithRef) -> z3.ArithRef: + return z3.If(a > b, a, b) # type: ignore[return-value] + + def min(self, a: z3.ArithRef, b: z3.ArithRef) -> z3.ArithRef: + return z3.If(a < b, a, b) # type: ignore[return-value] + + # Python semantics for 'Mod' is defined as: p % q = p - floordiv(p, q) * q + # It should work with both integer and reals. + def mod(self, p: z3.ArithRef, q: z3.ArithRef) -> z3.ArithRef: + return p - self.floordiv(p, q) * q + + def pow(self, base: z3.ArithRef, exp: z3.ArithRef) -> z3.ArithRef: + # Z3 can't handle complex numbers very well. + self.validator.add_assertion(z3.Or(base != 0, exp > 0)) # type: ignore[arg-type] + return base**exp + + def sqrt(self, number: z3.ArithRef) -> z3.ArithRef: + # Square-root: + # 1. Only work with reals + number = _Z3Ops.to_real(number) + # 2. The number should be positive or zero. + # Otherwise, Z3 returns 'unknown'. + self.validator.add_assertion(number >= 0) + return number**0.5 + + def abs(self, number: z3.ArithRef) -> z3.ArithRef: + return z3.Abs(number) + + def round_to_int(self, number: z3.ArithRef) -> z3.ArithRef: + # Pythons builtin 'round' implements the 'round half to even' strategy + # See https://en.wikipedia.org/wiki/Rounding#Rounding_half_to_even + # z3 has an equivalent z3.fpRoundToIntegral(z3.RoundNearestTiesToEven(), ...), but this only applies to + # floating point numbers, which is different from real numbers that we are dealing with here. + # Instead, we implement 'round half to even' in terms of 'round half up' (floor(x + 0.5)) and + # 'round half down' (ceil(x - 0.5)). + # Assuming 'round half up' is the default case, we need to correct ..., -3.5, -1.5, 0.5, 2.5, 4.5, ... + # to round down, i.e. use the 'round half down' strategy + return z3.If( + self.mod(number, z3.IntVal(2)) == 0.5, + self.ceil(number - 0.5), + self.floor(number + 0.5), + ) + + bitwise_and = _bitwise_op(operator.and_, z3.And) + bitwise_or = _bitwise_op(operator.or_, z3.Or) + lshift = _bitwise_op(operator.lshift, None) + rshift = _bitwise_op(operator.rshift, None) + + # Lifts a callable to be used in Z3. + # + # This function replaces the given 'op' by a function that: + # + # 1. Lifts the arguments into Z3 (i.e. make them inhabitants of Z3) + # + # 2. Calls an operation that corresponds to 'op', but works with Z3 + # inhabitants (left as is if it works as is) + def z3op(op: Callable, validator: "TranslationValidator") -> Callable: + # Operations that have booleans as their argument. + # This is needed because the argument of some FX nodes were + # literal integers, instead of booleans. So, whenever this flag + # is set, we also convert ints to booleans. + boolean_ops = {operator.not_} + as_bool = op in boolean_ops + + # Lifts the function into 'z3.ExprRef' domain. + def lift(func): + def wrap(a) -> z3.ExprRef: + if isinstance(a, (z3.ArithRef, z3.BoolRef)): + return a + # Convert it into a Z3 value, if it is some of the supported + # types below. + if isinstance(a, bool) or (as_bool and isinstance(a, int)): + return z3.BoolVal(bool(a)) + if isinstance(a, (int, sympy.Integer)): + return z3.IntVal(int(a)) + if isinstance(a, (float, sympy.Float)): + return z3.RealVal(float(a)) + raise ValueError(f"can't lift type: {type(a)}") + + @functools.wraps(func) + def wrapper(*args): + # Lifts the arguments into a list of Z3 inhabitants. + if len(args) == 1 and isinstance(args[0], (list, tuple)): + wrapped_args = (tuple(wrap(a) for a in args[0]),) + else: + wrapped_args = tuple(wrap(a) for a in args) + # Run the function on the Z3 expressions. + return func(*wrapped_args) + + return wrapper + + ops = _Z3Ops(validator) + replacement_map = { + # Operator module. + operator.not_: lift(z3.Not), + operator.and_: lift(ops.bitwise_and), + operator.or_: lift(ops.bitwise_or), + operator.lshift: lift(ops.lshift), + operator.rshift: lift(ops.rshift), + operator.floordiv: lift(ops.floordiv), + operator.truediv: lift(ops.div), + operator.mod: lift(ops.mod), + operator.abs: lift(ops.abs), + builtins.round: lift(ops.round_to_int), + # Math module. + math.ceil: lift(ops.ceil), + math.floor: lift(ops.floor), + math.trunc: lift(ops.trunc), + # Torch module. + torch.sym_float: lift(ops.to_real), + torch.sym_max: lift(ops.max), + torch.sym_min: lift(ops.min), + torch.sym_sum: lift(ops.sym_sum), + torch.sym_ite: lift(lambda b, t, f: t if b else f), + torch._sym_sqrt: lift(ops.sqrt), # type: ignore[attr-defined] + # Not lifted because we only use this function as a + # marker for adding the expression as validator input. + torch._assert: torch._assert, + } + return replacement_map[op] if op in replacement_map else lift(op) + + # Processes an FX graph, populating the given validator. + # + # [Note: PopulateValidator] + # This class walks through each node in the FX graph, translating + # them into the Z3 world. + # + # Then, whenever it finds an 'torch._assert' call_function operation, + # it adds the Z3 expression corresponding to the argument as validator + # input. + class PopulateValidator(torch.fx.Interpreter): + def __init__(self, graph: torch.fx.Graph, validator: "TranslationValidator"): + # Reference to the translation validator. + self.validator = validator + + # Build the graph module and call `Interpreter` constructor. + module = torch.fx.GraphModule(root={}, graph=graph) + super().__init__(module, garbage_collect_values=True) + + def placeholder( + self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + symbol = fx_traceback.get_current_meta()["symbol"] + return self.validator.z3var(symbol) + + def call_function( + self, target: Target, args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + if target is not torch._assert: + # Lift and runs the node target function + return super().call_function(z3op(target, self.validator), args, kwargs) # type: ignore[arg-type] + # Adds the Z3 expression corresponding to the first argument + # as a validator input. + assert len(args) == 1, ( + f"expected 1 argument on assertion. Got: {len(args)} " + ) + self.validator.add_source_expr(args[0]) # type: ignore[arg-type] + + # Translates SymPy expressions into Z3 expressions. + # + # [Note: SympyToZ3] + # At the time of the translation, all free variables present in the + # SymPy expression being translated must be already mapped to a Z3 + # integer variable. + class SympyToZ3: + OPERATOR_HANDLES = {"add", "mul", "eq", "ne", "lt", "gt", "le", "ge"} + + def __init__( + self, + validator: "TranslationValidator", + ) -> None: + self._validator = validator + self._ops = _Z3Ops(self._validator) + + def constant(self, value: Any, dtype: torch.dtype) -> z3.ExprRef: + # TODO: Probably OK to relax this and allow lower precision + if dtype is torch.int64: + return z3.IntVal(int(value)) + if dtype is torch.double: + return z3.RealVal(float(value)) + if dtype is torch.bool: + return z3.BoolVal(bool(value)) + raise ValueError(f"unsupported dtype (SympyToZ3): {dtype}") + + def to_dtype(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef: + if dtype == torch.float64: + return z3.ToReal(x) + raise NotImplementedError(f"to_dtype {dtype} NYI") + + def trunc_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef: + return z3.ToInt(x) + + def round_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef: + return self._ops.round_to_int(x) + + def int_truediv( + self, numerator: z3.ArithRef, denominator: z3.ArithRef + ) -> z3.ArithRef: + return self._ops.div(numerator, denominator) + + def truediv( + self, numerator: z3.ArithRef, denominator: z3.ArithRef + ) -> z3.ArithRef: + return self._ops.div(numerator, denominator) + + def floordiv( + self, numerator: z3.ArithRef, denominator: z3.ArithRef + ) -> z3.ArithRef: + return self._ops.floordiv(numerator, denominator) + + def div(self, numerator: z3.ArithRef, denominator: z3.ArithRef) -> z3.ArithRef: + return self._ops.floordiv(numerator, denominator) + + def pow(self, base: z3.ArithRef, exp: z3.ArithRef) -> z3.ArithRef: + return self._ops.pow(base, exp) + + def pow_by_natural(self, base: z3.ArithRef, exp: z3.ArithRef) -> z3.ArithRef: + return self._ops.pow(base, exp) + + def mod(self, p: z3.ArithRef, q: z3.ArithRef) -> z3.ArithRef: + return self._ops.mod(p, q) + + def python_mod(self, p: z3.ArithRef, q: z3.ArithRef) -> z3.ArithRef: + return self._ops.mod(p, q) + + def ceil_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef: + return self._ops.ceil(x) + + def floor_to_int(self, x: z3.ArithRef, dtype: torch.dtype) -> z3.ArithRef: + return self._ops.floor(x) + + def __getattr__(self, name: str) -> Any: + REPLACEMENT = { + "and_": z3.And, + "or_": z3.Or, + "not_": z3.Not, + "bitwise_and": self._ops.bitwise_and, + "bitwise_or": self._ops.bitwise_or, + "lshift": self._ops.lshift, + "rshift": self._ops.rshift, + "floor": self._ops.floor, + "ceil": self._ops.ceil, + "minimum": self._ops.min, + "maximum": self._ops.max, + } + + if name in REPLACEMENT: + return REPLACEMENT[name] + if name in self.OPERATOR_HANDLES: + return getattr(operator, name) + raise AttributeError(f"unhandled operator: {name}") + + def run(self, expr: sympy.Basic) -> z3.ExprRef: + return sympy_interp(self, self._validator.symbols, expr) # type: ignore[arg-type] + + # Dynamo guards translation validator. + # + # [Note: TranslationValidator] + # Verifies whether the guards issued by 'ShapeEnv.produce_guards' are sound. + # That is: whether those (target) guards only yield TRUE whenever the original, + # unoptimized, (source) guards yield TRUE. + # + # More concretely, given 'source' and 'target' guard expressions, we wish to + # check whether the following expression holds: + # + # Not(And(source)) AND And(target) + # + # i.e. whether there is an assignment of the free variables where the opposite + # happens: target is TRUE, but source is FALSE. + class TranslationValidator: + def __init__(self) -> None: + log.debug("new instance") + + # Mapping of SymPy symbols to Z3 variables. + self.symbols: dict[sympy.Symbol, z3.ExprRef] = {} + + # Set of source Z3 expressions. + # They represent the generated guards without any kind of + # simplification or transformation. + self._source_exprs: set[z3.BoolRef] = set() + + # Set of target Z3 expressions. + # They represent the actual checked guards at runtime. They might + # be simplified or transformed versions of the source guards. + self._target_exprs: set[z3.BoolRef] = set() + + # Set of Z3 expressions representing assertions over both the + # source and target expressions. + self._assertions: set[z3.BoolRef] = set() + + # Retrieves the corresponding Z3 variable. + def z3var(self, symbol: sympy.Symbol) -> z3.ExprRef: + assert symbol in self.symbols, f"Z3 variable not found for: {symbol}" + return self.symbols[symbol] + + # Create a variable in Z3 of 'type' for 'symbol', if it doesn't already exists. + def add_var(self, symbol: sympy.Symbol, type: type) -> z3.ExprRef: + if symbol in self.symbols: + return self.symbols[symbol] + + log.debug("new variable: %s (%s)", symbol.name, type.__name__) + + if type is int: + var = z3.Int(symbol.name) + + # If 'symbol' is positive (SymPy assumption), we have to + # convey it to Z3 as well. + if symbol.is_positive: # type: ignore[attr-defined] + self._target_exprs.add(var > 0) + elif type is float: + var = z3.Real(symbol.name) + elif type is bool: + var = z3.Bool(symbol.name) + else: + raise RuntimeError(f"unsupported type for Z3 variable: {type}") + + self.symbols[symbol] = var + return var + + # Checks whether all symbols were already added. + def _check_freesymbols(self, e: sympy.Basic) -> None: + for s in e.free_symbols: + assert isinstance(s, sympy.Symbol) + # Call 'z3var' just to check whether there's already a + # Z3 variable corresponding to 's'. + self.z3var(s) + + def to_z3_boolean_expr(self, e: sympy.Basic) -> z3.BoolRef: + z3expr = SympyToZ3(self).run(e) + assert isinstance(z3expr, z3.BoolRef), ( + f"expected boolean expression. Got: {z3expr}" + ) + return z3expr + + def add_source_expr(self, e: z3.BoolRef) -> None: + if e not in self._source_exprs: + log.debug("add source guard: %s", z3str(e)) + self._source_exprs.add(e) + + def add_target_expr(self, e: "sympy.logic.boolalg.Boolean") -> None: + self._check_freesymbols(e) + z3expr = self.to_z3_boolean_expr(e) + if e not in self._target_exprs: + log.debug("add target guard: %s", z3str(z3expr)) + self._target_exprs.add(z3expr) + + def add_assertion(self, e: Union[z3.BoolRef, sympy.Basic]) -> None: + if isinstance(e, sympy.Basic): + self._check_freesymbols(e) + ref = self.to_z3_boolean_expr(e) + else: + ref = e + assert isinstance(ref, z3.BoolRef) + if ref not in self._assertions: + log.debug("add assertion: %s", z3str(ref)) + self._assertions.add(ref) + + def validate(self) -> None: + with dynamo_timed("TranslationValidator.validate"): + return self._validate() + + def _validate(self) -> None: + if len(self._source_exprs) == 0 or len(self._target_exprs) == 0: + # If there are no source/target expressions, there's nothing we really + # wish to prove. So, we just return. + return None + + # Here, we use "QF_NRA" logic for the solver: + # "Quantifier-free Non-linear Real Arithmetic". + # + # Most of the guards expressions have: + # 1. arithmetic between integer and reals + # 2. no quantifiers + # 3. potentially non-linear. + # + # Although there's also "QF_NIRA" (mixed integer-real arithmetic), + # "QF_NRA" seems to work better on 'dynamo/test_dynamic_shapes.py'. + solver = z3.SolverFor("QF_NRA") + # Set a timeout for finding a solution. + solver.set(timeout=translation_validation_timeout()) + + # Add all the assertions to the solver. + for assertion in self._assertions: + solver.add(assertion) + + # "Is there any case where it's TRUE for the target expressions, + # but FALSE for the source expressions?" + solver.add(z3.Not(z3.And(*self._source_exprs))) + solver.add(*self._target_exprs) + + log.debug("translation validation: start") + r = solver.check() + if r == z3.sat: + # Target expressions are unsound. + # Log the found model and the source expressions that failed. + model = solver.model() + raise ValidationException( + model, + self._assertions, + self._target_exprs, + failed_source_exprs=[ + inp for inp in self._source_exprs if not model.evaluate(inp) + ], + ) + else: + if r == z3.unknown: + # Could not find a solution. It didn't fail, but it also + # didn't succeed. Canceling the validation execution (keyboard + # interrupt) also gets to this branch. + log.warning( + "translation validation: could not validate: got z3.unknown" + ) + else: + # Target expressions are sound. + assert r == z3.unsat + log.debug("translation validation: success") + +except ImportError: + _HAS_Z3 = False + + __all__ = [ + "translation_validation_enabled", + "translation_validation_timeout", + "ValidationException", + "BisectValidationException", + ] + +else: + _HAS_Z3 = True + + __all__ = [ + "z3str", + "z3op", + "PopulateValidator", + "SympyToZ3", + "TranslationValidator", + "translation_validation_enabled", + "translation_validation_timeout", + "ValidationException", + "BisectValidationException", + ] + +from torch.fx.experimental import _config as config + + +def translation_validation_enabled() -> bool: + # Checks every time this function is called, in case the Dynamo + # option is set, but Z3 is not installed. + _assert_z3_installed_if_tv_set() + return _HAS_Z3 and config.translation_validation + + +def translation_validation_timeout() -> int: + return config.translation_validation_timeout + + +def _assert_z3_installed_if_tv_set(): + assert _HAS_Z3 or not config.translation_validation, ( + "translation validation requires Z3 package. Please, either install " + "z3-solver or disable translation validation." + ) + + +class ValidationException(TorchDynamoException): + def __init__(self, model, assertions, target_exprs, failed_source_exprs): + assert _HAS_Z3 + + def symbolstr(sym) -> str: + return f"{sym}: {model[sym]}" + + def joinlines(xs) -> str: + return "\n".join(f" ==> {x}" for x in xs) + + model_str = joinlines(sorted(map(symbolstr, model))) + assertions_str = joinlines(sorted(map(z3str, assertions))) + target_exprs_str = joinlines(sorted(map(z3str, target_exprs))) + failed_source_exprs_str = joinlines(sorted(map(z3str, failed_source_exprs))) + + self.msg = "translation validation failed." + self.details = f"""\ +Model: +{model_str} + +Assertions: +{assertions_str} + +Target Expressions: +{target_exprs_str} + +Failed Source Expressions: +{failed_source_exprs_str}""" + + def __str__(self): + return f"{self.msg}\n\n{self.details}" + + +class BisectValidationException(TorchDynamoException): + def __init__(self, validation_exc, expr, failed_action, traced_node): + self.msg = f"translation validation failed when {failed_action}: {expr}" + self.details = f"""\ +Failure occurred while running node: + {traced_node.format_node()} + +{validation_exc.details}""" + + def __str__(self): + return f"{self.msg}\n\n{self.details}" + + +# Checks when this module is loaded. +_assert_z3_installed_if_tv_set() + + +# Translation validation bisection. +# +# Bisect into the torch._assert nodes recorded in the shape_env FX graph, and raise +# the earliest ValidationException. +# +# As guards are added by ShapeEnv.evaluate_expr calls, some simplification errors +# might be silently happening. This function tries to nail down exactly at which +# point things went wrong from a validation perspective. +def bisect(shape_env): + from torch.fx.experimental.recording import ( + FakeTensorMeta, + replay_shape_env_events, + ShapeEnvEvent, + ) + from torch.fx.experimental.symbolic_shapes import ( + CURRENT_NODE_KEY, + ShapeEnv, + SHAPEENV_EVENT_KEY, + ) + + events = shape_env.events + + # Retrieves the ShapeEnvEvent associated with node. + def get_node_event(node: torch.fx.Node) -> ShapeEnvEvent: + assert SHAPEENV_EVENT_KEY in node.meta + return events[node.meta[SHAPEENV_EVENT_KEY]] + + # Creates a new instance of fake, but updating every symbolic value's ShapeEnv + # reference to the one given as argument. + # + # This is needed so as not to simplify a symbolic expression using a ShapeEnv + # "from the future", where it may have a different set of replacements. + def new_with_shape_env(shape_env: ShapeEnv, fake) -> Any: + if isinstance(fake, int): + return fake + if isinstance(fake, torch.SymInt): + return torch.SymInt(fake.node.with_shape_env(shape_env)) + if isinstance(fake, torch.SymFloat): + return torch.SymFloat(fake.node.with_shape_env(shape_env)) + assert isinstance(fake, FakeTensorMeta) + return FakeTensorMeta( + tuple(new_with_shape_env(shape_env, s) for s in fake.size()), + tuple(new_with_shape_env(shape_env, s) for s in fake.stride()), + new_with_shape_env(shape_env, fake.storage_offset()), + fake.is_nested, + ) + + # Checks whether the given shape_env fails when produce_guards is called. + def check_shapeenv_fails( + shape_env: ShapeEnv, tracked_fakes: Optional[list[Any]] + ) -> Optional[ValidationException]: + assert tracked_fakes is not None + try: + # This produce_guards call is a best-effort replication, since we + # don't populate EqualityConstraint list. Reason: we would also have + # to save OutputGraph.tracked_fakes_id_to_source. + shape_env.produce_guards( + [new_with_shape_env(shape_env, a.fake) for a in tracked_fakes], + [a.source for a in tracked_fakes], + input_contexts=[a.symbolic_context for a in tracked_fakes], + ) + return None + except ValidationException as e: + return e + + # Checks whether the ShapeEnv reconstructed by replaying the events until + # node is created fails when produce_guards is called. + def check_node_fails(node: torch.fx.Node) -> Optional[ValidationException]: + number = node.meta[SHAPEENV_EVENT_KEY] + # Reconstruct shape_env until the event at event_number. + shape_env = replay_shape_env_events(events[: number + 1]) + shape_env.graph.lint() + return check_shapeenv_fails(shape_env, events[number].tracked_fakes) + + last_exception = check_shapeenv_fails( + shape_env, shape_env._snapshot_tracked_fakes() + ) + + if not last_exception: + # We don't actually fail due to a produce_guards call. + # Stop and don't bisect. + log.info("translation validation succeeded: no errors found.") + return + + if not shape_env.should_record_events or config.translation_validation_no_bisect: + # Bisection is off. + # Return the last ValidationException we got. + raise last_exception + + # Cache the raised exception (if any) at each bisection point. + exception = {} + + # Bisection happens on the assertion nodes of the recorded FX graph for + # dynamic shapes. + assert_nodes = [ + node for node in shape_env.graph.nodes if node.target is torch._assert + ] + + # Preparing the indices for binary search. + # The overall invariants are + # - for all i < left, assert_node[i] doesn't fail + # - for all i >= right, assert_node[i] fails + # - `right in exception` always holds + # - `left <= right` always holds + left, mid, right = 0, 0, len(assert_nodes) - 1 + exception[right] = check_node_fails(assert_nodes[right]) + + while left < right: + mid = (left + right) // 2 + + node = assert_nodes[mid] + log.debug("bisecting at %s: %s", mid, get_node_event(node)) + + # Check whether the new shape_env raises a ValidationException or not. + exception[mid] = check_node_fails(node) + + if exception[mid]: + right = mid + else: + left = mid + 1 + + assert left in exception and isinstance(exception[left], ValidationException) + + node = assert_nodes[left] + event = get_node_event(node) + + if event.is_evaluate_expr(): + failed_action = "evaluating" + else: + assert event.is_defer_runtime_assert(), f"unexpected event type: {event}" + failed_action = "adding runtime assert" + + args = event.args + assert args is not None + assert len(args) >= 2, ( + f"bisecting expects {event.name} to have at least 2 positional arguments. " + f"Got: {len(args)}" + ) + assert isinstance(args[1], sympy.Basic), ( + f"bisecting expects {event.name} to have a SymPy expression as its second argument. " + f"Got: {type(args[1])}" + ) + + raise BisectValidationException( + exception[left], + expr=args[1], + failed_action=failed_action, + traced_node=node.meta[CURRENT_NODE_KEY], + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/graph.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/graph.py new file mode 100644 index 0000000000000000000000000000000000000000..03cba403350becac369ca01ce17a43bbd184a455 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/graph.py @@ -0,0 +1,2316 @@ +# mypy: allow-untyped-defs +import builtins +import contextlib +import copy +import enum +import functools +import inspect +import keyword +import logging +import math +import os +import pprint +import re +import types +import typing +import warnings +from collections import defaultdict +from collections.abc import Callable, Iterable, Iterator +from contextlib import contextmanager +from dataclasses import dataclass +from typing import Any, Literal, NamedTuple, Optional, TYPE_CHECKING + +import torch +import torch.utils._pytree as pytree +from torch._C import _fx_map_arg as map_arg, _NodeIter +from torch._library.opaque_object import is_opaque_value_type +from torch.utils._dtype_abbrs import dtype_abbrs + +from . import _pytree as fx_pytree +from ._compatibility import compatibility +from .immutable_collections import immutable_dict +from .node import _get_qualified_name, _type_repr, Argument, Node, Target + + +log = logging.getLogger(__name__) + +__all__ = ["PythonCode", "CodeGen", "Graph"] + +if TYPE_CHECKING: + from ._symbolic_trace import Tracer # noqa: F401 + from .graph_module import GraphModule # noqa: F401 + + +# Mapping of builtins to their `typing` equivalent. +# (PEP585: See D68459095 test plan) +_origin_type_map = { + list: typing.List, # noqa: UP006 + dict: typing.Dict, # noqa: UP006 + set: typing.Set, # noqa: UP006 + frozenset: typing.FrozenSet, # noqa: UP006 + tuple: typing.Tuple, # noqa: UP006 +} + +_legal_ops = dict.fromkeys( + ["call_function", "call_method", "get_attr", "call_module", "placeholder", "output"] +) + + +# Signature for functions thattransforms the body (`list[str]`) of the +# generated code +TransformCodeFunc = Callable[[list[str]], list[str]] + + +class _CustomBuiltin(NamedTuple): + """Additional objs that we add to every graph's globals. + + The repr() for some standard library objects is not valid Python code without + an import. For common objects of this sort, we bundle them in the globals of + every FX graph. + """ + + # How to import this object from the standard library. + import_str: str + # The actual object, produced from that import string. + obj: Any + + +# Combined dict of disallowed variable names so we can check with one lookup +_illegal_names = {k: object() for k in keyword.kwlist} +_illegal_names.update(builtins.__dict__) # can't shadow a builtin name + +_custom_builtins: dict[str, _CustomBuiltin] = {} + + +def _register_custom_builtin(name: str, import_str: str, obj: Any): + _custom_builtins[name] = _CustomBuiltin(import_str, obj) + _illegal_names[name] = obj + + +_register_custom_builtin("inf", "from math import inf", math.inf) +_register_custom_builtin("nan", "from math import nan", math.nan) +_register_custom_builtin("NoneType", "NoneType = type(None)", type(None)) +_register_custom_builtin("torch", "import torch", torch) +_register_custom_builtin("device", "from torch import device", torch.device) +_register_custom_builtin("fx_pytree", "import torch.fx._pytree as fx_pytree", fx_pytree) +_register_custom_builtin("pytree", "import torch.utils._pytree as pytree", pytree) + + +def _is_magic(x: str) -> bool: + return x.startswith("__") and x.endswith("__") + + +def _snake_case(s: str) -> str: + """ + Transforms the given string ``s`` to a Python-style variable name + + Examples: + ``mod.snake_case`` -> ``mod.snake_case`` + ``mod.pascalCase``-> ``mod.pascal_case`` + ``mod.ALL_CAPS`` -> ``mod.all_caps`` + """ + return _snake_case_sub(s).lower() + + +# Replace occurrences where a lowercase letter is followed by an uppercase letter +_snake_case_sub = functools.partial(re.compile(r"(?<=[a-z])([A-Z])").sub, r"_\1") + +# Find chars that can't be in a Python identifier +_illegal_char_regex = re.compile("[^0-9a-zA-Z_]+") + +# Combined check for variable names: +# 1) Checks name is not empty +# 2) Checks first character is not a digit +# 3) Checks name has no illegal characters (_illegal_char_regex) +# 3) Splits off the number suffix (if present) +_name_regex = re.compile(r"^([a-zA-Z_][0-9a-zA-Z_]*?)(?:_(\d+))?$") + +# starts with torch but does not start with torch._dynamo. or torch._inductor. +_torch_but_not_dynamo = re.compile( + r"^torch(?:\.(?!_dynamo\.|_inductor\.)[^.]+)*$" +).fullmatch + + +def _is_from_torch(obj: Any) -> bool: + module_name = getattr(obj, "__module__", None) + if module_name is not None: + return _torch_but_not_dynamo(module_name) is not None + + name = getattr(obj, "__name__", None) + # exclude torch because torch.torch.torch.torch works. idk mang + if name is not None and name != "torch": + for guess in [torch, torch.nn.functional]: + if getattr(guess, name, None) is obj: + return True + + return False + + +class _Namespace: + """A context for associating names uniquely with objects. + + The following invariants are enforced: + - Each object gets a single name. + - Each name is unique within a given namespace. + - Names generated do not shadow builtins, unless the object is indeed that builtin. + """ + + def __init__(self): + self._obj_to_name: dict[Any, str] = {} + self._used_names: set[str] = set() + self._base_count: dict[str, int] = {} + + def create_name(self, candidate: str, obj: Optional[Any]) -> str: + """Create a unique name. + + Arguments: + candidate: used as the basis for the unique name, relevant to the user. + obj: If not None, an object that will be associated with the unique name. + """ + if obj is not None and obj in self._obj_to_name: + return self._obj_to_name[obj] + + # optimistically check if candidate is already a valid name + match = _name_regex.match(candidate) + if match is None: + # delete all characters that are illegal in a Python identifier + candidate = _illegal_char_regex.sub("_", candidate) + + if not candidate: + candidate = "_unnamed" + + if candidate[0].isdigit(): + candidate = f"_{candidate}" + + match = _name_regex.match(candidate) + assert match is not None + + base, num = match.group(1, 2) + if num is None or candidate in self._used_names: + num = self._base_count.get(candidate, 0) + if _illegal_names.get(candidate, obj) is not obj: + num += 1 + candidate = f"{base}_{num}" + # assume illegal names don't end in _\d so no need to check again + else: + num = int(num) + + while candidate in self._used_names: + num += 1 + candidate = f"{base}_{num}" + + self._used_names.add(candidate) + self._base_count[base] = num + if obj is not None: + self._obj_to_name[obj] = candidate + return candidate + + def associate_name_with_obj(self, name: str, obj: Any): + """Associate a unique name with an object. + + Neither `name` nor `obj` should be associated already. + """ + maybe_existing = self._obj_to_name.setdefault(obj, name) + assert maybe_existing is name, "obj is already associated" + + def _rename_object(self, obj: Any, name: str): + assert obj in self._obj_to_name + self._obj_to_name[obj] = name + self._used_names.add(name) + + +@compatibility(is_backward_compatible=True) +@dataclass +class PythonCode: + """ + Represents all the information necessary to exec or save a graph as Python code. + """ + + # Python source code for the forward function definition. + src: str + # Values in global scope during execution of `src_def`. + globals: dict[str, Any] + # Optional mapping from the forward function's line number to + # node index. Line number starts at the prologue (i.e. forward()). + _lineno_map: Optional[dict[int, Optional[int]]] + # The line number of prologue in fn_code + _prologue_start: int = 0 + + +def _format_target(base: str, target: str) -> str: + elems = target.split(".") + r = base + for e in elems: + if not e.isidentifier(): + r = f'getattr({r}, "{e}")' + else: + r = f"{r}.{e}" + return r + + +class _InsertPoint: + def __init__(self, graph, new_insert): + self.graph = graph + self.orig_insert, graph._insert = graph._insert, new_insert + + def __enter__(self): + pass + + def __exit__(self, type, value, tb): + self.graph._insert = self.orig_insert + + +class _node_list: + def __init__(self, graph: "Graph", direction: Literal["_prev", "_next"] = "_next"): + assert direction in ("_next", "_prev") + self.graph = graph + self.direction = direction + + def __len__(self): + return self.graph._len + + def __iter__(self): + return _NodeIter(self.graph._root, self.direction == "_prev") + + def __reversed__(self): + return _node_list(self.graph, "_next" if self.direction == "_prev" else "_prev") + + +class _PyTreeInfo(NamedTuple): + """ + Contains extra info stored when we're using Pytrees + """ + + orig_args: list[str] + in_spec: pytree.TreeSpec + out_spec: Optional[pytree.TreeSpec] + + +@dataclass(frozen=True) +class _ParsedStackTrace: + """ + Represents the top-most frame of a parsed stack trace + """ + + file: str + lineno: str + name: str + code: str + + def get_summary_str(self): + return f"File: {self.file}:{self.lineno} in {self.name}, code: {self.code}" + + +# get File:lineno code from stack_trace +def _parse_stack_trace( + stack_trace: str, filter_fn: Optional[Callable[[str, str, str], bool]] = None +): + if stack_trace is None: + return None + pattern = re.compile(r"^File \"(.+)\", line (\d+), in (.+)$") + lines = stack_trace.strip().split("\n") + # stacktrace should have innermost frame last, so we + # iterate backwards to find the first line that starts + # with 'File ' + for idx in range(len(lines) - 2, -1, -1): + line = lines[idx].strip() + matches = pattern.match(line) + if matches: + file = matches.group(1) + lineno = matches.group(2) + name = matches.group(3) + # next line should be the code + code = lines[idx + 1].strip() + if filter_fn and not filter_fn(file, name, code): + continue + return _ParsedStackTrace(file, lineno, name, code) + return None + + +@compatibility(is_backward_compatible=False) +class CodeGen: + # This is an override hook so we can customize the SymNode printer. + _sym_repr: Callable[["torch.types.PySymType"], str] = lambda x: repr(x) + + def __init__(self): + self._body_transformer: Optional[TransformCodeFunc] = None + self._func_name: str = "forward" + + def _format_multiline_args(self, args: list[str]) -> str: + """Helper to format function arguments in expanded multiline format.""" + return "".join(self._format_single_arg(arg) for arg in args) + + def _format_single_arg(self, arg: str) -> str: + """Helper to format a single argument with optional comment.""" + if "#" in arg: + arg_part, comment_part = arg.split("#", 1) + return f" {arg_part.rstrip()}, # {comment_part.lstrip()}\n" + else: + return f" {arg},\n" + + def _get_delimiters(self, container) -> tuple[str, str]: + """Helper to get opening and closing delimiters for containers.""" + return ("(", ")") if isinstance(container, tuple) else ("[", "]") + + def _format_multiline_container(self, items, descs=None, prefix="") -> str: + """Helper to format containers (lists/tuples) in multiline format.""" + ldelim, rdelim = self._get_delimiters(items) + desc_trailers = self._get_desc_trailers(items, descs) + + return ( + f"{prefix}{ldelim}\n" + + "".join( + f" {item},{trailer}\n" for item, trailer in zip(items, desc_trailers) + ) + + f"{rdelim}" + ) + + def _get_desc_trailers(self, items, descs): + """Helper to generate description trailers for items.""" + if descs is None: + return [""] * len(items) + return [f" # {desc}" for desc in descs] + + def _call_method_with_signature_check(self, method, *args, **kwargs): + """Helper to call a method with optional parameters based on signature.""" + sig = inspect.signature(method) + # Filter kwargs to only include parameters that exist in the method signature + filtered_kwargs = {k: v for k, v in kwargs.items() if k in sig.parameters} + return method(*args, **filtered_kwargs) + + def gen_fn_def( + self, + free_vars: list[str], + maybe_return_annotation: str, + *, + expanded_def: bool = False, + ) -> str: + """ + Given the free variables and a return annotation, generates the beginning of the FX function. + By default, `gen_fn_def(['a', 'b'], '') == 'def {self._func_name}(a, b):'` + """ + # If the original function didn't have self as its first argument, we + # would have added it. + if len(free_vars) == 0 or free_vars[0] != "self": + free_vars.insert(0, "self") + + if expanded_def: + args_formatted = self._format_multiline_args(free_vars) + return ( + f"def {self._func_name}(\n{args_formatted}){maybe_return_annotation}:" + ) + else: + return f"def {self._func_name}({', '.join(free_vars)}){maybe_return_annotation}:" + + def generate_output( + self, output_args: Argument, *, descs: Optional[Any] = None + ) -> str: + """ + Given the output arguments, generates the return statement of the FX function. + Note: The returned statement should not be indented. + """ + if descs is not None and isinstance(output_args, (list, tuple)): + return self._format_multiline_container(output_args, descs, "return ") + else: + return f"return {repr(output_args)}" + + def process_inputs(self, *args: Any) -> Any: + """ + Transforms the inputs so that the graph can take them as arguments, as + non-default codegen may result in the inputs to the function being + different from the inputs to the graph. + + If the graph was directly runnable, this invariant should hold true + `f.graph.process_outputs(f.graph(*f.graph.process_inputs(*inputs))) == f(*inputs)` + """ + return args + + def process_outputs(self, outputs: Any) -> Any: + """ + Transforms the outputs of the graph to be identical to the codegen. + + See ``process_inputs`` for more details. + """ + return outputs + + def additional_globals(self) -> list[tuple[str, Any]]: + """ + If your codegen uses extra global values, add tuples of (identifier,reference to the value) here. + For example, return ['List', typing.List] if you need ``List`` in the global context. + """ + return [] + + def _gen_python_code( + self, + nodes, + root_module: str, + namespace: _Namespace, + *, + verbose: bool = False, + include_stride: bool = False, + include_device: bool = False, + colored: bool = False, + # Render each argument on its own line + expanded_def: bool = False, + record_func: bool = False, + ) -> PythonCode: + free_vars: list[str] = [] + body: list[str] = [] + globals_: dict[str, Any] = {} + wrapped_fns: dict[str, None] = {} + + # Wrap string in list to pass by reference + maybe_return_annotation: list[str] = [""] + include_stride = include_stride or ( + os.environ.get("FX_GRAPH_SHOW_STRIDE", "0") == "1" + ) + include_device = include_device or ( + os.environ.get("FX_GRAPH_SHOW_DEVICE", "0") == "1" + ) + include_meta = os.environ.get("FX_GRAPH_SHOW_META", "0") == "1" + + def add_global(name_hint: str, obj: Any): + """Add an obj to be tracked as a global. + + We call this for names that reference objects external to the + Graph, like functions or types. + + Returns: the global name that should be used to reference 'obj' in generated source. + """ + if ( + _is_from_torch(obj) and obj != torch.device + ): # to support registering torch.device + # HACK: workaround for how torch custom ops are registered. We + # can't import them like normal modules so they must retain their + # fully qualified name. + return _get_qualified_name(obj) + + # normalize the name hint to get a proper identifier + global_name = namespace.create_name(name_hint, obj) + + if global_name in globals_: + assert globals_[global_name] == obj + return global_name + globals_[global_name] = obj + return global_name + + # Pre-fill the globals table with registered builtins. + for name, (_, obj) in _custom_builtins.items(): + add_global(name, obj) + + def type_repr(o: Any): + if o == (): + # Empty tuple is used for empty tuple type annotation Tuple[()] + return "()" + + typename = _type_repr(o) + if isinstance(o, types.UnionType) and "|" in typename: + # str | int + args = [type_repr(arg) for arg in o.__args__] + return "|".join(args) + + if origin_type := getattr(o, "__origin__", None): + # list[...], typing.List[...], TensorType[...] + + if isinstance(o, typing._GenericAlias): # type: ignore[attr-defined] + # This is a generic pre-PEP585 type, e.g. typing.List[torch.Tensor] + origin_type = _origin_type_map.get(origin_type, origin_type) + + origin_typename = add_global(_type_repr(origin_type), origin_type) + + if hasattr(o, "__args__") and o.__args__: + args = [type_repr(arg) for arg in o.__args__] + return f"{origin_typename}[{','.join(args)}]" + else: + return origin_typename + + # Common case: this is a regular module name like 'foo.bar.baz' + return add_global(typename, o) + + if colored: + red = _color_fns["red"] + dim_green = _color_fns["dim_green"] + dim = _color_fns["dim"] + dim_blue = _color_fns["dim_blue"] + blue = _color_fns["blue"] + else: + red = _identity + dim_green = _identity + dim = _identity + dim_blue = _identity + blue = _identity + + def _get_repr(arg: Any) -> str: + if isinstance(arg, Node): # first because common + return repr(arg) + elif isinstance(arg, tuple) and hasattr(arg, "_fields"): + # Handle NamedTuples (if it has `_fields`) via add_global. + qualified_name = _get_qualified_name(type(arg)) + global_name = add_global(qualified_name, type(arg)) + return f"{global_name}{repr(tuple(arg))}" + elif isinstance( + arg, (torch._ops.OpOverload, torch._ops.HigherOrderOperator) + ): + qualified_name = _get_qualified_name(arg) + global_name = add_global(qualified_name, arg) + return f"{global_name}" + elif isinstance(arg, enum.Enum): + cls = arg.__class__ + clsname = add_global(cls.__name__, cls) + return f"{clsname}.{arg.name}" + elif isinstance(arg, torch.Tensor): + size = list(arg.size()) + dtype = str(arg.dtype).split(".")[-1] + return f"torch.Tensor(size={size}, dtype={dtype})" + elif isinstance(arg, tuple): + if len(arg) == 1: + return f"({_get_repr(arg[0])},)" + else: + return "(" + ", ".join(_get_repr(a) for a in arg) + ")" + elif isinstance(arg, list): + return "[" + ", ".join(_get_repr(a) for a in arg) + "]" + elif isinstance(arg, slice): + return f"slice({_get_repr(arg.start)}, {_get_repr(arg.stop)}, {_get_repr(arg.step)})" + elif is_opaque_value_type(type(arg)): + arg_type = type(arg) + add_global(arg_type.__name__, arg_type) + return repr(arg) + else: + return blue(repr(arg)) + + def _format_args( + args: tuple[Argument, ...], kwargs: dict[str, Argument] + ) -> str: + res = [_get_repr(a) for a in args] + res.extend([f"{k} = {_get_repr(v)}" for k, v in kwargs.items()]) + return ", ".join(res) + + # Run through reverse nodes and record the first instance of a use + # of a given node. This represents the *last* use of the node in the + # execution order of the program, which we will use to free unused + # values + node_to_last_use: dict[Node, Node] = {} + user_to_last_uses: dict[Node, list[Node]] = {} + + def register_last_uses(n: Node, user: Node): + if n not in node_to_last_use: + node_to_last_use[n] = user + user_to_last_uses.setdefault(user, []).append(n) + + for node in reversed(nodes): + for input_node in node._input_nodes: + register_last_uses(input_node, node) + + def delete_unused_values(user: Node): + """ + Delete values after their last use. This ensures that values that are + not used in the remainder of the code are freed and the memory usage + of the code is optimal. + """ + if user.op == "placeholder": + return + if user.op == "output": + body.append("\n") + return + nodes_to_delete = user_to_last_uses.get(user, []) + + if len(user.users.keys()) == 0: + # This node is not used by any others. however it's also not + # removed by DCE since side-effect. We want to free it's outputs + # right after its execution done to save memory. + nodes_to_delete.append(user) + + if len(nodes_to_delete): + to_delete_str = " = ".join( + [repr(n) for n in nodes_to_delete] + ["None"] + ) + body.append(f"; {dim(to_delete_str)}\n") + else: + body.append("\n") + + prev_summary_str = None + + def append_stacktrace_summary(node: Node): + """ + Append a summary of the stacktrace to the generated code. This is + useful for debugging. + """ + nonlocal prev_summary_str + + if node.op not in {"placeholder", "output"}: + annotation_str = "" + annotation = node.meta.get("custom", {}) + if annotation: + annotation_str = f" Annotation: {annotation}" + + stack_trace_str = "No stacktrace found for following nodes" + if stack_trace := node.stack_trace: + if parsed_stack_trace := _parse_stack_trace(stack_trace): + stack_trace_str = parsed_stack_trace.get_summary_str() + + maybe_recompute_info = "" + if hasattr(node, "meta") and node.meta: + # recompute tags are generated by torch.compile and put in the joint graph. + # These tags are load bearing enough that we want them to show up by default + # in tlparse, when you run torch.compile. + recompute = node.meta.get("recompute", None) + ac_graph_id = node.meta.get("ac_graph_id", None) + + if recompute is not None and ac_graph_id is not None: + maybe_recompute_info = f" # ac_graph_id: {str(ac_graph_id)} - {str(recompute.name)}" + elif recompute is not None: + maybe_recompute_info = f" # recompute: {str(recompute.name)}" + elif ac_graph_id is not None: + maybe_recompute_info = f" # ac_graph_id: {str(ac_graph_id)}" + + summary_str = f"\n{dim(f'#{annotation_str}{maybe_recompute_info} {stack_trace_str}')}\n" + + if summary_str != prev_summary_str: + prev_summary_str = summary_str + body.append(summary_str) + + def stringify_shape(shape: Iterable) -> str: + return f"[{', '.join([str(x) for x in shape])}]" + + def emit_node(node: Node): + maybe_type_annotation = ( + "" if node.type is None else f" : {type_repr(node.type)}" + ) + maybe_comment = "" + + if verbose: + # override annotation with more detailed information + try: + from torch.distributed.tensor._api import DTensor, DTensorSpec + + dtensorspec_format_shard_order_str = ( + DTensorSpec.format_shard_order_str + ) + except ModuleNotFoundError: + DTensor = None # type: ignore[assignment,misc] + dtensorspec_format_shard_order_str = None + from torch.fx.experimental.proxy_tensor import py_sym_types + from torch.fx.passes.shape_prop import TensorMetadata + + meta_val = node.meta.get( + "val", + node.meta.get("tensor_meta", node.meta.get("example_value", None)), + ) + + def _tensor_annotation(t: torch.Tensor) -> str: + stride = stringify_shape(t.stride()) if include_stride else "" + device = f"{t.device}" if include_device else "" + return ( + f"{red(dtype_abbrs[t.dtype])}" + f"{blue(stringify_shape(t.shape))}" + f"{dim_blue(stride)}" + f"{dim_green(device)}" + ) + + # use string as annotation, to make it valid python code + if isinstance(meta_val, torch.Tensor) and meta_val.layout not in ( + torch.sparse_csc, + torch.sparse_csr, + ): + # Fake tensors cause tests to wobble, so do not custom print them. + is_plain = type(meta_val) is torch.Tensor or isinstance( + meta_val, torch._subclasses.FakeTensor + ) + core = _tensor_annotation(meta_val) + if is_plain: + maybe_type_annotation = f': "{core}"' + elif type(meta_val) is DTensor: + assert dtensorspec_format_shard_order_str is not None + dtensor_meta = dtensorspec_format_shard_order_str( + meta_val._spec.placements, # type: ignore[attr-defined] + meta_val._spec.shard_order, # type: ignore[attr-defined] + ) + cls = meta_val.__class__.__name__ + maybe_type_annotation = ( + f': "{cls}({core}, {dim_green(dtensor_meta)})"' + ) + else: + cls = meta_val.__class__.__name__ + maybe_type_annotation = f': "{cls}({core})"' + + elif isinstance(meta_val, py_sym_types): + val_str = CodeGen._sym_repr(meta_val) + maybe_type_annotation = f': "Sym({val_str})"' + + elif isinstance(meta_val, TensorMetadata): + maybe_type_annotation = f': "{dtype_abbrs[meta_val.dtype]}{stringify_shape(meta_val.shape)}"' + + desc = None + if expanded_def: + desc = node.meta.get("desc", None) + if desc is not None and node.op == "placeholder": + maybe_comment += f" # {desc}" + # output is handled specially + + if include_meta and hasattr(node, "meta") and node.meta: + body.append('"""\n') + for k, v in node.meta.items(): + # use str over repr since repr is susceptible to sympy + # errors such as "cannot determine truth value of Relational" + # Pretty print the high-level dict with str() for values + body.append( + f"{k}: {pprint.pformat(str(v), width=80, compact=True)}\n" + ) + body.append('"""\n') + + if node.op == "placeholder": + assert isinstance(node.target, str) + maybe_default_arg = ( + "" if not node.args else f" = {_get_repr(node.args[0])}" + ) + free_vars.append( + f"{node.target}{maybe_type_annotation}{maybe_default_arg}{maybe_comment}" + ) + raw_name = node.target.replace("*", "") + if raw_name != repr(node): + body.append(f"{repr(node)} = {raw_name}\n") + return + elif node.op == "call_method": + assert isinstance(node.target, str) + body.append( + f"{repr(node)}{maybe_type_annotation} = {_format_target(_get_repr(node.args[0]), node.target)}" + f"({_format_args(node.args[1:], node.kwargs)})" + ) + return + elif node.op == "call_function": + assert callable(node.target) + # pretty print operators + if ( + getattr(node.target, "__module__", "") == "_operator" + and node.target.__name__ in magic_methods + ): + assert isinstance(node.args, tuple) + body.append( + f"{repr(node)}{maybe_type_annotation} = " + f"{magic_methods[node.target.__name__].format(*(_get_repr(a) for a in node.args))}" + ) + return + + # pretty print inplace operators; required for jit.script to work properly + # not currently supported in normal FX graphs, but generated by torchdynamo + if ( + getattr(node.target, "__module__", "") == "_operator" + and node.target.__name__ in inplace_methods + ): + body.append( + f"{inplace_methods[node.target.__name__].format(*(_get_repr(a) for a in node.args))}; " + f"{repr(node)}{maybe_type_annotation} = {_get_repr(node.args[0])}" + ) + return + + qualified_name = _get_qualified_name(node.target) + global_name = add_global(qualified_name, node.target) + # special case for getattr: node.args could be 2-argument or 3-argument + # 2-argument: attribute access; 3-argument: fall through to attrib function call with default value + if ( + global_name == "getattr" + and isinstance(node.args, tuple) + and isinstance(node.args[1], str) + and node.args[1].isidentifier() + and len(node.args) == 2 + ): + body.append( + f"{repr(node)}{maybe_type_annotation} = {_format_target(_get_repr(node.args[0]), node.args[1])}" + ) + return + body.append( + f"{repr(node)}{maybe_type_annotation} = {global_name}({_format_args(node.args, node.kwargs)})" + ) + if node.meta.get("is_wrapped", False): + wrapped_fns.setdefault(global_name) + return + elif node.op == "call_module": + assert isinstance(node.target, str) + body.append( + f"{repr(node)}{maybe_type_annotation} = " + f"{_format_target(root_module, node.target)}({_format_args(node.args, node.kwargs)})" + ) + return + elif node.op == "get_attr": + assert isinstance(node.target, str) + body.append( + f"{repr(node)}{maybe_type_annotation} = {_format_target(root_module, node.target)}" + ) + return + elif node.op == "output": + if node.type is not None: + maybe_return_annotation[0] = f" -> {type_repr(node.type)}" + body.append( + self._call_method_with_signature_check( + self.generate_output, + node.args[0], + descs=desc if expanded_def else None, + ) + ) + return + raise NotImplementedError(f"node: {node.op} {node.target}") + + if record_func: + body.append( + "_rf = torch._C._profiler._RecordFunctionFast('## ENTER_GRAPH_PLACEHOLDER_KEY ##'); _rf.__enter__()\n" + ) + for i, node in enumerate(nodes): + # NOTE: emit_node does not emit a string with newline. It depends + # on delete_unused_values to append one + if verbose: + append_stacktrace_summary(node) + # emit a counter comment to keep track of + # node index, which will be deleted later + # after going through _body_transformer + body.append(f"# COUNTER: {i}\n") + do_record = record_func and node.op in ( + "call_function", + "call_method", + "call_module", + ) + if do_record: + # The double hash ## convention is used by post-processing to find the fx markers + body.append( + f"_rf_{node.name} = torch._C._profiler._RecordFunctionFast('## {i} ##'); _rf_{node.name}.__enter__()\n" + ) + emit_node(node) + delete_unused_values(node) + if do_record: + body.append(f"_rf_{node.name}.__exit__(None, None, None)\n") + if record_func: + body.append("_rf.__exit__(None, None, None)\n") + + if len(body) == 0: + # If the Graph has no non-placeholder nodes, no lines for the body + # have been emitted. To continue to have valid Python code, emit a + # single pass statement + body.append("pass\n") + + if len(wrapped_fns) > 0: + wrap_name = add_global("wrap", torch.fx.wrap) + wrap_stmts = "\n".join([f'{wrap_name}("{name}")' for name in wrapped_fns]) + else: + wrap_stmts = "" + + if self._body_transformer: + body = self._body_transformer(body) + + for name, value in self.additional_globals(): + add_global(name, value) + + prologue = self._call_method_with_signature_check( + self.gen_fn_def, + free_vars, + maybe_return_annotation[0], + expanded_def=expanded_def, + ) + + # remove counter and generate lineno to node index mapping + lineno_map: dict[int, Optional[int]] = {} + prologue_len = prologue.count("\n") + 1 + new_lines: list[str] = [] + cur_idx = None + for line in "".join(body).split("\n"): + counter = _counter_regexp.search(line) + if counter is not None: + cur_idx = int(counter.group(1)) + else: + lineno_map[len(new_lines) + prologue_len] = cur_idx + new_lines.append(line) + + code = "\n".join(new_lines).lstrip("\n") + code = "\n".join(" " + line for line in code.split("\n")) + + fn_code = f""" +{wrap_stmts} + +{prologue} +{code}""" + # The +4 accounts for the empty lines before prologue in fn_code + prologue_start = wrap_stmts.count("\n") + 4 + return PythonCode( + fn_code, + globals_, + _lineno_map=lineno_map, + _prologue_start=prologue_start, + ) + + +# Ideally, we'd like to refactor all of the pytree logic into this codegen +# class. Unfortunately, there are 3 areas we currently need extra logic in FX. +# 1. In the initial symbolic trace, the pytree logic is tied up with `concrete_args`. +# 2. In the FX graph, we need to access 2 attributes - in_spec and out_spec. +# Since we can't access .graph within the FX forward, we need to copy the attribute to the module. +# 3. We currently can't register the pytree imports with `add_global` - not sure why. +class _BoxedCodeGen(CodeGen): + """ + CodeGen subclass that generates code using the "boxed" calling convention. + + The boxed calling convention takes a single list argument and clears it + after extracting the arguments, which allows for early deallocation of + input tensors. + """ + + def gen_fn_def( + self, free_vars, maybe_return_annotation, *, expanded_def: bool = False + ): + """ + Generate function definition for boxed calling convention. + + Instead of taking individual arguments, the generated function takes + a single 'args_list' parameter, extracts placeholder values from it, + and clears the list. + """ + # Generate the function signature with args_list parameter + fn_def = f"def {self._func_name}(self, args_list){maybe_return_annotation}:" + + if free_vars: + # This is horribly manual but we don't get the "raw" free vars + # without a bigger refactor. + placeholder_vars = [ + v.split(":")[0].split("=")[0].strip() for v in free_vars if v != "self" + ] + + if placeholder_vars: + fn_def += "\n args_iter = iter(args_list)" + for var in placeholder_vars: + fn_def += f"\n {var} = next(args_iter)" + fn_def += "\n args_list.clear()" + + return fn_def + + +class _PyTreeCodeGen(CodeGen): + def __init__(self, pytree_info: _PyTreeInfo): + super().__init__() + self.pytree_info: _PyTreeInfo = pytree_info + + def process_inputs(self, *inputs: Any) -> Any: + flat_args = pytree.arg_tree_leaves(*inputs) + return flat_args + + def process_outputs(self, out: Any) -> Any: + if self.pytree_info is None or self.pytree_info.out_spec is None: + return out + if not isinstance(out, (list, tuple)): + out = [out] + assert self.pytree_info.out_spec is not None + return pytree.tree_unflatten(out, self.pytree_info.out_spec) + + def _format_annotations(self, free_vars: list[str], expanded_def: bool) -> str: + """Helper to format annotations for variables in pytree codegen.""" + if not free_vars: + return "" + + has_annotation = [x for x in free_vars if ":" in x] + if not has_annotation: + return "" + + if expanded_def: + return "\n " + "\n ".join(has_annotation) + else: + return "\n " + "".join(x + "; " for x in has_annotation) + "\n" + + def gen_var_bindings(self, fn_args, free_vars, expanded_def) -> str: + in_spec = self.pytree_info.in_spec + # when kwargs is present, in_spec is tuple(args, kwargs) + has_args_kwargs_tuple = ( + in_spec.type is tuple + and in_spec.num_children == 2 + and in_spec.child(0).type is tuple + and in_spec.child(1).type is dict + ) + fn_kwargs = "{}" + fn_signature = f"[{', '.join(fn_args)}], self._in_spec" + if has_args_kwargs_tuple: + count_args = in_spec.child(0).num_children + fn_args = self.pytree_info.orig_args[:count_args] + fn_kwargs = ( + "{" + + ", ".join( + f"'{k}':{v}" + for k, v in zip( + in_spec.child(1).context, + self.pytree_info.orig_args[count_args:], + ) + ) + + "}" + ) + fn_signature = f"([{', '.join(fn_args)}], {fn_kwargs}), self._in_spec" + + # in Python, `var1: annotation1, var2: annotation2 = function_call()` is invalid. + # we need to split it to two lines: + # one for annotation: `var1: annotation1; var2: annotation2;` (note the semicolon) + # one for code: `var1, var2, = function_call()` + without_annotation = [x.split(":")[0].split("#")[0] for x in free_vars] + bindings = self._format_annotations(free_vars, expanded_def) + bindings += f""" + {", ".join(without_annotation)}, = fx_pytree.tree_flatten_spec({fn_signature})""" + return bindings + + def gen_fn_def( + self, free_vars, maybe_return_annotation, *, expanded_def: bool = False + ): + # Given a user function/model: + # myargs = [myargs0, myargs1] + # mykwargs = {'mykwargs0': ..., 'mykwargs1': ...} + # def forward(self, mypos, *myargs, mykey=None, **mykwargs): + # + # The generated code flattens all keywords into positional arguments for `forward()` + # e.g forward(self, mypos, myargs0, myargs1, mykey, mykwargs0, mykwargs1): + # + # Within `forward`, `tree_flatten_spec``still parses args and kwargs separately + # e.g. tree_flatten_spec(([mypos, myargs0, myargs1], + # {'mykey':mykey, 'mykwargs0':mykwargs0, 'mykwargs1':mykwargs1}), + # self._in_spec) + # + # If the user function/model does not have keywords, the dict is suppressed from tree_flatten_spec + # e.g. tree_flatten_spec([mypos, myargs0, myargs1]), self._in_spec) + if self.pytree_info is None: + return super().gen_fn_def( + free_vars, maybe_return_annotation, expanded_def=expanded_def + ) + + fn_args = self.pytree_info.orig_args + has_orig_self = (fn_args[0] == "self") if len(fn_args) > 0 else False + if has_orig_self: + free_vars.insert(0, "self") + fn_definition = super().gen_fn_def( + fn_args[:], maybe_return_annotation, expanded_def=expanded_def + ) + + if len(free_vars) > 0: # pytree has placeholders in it + fn_definition += self.gen_var_bindings(fn_args, free_vars, expanded_def) + return fn_definition + + def generate_output(self, output_args, *, descs: Optional[Any] = None): + if self.pytree_info and self.pytree_info.out_spec: + if descs is not None and isinstance(output_args, (list, tuple)): + return ( + self._format_multiline_container( + output_args, descs, "return pytree.tree_unflatten(" + ) + + ", self._out_spec)" + ) + else: + return ( + f"return pytree.tree_unflatten({repr(output_args)}, self._out_spec)" + ) + else: + return super().generate_output(output_args, descs=descs) + + +class _ExportCodeGen(_PyTreeCodeGen): + def __init__( + self, + pytree_info: _PyTreeInfo, + in_shuffle_graph: "GraphModule", + out_shuffle_graph: "GraphModule", + tree_leaf_names: list[str], + root: Optional[torch.nn.Module], + ): + super().__init__(pytree_info) + self.in_shuffle_graph = in_shuffle_graph + self.out_shuffle_graph = out_shuffle_graph + self.tree_leaf_names = tree_leaf_names + self.root = root + + def process_inputs(self, *inputs: Any) -> Any: + flat_args = super().process_inputs(*inputs) + if self.root is not None: + flat_args = (self.root, *flat_args) + self.flat_args = flat_args + return self.in_shuffle_graph(*flat_args) + + def process_outputs(self, out: Any) -> Any: + flat_outs = self.out_shuffle_graph(*self.flat_args, *out) + del self.flat_args + ret = super().process_outputs(flat_outs) + return ret + + def gen_fn_def(self, *args, **kwargs) -> str: + fn_def = super().gen_fn_def(*args, **kwargs) + return fn_def + + def gen_var_bindings(self, fn_args, free_vars, expanded_def) -> str: + without_annotation = [x.split(":")[0].split("#")[0] for x in free_vars] + fn_signature: str = f"{', '.join(fn_args)}" + if self.root is not None: + fn_signature = f"self, {fn_signature}" + return f""" + {", ".join(self.tree_leaf_names)}, = pytree.tree_leaves(({fn_signature},)) + {", ".join(without_annotation)}, = self._in_shuffle_graph({", ".join(self.tree_leaf_names)})""" + + def generate_output(self, output_args, *args, **kwargs) -> str: + output = f"self._out_shuffle_graph({', '.join(self.tree_leaf_names)}, {', '.join([str(a) for a in output_args])})" + return f"return pytree.tree_unflatten({output}, self._out_spec)" + + +class _FindNodesLookupTable: + """ + Side table for the graph for the purpose of doing fast queries + """ + + def __init__(self): + self.table: dict[tuple[str, Optional[Target]], dict[Node, None]] = defaultdict( + dict + ) + + def _key(self, node) -> tuple[str, Optional[Target]]: + return (node.op, node.target if node.op == "call_function" else None) + + def __contains__(self, node) -> bool: + return node in self.table[self._key(node)] + + def insert(self, node: Node) -> None: + self.table[self._key(node)][node] = None + + def remove(self, node: Node) -> None: + self.table[self._key(node)].pop(node) + + def find_nodes(self, *, op: str, target: Optional["Target"] = None): + if op == "call_function": + assert target is not None + return [*self.table[(op, target)].keys()] + + if target is None: + return [*self.table[(op, None)].keys()] + + # op is call_method, get_attr, call_module + return [node for node in self.table[(op, None)] if node.target == target] + + +@compatibility(is_backward_compatible=True) +class Graph: + """ + ``Graph`` is the main data structure used in the FX Intermediate Representation. + It consists of a series of ``Node`` s, each representing callsites (or other + syntactic constructs). The list of ``Node`` s, taken together, constitute a + valid Python function. + + For example, the following code + + .. code-block:: python + + import torch + import torch.fx + + + class MyModule(torch.nn.Module): + def __init__(self): + super().__init__() + self.param = torch.nn.Parameter(torch.rand(3, 4)) + self.linear = torch.nn.Linear(4, 5) + + def forward(self, x): + return torch.topk( + torch.sum(self.linear(x + self.linear.weight).relu(), dim=-1), 3 + ) + + + m = MyModule() + gm = torch.fx.symbolic_trace(m) + + Will produce the following Graph:: + + print(gm.graph) + + .. code-block:: text + + graph(x): + %linear_weight : [num_users=1] = self.linear.weight + %add_1 : [num_users=1] = call_function[target=operator.add](args = (%x, %linear_weight), kwargs = {}) + %linear_1 : [num_users=1] = call_module[target=linear](args = (%add_1,), kwargs = {}) + %relu_1 : [num_users=1] = call_method[target=relu](args = (%linear_1,), kwargs = {}) + %sum_1 : [num_users=1] = call_function[target=torch.sum](args = (%relu_1,), kwargs = {dim: -1}) + %topk_1 : [num_users=1] = call_function[target=torch.topk](args = (%sum_1, 3), kwargs = {}) + return topk_1 + + For the semantics of operations represented in the ``Graph``, please see :class:`Node`. + """ + + @compatibility(is_backward_compatible=True) + def __init__( + self, + owning_module: Optional["GraphModule"] = None, + tracer_cls: Optional[type["Tracer"]] = None, + tracer_extras: Optional[dict[str, Any]] = None, + ): + """ + Construct an empty Graph. + """ + self._root: Node = Node(self, "", "root", "", (), {}) + self._used_names: dict[str, int] = {} # base name -> number + self._insert = self._root.prepend + self._len = 0 + self._graph_namespace = _Namespace() + self._owning_module = owning_module + self._tracer_cls = tracer_cls + self._tracer_extras = tracer_extras + self._codegen = CodeGen() + self._co_fields: dict[str, Any] = {} + self._find_nodes_lookup_table = _FindNodesLookupTable() + + @property + def owning_module(self): + return self._owning_module + + @owning_module.setter + def owning_module(self, mod: Optional["GraphModule"]): + self._owning_module = mod + + @property + def nodes(self) -> _node_list: + """ + Get the list of Nodes that constitute this Graph. + + Note that this ``Node`` list representation is a doubly-linked list. Mutations + during iteration (e.g. delete a Node, add a Node) are safe. + + Returns: + + A doubly-linked list of Nodes. Note that ``reversed`` can be called on + this list to switch iteration order. + """ + return _node_list(self) + + @compatibility(is_backward_compatible=False) + def output_node(self) -> Node: + output_node = next(iter(reversed(self.nodes))) + assert output_node.op == "output" + return output_node + + @compatibility(is_backward_compatible=False) + def find_nodes( + self, *, op: str, target: Optional["Target"] = None, sort: bool = True + ): + """ + Allows for fast query of nodes + + Args: + + op (str): the name of the operation + + target (Optional[Target]): the target of the node. For call_function, + the target is required. For other ops, the target is optional. + + sort (bool): whether to return nodes in the order they appear on + on the graph. + + Returns: + + Iterable of nodes with the requested op and target. + """ + node_list = self._find_nodes_lookup_table.find_nodes(op=op, target=target) + if sort: + return sorted(node_list) + return node_list + + @compatibility(is_backward_compatible=True) + def graph_copy( + self, g: "Graph", val_map: dict[Node, Node], return_output_node=False + ) -> "Optional[Argument]": + """ + Copy all nodes from a given graph into ``self``. + + Args: + + g (Graph): The source graph from which to copy Nodes. + + val_map (Dict[Node, Node]): a dictionary that will be populated with a mapping + from nodes in ``g`` to nodes in ``self``. Note that ``val_map`` can be passed + in with values in it already to override copying of certain values. + + Returns: + + The value in ``self`` that is now equivalent to the output value in ``g``, + if ``g`` had an ``output`` node. ``None`` otherwise. + """ + for node in g.nodes: + if node in val_map: + continue + if node.op == "output": + rv = map_arg(node.args[0], lambda n: val_map[n]) + return rv if not return_output_node else (rv, node) + val_map[node] = self.node_copy(node, lambda n: val_map[n]) + return None + + def __deepcopy__(self, memo=None) -> "Graph": + """ + Explicitly implement __deepcopy__ to prevent excessive recursion depth + from the default implementation. This uses graph_copy to copy the nodes + in an iterative way, rather than recursive. It also populates the + memoization table to prevent unnecessary copies (e.g. references to + nodes or other parts of the Graph from a custom GraphModule implementation. + """ + memo = memo if memo else {} + g = Graph(tracer_cls=self._tracer_cls) + output_vals = g.graph_copy(self, val_map=memo, return_output_node=True) + g._codegen = copy.deepcopy(self._codegen) + if output_vals is not None: + assert isinstance(output_vals, tuple) + output_val, old_output_node = output_vals + new_output_node = g.output( + output_val, type_expr=getattr(old_output_node, "type", None) + ) + new_output_node.meta = copy.copy(old_output_node.meta) + return g + + @compatibility(is_backward_compatible=True) + def create_node( + self, + op: str, + target: "Target", + args: Optional[tuple["Argument", ...]] = None, + kwargs: Optional[dict[str, "Argument"]] = None, + name: Optional[str] = None, + type_expr: Optional[Any] = None, + ) -> Node: + """ + Create a ``Node`` and add it to the ``Graph`` at the current insert-point. + Note that the current insert-point can be set via :meth:`Graph.inserting_before` + and :meth:`Graph.inserting_after`. + + Args: + op (str): the opcode for this Node. One of 'call_function', 'call_method', 'get_attr', + 'call_module', 'placeholder', or 'output'. The semantics of these opcodes are + described in the ``Graph`` docstring. + + args (Optional[Tuple[Argument, ...]]): is a tuple of arguments to this node. + + kwargs (Optional[Dict[str, Argument]]): the kwargs of this Node + + name (Optional[str]): an optional string name for the ``Node``. + This will influence the name of the value assigned to in the + Python generated code. + + type_expr (Optional[Any]): an optional type annotation representing the + Python type the output of this node will have. + + Returns: + + The newly-created and inserted node. + """ + # `target in _legal_ops` is checked in Node.__init__ + if not args: + args = () + else: + assert isinstance(args, tuple), "args must be a tuple" + if not kwargs: + kwargs = immutable_dict() + else: + assert isinstance(kwargs, dict), "kwargs must be a dict" + + candidate = name if name is not None else self._target_to_str(target) + name = self._graph_namespace.create_name(candidate, None) + n = Node(self, name, op, target, args, kwargs, type_expr) + + if ( + self.owning_module is not None + and getattr(self.owning_module, "_create_node_hooks", None) is not None + ): + for f in self.owning_module._create_node_hooks: + f(n) + + self._graph_namespace.associate_name_with_obj(name, n) + + self._insert(n) + self._find_nodes_lookup_table.insert(n) + self._len += 1 + return n + + @compatibility(is_backward_compatible=False) + def process_inputs(self, *args): + """ + Processes args so that they can be passed to the FX graph. + """ + return self._codegen.process_inputs(*args) + + @compatibility(is_backward_compatible=False) + def process_outputs(self, out): + return self._codegen.process_outputs(out) + + @compatibility(is_backward_compatible=True) + def erase_node(self, to_erase: Node) -> None: + """ + Erases a ``Node`` from the ``Graph``. Throws an exception if + there are still users of that node in the ``Graph``. + + Args: + + to_erase (Node): The ``Node`` to erase from the ``Graph``. + """ + if len(to_erase.users) > 0: + raise RuntimeError( + f"Tried to erase Node {to_erase} but it still had {len(to_erase.users)} " + f"users in the graph: {to_erase.users}!" + ) + if to_erase.graph != self: + raise RuntimeError(f"Attempting to remove {to_erase} from wrong graph!") + if to_erase._erased: + warnings.warn(f"erase_node({to_erase}) on an already erased node") + return + + if ( + self.owning_module is not None + and getattr(self.owning_module, "_erase_node_hooks", None) is not None + ): + for f in self.owning_module._erase_node_hooks: + f(to_erase) + + self._find_nodes_lookup_table.remove(to_erase) + # pyrefly: ignore [missing-attribute] + to_erase._remove_from_list() + to_erase._erased = True # iterators may retain handles to erased nodes + self._len -= 1 + + # Null out this Node's argument nodes so that the Nodes referred to + # can update their ``users`` accordingly + to_erase._update_args_kwargs( + map_arg(to_erase._args, lambda n: None), + map_arg(to_erase._kwargs, lambda n: None), + ) + + @compatibility(is_backward_compatible=True) + def inserting_before(self, n: Optional[Node] = None): + """Set the point at which create_node and companion methods will insert into the graph. + When used within a 'with' statement, this will temporary set the insert point and + then restore it when the with statement exits:: + + with g.inserting_before(n): + ... # inserting before node n + ... # insert point restored to what it was previously + g.inserting_before(n) # set the insert point permanently + + Args: + + n (Optional[Node]): The node before which to insert. If None this will insert before + the beginning of the entire graph. + + Returns: + A resource manager that will restore the insert point on ``__exit__``. + """ + if n is None: + return self.inserting_after(self._root) + assert n.graph == self, "Node to insert before is not in graph." + return _InsertPoint(self, n.prepend) + + @compatibility(is_backward_compatible=True) + def inserting_after(self, n: Optional[Node] = None): + """Set the point at which create_node and companion methods will insert into the graph. + When used within a 'with' statement, this will temporary set the insert point and + then restore it when the with statement exits:: + + with g.inserting_after(n): + ... # inserting after node n + ... # insert point restored to what it was previously + g.inserting_after(n) # set the insert point permanently + + Args: + + n (Optional[Node]): The node before which to insert. If None this will insert after + the beginning of the entire graph. + + Returns: + A resource manager that will restore the insert point on ``__exit__``. + """ + if n is None: + return self.inserting_before(self._root) + assert n.graph == self, "Node to insert after is not in graph." + return _InsertPoint(self, n.append) + + @compatibility(is_backward_compatible=True) + def placeholder( + self, + name: str, + type_expr: Optional[Any] = None, + default_value: Any = inspect.Signature.empty, + ) -> Node: + """ + Insert a ``placeholder`` node into the Graph. A ``placeholder`` represents + a function input. + + Args: + + name (str): A name for the input value. This corresponds to the name + of the positional argument to the function this ``Graph`` represents. + + type_expr (Optional[Any]): an optional type annotation representing the + Python type the output of this node will have. This is needed in some + cases for proper code generation (e.g. when the function is used + subsequently in TorchScript compilation). + + default_value (Any): The default value this function argument should take + on. NOTE: to allow for `None` as a default value, `inspect.Signature.empty` + should be passed as this argument to specify that the parameter does _not_ + have a default value. + + .. note:: + The same insertion point and type expression rules apply for this method + as ``Graph.create_node``. + """ + args = () if default_value is inspect.Signature.empty else (default_value,) + return self.create_node("placeholder", name, args=args, type_expr=type_expr) + + @compatibility(is_backward_compatible=True) + def get_attr(self, qualified_name: str, type_expr: Optional[Any] = None) -> Node: + """ + Insert a ``get_attr`` node into the Graph. A ``get_attr`` ``Node`` represents the + fetch of an attribute from the ``Module`` hierarchy. + + Args: + + qualified_name (str): the fully-qualified name of the attribute to be retrieved. + For example, if the traced Module has a submodule named ``foo``, which has a + submodule named ``bar``, which has an attribute named ``baz``, the qualified + name ``foo.bar.baz`` should be passed as ``qualified_name``. + + type_expr (Optional[Any]): an optional type annotation representing the + Python type the output of this node will have. + + + Returns: + + The newly-created and inserted ``get_attr`` node. + + .. note:: + The same insertion point and type expression rules apply for this method + as ``Graph.create_node``. + """ + + def _get_attr_reference_exists( + mod: torch.nn.Module, qualified_name: str + ) -> bool: + module_path, _, name = qualified_name.rpartition(".") + + try: + submod: torch.nn.Module = mod.get_submodule(module_path) + except AttributeError: + warnings.warn(f"Failed to fetch module {module_path}!") + return False + + if not hasattr(submod, name): + return False + + res = getattr(submod, name) + + if ( + not isinstance(res, torch.nn.Module) + and not isinstance(res, torch.nn.Parameter) + and name not in submod._buffers + ): + return False + + return True + + if self.owning_module and not _get_attr_reference_exists( + self.owning_module, qualified_name + ): + warnings.warn( + "Attempted to insert a get_attr Node with no " + "underlying reference in the owning " + "GraphModule! Call " + "GraphModule.add_submodule to add the " + "necessary submodule, " + "GraphModule.add_parameter to add the " + "necessary Parameter, or " + "nn.Module.register_buffer to add the " + "necessary buffer", + stacklevel=2, + ) + return self.create_node("get_attr", qualified_name, type_expr=type_expr) + + @compatibility(is_backward_compatible=True) + def call_module( + self, + module_name: str, + args: Optional[tuple["Argument", ...]] = None, + kwargs: Optional[dict[str, "Argument"]] = None, + type_expr: Optional[Any] = None, + ) -> Node: + """ + Insert a ``call_module`` ``Node`` into the ``Graph``. A ``call_module`` node + represents a call to the forward() function of a ``Module`` in the ``Module`` + hierarchy. + + Args: + + module_name (str): The qualified name of the ``Module`` in the ``Module`` + hierarchy to be called. For example, if the traced ``Module`` has a + submodule named ``foo``, which has a submodule named ``bar``, the + qualified name ``foo.bar`` should be passed as ``module_name`` to + call that module. + + args (Optional[Tuple[Argument, ...]]): The positional arguments to be passed + to the called method. Note that this should *not* include a ``self`` argument. + + kwargs (Optional[Dict[str, Argument]]): The keyword arguments to be passed + to the called method + + type_expr (Optional[Any]): an optional type annotation representing the + Python type the output of this node will have. + + Returns: + + The newly-created and inserted ``call_module`` node. + + .. note:: + The same insertion point and type expression rules apply for this method + as :meth:`Graph.create_node`. + """ + if self.owning_module and self.owning_module.get_submodule(module_name) is None: + warnings.warn( + "Attempted to insert a call_module Node with " + "no underlying reference in the owning " + "GraphModule! Call " + "GraphModule.add_submodule to add the " + "necessary submodule" + ) + return self.create_node( + "call_module", module_name, args, kwargs, type_expr=type_expr + ) + + @compatibility(is_backward_compatible=True) + def call_method( + self, + method_name: str, + args: Optional[tuple["Argument", ...]] = None, + kwargs: Optional[dict[str, "Argument"]] = None, + type_expr: Optional[Any] = None, + ) -> Node: + """ + Insert a ``call_method`` ``Node`` into the ``Graph``. A ``call_method`` node + represents a call to a given method on the 0th element of ``args``. + + Args: + + method_name (str): The name of the method to apply to the self argument. + For example, if args[0] is a ``Node`` representing a ``Tensor``, + then to call ``relu()`` on that ``Tensor``, pass ``relu`` to ``method_name``. + + args (Optional[Tuple[Argument, ...]]): The positional arguments to be passed + to the called method. Note that this *should* include a ``self`` argument. + + kwargs (Optional[Dict[str, Argument]]): The keyword arguments to be passed + to the called method + + type_expr (Optional[Any]): an optional type annotation representing the + Python type the output of this node will have. + + Returns: + + The newly created and inserted ``call_method`` node. + + .. note:: + The same insertion point and type expression rules apply for this method + as :meth:`Graph.create_node`. + """ + return self.create_node( + "call_method", method_name, args, kwargs, type_expr=type_expr + ) + + @compatibility(is_backward_compatible=True) + def call_function( + self, + the_function: Callable[..., Any], + args: Optional[tuple["Argument", ...]] = None, + kwargs: Optional[dict[str, "Argument"]] = None, + type_expr: Optional[Any] = None, + name: Optional[str] = None, + ) -> Node: + """ + Insert a ``call_function`` ``Node`` into the ``Graph``. A ``call_function`` node + represents a call to a Python callable, specified by ``the_function``. + + Args: + + the_function (Callable[..., Any]): The function to be called. Can be any PyTorch + operator, Python function, or member of the ``builtins`` or ``operator`` + namespaces. + + args (Optional[Tuple[Argument, ...]]): The positional arguments to be passed + to the called function. + + kwargs (Optional[Dict[str, Argument]]): The keyword arguments to be passed + to the called function + + type_expr (Optional[Any]): an optional type annotation representing the + Python type the output of this node will have. + + name (Optional[str]): The name of the node. If not specified, set to None + + Returns: + + The newly created and inserted ``call_function`` node. + + .. note:: + The same insertion point and type expression rules apply for this method + as :meth:`Graph.create_node`. + """ + return self.create_node( + "call_function", the_function, args, kwargs, name=name, type_expr=type_expr + ) + + @compatibility(is_backward_compatible=True) + def node_copy( + self, node: Node, arg_transform: Callable[[Node], "Argument"] = lambda x: x + ) -> Node: + """ + Copy a node from one graph into another. ``arg_transform`` needs to transform arguments from + the graph of node to the graph of self. Example:: + + # Copying all the nodes in `g` into `new_graph` + g: torch.fx.Graph = ... + new_graph = torch.fx.graph() + value_remap = {} + for node in g.nodes: + value_remap[node] = new_graph.node_copy(node, lambda n: value_remap[n]) + + Args: + + node (Node): The node to copy into ``self``. + + arg_transform (Callable[[Node], Argument]): A function that transforms + ``Node`` arguments in node's ``args`` and ``kwargs`` into the + equivalent argument in ``self``. In the simplest case, this should + retrieve a value out of a table mapping Nodes in the original + graph to ``self``. + """ + args = map_arg(node.args, arg_transform) + kwargs = map_arg(node.kwargs, arg_transform) + assert isinstance(args, tuple) + assert isinstance(kwargs, dict) + result_node = self.create_node( + node.op, node.target, args, kwargs, node.name, node.type + ) + result_node.meta = copy.copy(node.meta) + return result_node + + @compatibility(is_backward_compatible=True) + def output(self, result: "Argument", type_expr: Optional[Any] = None): + """ + Insert an ``output`` ``Node`` into the ``Graph``. An ``output`` node represents + a ``return`` statement in Python code. ``result`` is the value that should + be returned. + + Args: + + result (Argument): The value to be returned. + + type_expr (Optional[Any]): an optional type annotation representing the + Python type the output of this node will have. + + .. note:: + + The same insertion point and type expression rules apply for this method + as ``Graph.create_node``. + """ + return self.create_node( + op="output", target="output", args=(result,), type_expr=type_expr + ) + + def _target_to_str(self, target: Optional[Target]) -> str: + if callable(target): + op = target.__name__ + else: + assert isinstance(target, str) + op = target + if _is_magic(op): + op = op[2:-2] + op = _snake_case(op) + return op + + @compatibility(is_backward_compatible=True) + def python_code( + self, + root_module: str, + *, + verbose: bool = False, + include_stride: bool = False, + include_device: bool = False, + colored: bool = False, + expanded_def: bool = False, + record_func: bool = False, + ) -> PythonCode: + """ + Turn this ``Graph`` into valid Python code. + + Args: + + root_module (str): The name of the root module on which to look-up + qualified name targets. This is usually 'self'. + + Returns: + + A PythonCode object, consisting of two fields: + src: the Python source code representing the object + globals: a dictionary of global names in `src` -> the objects that they reference. + """ + # NOTE: [Graph Namespaces] + # + # There are two types of symbols in generated Python source code: + # locals and globals. + # Locals are locally defined by the output of a node in the Graph. + # Globals are references to external objects, like functions or types. + # + # When generating Python code, we need to make sure to name things + # appropriately. In particular: + # - All names should be unique, to avoid weird shadowing bugs. + # - These names need to be consistent, e.g. a object should always be + # referenced by the same name. + # + # To do this, we create a new namespace just for this source. All names + # that get printed must come from this namespace. + # + # Why can't we reuse node.name? Because it was generated within the + # namespace `self._graph_namespace`. In order to provide uniqueness + # over both locals (node.name) *and* globals, we create a completely + # new namespace to put all identifiers in. + namespace = _Namespace() + + # Override Node's repr to generate a valid name within our namespace. + # Since repr() is designed to produce a valid Python expression, it + # makes sense to reuse it. This way, it's easy to print something like + # Tuple[Node, Node] by simply calling repr() on it. Node's __repr__ is + # implemented cooperatively to allow this. + def node_repr(n: Node): + return namespace.create_name(n.name, n) + + @contextmanager + def override_node_repr(graph: Graph): + orig_repr_fns = {} + for node in graph.nodes: + orig_repr_fns[node] = node._repr_fn + node._repr_fn = node_repr + try: + yield None + finally: + # restore the original repr functions + for node in graph.nodes: + node._repr_fn = orig_repr_fns[node] + + with override_node_repr(self): + return self._python_code( + root_module, + namespace, + verbose=verbose, + include_stride=include_stride, + include_device=include_device, + colored=colored, + expanded_def=expanded_def, + record_func=record_func, + ) + + def _python_code( + self, + root_module: str, + namespace: _Namespace, + *, + verbose: bool = False, + include_stride: bool = False, + include_device: bool = False, + colored: bool = False, + expanded_def: bool = False, + record_func: bool = False, + ) -> PythonCode: + return self._codegen._gen_python_code( + self.nodes, + root_module, + namespace, + verbose=verbose, + include_stride=include_stride, + include_device=include_device, + colored=colored, + expanded_def=expanded_def, + record_func=record_func, + ) + + def __str__(self) -> str: + """ + Return a human-readable (not machine-readable) string representation + of this Graph + """ + placeholder_names: list[str] = [] + # This is a one-element array just so ``format_node`` can modify the closed + # over value + maybe_return_typename: list[str] = [""] + + node_strs = [node.format_node(placeholder_names) for node in self.nodes] + param_str = ", ".join(placeholder_names) + s = f"graph({param_str}){maybe_return_typename[0]}:" + for node_str in node_strs: + if node_str: + s += "\n " + node_str + return s + + @compatibility(is_backward_compatible=True) + def print_tabular(self): + """ + Prints the intermediate representation of the graph in tabular + format. Note that this API requires the ``tabulate`` module to be + installed. + """ + try: + from tabulate import tabulate + except ImportError: + print( + "`print_tabular` relies on the library `tabulate`, " + "which could not be found on this machine. Run `pip " + "install tabulate` to install the library." + ) + raise + + node_specs = [[n.op, n.name, n.target, n.args, n.kwargs] for n in self.nodes] + print( + tabulate(node_specs, headers=["opcode", "name", "target", "args", "kwargs"]) + ) + + @compatibility(is_backward_compatible=True) + def lint(self): + """ + Runs various checks on this Graph to make sure it is well-formed. In + particular: + - Checks Nodes have correct ownership (owned by this graph) + - Checks Nodes appear in topological order + - If this Graph has an owning GraphModule, checks that targets + exist in that GraphModule + """ + + # Check topo order + def check_arg(arg: Node, n: Optional[Node] = None) -> None: + context_str = f" of Node '{n}' " if n else " " + if arg.graph is not self: + raise RuntimeError( + f"Argument '{arg}'{context_str}does not belong to this Graph, " + f"but was used as an argument! If you are copying nodes from another graph, make " + f"sure to use ``arg_transform`` on node_copy() to remap values\n{self}" + ) + if arg not in seen_values: + raise RuntimeError( + f"Argument '{arg}'{context_str}was used before it has been " + f"defined! Please check that Nodes in the graph are topologically ordered\n{self}" + ) + + seen_names: set[str] = set() + seen_values: set[Node] = set() + for node in self.nodes: + if node.op not in _legal_ops: + raise RuntimeError(f"Node {node} had unknown opcode {node.op}!") + if node.graph is not self: + raise RuntimeError(f"Node '{node}' does not belong to this Graph!") + if node not in self._find_nodes_lookup_table: + raise RuntimeError(f"Node '{node}' is not added to the side table") + for arg in node._input_nodes: + check_arg(arg, node) + seen_values.add(node) + + if node.name in seen_names: + raise RuntimeError(f"Node redefined name {node.name}!") + seen_names.add(node.name) + + # Check targets are legit + if self.owning_module: + for node in self.nodes: + if node.op == "call_function": + if not callable(node.target): + raise ValueError( + f"Node {node} target {node.target} has type {torch.typename(node.target)} but " + "a Callable is expected" + ) + else: + if not isinstance(node.target, str): + raise ValueError( + f"Node {node} target {node.target} has type {torch.typename(node.target)} but " + "a str is expected" + ) + if node.op in ["get_attr", "call_module"]: + # pyrefly: ignore [missing-attribute] + target_atoms = node.target.split(".") + m_itr = self.owning_module + for i, atom in enumerate(target_atoms): + new_m_itr = getattr(m_itr, atom, None) + seen_qualname = ".".join(target_atoms[:i]) + if new_m_itr is None: + raise RuntimeError( + f"Node {node} target {node.target} references nonexistent attribute " + f"{atom} of {seen_qualname}" + ) + if node.op == "call_module" and not isinstance( + new_m_itr, torch.nn.Module + ): + raise RuntimeError( + f"Node {node} target {node.target} {atom} of {seen_qualname} does " + "not reference an nn.Module" + ) + + m_itr = new_m_itr + + @compatibility(is_backward_compatible=True) + def eliminate_dead_code( + self, is_impure_node: Optional[Callable[[Node], bool]] = None + ) -> bool: + """ + Remove all dead code from the graph, based on each node's number of + users, and whether the nodes have any side effects. The graph must be + topologically sorted before calling. + + Args: + is_impure_node (Optional[Callable[[Node], bool]]): A function that returns + whether a node is impure. If this is None, then the default behavior is to + use Node.is_impure. + + Returns: + bool: Whether the graph was changed as a result of the pass. + + Example: + + Before dead code is eliminated, `a` from `a = x + 1` below has no users + and thus can be eliminated from the graph without having an effect. + + .. code-block:: python + + def forward(self, x): + a = x + 1 + return x + self.attr_1 + + After dead code is eliminated, `a = x + 1` has been removed, and the rest + of `forward` remains. + + .. code-block:: python + + def forward(self, x): + return x + self.attr_1 + + .. warning:: + + Dead code elimination has some heuristics to avoid removing + side-effectful nodes (see Node.is_impure) but in general coverage + is very bad, so you should assume that this method is not sound + to call unless you know that your FX graph consists entirely + of functional operations or you supply your own custom + function for detecting side-effectful nodes. + """ + from torch.utils._ordered_set import OrderedSet + + # Lint the graph first to make sure its topologically sorted, otherwise + # DCE below will not behave as expected. + self.lint() + + impure_random = True + if torch._guards.TracingContext.try_get(): + impure_random = torch._inductor.config.fallback_random + + def has_side_effect(node): + if is_impure_node is not None: + return is_impure_node(node) + return node.is_impure(impure_random) + + # Reverse iterate so that when we remove a node, any nodes used as an + # input to that node have an updated user count that no longer reflects + # the removed node. + removed_nodes = set() + for node in reversed(self.nodes): + if not has_side_effect(node) and len(node.users) == 0: + self.erase_node(node) + removed_nodes.add(node.name) + + changed = len(removed_nodes) > 0 + if changed: + log.info("The following nodes were dead code eliminated: %s", removed_nodes) + + # Call DCE on the subgraphs + if self.owning_module is not None: + subgraph_names = OrderedSet( + x.target for x in self.find_nodes(op="get_attr") + ) + for child_name, child_module in self.owning_module.named_children(): + # Sometimes an owning_module can have unused children. Skip them + # by checking them from get_attr node targets. + if child_name in subgraph_names and isinstance( + child_module, torch.fx.GraphModule + ): + changed |= child_module.graph.eliminate_dead_code() + child_module.recompile() + + return changed + + @compatibility(is_backward_compatible=False) + def set_codegen(self, codegen: CodeGen): + self._codegen = codegen + + @compatibility(is_backward_compatible=False) + def on_generate_code( + self, + make_transformer: Callable[[Optional[TransformCodeFunc]], TransformCodeFunc], + ): + """Register a transformer function when python code is generated + + Args: + make_transformer (Callable[[Optional[TransformCodeFunc]], TransformCodeFunc]): + a function that returns a code transformer to be registered. + This function is called by `on_generate_code` to obtain the + code transformer. + + This function is also given as its input the currently + registered code transformer (or None if nothing is registered), + in case it is not desirable to overwrite it. This is useful to + chain code transformers together. + + Returns: + a context manager that when used in a `with` statement, to automatically + restore the previously registered code transformer. + + Example: + + .. code-block:: python + + + gm: fx.GraphModule = ... + + + # This is a code transformer we want to register. This code + # transformer prepends a pdb import and trace statement at the very + # beginning of the generated torch.fx code to allow for manual + # debugging with the PDB library. + def insert_pdb(body): + return ["import pdb; pdb.set_trace()\\n", *body] + + + # Registers `insert_pdb`, and overwrites the current registered + # code transformer (given by `_` to the lambda): + gm.graph.on_generate_code(lambda _: insert_pdb) + + # Or alternatively, registers a code transformer which first + # runs `body` through existing registered transformer, then + # through `insert_pdb`: + gm.graph.on_generate_code( + lambda current_trans: ( + lambda body: insert_pdb( + current_trans(body) if current_trans else body + ) + ) + ) + + gm.recompile() + gm(*inputs) # drops into pdb + + + This function can also be used as a context manager, with the benefit to + automatically restores the previously registered code transformer: + + .. code-block:: python + + # ... continue from previous example + + with gm.graph.on_generate_code(lambda _: insert_pdb): + # do more stuff with `gm`... + gm.recompile() + gm(*inputs) # drops into pdb + + # now previous code transformer is restored (but `gm`'s code with pdb + # remains - that means you can run `gm` with pdb here too, until you + # run next `recompile()`). + """ + on_gen_code_old = self._codegen._body_transformer + self._codegen._body_transformer = make_transformer(on_gen_code_old) + + @contextlib.contextmanager + def on_generate_code_context_manager(): + try: + yield + finally: + self._codegen._body_transformer = on_gen_code_old + + return on_generate_code_context_manager() + + def _clear_nodes(self) -> None: + for node in reversed(self.nodes): + node.meta.clear() + self.erase_node(node) + + +@contextmanager +def _override_sym_repr( + override: Callable[["torch.types.PySymType"], str], +) -> Iterator[None]: + tmp = CodeGen._sym_repr + try: + CodeGen._sym_repr = override + yield + finally: + CodeGen._sym_repr = tmp + + +def _identity(x): + return x + + +def _make_color_fn(code): + def f(s): + reset = "\033[0m" + return f"{code}{s}{reset}" + + return f + + +_color_codes = { + "yellow": "\033[33m", + "cyan": "\033[36m", + "green": "\033[32m", + "blue": "\033[34m", + "red": "\033[31m", + "dim": "\033[2m", + "dim_blue": "\033[2m\033[34m", + "dim_green": "\033[2m\033[32m", +} +_color_fns = {k: _make_color_fn(v) for k, v in _color_codes.items()} +_counter_regexp = re.compile(r"# COUNTER: (\d+)") + + +reflectable_magic_methods = { + "add": "{} + {}", + "sub": "{} - {}", + "mul": "{} * {}", + "floordiv": "{} // {}", + "truediv": "{} / {}", + "div": "{} / {}", + "mod": "{} % {}", + "pow": "{} ** {}", + "lshift": "{} << {}", + "rshift": "{} >> {}", + "and_": "{} & {}", + "or_": "{} | {}", + "xor": "{} ^ {}", + "getitem": "{}[{}]", + "matmul": "{} @ {}", +} + +magic_methods = { + "eq": "{} == {}", + "ne": "{} != {}", + "lt": "{} < {}", + "gt": "{} > {}", + "le": "{} <= {}", + "ge": "{} >= {}", + "pos": "+{}", + "neg": "-{}", + "invert": "~{}", + **reflectable_magic_methods, +} + +inplace_methods = { + "iadd": "{} += {}", + "iand": "{} &= {}", + "ifloordiv": "{} //= {}", + "ilshift": "{} <<= {}", + "imod": "{} %= {}", + "imul": "{} *= {}", + "imatmul": "{} @= {}", + "ior": "{} |= {}", + "ipow": "{} **= {}", + "irshift": "{} >>= {}", + "isub": "{} -= {}", + "itruediv": "{} /= {}", + "ixor": "{} ^= {}", + "setitem": "{}[{}] = {}", +} diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/graph_module.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/graph_module.py new file mode 100644 index 0000000000000000000000000000000000000000..ab33d7bf321c9ba4e41eed45732c91e38c545593 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/graph_module.py @@ -0,0 +1,1183 @@ +# mypy: allow-untyped-defs +import base64 +import contextlib +import copy +import hashlib +import itertools +import linecache +import os +import sys +import traceback +import warnings +from collections.abc import Callable +from pathlib import Path +from typing import Any, Optional, Union + +import torch +import torch.nn as nn +import torch.overrides +from torch.nn.modules.module import _addindent +from torch.package import Importer, PackageExporter, PackageImporter, sys_importer + +from ._compatibility import compatibility +from .experimental import _config as fx_experimental_config +from .graph import ( + _BoxedCodeGen, + _custom_builtins, + _is_from_torch, + _override_sym_repr, + _PyTreeCodeGen, + Graph, + PythonCode, +) + + +__all__ = [ + "reduce_graph_module", + "reduce_package_graph_module", + "GraphModule", +] + +_USER_PRESERVED_ATTRIBUTES_KEY = "_user_preserved_attributes" +FX_GRAPH_MODULE_FILE_PREFIX = "fx_generated_" + + +# Normal exec loses the source code, however we can work with +# the linecache module to recover it. +# Using _exec_with_source will add it to our local cache +# and then tools like TorchScript will be able to get source info. +class _EvalCacheLoader: + def __init__(self): + self.eval_cache = {} + self.next_id = 0 + + def cache(self, src: str, globals: dict[str, Any], co_fields=None): + """Store the source in a private cache, and add a lazy entry in linecache + that allows the source to be retrieved by 'filename'. + + Args: + src (str): The module source to cache + globals (dict): The module globals + + Returns: + str: The cache key (and dummy filename) generated for src. + """ + + key = self._get_key() + if co_fields: + if "co_filename" in co_fields: + # If only co_filename is provided, use it directly as the key + if "co_firstlineno" not in co_fields or "co_name" not in co_fields: + key = co_fields["co_filename"] + else: + # Full co_fields with all three components + key += f" from {co_fields['co_filename']}:{co_fields['co_firstlineno']} in {co_fields['co_name']}" + self.eval_cache[key] = src + + # Don't mutate globals so that this loader is only used + # to populate linecache, and doesn't interact with other modules + # that might check `__loader__` + globals_copy = globals.copy() + globals_copy["__file__"] = key + globals_copy["__name__"] = key + globals_copy["__loader__"] = self + linecache.lazycache(key, globals_copy) + + return key + + # Part of the loader protocol (PEP 302) + # linecache will use this method when trying to find source code + def get_source(self, module_name) -> Optional[str]: + if module_name in self.eval_cache: + return self.eval_cache[module_name] + return None + + def _get_key(self): + key = f".{self.next_id}" + self.next_id += 1 + return key + + +_loader = _EvalCacheLoader() + + +def _exec_with_source(src: str, globals: dict[str, Any], co_fields=None): + key = _loader.cache(src, globals, co_fields) + exec(compile(src, key, "exec"), globals) + + +def _forward_from_src(src: str, globals: dict[str, Any], co_fields=None): + return _method_from_src( + method_name="forward", src=src, globals=globals, co_fields=co_fields + ) + + +def _method_from_src( + method_name: str, src: str, globals: dict[str, Any], co_fields=None +) -> Callable: + # avoid mutating the passed in dict + globals_copy = globals.copy() + _exec_with_source(src, globals_copy, co_fields) + fn = globals_copy[method_name] + del globals_copy[method_name] + return fn + + +def _format_import_statement(name: str, obj: Any, importer: Importer) -> str: + if name in _custom_builtins: + return _custom_builtins[name].import_str + if _is_from_torch(name): + return "import torch" + module_name, attr_name = importer.get_name(obj) + return f"from {module_name} import {attr_name} as {name}" + + +def _format_import_block(globals: dict[str, Any], importer: Importer): + import_strs: set[str] = { + _format_import_statement(name, obj, importer) for name, obj in globals.items() + } + # Sort the imports so we have a stable import block that allows us to + # hash the graph module and get a consistent key for use in a cache. + return "\n".join(sorted(import_strs)) + + +@compatibility(is_backward_compatible=True) +def reduce_graph_module(body: dict[Any, Any], import_block: str) -> torch.nn.Module: + # BC: attribute name was changed from `code` to `_code` to facilitate + # making `code` into a property and adding a docstring to it + fn_src = body.get("_code") or body["code"] + forward = _forward_from_src(import_block + fn_src, {}) + return _deserialize_graph_module(forward, body) + + +@compatibility(is_backward_compatible=True) +def reduce_package_graph_module( + importer: PackageImporter, body: dict[Any, Any], generated_module_name: str +) -> torch.nn.Module: + forward = importer.import_module(generated_module_name).forward + return _deserialize_graph_module(forward, body) + + +# We create a dummy class here because symbolic_trace pulls the forward() +# function off of the class, rather than the instance. This class is used +# in _deserialize_graph_module() below. +class _CodeOnlyModule(torch.nn.Module): + def __init__(self, body): + super().__init__() + self.__dict__ = body + + +def _deserialize_graph_module( + forward, body: dict[Any, Any], graph_module_cls=None +) -> torch.nn.Module: + """ + Deserialize a GraphModule given the dictionary of the original module, + using the code to reconstruct the graph. We delete the actual graph before + saving the dictionary so that changes to the in-memory graph format do not + get serialized. + """ + + # Try to retrieve the forward source in a backward-compatible way + _CodeOnlyModule.forward = forward + + tracer_cls = body.get("_tracer_cls") + if tracer_cls is None: + from ._symbolic_trace import Tracer + + tracer_cls = Tracer + + graphmodule_cls_name = body.get("_graphmodule_cls_name", "GraphModule") + + # This is a workaround for a mypy linter issue related to + # passing base class as an argument - https://github.com/python/mypy/issues/5865. + cls_tracer: Any = tracer_cls + + class KeepModules(cls_tracer): + # we shouldn't trace into any of the submodules, + # because they were not traced in the original GraphModule + def is_leaf_module(self, _: torch.nn.Module, __: str) -> bool: + return True + + com = _CodeOnlyModule(body) + + tracer_extras = body.get("_tracer_extras", {}) + graph = KeepModules().trace(com, **tracer_extras) + + # Recover node.meta["stack_trace"] after re-tracing + node_meta_stack_trace = body.get("_graphmodule_graph_node_meta_stack_trace") + if node_meta_stack_trace is not None: + del body["_graphmodule_graph_node_meta_stack_trace"] + for node in graph.nodes: + if node_meta_stack_trace.get(node.name, None) is not None: + node.meta["stack_trace"] = node_meta_stack_trace[node.name] + + # Manually set Tracer class on the reconstructed Graph, to avoid + # referencing the private local subclass KeepModules. + graph._tracer_cls = tracer_cls + from ._lazy_graph_module import _make_graph_module + + gm = _make_graph_module( + com, graph, class_name=graphmodule_cls_name, graph_module_cls=graph_module_cls + ) + + # The GraphModule constructor only retains attributes referenced by the graph. + # In this case, our goal is return a GraphModule as close to identical as the one + # put into the package. If any additional attributes were present in body, + # we should keep them. + for k, v in body.items(): + if not hasattr(gm, k): + setattr(gm, k, v) + return gm + + +# copy an attribute value with qualified name 'target' from 'from_module' to 'to_module' +# This installs empty Modules where none exist yet if they are subpaths of target +def _copy_attr(from_module: torch.nn.Module, to_module: torch.nn.Module, target: str): + *prefix, field = target.split(".") + for item in prefix: + f = getattr(from_module, item) + t = getattr(to_module, item, None) + if f is t: + # we have already installed one of its parents + # (e.g. target = root.linear.weight, but we have already installed root.linear) + # once we install a parent, we no longer need to copy the children + # since all the needed properties will already be present + return + + if t is None: + t = torch.nn.Module() + setattr(to_module, item, t) + from_module, to_module = f, t + + orig = getattr(from_module, field) + # If it is a tensor and not a parameter attribute of a module, it should be a named buffer. + # So, we register it as a named buffer in the target module. + if isinstance(orig, torch.Tensor) and not isinstance(orig, torch.nn.Parameter): + to_module.register_buffer(field, orig) + else: + setattr(to_module, field, orig) + + +# Assign attribute 'from_obj' to the qualified name 'target' on 'to_module +# This installs empty Modules where none exist yet if they are subpaths of target +def _assign_attr(from_obj: Any, to_module: torch.nn.Module, target: str): + *prefix, field = target.split(".") + for item in prefix: + t = getattr(to_module, item, None) + + if t is None: + t = torch.nn.Module() + setattr(to_module, item, t) + to_module = t + + # If it is a tensor and not a parameter attribute of a module, it should be a named buffer. + # So, we register it as a named buffer in the target module. + if isinstance(from_obj, torch.Tensor) and not isinstance( + from_obj, torch.nn.Parameter + ): + to_module.register_buffer(field, from_obj) + else: + setattr(to_module, field, from_obj) + + +# Recursively look up target from a graph module. +def _get_attr(model: torch.nn.Module, attr_name: str): + return _get_attr_via_attr_list(model, attr_name.split(".")) + + +def _del_attr(model: torch.nn.Module, attr_name: str): + attr_names = attr_name.split(".") + t = _get_attr_via_attr_list(model, attr_names[:-1]) + return delattr(t, attr_names[-1]) + + +def _get_attr_via_attr_list(model: torch.nn.Module, attr_list: list[str]): + if len(attr_list) == 0: + return model + *prefix, field = attr_list + t = model + for item in prefix: + t = getattr(t, item, None) # type: ignore[assignment] + assert t is not None + + return getattr(t, field) + + +def _has_attr(model: torch.nn.Module, attr_name: str): + *prefix, field = attr_name.split(".") + t = model + for item in prefix: + t = hasattr(t, item) # type: ignore[assignment] + if t is False: + return False + + return hasattr(t, field) + + +def _print_readable( + module, + module_name, + print_output=True, + include_stride=False, + include_device=False, + colored=False, + expanded_def=False, +): + graph = module.graph + assert graph is not None and isinstance(graph, torch.fx.Graph), ( + "print_readable must be used on a module with a graph" + ) + + verbose_python_code = graph.python_code( + root_module="self", + verbose=True, + include_stride=include_stride, + include_device=include_device, + colored=colored, + expanded_def=expanded_def, + ) + module_code = verbose_python_code.src + module_code = module_code.lstrip("\n") + module_code = f"class {module_name}(torch.nn.Module):\n" + module_code + module_code = _addindent(module_code, 4) + + submodule_code_list = [""] + for submodule_name, submodule in module.named_children(): + if hasattr(submodule, "graph"): + submodule_code_list.append( + _print_readable( + submodule, + submodule_name, + print_output=False, + include_stride=include_stride, + include_device=include_device, + colored=colored, + ) + ) + submodule_code = "\n".join(submodule_code_list) + submodule_code = _addindent(submodule_code, 4) + + output = module_code + submodule_code + if print_output: + print(module_code + submodule_code) + return output + + +def _metadata_hash(code: str, node_metadata: dict) -> str: + """ + Create a content-addressed hash from code and metadata. + + Args: + code: The source code string + lineno_map: Mapping from line numbers to node indices + node_metadata: Metadata for each node + + Returns: + A 51-character base32-encoded hash + """ + import json + + # Create a deterministic string representation of all components + # We use JSON to ensure consistent serialization + hash_data = { + "code": code, + "node_metadata": node_metadata, + } + hashing_str = json.dumps(hash_data).encode("utf-8") + + # [:51] to strip off the "Q====" suffix common to every hash value. + return ( + base64.b32encode(hashlib.sha256(hashing_str).digest())[:51] + .decode("utf-8") + .lower() + ) + + +class _WrappedCall: + def __init__(self, cls, cls_call): + self.cls = cls + self.cls_call = cls_call + + # Previously, if an error occurred when valid + # symbolically-traced code was run with an invalid input, the + # user would see the source of the error as coming from + # `File "`, where N is some number. We use + # this function to generate a more informative error message. We + # return the traceback itself, a message explaining that the + # error occurred in a traced Module's generated forward + # function, and five lines of context surrounding the faulty + # line + @staticmethod + def _generate_error_message(frame_summary: traceback.FrameSummary) -> str: + # auxiliary variables (for readability) + err_lineno = frame_summary.lineno + assert err_lineno is not None + line = frame_summary.line + assert line is not None + err_line_len = len(line) + all_src_lines = linecache.getlines(frame_summary.filename) + + # constituent substrings of the error message + tb_repr = torch._dynamo.disable( + traceback.format_exc, + reason="do not trace into traceback.format_exc when generating error message", + )() + custom_msg = ( + "Call using an FX-traced Module, " + f"line {err_lineno} of the traced Module's " + "generated forward function:" + ) + before_err = "".join(all_src_lines[err_lineno - 2 : err_lineno]) + marker = "~" * err_line_len + "~~~ <--- HERE" + err_and_after_err = "\n".join(all_src_lines[err_lineno : err_lineno + 2]) + + # joined message + return "\n".join([tb_repr, custom_msg, before_err, marker, err_and_after_err]) + + def __call__(self, obj, *args, **kwargs): + try: + if self.cls_call is not None: + return self.cls_call(obj, *args, **kwargs) + else: + return super(self.cls, obj).__call__(*args, **kwargs) # type: ignore[misc] + except Exception as e: + assert e.__traceback__ + topmost_framesummary: traceback.FrameSummary = ( + traceback.StackSummary.extract(traceback.walk_tb(e.__traceback__))[-1] + ) + if "eval_with_key" in topmost_framesummary.filename: + print( + _WrappedCall._generate_error_message(topmost_framesummary), + file=sys.stderr, + ) + raise e.with_traceback(None) # noqa: B904 + else: + raise e + + +@compatibility(is_backward_compatible=True) +class GraphModule(torch.nn.Module): + """ + GraphModule is an nn.Module generated from an fx.Graph. Graphmodule has a + ``graph`` attribute, as well as ``code`` and ``forward`` attributes generated + from that ``graph``. + + .. warning:: + + When ``graph`` is reassigned, ``code`` and ``forward`` will be automatically + regenerated. However, if you edit the contents of the ``graph`` without reassigning + the ``graph`` attribute itself, you must call ``recompile()`` to update the generated + code. + """ + + def __new__(cls: "type[GraphModule]", *args, **kwargs): + # each instance of a graph module needs its own forward method + # so create a new singleton class for each instance. + # it is a subclass of the user-defined class, the only difference + # is an extra layer to install the forward method + + # address issue described at https://github.com/pytorch/pytorch/issues/63883 + # in other words, traverse class hierarchy to fix the redundant class definition problem + for t in cls.__mro__: + c = t.__qualname__.split(".")[-1] + if c != "GraphModuleImpl": + cls = t + break + + class GraphModuleImpl(cls): # type: ignore[misc, valid-type] + pass + + return super().__new__(GraphModuleImpl) + + @compatibility(is_backward_compatible=True) + def __init__( + self, + root: Union[torch.nn.Module, dict[str, Any]], + graph: Graph, + class_name: str = "GraphModule", + ): + """ + Construct a GraphModule. + + Args: + + root (Union[torch.nn.Module, Dict[str, Any]): + ``root`` can either be an nn.Module instance or a Dict mapping strings to any attribute type. + In the case that ``root`` is a Module, any references to Module-based objects (via qualified + name) in the Graph's Nodes' ``target`` field will be copied over from the respective place + within ``root``'s Module hierarchy into the GraphModule's module hierarchy. + In the case that ``root`` is a dict, the qualified name found in a Node's ``target`` will be + looked up directly in the dict's keys. The object mapped to by the Dict will be copied + over into the appropriate place within the GraphModule's module hierarchy. + + graph (Graph): ``graph`` contains the nodes this GraphModule should use for code generation + + class_name (str): ``name`` denotes the name of this GraphModule for debugging purposes. If it's unset, all + error messages will report as originating from ``GraphModule``. It may be helpful to set this + to ``root``'s original name or a name that makes sense within the context of your transform. + """ + super().__init__() + self.__class__.__name__ = class_name + if isinstance(root, torch.nn.Module): + if hasattr(root, "training"): + self.training = root.training + + # When we pickle/unpickle graph module, we don't want to drop any module or attributes. + if isinstance(root, _CodeOnlyModule): + for k, _ in root.named_children(): + _copy_attr(root, self, k) + + for k, _ in root.named_buffers(): + _copy_attr(root, self, k) + + for k, _ in root.named_parameters(): + _copy_attr(root, self, k) + + for node in graph.nodes: + if node.op in ["get_attr", "call_module"]: + assert isinstance(node.target, str) + _copy_attr(root, self, node.target) + elif isinstance(root, dict): + targets_to_copy = [] + for node in graph.nodes: + if node.op in ["get_attr", "call_module"]: + assert isinstance(node.target, str) + if node.target not in root: + raise RuntimeError( + "Node " + + str(node) + + " referenced target " + + node.target + + " but that target was not provided in ``root``!" + ) + targets_to_copy.append(node.target) + # Sort targets in ascending order of the # of atoms. + # This will ensure that less deeply nested attributes are assigned + # before more deeply nested attributes. For example, foo.bar + # will be assigned before foo.bar.baz. Otherwise, we might assign + # the user-provided ``foo.bar`` and wipe out the previously-assigned + # ``foo.bar.baz`` + targets_to_copy.sort(key=lambda t: t.count(".")) + for target_to_copy in targets_to_copy: + _assign_attr(root[target_to_copy], self, target_to_copy) + else: + raise RuntimeError("Unsupported type " + str(root) + " passed for root!") + + self.graph = graph + + # Store the Tracer class responsible for creating a Graph separately as part of the + # GraphModule state, except when the Tracer is defined in a local namespace. + # Locally defined Tracers are not pickleable. This is needed because torch.package will + # serialize a GraphModule without retaining the Graph, and needs to use the correct Tracer + # to re-create the Graph during deserialization. + self._tracer_cls = None + if ( + self.graph._tracer_cls + and "" not in self.graph._tracer_cls.__qualname__ + ): + # pyrefly: ignore [bad-assignment] + self._tracer_cls = self.graph._tracer_cls + + self._tracer_extras = {} + if self.graph._tracer_extras: + self._tracer_extras = self.graph._tracer_extras + + # Dictionary to store metadata + self.meta: dict[str, Any] = {} + self._replace_hooks: list[Callable] = [] + self._create_node_hooks: list[Callable] = [] + self._erase_node_hooks: list[Callable] = [] + # Used to remove hooks from deepcopied graph modules within a context manager. + self._deepcopy_hooks: list[Callable] = [] + self.shape_env = None # optional not always set even when dynamic shapes exist. + + # TorchScript breaks trying to compile the graph setter because of the + # continued string literal. Issue here: https://github.com/pytorch/pytorch/issues/44842 + # + # Shouldn't be an issue since these methods shouldn't be used in TorchScript anyway + __jit_unused_properties__ = ["graph", "_boxed_call"] + + @property + def _boxed_call(self) -> bool: + return isinstance(self._graph._codegen, _BoxedCodeGen) + + @property + def graph(self) -> Graph: + """ + Return the ``Graph`` underlying this ``GraphModule`` + """ + return self._graph + + @graph.setter + def graph(self, g: Graph) -> None: + """ + Set the underlying ``Graph`` for this ``GraphModule``. This will internally + recompile the ``GraphModule`` so that the generated ``forward()`` function + corresponds to ``g`` + """ + assert isinstance(g, Graph), f"Expected a Graph instance, but got {type(g)}" + self._graph = g + g.owning_module = self + self.recompile() + + @compatibility(is_backward_compatible=False) + def to_folder(self, folder: Union[str, os.PathLike], module_name: str = "FxModule"): + """Dumps out module to ``folder`` with ``module_name`` so that it can be + imported with ``from import `` + + Args: + + folder (Union[str, os.PathLike]): The folder to write the code out to + + module_name (str): Top-level name to use for the ``Module`` while + writing out the code + """ + folder = Path(folder) + Path(folder).mkdir(exist_ok=True) + torch.save(self.state_dict(), folder / "state_dict.pt") + tab = " " * 4 + custom_builtins = "\n".join([v.import_str for v in _custom_builtins.values()]) + model_str = f""" +import torch +{custom_builtins} + +from torch.nn import * +class {module_name}(torch.nn.Module): + def __init__(self): + super().__init__() +""" + + def _gen_model_repr(module_name: str, module: torch.nn.Module) -> Optional[str]: + safe_reprs = [ + nn.Linear, + nn.Conv1d, + nn.Conv2d, + nn.Conv3d, + nn.BatchNorm1d, + nn.BatchNorm2d, + nn.BatchNorm3d, + ] + if type(module) in safe_reprs: + return f"{module.__repr__()}" + else: + return None + + blobified_modules = [] + for module_name, module in self.named_children(): + module_str = _gen_model_repr(module_name, module) + if module_str is None: + module_file = folder / f"{module_name}.pt" + torch.save(module, module_file) + blobified_modules.append(module_name) + module_repr = module.__repr__().replace("\r", " ").replace("\n", " ") + # weights_only=False as this is legacy code that saves the model + module_str = ( + f"torch.load(r'{module_file}', weights_only=False) # {module_repr}" + ) + model_str += f"{tab * 2}self.{module_name} = {module_str}\n" + + for buffer_name, buffer in self._buffers.items(): + if buffer is None: + continue + model_str += f"{tab * 2}self.register_buffer('{buffer_name}', torch.empty({list(buffer.shape)}, dtype={buffer.dtype}))\n" # noqa: B950 + + for param_name, param in self._parameters.items(): + if param is None: + continue + model_str += f"{tab * 2}self.{param_name} = torch.nn.Parameter(torch.empty({list(param.shape)}, dtype={param.dtype}))\n" # noqa: B950 + + model_str += ( + f"{tab * 2}self.load_state_dict(torch.load(r'{folder}/state_dict.pt'))\n" + ) + model_str += f"{_addindent(self.code, 4)}\n" + + module_file = folder / "module.py" + module_file.write_text(model_str) + + init_file = folder / "__init__.py" + init_file.write_text("from .module import *") + + if len(blobified_modules) > 0: + warnings.warn( + "Was not able to save the following children modules as reprs -" + f"saved as pickled files instead: {blobified_modules}" + ) + + @compatibility(is_backward_compatible=True) + def add_submodule(self, target: str, m: torch.nn.Module) -> bool: + """ + Adds the given submodule to ``self``. + + This installs empty Modules where none exist yet if they are + subpaths of ``target``. + + Args: + target: The fully-qualified string name of the new submodule + (See example in ``nn.Module.get_submodule`` for how to + specify a fully-qualified string.) + m: The submodule itself; the actual object we want to + install in the current Module + + Return: + bool: Whether or not the submodule could be inserted. For + this method to return True, each object in the chain + denoted by ``target`` must either a) not exist yet, + or b) reference an ``nn.Module`` (not a parameter or + other attribute) + """ + *prefix, field = target.split(".") + mod: torch.nn.Module = self + + for item in prefix: + submod = getattr(mod, item, None) + + if submod is None: + submod = torch.nn.Module() + setattr(mod, item, submod) + + if not isinstance(submod, torch.nn.Module): + return False + + mod = submod + + mod.add_module(field, m) + return True + + @compatibility(is_backward_compatible=True) + def delete_submodule(self, target: str) -> bool: + """ + Deletes the given submodule from ``self``. + + The module will not be deleted if ``target`` is not a valid + target. + + Args: + target: The fully-qualified string name of the new submodule + (See example in ``nn.Module.get_submodule`` for how to + specify a fully-qualified string.) + + Returns: + bool: Whether or not the target string referenced a + submodule we want to delete. A return value of ``False`` + means that the ``target`` was not a valid reference to + a submodule. + """ + atoms = target.split(".") + path, target_submod = atoms[:-1], atoms[-1] + mod: torch.nn.Module = self + + # Get the parent module + for item in path: + if not hasattr(mod, item): + return False + + mod = getattr(mod, item) + + if not isinstance(mod, torch.nn.Module): + return False + + if not hasattr(mod, target_submod): + return False + + if not isinstance(getattr(mod, target_submod), torch.nn.Module): + return False + + delattr(mod, target_submod) + return True + + @compatibility(is_backward_compatible=True) + def delete_all_unused_submodules(self) -> None: + """ + Deletes all unused submodules from ``self``. + + A Module is considered "used" if any one of the following is + true: + 1. It has children that are used + 2. Its forward is called directly via a ``call_module`` node + 3. It has a non-Module attribute that is used from a + ``get_attr`` node + + This method can be called to clean up an ``nn.Module`` without + manually calling ``delete_submodule`` on each unused submodule. + """ + used: list[str] = [] + + for node in self.graph.nodes: + if node.op == "call_module" or node.op == "get_attr": + # A list of strings representing the different parts + # of the path. For example, `foo.bar.baz` gives us + # ["foo", "bar", "baz"] + fullpath = node.target.split(".") + + # If we're looking at multiple parts of a path, join + # join them with a dot. Otherwise, return that single + # element without doing anything to it. + def join_fn(x: str, y: str) -> str: + return ".".join([x, y] if y else [x]) + + # Progressively collect all the names of intermediate + # modules. For example, if we have the target + # `foo.bar.baz`, we'll add `foo`, `foo.bar`, and + # `foo.bar.baz` to the list. + used.extend(itertools.accumulate(fullpath, join_fn)) + + # For a `call_module` node, also register all recursive submodules + # as used + if node.op == "call_module": + try: + submod = self.get_submodule(node.target) + + for submod_name, _ in submod.named_modules(): + if submod_name != "": + used.append(".".join([node.target, submod_name])) + except AttributeError: + # Node referenced nonexistent submodule, don't need to + # worry about GCing anything + pass + + to_delete = [name for name, _ in self.named_modules() if name not in used] + + for name in to_delete: + self.delete_submodule(name) + + @property + def code(self) -> str: + """ + Return the Python code generated from the ``Graph`` underlying this + ``GraphModule``. + """ + if not hasattr(self, "_code"): + raise RuntimeError( + "Code has not been generated! Please report a bug to PyTorch" + ) + return self._code + + @compatibility(is_backward_compatible=True) + def recompile(self) -> PythonCode: + """ + Recompile this GraphModule from its ``graph`` attribute. This should be + called after editing the contained ``graph``, otherwise the generated + code of this ``GraphModule`` will be out of date. + """ + # Do not import anything inside recompile, it might slow down the + # function and cause perf regression. Import outside of the method instead. + if isinstance(self._graph._codegen, _PyTreeCodeGen): + self._in_spec = self._graph._codegen.pytree_info.in_spec + self._out_spec = self._graph._codegen.pytree_info.out_spec + + python_code = self._graph.python_code( + root_module="self", + record_func=fx_experimental_config.enrich_profiler_metadata, + ) + self._code = python_code.src + self._lineno_map = python_code._lineno_map + self._prologue_start = python_code._prologue_start + + cls = type(self) + co_fields = self._graph._co_fields if hasattr(self._graph, "_co_fields") else {} + + if fx_experimental_config.enrich_profiler_metadata: + # Generate metadata and register for profiler augmentation + node_metadata: dict[int, dict[str, Any]] = {} + for i, node in enumerate(self._graph.nodes): + node_metadata[i] = { + "name": node.name, + "op": node.op, + "target": str(node.target), + "stack_trace": node.meta.get("stack_trace", None), + } + + # Generate a content-addressed filename based on hash of code and metadata + # This ensures the same code+metadata always generates the same filename + hash_value = _metadata_hash(self._code, node_metadata) + file_stem = f"{FX_GRAPH_MODULE_FILE_PREFIX}_{hash_value}" + filename = f"{file_stem}.py" + + # Only include co_filename to use it directly as the cache key + co_fields = { + "co_filename": filename, + } + + # Store metadata in global in-memory registry + metadata = { + "lineno_map": python_code._lineno_map, + "prologue_start": python_code._prologue_start, + "node_metadata": node_metadata, + } + + # Register metadata in the global registry + from torch.fx.traceback import _register_fx_metadata + + _register_fx_metadata(filename, metadata) + + # Replace the placeholder in generated code with actual filename + # The double hash ## convention is used by post-processing to find the fx markers + self._code = self._code.replace( + "torch._C._profiler._RecordFunctionFast('## ENTER_GRAPH_PLACEHOLDER_KEY ##')", + f"torch._C._profiler._RecordFunctionFast('## {filename} ##')", + ) + + cls.forward = _forward_from_src(self._code, python_code.globals, co_fields) + + # Determine whether this class explicitly defines a __call__ implementation + # to wrap. If it does, save it in order to have wrapped_call invoke it. + # If it does not, wrapped_call can use a dynamic call to super() instead. + # In most cases, super().__call__ should be torch.nn.Module.__call__. + # We do not want to hold a reference to Module.__call__ here; doing so will + # bypass patching of torch.nn.Module.__call__ done while symbolic tracing. + cls_call = cls.__call__ if "__call__" in vars(cls) else None + + if "_wrapped_call" not in vars(cls): + cls._wrapped_call = _WrappedCall(cls, cls_call) # type: ignore[attr-defined] + + self._recompile_submodules() + + def call_wrapped(self, *args, **kwargs): + return self._wrapped_call(self, *args, **kwargs) + + cls.__call__ = call_wrapped # type: ignore[method-assign] + + return python_code + + def _recompile_submodules(self) -> list[tuple[str, PythonCode]]: + """ + Recompile all submodules of this graph module, returning their respective PythonCodes + in a similar format to named_children() + """ + results: list[tuple[str, PythonCode]] = [] + for name, mod in self.named_children(): + if isinstance(mod, GraphModule): + results.append((name, mod.recompile())) + return results + + # Passing Tracer as argument allows subclasses extending fx.GraphModule + # define their own Tracer (extending fx.Tracer). + + def __reduce_package__(self, exporter: PackageExporter): + dict_without_graph = self.__dict__.copy() + dict_without_graph["_graphmodule_cls_name"] = self.__class__.__name__ + del dict_without_graph["_graph"] + + # Store node.meta["stack_trace"] so we can recover them after re-tracing during deserialization + node_meta_stack_trace = { + node.name: node.meta["stack_trace"] + for node in self.graph.nodes + if "stack_trace" in node.meta + } + dict_without_graph["_graphmodule_graph_node_meta_stack_trace"] = ( + node_meta_stack_trace + ) + + generated_module_name = f"fx-generated._{exporter.get_unique_id()}" + python_code = self.recompile() + import_block = _format_import_block(python_code.globals, exporter.importer) + module_code = import_block + self.code + exporter.save_source_string(generated_module_name, module_code) + return ( + reduce_package_graph_module, + (dict_without_graph, generated_module_name), + ) + + def __reduce__(self): + """ + Serialization of GraphModule. We serialize only the generated code, not + the underlying ``Graph``. This is because ``Graph`` does not have on-disk + backward-compatibility guarantees, whereas Python source code does. + On the deserialization side, we symbolically trace through the generated + code to regenerate the underlying ``Graph`` + """ + dict_without_graph = self.__dict__.copy() + + python_code = self.recompile() + import_block = _format_import_block(python_code.globals, sys_importer) + del dict_without_graph["_graph"] + return (reduce_graph_module, (dict_without_graph, import_block)) + + def _deepcopy_init(self): + return GraphModule.__init__ + + # because __reduce__ is defined for serialization, + # we need to define deepcopy otherwise it will call __reduce__ + # and cause symbolic tracing to occur every time we try to copy the object + def __deepcopy__(self, memo): + res = type(self).__new__(type(self)) + memo[id(self)] = res + fake_mod = _CodeOnlyModule(copy.deepcopy(self.__dict__, memo)) + self._deepcopy_init()(res, fake_mod, fake_mod.__dict__["_graph"]) + # hooks are lost during `GraphModule.__init__`, so we need to copy over + # them explicitly, note right now we are only copying state_dict related + # hooks, to reduce bc-related issues, we can copy forward/backward related + # hooks in the future as well if needed + extra_preserved_attrs = [ + "_state_dict_hooks", + "_load_state_dict_pre_hooks", + "_load_state_dict_post_hooks", + "_replace_hooks", + "_create_node_hooks", + "_erase_node_hooks", + "_deepcopy_hooks", + ] + for attr in extra_preserved_attrs: + if attr in self.__dict__: + setattr(res, attr, copy.deepcopy(self.__dict__[attr], memo)) + res.meta = copy.deepcopy(getattr(self, "meta", {}), memo) + if _USER_PRESERVED_ATTRIBUTES_KEY in res.meta: + for attr_name, attr in res.meta[_USER_PRESERVED_ATTRIBUTES_KEY].items(): + setattr(res, attr_name, attr) + if hasattr(self, "_deepcopy_hooks"): + for hook in self._deepcopy_hooks: + hook(res) + return res + + def __copy__(self): + from ._lazy_graph_module import _make_graph_module + + res = _make_graph_module(self, self.graph) + res.meta = getattr(self, "meta", {}) + return res + + @compatibility(is_backward_compatible=False) + def print_readable( + self, + print_output=True, + include_stride=False, + include_device=False, + colored=False, + *, + # If `fast_sympy_print` is True then we use a sympy printer which is faster + # but may result in less-readable output. + fast_sympy_print: bool = False, + expanded_def: bool = False, + ): + """ + Return the Python code generated for current GraphModule and its children GraphModules + """ + ctx_mgr = contextlib.ExitStack() + with ctx_mgr: + if fast_sympy_print: + from torch._inductor.utils import sympy_str + + def fast_repr(expr: torch.types.PySymType) -> str: + return sympy_str(expr.node.expr) + + ctx_mgr.enter_context(_override_sym_repr(fast_repr)) + + r = _print_readable( + self, + self._get_name(), + print_output, + include_stride, + include_device, + colored, + expanded_def, + ) + return r + + def __str__(self) -> str: + orig_str = super().__str__() + print_readable_reminder = ( + "# To see more debug info, please use `graph_module.print_readable()`" + ) + return "\n".join([orig_str, self._code, print_readable_reminder]) + + def _replicate_for_data_parallel(self): + new_gm = self.__copy__() + new_gm._is_replica = True + return new_gm + + @contextlib.contextmanager + def _set_replace_hook(self, f): + """ + Takes a callable which will be called every time when we replace a node + to a new node, or change the node's name. Callable takes three arguments: + the old node we're changing, and NAME of the new node, followed by the + user node which consumes the old node to be replaced. + """ + assert callable(f), "Replace hook must be a callable." + self._register_replace_node_hook(f) + try: + yield + finally: + self._unregister_replace_node_hook(f) + + def _register_replace_node_hook(self, f): + """ + Takes a callable which will be called every time when we replace a node + to a new node, or change the node's name. Callable takes three arguments: + the old node we're changing, and NAME of the new node, followed by the + user node which consumes the old node to be replaced. + """ + assert callable(f), "create_node hook must be a callable." + self._replace_hooks.append(f) + + def _unregister_replace_node_hook(self, f): + """ + Takes a callable which was previously registered to be called every time when we replace a node. + This function will unregister that callable so it is no longer invoked on node replacement. + """ + assert callable(f), "create_node hook must be a callable." + self._replace_hooks.remove(f) + + def _register_create_node_hook(self, f): + """ + Takes a callable which will be called after we create a new node. The + callable takes the newly created node as input and returns None. + """ + assert callable(f), "create_node hook must be a callable." + self._create_node_hooks.append(f) + + def _unregister_create_node_hook(self, f): + """ + Takes a callable which was previously registered to be called after we create a node. + This function will unregister that callable so it is no longer invoked on node creation. + """ + assert callable(f), "create_node hook must be a callable." + self._create_node_hooks.remove(f) + + def _register_erase_node_hook(self, f): + """ + Takes a callable which will be called after we erase a node. The + callable takes the node that is being erased as input and returns None. + """ + assert callable(f), "erase_node hook must be a callable." + self._erase_node_hooks.append(f) + + def _unregister_erase_node_hook(self, f): + """ + Takes a callable which was previously registered to be called after we erase a node. + This function will unregister that callable so it is no longer invoked on node erasure. + """ + assert callable(f), "erase_node hook must be a callable." + self._erase_node_hooks.remove(f) + + def _register_deepcopy_hook(self, f): + """ + Takes a callable which will be called when we deepcopy this graph module. The + callable takes the resulting deepcopied graph module. + """ + assert callable(f), "deepcopy hook must be a callable." + self._deepcopy_hooks.append(f) + + def _unregister_deepcopy_hook(self, f): + """ + Takes a callable which was previously registered to be called after deepcopy. + This function will unregister that callable so it is no longer invoked on deepcopy. + """ + assert callable(f), "deepcopy hook must be a callable." + self._deepcopy_hooks.remove(f) + + +# workarounds for issues in __torch_function__ + +# WAR for __torch_function__ not handling tensor lists, +# fix is in https://github.com/pytorch/pytorch/pull/34725 +# orig_cat = torch.cat +# def patched_cat(*args, **kwargs): +# tensors = args[0] +# for t in tensors: +# if isinstance(t, Proxy): +# return t.__torch_function__(patched_cat, (), args, kwargs) +# return orig_cat(*args, **kwargs) +# patched_cat.__module__ = 'torch' +# patched_cat.__name__ = 'cat' +# torch.cat = patched_cat diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/immutable_collections.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/immutable_collections.py new file mode 100644 index 0000000000000000000000000000000000000000..6c6204d520bc66af3e6c161b0254b9d81012c287 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/immutable_collections.py @@ -0,0 +1,122 @@ +from collections.abc import Iterable +from typing import Any, NoReturn, TypeVar +from typing_extensions import Self + +from torch.utils._pytree import ( + _dict_flatten, + _dict_flatten_with_keys, + _dict_unflatten, + _list_flatten, + _list_flatten_with_keys, + _list_unflatten, + Context, + register_pytree_node, +) + +from ._compatibility import compatibility + + +__all__ = ["immutable_list", "immutable_dict"] + + +_help_mutation = """ +If you are attempting to modify the kwargs or args of a torch.fx.Node object, +instead create a new copy of it and assign the copy to the node: + + new_args = ... # copy and mutate args + node.args = new_args +""".strip() + + +_T = TypeVar("_T") +_KT = TypeVar("_KT") +_VT = TypeVar("_VT") + + +def _no_mutation(self: Any, *args: Any, **kwargs: Any) -> NoReturn: + raise TypeError( + f"{type(self).__name__!r} object does not support mutation. {_help_mutation}", + ) + + +@compatibility(is_backward_compatible=True) +class immutable_list(list[_T]): + """An immutable version of :class:`list`.""" + + __delitem__ = _no_mutation + __iadd__ = _no_mutation + __imul__ = _no_mutation + __setitem__ = _no_mutation + append = _no_mutation + clear = _no_mutation + extend = _no_mutation + insert = _no_mutation + pop = _no_mutation + remove = _no_mutation + reverse = _no_mutation + sort = _no_mutation + + def __hash__(self) -> int: # type: ignore[override] + return hash(tuple(self)) + + def __reduce__(self) -> tuple[type[Self], tuple[tuple[_T, ...]]]: + return (type(self), (tuple(self),)) + + +@compatibility(is_backward_compatible=True) +class immutable_dict(dict[_KT, _VT]): + """An immutable version of :class:`dict`.""" + + __delitem__ = _no_mutation + __ior__ = _no_mutation + __setitem__ = _no_mutation + clear = _no_mutation + pop = _no_mutation + popitem = _no_mutation + setdefault = _no_mutation + update = _no_mutation # type: ignore[assignment] + + def __hash__(self) -> int: # type: ignore[override] + return hash(frozenset(self.items())) + + def __reduce__(self) -> tuple[type[Self], tuple[tuple[tuple[_KT, _VT], ...]]]: + return (type(self), (tuple(self.items()),)) + + +# Register immutable collections for PyTree operations +def _immutable_list_flatten(d: immutable_list[_T]) -> tuple[list[_T], Context]: + return _list_flatten(d) + + +def _immutable_list_unflatten( + values: Iterable[_T], + context: Context, +) -> immutable_list[_T]: + return immutable_list(_list_unflatten(values, context)) + + +def _immutable_dict_flatten(d: immutable_dict[Any, _VT]) -> tuple[list[_VT], Context]: + return _dict_flatten(d) + + +def _immutable_dict_unflatten( + values: Iterable[_VT], + context: Context, +) -> immutable_dict[Any, _VT]: + return immutable_dict(_dict_unflatten(values, context)) + + +register_pytree_node( + immutable_list, + _immutable_list_flatten, + _immutable_list_unflatten, + serialized_type_name="torch.fx.immutable_collections.immutable_list", + flatten_with_keys_fn=_list_flatten_with_keys, +) +register_pytree_node( + immutable_dict, + _immutable_dict_flatten, + _immutable_dict_unflatten, + serialized_type_name="torch.fx.immutable_collections.immutable_dict", + flatten_with_keys_fn=_dict_flatten_with_keys, +) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/interpreter.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/interpreter.py new file mode 100644 index 0000000000000000000000000000000000000000..5b40e8a66147f410e03e349560571a3da0859f19 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/interpreter.py @@ -0,0 +1,656 @@ +# mypy: allow-untyped-defs +import inspect +import logging +from contextlib import contextmanager +from typing import Any, Optional, TYPE_CHECKING, Union + +import torch +import torch.fx.traceback as fx_traceback +from torch._logging import LazyString, trace_structured +from torch.hub import tqdm + +from . import config +from ._compatibility import compatibility +from ._lazy_graph_module import _make_graph_module +from ._symbolic_trace import Tracer +from .graph import Graph +from .graph_module import GraphModule +from .node import Argument, map_aggregate, map_arg, Node, Target +from .proxy import Proxy + + +if TYPE_CHECKING: + from collections.abc import Iterator + +log = logging.getLogger(__name__) + +__all__ = ["Interpreter", "Transformer"] + + +def _format_fx_node(n): + """ + Format a torch.fx.Node into a human-readable string for debug logging. + + Args: + n (torch.fx.Node): The FX node being executed. + + Returns: + str: A formatted string describing the node operation, including its + name, target, positional arguments, and keyword arguments. + """ + module_prefix = getattr(n.target, "__module__", "") + module_prefix = f"{module_prefix}." if module_prefix else "" + + # Handle positional and keyword arguments + args = ", ".join(map(str, n.args)) + kwargs = ", ".join(f"{k}={v}" for k, v in n.kwargs.items()) + joined = ", ".join(filter(None, [args, kwargs])) + + return ( + f"{n.name} = {module_prefix}{getattr(n.target, '__name__', n.target)}({joined})" + ) + + +@compatibility(is_backward_compatible=True) +class Interpreter: + """ + An Interpreter executes an FX graph Node-by-Node. This pattern + can be useful for many things, including writing code + transformations as well as analysis passes. + + Methods in the Interpreter class can be overridden to customize + the behavior of execution. The map of overridable methods + in terms of call hierarchy:: + + run() + +-- run_node + +-- placeholder() + +-- get_attr() + +-- call_function() + +-- call_method() + +-- call_module() + +-- output() + + Example: + + Suppose we want to swap all instances of ``torch.neg`` with + ``torch.sigmoid`` and vice versa (including their ``Tensor`` + method equivalents). We could subclass Interpreter like so:: + + class NegSigmSwapInterpreter(Interpreter): + def call_function( + self, target: Target, args: Tuple, kwargs: Dict + ) -> Any: + if target is torch.sigmoid: + return torch.neg(*args, **kwargs) + return super().call_function(target, args, kwargs) + + def call_method(self, target: Target, args: Tuple, kwargs: Dict) -> Any: + if target == "neg": + call_self, *args_tail = args + return call_self.sigmoid(*args_tail, **kwargs) + return super().call_method(target, args, kwargs) + + + def fn(x): + return torch.sigmoid(x).neg() + + + gm = torch.fx.symbolic_trace(fn) + input = torch.randn(3, 4) + result = NegSigmSwapInterpreter(gm).run(input) + torch.testing.assert_close(result, torch.neg(input).sigmoid()) + + Args: + module (torch.nn.Module): The module to be executed + garbage_collect_values (bool): Whether to delete values after their last + use within the Module's execution. This ensures optimal memory usage during + execution. This can be disabled to, for example, examine all of the intermediate + values in the execution by looking at the ``Interpreter.env`` attribute. + graph (Optional[Graph]): If passed, the interpreter will execute this + graph instead of `module.graph`, using the provided `module` + argument to satisfy any requests for state. + """ + + @compatibility(is_backward_compatible=True) + def __init__( + self, + module: torch.nn.Module, + garbage_collect_values: bool = True, + graph: Optional[Graph] = None, + ): + self.module = module + self.submodules = dict(self.module.named_modules()) + if graph is not None: + self.graph = graph + else: + self.graph = self.module.graph # type: ignore[assignment] + self.env: dict[Node, Any] = {} + self.name = "Interpreter" + self.garbage_collect_values = garbage_collect_values + self.extra_traceback = True + + if self.garbage_collect_values: + # Run through reverse nodes and record the first instance of a use + # of a given node. This represents the *last* use of the node in the + # execution order of the program, which we will use to free unused + # values + node_to_last_use: dict[Node, Node] = {} + self.user_to_last_uses: dict[Node, list[Node]] = {} + + def register_last_uses(n: Node, user: Node): + if n not in node_to_last_use: + node_to_last_use[n] = user + self.user_to_last_uses.setdefault(user, []).append(n) + + for node in reversed(self.graph.nodes): + for n in node._input_nodes: + register_last_uses(n, node) + + @compatibility(is_backward_compatible=True) + def run( + self, + *args, + initial_env: Optional[dict[Node, Any]] = None, + enable_io_processing: bool = True, + ) -> Any: + """ + Run `module` via interpretation and return the result. + + Args: + *args: The arguments to the Module to run, in positional order + initial_env (Optional[Dict[Node, Any]]): An optional starting environment for execution. + This is a dict mapping `Node` to any value. This can be used, for example, to + pre-populate results for certain `Nodes` so as to do only partial evaluation within + the interpreter. + enable_io_processing (bool): If true, we process the inputs and outputs with graph's process_inputs and + process_outputs function first before using them. + + Returns: + Any: The value returned from executing the Module + """ + self.env = initial_env if initial_env is not None else {} + + # Positional function args are consumed left-to-right by + # `placeholder` nodes. Use an iterator to keep track of + # position and extract those values. + if enable_io_processing: + args = self.graph.process_inputs(*args) + self.args_iter: Iterator[Any] = iter(args) + pbar = tqdm( + total=len(self.graph.nodes), + desc=f"{self.name}: {str(list(self.graph.nodes)) if config.verbose_progress else ''}", + initial=0, + position=0, + leave=True, + disable=config.disable_progress, + delay=0, + ) + + for node in self.graph.nodes: + pbar.update(1) + if node in self.env: + # Short circuit if we have this value. This could + # be used, for example, for partial evaluation + # where the caller has pre-populated `env` with + # values for a subset of the program. + continue + + try: + self.env[node] = self.run_node(node) + except Exception as e: + if self.extra_traceback: + msg = f"While executing {node.format_node()}" + msg = f"{e.args[0]}\n\n{msg}" if e.args else str(msg) + msg += f"\nOriginal traceback:\n{node.stack_trace}" + if ( + isinstance(self.module, GraphModule) + and self.module.graph is not None + and isinstance(self.module.graph, torch.fx.Graph) + ): + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "fx_interpreter_error", + "encoding": "string", + }, + payload_fn=lambda: ( + f"{msg}\nGraphModule: " + f"{self.module.print_readable(print_output=False, include_stride=True)}" # type: ignore[operator] + ), + ) + + msg += "\nUse tlparse to see full graph. " + msg += "(https://github.com/pytorch/tlparse?tab=readme-ov-file#tlparse-parse-structured-pt2-logs)" + e.args = (msg,) + e.args[1:] + if isinstance(e, KeyError): + raise RuntimeError(*e.args) from e + raise + + if self.garbage_collect_values: + for to_delete in self.user_to_last_uses.get(node, []): + del self.env[to_delete] + + if node.op == "output": + output_val = self.env[node] + return ( + self.graph.process_outputs(output_val) + if enable_io_processing + else output_val + ) + + @compatibility(is_backward_compatible=True) + def boxed_run(self, args_list): + """ + Run `module` via interpretation and return the result. This uses the "boxed" + calling convention, where you pass a list of arguments, which will be cleared + by the interpreter. This ensures that input tensors are promptly deallocated. + """ + # Collect placeholder nodes first + placeholder_nodes = [n for n in self.graph.nodes if n.op == "placeholder"] + + # Check argument count + if len(args_list) != len(placeholder_nodes): + detail = ( + "extra arguments" + if len(args_list) > len(placeholder_nodes) + else "missing arguments" + ) + raise RuntimeError( + f"Interpreter.boxed_run expected {len(placeholder_nodes)} arguments for placeholders " + f"but received {len(args_list)} ({detail})" + ) + + # Assign arguments to placeholders + env = dict(zip(placeholder_nodes, args_list)) + args_list.clear() + return self.run(initial_env=env) + + @contextmanager + def _set_current_node(self, node): + with fx_traceback.set_current_meta( + node, f"Interpreter_{self.__class__.__name__}" + ): + yield + + @compatibility(is_backward_compatible=True) + def run_node(self, n: Node) -> Any: + """ + Run a specific node ``n`` and return the result. + Calls into placeholder, get_attr, call_function, + call_method, call_module, or output depending + on ``node.op`` + + Args: + n (Node): The Node to execute + + Returns: + Any: The result of executing ``n`` + """ + log.debug("run_node %s", LazyString(lambda: _format_fx_node(n))) + with self._set_current_node(n): + args, kwargs = self.fetch_args_kwargs_from_env(n) + assert isinstance(args, tuple) + assert isinstance(kwargs, dict) + return getattr(self, n.op)(n.target, args, kwargs) + + # Main Node running APIs + @compatibility(is_backward_compatible=True) + def placeholder( + self, target: "Target", args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + """ + Execute a ``placeholder`` node. Note that this is stateful: + ``Interpreter`` maintains an internal iterator over + arguments passed to ``run`` and this method returns + next() on that iterator. + + Args: + target (Target): The call target for this node. See + `Node `__ for + details on semantics + args (Tuple): Tuple of positional args for this invocation + kwargs (Dict): Dict of keyword arguments for this invocation + + Returns: + Any: The argument value that was retrieved. + """ + assert isinstance(target, str) + if target.startswith("*"): + # For a starred parameter e.g. `*args`, retrieve all + # remaining values from the args list. + return list(self.args_iter) + else: + try: + return next(self.args_iter) + except StopIteration as si: + if len(args) > 0: + return args[0] + else: + raise RuntimeError( + f"Expected positional argument for parameter {target}, but one was not passed in!" + ) from si + + @compatibility(is_backward_compatible=True) + def get_attr( + self, target: "Target", args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + """ + Execute a ``get_attr`` node. Will retrieve an attribute + value from the ``Module`` hierarchy of ``self.module``. + + Args: + target (Target): The call target for this node. See + `Node `__ for + details on semantics + args (Tuple): Tuple of positional args for this invocation + kwargs (Dict): Dict of keyword arguments for this invocation + + Return: + Any: The value of the attribute that was retrieved + """ + assert isinstance(target, str) + return self.fetch_attr(target) + + @compatibility(is_backward_compatible=True) + def call_function( + self, target: "Target", args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + """ + Execute a ``call_function`` node and return the result. + + Args: + target (Target): The call target for this node. See + `Node `__ for + details on semantics + args (Tuple): Tuple of positional args for this invocation + kwargs (Dict): Dict of keyword arguments for this invocation + + Return + Any: The value returned by the function invocation + """ + assert not isinstance(target, str) + + # Execute the function and return the result + return target(*args, **kwargs) + + @compatibility(is_backward_compatible=True) + def call_method( + self, target: "Target", args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + """ + Execute a ``call_method`` node and return the result. + + Args: + target (Target): The call target for this node. See + `Node `__ for + details on semantics + args (Tuple): Tuple of positional args for this invocation + kwargs (Dict): Dict of keyword arguments for this invocation + + Return + Any: The value returned by the method invocation + """ + # args[0] is the `self` object for this method call + self_obj, *args_tail = args + + # Execute the method and return the result + assert isinstance(target, str) + return getattr(self_obj, target)(*args_tail, **kwargs) + + @compatibility(is_backward_compatible=True) + def call_module( + self, target: "Target", args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + """ + Execute a ``call_module`` node and return the result. + + Args: + target (Target): The call target for this node. See + `Node `__ for + details on semantics + args (Tuple): Tuple of positional args for this invocation + kwargs (Dict): Dict of keyword arguments for this invocation + + Return + Any: The value returned by the module invocation + """ + # Retrieve executed args and kwargs values from the environment + + # Execute the method and return the result + assert isinstance(target, str) + submod = self.fetch_attr(target) + + return submod(*args, **kwargs) + + @compatibility(is_backward_compatible=True) + def output( + self, target: "Target", args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + """ + Execute an ``output`` node. This really just retrieves + the value referenced by the ``output`` node and returns it. + + Args: + target (Target): The call target for this node. See + `Node `__ for + details on semantics + args (Tuple): Tuple of positional args for this invocation + kwargs (Dict): Dict of keyword arguments for this invocation + + Return: + Any: The return value referenced by the output node + """ + return args[0] + + # Helper methods + @compatibility(is_backward_compatible=True) + def fetch_attr(self, target: str): + """ + Fetch an attribute from the ``Module`` hierarchy of ``self.module``. + + Args: + target (str): The fully-qualified name of the attribute to fetch + + Return: + Any: The value of the attribute. + """ + target_atoms = target.split(".") + attr_itr = self.module + for i, atom in enumerate(target_atoms): + if not hasattr(attr_itr, atom): + raise RuntimeError( + f"Node referenced nonexistent target {'.'.join(target_atoms[: i + 1])}" + ) + attr_itr = getattr(attr_itr, atom) + return attr_itr + + @compatibility(is_backward_compatible=True) + def fetch_args_kwargs_from_env(self, n: Node) -> tuple[tuple, dict]: + """ + Fetch the concrete values of ``args`` and ``kwargs`` of node ``n`` + from the current execution environment. + + Args: + n (Node): The node for which ``args`` and ``kwargs`` should be fetched. + + Return: + Tuple[Tuple, Dict]: ``args`` and ``kwargs`` with concrete values for ``n``. + """ + args = self.map_nodes_to_values(n.args, n) + assert isinstance(args, tuple) + kwargs = self.map_nodes_to_values(n.kwargs, n) + assert isinstance(kwargs, dict) + return args, kwargs + + @compatibility(is_backward_compatible=True) + def map_nodes_to_values(self, args: Argument, n: Node) -> Argument: + """ + Recursively descend through ``args`` and look up the concrete value + for each ``Node`` in the current execution environment. + + Args: + args (Argument): Data structure within which to look up concrete values + + n (Node): Node to which ``args`` belongs. This is only used for error reporting. + """ + + def load_arg(n_arg: Node) -> Any: + if n_arg not in self.env: + raise RuntimeError( + f"Node {n} referenced nonexistent value {n_arg}! Run Graph.lint() " + f"to diagnose such issues" + ) + return self.env[n_arg] + + return map_arg(args, load_arg) + + +@compatibility(is_backward_compatible=True) +class Transformer(Interpreter): + """ + ``Transformer`` is a special type of interpreter that produces a + new ``Module``. It exposes a ``transform()`` method that returns + the transformed ``Module``. ``Transformer`` does not require + arguments to run, as ``Interpreter`` does. ``Transformer`` works + entirely symbolically. + + Example: + + Suppose we want to swap all instances of ``torch.neg`` with + ``torch.sigmoid`` and vice versa (including their ``Tensor`` + method equivalents). We could subclass ``Transformer`` like so:: + + class NegSigmSwapXformer(Transformer): + def call_function( + self, + target: "Target", + args: Tuple[Argument, ...], + kwargs: Dict[str, Any], + ) -> Any: + if target is torch.sigmoid: + return torch.neg(*args, **kwargs) + return super().call_function(target, args, kwargs) + + def call_method( + self, + target: "Target", + args: Tuple[Argument, ...], + kwargs: Dict[str, Any], + ) -> Any: + if target == "neg": + call_self, *args_tail = args + return call_self.sigmoid(*args_tail, **kwargs) + return super().call_method(target, args, kwargs) + + + def fn(x): + return torch.sigmoid(x).neg() + + + gm = torch.fx.symbolic_trace(fn) + + transformed: torch.nn.Module = NegSigmSwapXformer(gm).transform() + input = torch.randn(3, 4) + torch.testing.assert_close(transformed(input), torch.neg(input).sigmoid()) + + Args: + module (GraphModule): The ``Module`` to be transformed. + """ + + @compatibility(is_backward_compatible=True) + def __init__(self, module): + super().__init__(module) + self.new_graph = Graph() + self.new_graph.set_codegen(module.graph._codegen) + + class TransformerTracer(Tracer): + def __init__(self, graph: Graph): + super().__init__() + self.graph = graph + self.tensor_attrs: dict[torch.Tensor, str] = {} # type: ignore[assignment] + + def is_leaf_module(self, _, __) -> bool: + return True + + self.tracer = TransformerTracer(self.new_graph) + self.tracer.root = module + + @compatibility(is_backward_compatible=True) + def placeholder( + self, target: "Target", args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Proxy: + """ + Execute a ``placeholder`` node. In ``Transformer``, this is + overridden to insert a new ``placeholder`` into the output + graph. + + Args: + target (Target): The call target for this node. See + `Node `__ for + details on semantics + args (Tuple): Tuple of positional args for this invocation + kwargs (Dict): Dict of keyword arguments for this invocation + """ + assert isinstance(target, str) + default_value = next(iter(args)) if args else inspect.Signature.empty + return Proxy( + self.new_graph.placeholder(target, default_value=default_value), self.tracer + ) + + @compatibility(is_backward_compatible=True) + def get_attr( + self, target: "Target", args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Proxy: + """ + Execute a ``get_attr`` node. In ``Transformer``, this is + overridden to insert a new ``get_attr`` node into the output + graph. + + Args: + target (Target): The call target for this node. See + `Node `__ for + details on semantics + args (Tuple): Tuple of positional args for this invocation + kwargs (Dict): Dict of keyword arguments for this invocation + """ + assert isinstance(target, str) + return self.tracer.create_proxy("get_attr", target, args, kwargs) + + @compatibility(is_backward_compatible=True) + def call_module( + self, target: "Target", args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + # Override so that the leaf module policy from `self.tracer` is respected. + assert isinstance(target, str) + submod = self.fetch_attr(target) + return self.tracer.call_module(submod, submod.forward, args, kwargs) + + @compatibility(is_backward_compatible=True) + def call_function( + self, target: "Target", args: tuple[Argument, ...], kwargs: dict[str, Any] + ) -> Any: + # Override so that functions that were wrapped are still wrapped. + return self.tracer.create_proxy("call_function", target, args, kwargs) + + @compatibility(is_backward_compatible=True) + def transform(self) -> GraphModule: + """ + Transform ``self.module`` and return the transformed + ``GraphModule``. + """ + with fx_traceback.preserve_node_meta(): + result = super().run(enable_io_processing=False) + if result is not None: + + def strip_proxy(a: Union[Argument, Proxy]) -> Any: + return a.node if isinstance(a, Proxy) else a + + new_output_node = self.new_graph.output(map_aggregate(result, strip_proxy)) + # also preserve the metadata from the old output node, if it exists + old_output_node = list(self.graph.nodes)[-1] + assert old_output_node.op == "output" + for k, v in old_output_node.meta.items(): + new_output_node.meta[k] = v + + return _make_graph_module(self.module, self.new_graph) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/node.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/node.py new file mode 100644 index 0000000000000000000000000000000000000000..4af5ed9d82fe202265e4c3dc17665c91439197de --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/node.py @@ -0,0 +1,893 @@ +# Nodes represent a definition of a value in our graph of operators. +import builtins +import inspect +import logging +import operator +import types +import typing +from collections.abc import Callable, Iterable, Mapping, Sequence +from typing import Any, Optional, TYPE_CHECKING, TypeAlias, Union +from typing_extensions import ParamSpec, TypeVar + +import torch +from torch._C import _fx_map_aggregate, _fx_map_arg, _NodeBase +from torch.fx.operator_schemas import ( + ArgsKwargsPair, + normalize_function, + normalize_module, +) +from torch.utils._dtype_abbrs import dtype_abbrs + +from .._ops import ops as _ops +from ._compatibility import compatibility + + +if TYPE_CHECKING: + from .graph import Graph + +__all__ = ["Node", "map_arg", "map_aggregate", "has_side_effect"] + +log = logging.getLogger(__name__) + +BaseArgumentTypes = Union[ + str, + int, + float, + bool, + complex, + torch.dtype, + torch.Tensor, + torch.device, + torch.memory_format, + torch.layout, + torch._ops.OpOverload, + torch.SymInt, + torch.SymBool, + torch.SymFloat, +] +base_types = typing.get_args(BaseArgumentTypes) + +Target: TypeAlias = Union[Callable[..., Any], str] + +Argument = Optional[ + Union[ + tuple["Argument", ...], + Sequence["Argument"], + Mapping[str, "Argument"], + slice, # Slice[Argument, Argument, Argument], but slice is not a templated type in typing + range, + "Node", + BaseArgumentTypes, + ] +] +# pyrefly: ignore [invalid-annotation] +ArgumentT = TypeVar("ArgumentT", bound=Argument) +_P = ParamSpec("_P") +_R = TypeVar("_R") + +_legal_ops = dict.fromkeys( + [ + "placeholder", + "call_method", + "call_module", + "call_function", + "get_attr", + "output", + "root", + ] +) + +# Dynamo is unable to trace global set[Callable].__contains__. +# See https://github.com/pytorch/pytorch/issues/145761. Since we only have +# a handful of ops so switch to list of callables. +_side_effectful_need_to_be_preserved_pre_dispatch: list[Callable[..., Any]] = [ + torch._C._set_grad_enabled, + torch.amp._enter_autocast, + torch.amp._exit_autocast, +] + +# TODO: Either refactor this into 2 functions 1 dce for functional graphs and 1 dce for all graphs, +# or add logic to correctly mark all inplace ops as side effectful. +# +# NOTE: For new operators, please do not add to this set! +# Instead, consider using the effects system via +# torch.library._register_effectful_op() for operators. +# +# This _side_effectful_functions set is only for: +# - Legacy functions that aren't operators (e.g., profiler ops, asserts) +# - Things that cannot be marked via the normal effects system +_side_effectful_functions: set[Callable[..., Any]] = { + torch._assert, + torch._assert_async, + _ops.aten._assert_async.msg, + _ops.aten._assert_scalar.default, + _ops.aten._assert_tensor_metadata.default, + _ops.aten.sym_constrain_range.default, + _ops.aten.sym_constrain_range_for_size.default, + _ops.profiler._record_function_enter, + _ops.profiler._record_function_enter_new, + _ops.profiler._record_function_exit, + _ops.inductor.accumulate_grad_.default, + operator.setitem, + *_side_effectful_need_to_be_preserved_pre_dispatch, +} + +if hasattr(_ops.inductor, "resize_storage_bytes_"): + _side_effectful_functions.add(_ops.inductor.resize_storage_bytes_.default) + + +@compatibility(is_backward_compatible=False) +def has_side_effect(fn: Callable[_P, _R]) -> Callable[_P, _R]: + """ + Registers a function to not be dead code eliminated by + fx.graph.eliminate_dead_code + + NOTE: For new operators, please do not add to this set! + Instead, consider using the effects system via + torch.library._register_effectful_op() for operators. + + This _side_effectful_functions set is only for: + - Legacy functions that aren't operators (e.g., profiler ops, asserts) + - Things that cannot be marked via the normal effects system + """ + _side_effectful_functions.add(fn) + return fn + + +# this is fixed on master, WAR for 1.5 +def _find_module_of_method(orig_method: Callable[..., Any]) -> str: + name = orig_method.__name__ + module = orig_method.__module__ + if module is not None: + return module + for guess in [torch, torch.nn.functional]: + if getattr(guess, name, None) is orig_method: + return guess.__name__ + raise RuntimeError(f"cannot find module for {orig_method}") + + +# Borrowed from CPython typing module +# https://github.com/python/cpython/blob/f90dc36c15d7fee0efaf6d39e97be0bdf2683e93/Lib/typing.py#L156 +def _type_repr(obj: object) -> str: + """Return the repr() of an object, special-casing types (internal helper). + If obj is a type, we return a shorter version than the default + type.__repr__, based on the module and qualified name, which is + typically enough to uniquely identify a type. For everything + else, we fall back on repr(obj). + """ + # Extension: If we don't ignore GenericAlias then `list[int]` will print + # simply "list". + if isinstance(obj, type) and not isinstance(obj, types.GenericAlias): + if obj.__module__ == "builtins": + return obj.__qualname__ + return f"{obj.__module__}.{obj.__qualname__}" + if obj is ...: + return "..." + if isinstance(obj, types.FunctionType): + return obj.__name__ + return repr(obj) + + +def _get_qualified_name(func: Callable[..., Any]) -> str: + # things like getattr just appear in builtins + if getattr(builtins, func.__name__, None) is func: + return func.__name__ + # torch.Tensor.{fn} + if ( + isinstance(func, (types.MethodDescriptorType, types.WrapperDescriptorType)) + and func is getattr(torch.Tensor, func.__name__, None) + ) or ( + func.__module__ == torch._tensor.__name__ + and func.__qualname__ == f"Tensor.{func.__name__}" + ): + return f"torch.Tensor.{func.__name__}" + name = func.__name__ + if name == "": + # For lambdas, try to get their defining name in the module + try: + name = inspect.getsource(func).split("=")[0].strip() + except Exception as e: + raise RuntimeError("Unable to represent lambda") from e + module = _find_module_of_method(func) + module = module.replace( + "torch._ops", "torch.ops" + ) # WAR for bug in how torch.ops assigns module + # Fixup segment_reduce mismatch + if module == "torch" and name == "segment_reduce": + name = "_" + name + if module == "torch.nn.functional" and name in ("_ScalingType", "_SwizzleType"): + name = name.removeprefix("_") + return f"{module}.{name}" + + +def _format_arg(arg: object, max_list_len: float = float("inf")) -> str: + if hasattr(arg, "_custom_fx_repr_fn"): + return arg._custom_fx_repr_fn() + elif isinstance(arg, list): + items = ", ".join( + _format_arg(a) for idx, a in enumerate(arg) if idx < max_list_len + ) + maybe_len = ( + "" if len(arg) < max_list_len + 1 else f", ...[total_len={len(arg)}]" + ) + return f"[{items}{maybe_len}]" + elif isinstance(arg, tuple): + items = ", ".join( + _format_arg(a) for idx, a in enumerate(arg) if idx < max_list_len + ) + maybe_len = ( + "" if len(arg) < max_list_len + 1 else f", ...[total_len={len(arg)}]" + ) + maybe_comma = "," if len(arg) == 1 else "" + return f"({items}{maybe_comma}{maybe_len})" + elif isinstance(arg, dict): + items_str = ", ".join(f"{k}: {_format_arg(v)}" for k, v in arg.items()) + return f"{{{items_str}}}" + + if isinstance(arg, Node): + return "%" + str(arg) + else: + return str(arg) + + +@compatibility(is_backward_compatible=True) +class Node(_NodeBase): + """ + ``Node`` is the data structure that represents individual operations within + a ``Graph``. For the most part, Nodes represent callsites to various entities, + such as operators, methods, and Modules (some exceptions include nodes that + specify function inputs and outputs). Each ``Node`` has a function specified + by its ``op`` property. The ``Node`` semantics for each value of ``op`` are as follows: + + - ``placeholder`` represents a function input. The ``name`` attribute specifies the name this value will take on. + ``target`` is similarly the name of the argument. ``args`` holds either: 1) nothing, or 2) a single argument + denoting the default parameter of the function input. ``kwargs`` is don't-care. Placeholders correspond to + the function parameters (e.g. ``x``) in the graph printout. + - ``get_attr`` retrieves a parameter from the module hierarchy. ``name`` is similarly the name the result of the + fetch is assigned to. ``target`` is the fully-qualified name of the parameter's position in the module hierarchy. + ``args`` and ``kwargs`` are don't-care + - ``call_function`` applies a free function to some values. ``name`` is similarly the name of the value to assign + to. ``target`` is the function to be applied. ``args`` and ``kwargs`` represent the arguments to the function, + following the Python calling convention + - ``call_module`` applies a module in the module hierarchy's ``forward()`` method to given arguments. ``name`` is + as previous. ``target`` is the fully-qualified name of the module in the module hierarchy to call. + ``args`` and ``kwargs`` represent the arguments to invoke the module on, *excluding the self argument*. + - ``call_method`` calls a method on a value. ``name`` is as similar. ``target`` is the string name of the method + to apply to the ``self`` argument. ``args`` and ``kwargs`` represent the arguments to invoke the module on, + *including the self argument* + - ``output`` contains the output of the traced function in its ``args[0]`` attribute. This corresponds to the "return" statement + in the Graph printout. + """ + + _args: tuple["Argument", ...] + _kwargs: dict[str, "Argument"] + graph: "Graph" + # unique name of value being created + name: str + # the kind of operation = placeholder|call_method|call_module|call_function|get_attr + op: str + # for method/module/function, the name of the method/module/function/attr + # being invoked, e.g add, layer1, or torch.add + target: "Target" + # All `Node`-valued inputs. Key is the Node, value is don't-care. + # The public API for this is `all_input_nodes`, this private attribute + # should not be accessed directly. + _input_nodes: dict["Node", None] + # All of the nodes that use the value produced by this Node + # Note one user may correspond to several uses, e.g. the node for ``x + x`` + # would appear once here, but represents two uses. + # Is a dict to act as an "ordered set". Keys are significant, value dont-care + users: dict["Node", None] + # Type expression representing the output value of this node. + # This should contain the same class of Type objects that would appear + # as type annotations for function inputs/outputs. + # + # For placeholder nodes, this value will be used to type-annotate the + # generated function parameters. + # For the return node, this value will be used to type-annotate the + # generated function return type. (Note this is a special case. ``return`` + # does not produce a value, it's more of a notation. Thus, this value + # describes the type of args[0] in the ``return`` node. + type: Optional[Any] + _sort_key: Any + # If set, use this fn to print this node + _repr_fn: Optional[Callable[["Node"], str]] + # Dictionary to store metadata passes need to do their + # transformations. This metadata is preserved across node copies + meta: dict[str, Any] + + @compatibility(is_backward_compatible=True) + def __init__( + self, + graph: "Graph", + name: str, + op: str, + target: "Target", + args: tuple["Argument", ...], + kwargs: dict[str, "Argument"], + return_type: Optional[Any] = None, + ) -> None: + """ + Instantiate an instance of ``Node``. Note: most often, you want to use the + Graph APIs, i.e. ``Graph.call_module``, ``Graph.call_method``, etc. rather + than instantiating a ``Node`` directly. + + Args: + graph (Graph): The ``Graph`` to which this ``Node`` should belong. + + name (str): The name to which the output of this ``Node`` should be assigned + + op (str): The opcode for this ``Node``. Can be one of 'placeholder', + 'call_method', 'call_module', 'call_function', 'get_attr', + 'output' + + target ('Target'): The target this op should call. See the broader + ``Node`` docstring for more details. + + args (Tuple['Argument']): The args to be passed to ``target`` + + kwargs (Dict[str, 'Argument']): The kwargs to be passed to ``target`` + + return_type (Optional[Any]): The python type expression representing the + type of the output of this node. This field can be used for + annotation of values in the generated code or for other types + of analyses. + """ + if op == "call_function": + if not callable(target): + raise ValueError( + f"Node [graph = {graph}, name = '{name}'] target {target} has type {torch.typename(target)} " + "but a Callable is expected" + ) + else: + assert op in _legal_ops + if not isinstance(target, str): + raise ValueError( + f"Node [graph = {graph}, name = '{name}'] target {target} has type {torch.typename(target)} " + "but a str is expected" + ) + super().__init__(graph, name, op, target, return_type) + self._update_args_kwargs(args, kwargs) + + def __getstate__(self) -> dict[str, Any]: + return { + **self.__dict__, + "graph": self.graph, + "name": self.name, + "op": self.op, + "target": self.target, + "type": self.target, + "_sort_key": self._sort_key, + "_args": self._args, + "_kwargs": self._kwargs, + "_erased": self._erased, + "_prev": self._prev, + "_next": self._next, + "_input_nodes": self._input_nodes, + "users": self.users, + "_repr_fn": self._repr_fn, + "meta": self.meta, + } + + def __setstate__(self, state: dict[str, Any]) -> None: + for k, v in state.items(): + setattr(self, k, v) + + @property + def next(self) -> "Node": + """ + Returns the next ``Node`` in the linked list of Nodes. + + Returns: + + The next ``Node`` in the linked list of Nodes. + """ + return self._next + + @property + def prev(self) -> "Node": + """ + Returns the previous ``Node`` in the linked list of Nodes. + + Returns: + + The previous ``Node`` in the linked list of Nodes. + """ + return self._prev + + @compatibility(is_backward_compatible=True) + def prepend(self, x: "Node") -> None: + """ + Insert x before this node in the list of nodes in the graph. Example:: + + Before: p -> self + bx -> x -> ax + After: p -> x -> self + bx -> ax + + Args: + x (Node): The node to put before this node. Must be a member of the same graph. + """ + # pyrefly: ignore [missing-attribute] + self._prepend(x) + + @compatibility(is_backward_compatible=True) + def append(self, x: "Node") -> None: + """ + Insert ``x`` after this node in the list of nodes in the graph. + Equivalent to ``self.next.prepend(x)`` + + Args: + x (Node): The node to put after this node. Must be a member of the same graph. + """ + # pyrefly: ignore [missing-attribute] + self._next._prepend(x) + + @property + def args(self) -> tuple[Argument, ...]: + """ + The tuple of arguments to this ``Node``. The interpretation of arguments + depends on the node's opcode. See the :class:`Node` docstring for more + information. + + Assignment to this property is allowed. All accounting of uses and users + is updated automatically on assignment. + """ + return self._args + + @args.setter + def args(self, a: tuple[Argument, ...]) -> None: + """ + Set the tuple of arguments to this Node. The interpretation of arguments + depends on the node's opcode. See the ``fx.Graph`` docstring for more + information. + """ + # DO NOT CALL `_update_args_kwargs` directly. The correct way to + # set `args` is via direct assignment, i.e. `node.args = new_args` + self._update_args_kwargs(a, self._kwargs) + + @property + def kwargs(self) -> dict[str, Argument]: + """ + The dict of keyword arguments to this ``Node``. The interpretation of arguments + depends on the node's opcode. See the :class:`Node` docstring for more + information. + + Assignment to this property is allowed. All accounting of uses and users + is updated automatically on assignment. + """ + return self._kwargs + + @kwargs.setter + def kwargs(self, k: dict[str, Argument]) -> None: + """ + Set the dict of kwargs to this Node. The interpretation of arguments + depends on the node's opcode. See the ``fx.Graph`` docstring for more + information. + """ + # DO NOT CALL `_update_args_kwargs` directly. The correct way to + # set `args` is via direct assignment, i.e. `node.kwargs = new_kwargs` + self._update_args_kwargs(self._args, k) + + @property + def all_input_nodes(self) -> list["Node"]: + """ + Return all Nodes that are inputs to this Node. This is equivalent to + iterating over ``args`` and ``kwargs`` and only collecting the values that + are Nodes. + + Returns: + + List of ``Nodes`` that appear in the ``args`` and ``kwargs`` of this + ``Node``, in that order. + """ + return list(self._input_nodes.keys()) + + @compatibility(is_backward_compatible=True) + def update_arg(self, idx: int, arg: Argument) -> None: + """ + Update an existing positional argument to contain the new value + ``arg``. After calling, ``self.args[idx] == arg``. + + Args: + + idx (int): The index into ``self.args`` of the element to update + arg (Argument): The new argument value to write into ``args`` + """ + args = list(self.args) + args[idx] = arg + self.args = tuple(args) + + @compatibility(is_backward_compatible=True) + def insert_arg(self, idx: int, arg: Argument) -> None: + """ + Insert an positional argument to the argument list with given index. + + Args: + + idx (int): The index of the element in ``self.args`` to be inserted before. + arg (Argument): The new argument value to insert into ``args`` + """ + assert 0 <= idx <= len(self.args), ( + "insert_args index must be between 0 and len(self.args)" + ) + args_left = self.args[:idx] + args_right = self.args[idx:] + + self._args = args_left + (arg,) + args_right + + _new_input_nodes: dict[Node, None] = {} + _fx_map_arg(arg, _new_input_nodes.setdefault) + + for new_use in _new_input_nodes: + if new_use not in self._input_nodes: + self._input_nodes.setdefault(new_use) + new_use.users.setdefault(self) + + @compatibility(is_backward_compatible=True) + def update_kwarg(self, key: str, arg: Argument) -> None: + """ + Update an existing keyword argument to contain the new value + ``arg``. After calling, ``self.kwargs[key] == arg``. + + Args: + + key (str): The key in ``self.kwargs`` of the element to update + arg (Argument): The new argument value to write into ``kwargs`` + """ + self.kwargs = {**self.kwargs, key: arg} + + @property + def stack_trace(self) -> Optional[str]: + """ + Return the Python stack trace that was recorded during tracing, if any. + When traced with fx.Tracer, this property is usually populated by + `Tracer.create_proxy`. To record stack traces during tracing for debug purposes, + set `record_stack_traces = True` on the `Tracer` instance. + When traced with dynamo, this property will be populated by default by + `OutputGraph.create_proxy`. + + stack_trace would have the innermost frame at the end of the string. + """ + return self.meta.get("stack_trace", None) + + @stack_trace.setter + def stack_trace(self, trace: Optional[str]) -> None: + self.meta["stack_trace"] = trace + + def __repr__(self) -> str: + if self._repr_fn: + return self._repr_fn(self) + return self.name + + @staticmethod + def _pretty_print_target(target: object) -> str: + """ + Make target printouts more user-friendly. + 1) builtins will be printed as `builtins.xyz` + 2) operators will be printed as `operator.xyz` + 3) other callables will be printed with qualified name, e.g. torch.add + """ + if isinstance(target, str): + return target + if hasattr(target, "__module__"): + name = getattr(target, "__name__", None) + if name is None: + # Just to be defensive, if we don't have `__name__`, get the + # qualname. Not sure if this happens for any members of `operator` + # or `builtins`. This fallback path is not as good, since e.g. + # things in `operator` have `_operator` as their __module__. + # TODO: THIS IS BROKEN: _get_qualified_name calls `__name__` + return _get_qualified_name(target) # type: ignore[arg-type] + if target.__module__ == "builtins": + return f"builtins.{name}" + elif target.__module__ == "_operator": + return f"operator.{name}" + return _get_qualified_name(target) # type: ignore[arg-type] + + @compatibility(is_backward_compatible=True) + def format_node( + self, + placeholder_names: Optional[list[str]] = None, + maybe_return_typename: Optional[list[str]] = None, + *, + include_tensor_metadata: bool = False, + ) -> Optional[str]: + """ + Return a descriptive string representation of ``self``. + + This method can be used with no arguments as a debugging + utility. + + This function is also used internally in the ``__str__`` method + of ``Graph``. Together, the strings in ``placeholder_names`` + and ``maybe_return_typename`` make up the signature of the + autogenerated ``forward`` function in this Graph's surrounding + GraphModule. ``placeholder_names`` and ``maybe_return_typename`` + should not be used otherwise. + + Args: + placeholder_names: A list that will store formatted strings + representing the placeholders in the generated + ``forward`` function. Internal use only. + maybe_return_typename: A single-element list that will store + a formatted string representing the output of the + generated ``forward`` function. Internal use only. + include_tensor_metadata: Whether to include tensor metadata + + Returns: + str: If 1) we're using ``format_node`` as an internal helper + in the ``__str__`` method of ``Graph``, and 2) ``self`` + is a placeholder Node, return ``None``. Otherwise, + return a descriptive string representation of the + current Node. + """ + if self.op == "placeholder": + assert isinstance(self.target, str) + arg_str = self.target + arg_str += arg_str + f": {_type_repr(self.type)}" if self.type else "" + if placeholder_names: + placeholder_names.append(arg_str) + return None + maybe_typename = f"{_type_repr(self.type)} " if self.type else "" + default_val = "(default=" + str(self.args[0]) + ")" if self.args else "" + return f"%{self.name} : {maybe_typename}[num_users={len(self.users)}] = {self.op}[target={self.target}]{default_val}" + elif self.op == "get_attr": + maybe_typename = ( + f"{_type_repr(self.type)} " if self.type is not None else "" + ) + return ( + f"%{self.name} : {maybe_typename}[num_users={len(self.users)}] = " + f"{self.op}[target={self._pretty_print_target(self.target)}]" + ) + elif self.op == "output": + if self.type and maybe_return_typename: + maybe_return_typename[0] = f" -> {_type_repr(self.type)}" + return f"return {self.args[0]}" + else: + + def stringify_shape(shape: Iterable) -> str: + return f"[{', '.join([str(x) for x in shape])}]" + + meta_val = self.meta.get( + "val", + self.meta.get("tensor_meta", self.meta.get("example_value", None)), + ) + type_annotation = "" + if ( + include_tensor_metadata + and isinstance(meta_val, torch.Tensor) + and meta_val.layout + not in ( + torch.sparse_csc, + torch.sparse_csr, + ) + ): + stride_annotation = f"{stringify_shape(meta_val.stride())}" + device_annotation = f"{meta_val.device}" + type_annotation = ( + f'Tensor "{dtype_abbrs[meta_val.dtype]}{stringify_shape(meta_val.shape)}' + f'{stride_annotation}{device_annotation}"' + ) + else: + type_annotation = ( + f"{_type_repr(self.type)} " if self.type is not None else "" + ) + return ( + f"%{self.name} : {type_annotation}[num_users={len(self.users)}] = " + f"{self.op}[target={self._pretty_print_target(self.target)}](" + f"args = {_format_arg(self.args)}, kwargs = {_format_arg(self.kwargs)})" + ) + + @compatibility(is_backward_compatible=True) + def replace_all_uses_with( + self, + replace_with: "Node", + delete_user_cb: Optional[Callable[["Node"], bool]] = None, + *, + propagate_meta: bool = False, + ) -> list["Node"]: + """ + Replace all uses of ``self`` in the Graph with the Node ``replace_with``. + + Args: + + replace_with (Node): The node to replace all uses of ``self`` with. + delete_user_cb (Callable): Callback that is called to determine + whether a given user of the self node should be removed. + propagate_meta (bool): Whether or not to copy all properties + on the .meta field of the original node onto the replacement node. + For safety, this is only valid to do if the replacement node + doesn't already have an existing .meta field. + + Returns: + + The list of Nodes on which this change was made. + """ + if propagate_meta: + assert len(replace_with.meta) == 0, ( + "Called node.replace_all_uses_with(replace_with, propagate_meta=True), " + "but replace_with already has .meta keys" + ) + for k, v in self.meta.items(): + replace_with.meta[k] = v + to_process = [*self.users] + replace_hooks = getattr(self.graph.owning_module, "_replace_hooks", None) + result = [] + for use_node in to_process: + if delete_user_cb is not None and not delete_user_cb(use_node): + continue + result.append(use_node) + if replace_hooks: + for replace_hook in replace_hooks: + replace_hook(old=self, new=replace_with.name, user=use_node) + # pyrefly: ignore [missing-attribute] + use_node._replace_input_with(self, replace_with) # type: ignore[attr-defined] + return result + + @compatibility(is_backward_compatible=False) + def is_impure(self, impure_random: bool = True) -> bool: + """ + Returns whether this op is impure, i.e. if its op is a placeholder or + output, or if a call_function or call_module which is impure. + + Args: + impure_random (bool): Whether to treat rand op as impure. + + Returns: + + bool: If the op is impure or not. + """ + # Placeholders and outputs are always impure for DCE purposes + if self.op in {"placeholder", "output"}: + return True + + # Check if an impure module. + if self.op == "call_module": + assert self.graph.owning_module is not None, ( + "self.graph.owning_module not set for purity check" + ) + target_mod = self.graph.owning_module.get_submodule(self.target) + assert target_mod is not None, ( + f"Did not find expected submodule target {self.target}" + ) + # NOTE: here we can end up considering GraphModule submodules pure, + # even if they contain impure ops. It may not be safe to change + # because this function is used by graph.eliminate_dead_code, + # and some users depend on current elimination behavior. + return getattr(target_mod, "_is_impure", False) + + # For call_function, delegate to the unified has_side_effects function + if self.op == "call_function": + from torch._library.utils import is_impure + + return is_impure( + self.target, # pyrefly: ignore[bad-argument-type] + args=self.args, + kwargs=self.kwargs, + impure_random=impure_random, + ) + + return False + + @compatibility(is_backward_compatible=False) + def normalized_arguments( + self, + root: torch.nn.Module, + arg_types: Optional[tuple[Any]] = None, + kwarg_types: Optional[dict[str, Any]] = None, + normalize_to_only_use_kwargs: bool = False, + ) -> Optional[ArgsKwargsPair]: + """ + Returns normalized arguments to Python targets. This means that + `args/kwargs` will be matched up to the module/functional's + signature and return exclusively kwargs in positional order + if `normalize_to_only_use_kwargs` is true. + Also populates default values. Does not support positional-only + parameters or varargs parameters. + + Supports module calls. + + May require `arg_types` and `kwarg_types` in order to disambiguate overloads. + + Args: + root (torch.nn.Module): Module upon which to resolve module targets. + arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args + kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs + normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs. + + Returns: + + Returns NamedTuple ArgsKwargsPair, or `None` if not successful. + """ + if self.op == "call_function": + assert callable(self.target) + return normalize_function( + self.target, + self.args, # type: ignore[arg-type] + self.kwargs, + arg_types, + kwarg_types, + normalize_to_only_use_kwargs=normalize_to_only_use_kwargs, + ) + elif self.op == "call_module": + assert isinstance(self.target, str) + return normalize_module( + root, + self.target, + self.args, # type: ignore[arg-type] + self.kwargs, + normalize_to_only_use_kwargs=normalize_to_only_use_kwargs, + ) + + return None + + @compatibility(is_backward_compatible=True) + def replace_input_with(self, old_input: "Node", new_input: "Node") -> None: + """ + Loop through input nodes of ``self``, and replace all instances of + ``old_input`` with ``new_input``. + + Args: + + old_input (Node): The old input node to be replaced. + new_input (Node): The new input node to replace ``old_input``. + """ + + m = self.graph.owning_module + if getattr(m, "_replace_hooks", None): + for replace_hook in m._replace_hooks: + replace_hook(old=old_input, new=new_input.name, user=self) + + # pyrefly: ignore [missing-attribute] + self._replace_input_with(old_input, new_input) # type: ignore[attr-defined] + + def _rename(self, candidate: str) -> None: + if candidate == self.name: + return + name = self.graph._graph_namespace.create_name(candidate, None) + self.name = name + self.graph._graph_namespace._rename_object(self, name) + + def __setattr__(self, name: str, value: Any) -> None: + if name == "name" and hasattr(self, "name"): + m = self.graph.owning_module + if getattr(m, "_replace_hooks", None): + assert isinstance(value, str) + for user in self.users: + for replace_hook in m._replace_hooks: + replace_hook(old=self, new=value, user=user) + update = False + if ( + hasattr(self, name) + and hasattr(self.graph, "_find_nodes_lookup_table") + and self in self.graph._find_nodes_lookup_table + ): + update = True + self.graph._find_nodes_lookup_table.remove(self) + object.__setattr__(self, name, value) + if update: + self.graph._find_nodes_lookup_table.insert(self) + + +@compatibility(is_backward_compatible=True) +def map_arg(a: ArgumentT, fn: Callable[[Node], Argument]) -> ArgumentT: + """ + Apply fn recursively to each Node appearing in arg. + + arg may be a list, tuple, slice, or dict with string keys: the return value will + have the same type and structure. + """ + assert callable(fn), "torch.fx.map_arg(a, fn): fn must be a callable" + return _fx_map_arg(a, fn) + + +@compatibility(is_backward_compatible=True) +def map_aggregate(a: ArgumentT, fn: Callable[[Argument], Argument]) -> ArgumentT: + """ + Apply fn recursively to each object appearing in arg. + + arg may be a list, tuple, slice, or dict with string keys: the return value will + have the same type and structure. + """ + return _fx_map_aggregate(a, fn) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/operator_schemas.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/operator_schemas.py new file mode 100644 index 0000000000000000000000000000000000000000..397d4c5996ee9024ecebf2e306d45a4d27b36c7f --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/operator_schemas.py @@ -0,0 +1,570 @@ +# mypy: allow-untyped-defs +import enum +import inspect +import numbers +import types +import typing +import warnings +from collections.abc import Callable +from typing import Any, cast, NamedTuple, Optional, TYPE_CHECKING + +import torch +from torch._jit_internal import boolean_dispatched +from torch._ops import OpOverload, OpOverloadPacket + +from ._compatibility import compatibility + + +if TYPE_CHECKING: + from .node import Argument + +__all__ = [ + "ArgsKwargsPair", + "check_for_mutable_operation", + "get_signature_for_torch_op", + "create_type_hint", + "type_matches", + "normalize_function", + "normalize_module", +] + + +@compatibility(is_backward_compatible=False) +class ArgsKwargsPair(NamedTuple): + """ + Simple named tuple for wrapping args/kwargs pairs. + """ + + args: tuple[Any, ...] + kwargs: dict[str, Any] + + +_manual_overrides: dict[Callable, list[inspect.Signature]] = {} + + +def _nonzero_schemas(): + signatures = [] + + def nonzero(self): + pass + + signatures.append(inspect.signature(nonzero)) + + def nonzero(self, *, as_tuple: bool): # type: ignore[no-redef] + pass + + signatures.append(inspect.signature(nonzero)) + + return signatures + + +_manual_overrides[torch.nonzero] = _nonzero_schemas() + + +class _FakeGlobalNamespace: + def __getattr__(self, name): + if name == "torch": + return torch + raise RuntimeError("Expected a torch namespace lookup") + + +_type_eval_globals = { + "Tensor": torch.Tensor, + "Device": torch.device, + "Layout": torch.layout, + "number": numbers.Number, + "Future": torch.jit.Future, + "AnyEnumType": enum.Enum, + "QScheme": torch.qscheme, + "__torch__": _FakeGlobalNamespace(), + "NoneType": type(None), + "Storage": torch.UntypedStorage, + "t": typing.TypeVar("t"), + "PyObject": Any, +} +for k in dir(typing): + _type_eval_globals[k] = getattr(typing, k) + + +def _torchscript_type_to_python_type(ts_type: "torch._C.JitType") -> Any: + """ + Convert a TorchScript type to a Python type (including subtypes) via + eval'ing the annotation_str. _type_eval_globals sets up expressions + like "List" and "Future" to map to actual types (typing.List and jit.Future) + """ + return eval(ts_type.annotation_str, _type_eval_globals) + + +def _torchscript_schema_to_signature_impl( + ts_schema: torch._C.FunctionSchema, +) -> inspect.Signature: + from inspect import Parameter + + parameters: list[Parameter] = [] + for arg in ts_schema.arguments: + arg_type = _torchscript_type_to_python_type(arg.type) + default = arg.default_value if arg.has_default_value() else Parameter.empty + # TODO: Figure out if this is safe. It seems like when generating the type signatures for + # PythonArgParser, we emit signatures with `input` instead of `self` as the first tensor + # argument name. Downstream, if someone converts that positional argument to a keyword + # argument, the name mismatch will break things, so here we're going to normalize the + # name to "input" + name = arg.name if arg.name != "self" else "input" + kind = ( + Parameter.KEYWORD_ONLY + if arg.kwarg_only + else Parameter.POSITIONAL_OR_KEYWORD + ) + # "from" is a keyword therefore it must be a POSITIONAL_ONLY argument + if name == "from": + assert kind == Parameter.POSITIONAL_OR_KEYWORD + # ParameterKind type is internal implementation detail to inspec package + # which makes it hard to do type annotation + kind = Parameter.POSITIONAL_ONLY # type: ignore[assignment] + # This renders all previous arguments to positional only + + for idx, p in enumerate(parameters): + assert p.kind == Parameter.POSITIONAL_OR_KEYWORD + parameters[idx] = Parameter( + name=p.name, + kind=Parameter.POSITIONAL_ONLY, + default=p.default, + annotation=p.annotation, + ) + + parameters.append( + Parameter(name=name, kind=kind, default=default, annotation=arg_type) + ) + return_types = [ + _torchscript_type_to_python_type(ret.type) for ret in ts_schema.returns + ] + if len(return_types) == 0: + return_type = None + elif len(return_types) == 1: + return_type = return_types[0] + else: + return_type = tuple(return_types) + + return inspect.Signature(parameters, return_annotation=return_type) + + +_SCHEMA_TO_SIGNATURE_CACHE: dict[tuple[str, str], inspect.Signature] = {} + + +def _torchscript_schema_to_signature( + ts_schema: torch._C.FunctionSchema, +) -> inspect.Signature: + # Cached as it's called in the hot path of FakeTensor dispatch + cache_key = ts_schema.name, ts_schema.overload_name + cache_val = _SCHEMA_TO_SIGNATURE_CACHE.get(cache_key) + if cache_val is not None: + return cache_val + + res = _torchscript_schema_to_signature_impl(ts_schema) + _SCHEMA_TO_SIGNATURE_CACHE[cache_key] = res + return res + + +@compatibility(is_backward_compatible=False) +def check_for_mutable_operation( + target: Callable, args: tuple["Argument", ...], kwargs: dict[str, "Argument"] +): + signatures, schemas = get_signature_for_torch_op(target, return_schemas=True) + + if signatures and schemas: + matched_schemas = [] + + # Iterate through all of the schema until we find one that matches + # If one matches, populate `new_args_and_kwargs` with the new args/kwargs + # values. If none matches, `new_args_and_kwargs` will be None + for candidate_signature, schema in zip(signatures, schemas): + try: + candidate_signature.bind(*args, **kwargs) + matched_schemas.append((candidate_signature, schema)) + except TypeError: + continue + + def throw_if_mutable(schema): + if schema.is_mutable: + raise RuntimeError( + f"Tried to trace mutable operation {schema}. FX only supports functional " + f"code, so operations that mutate operands in-place (e.g. via `out` arguments) " + f"are not supported" + ) + + if len(matched_schemas) == 0: + # Did not match any schema. Cannot check for mutation + pass + elif len(matched_schemas) == 1: + # Matched exactly one schema, unambiguous + _, schema_to_check = matched_schemas[0] + throw_if_mutable(schema_to_check) + else: + # Ambiguous schema match. Since mutability checking is best effort, + # do nothing. + pass + + +@compatibility(is_backward_compatible=False) +def get_signature_for_torch_op(op: Callable, return_schemas: bool = False): + """ + Given an operator on the `torch` namespace, return a list of `inspect.Signature` + objects corresponding to the overloads of that op.. May return `None` if a signature + could not be retrieved. + + Args: + op (Callable): An operator on the `torch` namespace to look up a signature for + + Returns: + Optional[List[inspect.Signature]]: A list of signatures for the overloads of this + operator, or None if the operator signatures could not be retrieved. If + return_schemas=True, returns a tuple containing the optional Python signatures + and the optional TorchScript Function signature + """ + if isinstance(op, OpOverload): + schemas = [op._schema] + elif isinstance(op, OpOverloadPacket): + schemas = [getattr(op, overload)._schema for overload in op.overloads()] + else: + override = _manual_overrides.get(op) + if override: + return (override, None) if return_schemas else None + + aten_fn = torch.jit._builtins._find_builtin(op) + + if aten_fn is None: + return (None, None) if return_schemas else None + schemas = torch._C._jit_get_schemas_for_operator(aten_fn) + + signatures = [_torchscript_schema_to_signature(schema) for schema in schemas] + return (signatures, schemas) if return_schemas else signatures + + +@compatibility(is_backward_compatible=False) +def create_type_hint(x): + """ + Produces a type hint for the given argument. + + The :func:`create_type_hint` looks for a type hint compatible with the input argument `x`. + + If `x` is a `list` or `tuple`, it looks for an object in the list whose type is a superclass + of the rest, and uses that as `base_type` for the `List` or `Tuple` to be returned. + If no such object is found, it defaults to `List[Any]`. + + If `x` is neither a `list` nor a `tuple`, it returns `x`. + """ + try: + if isinstance(x, (list, tuple)): + # todo(chilli): Figure out the right way for mypy to handle this + if isinstance(x, list): + + def ret_type(x): + return list[x] # type: ignore[valid-type] + + else: + + def ret_type(x): + return tuple[x, ...] # type: ignore[valid-type] + + if len(x) == 0: + return ret_type(Any) + base_type = x[0] + for t in x: + if issubclass(t, base_type): + continue + elif issubclass(base_type, t): + base_type = t + else: + return ret_type(Any) + return ret_type(base_type) + except Exception: + # We tried to create a type hint for list but failed. + warnings.warn( + f"We were not able to successfully create type hint from the type {x}" + ) + return x + + +@compatibility(is_backward_compatible=False) +def type_matches(signature_type: Any, argument_type: Any): + sig_origin_type = getattr(signature_type, "__origin__", signature_type) + + if signature_type is argument_type: + return True + + # Union types in signature. Given type needs to match one of the + # contained types in the Union + if sig_origin_type is typing.Union and signature_type != argument_type: + sig_contained = signature_type.__args__ + return any(type_matches(c, argument_type) for c in sig_contained) + + if getattr(signature_type, "__origin__", None) is list: + sig_el_type = signature_type.__args__[0] + + # int can be promoted to list[int] + if argument_type is int and sig_el_type is int: + return True + + if not inspect.isclass(sig_el_type): + warnings.warn( + f"Does not support nested parametric types, got {signature_type}. Please file a bug." + ) + return False + if getattr(argument_type, "__origin__", None) is list: + return issubclass(argument_type.__args__[0], sig_el_type) + + def is_homogeneous_tuple(t): + if getattr(t, "__origin__", None) is not tuple: + return False + contained = t.__args__ + if t.__args__ == ((),): # Tuple[()].__args__ == ((),) for some reason + return True + return all((c is Ellipsis) or issubclass(c, sig_el_type) for c in contained) + + # Tuple[T] is accepted for List[T] parameters + return is_homogeneous_tuple(argument_type) + + # Dtype is an int in schemas + if signature_type is int and argument_type is torch.dtype: + return True + + if signature_type is numbers.Number and argument_type in {int, float}: + return True + if inspect.isclass(argument_type) and inspect.isclass(signature_type): + return issubclass(argument_type, signature_type) + + return False + + +@compatibility(is_backward_compatible=False) +def normalize_function( + target: Callable, + args: tuple[Any, ...], + kwargs: Optional[dict[str, Any]] = None, + arg_types: Optional[tuple[Any]] = None, + kwarg_types: Optional[dict[str, Any]] = None, + normalize_to_only_use_kwargs: bool = False, +) -> Optional[ArgsKwargsPair]: + """ + Returns normalized arguments to PyTorch functions. This means that + `args/kwargs` will be matched up to the functional's + signature and return exclusively kwargs in positional order if + `normalize_to_only_use_kwargs` is True. + Also populates default values. Does not support positional-only + parameters or varargs parameters (*args, **kwargs). Does not support modules. + + May require `arg_types` and `kwarg_types` in order to disambiguate overloads. + + Args: + target (Callable): Function that we are normalizing + args (Tuple[Any]): Tuple of args to the function + kwargs (Optional[Dict[str, Any]]): Dict of kwargs to the function + arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args + kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs + normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs. + + Returns: + + Returns normalized_args_and_kwargs, or `None` if not successful. + """ + if kwargs is None: + kwargs = {} + new_args_and_kwargs = None + if ( + not isinstance(target, types.BuiltinFunctionType) + and not (isinstance(target, (OpOverloadPacket, OpOverload))) + and hasattr(target, "_op") + ): + # ExecuTorch's EdgeOpOverload are a wrapper around PyTorch's OpOverload, + # so we can unwrap it here to get its schema + # Can't import EdgeOpOverload directly because of a circular dependency, + # so checking for "_op" existing is the next best thing. + target = target._op + + # Repeat the condition after checking for the inner _op field. + if not isinstance(target, types.BuiltinFunctionType) and not ( + isinstance(target, (OpOverloadPacket, OpOverload)) + ): + target_for_analysis = target + if target in boolean_dispatched: + # HACK: `boolean_dispatch` as used in `torch.nn.functional` makes it so that we have + # a 2-way dispatch based on a boolean value. Here we check that the `true` and `false` + # branches of the dispatch have exactly the same signature. If they do, use the `true` + # branch signature for analysis. Otherwise, leave this un-normalized + assert not isinstance(target, str) + dispatched = boolean_dispatched[target] + if_true, if_false = dispatched["if_true"], dispatched["if_false"] + if ( + inspect.signature(if_true).parameters + != inspect.signature(if_false).parameters + ): + return None + target_for_analysis = if_true + + assert callable(target_for_analysis) + sig = inspect.signature(inspect.unwrap(target_for_analysis)) + new_args_and_kwargs = _args_kwargs_to_normalized_args_kwargs( + sig, args, kwargs, normalize_to_only_use_kwargs + ) + else: + assert callable(target) + torch_op_schemas = get_signature_for_torch_op(target) + matched_schemas = [] + if torch_op_schemas: + # Iterate through all of the schema until we find one that matches + # If one matches, populate `new_args_and_kwargs` with the new args/kwargs + # values. If none matches, `new_args_and_kwargs` will be None + for candidate_signature in torch_op_schemas: + try: + candidate_signature.bind(*args, **kwargs) + matched_schemas.append(candidate_signature) + except TypeError: + continue + + if len(matched_schemas) == 0: + # Did not match any schema. Cannot normalize + pass + elif len(matched_schemas) == 1: + # Matched exactly one schema, unambiguous + new_args_and_kwargs = _args_kwargs_to_normalized_args_kwargs( + matched_schemas[0], args, kwargs, normalize_to_only_use_kwargs + ) + else: + if arg_types is not None or kwarg_types is not None: + arg_types = arg_types if arg_types else cast(tuple[Any], ()) + kwarg_types = kwarg_types if kwarg_types else {} + for candidate_signature in torch_op_schemas: + sig_matches = True + try: + bound_types = candidate_signature.bind( + *arg_types, **kwarg_types + ) + for arg_name, arg_type in bound_types.arguments.items(): + param = candidate_signature.parameters[arg_name] + sig_matches = sig_matches and type_matches( + param.annotation, arg_type + ) + except TypeError: + sig_matches = False + if sig_matches: + new_args_and_kwargs = ( + _args_kwargs_to_normalized_args_kwargs( + candidate_signature, + args, + kwargs, + normalize_to_only_use_kwargs, + ) + ) + break + else: + # Matched more than one schema. In this situation, the caller must provide the types of + # the arguments of the overload they expect. + schema_printouts = "\n".join( + str(schema) for schema in matched_schemas + ) + raise RuntimeError( + f"Tried to normalize arguments to {torch.typename(target)} but " + f"the schema match was ambiguous! Please provide argument types to " + f"the normalize_arguments() call. Available schemas:\n{schema_printouts}" + ) + + return new_args_and_kwargs + + +@compatibility(is_backward_compatible=False) +def normalize_module( + root: torch.nn.Module, + target: str, + args: tuple[Any], + kwargs: Optional[dict[str, Any]] = None, + normalize_to_only_use_kwargs: bool = False, +) -> Optional[ArgsKwargsPair]: + """ + Returns normalized arguments to PyTorch modules. This means that + `args/kwargs` will be matched up to the functional's + signature and return exclusively kwargs in positional order if + `normalize_to_only_use_kwargs` is True. + Also populates default values. Does not support positional-only + parameters or varargs parameters (*args, **kwargs). + + Args: + root (nn.Module): root module upon which we query modules + target (Callable): Function that we are normalizing + args (Tuple[Any]): Tuple of args to the function + kwargs (Optional[Dict[str, Any]]): Dict of kwargs to the function + normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs. + + Returns: + + Returns normalized_args_and_kwargs, or `None` if not successful. + """ + try: + submod = root.get_submodule(target) + except AttributeError as e: + raise RuntimeError( + f"Tried to normalize node with target {target} but root did not " + f"have that target!" + ) from e + if hasattr(submod.__class__, "__name__"): + classname = submod.__class__.__name__ + if getattr(torch.nn, classname, None) == submod.__class__: + sig = inspect.signature(inspect.unwrap(submod.forward)) + if kwargs is None: + kwargs = {} + new_args_and_kwargs = _args_kwargs_to_normalized_args_kwargs( + sig, args, kwargs, normalize_to_only_use_kwargs + ) + return new_args_and_kwargs + return None + + +def _args_kwargs_to_normalized_args_kwargs( + sig: inspect.Signature, + args: tuple[Any, ...], + kwargs: dict[str, Any], + normalize_to_only_use_kwargs: bool, +) -> Optional[ArgsKwargsPair]: + """ + Given a call target, args, and kwargs, return the arguments normalized into + an ArgsKwargsPair, or None if the type signature is not supported by + this normalization. + + Args: + + sig (inspect.Signature): Signature object for the target + args (Tuple): Arguments that appear at the callsite for `target` + kwargs (Dict): Keyword arguments that appear at the callsite for `target` + normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs. + + Returns: + + Optional[ArgsKwargsPair]: Normalized args and kwargs for `target`, or `None` if + this target is not supported. + """ + + # Don't currently support positional-only + # or varargs (*args, **kwargs) signatures + supported_parameter_types = { + inspect.Parameter.POSITIONAL_OR_KEYWORD, + inspect.Parameter.KEYWORD_ONLY, + } + if any(p.kind not in supported_parameter_types for p in sig.parameters.values()): + # Add an exception for one signature, which is common for random/uniform, i.e.: + # Tensor(a!) self, float from=0, float to=1, *, Generator? generator=None + # `from` is Python keyword and as such functions with that signature should have + # positional-only args, but at the same time they could be dispatched as kwargs + if list(sig.parameters.keys()) != ["input", "from", "to", "generator"]: + return None + + bound_args = sig.bind(*args, **kwargs) + bound_args.apply_defaults() + + new_kwargs: dict[str, Any] = {} + new_args: list[Any] = [] + for i, param in enumerate(sig.parameters): + if not normalize_to_only_use_kwargs and i < len(args): + new_args.append(bound_args.arguments[param]) + else: + new_kwargs[param] = bound_args.arguments[param] + + return ArgsKwargsPair(tuple(new_args), new_kwargs) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3bcb6e1d75a17cbbcf2881b48edc713bd66aa303 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/__init__.py @@ -0,0 +1,15 @@ +from . import ( + graph_drawer, + graph_manipulation, + net_min_base, + operator_support, + param_fetch, + regional_inductor, + reinplace, + runtime_assert, + shape_prop, + split_module, + split_utils, + splitter_base, + tools_common, +) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/_tensorify_python_scalars.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/_tensorify_python_scalars.py new file mode 100644 index 0000000000000000000000000000000000000000..3e4c6c56bddf9244276680aee1935b5a6c1cb048 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/_tensorify_python_scalars.py @@ -0,0 +1,407 @@ +from __future__ import annotations + +import logging +import os +from typing import Any, TYPE_CHECKING, Union + +from sympy import Integer, Number, Symbol +from sympy.logic.boolalg import BooleanAtom + +import torch +import torch.fx as fx +from torch._dynamo.exc import TensorifyScalarRestartAnalysis +from torch._dynamo.symbolic_convert import TensorifyState +from torch._dynamo.utils import get_metrics_context +from torch._prims_common import get_computation_dtype +from torch._subclasses.fake_tensor import FakeTensor +from torch._utils_internal import justknobs_check +from torch.fx._utils import lazy_format_graph_code +from torch.fx.experimental.symbolic_shapes import ( + guard_scalar, + has_free_symbols, + ShapeEnv, +) + +# TODO: refactor +from torch.fx.passes.runtime_assert import _get_sym_val +from torch.fx.proxy import MetaProxy +from torch.utils._sympy.interp import _run_sympy_handler, sympy_interp +from torch.utils._sympy.reference import TensorReferenceAnalysis +from torch.utils._sympy.symbol import symbol_is_type, SymT + + +if TYPE_CHECKING: + from torch._subclasses import fake_tensor + from torch.fx.graph_module import GraphModule + + +__all__: list[str] = [] + +log = logging.getLogger(__name__) +graph_code_log = torch._logging.getArtifactLogger(__name__, "graph_code_verbose") + +# The general shape of this transformation is to look for Tensor operations +# that take a backed SymFloat as an argument, and then redo them as tensor +# compute (with ints and tensors as inputs). For example, add(Tensor, Scalar) +# can be translated into add(Tensor, Tensor). Because Dynamo has already +# arranged for floats to be Tensor inputs to the graph, for typical float +# compute you can entirely translate the Python float operations into Tensor +# operations with only Tensor inputs. +# +# This pass is also responsible for doing CSE on the fly as we do this, since +# you don't want to keep recomputing the same quantity over and over again if +# it's used multiple times. +# +# This pass runs on the JOINT graph produced by AOT Autograd, prior to partitioning. +# The primary goal of this pass is to eliminate floats by replacing TensorScalar +# operations with TensorTensor operations and then Dead Code Elimination (DCE) of +# the item calls, which effectively removes the floats. +# +# This needs to happen before partitioning because it influences partitioning decisions, +# specifically by ensuring that we don't need to save floats across partitions. +# Additionally, there is a separate pass that changes which device computations +# occur on. That pass must be run after this one, but still before partitioning. +# +# HISTORY NOTE: Originally, I wanted to formulate this pass as pushing item() +# calls down, transforming float compute into int compute as we went. If you +# manage to eliminate all float compute, this ends up being equivalent, but +# there is a critical difference when some floats cannot be eliminated: when +# we call item() on them, what should it's SymFloat be? Ideally, it would +# be the same backed SymFloat we had before. But without symbolic expression +# propagation on tensor quantities, repropagating would instead give you an +# unbacked SymFloat. Maybe it is a good idea to implement symbolic propagation +# on 0d scalar tensors, but I decided to go for something simpler to start. +# +# The boring stuff: +# +# * What operators can I Tensor-ify? (Anything with a Scalar argument) +# * How do I Tensor-ify a SymFloat sympy expression (Sympy -> Op Handler -> Tensor) +# +# TODO: make sure this runs before CPU->CUDA pass for cudagraph friendliness + + +SUPPORTED_OPS = { + torch.ops.aten.mul.Tensor: torch.ops.aten.mul.Tensor, + torch.ops.aten.add.Tensor: torch.ops.aten.add.Tensor, + torch.ops.aten.sub.Tensor: torch.ops.aten.sub.Tensor, + torch.ops.aten.div.Tensor: torch.ops.aten.div.Tensor, + torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor, + torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor, + torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor, + torch.ops.aten.le.Scalar: torch.ops.aten.le.Tensor, + torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor, + torch.ops.aten.ne.Scalar: torch.ops.aten.ne.Tensor, +} + + +@torch.fx._compatibility.compatibility(is_backward_compatible=False) +def tensorify_python_scalars( + gm: GraphModule, shape_env: ShapeEnv, fake_mode: fake_tensor.FakeTensorMode +) -> None: + """ + Converts Python scalar operations into Tensor operations within the graph. This pass looks for + Tensor operations that involve SymFloat arguments and transforms them into equivalent operations + that use only Tensor inputs. + + Args: + gm: The FX graph module representing the computation graph. + shape_env: The shape environment responsible for symbolic shape tracking and propagation + during graph transformations. + + Returns: + None + """ + import sympy + + knob = True + if (env := os.getenv("TENSORIFY_PYTHON_SCALARS")) is not None: + if env in ("0", "FALSE"): + knob = False + else: + knob = justknobs_check("pytorch/compiler:tensorify_python_scalars") + if not knob: + return None + + graph = gm.graph + tracer = fx.proxy.GraphAppendingTracer(graph) + expr_to_sym_proxy: dict[sympy.Expr, MetaProxy] = {} + expr_to_tensor_proxy: dict[sympy.Expr, MetaProxy] = {} + tensorified_symbols: set[sympy.Symbol] = set() + should_restart = False + + first_non_placeholder = None + placeholders = set() + for node in graph.nodes: + if node.op != "placeholder": + first_non_placeholder = node + break + else: + placeholders.add(node) + + Analysis = TensorReferenceAnalysis + + def _sympy_interp(expr: sympy.Expr) -> MetaProxy: + # sympy_interp() with hash consing, and special handling for + # generating constants correctly + + # hash cons + if isinstance(expr, Symbol) and expr not in expr_to_tensor_proxy: + # This is guaranteed to be populated by invariant established by + # insert_deferred_runtime_asserts + expr_to_tensor_proxy[expr] = torch.ops.aten.scalar_tensor.default( + expr_to_sym_proxy[expr] + ) + + # cache constants, why not + if isinstance(expr, (Integer, Number, BooleanAtom)): + dtype = None + c: Union[bool, int, float] + if isinstance(expr, BooleanAtom): + dtype = torch.bool + c = bool(expr) + elif isinstance(expr, sympy.Integer): + dtype = torch.int64 + c = int(expr) + elif isinstance(expr, sympy.Number): + dtype = torch.float64 + c = float(expr) + + node = graph.call_function( + torch.ops.aten.scalar_tensor.default, + # pyrefly: ignore [unbound-name] + (c,), + {"dtype": dtype}, + ) + with fake_mode: + # pyrefly: ignore [unbound-name] + node.meta["val"] = torch.ops.aten.scalar_tensor.default(c, dtype=dtype) + expr_to_tensor_proxy[expr] = MetaProxy( + node, + tracer=tracer, + fake_mode=fake_mode, + ) + + if expr in expr_to_tensor_proxy: + return expr_to_tensor_proxy[expr] + + # don't cache + if isinstance(expr, Symbol): + return sympy_interp(Analysis, expr_to_tensor_proxy, expr) # type: ignore[arg-type] + + # hash cons on arguments, run expr handler + expr_to_tensor_proxy[expr] = _run_sympy_handler( + Analysis, + [_sympy_interp(arg) for arg in expr.args], # type: ignore[arg-type] + expr, + ) + + return expr_to_tensor_proxy[expr] + + failed_tensorify_ops: set[str] = set() + nodes = list(graph.nodes) + for i, node in enumerate(nodes[:-1]): + with graph.inserting_before( + nodes[i + 1] if node not in placeholders else first_non_placeholder + ): + # Look for tensor.item() calls on placeholders + if ( + node is not None + and node.op == "call_function" + and node.target is torch.ops.aten._local_scalar_dense.default + ): + dtype = node.args[0].meta["val"].dtype + + assert isinstance(node.args[0], fx.Node), node.args[0] + + s = node.meta["val"].node.expr + + expr_to_sym_proxy[s] = MetaProxy( + node, tracer=tracer, fake_mode=fake_mode + ) + + # only tensorify if the dtype is floating point + if not dtype.is_floating_point: + continue + + expr_to_tensor_proxy[s] = MetaProxy( + node.args[0], tracer=tracer, fake_mode=fake_mode + ) + # Upcast the float tensor to torch.float64 to avoid precision problem + expr_to_tensor_proxy[s] = torch.ops.prims.convert_element_type.default( + expr_to_tensor_proxy[s], torch.float64 + ) + + # pyrefly: ignore [bad-argument-type] + elif (sym_expr := _get_sym_val(node)) is not None: + if sym_expr not in expr_to_sym_proxy and not isinstance( + sym_expr, (sympy.Number, sympy.logic.boolalg.BooleanAtom) + ): + expr_to_sym_proxy[sym_expr] = MetaProxy( + # pyrefly: ignore [bad-argument-type] + node, + tracer=tracer, + fake_mode=fake_mode, + ) + + # Specialize all dimensions that contain symfloats. Here's + # an example test that requires this: + # PYTORCH_OPINFO_SAMPLE_INPUT_INDEX=4 python test/inductor/test_torchinductor_opinfo.py TestInductorOpInfoCUDA.test_comprehensive_nn_functional_interpolate_bicubic_cuda_float32 # noqa: B950 + # pyrefly: ignore [missing-attribute] + val = node.meta.get("val") + if isinstance(val, FakeTensor): + for dim in val.shape: + if isinstance(dim, torch.SymInt): + for s in dim.node.expr.free_symbols: + name = str(s) + if symbol_is_type( + s, SymT.FLOAT + ) and not TensorifyState.should_specialize(name): + # In principle, we could support float input that + # is used to do size compute. The problem is that + # we don't actually want to tensorify the compute + # in this case, which means we need codegen support for + # all symfloats. + TensorifyState.specialize(name) + should_restart = True + + # Look for functions to convert + # pyrefly: ignore [missing-attribute] + if node.op == "call_function" and ( + # pyrefly: ignore [missing-attribute] + replacement_op := SUPPORTED_OPS.get(node.target) + ): + args: list[Any] = [] + transform = False + # pyrefly: ignore [missing-attribute] + compute_dtype = get_computation_dtype(node.meta["val"].dtype) + + # pyrefly: ignore [missing-attribute] + for a in node.args: + if ( + isinstance(a, fx.Node) + and "val" in a.meta + and isinstance(zf := a.meta["val"], torch.SymFloat) + ): + transform = True + try: + proxy = _sympy_interp(zf.node.expr) + except NotImplementedError: + transform = False + break + + # We use _expr instead of expr b/c we want the symbol not the replacement + tensorified_symbols.add(a.meta["val"].node._expr) + + # The upcasting is irrelevant when the compute dtype is bool. This happens + # in cases where we are tensorifying a comparison operator such as + # torch.ops.aten.gt.Tensor + if ( + compute_dtype != torch.bool + and proxy.node.meta["val"].dtype != compute_dtype + ): + proxy = torch.ops.prims.convert_element_type.default( + proxy, compute_dtype + ) + + args.append(proxy) + elif isinstance(a, fx.Node): + args.append(MetaProxy(a, tracer=tracer, fake_mode=fake_mode)) + else: + args.append(a) + + if transform: + replacement_proxy = replacement_op(*args) + + # pyrefly: ignore [missing-attribute] + if compute_dtype != node.meta["val"].dtype: + replacement_proxy = ( + torch.ops.prims.convert_element_type.default( + replacement_proxy, + node.meta["val"].dtype, + ) + ) + + # pyrefly: ignore [missing-attribute] + node.replace_all_uses_with(replacement_proxy.node) + # pyrefly: ignore [bad-argument-type] + graph.erase_node(node) + + metrics_context = get_metrics_context() + if metrics_context.in_progress(): + metrics_context.set( + "tensorify_float_success", True, overwrite=True + ) + else: + # pyrefly: ignore [missing-attribute] + for a in node.args: + if ( + isinstance(a, fx.Node) + and "val" in a.meta + and isinstance(zf := a.meta["val"], torch.SymFloat) + ): + # pyrefly: ignore [missing-attribute] + failed_tensorify_ops.update(str(node.target)) + # pyrefly: ignore [missing-attribute] + log.info("Failed to tensorify %s", str(node.target)) + + # Now do one more pass that specializes all symfloats we didn't manage + # to tensorify away. + for node in reversed(graph.nodes): + if node.op == "output" or node.op == "placeholder": + continue + + with graph.inserting_before(node): + if len(node.users) == 0 and not node.is_impure(): + graph.erase_node(node) + continue + + if isinstance( + (val := node.meta.get("val")), + (torch.SymFloat, torch.SymInt, torch.SymBool), + ): + if has_free_symbols(val.node.expr) and all( + symbol_is_type(s, SymT.FLOAT) for s in val.node.expr.free_symbols + ): + # If all symbols are backed symfloats, we can just specialize the whole node + # and get more precise guards. eg. + # + # zf = a.item() + # zf2 = zf // 2 + # op(.. zf2 ..) + # + # It's better to guard on zf // 2 == 2.0 than zf == 5.0 + + node.replace_all_uses_with(guard_scalar(val)) + graph.erase_node(node) + + # Sometimes by the time we get to tensorify, there have already been + # specializations, eg. in python_arg_parser.h. In these cases, + # placeholder nodes no longer have a reference to their original + # symfloat and thus we need to deduce specializations have happened + # via shape_env.replacements. NB: there's an important invariant here + # that symfloats keep consistent names across restarts. + for k, v in shape_env.var_to_val.items(): + if symbol_is_type(k, SymT.FLOAT) and isinstance(v, sympy.core.numbers.Float): + name = str(k) + if ( + not TensorifyState.should_specialize(name) + and k not in tensorified_symbols + ): + TensorifyState.specialize(name) + should_restart = True + + if should_restart: + # Sledgehammer time. Restart dynamo analysis, keeping track of which input sources + # are no longer needed and should be specialized. Restarting analysis is necessary + # because we need to instruct Dynamo to NOT make these as inputs. + metrics_context = get_metrics_context() + if metrics_context.in_progress(): + metrics_context.set( + "tensorify_float_failure", failed_tensorify_ops, overwrite=True + ) + metrics_context.set("tensorify_float_success", True, overwrite=True) + raise TensorifyScalarRestartAnalysis + + graph_code_log.debug( + "%s", lazy_format_graph_code("tensorify_python_scalars", gm, colored=True) + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/annotate_getitem_nodes.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/annotate_getitem_nodes.py new file mode 100644 index 0000000000000000000000000000000000000000..17b77f6396206e37e51bbb1ff68479b55bc062fd --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/annotate_getitem_nodes.py @@ -0,0 +1,59 @@ +import operator + +import torch + + +def annotate_getitem_nodes(graph: torch.fx.Graph) -> None: + """ + Annotate the type of getitem nodes, inferred from the type of sequence node. + If sequence node is not annotated with a type, do nothing. + Currently support getitem nodes from tuple, list, and NamedTuple sequence node. + + This is helpful since annotations on local names within function are lost during FX transforms. + Adding back known type annotation for getitem nodes to improve jit scriptability. + + Args: + graph (Graph): The graph to be annotated + """ + for node in graph.nodes: + if node.target is operator.getitem: + sequence_node, index_node = node.args + if not sequence_node.type: + continue + # container types + if hasattr(sequence_node.type, "_name"): + parameterized_types = sequence_node.type.__args__ + if sequence_node.type._name == "Tuple": + if len(parameterized_types) == 2 and isinstance( + parameterized_types[1], type(...) + ): + node.type = parameterized_types[0] + else: + assert len(parameterized_types) > index_node + node_type = parameterized_types[index_node] + node.type = node_type + elif sequence_node.type._name == "List": + assert len(parameterized_types) == 1 + node.type = parameterized_types[0] + # Generic Alias Type + elif hasattr(sequence_node.type, "__origin__"): + parameterized_types = sequence_node.type.__args__ + if sequence_node.type.__origin__ is tuple: + if len(parameterized_types) == 2 and isinstance( + parameterized_types[1], type(...) + ): + node.type = parameterized_types[0] + else: + assert len(parameterized_types) > index_node + node_type = parameterized_types[index_node] + node.type = node_type + elif sequence_node.type.__origin__ is list: + assert len(parameterized_types) == 1 + node.type = parameterized_types[0] + # NamedTuple type + elif hasattr(sequence_node.type, "__annotations__"): + if sequence_node.type == torch.Tensor: + continue + sequence_node_field_types = sequence_node.type.__annotations__ + field_name = sequence_node.type._fields[index_node] + node.type = sequence_node_field_types[field_name] diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/backends/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/backends/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/backends/cudagraphs.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/backends/cudagraphs.py new file mode 100644 index 0000000000000000000000000000000000000000..97496fbc9b2a2439b687bc09c58bb4031b8fc670 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/backends/cudagraphs.py @@ -0,0 +1,61 @@ +# mypy: allow-untyped-defs +import operator + +import torch +from torch.fx.passes.fake_tensor_prop import FakeTensorProp +from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner +from torch.fx.passes.operator_support import OperatorSupport +from torch.fx.passes.tools_common import CALLABLE_NODE_OPS +from torch.utils import _pytree as pytree + + +class CudaGraphsSupport(OperatorSupport): + # TODO: why is submodules passed here + def is_node_supported(self, submodules, node: torch.fx.Node) -> bool: + if node.op not in CALLABLE_NODE_OPS: + return False + + if node.target is torch.ops.aten.embedding_dense_backward.default: + return False + + if node.target is operator.getitem: + return True + + found_not_cuda = False + + def meta_fk(meta): + return meta["val"] if "val" in meta else meta["fake_result"] + + def find_not_cuda(t): + nonlocal found_not_cuda + if isinstance(t, torch.Tensor) and t.device.type != "cuda": + found_not_cuda = True + + for n in node.all_input_nodes: + pytree.tree_map_(find_not_cuda, meta_fk(n.meta)) + + pytree.tree_map_(find_not_cuda, meta_fk(node.meta)) + + # NB: factory function is accounted for because the result would be + # cpu or cuda + + return not found_not_cuda + + +def partition_cudagraphs(gm, inputs): + """ + Partition an FX graph into sub-GraphModules that can be validly run under + CUDA graphs. For a subgraph to be runnable under CUDA, all of the operations + must involve CUDA tensors only/ + """ + + FakeTensorProp(gm).propagate(*inputs) + supported_ops = CudaGraphsSupport() + # TODO: single node partition may be wrong due to the pessimization + # from copying in and out the data. Check in benchmarks, perhaps + partitioner = CapabilityBasedPartitioner( + gm, supported_ops, allows_single_node_partition=True + ) + partitions = partitioner.propose_partitions() + fused_graph = partitioner.fuse_partitions(partitions) + return fused_graph diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/dialect/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/dialect/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/dialect/common/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/dialect/common/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/dialect/common/cse_pass.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/dialect/common/cse_pass.py new file mode 100644 index 0000000000000000000000000000000000000000..e5889375bb07ae0f56917aff9950db67ff3f4bec --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/dialect/common/cse_pass.py @@ -0,0 +1,155 @@ +# mypy: allow-untyped-defs +from typing import Any + +import torch +from torch.fx import Graph, GraphModule, Node +from torch.fx.passes.infra.pass_base import PassBase, PassResult +from torch.utils._pytree import tree_flatten + + +aten = torch.ops.aten + + +# stateful ops are banned from CSE +rand_ops = { + aten.dropout, + aten._fused_dropout, + aten._standard_gamma, + aten.bernoulli, + aten.multinomial, + aten.native_dropout, + aten.normal, + aten.poisson, + aten.binomial, + aten.rrelu, + aten.rand_like, + aten.rand, + aten.randint, + aten.randn, + aten.randperm, +} # noqa: E501,B950 + +inplace_ops = { + aten.add_, + aten.sub_, + aten.mul_, + aten.div_, + aten.pow_, + aten.lerp_, + aten.relu_, + aten.sigmoid_, + aten.tanh_, +} # noqa: E501 + + +@torch.fx._compatibility.compatibility(is_backward_compatible=False) +def get_CSE_banned_ops(): + return rand_ops.union(inplace_ops) + + +@torch.fx._compatibility.compatibility(is_backward_compatible=False) +class CSEPass(PassBase): + def __init__(self, banned_ops=None): + """ + This version of CSE Pass aims to be dialect agnostic, and it's implemented purely based on the connectivity between fx.Node. + + For functional dialects, user would only need to specify the random ops in ban list. + + Warning: CSE Pass cannot be safely applied on a FX graph in non-functional dialects. + If your dialect contains stateful operators, please customized the banned_ops. + + """ + if banned_ops is None: + banned_ops = set() + self.banned_ops = banned_ops + super().__init__() + + def call(self, graph_module: GraphModule) -> PassResult: + """ + Return a new copy of torch.fx.GraphModule with CSE applied to the input graph + + Example usage: + + from torch.fx.experimental.proxy_tensor import make_fx + def f(a): + b = a * a + c = a * a + return b+c + + p = CSEPass() + traced_graph = make_fx(f)(torch.tensor(1)) + print(traced_graph) + result = p(traced_graph) + print(result.graph_module) + """ + + def get_aten_target(node): + if hasattr(node.target, "overloadpacket"): + return node.target.overloadpacket + return node.target + + modified = False + new_graph = Graph() + env: dict[ + Node, Node + ] = {} # map from node in the old graph to node in the new graph + hash_env: dict[ + tuple[torch._ops.OpOverload, int], Node + ] = {} # map from hash to a node in the new graph + token_map: dict[ + tuple[torch._ops.OpOverload, int], dict[str, Any] + ] = {} # map from hash to token + for n in graph_module.graph.nodes: + # The placeholder, output, and get_attr nodes are copied to the new graph without change + # do not CSE away random operations + if ( + n.op == "placeholder" + or n.op == "output" + or n.op == "get_attr" + or get_aten_target(n) in self.banned_ops + ): + new_node = new_graph.node_copy(n, lambda x: env[x]) + env[n] = new_node + else: # n.op == 'call_function', should never see n.op == 'call_module' or 'call_method' + # substitute args and kwargs members to their mapping in env if exists + # specs can be used to reconstruct nested list/dictionaries + def substitute(arg_list): + arg_list, spec = tree_flatten(arg_list) + for i in range(len(arg_list)): + v = arg_list[i] + if isinstance(v, Node) and v in env: + arg_list[i] = env[v] + return tuple(arg_list), spec + + args, args_spec = substitute(n.args) + kwargs, kwargs_spec = substitute(n.kwargs) + + # each token corresponds to a unique node + # nodes with the same token can be substituted + token = { + "target": n.target, + "args": args, + "args_spec": args_spec, + "kwargs": kwargs, + "kwargs_spec": kwargs_spec, + } + + # hash substituted args to a number, do not hash specs because specs are not hashable + hash_arg = hash((args, kwargs)) + hash_val = (n.target, hash_arg) + + # check if a node has a substitute and can be eliminated + hash_val_in_hash_env = hash_val in hash_env + if hash_val_in_hash_env and token_map[hash_val] == token: + modified = True # substitution happens and the graph is modified + env[n] = hash_env[hash_val] + continue + + new_node = new_graph.node_copy(n, lambda x: env[x]) + env[n] = new_node + if not hash_val_in_hash_env: + hash_env[hash_val] = new_node + token_map[hash_val] = token + + csed_gm = GraphModule(graph_module, new_graph) + return PassResult(csed_gm, modified) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/fake_tensor_prop.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/fake_tensor_prop.py new file mode 100644 index 0000000000000000000000000000000000000000..43dbe86c7370f66aa30b5fbc5853d5a0d12cd8ad --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/fake_tensor_prop.py @@ -0,0 +1,109 @@ +# mypy: allow-untyped-defs +from typing import Optional + +import torch.fx +from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode +from torch.fx import Node +from torch.fx._compatibility import compatibility +from torch.fx.experimental.proxy_tensor import py_sym_types, snapshot_fake +from torch.fx.node import map_aggregate +from torch.utils._ordered_set import OrderedSet + + +__all__ = ["FakeTensorProp"] + + +@compatibility(is_backward_compatible=False) +class FakeTensorProp(torch.fx.Interpreter): + """ + Execute an FX graph Node-by-Node and record a fake tensor representing + the metadata for the node. Unlike ShapeProp, (1) this propagation + is cheap--it does the propagation with meta tensors which do not actually + store data, and (2) the fake tensors have much more fine grained information, + e.g., they have accurate alias information that can be consulted by looking + at the storages. + + Args: + module (GraphModule): The module to be executed + mode (Optional[FakeTensorMode]): The dispatch mode used to execute computation indicated by each FX Node. + """ + + def __init__( + self, module: torch.fx.GraphModule, mode: Optional[FakeTensorMode] = None + ): + super().__init__(module) + if mode is None: + mode = FakeTensorMode() + self._mode = mode + mode.epoch += 1 + mode.reset_nt_tensor_id_counter() + self.seen_subgraphs: OrderedSet[str] = OrderedSet() + + def run_node(self, n: Node): + from torch.fx.experimental.symbolic_shapes import ( + compute_unbacked_bindings, + rebind_unbacked, + ) + + if ( + n.op == "call_function" + and n.target is torch.ops.higher_order.invoke_subgraph + and n.args[1] not in self.seen_subgraphs + ): + # Prevent redundant fake tensor prop for invoke_subgraphs. Note that + # there is also fake tensor caching for the entire subgraph. This + # happens the next time we call `run_node` for the same subgraph, + # which goes through super.run_node and caches the fake tensor prop. + # Therefore, we are propagating fake tensor through the subgraphs + # twice. + assert isinstance(n.args[1], str) + assert ( + isinstance(n.args[0], torch.fx.Node) + and n.args[0].op == "get_attr" + and isinstance(n.args[0].target, str) + ) + self.seen_subgraphs.add(n.args[1]) + operands = n.args[2:] + example_inputs = [] + for operand in operands: + assert isinstance(operand, torch.fx.Node) and "val" in operand.meta + example_inputs.append(operand.meta["val"]) + return FakeTensorProp( + getattr(self.module, n.args[0].target), mode=self._mode + ).propagate(*example_inputs) + + result = super().run_node(n) + rebind_unbacked(self._mode.shape_env, n, result) + + def extract_val(obj): + if isinstance(obj, FakeTensor): + return snapshot_fake(obj) + elif isinstance(obj, torch.Tensor): + # TODO: How is it possible that we get a non fake tensor? We + # should be running under the mode... + return snapshot_fake(self._mode.from_tensor(obj, static_shapes=True)) + elif isinstance(obj, py_sym_types): + return obj + else: + return None + + meta = map_aggregate(result, extract_val) + if meta is not None: + n.meta["val"] = meta + if (shape_env := self._mode.shape_env) and ( + symbol_to_path := compute_unbacked_bindings(shape_env, result) + ): + n.meta["unbacked_bindings"] = symbol_to_path + + return result + + def propagate(self, *args): + fake_args = [ + self._mode.from_tensor(a) if isinstance(a, torch.Tensor) else a + for a in args + ] + return self.propagate_dont_convert_inputs(*fake_args) + + def propagate_dont_convert_inputs(self, *args): + with self._mode: + return super().run(*args) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/graph_drawer.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/graph_drawer.py new file mode 100644 index 0000000000000000000000000000000000000000..92ce645df8fa92e03e912da7d66f9b8622edeec7 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/graph_drawer.py @@ -0,0 +1,504 @@ +# mypy: allow-untyped-defs + +import hashlib +from itertools import chain +from types import ModuleType +from typing import Any, Optional, TYPE_CHECKING + +import torch +import torch.fx +from torch.fx._compatibility import compatibility +from torch.fx.graph import _parse_stack_trace +from torch.fx.node import _format_arg, _get_qualified_name +from torch.fx.operator_schemas import normalize_function +from torch.fx.passes.shape_prop import TensorMetadata + + +if TYPE_CHECKING: + import pydot + + HAS_PYDOT = True +else: + pydot: Optional[ModuleType] + try: + import pydot + + HAS_PYDOT = True + except ModuleNotFoundError: + HAS_PYDOT = False + pydot = None + + +__all__ = ["FxGraphDrawer"] + +_COLOR_MAP = { + "placeholder": '"AliceBlue"', + "call_module": "LemonChiffon1", + "get_param": "Yellow2", + "get_attr": "LightGrey", + "output": "PowderBlue", +} + +_HASH_COLOR_MAP = [ + "CadetBlue1", + "Coral", + "DarkOliveGreen1", + "DarkSeaGreen1", + "GhostWhite", + "Khaki1", + "LavenderBlush1", + "LightSkyBlue", + "MistyRose1", + "MistyRose2", + "PaleTurquoise2", + "PeachPuff1", + "Salmon", + "Thistle1", + "Thistle3", + "Wheat1", +] + +_WEIGHT_TEMPLATE = { + "fillcolor": "Salmon", + "style": '"filled,rounded"', + "fontcolor": "#000000", +} + +if HAS_PYDOT: + + @compatibility(is_backward_compatible=False) + class FxGraphDrawer: + """ + Visualize a torch.fx.Graph with graphviz + Basic usage: + g = FxGraphDrawer(symbolic_traced, "resnet18") + g.get_dot_graph().write_svg("a.svg") + """ + + def __init__( + self, + graph_module: torch.fx.GraphModule, + name: str, + ignore_getattr: bool = False, + ignore_parameters_and_buffers: bool = False, + skip_node_names_in_args: bool = True, + parse_stack_trace: bool = False, + dot_graph_shape: Optional[str] = None, + normalize_args: bool = False, + ): + self._name = name + self.dot_graph_shape = ( + dot_graph_shape if dot_graph_shape is not None else "record" + ) + self.normalize_args = normalize_args + _WEIGHT_TEMPLATE["shape"] = self.dot_graph_shape + + self._dot_graphs = { + name: self._to_dot( + graph_module, + name, + ignore_getattr, + ignore_parameters_and_buffers, + skip_node_names_in_args, + parse_stack_trace, + ) + } + + for node in graph_module.graph.nodes: + if node.op != "call_module": + continue + + leaf_node = self._get_leaf_node(graph_module, node) + + if not isinstance(leaf_node, torch.fx.GraphModule): + continue + + self._dot_graphs[f"{name}_{node.target}"] = self._to_dot( + leaf_node, + f"{name}_{node.target}", + ignore_getattr, + ignore_parameters_and_buffers, + skip_node_names_in_args, + parse_stack_trace, + ) + + def get_dot_graph(self, submod_name=None) -> pydot.Dot: + """ + Visualize a torch.fx.Graph with graphviz + Example: + >>> # xdoctest: +REQUIRES(module:pydot) + >>> # xdoctest: +REQUIRES(module:ubelt) + >>> # define module + >>> class MyModule(torch.nn.Module): + >>> def __init__(self) -> None: + >>> super().__init__() + >>> self.linear = torch.nn.Linear(4, 5) + >>> def forward(self, x): + >>> return self.linear(x).clamp(min=0.0, max=1.0) + >>> module = MyModule() + >>> # trace the module + >>> symbolic_traced = torch.fx.symbolic_trace(module) + >>> # setup output file + >>> import ubelt as ub + >>> dpath = ub.Path.appdir("torch/tests/FxGraphDrawer").ensuredir() + >>> fpath = dpath / "linear.svg" + >>> # draw the graph + >>> g = FxGraphDrawer(symbolic_traced, "linear") + >>> g.get_dot_graph().write_svg(fpath) + """ + if submod_name is None: + return self.get_main_dot_graph() + else: + return self.get_submod_dot_graph(submod_name) + + def get_main_dot_graph(self) -> pydot.Dot: + return self._dot_graphs[self._name] + + def get_submod_dot_graph(self, submod_name) -> pydot.Dot: + return self._dot_graphs[f"{self._name}_{submod_name}"] + + def get_all_dot_graphs(self) -> dict[str, pydot.Dot]: + return self._dot_graphs + + def _get_node_style(self, node: torch.fx.Node) -> dict[str, str]: + template = { + "shape": self.dot_graph_shape, + "fillcolor": "#CAFFE3", + "style": '"filled,rounded"', + "fontcolor": "#000000", + } + if node.op in _COLOR_MAP: + template["fillcolor"] = _COLOR_MAP[node.op] + else: + # Use a random color for each node; based on its name so it's stable. + target_name = node._pretty_print_target(node.target) + target_hash = int( + hashlib.md5( + target_name.encode(), usedforsecurity=False + ).hexdigest()[:8], + 16, + ) + template["fillcolor"] = _HASH_COLOR_MAP[ + target_hash % len(_HASH_COLOR_MAP) + ] + return template + + def _get_leaf_node( + self, module: torch.nn.Module, node: torch.fx.Node + ) -> torch.nn.Module: + py_obj = module + assert isinstance(node.target, str) + atoms = node.target.split(".") + for atom in atoms: + if not hasattr(py_obj, atom): + raise RuntimeError( + str(py_obj) + " does not have attribute " + atom + "!" + ) + py_obj = getattr(py_obj, atom) + return py_obj + + def _typename(self, target: Any) -> str: + if isinstance(target, torch.nn.Module): + ret = torch.typename(target) + elif isinstance(target, str): + ret = target + else: + ret = _get_qualified_name(target) + + # Escape "{" and "}" to prevent dot files like: + # https://gist.github.com/SungMinCho/1a017aab662c75d805c5954d62c5aabc + # which triggers `Error: bad label format (...)` from dot + return ret.replace("{", r"\{").replace("}", r"\}") + + # shorten path to avoid drawing long boxes + # for full path = '/home/weif/pytorch/test.py' + # return short path = 'pytorch/test.py' + def _shorten_file_name( + self, + full_file_name: str, + truncate_to_last_n: int = 2, + ): + splits = full_file_name.split("/") + if len(splits) >= truncate_to_last_n: + return "/".join(splits[-truncate_to_last_n:]) + return full_file_name + + def _get_node_label( + self, + module: torch.fx.GraphModule, + node: torch.fx.Node, + skip_node_names_in_args: bool, + parse_stack_trace: bool, + ) -> str: + def _get_str_for_args_kwargs(arg): + if isinstance(arg, tuple): + prefix, suffix = r"|args=(\l", r",\n)\l" + arg_strs_list = [_format_arg(a, max_list_len=8) for a in arg] + elif isinstance(arg, dict): + prefix, suffix = r"|kwargs={\l", r",\n}\l" + arg_strs_list = [ + f"{k}: {_format_arg(v, max_list_len=8)}" for k, v in arg.items() + ] + else: # Fall back to nothing in unexpected case. + return "" + + # Strip out node names if requested. + if skip_node_names_in_args: + arg_strs_list = [a for a in arg_strs_list if "%" not in a] + if len(arg_strs_list) == 0: + return "" + arg_strs = prefix + r",\n".join(arg_strs_list) + suffix + if len(arg_strs_list) == 1: + arg_strs = arg_strs.replace(r"\l", "").replace(r"\n", "") + return arg_strs.replace("{", r"\{").replace("}", r"\}") + + label = "{" + f"name=%{node.name}|op_code={node.op}\n" + + if node.op == "call_module": + leaf_module = self._get_leaf_node(module, node) + label += r"\n" + self._typename(leaf_module) + r"\n|" + extra = "" + if hasattr(leaf_module, "__constants__"): + extra = r"\n".join( + [ + f"{c}: {getattr(leaf_module, c)}" + for c in leaf_module.__constants__ # type: ignore[union-attr] + ] # type: ignore[union-attr] + ) + label += extra + r"\n" + else: + label += f"|target={self._typename(node.target)}" + r"\n" + if self.normalize_args: + try: + args, kwargs = normalize_function( # type: ignore[misc] + node.target, # type: ignore[arg-type] + node.args, # type: ignore[arg-type] + node.kwargs, + normalize_to_only_use_kwargs=True, + ) + except Exception: + # Fallback to not normalizing if there's an exception. + # Some functions need overloads specified to normalize. + args, kwargs = node.args, node.kwargs + else: + args, kwargs = node.args, node.kwargs + if len(args) > 0: + label += _get_str_for_args_kwargs(args) + if len(kwargs) > 0: + label += _get_str_for_args_kwargs(kwargs) + label += f"|num_users={len(node.users)}" + r"\n" + + tensor_meta = node.meta.get("tensor_meta") + label += self._tensor_meta_to_label(tensor_meta) + + # for original fx graph + # print buf=buf0, n_origin=6 + buf_meta = node.meta.get("buf_meta", None) + if buf_meta is not None: + label += f"|buf={buf_meta.name}" + r"\n" + label += f"|n_origin={buf_meta.n_origin}" + r"\n" + + # for original fx graph + # print file:lineno code + if parse_stack_trace and node.stack_trace is not None: + parsed_stack_trace = _parse_stack_trace(node.stack_trace) + fname = self._shorten_file_name(parsed_stack_trace.file) + label += ( + f"|file={fname}:{parsed_stack_trace.lineno} {parsed_stack_trace.code}" + + r"\n" + ) + + return label + "}" + + def _tensor_meta_to_label(self, tm) -> str: + if tm is None: + return "" + elif isinstance(tm, TensorMetadata): + return self._stringify_tensor_meta(tm) + elif isinstance(tm, list): + result = "" + for item in tm: + result += self._tensor_meta_to_label(item) + return result + elif isinstance(tm, dict): + result = "" + for v in tm.values(): + result += self._tensor_meta_to_label(v) + return result + elif isinstance(tm, tuple): + result = "" + for item in tm: + result += self._tensor_meta_to_label(item) + return result + else: + raise RuntimeError(f"Unsupported tensor meta type {type(tm)}") + + def _stringify_tensor_meta(self, tm: TensorMetadata) -> str: + result = "" + if not hasattr(tm, "dtype"): + print("tm", tm) + result += "|" + "dtype" + "=" + str(tm.dtype) + r"\n" + result += "|" + "shape" + "=" + str(tuple(tm.shape)) + r"\n" + result += "|" + "requires_grad" + "=" + str(tm.requires_grad) + r"\n" + result += "|" + "stride" + "=" + str(tm.stride) + r"\n" + if tm.is_quantized: + assert tm.qparams is not None + assert "qscheme" in tm.qparams + qscheme = tm.qparams["qscheme"] + if qscheme in { + torch.per_tensor_affine, + torch.per_tensor_symmetric, + }: + result += "|" + "q_scale" + "=" + str(tm.qparams["scale"]) + r"\n" + result += ( + "|" + + "q_zero_point" + + "=" + + str(tm.qparams["zero_point"]) + + r"\n" + ) + elif qscheme in { + torch.per_channel_affine, + torch.per_channel_symmetric, + torch.per_channel_affine_float_qparams, + }: + result += ( + "|" + + "q_per_channel_scale" + + "=" + + str(tm.qparams["scale"]) + + r"\n" + ) + result += ( + "|" + + "q_per_channel_zero_point" + + "=" + + str(tm.qparams["zero_point"]) + + r"\n" + ) + result += ( + "|" + + "q_per_channel_axis" + + "=" + + str(tm.qparams["axis"]) + + r"\n" + ) + else: + raise RuntimeError(f"Unsupported qscheme: {qscheme}") + result += "|" + "qscheme" + "=" + str(tm.qparams["qscheme"]) + r"\n" + return result + + def _get_tensor_label(self, t: torch.Tensor) -> str: + return str(t.dtype) + str(list(t.shape)) + r"\n" + + # when parse_stack_trace=True + # print file:lineno code + def _to_dot( + self, + graph_module: torch.fx.GraphModule, + name: str, + ignore_getattr: bool, + ignore_parameters_and_buffers: bool, + skip_node_names_in_args: bool, + parse_stack_trace: bool, + ) -> pydot.Dot: + """ + Actual interface to visualize a fx.Graph. Note that it takes in the GraphModule instead of the Graph. + If ignore_parameters_and_buffers is True, the parameters and buffers + created with the module will not be added as nodes and edges. + """ + + # "TB" means top-to-bottom rank direction in layout + dot_graph = pydot.Dot(name, rankdir="TB") + + buf_name_to_subgraph = {} + + for node in graph_module.graph.nodes: + if ignore_getattr and node.op == "get_attr": + continue + + style = self._get_node_style(node) + dot_node = pydot.Node( + node.name, + label=self._get_node_label( + graph_module, node, skip_node_names_in_args, parse_stack_trace + ), + **style, # type: ignore[arg-type] + ) + + current_graph = dot_graph + + buf_meta = node.meta.get("buf_meta", None) + if buf_meta is not None and buf_meta.n_origin > 1: + buf_name = buf_meta.name + if buf_name not in buf_name_to_subgraph: + buf_name_to_subgraph[buf_name] = pydot.Cluster( + buf_name, label=buf_name + ) + current_graph = buf_name_to_subgraph.get(buf_name) # type: ignore[assignment] + + # pyrefly: ignore [missing-attribute] + current_graph.add_node(dot_node) + + def get_module_params_or_buffers(): + for pname, ptensor in chain( + leaf_module.named_parameters(), + # pyrefly: ignore [bad-argument-type] + leaf_module.named_buffers(), + ): + pname1 = node.name + "." + pname + label1 = ( + pname1 + "|op_code=get_" + "parameter" + if isinstance(ptensor, torch.nn.Parameter) + else "buffer" + r"\l" + ) + dot_w_node = pydot.Node( + pname1, + label="{" + label1 + self._get_tensor_label(ptensor) + "}", + **_WEIGHT_TEMPLATE, # type: ignore[arg-type] + ) + dot_graph.add_node(dot_w_node) + dot_graph.add_edge(pydot.Edge(pname1, node.name)) + + if node.op == "call_module": + leaf_module = self._get_leaf_node(graph_module, node) + + if not ignore_parameters_and_buffers and not isinstance( + leaf_module, torch.fx.GraphModule + ): + get_module_params_or_buffers() + + for subgraph in buf_name_to_subgraph.values(): + subgraph.set("color", "royalblue") + subgraph.set("penwidth", "2") + dot_graph.add_subgraph(subgraph) # type: ignore[arg-type] + + for node in graph_module.graph.nodes: + if ignore_getattr and node.op == "get_attr": + continue + + for user in node.users: + dot_graph.add_edge(pydot.Edge(node.name, user.name)) + + return dot_graph + +else: + if not TYPE_CHECKING: + + @compatibility(is_backward_compatible=False) + class FxGraphDrawer: + def __init__( + self, + graph_module: torch.fx.GraphModule, + name: str, + ignore_getattr: bool = False, + ignore_parameters_and_buffers: bool = False, + skip_node_names_in_args: bool = True, + parse_stack_trace: bool = False, + dot_graph_shape: Optional[str] = None, + normalize_args: bool = False, + ): + raise RuntimeError( + "FXGraphDrawer requires the pydot package to be installed. Please install " + "pydot through your favorite Python package manager." + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/graph_manipulation.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/graph_manipulation.py new file mode 100644 index 0000000000000000000000000000000000000000..6026e9ca25c05cfb4bdc941d5beb638175d00fc6 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/graph_manipulation.py @@ -0,0 +1,113 @@ +# mypy: allow-untyped-defs +from typing import Any, NamedTuple, Optional + +import torch +from torch.fx._compatibility import compatibility +from torch.fx.graph import Graph +from torch.fx.graph_module import GraphModule +from torch.fx.node import map_arg, Node, Target +from torch.fx.passes.shape_prop import ShapeProp + + +__all__ = [ + "replace_target_nodes_with", + "size_bytes", + "get_size_of_all_nodes", + "get_tensor_meta", + "get_size_of_node", +] + + +@compatibility(is_backward_compatible=False) +def replace_target_nodes_with( + fx_module: GraphModule, + old_op: str, + old_target: Target, + new_op: str, + new_target: Target, +): + """Modifies all nodes in fx_module.graph.nodes which match the specified op code and target, + and updates them to match the new op code and target""" + new_graph = Graph() + val_map: dict[Node, Node] = {} + for node in fx_module.graph.nodes: + if node.op == old_op and node.target == old_target: + args = map_arg(node.args, lambda n: val_map[n]) + kwargs = map_arg(node.kwargs, lambda n: val_map[n]) + assert isinstance(args, tuple) + assert isinstance(kwargs, dict) + val_map[node] = new_graph.create_node( + new_op, new_target, args, kwargs, node.name + ) + else: + val_map[node] = new_graph.node_copy(node, lambda n: val_map[n]) + fx_module.graph = new_graph + + +@compatibility(is_backward_compatible=False) +class size_bytes(NamedTuple): + output_size: int + total_size: int + + +@compatibility(is_backward_compatible=False) +def get_size_of_all_nodes( + fx_module: GraphModule, args: Optional[list[torch.Tensor]] = None +) -> None: + """Given a fx graph module, update each node with its total size (weights + bias + output) + and its output_size(output). For a non-module node, the total size is the output size. + return total size""" + if args is not None: + # Mark shape and dtype for each node (node.shape and node.dtype) + ShapeProp(fx_module).propagate(*args) + # Calculate the total size of the whole fx graph + for node in fx_module.graph.nodes: + if node.op == "output": + break + node.size_bytes = get_size_of_node(fx_module, node) + return + + +@compatibility(is_backward_compatible=False) +def get_tensor_meta(node: Node) -> Any: + tensor_meta = node.meta.get("tensor_meta") + + if not tensor_meta: + raise RuntimeError( + f"Node {node} has no tensor metadata associated with it! " + f"Check that shape propagation has run." + ) + + return tensor_meta + + +@compatibility(is_backward_compatible=False) +def get_size_of_node(fx_module: GraphModule, node: Node) -> size_bytes: + """Given a node with node.dtype and node.shape, return its total size and its output size. + total_size = weights + bias + output_size + """ + # Total num of elements + total_num_of_elems = 0 + # For a module, consider all parameters + if node.op == "call_module": + submodule_dict = dict(fx_module.named_modules()) + submodule = submodule_dict[node.target] + parameters = submodule.named_parameters() + # Parameters are named tuples + for _name, p in parameters: + total_num_of_elems += p.numel() + # Don't forget the output size + # node.shape is the shape of this node's output + tensor_meta = get_tensor_meta(node) + output_elem = tensor_meta.shape.numel() + total_num_of_elems += output_elem + # Assume for now if it's quantized then it's qint8 or quint8 + if tensor_meta.is_quantized: + size_per_elem_bytes = torch._empty_affine_quantized( + [], dtype=tensor_meta.dtype + ).element_size() + else: + size_per_elem_bytes = torch.tensor([], dtype=tensor_meta.dtype).element_size() + total_size = size_per_elem_bytes * total_num_of_elems + output_size = size_per_elem_bytes * output_elem + return size_bytes(output_size, total_size) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/graph_transform_observer.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/graph_transform_observer.py new file mode 100644 index 0000000000000000000000000000000000000000..e762b8a60d10cf9dea401a501b9cd3840411ed17 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/graph_transform_observer.py @@ -0,0 +1,229 @@ +# mypy: allow-untyped-defs +import os +from collections.abc import Callable +from typing import Optional, TypeVar + +from torch.fx import Graph, Node +from torch.fx._compatibility import compatibility +from torch.fx.graph_module import GraphModule +from torch.fx.traceback import NodeSource, NodeSourceAction + + +T = TypeVar("T") + + +from .graph_drawer import FxGraphDrawer + + +__all__ = ["GraphTransformObserver"] + + +@compatibility(is_backward_compatible=False) +class GraphTransformObserver: + __pass_count = 0 + + def __init__( + self, + gm: GraphModule, + passname: str, + subsystem: Optional[str] = None, + log_url: Optional[str] = None, + ): + """ + log_url is inferred to be torch._inductor.config.trace.log_url_for_graph_xform unless otherwise specified + """ + from torch._inductor import config as inductor_config + + self.gm = gm + self.passname = passname + self.subsystem = subsystem + + if log_url is None: + log_url = inductor_config.trace.log_url_for_graph_xform + + self.log_url = log_url + + self.active = ( + self.log_url is not None + or inductor_config.trace.provenance_tracking_level == 1 + ) + + if self.active: + self.erased_nodes: set[str] = set() + self.created_nodes: set[str] = set() + self.name_to_node: dict[str, Node] = {} + # record graph modules deepcopied from self.gm, so we can remove hooks on them when exiting the context + self.copied_gms: list[GraphModule] = [] + + self._node_creation_hook = self.get_node_creation_hook() + self._node_erase_hook = self.get_node_erase_hook() + self._node_replace_hook = self.get_node_replace_hook() + self._deepcopy_hook = self.get_deepcopy_hook() + + # If log_url is None, we don't log anything + if self.log_url is None: + return + GraphTransformObserver.__pass_count += 1 + + self.input_dot_graph = FxGraphDrawer( + self.gm, + self.passname, + ignore_getattr=True, + ignore_parameters_and_buffers=True, + ).get_dot_graph() + + @classmethod + def get_current_pass_count(cls): + return cls.__pass_count + + def apply_gm_pass(self, pass_fn: Callable[[GraphModule], T]) -> Optional[T]: + with self: + if not self._check_disable_pass(): + return pass_fn(self.gm) + + return None + + def apply_graph_pass(self, pass_fn: Callable[[Graph], T]) -> Optional[T]: + with self: + if not self._check_disable_pass(): + return pass_fn(self.gm.graph) + + return None + + def _check_disable_pass(self): + if self.subsystem is None: + return False + + debug_info = lambda: self.passname # noqa: E731 + from torch._inductor.compiler_bisector import CompilerBisector + + return CompilerBisector.disable_subsystem( + "inductor", self.subsystem, debug_info + ) + + def __enter__(self): + if not self.active: + return self + self.gm._register_create_node_hook(self._node_creation_hook) + self.gm._register_erase_node_hook(self._node_erase_hook) + self.gm._register_replace_node_hook(self._node_replace_hook) + self.gm._register_deepcopy_hook(self._deepcopy_hook) + + self.erased_nodes.clear() + self.created_nodes.clear() + self.name_to_node.clear() + self.copied_gms.clear() + + for node in self.gm.graph.nodes: + self.name_to_node[node.name] = node + + return self + + def __exit__(self, type, value, tb): + if not self.active: + return + for gm in self.copied_gms + [self.gm]: + gm._unregister_create_node_hook(self._node_creation_hook) + gm._unregister_erase_node_hook(self._node_erase_hook) + gm._unregister_replace_node_hook(self._node_replace_hook) + gm._unregister_deepcopy_hook(self._deepcopy_hook) + + if self.log_url is None: + return + + if len(self.created_nodes) > 0 or len(self.erased_nodes) > 0: + for e in self.input_dot_graph.get_node_list(): + if e.get_name() in self.erased_nodes: + e.obj_dict["attributes"]["fillcolor"] = "yellow" + else: + e.obj_dict["attributes"]["fillcolor"] = "grey" + assert self.log_url is not None + self.input_dot_graph.write( + os.path.join( + self.log_url, + f"pass_{GraphTransformObserver.__pass_count}_{self.passname}_input_graph.dot", + ) + ) + + output_dot_graph = FxGraphDrawer( + self.gm, + self.passname, + ignore_getattr=True, + ignore_parameters_and_buffers=True, + ).get_dot_graph() + for e in output_dot_graph.get_node_list(): + if e.get_name() in self.created_nodes: + e.obj_dict["attributes"]["fillcolor"] = "yellow" + else: + e.obj_dict["attributes"]["fillcolor"] = "grey" + output_dot_graph.write( + os.path.join( + self.log_url, + f"pass_{GraphTransformObserver.__pass_count}_{self.passname}_output_graph.dot", + ) + ) + + def get_node_creation_hook(self): + # We have to return a function instead of using a class method directly + # to avoid max recursion issue when deepcopy a graph module within the context manager. + def on_node_creation(node): + self.created_nodes.add(node.name) + self.name_to_node[node.name] = node + source = NodeSource(None, self.passname, NodeSourceAction.CREATE) + if "from_node" not in node.meta: + node.meta["from_node"] = [source] + else: + node.meta["from_node"].append(source) + + return on_node_creation + + def get_node_erase_hook(self): + def on_node_erase(node): + self.erased_nodes.add(node.name) + self.name_to_node.pop(node.name, None) + + return on_node_erase + + def get_node_replace_hook(self): + def on_node_replace(old: Node, new: str, user: Node): + # Update node meta when replacing old node with new node + new_node = self.name_to_node.get(new, None) + + if not new_node: + return + + assert isinstance(new_node, Node) + + # replace hook is called once for each user of old + # this avoids adding duplicated source nodes + added_nodes = {s.name for s in new_node.meta.get("from_node", [])} + if old.name in added_nodes: + return + + action = [NodeSourceAction.REPLACE] + if new_node.name in self.created_nodes: + action.append(NodeSourceAction.CREATE) + + def created_this_pass(source): + return source.pass_name == self.passname and source.action == [ + NodeSourceAction.CREATE + ] + + # remove redundant source added on node creation + new_from_node = new_node.meta.get("from_node", []) + new_from_node = [ + source for source in new_from_node if not created_this_pass(source) + ] + + # add new source + new_node_source = NodeSource(old, self.passname, action) + new_from_node.append(new_node_source) + new_node.meta["from_node"] = new_from_node + + return on_node_replace + + def get_deepcopy_hook(self): + def on_deepcopy(gm): + self.copied_gms.append(gm) + + return on_deepcopy diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..939157f1302e75e3cf17ec3c1e93d1b8993d67a0 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/__init__.py @@ -0,0 +1 @@ +from . import pass_manager diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/partitioner.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/partitioner.py new file mode 100644 index 0000000000000000000000000000000000000000..7bb536dbba9399d9ae5d5df53966f75f3a2a18a8 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/partitioner.py @@ -0,0 +1,400 @@ +# mypy: allow-untyped-defs +import collections +import itertools +import logging +import operator +from collections.abc import Iterable, Sequence +from typing import Optional + +from torch.fx.graph_module import GraphModule +from torch.fx.node import _get_qualified_name, Node +from torch.fx.passes.operator_support import OperatorSupportBase +from torch.fx.passes.utils.fuser_utils import fuse_by_partitions + + +logger = logging.getLogger(__name__) +logger.setLevel(logging.WARNING) + + +class Partition: + def __init__( + self, + id: Optional[int] = None, + nodes: Optional[Iterable[Node]] = None, + node_orders: Optional[Iterable[int]] = None, + ): + self.id = id + self.nodes: dict[Node, Optional[int]] = {} + if nodes is not None: + if node_orders is None: + self.nodes = dict.fromkeys(nodes, None) + else: + nodes_list = list(nodes) + node_orders_list = list(node_orders) + assert len(nodes_list) == len(node_orders_list), ( + "nodes and node_orders must have the same length" + ) + self.nodes = dict(zip(nodes_list, node_orders_list)) + + def __repr__(self) -> str: + return str(self.nodes) + + def add_node(self, node: Node, node_order: Optional[int] = None): + self.nodes.update({node: node_order}) + + def remove_node(self, node: Node): + del self.nodes[node] + + def size(self): + return len(self.nodes) + + +class _DependencyViewer: + def __init__(self, graph_module: GraphModule): + self.downstreams = collections.defaultdict(set) + + for node in reversed(graph_module.graph.nodes): + for output_node in node.users: + # add output_node and output_node's downstream dependency + self.downstreams[node].add(output_node) + self.downstreams[node].update(self.downstreams[output_node]) + + def downstreams_of(self, node: Node) -> set[Node]: + return self.downstreams[node] + + +class CapabilityBasedPartitioner: + def __init__( + self, + graph_module: GraphModule, + operator_support: OperatorSupportBase, + allows_single_node_partition: bool = False, + non_compute_ops: Optional[Sequence[str]] = None, + allowed_single_node_partition_ops: Optional[Sequence[str]] = None, + ) -> None: + self.graph_module = graph_module + self.operator_support = operator_support + self.allows_single_node_partition = allows_single_node_partition + self.non_compute_ops = non_compute_ops if non_compute_ops is not None else [] + self.allowed_single_node_partition_ops = ( + allowed_single_node_partition_ops + if allowed_single_node_partition_ops is not None + else [] + ) + self.dependency_viewer = _DependencyViewer(graph_module) + + def _is_node_supported(self, node: Node) -> bool: + return self.operator_support.is_node_supported( + dict(self.graph_module.named_modules()), node + ) + + def propose_partitions(self) -> list[Partition]: + # partition_map is a mapping from partition id to a set of partition id's. + # The value set contains all the partition ids that can be reached by doing a + # DFS starting from the partition id in the key. + partition_map: dict[int, set] = collections.defaultdict(set) + + # assumptions: nodes in candidate list is sorted in topological order + assignment: dict[Node, int] = {} # mapping from node to partition_id + partitions_by_id: dict[ + int, Partition + ] = {} # mapping from partition_id to partition + nodes_order: dict[ + Node, int + ] = {} # mapping from nodes to reversed topological order + partitions_order: dict[ + int, int + ] = {} # mapping from partition_id to minimum topo order of nodes in partition + partition_users: dict[ + int, set + ] = {} # mapping from partition_id to partition users + new_partition_id = itertools.count() + + # try to merge partition other_id into partition self_id + # merge only happens if the end graph doesn't contain cyclic dependency + # returns `True` when merge happens, `False` otherwise. + def maybe_merge_partition(self_id: int, other_id: int): + # merged_nodes is the union of nodes in two partition to-be-merged + self_nodes = partitions_by_id[self_id].nodes + other_nodes = partitions_by_id[other_id].nodes + + def dfs_iter_find_cycle(all_user_nodes: set[Node]): + for user_node in all_user_nodes: + visited_partition_ids = set() + + for path_node in self.dependency_viewer.downstreams_of(user_node): + # If any of the nodes in the dfs path of this node are in the merged_nodes + # list then there is a cycle in the graph. + if path_node in self_nodes or path_node in other_nodes: + return True + + # If any of the nodes in the dfs path of this node are in the assignment + # map then we have to make sure that the partitions that these nodes belong + # to do not form a cycle with the current partitions being merged. This means + # iterating through all the nodes in all the parititons that are traversed in + # the dfs path and checking if they are in the merged_nodes list. + if path_node in assignment: + partition_id = assignment[path_node] + # If the partition id has already been visited then we know that it doesn't + # form a cycle with the current partitions being merged. + if partition_id in visited_partition_ids: + continue + p_map = partition_map[partition_id] + if self_id in p_map or other_id in p_map: + return True + + visited_partition_ids.add(partition_id) + + return False + + # find new partition users if merge. + all_user_nodes = partition_users[self_id] | partition_users[other_id] + all_user_nodes.difference_update(other_nodes, self_nodes) + + # check if merge would create cyclic dependency. + if dfs_iter_find_cycle(all_user_nodes): + # return false indicating cyclic dependency found and + # merge is aborted + return self_id, False + + # merge the smaller partition into the larger. + merge_id, removed_id = self_id, other_id + if len(self_nodes) < len(other_nodes): + merge_id, removed_id = removed_id, merge_id + # no cyclic dependency found, move forward with the merge + # updating partition nodes + partitions_by_id[merge_id].nodes.update(partitions_by_id[removed_id].nodes) + # updating assignment map + for node in partitions_by_id[removed_id].nodes: + assignment[node] = merge_id + # delete other partition + del partitions_by_id[removed_id] + + partitions_order[merge_id] = min( + partitions_order[merge_id], partitions_order[removed_id] + ) + del partitions_order[removed_id] + + partition_map[merge_id] = partition_map[merge_id].union( + partition_map[removed_id] + ) + del partition_map[removed_id] + + partition_users[merge_id] = all_user_nodes + del partition_users[removed_id] + + return merge_id, True + + def merge_single_node(node: Node, node_order: Optional[int], id: Optional[int]): + def _update_partition_map(node: Node, id: int): + # Iterate through all the users of this node and update the partition map to indicate + # that there is a path from the partition id of this node to the target partition id. + for user_node in node.users: + target_id = assignment.get(user_node) + if target_id is not None: + partition_map[id].add(target_id) + partition_map[id].update(partition_map[target_id]) + + if node in assignment: + partitions_by_id[assignment[node]].remove_node(node) + + if id is None: + assignment.pop(node) + elif id not in partitions_by_id: + assignment[node] = id + assert node_order is not None + partitions_by_id[id] = Partition( + id=id, nodes=[node], node_orders=[node_order] + ) + partition_users[id] = set(node.users) + _update_partition_map(node, id) + else: + assignment[node] = id + partitions_by_id[id].add_node(node, node_order) + + logger.debug("Proposing partitions...") + + for node_order, node in enumerate(reversed(self.graph_module.graph.nodes)): + # use Dict as an ordered set to ensure deterministic partitioning result, don't care value + merge_candidates: dict[int, None] = {} + + # Note a limited horizontal fusion is enabled: + # when `node` is not supported, the code below attempts to fuse consumer of `node`. + # + # I don't see a need to add a knob to disable horizontal fusion yet, we can short-cut + # the fusion by adding an `else` block here to skip horizontal fusion. + if self._is_node_supported(node) and node not in assignment: + partition_id = next(new_partition_id) + nodes_order[node] = partition_id + partitions_order[partition_id] = partition_id + merge_single_node(node, node_order, partition_id) + merge_candidates[partition_id] = None + + # merge all possible partitions + for partition_id, _ in sorted( + partitions_order.items(), key=operator.itemgetter(1) + ): + merge_candidates[partition_id] = None + + merge_candidates_list = list(merge_candidates.keys()) + if len(merge_candidates_list) > 1: + self_id = merge_candidates_list[0] + for other_id in merge_candidates_list[1:]: + # note: merge partitions if it doesn't create cyclic dependency + # in the graph, otherwise, this is a no-op + self_id, _ = maybe_merge_partition(self_id, other_id) + + # sort partition nodes based on descending node order + for partition in partitions_by_id.values(): + partition.nodes = dict( + sorted( + partition.nodes.items(), key=operator.itemgetter(1), reverse=True + ) + ) + + # post processing to re-assign "getitem" nodes into upstream partition + logger.debug("Reassigning getitem nodes to its producer node's partition...") + nodes_reassignment: dict[Node, int] = {} + for node in self.graph_module.graph.nodes: + is_tuple_output = True + for user in node.users: + if ( + user.op != "call_function" + or _get_qualified_name(user.target) != "_operator.getitem" + ): # type: ignore[arg-type] + is_tuple_output = False + break + + # node has tuple outputs, re-assign all following getitem node into node's partition + if is_tuple_output: + id = assignment.get(node) # type: ignore[arg-type] + for user in node.users: + if assignment.get(user) != id: # type: ignore[arg-type] + nodes_reassignment[user] = id # type: ignore[assignment] + for node, id in nodes_reassignment.items(): + merge_single_node(node, None, id) + + # filter out single node partitions + if not self.allows_single_node_partition: + logger.debug("Filtering out single node partitions...") + default_non_compute_ops = {"torch.ops.aten.view", "_operator.getitem"} + non_compute_ops = default_non_compute_ops.union(set(self.non_compute_ops)) + partitions_to_remove: list[int] = [] + for id, partition in partitions_by_id.items(): + compute_node_count = 0 + for node in partition.nodes: + if node.op == "call_function": + assert callable(node.target) + if _get_qualified_name(node.target) not in non_compute_ops: + compute_node_count += 1 + if ( + _get_qualified_name(node.target) + in self.allowed_single_node_partition_ops + ): + compute_node_count += 1 + if compute_node_count <= 1: + partitions_to_remove.append(id) + for id in partitions_to_remove: + del partitions_by_id[id] + + logger.debug("Partitions proposed:") + for id, partition in partitions_by_id.items(): + logger.debug( + "partition #%s: %s", id, [node.name for node in partition.nodes] + ) + + return [ + partition for partition in partitions_by_id.values() if partition.size() > 0 + ] + + def fuse_partitions( + self, partitions: list[Partition], prefix: str = "fused_" + ) -> GraphModule: + logger.debug("Fusing partitions...") + # fuse_by_partitions expects partitions in List[Dict[Node, None]]: [ {node0 : None}, {node1 : None} ] + return fuse_by_partitions( + self.graph_module, + [partition.nodes for partition in partitions], + prefix=prefix, + ) + + # remove non-compute-ops that sits at the boundary of a partition. + def remove_bookend_non_compute_ops(self, partitions: list[Partition]): + non_compute_ops = set(self.non_compute_ops) + + def is_non_compute_node(node: Node): + return ( + node.op == "call_function" + and _get_qualified_name(node.target) in non_compute_ops # type: ignore[arg-type] + ) + + # cache transparent nodes + transparent_input_nodes: dict[Node, bool] = {} + transparent_output_nodes: dict[Node, bool] = {} + + def is_transparent_input_node( + node: Node, partition: set[Node], removed_nodes: set[Node] + ): + if ( + node.op == "placeholder" + or (node not in partition) + or (node in removed_nodes) + ): + return True + if node in transparent_input_nodes: + return transparent_input_nodes[node] + if is_non_compute_node(node): + for input_n in node.all_input_nodes: + if not is_transparent_input_node(input_n, partition, removed_nodes): + transparent_input_nodes[node] = False + return False + transparent_input_nodes[node] = True + return True + transparent_input_nodes[node] = False + return False + + def is_transparent_output_node( + node: Node, partition: set[Node], removed_nodes: set[Node] + ): + if ( + node.op == "placeholder" + or (node not in partition) + or (node in removed_nodes) + ): + return True + if node in transparent_output_nodes: + return transparent_output_nodes[node] + if is_non_compute_node(node): + for output_n in node.users: + if not is_transparent_output_node( + output_n, partition, removed_nodes + ): + transparent_output_nodes[node] = False + return False + transparent_output_nodes[node] = True + return True + transparent_output_nodes[node] = False + return False + + for partition in partitions: + # Note it's ok to use `set` here, since we are only query if a node + # has been removed. We are NEVER going to iterate on nodes inside + # the set. + remove_node: set[Node] = set() + for node in partition.nodes: + if is_non_compute_node(node) and ( + is_transparent_input_node(node, set(partition.nodes), remove_node) + or is_transparent_output_node( + node, set(partition.nodes), remove_node + ) + ): + remove_node.add(node) + + if len(remove_node) != 0: + for node in remove_node: + partition.nodes.pop(node, None) + + def partition_and_fuse(self, prefix: str = "fused_") -> GraphModule: + partitions = self.propose_partitions() + fused_gm = self.fuse_partitions(partitions, prefix=prefix) + return fused_gm diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/pass_base.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/pass_base.py new file mode 100644 index 0000000000000000000000000000000000000000..32c641031b31f2c49ca76daac6751b356e740213 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/pass_base.py @@ -0,0 +1,79 @@ +# mypy: allow-untyped-defs +import abc +from collections import namedtuple +from typing import Optional + +from torch.fx._compatibility import compatibility +from torch.fx.graph_module import GraphModule + + +__all__ = ["PassResult", "PassBase"] + + +@compatibility(is_backward_compatible=False) +# pyrefly: ignore [invalid-inheritance] +class PassResult(namedtuple("PassResult", ["graph_module", "modified"])): + """ + Result of a pass: + graph_module: The modified graph module + modified: A flag for if the pass has modified the graph module + """ + + __slots__ = () + + def __new__(cls, graph_module, modified): + return super().__new__(cls, graph_module, modified) + + +@compatibility(is_backward_compatible=False) +class PassBase(abc.ABC): + """ + Base interface for implementing passes. + + It is required to implement the `call` function so that we can directly + pass instances of the Pass directly to the PassManager and call them as a + function. + + We can directly pass an instance of a class implementing this interface into + the PassManager's `passes` attribute. + """ + + def __call__(self, graph_module: GraphModule) -> Optional[PassResult]: + """ + Runs the precondition check, the pass itself, and the postcondition check. + """ + + self.requires(graph_module) + res = self.call(graph_module) + self.ensures(graph_module) + return res + + @abc.abstractmethod + def call(self, graph_module: GraphModule) -> Optional[PassResult]: + """ + The pass that is run through the given graph module. To implement a + pass, it is required to implement this function. + + Args: + graph_module: The graph module we will run a pass on + """ + + def requires(self, graph_module: GraphModule) -> None: # noqa: B027 + """ + This function will be called before the pass is run and will check that + the given graph module contains the preconditions needed to run the + pass. It is not required to implement this function. + + Args: + graph_module: The graph module we will run checks on + """ + + def ensures(self, graph_module: GraphModule) -> None: # noqa: B027 + """ + This function will be called after the pass is run and will check that + the given graph module contains the postconditions needed to run the + pass. It is not required to implement this function. + + Args: + graph_module: The graph module we will run checks on + """ diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/pass_manager.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/pass_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..87fb6e70037f9a00c46143f87efc5a832a7db3ae --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/infra/pass_manager.py @@ -0,0 +1,310 @@ +# mypy: allow-untyped-defs +import inspect +import logging +from collections.abc import Callable +from functools import wraps +from queue import Queue + +import torch.nn as nn +from torch.fx._compatibility import compatibility +from torch.fx.graph_module import GraphModule +from torch.fx.passes.infra.pass_base import PassResult + + +logger = logging.getLogger(__name__) +logger.setLevel(logging.WARNING) + +__all__ = ["pass_result_wrapper", "this_before_that_pass_constraint", "PassManager"] + + +@compatibility(is_backward_compatible=False) +def pass_result_wrapper(fn: Callable) -> Callable: + """ + Wrapper for passes which currently do not return a PassResult. + This wrapper makes them return a PassResult containing the modified object + and True for the "modified" flag. + + Args: + fn (Callable[Module, Any]) + + Returns: + wrapped_fn (Callable[Module, PassResult]) + """ + if fn is None: + # pyrefly: ignore [bad-return] + return None + + @wraps(fn) + def wrapped_fn(gm): + res = fn(gm) + if res is None: + return PassResult(gm, True) + if isinstance(res, PassResult): + return res + elif isinstance(res, nn.Module): + return PassResult(res, True) + + if not inspect.isfunction(fn): + wrapped_fn.__name__ = type(fn).__name__ + + return wrapped_fn + + +def _validate_pass_schedule_constraint( + constraint: Callable[[Callable, Callable], bool], passes: list[Callable] +) -> None: + for i, a in enumerate(passes): + for j, b in enumerate(passes[i + 1 :]): + if constraint(a, b): + continue + raise RuntimeError( + f"pass schedule constraint violated. Expected {a} before {b}" + f" but found {a} at index {i} and {b} at index{j} in pass" + f" list." + ) + + +def _topological_sort_passes( + passes: list[Callable], constraints: list[Callable] +) -> list[Callable]: + """ + Args + passes: Passes that we are ordering + constraints: Constraints applied on these passes + + Returns + A sorted list of callables and a boolean of if a circular dependency + existed + """ + if len(constraints) == 0: + return passes + + # Construct a graph mapping nodes to a list of their users + graph: dict[Callable, list[Callable]] = {p: [] for p in passes} + indegree_map: dict[Callable, int] = dict.fromkeys(passes, 0) + candidates: Queue = Queue() + for a in passes: + for b in passes: + if a == b: + continue + + for constraint in constraints: + if not constraint(a, b): + graph[b].append(a) + indegree_map[a] += 1 + + if indegree_map[a] == 0: + candidates.put(a) + + visited: dict[Callable, bool] = dict.fromkeys(passes, False) + sorted_passes: list[Callable] = [] + + while not candidates.empty(): + p = candidates.get() + sorted_passes.append(p) + visited[p] = True + + for n in graph[p]: + if not visited[n]: + indegree_map[n] -= 1 + if indegree_map[n] == 0: + candidates.put(n) + + # Check if there are unvisited nodes (aka cycles in the graph) + cycle_passes = list(filter(lambda p: indegree_map[p] != 0, indegree_map.keys())) + if len(cycle_passes) != 0: + error = ( + f"Circular dependency detected within the following passes: {cycle_passes}" + ) + raise RuntimeError(error) + + return sorted_passes + + +@compatibility(is_backward_compatible=False) +def this_before_that_pass_constraint(this: Callable, that: Callable) -> Callable: + """ + Defines a partial order ('depends on' function) where `this` must occur + before `that`. + + For example, the following pass list and constraint list would be invalid. + ``` + passes = [pass_b, pass_a] + + constraints = [this_before_that_pass_constraint(pass_a, pass_b)] + ``` + + Args: + this (Callable): pass which should occur first + that (Callable): pass which should occur later + + Returns: + depends_on (Callable[[Object, Object], bool] + """ + + def depends_on(a: Callable, b: Callable): + return a != that or b != this + + return depends_on + + +@compatibility(is_backward_compatible=False) +class PassManager: + """ + Construct a PassManager. + + Collects passes and constraints. This defines the pass schedule, manages + pass constraints and pass execution. + + Args: + passes (Optional[List[Callable]]): List of passes. A pass is a + callable which modifies an object and returns a PassResult + constraint (Optional[List[Callable]]): List of constraints. A + constraint is a callable which takes two passes (A, B) and returns + True if A depends on B and False otherwise. See implementation of + `this_before_that_pass_constraint` for example. + steps (int): Max number of times we run the passes (default = 1). + run_checks_after_each_pass (bool): Whether to run checks and linting + after each pass + suppress_check_failures (bool): Whether to raise errors when running + checks + """ + + passes: list[Callable[[nn.Module], PassResult]] + constraints: list[Callable[[Callable, Callable], bool]] + _validated: bool = False + steps: int = 1 + + def __init__( + self, + passes=None, + constraints=None, + steps=None, + run_checks_after_each_pass: bool = False, + suppress_check_failures: bool = False, + ): + self.passes = passes or [] + self.constraints = constraints or [] + if steps: + self.steps = steps + + self.run_checks_after_each_pass = run_checks_after_each_pass + self.suppress_check_failures = suppress_check_failures + + def add_pass(self, _pass: Callable): + """ + Adds a pass into the current list of passes. + """ + self.passes.append(_pass) + self._validated = False + + def add_constraint(self, constraint: Callable): + """ + Adds a constraint into the current list of constraints. + """ + self.constraints.append(constraint) + self._validated = False + + def validate_constraints(self): + """ + Validates that current pass schedule defined by `self.passes` is valid + according to all constraints in `self.constraints` + """ + if self._validated: + return + for constraint in self.constraints: + _validate_pass_schedule_constraint(constraint, self.passes) + self._validated = True + + def solve_constraints(self): + """ + Finds a valid traversal order based on the given constraints and orders + the passes based on this order. + + If a circular dependency exists between the constraints and steps = 1, + then we will raise an error because if steps != 1 this means that we + will re-run the passes, allowing for circular dependencies. + """ + self.passes = _topological_sort_passes(self.passes, self.constraints) + self._validated = True + + def add_checks(self, check: Callable) -> None: + """ + Adds a function which takes runs various checks on a given graph module. + This function is run before and after each pass if the + `run_checks_after_each_pass` flag is enabled. + """ + sig = inspect.signature(check) + + if len(list(sig.parameters.values())) != 1: + raise TypeError( + "PassManager check function should only take in one variable, a module" + ) + + setattr(self, "check", check) # noqa: B010 + + def check(self, module: nn.Module) -> None: + pass + + def __call__(self, module: nn.Module) -> PassResult: + """ + Runs a list of passes in the order based on `self.passes` on the given + graph module. Each time a pass is run, checks and linting will be run on + the graph module if `run_checks_after_each_pass` is set. + + If the module is a graph module, we will run the list of passes until + the graph stops changing, or until `steps` number of times. + """ + # Order the passes based on the constraints + if not self._validated: + self.solve_constraints() + + # Check graph invariants + self.check(module) + + # Run the set of passes `steps` number of times or until the graph stops + # changing + overall_modified = False + for _ in range(self.steps): + modified = False + + # Run the set of passes on the graph module + for i, fn in enumerate(self.passes): + fn_name = fn.__name__ if inspect.isfunction(fn) else type(fn).__name__ + logger.debug("Running pass '%s'", fn_name) + + try: + res = fn(module) + + if not isinstance(res, PassResult) and not hasattr( + res, "graph_module" + ): + raise TypeError( + f"The result of the pass {fn_name} should be type PassResult." + + "Please wrap it with pass_result_wrapper()" + ) + module = res.graph_module + modified = modified or res.modified + + if isinstance(module, GraphModule): + logger.debug("Graph after pass '%s': %s", fn_name, module.graph) + module.recompile() + + # Check graph invariants + if self.run_checks_after_each_pass: + self.check(module) + + except Exception as e: + prev_pass_names = [ + p.__name__ if inspect.isfunction(p) else type(p).__name__ + for p in self.passes[:i] + ] + msg = f"An error occurred when running the '{fn_name}' pass after the following passes: {prev_pass_names}" + raise Exception(msg) from e # noqa: TRY002 + + # If the graph no longer changes, then we can stop running these passes + overall_modified = overall_modified or modified + if not modified: + break + + return PassResult(module, overall_modified) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/net_min_base.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/net_min_base.py new file mode 100644 index 0000000000000000000000000000000000000000..e98bad06e5a55eedbc5177f7053d000c25f23e8a --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/net_min_base.py @@ -0,0 +1,983 @@ +# mypy: allow-untyped-defs +import logging +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, cast, Optional + +import torch +import torch.fx +from torch.fx._compatibility import compatibility +from torch.fx.node import map_arg + +from .shape_prop import ShapeProp +from .split_utils import split_by_tags +from .tools_common import ( + CALLABLE_NODE_OPS, + FxNetAccFusionsFinder, + Names, + NodeList, + NodeSet, + TensorOrTensors, + Tensors, +) + + +__all__ = [ + "FxNetMinimizerBadModuleError", + "FxNetMinimizerRunFuncError", + "FxNetMinimizerResultMismatchError", +] + +_LOGGER = logging.getLogger(__name__) + + +@compatibility(is_backward_compatible=False) +class FxNetMinimizerBadModuleError(Exception): + """ + Raised if failed to split out a minimize module + """ + + +@compatibility(is_backward_compatible=False) +class FxNetMinimizerRunFuncError(Exception): + """ + Raised if error occurs during run_a or run_b functions + """ + + +@compatibility(is_backward_compatible=False) +class FxNetMinimizerResultMismatchError(Exception): + """ + Raised if comparing function thinks the results are mismatching. + """ + + +@dataclass +class _MinimizerSettingBase: + """ + Args: + `accumulate_error`: Instead of using a's input for both converted module to verify + , use the previous outputs of each converted module as input to accumulate the + errors. + + `traverse_method`: "sequential" or "binary" or "accumulate" + Determine the way of traverse the nodes in FX module. + + `find_all`: Minimizer will go through the entire model and return all problematic nodes. + + `return_intermediate`: If true, when using `run_nodes()` function to run the + model, intermediate results of all the ops will be returned as output. + + `all_outputs`: If true, when using `_run_and_compare()` function, + all the output nodes in the subgraph will be used for comparison. + """ + + accumulate_error: bool = False + traverse_method: str = "sequential" + find_all: bool = False + return_intermediate: bool = False + all_outputs: bool = False + + def __str__(self): + settings_str = "FX Minimizer Settings:\n" + + for k, v in vars(self).items(): + settings_str += f"\t{k}: {v}\n" + + return settings_str + + +class _MinimizerBase: + """ + This class is used to automatically find problematic nodes in a model. It takes a FX + graphmodule and generate some submodules while traverse the graph. Then two functions + `run_a` and `run_b` will be used to run the same submodule and a function `compare_fn` + will be used to compare the results. + + Currently we provides two ways to traverse the graph and generate submodules. + 1. Sequential traversal: this will traverse the graph node by node and generate + one submodule with one single node. + 2. Binary searching: this will do a binary search style traversal on the graph. + + For internal Users, a guide can be found here https://fb.quip.com/HDtuAgiKGfkP. + """ + + def __init__( + self, + module: torch.fx.GraphModule, + sample_input: Tensors, + compare_fn: Callable[ + [TensorOrTensors, TensorOrTensors, Names], tuple[float, bool] + ], + settings: _MinimizerSettingBase, + module_exporter: Optional[ + Callable[[Tensors, torch.fx.GraphModule, str], None] + ] = None, + exclusion_fn: Optional[Callable[[NodeList, int, int], None]] = None, + ): + assert isinstance(module, torch.fx.GraphModule) + + self.module = module + self.sample_input = sample_input + self.compare_fn = compare_fn + self.module_exporter = module_exporter + self.settings = settings + self.exclusion_fn = exclusion_fn + + # Stores outputs of run_a function + self.a_outputs: dict[str, Any] = {} + + # Stores outputs of run_b function + self.b_outputs: dict[str, Any] = {} + + # Stores the results of compare_fn + self.results: dict[Any, Any] = {} + + # Stores the report for the runs + self.reports: list[list[str]] = [] + + # Current iteration + self.iteration: int = 0 + + callable_nodes = { + node for node in self.module.graph.nodes if node.op in CALLABLE_NODE_OPS + } + self.run_shape_prop() + self.fusions = FxNetAccFusionsFinder(self.module, callable_nodes)() + + # Check if number of input in sample_input matches the number of placeholders + placeholders = [ + node.name for node in self.module.graph.nodes if node.op == "placeholder" + ] + assert len(placeholders) == len(self.sample_input) + + # Store sample_input + for i, name in enumerate(placeholders): + self.a_outputs[name] = sample_input[i] + self.b_outputs[name] = sample_input[i] + + def run_shape_prop(self) -> None: + """ + Helper function to run shape propagation on module. Can be overridden by + subclasses for custom shape propagation logic. + """ + ShapeProp(self.module).propagate(*self.sample_input) + + def run_a( + self, mod: torch.fx.GraphModule, inputs: Tensors, report_idx: int = -1 + ) -> TensorOrTensors: + """ + Run `mod` with `inputs` and generate output. The output will be compared with + output of run_b(). + """ + raise RuntimeError("run_a() is not implemented.") + + def run_b( + self, mod: torch.fx.GraphModule, inputs: Tensors, report_idx: int = -1 + ) -> TensorOrTensors: + """ + Run `mod` with `inputs` and generate output. The output will be compared with + output of run_a(). + """ + raise RuntimeError("run_b() is not implemented.") + + def _store_outputs( + self, + a_result: TensorOrTensors, + b_result: TensorOrTensors, + submodule: torch.fx.GraphModule, + ): + """ + Store the outputs of self.run_a() and self.run_b() into self.a_outputs and + self.b_outputs, so that we can use them when execute preceding nodes that + use those outputs as inputs. + + Args: + a_result: Output of self.run_a(). Could be a tensor or tensors. + b_result: Output of self.run_b(). Could be a tensor or tensors. + submodule: The module that generates a_result and b_result. + """ + output_node = next( + node for node in submodule.graph.nodes if node.op == "output" + ) + + # Only one output + if isinstance(output_node.args[0], torch.fx.Node): + self.a_outputs[output_node.args[0].name] = a_result + self.b_outputs[output_node.args[0].name] = b_result + # Multiple outputs + else: + for i, arg in enumerate(output_node.args[0]): + self.a_outputs[arg.name] = a_result[i] + self.b_outputs[arg.name] = b_result[i] + + def _get_submod_inputs( + self, main_module: torch.fx.GraphModule, submod_path: str + ) -> tuple[Tensors, Tensors]: + """ + Try get submodule inputs from stored outputs. If not found then use + torch_glow.get_submod_inputs to get the inputs. + + If accumulate_error is False, use a_input for run_a() and run_b() + otherwise use a_input for run_a and b_input for run_b. + + Args: + main_module: Top-levlel fx module. + submod_path: Path to the submodule we want to run and compare results. + + Returns: + a_input: List of tensor(s) that will be used by run_a() as submodule inputs. + b_input: List of tensor(s) that will be used by run_b() as submodule inputs. + """ + a_input = [] + b_input = [] + submodule = getattr(main_module, submod_path) + placeholders = [ + node.name for node in submodule.graph.nodes if node.op == "placeholder" + ] + + # If all placeholder can be found in stored outputs, use stored + # outputs as inputs. Otherwise, use `torch_glow.get_submod_inputs` + # to get the inputs. + if set(placeholders) <= self.a_outputs.keys(): + for name in placeholders: + a_input.append(self.a_outputs[name]) + b_input.append(self.b_outputs[name]) + else: + if self.settings.accumulate_error: + print(f"Can't find previous stored outputs named {placeholders}!") + + def get_inputs(self: torch.nn.Module, inputs: Any): + nonlocal a_input + a_input = inputs + + # Use forward hook to get the inputs to the submodule + handle = submodule.register_forward_pre_hook(get_inputs) + main_module(*self.sample_input) + handle.remove() + + b_input = a_input + + if not self.settings.accumulate_error: + return a_input, a_input + + return a_input, b_input + + def _tag_nodes(self, selected_nodes: NodeSet): + """ + Tag selected nodes with tag "minimize". Nodes with the same tags will + be split to the same submodule afterwards. + + Args: + selected_nodes: Nodes that we want to minimize. We will tag those nodes + with "minimize", all preceding nodes with "main_0" and all following + nodes with "main_1". + """ + for node in self.module.graph.nodes: + if node.op not in CALLABLE_NODE_OPS: + continue + + if node in selected_nodes: + node.tag = "minimize" + elif any( + n.tag in {"minimize", "main_1"} + for n in node.all_input_nodes + if n.op in CALLABLE_NODE_OPS + ): + node.tag = "main_1" + else: + node.tag = "main_0" + + def _build_submodule(self, nodes: NodeSet) -> tuple[torch.fx.GraphModule, str]: + """ + Split self.module so that one submodule consists of `nodes` and only `nodes`. + + Args: + nodes: Nodes that we want to include in the minimize submodule. + + Returns: + split_module (torch.fx.GraphModule): the module after split. + submodule_name (str): the name of the submodule that consists of `nodes`. + """ + # Color provided nodes + self._tag_nodes(nodes) + + # Split module based on coloring + split_module = split_by_tags(self.module, ["main_0", "minimize", "main_1"]) + + # Find submodule containing colored nodes + submodule_name: str = "" + for child_name, _ in split_module.named_children(): # type: ignore[union-attr] + # Skip submodules we're not interested in at the moment + if "minimize" not in child_name: + continue + + if submodule_name == "": + submodule_name = child_name + else: + raise FxNetMinimizerBadModuleError( + f"Expected only one minimize submodule with nodes {nodes}" + ) + + if submodule_name == "": + raise FxNetMinimizerBadModuleError( + f"Minimize submodule was not found with nodes {nodes}" + ) + + return split_module, submodule_name # type: ignore[return-value] + + def _run_and_compare( + self, + split_module: torch.fx.GraphModule, + submod_name: str, + output_names: Names, + report_idx: int = -1, + ): + """ + Run the submodule in `split_module` that has name `submod_name` + using `self.run_a` and `self.run_b` and compare their results. + + Args: + split_module: Main module that contains the minimize submodule. + submod_name: Name of the minimize submodule. + output_names: Names of the node we want to output. If None, we + will use the original output. + """ + submodule = getattr(split_module, submod_name) + a_input, b_input = self._get_submod_inputs(split_module, submod_name) + + if len(self.reports) == 0: + self.reports.append([]) + self.iteration = 1 + + report = self.reports[report_idx if report_idx >= 0 else self.iteration - 1] + report.append("Run and compare ...") + + if output_names and not self.settings.all_outputs: + output_nodes: NodeList = [] + for node in submodule.graph.nodes: + if node.op == "output": + submodule.graph.erase_node(node) + + if node.name in output_names: + output_nodes.append(node) + + submodule.graph.output( + output_nodes[0] if len(output_nodes) == 1 else tuple(output_nodes) + ) + submodule.graph.lint() + submodule.recompile() + + # Use name of args in output node as key to store comparison result + for node in submodule.graph.nodes: + if node.op == "output": + result_key = map_arg(node.args, lambda x: x.name) + + try: + a_result = self.run_a(submodule, a_input, report_idx) + b_result = self.run_b(submodule, b_input, report_idx) + self._store_outputs(a_result, b_result, submodule) + except Exception as e: + report.append(f"Exception raised when running {submod_name}: {e}") + raise FxNetMinimizerRunFuncError( # noqa: B904 + f"Exception raised when running {submod_name}: {e}" + ) + + # Compare results + names: Names = output_names + if output_names is None: + names = [str(v) for v in result_key] # type: ignore[possibly-undefined] + + numeric_result, bool_result = self.compare_fn(a_result, b_result, names) + + self.results[result_key] = numeric_result # type: ignore[possibly-undefined] + report.append(f"Numerical accuracy = {numeric_result}") + if not bool_result: + report.append(f"Result mismatch for {result_key}") # type: ignore[possibly-undefined] + if self.module_exporter: + if isinstance(result_key, tuple): # type: ignore[possibly-undefined] + # pyrefly: ignore [unbound-name] + result_key = result_key[-1] + # If the result is still a tuple (happens in non-sequential mode), + # we only use the first element as name. + if isinstance(result_key, tuple): # type: ignore[possibly-undefined] + # pyrefly: ignore [unbound-name] + result_key = str(result_key[0]) + # pyre-ignore[29]: not a function + self.module_exporter( + a_input, + submodule, + # pyrefly: ignore [unbound-name] + result_key + "_cpu", + ) + # pyre-ignore[29]: not a function + self.module_exporter( + b_input, + submodule, + # pyrefly: ignore [unbound-name] + result_key + "_acc", + ) + raise FxNetMinimizerResultMismatchError(f"Result mismatch for {result_key}") # type: ignore[possibly-undefined] + + def _binary_search_impl( + self, all_nodes: NodeList, start_idx: int, end_idx: int + ) -> NodeSet: + """ + Recursive binary search implementation. + """ + culprits: NodeSet = set() + nodes: NodeList = all_nodes[start_idx:end_idx] + + report: list[str] = [] + if self.exclusion_fn is not None: + self.exclusion_fn(nodes, start_idx, end_idx) + if len(nodes) == 0: + report = ["All nodes are excluded by user"] + self.reports.append(report) + return culprits + + first_node_name = nodes[0].name + output_node_name = nodes[-1].name + self.iteration += 1 + self.reports.append(report) + report.append(f"Binary search iteration {self.iteration}") + report.append( + f"From node index {start_idx}:{first_node_name} to {end_idx - 1}:{output_node_name}. " + f"Size of the interested node list is {len(nodes)}" + ) + cur_nodes: NodeSet = set(nodes) + + try: + split_module, submod_name = self._build_submodule(cur_nodes) + self._run_and_compare(split_module, submod_name, [output_node_name]) + + except (FxNetMinimizerRunFuncError, FxNetMinimizerResultMismatchError): + if len(nodes) == 1: + report.append( + f"This is the last node in the sub-module. " + f"Search in the current branch is successful with culprit = {cur_nodes}." + ) + self.print_report(report) + return cur_nodes + + report.append( + "Proceed to split and lower the halves of the current " + "sub-module individually." + ) + self.print_report(report) + + mid = len(nodes) // 2 + culprits = self._binary_search_impl(all_nodes, start_idx, start_idx + mid) + + if len(culprits) != 0 and not self.settings.find_all: + return culprits + + culprits = self._binary_search_impl(all_nodes, start_idx + mid, end_idx) + + if len(culprits) == 0: + report.append( + f"Further split and lowering found no errors. " + f"Unable to minimize the submodule with list of nodes: {nodes}" + ) + self.print_report(report) + + return culprits + else: + report.append("No discrepancy found.") + self.print_report(report) + return set() + + def _binary_traverse(self, nodes: NodeList) -> NodeSet: + """ + Binary search on `nodes` for culprit. + """ + return self._binary_search_impl(nodes, 0, len(nodes)) + + def _sequential_traverse(self, nodes: NodeList) -> NodeSet: + """ + Traverse `nodes` one by one and determine if any of them is a culprit. + """ + culprits: NodeSet = set() + + for node in nodes: + report: list[str] = [] + self.reports.append(report) + self.iteration += 1 + report.append(f"Sequential traverse iteration {self.iteration}.") + report.append(f"Visit node: {node.name}") + + _LOGGER.info("Visit node: %s", node.name) + node_list: NodeList = [node] + if self.exclusion_fn is not None: + self.exclusion_fn(node_list, -1, -1) + if len(node_list) == 0: + report.append(f"User exclusion : {node.name}") + self.print_report(report) + if not self.settings.find_all: + return culprits + else: + continue + + cur_nodes: NodeSet = {node} + + if node in self.fusions: + cur_nodes = self.fusions[node] + + try: + split_module, submod_name = self._build_submodule(cur_nodes) + self._run_and_compare(split_module, submod_name, [node.name]) + self.print_report(report) + except FxNetMinimizerResultMismatchError: + culprits.add(node) + report.append(f"Found culprit from numeric error: {node}") + self.print_report(report) + if not self.settings.find_all: + return culprits + except FxNetMinimizerRunFuncError: + culprits.update(cur_nodes) + report.append(f"Found culprit from run error: {node}") + self.print_report(report) + if not self.settings.find_all: + return culprits + + return culprits + + def _block_traverse_impl( + self, nodes: NodeList, start_idx: int, end_idx: int, find_last_node: bool + ) -> Optional[int]: + """ + Recursive block search implementation. + find_last_node: If True, search for the last node which result in numerics difference + if False: find first node in sorted node list + """ + report: list[str] = [] + + mid = (start_idx + end_idx) // 2 + cur_nodes_list: NodeList = nodes[: mid + 1] if find_last_node else nodes[mid:] + + if self.exclusion_fn: + self.exclusion_fn(cur_nodes_list, -1, -1) + + cur_nodes = set(cur_nodes_list) + + first_node_name = cur_nodes_list[0].name + last_node_name = cur_nodes_list[-1].name + target_node_name = last_node_name if find_last_node else first_node_name + + self.iteration += 1 + self.reports.append(report) + report.extend( + [ + "=" * 30, + f"Block search iteration {self.iteration}", + ] + ) + report.extend( + [ + f"Search for {'last' if find_last_node else 'first'} node in culprits", + f"From node index {start_idx}:{nodes[start_idx].name} to {end_idx}:{nodes[end_idx].name}. ", + f"Subgraph constructed by {first_node_name} to {last_node_name}", + f"Targeting node: {target_node_name}", + f"Size of the interested node list is {end_idx - start_idx + 1}", + ] + ) + report_idx = len(self.reports) - 1 + + try: + split_module, submod_name = self._build_submodule(cur_nodes) + self._run_and_compare( + split_module, submod_name, [last_node_name], report_idx + ) + except (FxNetMinimizerResultMismatchError, FxNetMinimizerRunFuncError): + report.append( + f"Culprits found from node {first_node_name} to {last_node_name}." + ) + + if start_idx == mid == end_idx: + report.extend( + [ + "This is the last node in the sub-module. ", + "Search in the current branch is successful with node :", + f"{start_idx}, node name: {nodes[start_idx].name}.", + ] + ) + self.print_report(report) + return start_idx + + report.append( + "Proceed to split and lower the halves of the current " + "sub-module individually." + ) + self.print_report(report) + + if find_last_node: + return self._block_traverse_impl(nodes, start_idx, mid, find_last_node) + else: + return self._block_traverse_impl( + nodes, mid + 1, end_idx, find_last_node + ) + else: + report.append( + f"Culprits not found from node start to {mid}:{nodes[mid].name}." + ) + + if start_idx == mid == end_idx: + # We did not find anything if the pointers have not moved + if (start_idx == 0 and not find_last_node) or ( + start_idx == len(nodes) - 1 and find_last_node + ): + report.append( + f"At {'last' if find_last_node else 'first'} node, no culprits found." + ) + self.print_report(report) + return None + + # Otherwise, we have converged on the border between discrepancy and valid + return start_idx + (1 if find_last_node else -1) + + report.append( + "Proceed to split and lower the halves of the current " + "sub-module individually." + ) + self.print_report(report) + + if find_last_node: + return self._block_traverse_impl( + nodes, mid + 1, end_idx, find_last_node + ) + else: + return self._block_traverse_impl(nodes, start_idx, mid, find_last_node) + + def _block_traverse( + self, nodes: NodeList, find_last_node: Optional[bool] + ) -> NodeSet: + """ + Traverse topologically sorted node list + Find minimum block (start_idx, end_idx) which contains the culprit + 1st pass: search for end_idx by finding the last node in culprit block + where Numerical accuracy (0, end_idx) > threshold + 2nd pass: search for start_idx by finding the first node in culprit block + where Numerical accuracy (start_idx, end_idx) < threshold + Form minimum block by (start_idx - 1, end_idx) + """ + culprits: NodeSet = set() + first_node_name = nodes[0].name + last_node_name = nodes[-1].name + last_node_report = [f"Block search from {first_node_name} to {last_node_name}"] + last_node_report.append("*" * 50) + self.reports.append(last_node_report) + + start_idx = 0 + end_idx = len(nodes) - 1 + + final_start_idx: Optional[int] = start_idx + final_end_idx: Optional[int] = end_idx + + run_both = find_last_node is None + + # step 1: find (0, end_idx) of culprit block + if run_both or find_last_node: + last_node_report.append("Start searching for last node in culprit") + self.print_report(last_node_report) + final_end_idx = self._block_traverse_impl(nodes, start_idx, end_idx, True) + + if final_end_idx is None: + last_node_report.append("No culprits found") + self.print_report(last_node_report) + return culprits + + last_node_report.extend( + [ + "Finish Pass 1", + f"Find end_idx = {final_end_idx}:{nodes[final_end_idx].name}", + ] + ) + self.print_report(last_node_report) + + # step 2: reduce culprit block to (start_idx, end_idx) + if run_both or not find_last_node: + first_node_report = ["Start searching for first node in culprit"] + self.print_report(first_node_report) + final_start_idx = self._block_traverse_impl( + nodes[0 : end_idx + 1], start_idx, final_end_idx or end_idx, False + ) + + if final_start_idx is None: + last_node_report.append("No culprits found") + self.print_report(last_node_report) + return culprits + + first_node_report.append("*" * 50) + self.reports.append(first_node_report) + first_node_report.extend( + [ + "Finish Pass 2", + f"Find start_idx = {final_start_idx}:{nodes[final_start_idx].name}", + ] + ) + self.print_report(first_node_report) + + # step 3: form module with minimum culprits. These indexes are guaranteed to exist + range_start, range_end = cast(int, final_start_idx), cast(int, final_end_idx) + culprits.update(nodes[range_start : range_end + 1]) + result_report = [ + f"Finish searching, found minimum block ({nodes[range_start]},{nodes[range_end]})" + ] + self.reports.append(result_report) + self.print_report(result_report) + return culprits + + def _defined_traverse(self, nodes: NodeList) -> NodeSet: + """ + run user defined `nodes` and determine if it is a culprit. + """ + culprits: NodeSet = set() + if self.exclusion_fn is not None: + self.exclusion_fn(nodes, -1, -1) + if len(nodes) == 0: + report = ["All nodes are excluded by user"] + self.reports.append(report) + return culprits + + first_node_name = nodes[0].name + output_node_name = nodes[-1].name + report = [f"Defined graph from {first_node_name} to {output_node_name}"] + cur_nodes: NodeSet = set(nodes) + try: + split_module, submod_name = self._build_submodule(cur_nodes) + self._run_and_compare(split_module, submod_name, [output_node_name]) + self.print_report(report) + except (FxNetMinimizerResultMismatchError, FxNetMinimizerRunFuncError): + report.append(f"Found culprit {cur_nodes}") + self.print_report(report) + return culprits + + return culprits + + def _accumulate_traverse(self, nodes: NodeList) -> NodeSet: + culprits: NodeSet = set() + nodes_to_run: NodeSet = set() + + # find_all is not supported for accumulate traversal because all the + # ops run on NNPI. So we return after the first op that raises error. + if self.settings.find_all: + print("'Find All' mode is not supported in accumulate traversal.") + return culprits + + for node in nodes: + report: list[str] = [] + self.reports.append(report) + self.iteration += 1 + report.append(f"Accumulate traverse iteration {self.iteration}.") + + nodes_to_run.add(node) + + node_name = node.name + if node_name is not None and isinstance(node_name, tuple): + node_name = node_name[0] + assert node_name is not None and isinstance(node_name, str), ( + f"minimize: node_name: {node_name}" + ) + + report.append(f"Add node: {node_name}") + + try: + split_module, submod_name = self._build_submodule(nodes_to_run) + self._run_and_compare(split_module, submod_name, [node_name]) + self.print_report(report) + except (FxNetMinimizerResultMismatchError, FxNetMinimizerRunFuncError): + culprits.add(node) + report.append(f"Found culprit {node}") + self.print_report(report) + return culprits + + return culprits + + def _skip_traverse_impl( + self, all_nodes: NodeList, start_idx: int, end_idx: int + ) -> NodeSet: + """ + Skip certain nodes in graph based on settings + """ + culprits: NodeSet = set() + nodes: NodeList = all_nodes[start_idx:end_idx] + cur_nodes: NodeSet = set(nodes) + if self.exclusion_fn is not None: + self.exclusion_fn(nodes, start_idx, end_idx) + cur_nodes = set(nodes) + else: + for node in nodes: + if node in self.fusions: + cur_nodes.update(self.fusions[node]) + report: list[str] = [] + self.reports.append(report) + self.iteration += 1 + report.append(f" Nodes block {self.iteration}.") + report.append( + f"From node index {start_idx} to {end_idx - 1}. " + f"Size of the interested node list is {len(nodes)}" + ) + + try: + split_module, submod_name = self._build_submodule(cur_nodes) + self._run_and_compare(split_module, submod_name, []) + except FxNetMinimizerResultMismatchError: + culprits.update(cur_nodes) + report.append(f"Found culprit from numeric error: {cur_nodes}") + self.print_report(report) + return culprits + except FxNetMinimizerRunFuncError: + culprits.update(cur_nodes) + report.append(f"Found culprit from run error: {cur_nodes}") + self.print_report(report) + return culprits + else: + report.append("No discrepancy found.") + self.print_report(report) + return set() + + def _skip_traverse(self, all_nodes: NodeList, skip_nodes: list) -> NodeSet: + """ + Skip certain nodes in graph based on settings + """ + start_idx = 0 + num_nodes = len(all_nodes) + idx = 0 + culprits = set() + while idx < num_nodes: + node = all_nodes[idx] + if node.name in skip_nodes: # skip the node + if idx > start_idx: + culprits = self._skip_traverse_impl(all_nodes, start_idx, idx) + start_idx = idx + 1 + elif idx == num_nodes - 1 and start_idx <= idx: # last node + culprits = self._skip_traverse_impl(all_nodes, start_idx, idx + 1) + idx += 1 + + return culprits + + def _collect_nodes(self, start: Optional[str], end: Optional[str]) -> NodeList: + """ + Collect nodes in the model that between nodes with name of `start` and `end`. + These two nodes are also included. + """ + nodes: NodeList = [] + add_node = start is None + + for node in self.module.graph.nodes: + if node.op not in CALLABLE_NODE_OPS: + continue + + if node.name == start: + add_node = True + + if add_node: + nodes.append(node) + + if node.name == end: + break + + return nodes + + def run_nodes(self, start: Optional[str] = None, end: Optional[str] = None): + """ + Run part of the model from `start` node to `end` node. If `start` is None + then we start from the beginning of the model. If `end` is None then we + stop at the end of the model. + + Args: + start: The name of the node which is the first node of the submodule + we want to run. If set to None, then we'll start with the first + node of the model. + end: The name of the node which is the last node of the submodule we + want to run. If set to None, we'll end with the last node of the + model. + """ + nodes = self._collect_nodes(start, end) + cur_nodes = set(nodes) + + for node in nodes: + if node in self.fusions: + cur_nodes.update(self.fusions[node]) + + output_names = [] + if self.settings.return_intermediate: + output_names = [node.name for node in nodes] + + try: + split_module, submod_name = self._build_submodule(cur_nodes) + self._run_and_compare(split_module, submod_name, output_names) + except ( + FxNetMinimizerRunFuncError, + FxNetMinimizerResultMismatchError, + ) as e: + print(e) + + def print_report(self, report: list[str]): + for i in range(len(report)): + if i > 0: + print(" . " + report[i]) + else: + print(report[i]) + + def print_reports(self): + for report in self.reports: + self.print_report(report) + + def minimize( + self, + start: Optional[str] = None, + end: Optional[str] = None, + skip_nodes: Optional[list] = None, + find_last_node: Optional[bool] = None, + ) -> NodeSet: + """ + Minimizing the model from node with name `start` to node with name `end` base + on self.settings. Find culprits that causes FxNetMinimizerRunFuncError or + FxNetMinimizerResultMismatchError errors. + + Args: + start: The name of the node where we want to start minimizing. If set + to None, then we'll start with the first node of the model. + end: The name of the node where we want to terminate minimizing. If + set to None, we'll end with the last node of the model. + skip_nodes: The names of nodes where we want to skip during minimizing. + It'll create subgraphs without these skip nodes under the hood. + Only applicable in mode "skip". + find_last_node: True if only last_node of a culprits is needed in mode "block". + False if only the first_node of a culprits is needed. + Only applicable in mode "block". + + Returns: + nodes: A list of nodes that causes FxNetMinimizerRunFuncError or + FxNetMinimizerResultMismatchError errors during minimizing. + """ + + print(self.settings) + print(self.module.graph) + + nodes = self._collect_nodes(start, end) + + if self.settings.traverse_method == "sequential": + return self._sequential_traverse(nodes) + + if self.settings.traverse_method == "binary": + return self._binary_traverse(nodes) + + if self.settings.traverse_method == "accumulate": + return self._accumulate_traverse(nodes) + + if self.settings.traverse_method == "skip": + if skip_nodes is None: + raise RuntimeError( + "'skip_nodes' can't be None when 'traverse_method' is 'skip'." + ) + return self._skip_traverse(nodes, skip_nodes) + + if self.settings.traverse_method == "defined": + return self._defined_traverse(nodes) + + if self.settings.traverse_method == "block": + return self._block_traverse(nodes, find_last_node) + + raise RuntimeError(f"Unknown traverse method {self.settings.traverse_method}!") diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/operator_support.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/operator_support.py new file mode 100644 index 0000000000000000000000000000000000000000..6cb14d312b60b0209195706488dd48a359c40b3f --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/operator_support.py @@ -0,0 +1,229 @@ +# mypy: allow-untyped-defs +import abc +import typing as t + +import torch +import torch.fx +from torch.fx._compatibility import compatibility + +from .shape_prop import TensorMetadata +from .tools_common import CALLABLE_NODE_OPS, get_node_target + + +__all__ = [ + "OperatorSupportBase", + "OperatorSupport", + "create_op_support", + "chain", + "OpSupports", + "any_chain", +] + +# fx.Node.target typename, as returned by `get_node_target()` +TargetTypeName = str + +# Arguments' dtypes for a given node, see `OperatorSupport` +SupportedArgumentDTypes = t.Optional[ + tuple[ + t.Sequence[t.Sequence[torch.dtype]], + dict[str, t.Sequence[torch.dtype]], + ] +] + +SupportDict = t.Mapping[TargetTypeName, SupportedArgumentDTypes] + + +@compatibility(is_backward_compatible=False) +class OperatorSupportBase(abc.ABC): + """Interface for determining if a fx.Node is supported by a backend""" + + @abc.abstractmethod + def is_node_supported( + self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node + ) -> bool: + raise NotImplementedError + + +@compatibility(is_backward_compatible=False) +class OperatorSupport(OperatorSupportBase): + """ + `_support_dict` maps node.target typename to supported inputs dtypes. + + node.target typename is retrieved using helper function `get_node_target()` + + If supported inputs dtypes is None, it means any dtype is supported, else + we should see a tuple like (([dtypes], ...), {"name":[dtypes], ...}). + + The first tuple ([dtypes], ...) indicates what dtypes are supported for + inputs in node.args and the second dict {"name": [dtypes], ...} indicates + what dtypes are supported for inputs in node.kwargs. + + For inputs in args, if we don't want to check it, we can put None there, + e.g. (None, [torch.float]) indicates that we don't care about the type of + the first input in args. And for inputs in kwargs, if not listed, will not + be checked. + """ + + _support_dict: SupportDict + + def __init__(self, support_dict: t.Optional[SupportDict] = None): + self._support_dict = support_dict or {} + + def is_node_supported( + self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node + ) -> bool: + """ + Args: + `submodules`: mapping from module name to the module. This can be + retrieved by calling model.named_modules(). + + `node`: a Fx node that we want to determine whether it's supported. + + Returns: + `is_supported`: whether the arg `node` is supported. + """ + if node.op not in CALLABLE_NODE_OPS: + return True + + target = get_node_target(submodules, node) + + # Target not found in _support_dict meaning that we don't support this op at all + if target not in self._support_dict: + return False + + # The rule for target is None meaning that we accept any dtype + if self._support_dict[target] is None: + return True + + args_dtypes, kwargs_dtypes = self._support_dict[target] # type: ignore[misc] + + # Check args dtypes + for i, dtypes in enumerate(args_dtypes): + if len(node.args) <= i: + break + + # None indicates we don't care about the dtype of args[i] + if dtypes is None: + continue + + # If arg is not a node then we don't check it + if not isinstance(node.args[i], torch.fx.Node): + continue + + arg_dtype = _get_arg_dtype(node.args[i]) # type: ignore[arg-type] + if arg_dtype not in dtypes: + return False + + # Check kwargs dtypes + for k, dtypes in kwargs_dtypes.items(): + if k not in node.kwargs: + continue + + # If arg is not a node then we don't check it + if not isinstance(node.kwargs[k], torch.fx.Node): + continue + + kwarg_dtype = _get_arg_dtype(node.kwargs[k]) # type: ignore[arg-type] + if kwarg_dtype not in dtypes: + return False + + return True + + +# ====================================================================== +# Functional interfaces and utils for defining basic operator support logic +# and composing them into more complex ones +# ====================================================================== + +IsNodeSupported = t.Callable[[t.Mapping[str, torch.nn.Module], torch.fx.Node], bool] + + +@compatibility(is_backward_compatible=False) +def create_op_support(is_node_supported: IsNodeSupported) -> OperatorSupportBase: + """Wraps a `IsNodeSupported` function into an `OperatorSupportBase` instance + + `IsNodeSupported` has the same call signature as + `OperatorSupportBase.is_node_supported` + """ + + class FunctionalOperatorSupport(OperatorSupportBase): + def is_node_supported( + self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node + ) -> bool: + return is_node_supported(submodules, node) + + return FunctionalOperatorSupport() + + +@compatibility(is_backward_compatible=False) +def chain(*op_support: OperatorSupportBase) -> OperatorSupportBase: + """Combines a sequence of `OperatorSupportBase` instances to form a single `OperatorSupportBase` + instance by evaluating each input `OperatorSupportBase` instance, and returns False if + any of it reports False. + """ + + def _chain(submods, node) -> bool: + return all(x.is_node_supported(submods, node) for x in op_support) + + return create_op_support(_chain) + + +@compatibility(is_backward_compatible=False) +def any_chain(*op_support: OperatorSupportBase) -> OperatorSupportBase: + """Combines a sequence of `OperatorSupportBase` instances to form a single `OperatorSupportBase` + instance by evaluating each input `OperatorSupportBase` instance, and returns True if + any of it reports True. + """ + + def _any_chain(submods, node) -> bool: + return any(x.is_node_supported(submods, node) for x in op_support) + + return create_op_support(_any_chain) + + +@compatibility(is_backward_compatible=False) +class OpSupports: + """A set of atomic `OperatorSupportBase` instances that can be combined together + to form more complex operator support logic. + """ + + @classmethod + def decline_if_input_dtype(cls, dtype: torch.dtype) -> OperatorSupportBase: + """Report a node as non-supported, if any of its arguments is of dtype""" + + def _decline_if_input_dtype( + submodules: t.Mapping[str, torch.nn.Module], + node: torch.fx.Node, + ) -> bool: + for arg in node.all_input_nodes: + arg_dtype = _get_arg_dtype(arg) + if arg_dtype == dtype: + return False + return True + + return create_op_support(_decline_if_input_dtype) + + @classmethod + def decline_if_node_in_names(cls, disallow_set: set[str]) -> OperatorSupportBase: + """ + If a node has a name that is in the disallow set, reported it as non-supported. + """ + + def _decline_if_node_in_names( + submodules: t.Mapping[str, torch.nn.Module], + node: torch.fx.Node, + ) -> bool: + return node.name not in disallow_set + + return create_op_support(_decline_if_node_in_names) + + +def _get_arg_dtype(arg: torch.fx.Node) -> t.Any: + assert isinstance(arg, torch.fx.Node) + tensor_meta = arg.meta.get("tensor_meta") # type: ignore[union-attr] + dtype = ( + tensor_meta.dtype + if isinstance(tensor_meta, TensorMetadata) + else arg.meta["type"] + ) + return dtype diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/param_fetch.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/param_fetch.py new file mode 100644 index 0000000000000000000000000000000000000000..5e17a8040e6a9573200e10bb1fa670bb71219a26 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/param_fetch.py @@ -0,0 +1,97 @@ +from collections.abc import Callable +from typing import Any + +import torch +import torch.nn as nn +from torch.fx._compatibility import compatibility +from torch.fx.graph_module import GraphModule + + +__all__ = [ + "default_matching", + "extract_attrs_for_lowering", + "lift_lowering_attrs_to_nodes", +] + + +# Matching method matches the attribute name of current version to the attribute name of `target_version` +@compatibility(is_backward_compatible=False) +def default_matching(name: str, target_version: int) -> str: + """Default matching method""" + return name + + +# This dict maps the nn.Module class name to the attribute name list that we want to fetch for lowering. +# The first integer in the tuple is the version number of the nn.Module class when we create the parameter list. +# If there's a version mismatch then it means the parameter names in the book might be mismatched with nn.Module. +module_fetch_book: dict[type, tuple[int, list[str], Callable[[str, int], str]]] = { + torch.nn.modules.linear.Linear: (1, ["weight", "bias"], default_matching), + torch.nn.modules.conv.Conv2d: ( + 1, + [ + "weight", + "bias", + "kernel_size", + "stride", + "padding", + "dilation", + "groups", + "padding_mode", + ], + default_matching, + ), + torch.nn.modules.batchnorm.BatchNorm2d: ( + 2, + ["weight", "bias", "running_mean", "running_var", "eps"], + default_matching, + ), + torch.nn.modules.pooling.AdaptiveAvgPool2d: (1, [], default_matching), + torch.nn.modules.pooling.MaxPool2d: ( + 1, + ["kernel_size", "stride", "padding", "dilation", "return_indices", "ceil_mode"], + default_matching, + ), + torch.nn.modules.activation.ReLU: (1, ["inplace"], default_matching), +} + + +@compatibility(is_backward_compatible=False) +def extract_attrs_for_lowering(mod: nn.Module) -> dict[str, Any]: + """If `mod` is in `module_fetch_book`, fetch the mod's attributes that in the `module_fetch_book` + after checking module's version is compatible with the `module_fetch_book`. + """ + attrs_for_lowering: dict[str, Any] = {} + attrs_for_lowering["name"] = torch.typename(mod) + + if type(mod) in module_fetch_book: + version, param_to_fetch, matching_method = module_fetch_book[type(mod)] + if version < mod._version: + raise RuntimeError( + f"Fetcher version {version} try to fetch {torch.typename(mod)} version {mod._version}, " + "please upgrade the module_fetch_book, open an issue and @842974287 " + "or report a bug to AIACC team directly." + ) + for attr in param_to_fetch: + attrs_for_lowering[attr] = getattr(mod, matching_method(attr, mod._version)) + else: + raise RuntimeError( + f"{torch.typename(mod)} is not in the module_fetch_book yet, " + "please add it to the module_fetch_book, open an issue and @842974287 " + "or report a bug to AIACC team directly." + ) + return attrs_for_lowering + + +@compatibility(is_backward_compatible=False) +def lift_lowering_attrs_to_nodes(fx_module: GraphModule) -> None: + """Recursively traverse all `fx_module` nodes and fetch the module's attributes if the node is a leaf module.""" + submodules = dict(fx_module.named_modules()) + + for node in fx_module.graph.nodes: + if node.op == "call_module": + if isinstance(submodules[node.target], GraphModule): + lift_lowering_attrs_to_nodes(submodules[node.target]) + else: + node.attrs_for_lowering = extract_attrs_for_lowering( + submodules[node.target] + ) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/pass_manager.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/pass_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..297d50a68f474dd8ba791f734027c4edc0e5ff70 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/pass_manager.py @@ -0,0 +1,254 @@ +# mypy: allow-untyped-defs +import logging +from collections.abc import Callable +from functools import wraps +from inspect import unwrap +from typing import Optional + + +logger = logging.getLogger(__name__) + +__all__ = [ + "PassManager", + "inplace_wrapper", + "log_hook", + "loop_pass", + "this_before_that_pass_constraint", + "these_before_those_pass_constraint", +] + + +# for callables which modify object inplace and return something other than +# the object on which they act +def inplace_wrapper(fn: Callable) -> Callable: + """ + Convenience wrapper for passes which modify an object inplace. This + wrapper makes them return the modified object instead. + + Args: + fn (Callable[Object, Any]) + + Returns: + wrapped_fn (Callable[Object, Object]) + """ + + @wraps(fn) + def wrapped_fn(gm): + fn(gm) + return gm + + return wrapped_fn + + +def log_hook(fn: Callable, level=logging.INFO) -> Callable: + """ + Logs callable output. + + This is useful for logging output of passes. Note inplace_wrapper replaces + the pass output with the modified object. If we want to log the original + output, apply this wrapper before inplace_wrapper. + + + ``` + def my_pass(d: Dict) -> bool: + changed = False + if "foo" in d: + d["foo"] = "bar" + changed = True + return changed + + + pm = PassManager(passes=[inplace_wrapper(log_hook(my_pass))]) + ``` + + Args: + fn (Callable[Type1, Type2]) + level: logging level (e.g. logging.INFO) + + Returns: + wrapped_fn (Callable[Type1, Type2]) + """ + + @wraps(fn) + def wrapped_fn(gm): + val = fn(gm) + logger.log(level, "Ran pass %s\t Return value: %s", fn, val) + return val + + return wrapped_fn + + +def loop_pass( + base_pass: Callable, + n_iter: Optional[int] = None, + predicate: Optional[Callable] = None, +): + """ + Convenience wrapper for passes which need to be applied multiple times. + + Exactly one of `n_iter`or `predicate` must be specified. + + Args: + base_pass (Callable[Object, Object]): pass to be applied in loop + n_iter (int, optional): number of times to loop pass + predicate (Callable[Object, bool], optional): + + """ + assert (n_iter is not None) ^ (predicate is not None), ( + "Exactly one of `n_iter`or `predicate` must be specified." + ) + + @wraps(base_pass) + def new_pass(source): + output = source + if n_iter is not None and n_iter > 0: + for _ in range(n_iter): + output = base_pass(output) + elif predicate is not None: + while predicate(output): + output = base_pass(output) + else: + raise RuntimeError( + f"loop_pass must be given positive int n_iter (given " + f"{n_iter}) xor predicate (given {predicate})" + ) + return output + + return new_pass + + +# Pass Schedule Constraints: +# +# Implemented as 'depends on' operators. A constraint is satisfied iff a list +# has a valid partial ordering according to this comparison operator. +def _validate_pass_schedule_constraint( + constraint: Callable[[Callable, Callable], bool], passes: list[Callable] +): + for i, a in enumerate(passes): + for j, b in enumerate(passes[i + 1 :]): + if constraint(a, b): + continue + raise RuntimeError( + f"pass schedule constraint violated. Expected {a} before {b}" + f" but found {a} at index {i} and {b} at index{j} in pass" + f" list." + ) + + +def this_before_that_pass_constraint(this: Callable, that: Callable): + """ + Defines a partial order ('depends on' function) where `this` must occur + before `that`. + """ + + def depends_on(a: Callable, b: Callable): + return a != that or b != this + + return depends_on + + +def these_before_those_pass_constraint(these: Callable, those: Callable): + """ + Defines a partial order ('depends on' function) where `these` must occur + before `those`. Where the inputs are 'unwrapped' before comparison. + + For example, the following pass list and constraint list would be invalid. + ``` + passes = [ + loop_pass(pass_b, 3), + loop_pass(pass_a, 5), + ] + + constraints = [these_before_those_pass_constraint(pass_a, pass_b)] + ``` + + Args: + these (Callable): pass which should occur first + those (Callable): pass which should occur later + + Returns: + depends_on (Callable[[Object, Object], bool] + """ + + def depends_on(a: Callable, b: Callable): + return unwrap(a) != those or unwrap(b) != these + + return depends_on + + +class PassManager: + """ + Construct a PassManager. + + Collects passes and constraints. This defines the pass schedule, manages + pass constraints and pass execution. + + Args: + passes (Optional[List[Callable]]): list of passes. A pass is a + callable which modifies an object and returns modified object + constraint (Optional[List[Callable]]): list of constraints. A + constraint is a callable which takes two passes (A, B) and returns + True if A depends on B and False otherwise. See implementation of + `this_before_that_pass_constraint` for example. + """ + + passes: list[Callable] + constraints: list[Callable] + _validated: bool = False + + def __init__( + self, + passes=None, + constraints=None, + ): + self.passes = passes or [] + self.constraints = constraints or [] + + @classmethod + def build_from_passlist(cls, passes): + pm = PassManager(passes) + # TODO(alexbeloi): add constraint management/validation + return pm + + def add_pass(self, _pass: Callable): + self.passes.append(_pass) + self._validated = False + + def add_constraint(self, constraint): + self.constraints.append(constraint) + self._validated = False + + def remove_pass(self, _passes: list[str]): + if _passes is None: + return + passes_left = [ps for ps in self.passes if ps.__name__ not in _passes] + self.passes = passes_left + self._validated = False + + def replace_pass(self, _target, _replacement): + passes_left = [] + for ps in self.passes: + if ps.__name__ == _target.__name__: + passes_left.append(_replacement) + else: + passes_left.append(ps) + self.passes = passes_left + self._validated = False + + def validate(self): + """ + Validates that current pass schedule defined by `self.passes` is valid + according to all constraints in `self.constraints` + """ + if self._validated: + return + for constraint in self.constraints: + _validate_pass_schedule_constraint(constraint, self.passes) + self._validated = True + + def __call__(self, source): + self.validate() + out = source + for _pass in self.passes: + out = _pass(out) + return out diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/regional_inductor.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/regional_inductor.py new file mode 100644 index 0000000000000000000000000000000000000000..ae98950ab60b0348d80d44541b2d38ebed5d1d67 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/regional_inductor.py @@ -0,0 +1,240 @@ +# mypy: allow-untyped-defs + +import functools +import logging + +import torch +from torch.fx._compatibility import compatibility + + +logger = logging.getLogger(__name__) + +__all__ = ["regional_inductor"] + + +# standalone_inductor returns a callable class object - this does not sit well +# with Fx graph node op call_function which expects a function. So this is just +# a wrapper function to make Fx graph codegen happy. +def _dummy_wrapper(fn): + @functools.wraps(fn) + def inner(*args, **kwargs): + return fn(*args, **kwargs) + + return inner + + +def _partition_by_supported_nodes(gm, supported_ops, prefix): + from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner + from torch.fx.passes.utils.fuser_utils import fuse_by_partitions + + partitioner = CapabilityBasedPartitioner( + gm, supported_ops, allows_single_node_partition=True + ) + + candidate_partitions = partitioner.propose_partitions() + partitioned_gm = fuse_by_partitions( + partitioner.graph_module, + [partition.nodes for partition in candidate_partitions], + prefix=prefix, + always_return_tuple=True, + ) + + return partitioned_gm + + +def _compile_submod(gm, prefix): + from torch._inductor.standalone_compile import AOTCompiledArtifact + + for node in gm.graph.nodes: + if node.op == "call_module" and node.target.startswith(prefix): + fake_inputs = [] + for inp_node in node.all_input_nodes: + if hasattr(inp_node, "meta") and "val" in inp_node.meta: + fake_inputs.append(inp_node.meta["val"]) + else: + raise RuntimeError( + f"Partition is bad because non fake tensor value is seen {inp_node}" + ) + + submod = getattr(gm, node.target) + + # Get inductor configs from annotation + # TODO we should change partition when there are multiple differently + # annotated regions. + inductor_options = {} + for sub_node in submod.graph.nodes: + if hasattr(sub_node, "meta") and sub_node.meta.get("custom", None): + custom = sub_node.meta["custom"] + if isinstance(custom, dict) and "compile_with_inductor" in custom: + compile_value = custom["compile_with_inductor"] + if ( + isinstance(compile_value, dict) + and "inductor_configs" in compile_value + ): + inductor_options = compile_value["inductor_configs"] + break + + # Log the options being used + logger.info( + "Compiling submodule %s with inductor options: %s", + node.target, + inductor_options, + ) + + # Apply config patches before compilation + import torch._inductor.config as inductor_config + + # Validate that all config keys exist + for key in inductor_options: + if not hasattr(inductor_config, key): + raise ValueError( + f"Invalid inductor config key '{key}' in regional_inductor annotation. " + f"Available config keys can be found in torch._inductor.config" + ) + + with inductor_config.patch(inductor_options): + compiled_fn = torch._inductor.standalone_compile( + submod, fake_inputs, dynamic_shapes="from_tracing_context", aot=True + ) + assert isinstance(compiled_fn, AOTCompiledArtifact) + # _dummy_wrapper is to make call_function happy + compiled_submod = _dummy_wrapper(compiled_fn) + with gm.graph.inserting_after(node): + new_node = gm.graph.call_function( + compiled_submod, args=node.args, kwargs=node.kwargs + ) + new_node.meta = node.meta + node.replace_all_uses_with(new_node) + gm.graph.erase_node(node) + del gm._modules[node.target] + + gm.recompile() + return gm + + +def _needs_inductor_compile(node: torch.fx.Node): + return ( + node.op not in ("placeholder", "output") + and hasattr(node, "meta") + and node.meta.get("custom", None) + and "compile_with_inductor" in node.meta["custom"] + ) + + +class _RegionScooper: + """ + Scoops out the inductor marked regions. It does NOT compile them. + """ + + @staticmethod + def scoop_regions(gm): + from torch.fx.passes.operator_support import OperatorSupport + + found_marked_node = False + for node in gm.graph.nodes: + if _needs_inductor_compile(node): + found_marked_node = True + break + + if not found_marked_node: + logger.info("No inductor marked nodes found") + return gm + + class InductorMarkedNodes(OperatorSupport): + def is_node_supported(self, submodules, node: torch.fx.Node) -> bool: + return _needs_inductor_compile(node) + + marked_nodes = InductorMarkedNodes() + return _partition_by_supported_nodes( + gm, marked_nodes, "__marked_inductor_submod" + ) + + @staticmethod + def recursively_scoop_regions(gm): + for node in gm.graph.find_nodes(op="get_attr"): + if _needs_inductor_compile(node): + # If the get_attr itself is marked for compile, the outer graph will + # take care of it. If we dont do that, we end up with nested + # regional inductor compiles that do not work well. + continue + submod = getattr(gm, node.target) + if isinstance(submod, torch.fx.GraphModule): + _RegionScooper.recursively_scoop_regions(submod) + + return _RegionScooper.scoop_regions(gm) + + def __call__(self, gm): + with torch.fx.traceback.preserve_node_meta(enable=False): + return _RegionScooper.recursively_scoop_regions(gm) + + +class _RegionCompiler: + """ + Compiles the scooped out regions. + """ + + @staticmethod + def compile_region(gm): + from torch.fx.graph import _BoxedCodeGen + + gm = _compile_submod(gm, "__marked_inductor_submod") + gm.graph.set_codegen(_BoxedCodeGen()) + gm.recompile() + return gm + + @staticmethod + def recursively_compile_regions(gm): + # Find if the graph module has a scooped out region + found_region = False + for node in gm.graph.find_nodes(op="call_module"): + submod = getattr(gm, node.target) + if isinstance(submod, torch.fx.GraphModule): + if node.target.startswith("__marked_inductor_submod"): + found_region = True + + # Recurse through the subgraphs + for node in gm.graph.find_nodes(op="get_attr"): + submod = getattr(gm, node.target) + if isinstance(submod, torch.fx.GraphModule): + _RegionCompiler.recursively_compile_regions(submod) + + if found_region: + return _RegionCompiler.compile_region(gm) + return gm + + def __call__(self, gm): + with torch.fx.traceback.preserve_node_meta(enable=False): + return _RegionCompiler.recursively_compile_regions(gm) + + +def _create_inductor_marked_regions(gm): + with torch.fx.traceback.preserve_node_meta(enable=False): + return _RegionScooper()(gm) + + +def _compile_inductor_marked_regions(gm): + with torch.fx.traceback.preserve_node_meta(enable=False): + return _RegionCompiler()(gm) + + +@compatibility(is_backward_compatible=False) +def regional_inductor(gm, *example_args): + """ + Scoops out inductor marked regions and compiles them with inductor. + + Inductor options should be provided via the annotation API: + with fx_traceback.annotate({ + "compile_with_inductor": { + "inductor_configs": { + "max_autotune": True, + "triton.cudagraphs": False + } + } + }): + """ + # fuser utils create new nodes using create_proxy which retains the seq_nr + # metadata and cause issues + with torch.fx.traceback.preserve_node_meta(enable=False): + gm = _create_inductor_marked_regions(gm) + gm = _compile_inductor_marked_regions(gm) + return gm diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/reinplace.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/reinplace.py new file mode 100644 index 0000000000000000000000000000000000000000..2dba9f0ca12f0d5545cfab18e84edfd19a1a7758 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/reinplace.py @@ -0,0 +1,755 @@ +# mypy: allow-untyped-defs +import _operator +import itertools +from collections import defaultdict +from collections.abc import Callable +from enum import Enum +from typing import Any + +import torch +from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode +from torch.fx import Node +from torch.fx._compatibility import compatibility +from torch.multiprocessing.reductions import StorageWeakRef +from torch.utils import _pytree as pytree +from torch.utils._pytree import tree_map_only + + +__all__ = ["reinplace"] + + +class _ViewType(Enum): + NonView = 0 + SingleOutputView = 1 + MultiOutputView = 2 + + +def _is_view_op(tgt): + if tgt is not None and isinstance(tgt, torch._ops.OpOverload): + schema = tgt._schema + if len(schema.arguments) > 0: + first_arg = schema.arguments[0] + # check if op is a view + return ( + first_arg.alias_info is not None and not first_arg.alias_info.is_write + ) + + +def _get_view_type(tgt) -> _ViewType: + if tgt is not None and isinstance(tgt, torch._ops.OpOverload): + schema = tgt._schema + if len(schema.arguments) > 0: + first_arg = schema.arguments[0] + # check if op is a view + if first_arg.alias_info is not None and not first_arg.alias_info.is_write: + # check if op is a multi-output view + if "*" in first_arg.alias_info.after_set: + return _ViewType.MultiOutputView + else: + return _ViewType.SingleOutputView + return _ViewType.NonView + + +# Stores a bunch of metadata related to functionalization each node. +# Relevant metadata: +# n.meta['fake_result']: FakeTensor (same type as the output of the node, but with FakeTenors instead of Tensors) +# The fake tensor output from running the current node +# n.meta['view_of']: Node +# If the current node n is a view of some base tensor, the 'view_of' field tells us which +# view node was used to generate the current node (a view tensor). +# This information actually makes `fake_result` redundant, but we can use `fake_result` +# to sanity check that our aliasing information is correct. +@compatibility(is_backward_compatible=False) +class _FunctionalizationMetadataProp(torch.fx.Interpreter): + def run_node(self, node: Node): + self.node_counter += 1 + result = super().run_node(node) + node.meta["fake_result"] = result + node.meta["node_idx"] = self.node_counter + + # (1) Update metadata with the list of nodes that are used by this node + # copy_() doesn't read from its first argument; it writes to it, overwriting previous data. + # We don't want to treat it as "being used as an input". + node_args = node.args + if node.target is torch.ops.aten.copy_.default: + node_args = node_args[1:] + + # (2) Update metadata to track aliasing information about view tensor nodes. + if node.op == "call_function": + view_type = _get_view_type(node.target) + if view_type == _ViewType.SingleOutputView: + assert isinstance(node.args[0], Node) + node.meta["view_of"] = node.args[0] + elif view_type == _ViewType.MultiOutputView: + self.multi_output_view_nodes[node] = node.args[0] + + # Check if we returned a multi-output view, + # and we're now grabbing the individual views from the output. + # + # For multi-output views, we want to map each output view to the base, + # but this mapping involves two separate nodes in FX IR. + # e.g. "a, b = x_1.split(...)" becomes: + # %split_tensor : [num_users=2] = call_function[target=torch.ops.aten.split.Tensor](args = (%x_1, 2), kwargs = {}) + # %getitem : [num_users=1] = call_function[target=operator.getitem](args = (%split_tensor, 0), kwargs = {}) + # %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%split_tensor, 1), kwargs = {}) + # And we'd like to set: + # getitem1.meta['view_of'] = x_1 + elif node.target is _operator.getitem: + list_arg = node.args[0] + maybe_base_of_view = self.multi_output_view_nodes.get(list_arg, None) + if maybe_base_of_view is not None: + # Note: we could also track indexing info here for multi-output views. + # I don't think this metadata is strictly needed for de-functionalization. + assert isinstance(maybe_base_of_view, Node) + node.meta["view_of"] = maybe_base_of_view + + if "view_of" in node.meta: + # We're linking the current node with its first argument as views. + # Assert here that this is actually the case, and their storages are the same. + assert isinstance(node.meta["fake_result"], FakeTensor) + assert isinstance(node.meta["view_of"].meta["fake_result"], FakeTensor) + view_storage = StorageWeakRef(node.meta["fake_result"]._typed_storage()) + base_storage = StorageWeakRef( + node.meta["view_of"].meta["fake_result"]._typed_storage() + ) + assert view_storage == base_storage + return result + + def propagate(self, *args): + self.multi_output_view_nodes = {} + self.node_counter = -1 + + with FakeTensorMode() as mode: + fake_args = [ + mode.from_tensor(a) if isinstance(a, torch.Tensor) else a for a in args + ] + return super().run(*fake_args) + + +def _schemas_match(functional_schema, inplace_schema): + names_match = ( + inplace_schema.name.endswith("_") + and inplace_schema.name[:-1] == functional_schema.name + ) + arg_types_match = len(functional_schema.arguments) == len( + inplace_schema.arguments + ) and all( + a1.type == a2.type + for a1, a2 in zip(functional_schema.arguments, inplace_schema.arguments) + ) + # for the inplace op, its first argument should be mutable + assert ( + inplace_schema.arguments[0].alias_info is not None + and inplace_schema.arguments[0].alias_info.is_write + ) + # and its remaining arguments shouldn't be. + assert all(a.alias_info is None for a in inplace_schema.arguments[1:]) + return names_match and arg_types_match + + +# TODO: this should be beefed up to be able to properly re-inplace with: +# - mutating ops (e.g. _fused_moving_avg_obs_fq_helper) +# - out= ops (e.g. angle -> angle.out) +# TODO: we should also figure this info out using torchgen. +def _maybe_get_inplace_op(op): + # __module__ seems broken; it returns torch._ops.aten which doesn't exist + if not isinstance(op, torch._ops.OpOverload): + return None + # Some view ops have inplace variants (as_strided_, etc), + # but we do NOT want the reinplacing pass to directly add these into the program. + # (they'll require extra special handling, aren't aren't really useful for perf anyway) + if _is_view_op(op): + return None + op_namespace = op.__module__.split(".")[-1] + op_base_name = op.overloadpacket.__name__ + maybe_namespace_module = getattr(torch.ops, op_namespace) + maybe_inplace_op = ( + None + if maybe_namespace_module is None + else getattr(maybe_namespace_module, f"{op_base_name}_", None) + ) + if maybe_inplace_op is None: + return None + + inplace_overloads = [ + getattr(maybe_inplace_op, overload_name) + for overload_name in maybe_inplace_op.overloads() + ] + inplace_overloads_with_matching_schemas = [ + f for f in inplace_overloads if _schemas_match(op._schema, f._schema) + ] + # Just because foo() and foo_() are both existing operators, + # They aren't guaranteed to have compatible schemas. + # For example, pow.Scalar(Scalar self, Tensor exponent) has no valid inplace variant, + # Even though several overloads of pow_ exist. + if len(inplace_overloads_with_matching_schemas) == 0: + return None + assert len(inplace_overloads_with_matching_schemas) == 1 + inplace_op = inplace_overloads_with_matching_schemas[0] + return inplace_op + + +_VIEW_INVERSE_MAP: dict[Callable[..., Any], Callable[..., Any]] = { + torch.ops.aten.diagonal_scatter.default: torch.ops.aten.diagonal.default, + torch.ops.aten.select_scatter.default: torch.ops.aten.select.int, + torch.ops.aten.slice_scatter.default: torch.ops.aten.slice.Tensor, + torch.ops.aten.as_strided_scatter.default: torch.ops.aten.as_strided.default, +} + + +# This function, given a set of set of (aliased) tensor nodes, +# Returns any nodes in the graph that *use* any of the aliases, that occur *after* op_index +# in the node ordering. +def _get_all_later_node_usages(tensor_aliases: set[Node], op_index: int): + def _add_if_tensor(x, set_): + if isinstance(x, FakeTensor): + set_.add(StorageWeakRef(x._typed_storage())) + + nodes_used_after = set() + for t in tensor_aliases: + # get all nodes that use the current alias + usage_nodes = t.users + for n in usage_nodes: + # We only care about usages after the current node + if "node_idx" not in n.meta or n.meta["node_idx"] <= op_index: + continue + # We also don't care about intermediate view ops. + # They only matter if their output is then used elsewhere + # (either in an out-of-place op, or as an output to the function). + if n in tensor_aliases: + if ( + isinstance(n.target, torch._ops.OpOverload) + or n.target is _operator.getitem + ): + continue + nodes_used_after.add(n) + return nodes_used_after + + +# Given an op that we're trying to re-inplace, "b = foo(a)", +# And given a {view}_scatter op that shows up later in the graph, "y = {view}_scatter(base, x, args...)" +# Then re-inplacing `foo()` would allow us to remove the `{view}_scatter` op entirely, IF: +# If there are any aliases in the alias_set(a) that satisfy: +# (1) The base of "alias", "alias_base", has the same size/stride/offset metadata as "base" +# (2) The output of running {view}(alias, args...) gives you the same size/stride/offset metadata +# as "alias" +def _get_view_inverse_node_usages( + later_node_usages: set[Node], self_aliases: set[Node] +) -> set[Node]: + def matching_view_metadata(a, b): + return ( + a.size() == b.size() + and a.stride() == b.stride() + and a.storage_offset() == b.storage_offset() + ) + + view_inverse_nodes = set() + # Go through them in node order, so we can see chains of view_scatter ops. + for n in sorted(later_node_usages, key=lambda x: x.meta["node_idx"]): + if n.target not in _VIEW_INVERSE_MAP: + continue + base = n.args[0] + mutated_view = n.args[1] + assert isinstance(base, Node) + assert isinstance(base.meta["fake_result"], FakeTensor) + assert isinstance(mutated_view, Node) + assert isinstance(mutated_view.meta["fake_result"], FakeTensor) + assert not isinstance(n.target, str) + # Check that this view_inverse op actually corresponds to taking doing the inverse + # of one of our existing self_alias nodes. + original_view = _VIEW_INVERSE_MAP[n.target] + for self_alias in self_aliases: + # We're looking for some alias of the self arg, "alias", + # that was created from some op `alias = foo(base, args...)` + # such that the current _scatter op "inverts" that foo call. + # We can check that by running the original op again, and checking that the strides match. + if "view_of" not in self_alias.meta: + continue + self_alias_base = self_alias.meta["view_of"] + try: + # The we're trying to reuse the args from the view_scatter call inside of the corresponding + # view op, which might throw. This just indicates that view_scatter op isn't a valid inverse + # of the current alias we're looking at. + view_replay_metadata = original_view( + self_alias_base.meta["fake_result"], *n.args[2:], **n.kwargs + ) + expected_metadata = self_alias.meta["fake_result"] + # If the alias and its base both have matching metadata, then this view_scatter op is valid to re-inplace. + if matching_view_metadata( + self_alias_base.meta["fake_result"], base.meta["fake_result"] + ) and matching_view_metadata(view_replay_metadata, expected_metadata): + view_inverse_nodes.add(n) + except Exception: + continue + + return view_inverse_nodes + + +@compatibility(is_backward_compatible=True) +def reinplace(gm, *sample_args): + """ + Given an fx.GraphModule, modifies it to perform "reinplacing", + mutating the nodes of the graph. + We look for out-of-place op call sites like `b = a.add(...)`, + and convert them to be inplace (`b = a.add_(...)`), + as long as the input to the current operator ("a") isn't reused + anywhere later in the graph. + + This pass currently expects to operate on a **functional, ATen** graph. + This can be obtained by running `make_fx(functionalize(f))`. + + Sample inputs are needed to determine aliasing relationships of the inputs. + In general, we can't reinplace node `b = a.add(...)` if "a" aliases any of the + inputs to the program. + + Given a node "b = foo(a, args...) the algorithm for re-inplacing is as follows: + + (1) Perform some initial checks on the metadata of "a" and "args..." + that can disqualify them from being reinplaced. + + (1a) Check that the self argument we're attempting to reinplace + has acceptable dtype/size metadata to reinplace with. + + For example, if we have: + a = torch.ones(1) + b = torch.ones(10) + out = torch.add(a, b) + We can't turn that into + a.add_(b) + Because that would require resizing "a". + + Similarly, we can't convert torch.ge(a, b) into a.ge_(b), + because that would require changing a's dtype (from e.g. float32 to bool). + Note that in this specific example, we could technically do better.. + + If we see the pattern: + a_1 = a.ge(b) + a_2 = aten._to_copy(a_1, a.dtype) + Then we this should be valid to completely re-inplace + (this is exactly what functionalization will emit when it sees a.ge_(b)). + + This optimization is only really important for user programs + that directly use inplace comparison ops though. + + We also cannot re-inplace on tensors that have overlapping memory, + e.g. torch.ones(1).expand(4, 4).add_(1) + + (1b) Check if "a" is an alias of any of the program inputs. + + If it is, skip and move to the next node. + Inplace'ing an op that would cause it to mutate a program is not sound, + because that would be a side effect visible to the user. + + NOTE: there's a future optimization that we should make: + if "a" is a (alias of a) program input, but later in the program + there is a node that looks like "a.copy_(...)", + Then re-inplacing is ok to do - we are temporarily reusing a's buffer, + which will later be overwritten by the copy_() call. + + This will be an important optimization to have for programs that mutate + their inputs. It currently isn't implemented though. + + (1c) Check if "a" and "args..." alias + + For example, re-inplacing to create code like the below + isn't guaranteed to be sound: + + aten.mul_(a, a) + + (2) Check that "a" and all of its outstanding aliases are not used anywhere + later in the graph. If this is the case, then it's safe to re-inplace + to "b = foo_(a)". + + There are a few caveats to this, explained in more detail below: + (a) If "a" is used later as an argument to a view op, that is okay. + It's only a problem if "a" (or that view) is later passed + into a normal operator, or if it is returned as the program output. + (b) If "a" is a repeat argument in `foo()`, then don't reinplace. + Most ATen kernels don't make any guarantees that this is sound, + e.g. if you do aten.mul_(a, a). + So we'll just ban re-inplacing in this case. + It's only a problem if "a" (or that view) is later passed + (c) If "a" is used as an input into a view "inverse" / "scatter" + operator, it is potentially fine to re-inplace + (and remove that scatter operator from the graph). + See below for a more detailed example. + + NOTE: there is an optimization in this step that is crucial + to fully recovering performance from functionalization. + + Given this program: + def f(x): + a = torch.ops.aten.add(x, x) + b = torch.ops.aten.diagonal(a) + torch.ops.aten.fill_(b, 0) + return d + + Functionalization will emit the following: + def f(x): + a = torch.ops.aten.add(x, x) + b = torch.ops.aten.diagonal(a, 0, 1) + b_updated = torch.ops.aten.fill(b, 0) + a_updated = torch.ops.aten.diagonal_scatter(a, b_updated, 0, 1) + return a_updated + + Ordinarily, we would not be able to reinplace the fill, + because "b" aliases with "a" which is used by the diagonal_scatter call. + + "re-inplacing" is on the hook for figuring out that it is ok to + completely, the expensive diagonal_scatter call, if we re-inplace the add(). + + So, for every `alias in alias_set(a)`, instead of checking + that "alias" is not used anywhere later in the graph, + we check that + EITHER: + (a) alias is not used anywhere later in the graph + OR: + (b) alias is used exactly once later on in the graph, + in the following op: + + out = foo_scatter(alias, x, args...) + + where the following must hold: + (i) "foo_scatter" is the "inverse" operator for foo. + This only applies to "foo" ops that are view operators, + which view into a subset of the original tensor's memory. + In practice, there are ~4 operators where this applies: + diagonal -> diagonal_scatter + slice -> slice_scatter + select -> select_scatter + as_strided -> as_strided_scatter + (ii) "args..." are the same between the foo() and foo_scatter() calls. + + (3) Perform the actual re-inplacing on foo! + + (3b) is the common case, but special care is needed for {view}_scatter (3a) + + (3a) {view}_scatter ops. + + Consider this program: + a = torch.zeros(2, 2) + b = torch.ones(2) + a[0] = b + + Post functionalization, that will look like: + a = torch.zeros(2) + b = torch.ones(1) + a_updated = torch.select_scatter(a, b, 0, 0) + + In this case though, there is no "functional" op to re-inplace! + Instead, we'd like to directly remove toe select_scatter call. + We already know from (3) that this is valid, + because "a" has no later usages in the graph. + + We perform the re-inplacing on the {view}_scatter op like so + Before: + a_updated = torch.select_scatter(a, b, args...) + After: + a_slice = a.select(a, args...) + a_slice.copy_(b) + + (3b) Otherwise, replace the functional op with its inplace variant. + Before: + b = foo(a, args...) + After: + a.foo_(args...) + + (4) Finally, after converting either: + Before: + b = foo(a) + After: + foo_(a) + or + Before: + b = {slice}_scatter(a, mutated_slice, args...) + After: + slice = {slice}(a, args...) + slice.copy_(mutated_slice) + + We now need to find all later nodes that use "b" as an argument + and update them to take in "a" instead. + + Note that for the majority of inplace ops, this isn't actually necessary + (because most inplace ops return "self" as their output). + This isn't generally true for all mutable ops though, which is why + we need to actually replace all of the arguments. + + We also need to update our metadata of Dict[StorageWeakRef, Set[Node]], + That maps a given tensor storage to the set of all nodes that take in that storage + as an input. + Specifically, re-inplacing `b = foo(a)` causes "a" and "b"'s sets to get fused + together. + + (5) Any "view_inverse/scatter" nodes that were identified as "it's ok to ignore them" + during step (3) get manually deleted from the graph. + Their outputs are no longer used, so technically standard DCE would be able + to do this, but we can no longer run FX's DCE pass now that we have mutable + ops in the graph. + """ + _FunctionalizationMetadataProp(gm).propagate(*sample_args) + + # Useful debug printing + # def _print(x): + # if isinstance(x, FakeTensor): + # print(f'fake_result: {StorageWeakRef(x._typed_storage()).cdata}') + + # for n in gm.graph.nodes: + # print(n.format_node()) + # if hasattr(n, 'meta'): + # print(f'node_idx: {n.meta["node_idx"]}') + # if 'fake_result' in n.meta: + # tree_map(_print, n.meta['fake_result']) + # if 'view_of' in n.meta: + # print(f'view_of: {str(n.meta["view_of"])}') + # print() + + # We need to know which nodes correspond to inputs (or their aliases) + # so we know not to re-inplace them. + # NOTE: later, we'll need to add an optimization for fully recovering performance + # on programs that mutate inputs. + input_storages = { + StorageWeakRef(node.meta["fake_result"]._typed_storage()) + for node in gm.graph.nodes + if ( + node.op == "placeholder" + and isinstance(node.meta["fake_result"], torch.Tensor) + ) + } + + # We also need to know for a given node, what are all of its aliasing nodes. + storage_to_nodes: dict[StorageWeakRef, set[Node]] = defaultdict(set) + for n in gm.graph.nodes: + if "fake_result" in n.meta: + # Tree-mapping because some ops can return lists of tensors. + def _add_to_map(x): + if isinstance(x, FakeTensor): + storage_to_nodes[StorageWeakRef(x._typed_storage())].add(n) + + pytree.tree_map_(_add_to_map, n.meta["fake_result"]) + + # inplace-ify functional ops, subject to the constraints written below. + all_later_view_inverse_nodes_to_delete = set() + for node in gm.graph.nodes: + if node.op == "call_function": + # Today, the re-inplace pass on directly acts on: + # - functional ops with an inplace variant + # - {view}_scatter ops that can be potentially removed from the graph. + # Both of these ops take in tensor first args, so filtering on this condition + # makes the later code simpler. + # We should revisit this at some point though, particularly when we also want + # the reinplacer to be able to handle out= and mutable operators + # and tensorlist first args (like `_foreach_` ops). + if not isinstance(node.target, torch._ops.OpOverload): + continue + if len(node.target._schema.arguments) < 1: + continue + if type(node.target._schema.arguments[0].type) is not torch.TensorType: + continue + + # Step 1a: Check that the self argument we're attempting to reinplace + # has the same size/stride as the output. + # For example, we shouldn't try to reinplace torch.add(scalar_tensor, larger_tensor) + # As it would require resizing scalar_tensor. + # (We could potentially swizzle this into larger_tensor.add_(scalar_tensor), + # this is probably an optimization to revisit later). + self_arg = node.args[0] + self_flattened = pytree.tree_leaves(self_arg.meta["fake_result"]) + node_flattened = pytree.tree_leaves(node.meta["fake_result"]) + self_has_wrong_metadata = False + if len(self_flattened) == len(node_flattened): + for self_meta, node_meta in zip(self_flattened, node_flattened): + if self_meta.numel() != node_meta.numel(): + self_has_wrong_metadata = True + if self_meta.dtype != node_meta.dtype: + self_has_wrong_metadata = True + # We also cannot re-inplace on tensors that have internal memory overlap. + # e.g. torch.ones(1).expand(4, 4).add_(1) + if torch._debug_has_internal_overlap(self_meta) == 1: + self_has_wrong_metadata = True + # Here, we (optimistically) assume that a.resize(b) is valid to re-inplace, + # Since users should never really be calling the functional "torch.ops.aten.resize" + # op directly in their programs. + if self_has_wrong_metadata and node.target != torch.ops.aten.resize.default: + continue + + # Step 1b: ensure that the op we're trying to re-inplace isn't a program input + self_arg_storage = StorageWeakRef( + self_arg.meta["fake_result"]._typed_storage() + ) + if self_arg_storage in input_storages: + # TODO: later, add the optimization for handling `copy_()` calls in the graph. + continue + if len([x for x in node.args if x is self_arg]) > 1: + # Step 1c: + # Calling stuff like aten.mul_(a, a) isn't guaranteed to be sound, + # so we prevent re-inplacing in this case. + continue + + self_arg_storage = StorageWeakRef( + self_arg.meta["fake_result"]._typed_storage() + ) + self_aliases = storage_to_nodes[self_arg_storage] + + # First, we find all later usages of any of the aliases of self_arg. + later_node_usages = _get_all_later_node_usages( + self_aliases, node.meta["node_idx"] + ) + # Then, we check if any of those later usages are actually view_scatter ops + # that are safe to fully remove. + later_view_inverse_node_usages = _get_view_inverse_node_usages( + later_node_usages, self_aliases + ) + + # Step 2: Check to see if the input to the op is reused later in the graph. + # If not (same goes for its aliases), then this op is safe to re-in place. + # This is a slightly roundabout way to check that there are no later usages of the current self argument. + # (later_view_inverse_node_usages corresponds to "view_scatter" nodes that we are allowed to delete) + can_reinplace = len(later_node_usages - later_view_inverse_node_usages) == 0 + if not can_reinplace: + continue + + # Step 3a: Special handling for when we see *_scatter operators. + # When we see an operator like `b = torch.slice_scatter(a, ...)`, + # instead of trying to "inplace" it into a.slice_scatter_(..._), + # we would prefer to remove it from the graph entirely, + # and instead copy_() the slice directly into the larger tensor. + # See the description of the algorithm for a full example. + if ( + node.target in _VIEW_INVERSE_MAP + and node not in all_later_view_inverse_nodes_to_delete + ): + view_op = _VIEW_INVERSE_MAP[node.target] + # Before: + # base_updated = torch.ops.aten.slice_scatter.default(base, mutated_slice, args...) + # After: + # slice = torch.ops.aten.slice.default(base, args...) + # slice.copy_(mutated_slice) + with gm.graph.inserting_before(node): + mutated_slice_node = node.args[1] + remaining_slice_args = node.args[2:] + slice_node = gm.graph.create_node( + "call_function", + view_op, + (self_arg,) + tuple(remaining_slice_args), + node.kwargs, + ) + gm.graph.create_node( + "call_function", + torch.ops.aten.copy_.default, + ( + slice_node, + mutated_slice_node, + ), + {}, + ) + # Add the slice_scatter node to our "nodes to delete" list. + all_later_view_inverse_nodes_to_delete.add(node) + + else: + # Step 3b: Check to see if this operator has an inplace variant. + maybe_inplace_op = _maybe_get_inplace_op(node.target) + if maybe_inplace_op is None: + continue + # And if so, replace it with its inplace variant. + node.target = maybe_inplace_op + + # At this point, 'storage_to_nodes' will be stale. + # Now that we're inplacing `b = foo(a)`, we need to effectively + # union together the dict values for b and a's storage. + # Hmm... morally I think we also want to keep the `fake_result` metadata + # up to date here, but I'm not sure how easy it is to do. + # Maybe it's fine to wait until the end of the pass to update it. + curr_node_storage = StorageWeakRef( + node.meta["fake_result"]._typed_storage() + ) + storage_to_nodes[self_arg_storage].update( + storage_to_nodes[curr_node_storage] + ) + storage_to_nodes[curr_node_storage].update( + storage_to_nodes[self_arg_storage] + ) + + # Need to remember the view_scatter view nodes we found so we can remove them alter. + all_later_view_inverse_nodes_to_delete.update( + later_view_inverse_node_usages + ) + + # Step 4: + # Now that we've replaced b = a.foo() with a.foo_(), + # We need to replace any later usages of "b" with "a" + for old in itertools.chain([node], later_view_inverse_node_usages): + new = old.args[0] + nodes_to_update = [ + n for n in old.users if n.meta["node_idx"] > node.meta["node_idx"] + ] + for node_to_update in nodes_to_update: + + def replace_arg(a): + if a == old: + return new + return a + + # First, replace usages of "b" with "a" + node_to_update.args = tree_map_only( + Node, replace_arg, node_to_update.args + ) + node_to_update.kwargs = tree_map_only( + Node, replace_arg, node_to_update.kwargs + ) + + # Second, update our storage_to_nodes data structure. + old_flattened_res = pytree.tree_leaves(old.meta["fake_result"]) + node_flattened_res = pytree.tree_leaves( + node_to_update.meta["fake_result"] + ) + + old_res_storage = { + StorageWeakRef(x._typed_storage()) + for x in old_flattened_res + if isinstance(x, FakeTensor) + } + node_res_storage = { + StorageWeakRef(x._typed_storage()) + for x in node_flattened_res + if isinstance(x, FakeTensor) + } + + # This will happen if we're updating a view op, e.g. + # e.g. replacing + # x = view(old) + # x = view(new) + # When that happens, we need to make sure to keep our + # storage mapping up to date. + # + # We're checking for len(...) == 1 here because all view ops are guaranteed to return either a single tensor, + # or multiple tensors that all share the same storage. + # We can't just check equality because we might encounter FX nodes that return zero tensor outputs. + if ( + len(old_res_storage) == 1 + and len(node_res_storage) == 1 + and old_res_storage == node_res_storage + ): + new_flattened_res = pytree.tree_leaves(new.meta["fake_result"]) + new_res_storage = { + StorageWeakRef(x._typed_storage()) + for x in new_flattened_res + if isinstance(x, FakeTensor) + } + assert len(new_res_storage) == 1 + (new_ref,) = new_res_storage + (node_ref,) = node_res_storage + # Technically, "old_ref" and all its aliases will remain + # in our mapping. + # That should be fine though, since we deleted "old" + # from the graph at this point. + storage_to_nodes[node_ref].update(storage_to_nodes[new_ref]) + storage_to_nodes[new_ref].update(storage_to_nodes[node_ref]) + + # Step 4: delete any _scatter nodes that we de-functionalized + # Need to take care not to delete any of these nodes until after *all* modifications + # to the graph are finished. + for to_delete in all_later_view_inverse_nodes_to_delete: + gm.graph.erase_node(to_delete) + + gm.recompile() + return gm diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/runtime_assert.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/runtime_assert.py new file mode 100644 index 0000000000000000000000000000000000000000..e475a5bc9b6df55dc640d80dc6510ed263368c4a --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/runtime_assert.py @@ -0,0 +1,658 @@ +# mypy: allow-untyped-defs +import functools +import logging +import operator +import sys +from typing import Any, Optional, TYPE_CHECKING + + +# Import sympy and ShapeEnv during TYPE_CHECKING since importing sympy is slow +if TYPE_CHECKING: + import sympy + + from torch.fx.experimental.symbolic_shapes import ShapeEnv +else: + ShapeEnv = Any + +import torch +import torch.utils._pytree as pytree +from torch import fx +from torch._subclasses.meta_utils import is_sparse_any +from torch.fx._compatibility import compatibility +from torch.fx._utils import lazy_format_graph_code +from torch.fx.experimental.proxy_tensor import py_sym_types +from torch.fx.experimental.sym_node import SymNode +from torch.fx.graph_module import GraphModule + + +__all__ = ["insert_deferred_runtime_asserts"] + +log = logging.getLogger(__name__) +graph_code_log = torch._logging.getArtifactLogger(__name__, "graph_code_verbose") + + +def _get_example_value(node: fx.Node) -> Optional[str]: + """ + Get the example value key for a node, since dynamo uses "example_value" + while non-strict export uses "val. + """ + if "example_value" in node.meta: + return node.meta["example_value"] + elif "val" in node.meta: + return node.meta["val"] + else: + return None + + +def _get_sym_val(node: fx.Node) -> Optional["sympy.Expr"]: + val = _get_example_value(node) + if isinstance(val, py_sym_types): + return val.node.expr + return None + + +@compatibility(is_backward_compatible=True) +def insert_deferred_runtime_asserts( + gm: GraphModule, + shape_env: ShapeEnv, + name: str, + export: bool = False, +) -> None: + """ + During tracing, we may have discovered that some data-dependent values + had runtime assert on them; e.g., torch.empty(x.item()) induces a runtime + that x.item() >= 0. These asserts can happen unpredictably during fake + tensor propagation, so we cannot conveniently insert them into the FX graph + when they occur. Instead, we accumulate them in the ShapeEnv, and in this + pass insert them into the graph as proper tests. + + This pass also deduplicates size-related computation, CSE-ing ops that produce + symbolic values and/or are involved in runtime asserts. Additionally, shape calls + (size/stride/storage_offset) are turned into compute on input sizes if possible, + allowing intermediate tensors to be freed earlier. For example, here dynamo will + DCE the cat and repeat calls: + + z = torch.cat([x, x], dim=0) # 2*s0 + w = z.repeat(y.shape[0]) # 2*s0*s1 + _w = w.shape[0] + # something with _w, but not w ... + + # turns into -> + _w0 = 2 * s0 + _w = _w0 * s1 + + # where s0, s1 are either SymInt graph inputs, or the result of added size calls + + Redundant torch._check or torch.ops.aten._assert_scalar.default calls that assert + the same expression, and redundant constrain_range calls are also deduplicated. + Additionally, because single-symbol bound checks (e.g. u0 >= 0, u0 <= 5) accumulate + information in the ShapeEnv, the ShapeEnv contains min/max bounds for each symbol, + and we delete all previous calls, adding bound checks at the end of this pass. + """ + + # Import sympy locally + import sympy + + from torch._export.passes._node_metadata_hook import _set_node_metadata_hook + from torch.fx.experimental.symbolic_shapes import ( + _get_placeholder_expr, + _has_uninterpretable_sympy_function, + CallMethodKey, + cast_symbool_to_symint_guardless, + ConvertIntKey, + DivideByKey, + free_symbols, + InnerTensorKey, + resolve_unbacked_bindings, + ) + from torch.utils._sympy.numbers import int_oo + from torch.utils._sympy.reference import ( + OptimizedPythonReferenceAnalysis, + PythonReferenceAnalysis, + ) + from torch.utils._sympy.value_ranges import ValueRanges + + # TODO: Request simplification on runtime asserts before emitting them + ras_by_symbol = shape_env.deferred_runtime_asserts.copy() + graph = gm.graph + tracer = fx.proxy.GraphAppendingTracer(graph) + graph_code_log.debug( + "%s", + lazy_format_graph_code( + f"pre insert_deferred_runtime_asserts {name}", gm, colored=True + ), + ) + + # We are going to mutate the dict + expr_to_proxy: dict[sympy.Expr, fx.Proxy] = {} + placeholders = set() + first_non_placeholder = None + for node in graph.nodes: + if node.op != "placeholder": + first_non_placeholder = node + break + else: + placeholders.add(node) + + def _is_intermediate_tensor_sym_call(node: fx.Node) -> bool: + """ + If a size/stride/storage offset call on an intermediate tensor, + we can try to compute the value from input shapes instead. + """ + return ( + (val := _get_sym_val(node)) is not None + and not isinstance(val, sympy.Number) + # this holds back from reifying anything in torch.utils._sympy.functions.py that's unsupported + and not _has_uninterpretable_sympy_function(val) + and any( + isinstance(arg, fx.Node) + and isinstance(_get_example_value(arg), (torch.Tensor, torch.Size)) + and arg.op != "placeholder" + for arg in node.args + ) + ) + + # Figure out what key to use, val or example_value + val_key = "val" + for node in graph.nodes: + if "example_value" in node.meta: + val_key = "example_value" + break + elif "val" in node.meta: + break + + def _node_metadata_hook( + node: torch.fx.Node, + stack_trace: Optional[str] = None, + nn_module_stack: Optional[dict[str, Any]] = None, + custom: Optional[dict[str, Any]] = None, + ) -> None: + fake_args = pytree.tree_map( + lambda arg: ( + _get_example_value(arg) if isinstance(arg, torch.fx.Node) else arg + ), + node.args, + ) + try: + target = node.target + if node.op == "call_method": + assert isinstance(node.target, str) + target = getattr(fake_args[0], node.target) + fake_args = fake_args[1:] + node.meta[val_key] = target(*fake_args) # type: ignore[operator] + except NotImplementedError: + # This can happen when attempting to reify a symbol with an unsupported call_function node, + # e.g. with NestedTensors + sym_size.int via match_symbol(). + # This seems to be fine, as the node gets CSE'd and deleted later in favor of a SymInt graph input. + pass + if stack_trace is not None: + node.meta["stack_trace"] = stack_trace + if nn_module_stack is not None: + node.meta["nn_module_stack"] = nn_module_stack + if custom is not None: + node.meta["custom"] = custom + + # Track asserts/checks we've added + added_asserts: set[sympy.Expr] = set() + constrained_unbacked_symbols: set[sympy.Symbol] = set() + + Analysis = PythonReferenceAnalysis if export else OptimizedPythonReferenceAnalysis + + def _sympy_interp(expr_to_proxy, expr): + # sympy_interp() with hash consing + from sympy import Integer, Number, Symbol + from sympy.logic.boolalg import BooleanAtom + + from torch.utils._sympy.interp import _run_sympy_handler, sympy_interp + + # hash cons + if expr in expr_to_proxy: + return expr_to_proxy[expr] + # base cases, don't cache + if isinstance(expr, (Integer, Number, Symbol, BooleanAtom)): + return sympy_interp(Analysis, expr_to_proxy, expr) + + # hash cons on arguments, run expr handler + expr_to_proxy[expr] = _run_sympy_handler( + Analysis, + [_sympy_interp(expr_to_proxy, arg) for arg in expr.args], + expr, + ) + return expr_to_proxy[expr] + + def _is_bound_expr_for_symbol(expr: "sympy.Expr") -> bool: + # This is probably unnecessary, but since torch._check() calls for single-symbol bounds + # like u0 >= 0, 10 >= u0 accumulate range info in the ShapeEnv, we designate these calls as redundant + # and instead add 2 runtime asserts at the end of this pass, if the min/max bounds are non-trivial. + if len(expr.args) != 2 or expr.func not in (sympy.LessThan, sympy.GreaterThan): + return False + lhs, rhs = expr.args + return (isinstance(lhs, sympy.Symbol) and isinstance(rhs, sympy.Number)) or ( + isinstance(rhs, sympy.Symbol) and isinstance(lhs, sympy.Number) + ) + + def add_runtime_asserts(ras): + for ra in ras: + if ( + # redundant + ra.expr in added_asserts + # if we've already added a constrain_range call for this symbol, + # then single-symbol bound asserts like u0 >= 0, u0 <= 5 are redundant. + or ( + len(ra.expr.free_symbols) == 1 + and next(iter(ra.expr.free_symbols)) in constrained_unbacked_symbols + and _is_bound_expr_for_symbol(ra.expr) + ) + # don't try to reify sympy functions we can't turn into FX nodes + or _has_uninterpretable_sympy_function(ra.expr) + ): + continue + + log.debug("inserting runtime assert %s", ra.expr) + # Need to process ALL free symbols, not just unbacked ones + fvs = free_symbols(ra.expr) + missing = fvs - expr_to_proxy.keys() + if missing: + i1 = min(missing, key=str) + # TODO: Remove relaxing assert on unbacked_symint https://github.com/pytorch/pytorch/issues/119689 + # assert shape_env.is_unbacked_symint(i1), i1 + ras_by_symbol.setdefault(i1, []).append(ra) + else: + # Convert the sympy expression into a sequence of FX + # nodes + with _set_node_metadata_hook(gm, _node_metadata_hook): + res = _sympy_interp(expr_to_proxy, ra.expr).node + + graph.call_function( + torch.ops.aten._assert_scalar.default, + # TODO: use ra.msg here, but it's pretty + # useless right now + ( + res, + f"Runtime assertion failed for expression {ra.expr} on node '{res}'", + ), + ) + added_asserts.add(ra.expr) + + nodes = list(graph.nodes) + for i, node in enumerate(nodes[:-1]): + # Placeholders can match symbols, but when we destructure them + # with size we have to make sure we insert the nodes after all + # the placeholders + with graph.inserting_before( + nodes[i + 1] if node not in placeholders else first_non_placeholder + ): + # Unfortunately, this logic still must remain because manual + # make_fx calls may not explicitly bind all symbolic ints as + # arguments to the function, so we must infer it from the other + # arguments + if ( + node in placeholders + and (example_value := _get_example_value(node)) is not None + ): + + def match_symbol(symint, cb): + if ( + isinstance(symint, torch.SymInt) + and isinstance(symint.node, SymNode) + and isinstance( + s := _get_placeholder_expr(symint.node), sympy.Symbol + ) + and s not in expr_to_proxy + ): + with _set_node_metadata_hook(gm, _node_metadata_hook): + expr_to_proxy[s] = fx.Proxy(cb(), tracer=tracer) + + log.debug("expr_to_proxy[%s] = %s", s, expr_to_proxy[s]) + + match_symbol(example_value, lambda: node) + + if isinstance(t := example_value, torch.Tensor): + for i, s in enumerate(t.size()): + match_symbol( + s, + lambda: graph.call_function( + torch.ops.aten.sym_size.int, (node, i) + ), + ) + if not is_sparse_any(t): + for i, s in enumerate(t.stride()): + match_symbol( + s, + lambda: graph.call_function( + torch.ops.aten.sym_stride.int, (node, i) + ), + ) + match_symbol( + t.storage_offset(), + lambda: graph.call_function( + torch.ops.aten.sym_storage_offset.default, (node,) + ), + ) + + # Handle asserts that aren't associated with any symbol. This + # doesn't really have to be in the loop as it will only run once, + # it just needs to happen right after the placeholders. + # insert this after placeholders & added sym nodes, and before non-placeholders. + if node == first_non_placeholder: + add_runtime_asserts(ras_by_symbol.pop(None, [])) # type: ignore[call-overload] + + # deduplicate asserts already present in graph, and remove trivial asserts + if node.target in ( + torch._check, + torch.ops.aten._assert_scalar.default, + ): + cond = node.args[0] if node.args else node.kwargs.get("cond") + if ( + cond == True # noqa: E712 + or (assert_expr := _get_sym_val(cond)) in expr_to_proxy + and assert_expr in added_asserts + ): + arg = cond + gm.graph.erase_node(node) + if isinstance(arg, fx.Node) and not arg.users: + gm.graph.erase_node(arg) + else: + added_asserts.add(assert_expr) # type: ignore[arg-type] + + # hash cons, replace function calls that return torch.SymInts with direct references to + # FX nodes built up to reify the sympy expression. + if ( + node.op != "placeholder" + and (sym_expr := _get_sym_val(node)) is not None + ): + # this guards against deleting calls like item() that produce new untracked symbols + def has_new_untracked_symbols(): + # pyrefly: ignore [missing-attribute] + for symbol in sym_expr.free_symbols: + if symbol not in expr_to_proxy: + return True + return False + + # this guards against deleting calls that produce unbacked bindings we haven't yet seen. + # in this case looking at sym_expr.free_symbols might not be enough, if the example value has a hint + # (is backed), but produces an unbacked symbol. In this case keep the node alive. + resolved_unbacked_bindings = resolve_unbacked_bindings( + shape_env, node.meta.get("unbacked_bindings", {}) + ) + + def has_new_unbacked_bindings(): + assert resolved_unbacked_bindings is not None + for key in resolved_unbacked_bindings: + if key not in expr_to_proxy: + return True + return False + + # maybe re-reify expression, replace current node + if ( + sym_expr in expr_to_proxy + or ( # example value is redundant + _is_intermediate_tensor_sym_call(node) + # shape call on intermediate tensor, turn into computation on input shapes + and not has_new_untracked_symbols() + ) + ) and not has_new_unbacked_bindings(): + if _is_intermediate_tensor_sym_call( + node + ): # reify from input shapes + with _set_node_metadata_hook( + gm, + functools.partial( + _node_metadata_hook, + stack_trace=node.meta.get("stack_trace"), + nn_module_stack=node.meta.get("nn_module_stack"), + ), + ): + expr_to_proxy[sym_expr] = _sympy_interp( + expr_to_proxy, + sym_expr, + ) # type: ignore[arg-type] + # won't try DCE-ing tensor compute here + hash_node = expr_to_proxy[sym_expr].node # type: ignore[arg-type] + node.replace_all_uses_with(hash_node) + gm.graph.erase_node(node) + log.debug( + "CSE node %s -> %s for expr %s", + node, + hash_node, + sym_expr, + ) + + # store node in hash cons, don't delete/replace + + elif sym_expr not in expr_to_proxy and not isinstance( + sym_expr, + (sympy.Number, sympy.logic.boolalg.BooleanAtom), + ): # don't hash cons primitives + expr_to_proxy[sym_expr] = fx.Proxy(node, tracer=tracer) # type: ignore[arg-type] + + # We add sym_constrain_range calls for symbols later in any case if they're size-like or range-constrained, + # so calls before that are redundant. + if node.target in ( + torch.ops.aten.sym_constrain_range.default, + torch.ops.aten.sym_constrain_range_for_size.default, + ): + gm.graph.erase_node(node) + + defs = [] + + # AOTAutograd will create new symbols as the unbacked_bindings keys, which PropagateSymInts will set as + # equivalent, but the refinement calls we perform in this pass may struggle with associating the two. + # More concretely, when re-exporting/tracing, constraining only the new symbol may not communicate enough + # information about the old symbol when we re-export, raising errors on data-dependent guards. + # Call resolve_unbacked_bindings() to get the original symbol if present, otherwise we take it as is. + if unbacked_bindings := resolve_unbacked_bindings( + shape_env, node.meta.get("unbacked_bindings") + ): + for s, keypath in unbacked_bindings.items(): + defs.append(s) + + # TODO: some CSE when generating these nodes can probably + # help reduce graph size and improve compile time + def go(node, keypath): + if keypath == (): + return node + if ( + len(keypath) >= 2 + and isinstance(keypath[0], CallMethodKey) + and isinstance(keypath[1], pytree.SequenceKey) + ): + if keypath[0].name == "size": + return go( + graph.call_function( + torch.ops.aten.sym_size.int, + (node, keypath[1].idx), + ), + keypath[2:], + ) + if keypath[0].name == "stride": + return go( + graph.call_function( + torch.ops.aten.sym_stride.int, + (node, keypath[1].idx), + ), + keypath[2:], + ) + + return go( + graph.call_method( + keypath[0].name, (node, keypath[1].idx) + ), + keypath[2:], + ) + elif isinstance(keypath[0], CallMethodKey): + if keypath[0].name == "storage_offset": + return go( + graph.call_function( + torch.ops.aten.sym_storage_offset.default, + (node,), + ), + keypath[1:], + ) + + return go( + graph.call_method(keypath[0].name, (node,)), keypath[1:] + ) + elif isinstance(keypath[0], pytree.SequenceKey): + return go( + graph.call_function( + operator.getitem, (node, keypath[0].idx) + ), + keypath[1:], + ) + elif isinstance(keypath[0], ConvertIntKey): + return go( + graph.call_function( + cast_symbool_to_symint_guardless, (node,) + ), + keypath[1:], + ) + elif isinstance(keypath[0], DivideByKey): + # TODO: need to assert divisibility + return go( + graph.call_function( + operator.floordiv, (node, keypath[0].divisor) + ), + keypath[1:], + ) + elif isinstance(keypath[0], InnerTensorKey): + return go( + graph.call_function( + getattr, (node, keypath[0].inner_name) + ), + keypath[1:], + ) + else: + raise AssertionError(f"unrecognized keypath {keypath}") + + if s not in expr_to_proxy: + with _set_node_metadata_hook(gm, _node_metadata_hook): + expr_to_proxy[s] = fx.Proxy( + go(node, keypath), tracer=tracer + ) + log.debug("expr_to_proxy[%s] = %s", s, expr_to_proxy[s]) + + for i0 in defs: + ras = ras_by_symbol.pop(i0, []) + # Before we perform any asserts, first apply range + # refinement. This is important, because if we are going + # to retrace the graph (and we typically are if we send + # the graph to AOTAutograd), we need to make sure we apply + # range refinement (ala _check_is_size) first, BEFORE we + # run any of the asserts. Otherwise, we may decide to + # perform substitutions based on the asserts which we then + # can't back out, because value ranges can only be applied + # to asserts.) + # + # A perhaps better long term plan is to avoid this order + # dependence by making it possible to refine ranges on + # arbitrary expressions, not just symbols. But it is not + # so easy to make use of this information, see + # https://twitter.com/ezyang/status/1745801370299482492 + # We actually made an attempt at this in + # https://github.com/pytorch/pytorch/pull/119043 + # which didn't work. + # + # Another ideas for how to do this: + # - Have bound_sympy be the source of truth of the ranges of any expression + # - Cache intermediate results for every subexpression of bound_sympy + # - This cache should be possible to edit to refine ranges + # + # One issue with this proposal is that if + # we have a bound on 2x, we are not going to be able to + # apply it for 4x. Similarly, we may have bounds for an + # equivalent expression that we are not applying because + # it's not a perfect match (e.g. x < y vs y > x)". + # + # The first issue we already have it and it's impossible + # to solve in general, so any implementation on a best + # effort basis should do. + # + # The second issue is a preexisting one. It can be mitigated + # with a normalization algorithm. In general, it may also + # be on a best effort basis, but since our grammar is not + # terribly difficult, chances are we could even fully + # normalize SymPy expressions... who knows. + if i0 in constrained_unbacked_symbols: + continue # constrain symbol just once + + if i0 in shape_env.size_like: + if export: + graph.call_function( + torch.ops.aten.sym_constrain_range_for_size.default, + (expr_to_proxy[i0].node,), + ) + else: + graph.call_function( + torch._check_is_size, (expr_to_proxy[i0].node,) + ) + + vr = shape_env.var_to_range[i0] + if vr.is_int and vr.upper == sys.maxsize - 1: + # treat upper bound == sys.maxsize - 1 for int symbols as +oo + # to avoid redundant runtime assert + vr = ValueRanges(vr.lower, int_oo) + if not shape_env._default_unspecified_value_range().issubset(vr): + # The runtime range is constrained, so add a runtime + # assert and also explicitly refine the range + # (refinement should not be necessary once runtime + # asserts cause refinement, but that's NYI) + def convert(s): + if s in (int_oo, -int_oo): + return None + try: + return int(s) + except TypeError: + return None + + if ( + expr_to_proxy[i0].node.target + is not cast_symbool_to_symint_guardless + ): + # TODO(pianpwk): calling sym_constrain_range_for_size or adding bound asserts + # raises AOTAutograd errors on cast_symbool_to_symint_guardless + + with _set_node_metadata_hook( + gm, + functools.partial( + _node_metadata_hook, + stack_trace=node.meta.get("stack_trace"), + nn_module_stack=node.meta.get("nn_module_stack"), + # nodes added in `apply_runtime_assertion_pass` will have the same annotation + # as the input node to the assertion + custom=node.meta.get("custom"), + ), + ): + if (min_val := convert(vr.lower)) is not None: + ge = _sympy_interp(expr_to_proxy, i0 >= min_val).node + graph.call_function( + torch.ops.aten._assert_scalar.default, + ( + ge, + f"Runtime assertion failed for expression {i0 >= min_val} on node '{ge}'", + ), + ) + added_asserts.add(i0 >= min_val) + if (max_val := convert(vr.upper)) is not None: + le = _sympy_interp(expr_to_proxy, i0 <= max_val).node + graph.call_function( + torch.ops.aten._assert_scalar.default, + ( + le, + f"Runtime assertion failed for expression {i0 <= max_val} on node '{le}'", + ), + ) + added_asserts.add(i0 <= max_val) + + constrained_unbacked_symbols.add(i0) + add_runtime_asserts(ras) + + # delete unused reified symbols + for expr, proxy in expr_to_proxy.items(): + if ( + isinstance(expr, sympy.Symbol) + and proxy.node.op != "placeholder" # keep placeholders intact + and not proxy.node.users + ): + log.debug("deleting unused reified symbol for %s", expr) + gm.graph.erase_node(proxy.node) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/shape_prop.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/shape_prop.py new file mode 100644 index 0000000000000000000000000000000000000000..62ea218356138de640c7fb7a74fb2efbcb4b21e5 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/shape_prop.py @@ -0,0 +1,230 @@ +# mypy: ignore-errors + +import traceback +from typing import Any, NamedTuple, Optional + +import torch +import torch.fx +from torch._dispatch.python import enable_python_dispatcher +from torch._guards import detect_fake_mode +from torch._prims_common import is_contiguous_for_memory_format_or_false +from torch._subclasses.meta_utils import is_sparse_any +from torch.fx._compatibility import compatibility +from torch.fx.node import map_aggregate, Node + + +__all__ = ["TensorMetadata", "ShapeProp"] + + +@compatibility(is_backward_compatible=True) +class TensorMetadata(NamedTuple): + # TensorMetadata is a structure containing pertinent information + # about a tensor within a PyTorch program. + + # General Tensor metadata + shape: torch.Size + dtype: torch.dtype + requires_grad: bool + stride: tuple[int, ...] + memory_format: Optional[torch.memory_format] + + # Quantization metadata + is_quantized: bool + qparams: dict[str, Any] + + +# When include_contiguity is True, we will set contiguity when its always true for the tensor. +# Some tensors can represent both contiguous and non-contiguous tensors. e.g: (u0, u1) with (u2, u3). +# In such situation contiguity is not set. We could also make it a tri-state i.e: (def_contiguous, +# def_not_contiguous and unknown). +def _extract_tensor_metadata( + result: torch.Tensor, include_contiguity=True +) -> TensorMetadata: + """ + Extract a TensorMetadata NamedTuple describing `result`. + """ + shape = result.shape + dtype = result.dtype + requires_grad = result.requires_grad + stride = result.stride() if not is_sparse_any(result) else () + + memory_format = None + + if include_contiguity and not is_sparse_any(result): + memory_formats = ( + torch.contiguous_format, + torch.channels_last, + torch.channels_last_3d, + ) + for query_format in memory_formats: + if is_contiguous_for_memory_format_or_false( + result, memory_format=query_format + ): + memory_format = query_format + break + + is_quantized = result.is_quantized + qparams: dict[str, Any] = {} + if is_quantized: + qscheme = result.qscheme() + qparams["qscheme"] = qscheme + if qscheme in (torch.per_tensor_affine, torch.per_tensor_symmetric): + qparams["scale"] = result.q_scale() # type: ignore[assignment] + qparams["zero_point"] = result.q_zero_point() # type: ignore[assignment] + elif qscheme in ( + torch.per_channel_affine, + torch.per_channel_affine_float_qparams, + torch.per_channel_symmetric, + ): + # In this branch, scale and zero_point are expected to be tensors, + # we store the values as immutable_list in TensorMetadata for + # easier serialization downstream + qparams["scale"] = result.q_per_channel_scales().tolist() # type: ignore[assignment] + qparams["zero_point"] = result.q_per_channel_zero_points().tolist() # type: ignore[assignment] + qparams["axis"] = result.q_per_channel_axis() # type: ignore[assignment] + + return TensorMetadata( + shape, dtype, requires_grad, stride, memory_format, is_quantized, qparams + ) + + +@compatibility(is_backward_compatible=True) +class ShapeProp(torch.fx.Interpreter): + """ + Execute an FX graph Node-by-Node and + record the shape and type of the result + into the corresponding node. + + Example: + In this example, we record the shape + and data type of a module given + an example input ``torch.randn(50, D_in)``. + We print the name, shape and dtype of each node. + + class TwoLayerNet(torch.nn.Module): + def __init__(self, D_in, H, D_out): + super().__init__() + self.linear1 = torch.nn.Linear(D_in, H) + self.linear2 = torch.nn.Linear(H, D_out) + def forward(self, x): + h_relu = self.linear1(x).clamp(min=0) + y_pred = self.linear2(h_relu) + return y_pred + N, D_in, H, D_out = 64, 1000, 100, 10 + x = torch.randn(N, D_in) + y = torch.randn(N, D_out) + model = TwoLayerNet(D_in, H, D_out) + gm = torch.fx.symbolic_trace(model) + sample_input = torch.randn(50, D_in) + ShapeProp(gm).propagate(sample_input) + + for node in gm.graph.nodes: + print(node.name, node.meta['tensor_meta'].dtype, + node.meta['tensor_meta'].shape) + + The output of this code is: + + x torch.float32 torch.Size([50, 1000]) + linear1 torch.float32 torch.Size([50, 100]) + clamp_1 torch.float32 torch.Size([50, 100]) + linear2 torch.float32 torch.Size([50, 10]) + output torch.float32 torch.Size([50, 10]) + + Args: + module (GraphModule): The module to be executed + fake_mode (FakeTensorMode): A fake mode for copying the gm + + """ + + def __init__(self, gm, fake_mode=None): + super().__init__(gm) + if fake_mode is None: + fake_mode = detect_fake_mode() + if fake_mode is not None: + from torch._dynamo.utils import deepcopy_to_fake_tensor + + # Note: + # We need fake execution cause the inputs are fake, however, we cannot fakify the module + # - because we need to write to the tensor_meta of the real module. So we fakify to + # produce a result (L131 below), to extract tensor meta, and then keep going. + # + # If we were to fakify, we would write to the wrong node, and then downstream fusion + # would be missing the tensor_meta. + # + # See torch/_inductor/overrides.py for where this is called upstream of fusion. + self.fake_module = deepcopy_to_fake_tensor(self.module, fake_mode) + self.fake_mode = fake_mode + else: + self.fake_module = None + self.fake_mode = None + + self.real_module = self.module + + def run_node(self, n: Node) -> Any: + from torch.fx.experimental.symbolic_shapes import ( + compute_unbacked_bindings, + rebind_unbacked, + ) + + try: + if self.fake_module is not None: + # Hacky swap. Alternatively, we could do this with overriding + # call_module and get_attr. + self.module = self.fake_module + try: + if self.fake_mode is not None: + with self.fake_mode, enable_python_dispatcher(): + result = super().run_node(n) + rebind_unbacked(self.fake_mode.shape_env, n, result) + else: + result = super().run_node(n) + finally: + self.module = self.real_module + except Exception as e: + traceback.print_exc() + raise RuntimeError( + f"ShapeProp error for: node={n.format_node()} with meta={n.meta}" + ) from e + + found_tensor = False + + def extract_tensor_meta(obj): + if isinstance(obj, torch.Tensor): + nonlocal found_tensor + found_tensor = True + return _extract_tensor_metadata(obj) + else: + return obj + + meta = map_aggregate(result, extract_tensor_meta) + if found_tensor: + n.meta["tensor_meta"] = meta + + if self.fake_mode: + if (shape_env := self.fake_mode.shape_env) and ( + symbol_to_path := compute_unbacked_bindings(shape_env, result) + ): + n.meta["unbacked_bindings"] = symbol_to_path + + n.meta["type"] = type(result) + return result + + def propagate(self, *args): + """ + Run `module` via interpretation and return the result and + record the shape and type of each node. + + Args: + *args (Tensor): the sample input. + + Returns: + Any: The value returned from executing the Module + """ + if self.fake_mode is not None: + fake_args = [ + self.fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t + for t in args + ] + else: + fake_args = args + return super().run(*fake_args) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/split_module.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/split_module.py new file mode 100644 index 0000000000000000000000000000000000000000..a4b244750f33dc5f5a7b233afc70f4b1e1f26cd8 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/split_module.py @@ -0,0 +1,656 @@ +# mypy: allow-untyped-defs +import inspect +import logging +from collections import OrderedDict +from collections.abc import Callable +from typing import Any, Optional + +import torch +from torch.fx._compatibility import compatibility +from torch.fx._utils import lazy_format_graph_code +from torch.fx.graph_module import GraphModule +from torch.fx.node import Node + + +__all__ = ["Partition", "split_module"] +log = _LOGGER = logging.getLogger(__name__) + + +@compatibility(is_backward_compatible=True) +class Partition: + def __init__(self, name: str): + self.name: str = name + self.submod_name = f"submod_{name}" + self.node_names: list[str] = [] + self.inputs: dict[str, None] = {} + self.outputs: dict[str, None] = {} + self.dependencies: dict[str, None] = {} + self.dependents: dict[str, None] = {} + self.graph: torch.fx.graph.Graph = torch.fx.graph.Graph() + self.environment: dict[Node, Node] = {} + self.targets: dict[str, Any] = {} + + def __repr__(self) -> str: + return ( + f"name: {self.name},\n" + f" nodes: {self.node_names},\n" + f" inputs: {self.inputs},\n" + f" outputs: {self.outputs},\n" + f" partitions depended on: {self.dependencies},\n" + f" partition dependents: {self.dependents}" + ) + + +def _get_attr_from_qualname(mod: torch.nn.Module, qualname: str) -> Any: + attr_val = mod + for atom in qualname.split("."): # type: ignore[union-attr] + if not hasattr(attr_val, atom): + raise AttributeError(f"Node target {qualname} not found!") + attr_val = getattr(attr_val, atom) + return attr_val + + +# Creates subgraphs out of main graph +@compatibility(is_backward_compatible=True) +def split_module( + m: GraphModule, + root_m: torch.nn.Module, + split_callback: Callable[[Node], int], + qualname_map: Optional[dict[str, str]] = None, + keep_original_order: Optional[bool] = False, + keep_original_node_name: Optional[bool] = False, + keep_original_input_name: bool = True, + *, + partition_affix: Optional[str] = None, +): + """ + Creates subgraphs out of main graph + + Args: + m (GraphModule): Graph module to split + root_m (torch.nn.Module): root nn module. Not currently used. Included + because the root nn module is usually transformed via + torch.fx._symbolic_trace.symbolic_trace (see example below) + split_callback (Callable[[Node], int]): Callable function + that maps a given Node instance to a numeric partition identifier. + split_module will use this function as the policy for which operations + appear in which partitions in the output Module. + qualname_map: Optional[Dict[str, str]]: optional output parameter that returns a + mapping from new target names in the module after split to old target + names in the original module. + keep_original_order: Optional[bool]: keep the original order of the GraphModule + or use the Topological order of the new constructed GraphModule + keep_original_node_name: Optional[bool]: If the partitioned graphs should + have the same node names as the original graph. + keep_original_input_name: bool: If the partitioned graphs should + have the same input names as the original graph. + partition_affix: Optional[str]: If specified, the submodules' names will contain + the affix, e.g. "submod__". + + Returns: + GraphModule: the module after split. + + Example: + + This is a sample setup: + + import torch + from torch.fx._symbolic_trace import symbolic_trace + from torch.fx.graph_module import GraphModule + from torch.fx.node import Node + from torch.fx.passes.split_module import split_module + + class MyModule(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.param = torch.nn.Parameter(torch.rand(3, 4)) + self.linear = torch.nn.Linear(4, 5) + + def forward(self, x, y): + z = self.linear(x + self.param).clamp(min=0.0, max=1.0) + w = self.linear(y).clamp(min=0.0, max=1.0) + return z + w + + # symbolically trace model + my_module = MyModule() + my_module_traced = symbolic_trace(my_module) + + # random mod partitioning + partition_counter = 0 + NPARTITIONS = 3 + + def mod_partition(node: Node): + global partition_counter + partition = partition_counter % NPARTITIONS + partition_counter = (partition_counter + 1) % NPARTITIONS + return partition + + # split module in module with submodules + module_with_submodules = split_module( + my_module_traced, my_module, mod_partition + ) + + Output looks like this. Original graph is broken into partitions + + > print(module_with_submodules) + GraphModule( + (submod_0): GraphModule( + (linear): Linear(in_features=4, out_features=5, bias=True) + ) + (submod_1): GraphModule( + (linear): Linear(in_features=4, out_features=5, bias=True) + ) + (submod_2): GraphModule() + ) + + def forward(self, x, y): + param = self.param + submod_0 = self.submod_0(x, param, y); x = param = y = None + getitem = submod_0[0] + getitem_1 = submod_0[1]; submod_0 = None + submod_1 = self.submod_1(getitem, getitem_1); getitem = getitem_1 = None + getitem_2 = submod_1[0] + getitem_3 = submod_1[1]; submod_1 = None + submod_2 = self.submod_2(getitem_2, getitem_3); getitem_2 = getitem_3 = None + return submod_2 + + Output of split module is the same as output of input traced module. + This is an example within a test setting: + + > orig_out = my_module_traced(x, y) + > submodules_out = module_with_submodules(x, y) + > self.assertEqual(orig_out, submodules_out) + True + """ + + log.debug( + "%s", + lazy_format_graph_code("pre split_module", m, colored=True), + ) + + def construct_graph( + node: Node, + base_mod_env: dict[str, Node], + base_mod_attrs: dict[str, torch.fx.graph_module.GraphModule], + ): + if node.op == "placeholder": + default_value = ( + node.args[0] if len(node.args) > 0 else inspect.Signature.empty + ) + if keep_original_node_name: + args = ( + () if default_value is inspect.Signature.empty else (default_value,) + ) + base_mod_env[node.name] = base_mod_graph.create_node( + "placeholder", + node.name, + args=args, # type: ignore[arg-type] + type_expr=node.type, + ) + else: + base_mod_env[node.name] = base_mod_graph.placeholder( + node.target, # type: ignore[arg-type] + type_expr=node.type, + default_value=default_value, + ) + base_mod_env[node.name].meta = node.meta.copy() + elif node.op == "get_attr": + base_mod_env[node.name] = base_mod_graph.get_attr(node.target) # type: ignore[arg-type] + base_mod_env[node.name].meta = node.meta.copy() + assert isinstance(node.target, str) + attr_val = _get_attr_from_qualname(m, node.target) + base_mod_attrs[node.target] = attr_val # type: ignore[index] + return base_mod_env, base_mod_attrs + + import sympy + + partitions: dict[str, Partition] = {} + orig_nodes: dict[str, Node] = {} + symbol_to_node: dict[sympy.Symbol, Node] = {} + + def record_cross_partition_use(def_node: Node, use_node: Optional[Node]): + from torch.fx.experimental.symbolic_shapes import free_symbols + + defined = getattr(def_node, "_fx_partition", None) + used = getattr(use_node, "_fx_partition", None) + + log.debug( + "record_cross_partition_use %s (%s) %s (%s)", + def_node.name, + defined, + use_node.name if use_node is not None else "-", + used, + ) + + if defined != used: + if defined is not None: + def_partition = partitions[defined] + def_partition.outputs.setdefault(def_node.name) + if used is not None: + def_partition.dependents.setdefault(used) + + if used is not None: + use_partition = partitions[used] + use_partition.inputs.setdefault(def_node.name) + # We have made def_node an input to the use_partition. If + # this input has symbolic symbols in its size, those also must + # be made as inputs to the partition + if (def_val := def_node.meta.get("example_value")) is not None: + for s in sorted(free_symbols(def_val), key=str): + s_node = symbol_to_node[s] + use_partition.inputs.setdefault(s_node.name) + if symbol_to_node[s].op != "placeholder": + # If the node that defines the symbol is not a + # placeholder, we must make it an output of the + # partition. Note that this may be in a different + # partition than defined! Although, this doesn't + # really make a difference for correctness, since + # defined is guaranteed to have the symbol in + # scope and can return it; you just get less + # optimal codegen in this case. + s_defined = getattr(s_node, "_fx_partition", None) + if s_defined is not None: + s_def_partition = partitions[s_defined] + s_def_partition.outputs.setdefault(s_node.name) + s_def_partition.dependents.setdefault(used) + use_partition.dependencies.setdefault(s_defined) + if defined is not None: + use_partition.dependencies.setdefault(defined) + + def instantiate_node_partition_mapping(node): + partition_idx = split_callback(node) + partition_name = str(partition_idx) + if partition_affix is not None: + # For example, if user specifies partition_affix = "pp", then the + # partition name will be "pp_0", "pp_1", etc + partition_name = "_".join([partition_affix, partition_name]) + + log.debug( + "instantiate_node_partition_mapping %s (%s)", node.name, partition_name + ) + + # add node to partitions + partition = partitions.get(partition_name) + if partition is None: + partitions[partition_name] = partition = Partition(partition_name) + + partition.node_names.append(node.name) + node._fx_partition = partition_name + + # Global State Nodes are nodes which by their global state effects, + # "taint" all downstream nodes while they are active. + GLOBAL_STATE_NODES = [ + torch.amp._enter_autocast, + torch.amp._exit_autocast, + torch._C._set_grad_enabled, + ] + + # For grad regions: + # ------------------------ + # 1. first region: we do nothing + # 2. subsequent regions: we insert the set_grad at the beginning + grad_regions: OrderedDict[Node, set[int]] = OrderedDict() + + # For autocast regions: + # ------------------------ + # 1. first region: we will only insert the _exit at the end + # 2. intermediate regions: we will insert both the + # _enter at the beginning and _exit at the end + # 3. last region: we will only insert _enter at the beginning + # We will do so in the order in which the autocasts were instantiated. + autocast_regions: OrderedDict[Node, set[int]] = OrderedDict() + autocast_exits: dict[Node, Optional[Node]] = {} + + active_grad = None + active_autocasts = set() + + for node in m.graph.nodes: + # This will prefer placeholder bindings, because those come first. + # This is a little dangerous though: it is possible that an unbacked + # symbol is used without any binding site for it, in which case we + # will get a KeyError not able to find it. I'd like to fix this by + # having passes.runtime_assert establish some invariants that I can + # rely on later, but this needs some extra work. Quick fix first. + # See https://github.com/pytorch/pytorch/issues/130534 + if ( + (val := node.meta.get("example_value")) is not None + and isinstance(val, (torch.SymInt, torch.SymFloat)) + and isinstance(s0 := val.node.expr, sympy.Symbol) + and s0 not in symbol_to_node + ): + symbol_to_node[val.node.expr] = node + + if node.op in ["placeholder", "get_attr", "output"]: + continue + + instantiate_node_partition_mapping(node) + + if node.op == "call_function" and node.target in GLOBAL_STATE_NODES: + if node.target is torch._C._set_grad_enabled: + assert len(node.args) == 1 + assert isinstance(node.args[0], bool) + active_grad = node + grad_regions[active_grad] = set({split_callback(node)}) + elif node.target is torch.amp._enter_autocast: + # Should all be python constants + assert all(not isinstance(arg, Node) for arg in node.args) + active_autocasts.add(node) + autocast_regions[node] = set({split_callback(node)}) + autocast_exits[node] = None + elif node.target is torch.amp._exit_autocast: + assert len(node.args) == 1 + autocast_regions[node.args[0]].add(split_callback(node)) + active_autocasts.remove(node.args[0]) + autocast_exits[node.args[0]] = node + + if active_grad is not None: + grad_regions[active_grad].add(split_callback(node)) + + for a in active_autocasts: + autocast_regions[a].add(split_callback(node)) + + assert all(v is not None for v in autocast_exits.values()), "autocast must exit" + + # pyrefly: ignore [bad-assignment] + autocast_regions = {k: sorted(v) for k, v in autocast_regions.items()} + # pyrefly: ignore [bad-assignment] + grad_regions = {k: sorted(v) for k, v in grad_regions.items()} + + if _LOGGER.isEnabledFor(logging.DEBUG): + _LOGGER.debug("autocast_regions: %s", autocast_regions) + _LOGGER.debug("grad_regions: %s", grad_regions) + + assert_monotonically_increasing = bool(autocast_regions) or bool(grad_regions) + + # split nodes into partitions + highest_partition = -1 + for node in m.graph.nodes: + orig_nodes[node.name] = node + + # TODO currently placeholders/parameters aren't put into random partitions, + # rather they're added to the graphs where they are used down below + if node.op in ["placeholder", "get_attr"]: + continue + if node.op == "output": + torch.fx.graph.map_arg( + node.args[0], lambda n: record_cross_partition_use(n, None) + ) + continue + + if assert_monotonically_increasing: + pid = split_callback(node) + assert highest_partition <= pid, ( + "autocast or set_grad_enabled require monotonically increasing partitions:" + f"highest: {highest_partition}, this node's: {pid}" + ) + highest_partition = pid + + # do not capture cross-partition dependencies for global state nodes as they will be + # self-contained - their setup and unwind will be isolated to each partition submodule. + if node.target not in GLOBAL_STATE_NODES: + torch.fx.graph.map_arg( + node.args, lambda def_node: record_cross_partition_use(def_node, node) + ) + torch.fx.graph.map_arg( + node.kwargs, lambda def_node: record_cross_partition_use(def_node, node) + ) # noqa: B950 + + original_partition_order = list(partitions.keys()) + # find partitions with no dependencies + root_partitions: list[str] = [] + for partition_name, partition in partitions.items(): + if not len(partition.dependencies): + root_partitions.append(partition_name) + + # check partitions for circular dependencies and create topological partition ordering + sorted_partitions: list[str] = [] + while root_partitions: + root_partition = root_partitions.pop() + sorted_partitions.append(root_partition) + for dependent in partitions[root_partition].dependents: + partitions[dependent].dependencies.pop(root_partition) # noqa: B909 + if not partitions[dependent].dependencies: + root_partitions.append(dependent) + if len(sorted_partitions) != len(partitions): + raise RuntimeError("cycle exists between partitions!") + + # Enter prelude + for regions_mapping in [autocast_regions, grad_regions]: + for node, regions in regions_mapping.items(): + assert len(regions) > 0 + # pyrefly: ignore [index-error] + partitions[str(regions[0])].environment[node] = node + # pyrefly: ignore [index-error] + for r in regions[1:]: + partition = partitions[str(r)] + new_node = partition.graph.create_node( + op=node.op, + target=node.target, + args=tuple(arg for arg in node.args), + kwargs={}, + type_expr=node.type, + ) + new_node.meta = ( + node.meta.copy() + ) # is it really a good idea to copy this? + partition.environment[node] = new_node + + # add placeholders to partition inputs + for partition_name in sorted_partitions: + partition = partitions[partition_name] + new_inputs: dict[str, None] = {} + + counter = 0 + + for inp in partition.inputs: + orig_node = orig_nodes[inp] + # We don't pass in get_attr nodes as inputs to the partition, but + # instead set them as targets and use getattr within the module + + def add_placeholder(): + if keep_original_input_name: + name = inp + else: + nonlocal counter + name = f"arg_{counter}" + counter += 1 + placeholder = partition.graph.placeholder( + name, + type_expr=orig_nodes[inp].type, + ) + new_inputs[inp] = None + return placeholder + + if orig_node.op == "get_attr": + assert isinstance(orig_node.target, str) + + orig_attr = _get_attr_from_qualname(m, orig_node.target) + if isinstance(orig_attr, torch.nn.Module): + placeholder = partition.graph.get_attr(orig_node.target) + partition.targets[orig_node.target] = orig_attr + else: + placeholder = add_placeholder() + else: + placeholder = add_placeholder() + placeholder.meta = orig_nodes[inp].meta.copy() + partition.environment[orig_nodes[inp]] = placeholder + partition.inputs = new_inputs + + # Transform nodes and collect targets for partition's submodule + for node in m.graph.nodes: + if hasattr(node, "_fx_partition"): + partition = partitions[node._fx_partition] + + # swap out old graph nodes in kw/args with references to new nodes in this submodule + environment = partition.environment + gathered_args = torch.fx.graph.map_arg(node.args, lambda n: environment[n]) + gathered_kwargs = torch.fx.graph.map_arg( + node.kwargs, lambda n: environment[n] + ) + + if node.op not in ["call_module", "get_attr"]: + target = node.target + else: + target_attr = _get_attr_from_qualname(m, node.target) + target = node.target.replace(".", "_") + partition.targets[target] = target_attr + # Fill in the passed-in mapping from new qualname to old qualname + if qualname_map is not None: + # When creating the split module later, the submodules will have + # path prefix matching the corresponding partition's submod_name + qualname = f"{partition.submod_name}.{target}" + qualname_map[qualname] = node.target + + assert isinstance(gathered_args, tuple) + assert isinstance(gathered_kwargs, dict) + name = node.name if keep_original_node_name else None + new_node = partition.graph.create_node( + op=node.op, + target=target, + args=gathered_args, + kwargs=gathered_kwargs, + type_expr=node.type, + name=name, + ) + new_node.meta = node.meta.copy() + partition.environment[node] = new_node + + # Exit epilogue + for regions_mapping in [autocast_regions]: + for node in reversed(regions_mapping): + regions = regions_mapping[node] + assert len(regions) > 0 + # pyrefly: ignore [index-error] + for r in regions[:-1]: + partition = partitions[str(r)] + exit_node = autocast_exits[node] + assert exit_node is not None, "Missing exit node" + new_node = partition.graph.create_node( + op=exit_node.op, + target=exit_node.target, + args=(partition.environment[node],), + kwargs={}, + type_expr=exit_node.type, + ) + new_node.meta = ( + exit_node.meta.copy() + ) # is it really a good idea to copy this? + + # original module environment dict mapping node names to nodes + orig_mod_env: dict[str, Node] = {} + # Set up values to construct base module + base_mod_env: dict[str, Node] = {} + base_mod_graph: torch.fx.graph.Graph = torch.fx.graph.Graph() + base_mod_attrs: dict[str, torch.fx.graph_module.GraphModule] = {} + if not keep_original_order: + for node in m.graph.nodes: + base_mod_env, base_mod_attrs = construct_graph( + node, base_mod_env, base_mod_attrs + ) + + else: + # Go through the graph to construct the mapping dict + for node in m.graph.nodes: + orig_mod_env[node.name] = node + + # Do some things iterating over the partitions in topological order again: + # 1) Finish off submodule Graphs by setting corresponding outputs + # 2) Construct GraphModules for each submodule + # 3) Construct the base graph by emitting calls to those submodules in + # topological order or original order specified by keep_original_order + + construct_order_partitions = ( + sorted_partitions if not keep_original_order else original_partition_order + ) + + already_constructed_attr_nodes = set() + + # We actually need to insert the placeholder nodes in the original order + # otherwise graph signature will be wrong. + original_order = [node for node in m.graph.nodes if node.op == "placeholder"] + + for partition_name in construct_order_partitions: + partition = partitions[partition_name] + + # Set correct output values + output_vals = tuple( + partition.environment[orig_nodes[name]] for name in partition.outputs + ) + + # skip output node generation if there are no output values + num_output_vals = len(output_vals) + if num_output_vals == 1: + partition.graph.output(output_vals[0]) + elif num_output_vals > 1: + partition.graph.output(output_vals) + else: + # Invariant - Graph should always have an output node. + partition.graph.output(()) + + if keep_original_order: + # first get the attr nodes required by this partition + orig_mod_attr_nodes: list[Node] = [ + orig_mod_env[key] + for key in partition.inputs + if key not in original_order + ] + + for node in original_order: + if node in already_constructed_attr_nodes: + continue # already added this attr to the base graph + base_mod_env, _based_mod_attrs = construct_graph( + node, base_mod_env, base_mod_attrs + ) + already_constructed_attr_nodes.add(node) + + # Construct GraphModule for this partition + for node in orig_mod_attr_nodes: # type: ignore[attr-defined] + if node in already_constructed_attr_nodes: + continue + base_mod_env, base_mod_attrs = construct_graph( + node, base_mod_env, base_mod_attrs + ) + already_constructed_attr_nodes.add(node) + + base_mod_attrs[partition.submod_name] = torch.fx.graph_module.GraphModule( + partition.targets, partition.graph + ) # noqa: B950 + + # Emit call in base graph to this submodule + output_val = base_mod_graph.call_module( + partition.submod_name, + tuple(base_mod_env[name] for name in partition.inputs), + ) + + num_outputs = len(partition.outputs) + if num_outputs > 1: + # Unpack multiple return values from submodule + output_val_proxy = torch.fx.proxy.Proxy(output_val) + for i, output_name in enumerate(partition.outputs): + base_mod_env[output_name] = output_val_proxy[i].node # type: ignore[index] + elif num_outputs == 1: + base_mod_env[next(iter(partition.outputs))] = output_val + + # When keep_original_order=True and if the graph doesn't have any + # `call_function` node then `base_mod_graph`, `base_mod_env` and `base_mod_attrs` + # are never populated. + # For this case, we call `construct_graph` here which takes care of updating them. + if keep_original_order and not base_mod_env: + for node in m.graph.nodes: + base_mod_env, base_mod_attrs = construct_graph( + node, base_mod_env, base_mod_attrs + ) + + # Add output node to `base_mod_graph` (i.e. the split graph) which will be returned. + for node in m.graph.nodes: + if node.op == "output": + base_mod_graph.output( + torch.fx.graph.map_arg(node.args[0], lambda n: base_mod_env[n.name]) + ) # noqa: B950 + + ret = torch.fx.graph_module.GraphModule(base_mod_attrs, base_mod_graph) + log.debug( + "%s", + lazy_format_graph_code("post split_module", ret, colored=True), + ) + return ret diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/split_utils.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/split_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..88da7ac7c4f55fb5cf1c22546d09ceb3b406d6fb --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/split_utils.py @@ -0,0 +1,312 @@ +# mypy: allow-untyped-defs +import copy +from dataclasses import dataclass, field +from typing import Optional, Union + +import torch.fx +from torch.fx._compatibility import compatibility +from torch.fx.graph import map_arg +from torch.fx.passes.utils import HolderModule, lift_subgraph_as_module + +from .tools_common import NodeList + + +__all__ = ["getattr_recursive", "setattr_recursive", "Component", "split_by_tags"] + + +@compatibility(is_backward_compatible=False) +def getattr_recursive(obj, name): + for layer in name.split("."): + if isinstance(obj, torch.nn.ModuleList): + if hasattr(obj, "_modules") and layer in obj._modules: + obj = obj._modules[layer] + else: + return None + elif hasattr(obj, layer): + obj = getattr(obj, layer) + else: + return None + return obj + + +@compatibility(is_backward_compatible=False) +def setattr_recursive(obj, attr, value): + if "." not in attr: + setattr(obj, attr, value) + else: + layer = attr.split(".") + setattr_recursive(getattr(obj, layer[0]), ".".join(layer[1:]), value) + + +@compatibility(is_backward_compatible=False) +@dataclass +class Component: + """ + A component serves as a container for a subgraph we want to create afterwards. + """ + + graph: torch.fx.Graph + order: int + name: str + + # Stores the placeholder nodes in `graph`. + input_placeholders: list = field(default_factory=list) + + # Store the nodes in original graph that are placeholder in `graph`. + orig_inputs: list = field(default_factory=list) + + # Store the nodes in original graph that are outputs in `graph`. + orig_outputs: list = field(default_factory=list) + + # Mapping from get_attr node in original graph to get_attr node in `graph`. + getattr_maps: dict[torch.fx.Node, torch.fx.Node] = field(default_factory=dict) + constructor_args: list[str] = field(default_factory=list) + gm: Optional[torch.fx.GraphModule] = None + + +@compatibility(is_backward_compatible=False) +def split_by_tags( + gm: torch.fx.GraphModule, + tags: list[str], + return_fqn_mapping: bool = False, + return_tuple: bool = False, + GraphModuleCls: type[torch.fx.GraphModule] = torch.fx.GraphModule, +) -> Union[torch.fx.GraphModule, tuple[torch.fx.GraphModule, dict[str, str]]]: + """ + Splits a GraphModule using tags on its graph nodes. We honor the order of + tags. For example, we have tags = ["a", "b", "c"], the function will create + the initial submodules in the order of "a", "b", "c". + + To set a tag: + gm.graph.nodes[idx].tag = "mytag" + + This will result in all nodes with the same tag being extracted and placed in their + own submodule. For placeholder, output and get_attr node, the tag is ignored. placeholder + and output nodes are created when needed while get_attr nodes get copied to submodules + where they are used. + + Given the following module def: + + class SimpleModule(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.linear1 = torch.nn.Linear(...) + self.linear2 = torch.nn.Linear(...) + self.linear3 = torch.nn.Linear(...) + + def forward(self, in1, in2): + r1 = self.linear1(in1) + r2 = self.linear2(in2) + r3 = torch.cat([r1, r2]) + return self.linear3(r3) + + Marking the node corresponding to in1 with the tag sc.REQUEST_ONLY.lower() results in the following split: + + ro: + def forward(self, in1): + self = self.root + linear1 = self.linear1(in1) + return linear1 + + main: + def forward(self, in2, linear1): + self = self.root + linear2 = self.linear2(in2) + cat_1 = torch.cat([linear1, linear2]) + linear3 = self.linear3(cat_1) + return linear3 + + main: + def forward(self, in1, in2): + self = self.root + ro_0 = self.ro_0(in1) + main_1 = self.main_1(in2, ro_0) + return main_1 + + Returns: + split_gm: torch fx graph after split + orig_to_split_fqn_mapping: a map between the original fqn and the fqn + after split for call_module and get_attr. + """ + + def flatten(x: torch.fx.node.Argument) -> NodeList: + """ + Stores nodes in x to a list and returns the list. + """ + r: NodeList = [] + map_arg(x, r.append) + return r + + # Mapping from node in original module to node in created submodule. + node_remapping: dict[torch.fx.Node, torch.fx.Node] = {} + + # Mapping from node in original module or created submodules to + # corresponding component. + node_to_component: dict[torch.fx.Node, Component] = {} + + # Mapping from tag to the corresponding component. + tag_to_component: dict[str, Component] = {} + + # Stores all components. + all_components: list[Component] = [] + + # Stores nodes that will be used in main graph. + used_in_main: dict[torch.fx.Node, None] = {} + + # Main graph after split. + main_g = torch.fx.Graph() + + # Mapping from node in original module to node in main graph after split. + main_remapping: dict[torch.fx.Node, torch.fx.Node] = {} + + # Output node of original module. + output_node: Optional[torch.fx.Node] = None + + # Create a component for each tag, we don't expect to create other components afterwards. + for tag in tags: + comp = Component(torch.fx.Graph(), len(all_components), f"{tag}") + all_components.append(comp) + tag_to_component[tag] = comp + + # Traverse the nodes in original graph and take care of them. + for node in gm.graph.nodes: + if node.op == "output": + if output_node is not None: + raise RuntimeError("Multiple output nodes in graph!") + output_node = node + continue + + # Placeholders in the original graph get copied to main graph. + if node.op == "placeholder": + main_remapping[node] = main_g.placeholder(node.name, type_expr=node.type) + main_remapping[node].meta = copy.copy(node.meta) + continue + + # Get_attr nodes are ignored because we are not tagging them. + # Instead, we copy them directly to the submodules use them afterwards. + if node.op == "get_attr": + continue + + # Now we process callable nodes which are nodes with op of call_module, + # call_function or call_method. Every callable nodes should be tagged. + assert hasattr(node, "tag"), f"Node does not have tag: {node.format_node()}" + + upstream_components = [ + node_to_component[x] + for x in flatten(node.args) + flatten(node.kwargs) + if x.op not in {"placeholder", "get_attr"} + ] + + comp = tag_to_component[node.tag] + node_to_component[node] = comp + + # Max order of upperstream components. + mx = max((c.order for c in upstream_components), default=0) + + # Expect the component for `node` has higher order then its upstream components. + assert comp.order >= mx, ( + f"Component {comp.name} order must be >= max of its upstream components, order={comp.order} and max={mx}" + ) + + # Map a input of `node` to nodes in the component's graph. + def remap_func(x): + # If input is a get_attr node, copy it to current component's graph. + # Returns the get_attr node in current component's graph. + if x.op == "get_attr": + if x not in comp.getattr_maps: + comp.getattr_maps[x] = comp.graph.get_attr( + x.target, type_expr=x.type + ) + comp.getattr_maps[x].meta = copy.copy(x.meta) + return comp.getattr_maps[x] + + # If input is not a placeholder, it should have been put into a component + # already. If it's the current component then we return the corresponding + # node in the component. + if x.op != "placeholder" and node_to_component[x] == comp: + return node_remapping[x] + + # If input is a placeholder or it's in other components, we want to make it + # as a placeholder in current component's graph. + if x not in comp.orig_inputs: + comp.orig_inputs.append(x) + placeholder = comp.graph.placeholder(x.name, type_expr=x.type) + placeholder.meta = copy.copy(x.meta) + comp.input_placeholders.append(placeholder) + used_in_main[x] = None + + return comp.input_placeholders[comp.orig_inputs.index(x)] + + n = comp.graph.node_copy(node, remap_func) + n.tag = node.tag # type: ignore[attr-defined] + node_remapping[node] = n + node_to_component[n] = comp + + if output_node is None: + raise RuntimeError("Graph had no output node!") + + for x in flatten(output_node.args[0]): + if x.op == "get_attr": + # We don't need components mapping for nodes of type "get_attr" + # that are consumed by the output. Only need to make sure we create + # corresponding counterparts in the resulting graph. + main_remapping[x] = main_g.get_attr(x.name, type_expr=x.type) + else: + # All component results consumed by the output node should be + # marked as "used in main". + used_in_main[x] = None + + # If a node is used in main graph then we mark it as an output in the component + # it belongs to. + for n in used_in_main: + if n.op != "placeholder": + node_to_component[n].orig_outputs.append(n) + + # Now we create a graphmodule for each component. + orig_to_split_fqn_mapping: dict[str, str] = {} + for comp in all_components: + outs = tuple(map(node_remapping.__getitem__, comp.orig_outputs)) + + if return_tuple: + comp.graph.output(outs) + else: + # Take care of the args of FX output node. If there's a single + # output then the output node args is like (output_single), else + # if there're multiple outputs then the output node args is like + # ((output_0, output_1, ...)). + comp.graph.output(outs[0] if len(outs) == 1 else outs) + + comp.gm, comp_orig_to_split_fqn_mapping = lift_subgraph_as_module( + gm, subgraph=comp.graph, comp_name=comp.name + ) + orig_to_split_fqn_mapping.update(comp_orig_to_split_fqn_mapping) + + # Create a call_module node in main graph. + main_node = main_g.call_module( + comp.name, + args=tuple(map(main_remapping.__getitem__, comp.orig_inputs)), + kwargs=None, + ) + + if len(outs) == 1 and not return_tuple: + main_remapping[comp.orig_outputs[0]] = main_node + else: + for i, o in enumerate(comp.orig_outputs): + # Use Proxy to record getitem access. + main_remapping[o] = torch.fx.Proxy(main_node)[i].node # type: ignore[index] + + main_g.output(map_arg(output_node.args[0], main_remapping.__getitem__)) + main_root = HolderModule({comp.name: comp.gm for comp in all_components}) + main_g._codegen = gm.graph._codegen + + # If the output nodes consumes get_attr directly in the original graph, + # then we need to make sure get_attr is copied to the new graph. + for x in flatten(output_node.args[0]): + if x.op == "get_attr": + setattr(main_root, x.name, getattr_recursive(gm, x.target)) # type: ignore[arg-type] + + result_gm = GraphModuleCls(main_root, main_g) + if return_fqn_mapping: + return result_gm, orig_to_split_fqn_mapping + + return result_gm diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/splitter_base.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/splitter_base.py new file mode 100644 index 0000000000000000000000000000000000000000..8d90f9d55cfdb194e2d2a0577a84b5fd9d7f0262 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/splitter_base.py @@ -0,0 +1,1121 @@ +# mypy: allow-untyped-defs +import argparse +import copy +import json +import logging +import os +from collections import defaultdict +from collections.abc import Iterable, Sequence +from dataclasses import dataclass +from typing import Any, Literal, NamedTuple, Optional + +import torch +from torch._logging import trace_structured +from torch.fx._compatibility import compatibility +from torch.fx.node import map_arg +from torch.fx.passes.graph_manipulation import get_size_of_node + +from .graph_drawer import FxGraphDrawer +from .operator_support import get_node_target, OperatorSupportBase +from .shape_prop import ShapeProp +from .split_utils import split_by_tags +from .tools_common import ( + CALLABLE_NODE_OPS, + FxNetAccFusionsFinder, + is_node_output_tensor, + NodeList, + NodeSet, + Tensors, +) + + +__all__ = [ + "FxNetAccNodesFinder", + "FxNetSplitterInternalError", + "Subgraph", + "SplitResult", + "generate_inputs_for_submodules", + "NodeEvent", + "NodeEventTracker", +] +_LOGGER = logging.getLogger(__name__) + +DEFAULT_MIN_ACC_MODULE_SIZE = 1 +DEFAULT_SKIP_FUSION = False +DEFAULT_ALLOW_NON_TENSOR = False + +# ENV var and constants for node tracker + +TRACKER_DUMP_PATH = "_fx_net_tracker" +NODES_SUFFIX = "_nodes.txt" +ALL_SUFFIX = "_all.txt" + +ENV_FX_NET_ACC_SPLITTER_TRACKER_MODE = "FX_NET_ACC_SPLITTER_TRACKER_MODE" +ENV_FX_NET_ACC_SPLITTER_TRACKER_DUMP_PATH = "FX_NET_ACC_SPLITTER_TRACKER_DUMP_PATH" +ENV_FX_NET_ACC_SPLITTER_TRACKER_TRACKED_NODES = ( + "FX_NET_ACC_SPLITTER_TRACKER_TRACKED_NODES" +) + +DUMP_PREFIX = os.environ.get( + ENV_FX_NET_ACC_SPLITTER_TRACKER_DUMP_PATH, TRACKER_DUMP_PATH +) + +""" +Different modes of the event tracker for local debugging: +"0": No local dumps. Information available by setting breakpoints and visually inspect in pdb. +"1": Dump all events to DUMP_PREFIX_all.txt +"2": In addition to events dump, track nodes specified by ENV_FX_NET_ACC_SPLITTER_TRACKER_TRACKED_NODES + recursively and dump to DUMP_PREFIX_nodex.txt +"3": In addition to events dump, track all nodes with more than 1 event recursively and dump to DUMP_PREFIX_nodex.txt +In addition to the above local dumps, tracker is always enabled and dumps via trace_structured. +""" +TRACKER_MODE: Literal["0", "1", "2", "3"] = os.environ.get( + ENV_FX_NET_ACC_SPLITTER_TRACKER_MODE, "0" +) # type: ignore[assignment] + + +class _SplitterSettingBase: + def __init__( + self, + min_acc_module_size=DEFAULT_MIN_ACC_MODULE_SIZE, + skip_fusion=DEFAULT_SKIP_FUSION, + allow_non_tensor=DEFAULT_ALLOW_NON_TENSOR, + max_acc_splits: int = -1, + ): + parser = argparse.ArgumentParser() + parser.add_argument( + "--min-acc-module-size", + "--min_acc_module_size", + required=False, + type=int, + help="Minimum size limit of an accelerator subgraph.", + ) + parser.add_argument( + "--max-acc-splits", + "--max_acc_splits", + required=False, + type=int, + help="Enforce a maximum number of split subgraphs.", + ) + parser.add_argument( + "--skip-fusion", + "--skip_fusion", + default=False, + action="store_true", + help="If true then no fusion groups. Fusion group is used to " + "enforce no non-tensor data flow between submodules. If we don't " + "have this constrain, setting this to false is recommended as it " + "can reduce overhead.", + ) + parser.add_argument( + "--allow-non-tensor", + "--allow_non_tensor", + default=False, + action="store_true", + help="For some backends non-tensor data flow between cpu and them " + "are not allowed. Therefore, if a node supported by accelerator but " + "it has non-tensor inputs or outputs to a cpu node we would want to " + "consider it as a cpu node during splitting. However, for some backends " + "we might not care about non-tensor data flow and we can set this option " + "to true to disable the functionality that prevent non-tensor data flow.", + ) + args, _unknown = parser.parse_known_args() + + self.min_acc_module_size: int = ( + args.min_acc_module_size + if args.min_acc_module_size + else min_acc_module_size + ) + self.skip_fusion: bool = args.skip_fusion if args.skip_fusion else skip_fusion + self.allow_non_tensor: bool = ( + args.allow_non_tensor if args.allow_non_tensor else allow_non_tensor + ) + self.max_acc_splits: int = max_acc_splits + + +@compatibility(is_backward_compatible=False) +class NodeEvent: + """ + An event in graph split that happened on a node. + source: Subject of the event + desc: readable description + dep: Optional dependency, usually the node that caused the event. + """ + + def __init__( + self, source: torch.fx.Node, desc: str, dep: Optional[torch.fx.Node] = None + ): + self.source = source + self.desc = desc + self.dep = dep + + def to_str(self): + # source: The name of the subject of the event. + # desc: description of the event, in the format of | + # dep: The name of the cause of this event, which is another node, or # + # if it's caused by the subject node + return f"{self.source.name}: {self.desc} {self.dep.name if self.dep else '#'}" + + +@compatibility(is_backward_compatible=False) +class NodeEventTracker: + """ + Tracks node events during the splitter execution. + """ + + def __init__(self, tracker_mode, dump_prefix): + self.tracker_mode = tracker_mode + self.dump_prefix = dump_prefix + # list of events + self.events = [] + # dict from node name to event index + self.node_events = {} + self.writer = print + + def add(self, node: torch.fx.Node, desc: str, dep: Optional[torch.fx.Node] = None): + """ + Add a new event to the tracker. + """ + event = NodeEvent(node, desc, dep) + self.events.append(event) + if node.name not in self.node_events: + self.node_events[node.name] = [] + self.node_events[node.name].append(len(self.events) - 1) + + def print_node(self, node_name, recursive=False, tab="", writer=None): + """ + Print a node and its events. + @param recursive: if True, print nodes that caused the events on this current node. + @param tab: Indentation for dependencies. + @param writer: function to write to file. If None, use print. + """ + if not writer: + writer = self.writer + for idx in self.node_events.get(node_name, []): + event = self.events[idx] + writer(tab + event.to_str()) + if recursive and event.dep is not None: + self.print_node( + event.dep.name, recursive=True, tab="| " + tab, writer=writer + ) + + def to_dict(self): + """ + Create dict dump on all events. + """ + ret: dict[str, list[str]] = {} + for name in self.node_events: + ret[name] = [] + for idx in self.node_events.get(name, []): + event = self.events[idx] + ret[name].append(event.to_str()) + return ret + + def print_all(self, writer=None): + """ + Print all nodes in a list. + @param writer: function to write to file. If None, use print. + """ + if not writer: + writer = self.writer + for name in self.node_events: + writer(f"Node: {name}:") + self.print_node(name, recursive=False, tab=" ", writer=writer) + + def dump(self): + """ + Function to be invoked at the end of the finder execution to printout tracked events specified by the mode. + """ + # dump via trace_structured + trace_structured( + "artifact", + metadata_fn=lambda: { + "name": "fx_net_acc_splitter_finder_events", + "encoding": "json", + }, + payload_fn=lambda: json.dumps(self.to_dict()), + ) + + def writeln(f): + def fn(x): + return f.write(x + "\n") + + return fn + + # Mode 0: no local dump + # Mode >=1: Dump all events to file + if self.tracker_mode >= 1: + with open(self.dump_prefix + ALL_SUFFIX, "w") as f: + self.print_all(writeln(f)) + + def dump_selected_nodes(nodes): + with open(self.dump_prefix + NODES_SUFFIX, "w") as f: + for node_name in nodes: + writeln(f"===== Tracking node {node_name} =====") + self.print_node( + node_name, recursive=True, tab="|-", writer=writeln(f) + ) + writeln(f"===== End of tracking node {node_name} =====") + + # Mode 2: Dump specific nodes in recursive manner. + # Mode 3: Dump all nodes with more than 1 event in recursive manner. + if self.tracker_mode == 2 or self.tracker_mode == 3: + nodes = ( + os.environ.get(ENV_FX_NET_ACC_SPLITTER_TRACKER_TRACKED_NODES, "").split( + "," + ) + if self.tracker_mode == 2 + else [ + name for name, events in self.node_events.items() if len(events) > 1 + ] + ) + dump_selected_nodes(nodes) + + +@compatibility(is_backward_compatible=False) +class FxNetAccNodesFinder: + """ + Finds a set of nodes that can be supported on ACC, excluding nodes that have non-tensor + input/output to cpu nodes to prevent non-tensor data flow between backends and cpu. + + I.e. if we have a chain: + + ACC_NODE_1 -> ACC_NODE_2 -> ACC_NODE_3 -> CPU_NODE_1 + + where every ACC node produces non-tensor output, then they all should be treated as CPU nodes. + + This behavior can be turned off by passing allow_non_tensor=True. + """ + + def __init__( + self, + module: torch.fx.GraphModule, + operator_support: OperatorSupportBase, + allow_non_tensor: bool, + ): + self.module = module + self.operator_support = operator_support + self.allow_non_tensor = allow_non_tensor + self.acc_nodes: NodeSet = set() + + self.tracker = NodeEventTracker(int(TRACKER_MODE), DUMP_PREFIX) + + def reduce_acc_nodes_non_tensor_input_helper(self, cpu_worklist: NodeList): + """ + Transitively excludes nodes from ACC supported set. + For every node in the worklist: + - removes its downstream ACC nodes from ACC supported set, + - if any downstream ACC node produces non-tensor output, + then it gets added into the worklist. + """ + while cpu_worklist: + node = cpu_worklist.pop(0) + + for user in node.users: + if user in self.acc_nodes: + self.acc_nodes.remove(user) + self.tracker.add(user, "acc_del|user_of_new_cpu_node", node) + if not is_node_output_tensor(user): + self.tracker.add(user, "new_cpu_node|non_tensor_output") + cpu_worklist.append(user) + + def reduce_acc_nodes_non_tensor_input(self): + """ + Excludes nodes from ACC supported set that have direct + upstream CPU nodes that produce non-tensor outputs. + """ + non_tensor_cpu_nodes: NodeList = [] + + for node in self.module.graph.nodes: + if node.op not in CALLABLE_NODE_OPS: + continue + if node in self.acc_nodes: + continue + if is_node_output_tensor(node): + continue + self.tracker.add(node, "new_cpu_node|callable_non_tensor_input") + non_tensor_cpu_nodes.append(node) + + self.reduce_acc_nodes_non_tensor_input_helper(non_tensor_cpu_nodes) + + def reduce_acc_nodes_non_tensor_output(self): + """ + Excludes nodes from ACC supported set that produce non-tensor + outputs and have downstream CPU nodes. + """ + while True: + new_cpu_nodes: NodeList = [] + + for acc_node in self.acc_nodes: + if is_node_output_tensor(acc_node): + continue + for user in acc_node.users: + if user not in self.acc_nodes: + new_cpu_nodes.append(acc_node) + self.tracker.add( + acc_node, "acc_del|non_tensor_output_with_cpu_user", user + ) + break + + if not new_cpu_nodes: + break + + for new_cpu_node in new_cpu_nodes: + self.acc_nodes.remove(new_cpu_node) + + self.reduce_acc_nodes_non_tensor_input_helper(new_cpu_nodes) + + def __call__(self) -> NodeSet: + submodules = dict(self.module.named_modules()) + self.acc_nodes = set() + for n in self.module.graph.nodes: + if n.op not in CALLABLE_NODE_OPS: + self.tracker.add(n, "init_cpu|not_callable") + continue + if not self.operator_support.is_node_supported(submodules, n): + self.tracker.add(n, "init_cpu|operator_support") + continue + + self.tracker.add(n, "init_acc|callable_and_operator_supported") + self.acc_nodes.add(n) + + if not self.allow_non_tensor: + self.reduce_acc_nodes_non_tensor_input() + self.reduce_acc_nodes_non_tensor_output() + self.tracker.dump() + return self.acc_nodes + + +@compatibility(is_backward_compatible=False) +class FxNetSplitterInternalError(Exception): + pass + + +@compatibility(is_backward_compatible=False) +@dataclass +class Subgraph: + is_acc: bool + nodes: NodeList + device_ordinal: Optional[int] = None + + +@compatibility(is_backward_compatible=False) +class SplitResult(NamedTuple): + """ + Stores the results of the splitter. + + Attributes: + split_module: root module after splitting. + submodule_inputs: a dict that maps submodule name to its inputs. + non_acc_submodule_prefix: the prefix for non acc submodules. For + acc submodule the prefix is always "_run_on_acc_". + """ + + split_module: torch.fx.GraphModule + submodule_inputs: dict[str, Any] + non_acc_submodule_prefix: str + + +@compatibility(is_backward_compatible=False) +def generate_inputs_for_submodules( + model: torch.nn.Module, + inputs: Sequence[Any], + target_submodules: Iterable[str], + deepcopy: bool = False, +) -> dict[str, Any]: + """ + Generate inputs for targeting submdoules in the given model. Note that if two submodules refer to the same obj, this + function doesn't work. + + Args: + model: root model. + inputs: inputs to the root model. + target_submodules: submodules that we want to generate inputs for. + + Returns: + A dict that maps from submodule name to its inputs. + """ + + handles = [] + results = {} + submodule_to_names = {mod: name for name, mod in model.named_modules()} + + def pre_forward(module, module_inputs): + results[submodule_to_names[module]] = ( + copy.deepcopy(module_inputs) if deepcopy else module_inputs + ) + + for name, mod in model.named_modules(): + if name in target_submodules: + if not isinstance(mod, torch.jit.ScriptModule): + handles.append(mod.register_forward_pre_hook(pre_forward)) + + def clean_up_handles(): + for h in handles: + h.remove() + + try: + with torch.no_grad(): + model(*inputs) + except Exception as e: + clean_up_handles() + raise e + + clean_up_handles() + return results + + +class _SplitterBase: + """ + Splits a GraphModule into sub-GraphModules for execution on CPU or the accelerator. + Output is a GraphModule with supported and unsupported operators grouped into as few sub-GraphModules as possible. + Assumes that only "call_module", "call_function" and "call_method" from FX IR can potentially be executed on the accelerator. + + Given the following graph: + ==> b ==> + // \\ + a d + \\ // + ==> c ==> + + class SimpleModule(torch.nn.Module): + def forward(self, a): + b = torch.sin(a) + c = torch.cos(a) + d = b + c + return d + + and providing "operator_support" that indicates that 'b' and 'c' can be executed on the accelerator, + we will get the following split result: + + main: + def forward(self, a): + run_on_acc_0_0 = self._run_on_acc_0_0(a) + getitem = run_on_acc_0_0[0] + getitem_1 = run_on_acc_0_0[1] + run_on_cpu_1_1 = self._run_on_cpu_1_1(getitem, getitem_1) + return run_on_cpu_1_1 + + _run_on_acc_0_0: + def forward(self, a): + sin_1 = torch.sin(a) + cos_1 = torch.cos(a) + return (sin_1, cos_1) + + _run_on_cpu_1_1: + def forward(self, sin_1, cos_1): + add_1 = sin_1 + cos_1 + return add_1 + """ + + # PCIe bandwidth for the backend, default to 100 GB/s + PCIe_BW = 100 * 2**30 + + def __init__( + self, + module: torch.fx.GraphModule, + sample_input: Sequence[Any], + operator_support: OperatorSupportBase, + settings: _SplitterSettingBase, + non_acc_submodule_name: str = "_run_on_cpu_", + return_tuple: bool = False, + nodes_finder: Optional[FxNetAccNodesFinder] = None, + ): + """ + Preprocesses graph before splitting: + - finds nodes supported by ACC, + - finds fusion groups for ACC nodes having non-tensor IO, + - builds a graph of direct dependencies, + - builds a map of fused nodes to their fusions. + As a result we get self.acc_nodes, self.deps and self.fusions. + """ + assert isinstance(module, torch.fx.GraphModule) + + self.module = module + ShapeProp(self.module).propagate(*sample_input) + + self.settings = settings + self.operator_support = operator_support + self.sample_input = sample_input + if nodes_finder is None: + nodes_finder = FxNetAccNodesFinder( + self.module, self.operator_support, self.settings.allow_non_tensor + ) + self.acc_nodes = nodes_finder() + + if self.settings.skip_fusion: + self.fusions = {} + else: + self.fusions = FxNetAccFusionsFinder(module, self.acc_nodes)() + + # Modify deps to add more deps for fused nodes + self.deps = self.find_deps() + self.update_deps_for_fusions() + + self.non_acc_submodule_name = non_acc_submodule_name + self._node_submodule_map: dict[str, str] = {} + self._return_tuple = return_tuple + + self.tags: list[str] = [] + + # =============================================================== + # Helpers for ctor and initial state + # =============================================================== + + def get_node_submodule_map(self) -> dict[str, str]: + """Returns a map from node name to submodule name, e.g. + node: main_module_impl_impl_over_arch_unary_multiple_embedding + _pooling_embedding_pooling_sparse_entity_equivalence_key + _proxy_embedding_bag + maps to submodule name of: _run_on_acc_1 + """ + return self._node_submodule_map + + def find_deps(self) -> dict[torch.fx.Node, NodeSet]: + """ + Builds a graph of node dependencies. Leaf nodes don't have any + dependencies and the "output" node doesn't have nodes depending on it. + + Resulting graph has only direct dependencies, i.e. there are no + transitive dependencies. + """ + deps: dict[torch.fx.Node, NodeSet] = defaultdict(set) + for node in self.module.graph.nodes: + if node.op not in CALLABLE_NODE_OPS: + continue + + for user in node.users: + if user.op != "output": + deps[user].add(node) + return deps + + def update_deps_for_fusions(self): + """ + Updates graph of dependencies so that: + - nodes from the same fusion depend on the same set of outer nodes, + - outer nodes depending on a fusion depend on all nodes in that fusion. + """ + for node in self.fusions: + fusion = self.fusions[node] + for fused_neighbor in fusion: + self.deps[node].update(self.deps[fused_neighbor] - fusion) + + for user in fused_neighbor.users: + if user not in fusion: + self.deps[user].add(node) + + # =============================================================== + # Helpers for preview + # =============================================================== + + def _lower_model_to_backend( + self, mod: torch.fx.GraphModule, inputs: Tensors + ) -> torch.nn.Module: + """ + Lower the model to a backend. + """ + + return mod + + def _find_culprit(self, mod: torch.fx.GraphModule, inputs: Tensors) -> str: + """ + When an error occurs during lowering or running the lowered mod, we use this + function to find culprits in the `mod` that causes the error. + """ + + return "Unable to find a culprit because _find_culprit() function is not implemented." + + def _draw_graph_based_on_node_support( + self, mod: torch.fx.GraphModule, supported_nodes: NodeList + ): + color_map = { + "default": "AliceBlue", + "supported": "chartreuse1", + "unsupported": "crimson", + } + + class CustomDrawer(FxGraphDrawer): + def _get_node_style(self, node): + template = super()._get_node_style(node) + if node in supported_nodes: + template["fillcolor"] = color_map["supported"] + elif node.op in CALLABLE_NODE_OPS: + template["fillcolor"] = color_map["unsupported"] + else: + template["fillcolor"] = color_map["default"] + + return template + + drawer = CustomDrawer(mod, "node_support", ignore_getattr=True) + dot_graph = drawer.get_main_dot_graph() + # pyre-fixme[16]: `pydot.Dot` has no attribute `write_raw`. + dot_graph.write_raw("node_support.dot") # type: ignore[attr-defined] + + def node_support_preview(self, dump_graph: bool = False): + submodules = dict(self.module.named_modules()) + + supported_nodes: NodeList = [] + supported_node_types = defaultdict(set) + unsupported_node_types = defaultdict(set) + + def get_dtype(arg): + tensor_meta = arg.meta.get("tensor_meta") + return getattr(tensor_meta, "dtype", None) + + for node in self.module.graph.nodes: + if node.op not in CALLABLE_NODE_OPS: + continue + + target = get_node_target(submodules, node) + + # Store dtype of arg in node.args. If arg doesn't have dtype, i.e. not a tensor, we'll store None. + arg_dtypes = [ + get_dtype(arg) if isinstance(arg, torch.fx.Node) else None + for arg in node.args + ] + + # Find last non-None element. If all elements are None, return max_len. + last_index = len(arg_dtypes) - next( + ( + i + for i, dtype in enumerate(reversed(arg_dtypes)) + if dtype is not None + ), + len(arg_dtypes), + ) + + # Strip None elements at the end. + arg_dtypes_tuple = tuple(arg_dtypes[:last_index]) + kwarg_dtypes_tuple = tuple( + (k, get_dtype(arg)) + for k, arg in node.kwargs.items() + if isinstance(arg, torch.fx.Node) + ) + + if self.operator_support.is_node_supported(submodules, node): + supported_nodes.append(node) + supported_node_types[target].add((arg_dtypes_tuple, kwarg_dtypes_tuple)) + else: + unsupported_node_types[target].add( + (arg_dtypes_tuple, kwarg_dtypes_tuple) + ) + + if dump_graph: + self._draw_graph_based_on_node_support(self.module, supported_nodes) + + reports = "\nSupported node types in the model:\n" + for t, dtypes in supported_node_types.items(): + for arg_dtypes_tuple, kwarg_dtypes_tuple in dtypes: + reports += f"{t}: ({arg_dtypes_tuple}, {dict(kwarg_dtypes_tuple)})\n" + + reports += "\nUnsupported node types in the model:\n" + for t, dtypes in unsupported_node_types.items(): + for arg_dtypes_tuple, kwarg_dtypes_tuple in dtypes: + reports += f"{t}: ({arg_dtypes_tuple}, {dict(kwarg_dtypes_tuple)})\n" + + print(reports) + + # Return reports for testing purpose + return reports + + def split_preview(self, dump_graph: bool = False): + reports = "" + subgraphs = self.put_nodes_into_subgraphs() + acc_subgraphs_num = len([g for g in subgraphs if g.is_acc]) + cpu_subgraphs_num = len(subgraphs) - acc_subgraphs_num + reports += f"Before removing small acc subgraphs, total {len(subgraphs)} subgraphs are created:" + reports += f" {acc_subgraphs_num} acc subgraphs and {cpu_subgraphs_num} cpu subgraphs.\n" + + subgraphs = self.remove_small_acc_subgraphs(subgraphs) + acc_subgraphs_num = len([g for g in subgraphs if g.is_acc]) + cpu_subgraphs_num = len(subgraphs) - acc_subgraphs_num + reports += f"After removing small acc subgraphs, total {len(subgraphs)} subgraphs are created:" + reports += f" {acc_subgraphs_num} acc subgraphs and {cpu_subgraphs_num} cpu subgraphs.\n" + + for i, subgraph in enumerate(subgraphs): + reports += ( + f"_run_on_acc_{i}: " + if subgraph.is_acc + else f"{self.non_acc_submodule_name}{i}: " + ) + reports += f"{len(subgraph.nodes)} node(s)\n" + + self.tag(subgraphs) + split_mod = self.split(remove_tag=True) + split_mod.eval() + + if dump_graph: + drawer = FxGraphDrawer(split_mod, "preview", ignore_getattr=True) + dot_graphs = drawer.get_all_dot_graphs() + for name, dot_graph in dot_graphs.items(): + # pyre-fixme[16]: `pydot.Dot` has no attribute `write_raw`. + dot_graph.write_raw(f"{name}.dot") # type: ignore[attr-defined] + + max_qps: float = self.PCIe_BW + bottleneck_module = "" + + for node in split_mod.graph.nodes: + if node.op == "call_module" and "acc" in node.target: + reports += f"\nProcessing acc submodule {node.target}\n" + + submod = getattr(split_mod, node.target) + + def get_submod_inputs(main_mod, submod, example_inputs): + sub_inputs = None + + def get_inputs(self, inputs): + nonlocal sub_inputs + sub_inputs = inputs + + handle = submod.register_forward_pre_hook(get_inputs) + main_mod(*example_inputs) + handle.remove() + return sub_inputs + + submod_inputs = get_submod_inputs(split_mod, submod, self.sample_input) + ShapeProp(submod).propagate(*submod_inputs) + + total_input_bytes = 0 + total_output_bytes = 0 + + reports += "Checking inputs...\n" + for n in submod.graph.nodes: + if n.op == "placeholder": + if not is_node_output_tensor(n): + reports += f"Input {n.name} is not a tensor, this might cause problems during lowering!\n" + else: + total_input_bytes += get_size_of_node(submod, n)[0] + if n.op == "output": + output_node = n + + reports += "Checking outputs...\n" + + def get_bytes(node: torch.fx.Node): + nonlocal total_output_bytes + nonlocal reports + if not is_node_output_tensor(node): + reports += f"Output {node.name} is not a tensor, this might cause problems during lowering!\n" + else: + total_output_bytes += get_size_of_node(submod, node)[0] + + map_arg(output_node.args, get_bytes) # type: ignore[possibly-undefined] + qps = self.PCIe_BW / max(total_input_bytes, total_output_bytes) + reports += f"Total input size in bytes is {total_input_bytes}, total output size in bytes is {total_output_bytes}," + reports += f" theoretical max qps (bounds by PCIe bandwidth) for this submodule is {qps}.\n" + + if qps < max_qps: + max_qps = qps + bottleneck_module = node.target + + try: + lowered_submod = self._lower_model_to_backend(submod, submod_inputs) + except RuntimeError: + reports += "Run into an error during lowering!\n" + reports += self._find_culprit(submod, submod_inputs) + continue + + try: + lowered_submod(*submod_inputs) + except RuntimeError: + reports += "Run into an error during inference!\n" + reports += self._find_culprit(submod, submod_inputs) + else: + reports += "Lowering and running succeed!\n" + + reports += f"\nTheoretical max qps (bounds by PCIe bandwidth) for this model is {max_qps}," + reports += f" bottleneck is submodule {bottleneck_module}." + print(reports) + + # return the reports for testing purposes + return reports + + # =============================================================== + # Helpers for extend_acc_subgraph() method + # =============================================================== + + def find_reverse_deps( + self, tag_id: Optional[int] = None + ) -> dict[torch.fx.Node, NodeSet]: + """ + Builds reversed topological node dependencies, if tag_id is specified, + we ignore nodes that are in later subgraph i.e. nodes have greater tag_id. + """ + result: dict[torch.fx.Node, NodeSet] = defaultdict(set) + + for node in self.module.graph.nodes: + if node.op not in CALLABLE_NODE_OPS: + continue + + for user in node.users: + if user.op not in CALLABLE_NODE_OPS: + continue + + if tag_id is None or (int(user.tag.split("_")[-1]) < tag_id): + result[node].add(user) + + return result + + def update_reverse_deps_for_fusions(self, deps: dict[torch.fx.Node, NodeSet]): + processed_node = set() + + for node, fusion in self.fusions.items(): + if node in processed_node: + continue + + new_dep = set() + + # Create a new dependency set which include all the + # dependencies of the nodes in the fusion group + for n in fusion: + new_dep.update(deps[n]) + + # Exclude nodes in the fusion + new_dep.difference_update(fusion) + + # Update dependency + for n in fusion: + deps[n] = new_dep + + for arg in n.all_input_nodes: + if arg not in fusion: + deps[arg].update(fusion) + + processed_node.add(n) + + def find_parent_nodes_of_subgraph(self, tag: str) -> NodeSet: + """ + Finds parent nodes of the `tag` subgraph. + + Traverse the inputs of nodes in the subgraph, if input doesn't belong to the subgraph + and is not a placeholder, we consider it as the parent node of the subgraph. + """ + parent_nodes = set() + + for node in self.module.graph.nodes: + if node.op in CALLABLE_NODE_OPS and node.tag == tag: + for arg in node.all_input_nodes: + if arg.op in CALLABLE_NODE_OPS and arg.tag != tag: + parent_nodes.add(arg) + + return parent_nodes + + def extend_acc_subgraph(self, tag: str): + """ + Extend the acc subgraph with `tag` going the reversed topological direction. + """ + # Dict that maps node to its users and ignore users that + # are in the subgraph that has greater tag + deps = self.find_reverse_deps(tag_id=int(tag.rsplit("_", maxsplit=1)[-1])) + self.update_reverse_deps_for_fusions(deps) + + # Parent nodes of the subgraph + parent_nodes = self.find_parent_nodes_of_subgraph(tag) + + visited_nodes: NodeSet = set() + + while parent_nodes: + node = None + + # Find a acc node that depends on visited nodes only + for n in parent_nodes: + if deps[n] <= visited_nodes and n in self.acc_nodes: + node = n + break + + if node is None: + break + + # Put the node into `tag` subgraph + node.tag = tag # type: ignore[attr-defined] + parent_nodes.remove(node) + visited_nodes.add(node) + + # If node is in a fusion group, add all fusion buddies to parent nodes + if node in self.fusions: + for fusion_node in self.fusions[node]: + if fusion_node not in visited_nodes: + parent_nodes.add(fusion_node) + + # Add inputs of the node to parent nodes + for arg in node.all_input_nodes: + if arg.op in CALLABLE_NODE_OPS and arg not in visited_nodes: + parent_nodes.add(arg) + + # =============================================================== + # Helpers for split() method + # =============================================================== + + def starter_nodes(self) -> tuple[NodeSet, NodeSet]: + """ + Finds nodes that consume module inputs or get_attr nodes. + """ + starter_cpu_nodes: NodeSet = set() + starter_acc_nodes: NodeSet = set() + for node in self.module.graph.nodes: + # edge case, call_function, but with no dependencies + if node.op == "call_function" and len(node.all_input_nodes) == 0: + if node in self.acc_nodes: + starter_acc_nodes.add(node) + else: + starter_cpu_nodes.add(node) + + if node.op not in {"placeholder", "get_attr"}: + continue + + for user in node.users: + if user in self.acc_nodes: + starter_acc_nodes.add(user) + else: + starter_cpu_nodes.add(user) + + return starter_cpu_nodes, starter_acc_nodes + + def put_nodes_into_subgraphs(self) -> list[Subgraph]: + # We start graph traversal from leaf nodes + current_cpu_nodes, current_acc_nodes = self.starter_nodes() + visited_nodes: NodeSet = set() + + # Determine which subgraph to start from based on which subgraph has + # 0-dep node + acc_subgraph: bool = not any(len(self.deps[n]) == 0 for n in current_cpu_nodes) + + current_subgraph_nodes: NodeList = [] + + # Result accumulator + subgraphs: list[Subgraph] = [] + while current_cpu_nodes or current_acc_nodes: + # Find the first node that should belong to the current subgraph and has all dependencies resolved + current_nodes = current_acc_nodes if acc_subgraph else current_cpu_nodes + node = next( + (n for n in current_nodes if self.deps[n] <= visited_nodes), + None, + ) + + # If nothing was found, then it's time to flip the mode and start a new subgraph + if node is None: + if not current_subgraph_nodes: + raise FxNetSplitterInternalError("Subgraph can't be empty") + + subgraphs.append( + Subgraph(is_acc=acc_subgraph, nodes=current_subgraph_nodes) + ) + acc_subgraph = not acc_subgraph + current_subgraph_nodes = [] + continue + + current_nodes.remove(node) + visited_nodes.add(node) + current_subgraph_nodes.append(node) + + # Add fusion buddies + if node in self.fusions: + if node in self.acc_nodes: + current_acc_nodes.update(self.fusions[node] - visited_nodes) + else: + current_cpu_nodes.update(self.fusions[node] - visited_nodes) + + # Put depending nodes into the queue + for user in node.users: + if user.op not in CALLABLE_NODE_OPS: + continue + + # Add downstream nodes + if user in self.acc_nodes: + current_acc_nodes.add(user) + else: + current_cpu_nodes.add(user) + + # Check if the last subgraph was not created + if current_subgraph_nodes: + subgraphs.append( + Subgraph(is_acc=acc_subgraph, nodes=current_subgraph_nodes) + ) + + if not subgraphs: + raise FxNetSplitterInternalError("Couldn't create subgraphs") + + return subgraphs + + def remove_small_acc_subgraphs(self, subgraphs: list[Subgraph]) -> list[Subgraph]: + """ + This pass finds ACC submodules with less than specified size and merges + them with adjacent CPU submodules. + """ + result: list[Subgraph] = [] + for subgraph in subgraphs: + if subgraph.is_acc: + if len(subgraph.nodes) >= self.settings.min_acc_module_size: + result.append(subgraph) + else: + print( + "Eliminating acc subgraph because it's smaller than the threshold: " + f"{len(subgraph.nodes)} < {self.settings.min_acc_module_size}" + ) + if result: + result[-1].nodes.extend(subgraph.nodes) + else: + subgraph.is_acc = False + result.append(subgraph) + else: + if result and not result[-1].is_acc: + result[-1].nodes.extend(subgraph.nodes) + else: + result.append(subgraph) + return result + + def tag(self, subgraphs: list[Subgraph]): + self.tags = [] + for subgraph in subgraphs: + tag = ( + f"_run_on_acc_{len(self.tags)}" + if subgraph.is_acc + else f"{self.non_acc_submodule_name}{len(self.tags)}" + ) + self.tags.append(tag) + for node in subgraph.nodes: + if hasattr(node, "tag"): + raise FxNetSplitterInternalError(f"Node {node} was already tagged") + + node.tag = tag # type: ignore[attr-defined] + self._node_submodule_map[node.name] = tag + + def split(self, remove_tag: bool = False) -> torch.fx.GraphModule: + split_module = split_by_tags( + self.module, self.tags, return_tuple=self._return_tuple + ) + if remove_tag: + for node in self.module.graph.nodes: + if hasattr(node, "tag"): + del node.tag + return split_module # type: ignore[return-value] + + def __call__(self) -> torch.fx.GraphModule: + subgraphs = self.put_nodes_into_subgraphs() + subgraphs = self.remove_small_acc_subgraphs(subgraphs) + acc_subgraphs_count = len([s for s in subgraphs if s.is_acc]) + non_acc_subgraphs_count = len(subgraphs) - acc_subgraphs_count + print( + f"Got {acc_subgraphs_count} acc subgraphs and {non_acc_subgraphs_count} non-acc subgraphs" + ) + self.tag(subgraphs) + return self.split() + + def generate_split_results(self) -> SplitResult: + split_module = self() + submodule_names = [] + for name, _mod in split_module.named_children(): + submodule_names.append(name) + if ( + self.settings.max_acc_splits > 0 + and len(submodule_names) > self.settings.max_acc_splits + ): + raise ValueError( + "Cannot fulfill max_acc_splits limit. " + "This may cause split fragmentation and " + "result in performance issues." + ) + + submodule_inputs = generate_inputs_for_submodules( + split_module, self.sample_input, submodule_names + ) + return SplitResult(split_module, submodule_inputs, self.non_acc_submodule_name) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/tests/__init__.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/tests/test_pass_manager.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/tests/test_pass_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..157dc4017eda576f10793ef46b78cd97b0f5074b --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/tests/test_pass_manager.py @@ -0,0 +1,56 @@ +import unittest + +from ..pass_manager import ( + inplace_wrapper, + PassManager, + these_before_those_pass_constraint, + this_before_that_pass_constraint, +) + + +class TestPassManager(unittest.TestCase): + def test_pass_manager_builder(self) -> None: + passes = [lambda x: 2 * x for _ in range(10)] + pm = PassManager(passes) + pm.validate() + + def test_this_before_that_pass_constraint(self) -> None: + passes = [lambda x: 2 * x for _ in range(10)] + pm = PassManager(passes) + + # add unfulfillable constraint + pm.add_constraint(this_before_that_pass_constraint(passes[-1], passes[0])) + + self.assertRaises(RuntimeError, pm.validate) + + def test_these_before_those_pass_constraint(self) -> None: + passes = [lambda x: 2 * x for _ in range(10)] + constraint = these_before_those_pass_constraint(passes[-1], passes[0]) + pm = PassManager([inplace_wrapper(p) for p in passes]) + + # add unfulfillable constraint + pm.add_constraint(constraint) + + self.assertRaises(RuntimeError, pm.validate) + + def test_two_pass_managers(self) -> None: + """Make sure we can construct the PassManager twice and not share any + state between them""" + + passes = [lambda x: 2 * x for _ in range(3)] + constraint = these_before_those_pass_constraint(passes[0], passes[1]) + pm1 = PassManager() + for p in passes: + pm1.add_pass(p) + pm1.add_constraint(constraint) + output1 = pm1(1) + self.assertEqual(output1, 2**3) + + passes = [lambda x: 3 * x for _ in range(3)] + constraint = these_before_those_pass_constraint(passes[0], passes[1]) + pm2 = PassManager() + for p in passes: + pm2.add_pass(p) + pm2.add_constraint(constraint) + output2 = pm2(1) + self.assertEqual(output2, 3**3) diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/tools_common.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/tools_common.py new file mode 100644 index 0000000000000000000000000000000000000000..d6a8f0df8449749167c4ec3dedaf719d78fad577 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/passes/tools_common.py @@ -0,0 +1,390 @@ +# mypy: allow-untyped-defs +import collections +import heapq +import operator +from collections.abc import Mapping +from dataclasses import dataclass +from typing import Any, Optional, Union + +import torch +import torch.fx +from torch.fx._compatibility import compatibility +from torch.fx.node import _get_qualified_name + + +__all__ = [ + "get_acc_ops_name", + "get_node_target", + "is_node_output_tensor", + "FxNetAccFusionsFinder", + "legalize_graph", + "stable_topological_sort", +] + +Tensors = Union[tuple[torch.Tensor], list[torch.Tensor]] +TensorOrTensors = Union[torch.Tensor, Tensors] +NodeList = list[torch.fx.Node] +NodeSet = set[torch.fx.Node] +Names = list[str] +CALLABLE_NODE_OPS = {"call_module", "call_function", "call_method"} + + +@compatibility(is_backward_compatible=False) +def get_acc_ops_name(k): + if isinstance(k, str): + return k + elif k.__module__ and "acc_ops" in k.__module__: + return f"acc_ops.{k.__name__}" + else: + module = k.__module__.replace( + "torch._ops", "torch.ops" + ) # WAR for bug in how torch.ops assigns module + return f"{module if module else ''}.{k.__name__}" + + +@compatibility(is_backward_compatible=False) +def get_node_target( + submodules: Mapping[str, torch.nn.Module], node: torch.fx.Node +) -> str: + """ + Given a `node` returns its target typename. + + For "call_method" node, return node.target which is the name of that method being called. + This could potential lead to conflict but should be okay because normally it's on a tensor. + + For "call_function" node, return typename of node.target. + + For "call_module" node, return typename of the module that node.target point to. + + If seeing "_VariableFunctionsClass" in the target name string, it will be replaced by + "torch". e.g. _VariableFunctionsClass.relu would become torch.relu. + """ + + assert node.op in CALLABLE_NODE_OPS, ( + "Expect op types of " + ", ".join(CALLABLE_NODE_OPS) + f", but found {node.op}" + ) + + if node.op == "call_module": + assert isinstance(node.target, str) + submod = submodules[node.target] + submod_type = getattr(submod, "_base_class_origin", type(submod)) + return get_acc_ops_name(submod_type) + elif node.op == "call_function": + target: Any = node.target + return ( + f"acc_ops.{target.__name__}" + if target.__module__ is not None and "acc_ops" in target.__module__ + else _get_qualified_name(target) + ) + else: + assert isinstance(node.target, str) + return node.target + + +@compatibility(is_backward_compatible=False) +def is_node_output_tensor(node: torch.fx.Node) -> bool: + """Checks if the node output produces a Tensor or not. + + NOTE: This requires to run `ShapeProp` on the containing fx graph before + calling this function. This is because it works by checking the `type` + metadata on the node. This metadata is produced by the `ShapeProp`. + """ + type_ = node.meta.get("type", None) + return type_ is not None and issubclass(type_, torch.Tensor) + + +@compatibility(is_backward_compatible=False) +class FxNetAccFusionsFinder: + """ + Finds groups of connected ACC nodes that pass non-tensor data between each other. + Such groups are called fusion groups. + """ + + def __init__(self, module: torch.fx.GraphModule, acc_nodes: NodeSet): + self.module = module + self.nodes = list(module.graph.nodes) + self.acc_nodes = acc_nodes + + @dataclass + class FusionGroup: + # The smallest idx of nodes in the fusion group after topological sorting all the nodes in the model. + top_node_idx: int + + # Nodes in this fusion group. + nodes: NodeSet + + # Inputs to this fusion group. + inputs: NodeSet + + # Nodes that in the fusion group that haven't been processed yet. + nodes_need_process: NodeSet + + def add_node(self, node): + """ + Add a node to fusion group. + """ + if node in self.nodes: + return + + self.nodes_need_process.add(node) + self.nodes.add(node) + self.inputs.discard(node) + self.inputs.update( + { + n + for n in node.all_input_nodes + if n.op in CALLABLE_NODE_OPS and n not in self.nodes + } + ) + + def recursive_add_node( + self, + fusion_group: "FxNetAccFusionsFinder.FusionGroup", + inputs: Union[NodeSet, NodeList], + visited: Optional[NodeSet] = None, + ): + """ + Start from inputs and going reverse topological order. If any upstream node + is in the fusion group, add all the nodes in this path to fusion group. + """ + for arg in inputs: + # skip the node if already seen + if visited is not None: + if arg in visited: + continue + visited.add(arg) + + # Skip placeholder and get_attr because they won't be in the fusion group. + if arg.op not in CALLABLE_NODE_OPS: + continue + + # If the node has smaller idx, it's already an upstream node of the fusion + # group. We don't need to check it anymore. + if self.nodes.index(arg) < fusion_group.top_node_idx: + continue + + # If the node is in the fusion group, return True. + if arg in fusion_group.nodes: + return True + + # Check the upstream nodes of the node, if any of them is in the fusion group + # we'll add this node to fusion group and return True. + if self.recursive_add_node(fusion_group, arg.all_input_nodes, visited): + fusion_group.add_node(arg) + return True + + return False + + def __call__(self) -> dict[torch.fx.Node, NodeSet]: + result: dict[torch.fx.Node, NodeSet] = {} + acc_nodes = list(self.acc_nodes) + + for node in acc_nodes: + if node in result: + continue + if node.op not in CALLABLE_NODE_OPS: + continue + if "tensor_meta" in node.meta: + continue + if node not in self.acc_nodes: + continue + + fusion_group: FxNetAccFusionsFinder.FusionGroup = self.FusionGroup( + top_node_idx=self.nodes.index(node), + nodes={node}, + inputs=set(node.all_input_nodes), + nodes_need_process={node}, + ) + while fusion_group.nodes_need_process: + node = fusion_group.nodes_need_process.pop() + self.recursive_add_node( + fusion_group, + fusion_group.inputs, + visited=set(), + ) + + # Optionally add downstream nodes + if "tensor_meta" not in node.meta: + for user in node.users: + if user.op not in CALLABLE_NODE_OPS: + continue + if user in fusion_group.nodes: + continue + + fusion_group.add_node(user) + self.recursive_add_node( + fusion_group, + fusion_group.inputs, + visited=set(), + ) + + # Add some upstream nodes + for arg in node.all_input_nodes: + if arg.op not in CALLABLE_NODE_OPS: + continue + if "tensor_meta" in arg.meta: + continue + if arg in fusion_group.nodes: + continue + + fusion_group.add_node(arg) + fusion_group.top_node_idx = min( + fusion_group.top_node_idx, self.nodes.index(arg) + ) + self.recursive_add_node( + fusion_group, + fusion_group.inputs, + visited=set(), + ) + + if not (set(fusion_group.nodes) <= self.acc_nodes): + self.acc_nodes -= fusion_group.nodes + else: + for n in fusion_group.nodes: + result[n] = fusion_group.nodes + + return result + + +@compatibility(is_backward_compatible=False) +def legalize_graph(gm: torch.fx.GraphModule) -> torch.fx.GraphModule: + """ + Replace the graph of the given GraphModule with one that contains the same nodes as the + original, but in topologically sorted order. + + This is used by the merge_matmul transformation below, which disturbs the topologically sorted + order of its input GraphModule, so that this order is restored before further transformation. + + Arguments: + gm: The graph module to topologically sort. It is modified in-place. + + Returns: + The graph module in-place sorted + + Warning: + This topological sort is NOT stable, it will NOT preserve the original node order. + If you need a stable topological sort, use stable_topological_sort instead. + """ + + # These operators are used for making runtime assertions before any + # data-dependent operators occur. We want to prioritize sorting these to + # ensure that these assertions appear before any data-dependent operations + # in the graph. + PRIORITIZED_OPS = [ + operator.add, + operator.mul, + operator.sub, + operator.floordiv, + operator.truediv, + operator.mod, + operator.le, + operator.lt, + operator.ge, + operator.gt, + operator.eq, + operator.ne, + torch.ops.aten.sym_constrain_range.default, + torch.ops.aten.sym_constrain_range_for_size.default, + torch.ops.aten._assert_async.msg, + torch.ops.aten.scalar_tensor.default, + torch.ops.aten._assert_scalar.default, + ] + + indeg = dict.fromkeys(gm.graph.nodes, 0) + new_graph = torch.fx.Graph() + # Track how many unfulfilled dependencies each node has + for node in gm.graph.nodes: + for user in node.users: + indeg[user] += 1 + queue: collections.deque = collections.deque() + # Add all nodes with no dependencies to the queue + for node in gm.graph.nodes: + if indeg[node] == 0: + queue.append(node) + env: dict[torch.fx.Node, torch.fx.Node] = {} + # Pop nodes from the queue, and add nodes that have had all their + # dependencies fulfilled + while len(queue) > 0: + cur = queue.popleft() + env[cur] = new_graph.node_copy(cur, lambda x: env[x]) + for user in cur.users: + indeg[user] -= 1 + if indeg[user] == 0: + if user.op == "call_function" and user.target in PRIORITIZED_OPS: + queue.appendleft(user) + else: + queue.append(user) + # If the new graph's size is not as large as the old one, then there must be + # a cycle (i.e. some node's dependencies were not satisfied.) + if len(new_graph.nodes) < len(gm.graph.nodes): + raise RuntimeError( + f"Input graph has cycles, unable to add {[node for node in indeg if indeg[node] != 0]}" + ) + new_graph._codegen = gm.graph._codegen + gm.graph = new_graph + return gm + + +@compatibility(is_backward_compatible=False) +def stable_topological_sort(gm: torch.fx.GraphModule) -> torch.fx.GraphModule: + """ + Replace the graph of the given GraphModule with one that contains the same nodes as the + original, but in topologically sorted order while preserving the original node order + as much as possible. + + This function performs a stable topological sort where nodes appear in an order that: + 1. Respects data dependencies (topological ordering) + 2. Preserves the original node order when there are no dependency constraints + + The algorithm uses Kahn's algorithm with a priority queue: nodes with all dependencies + satisfied are added to a min-heap, ordered by their original position. This ensures + we always process the earliest node in the original order among ready nodes. + + Arguments: + gm: The graph module to topologically sort. It is modified in-place. + + Returns: + The graph module in-place sorted + """ + indeg = dict.fromkeys(gm.graph.nodes, 0) + new_graph = torch.fx.Graph() + + # Build node to original index mapping + node_to_id: dict[torch.fx.Node, int] = { + node: idx for idx, node in enumerate(gm.graph.nodes) + } + + # Track how many unfulfilled dependencies each node has + for node in gm.graph.nodes: + for user in node.users: + indeg[user] += 1 + + # Priority queue: (original_index, node) + # Use min-heap to always process the node with smallest original index + ready_queue: list[tuple[int, torch.fx.Node]] = [] + for node in gm.graph.nodes: + if indeg[node] == 0: + heapq.heappush(ready_queue, (node_to_id[node], node)) + + env: dict[torch.fx.Node, torch.fx.Node] = {} + + # Process nodes + while ready_queue: + # Pop node with smallest original index + _, cur = heapq.heappop(ready_queue) + env[cur] = new_graph.node_copy(cur, lambda x: env[x]) + + # Update in-degrees and add newly ready nodes + for user in cur.users: + indeg[user] -= 1 + if indeg[user] == 0: + heapq.heappush(ready_queue, (node_to_id[user], user)) + + # Check if all nodes were processed + assert len(new_graph.nodes) == len(gm.graph.nodes), ( + f"Input graph has cycles, unable to add {[node for node in indeg if indeg[node] != 0]}" + ) + + new_graph._codegen = gm.graph._codegen + gm.graph = new_graph + return gm diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/proxy.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/proxy.py new file mode 100644 index 0000000000000000000000000000000000000000..c38c31c4d211278484edd53fc176effb65945aef --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/proxy.py @@ -0,0 +1,850 @@ +# mypy: ignore-errors + +import collections +import copy +import dis +import enum +import inspect +import logging +import operator +import sys +import traceback +from collections import OrderedDict +from collections.abc import Callable, Iterator +from dataclasses import fields, is_dataclass +from typing import Any, Optional + +import torch +import torch.fx.traceback as fx_traceback +from torch._C import _fx_map_aggregate as map_aggregate, _fx_map_arg as map_arg +from torch._library.opaque_object import is_opaque_value_type +from torch._logging import getArtifactLogger +from torch.utils._traceback import CapturedTraceback + +from ._compatibility import compatibility +from .graph import Graph, magic_methods, reflectable_magic_methods +from .immutable_collections import immutable_dict, immutable_list +from .node import Argument, base_types, Node, Target +from .operator_schemas import check_for_mutable_operation + + +__all__ = [ + "TracerBase", + "GraphAppendingTracer", + "TraceError", + "Proxy", + "MetaProxy", + "Attribute", + "ParameterProxy", + "Scope", + "ScopeContextManager", +] + + +log = logging.getLogger(__name__) +annotation_log = getArtifactLogger(__name__, "annotation") + + +@compatibility(is_backward_compatible=False) +class Scope: + """Scope object that records the module path and the module type + of a module. Scope is used to track the information of the module + that contains a Node in a Graph of GraphModule. For example:: + + class Sub(torch.nn.Module): + def forward(self, x): + # This will be a call_method Node in GraphModule, + # scope for this would be (module_path="sub", module_type=Sub) + return x.transpose(1, 2) + + + class M(torch.nn.Module): + def __init__(self) -> None: + self.sub = Sub() + + def forward(self, x): + # This will be a call_method Node as well, + # scope for this would be (module_path="", None) + x = x.transpose(1, 2) + x = self.sub(x) + return x + + """ + + def __init__(self, module_path: str, module_type: Any): + super().__init__() + self.module_path = module_path + self.module_type = module_type + + +@compatibility(is_backward_compatible=False) +class ScopeContextManager: + """A context manager to track the Scope of Node during symbolic tracing. + When entering a forward function of a Module, we'll update the scope information of + the current module, and when we exit, we'll restore the previous scope information. + """ + + def __init__( + self, + scope: Scope, + current_scope: Scope, + ): + super().__init__() + # Keep a copy of prev scope to restore on exit + self._prev_scope = copy.copy(scope) + # Update scope to current scope + scope.module_path = current_scope.module_path + scope.module_type = current_scope.module_type + # Save a reference so we can restore it + self._scope = scope + + def __enter__(self): + return self._scope + + def __exit__(self, *args): + self._scope.module_path = self._prev_scope.module_path + self._scope.module_type = self._prev_scope.module_type + return + + +_COPY_META_FIELDS = [ + "nn_module_stack", + "torch_fn", + "source_fn_stack", + "original_aten", + "recompute", + "ac_graph_id", + "has_backward_hook", + "from_node", + "quantization_tag", # TODO deprecated + "_numeric_debug_handle", # TODO deprecated + "custom", + "partitioner_tag", +] + + +@compatibility(is_backward_compatible=True) +class TracerBase: + graph: Graph + record_stack_traces: bool = False + # When record_stack_traces is True, only reocrd stack traces + # with forward function names. + # This helps when we want stack trace back to model code + _record_forward_stack_traces_only: bool = False + # Feature flag for mutable schema checking + # Enableby default in 1.12 + check_mutable_operations: bool = False + # Feature flag for assert tracing + trace_asserts: bool = False + # Feature flag for proxying accesses to buffer values + proxy_buffer_attributes: bool = False + + # Name of the function to be traced. It will only be used when + # ``root`` is an instance of ``nn.Module`` + traced_func_name: str = "forward" + + # Maps the containing module's name to the operator name + scope: Scope + + # Records the module call stack + module_stack: OrderedDict[str, tuple[str, Any]] + + # Mapping of node name to module scope + node_name_to_scope: dict[str, tuple[str, type]] + + @compatibility(is_backward_compatible=True) + def create_node( + self, + kind: str, + target: Target, + args: tuple[Argument, ...], + kwargs: dict[str, Argument], + name: Optional[str] = None, + type_expr: Optional[Any] = None, + ) -> Node: + """ + Inserts a graph node given target, args, kwargs, and name. + + This method can be overridden to do extra checking, validation, or + modification of values used in node creation. For example, one might + want to disallow in-place operations from being recorded. + """ + + if kind == "call_function" and self.check_mutable_operations: + check_for_mutable_operation(target, args, kwargs) + + node = self.graph.create_node(kind, target, args, kwargs, name, type_expr) + # TODO node_name_to_scope will be depreciated in favor of + # node.meta['nn_module_stack'] + self.node_name_to_scope[node.name] = ( + self.scope.module_path, + self.scope.module_type, + ) + + # Optionally set stack trace on the created Node for debugging purposes + if fx_traceback.has_preserved_node_meta(): + current_meta: dict[str, Any] = fx_traceback.get_current_meta() + + stack_trace = current_meta.get("stack_trace") + if stack_trace: + node.stack_trace = stack_trace + + if fx_traceback.GRADIENT_ACC_SPECIAL_STACK in stack_trace: + node.meta["is_gradient_acc"] = True + + # Explicitly set the stack_trace, nn_module_stack and source_fn on the node.meta + # If other meta fields are needed, they can be added here + for field in _COPY_META_FIELDS: + if field in current_meta: + node.meta[field] = copy.copy(current_meta[field]) + + # Here we decrement to account for the sequence_nr having + # just been incremented while tracing this lowered aten op. + new_seq_nr = torch.autograd._get_sequence_nr() - 1 + # The sequence_nr increments every time a new autograd Node + # is created. During the FWD pass we store the sequence_nr + # corresponding to the last autograd Node created on this fx + # node's meta. A single aten op can create multiple autograd + # nodes as is the case with in-place foreach ops. During the + # BWD pass we retrieve the sequence_nr stored on the current + # executing autograd Node. See NOTE [ Sequence Number ]. + if current_meta.get("in_grad_fn", 0) > 0: + annotation_log.debug("seq_nr from current_meta") + new_seq_nr = current_meta["grad_fn_seq_nr"][-1] + + # See Note [Functionalization View Replay Annotation] + # Overriding some node meta with the original node meta of the + # regenerated node. + replay_node: Node = fx_traceback.get_current_replay_node() + if replay_node is not None: + node.meta["is_functional_regenerated"] = True + if "seq_nr" in replay_node.meta: + annotation_log.debug("seq_nr from replay_node") + new_seq_nr = replay_node.meta["seq_nr"] + if "custom" in replay_node.meta: + node.meta["custom"] = replay_node.meta.get("custom") + if "stack_trace" in replay_node.meta: + node.stack_trace = replay_node.meta.get("stack_trace") + + annotation_log.debug("Assigning new_seq_nr %s to %s", new_seq_nr, node.name) + node.meta["seq_nr"] = new_seq_nr + + elif self.module_stack: + node.meta["nn_module_stack"] = copy.copy(self.module_stack) + + if self.record_stack_traces and not node.stack_trace: + user_stack_summary = CapturedTraceback.extract().summary() + if user_stack_summary: + user_stack_summary = self._filter_traceback_frames(user_stack_summary) + if user_stack_summary: + node.stack_trace = "".join(user_stack_summary.format()).strip() + + log.debug("create_node %s", node) + return node + + def _filter_traceback_frames( + self, user_stack_summary: traceback.StackSummary + ) -> traceback.StackSummary: + # This method can be overridden to customize the frame filtering logic + # for the recorded stack trace + user_frames = [] + if self._record_forward_stack_traces_only: + user_frames = [ + frame + for frame in user_stack_summary + if ( + frame.name == "forward" + or frame.filename.endswith("torch/__init__.py") + ) + ] + else: + first_forward = -1 + for i, frame in enumerate(user_stack_summary): + if frame.name == "forward": + user_frames = user_stack_summary[i:] + first_forward = i + break + + # Not having a "forward" call in the stacktrace implies the + # stacktrace will probably be irrelevant + if first_forward == -1: + user_frames = [] + + from torch.fx.experimental.symbolic_shapes import uninteresting_files + + user_frames = [ + frame + for frame in user_frames + if frame.filename not in uninteresting_files() + ] + + return traceback.StackSummary.from_list(user_frames) + + @compatibility(is_backward_compatible=True) + def proxy(self, node: Node) -> "Proxy": + return Proxy(node, self) + + @compatibility(is_backward_compatible=True) + def create_proxy( + self, + kind: str, + target: Target, + args: tuple[Any, ...], + kwargs: dict[str, Any], + name: Optional[str] = None, + type_expr: Optional[Any] = None, + # fix noqa when updating bc tests + proxy_factory_fn: Callable[[Node], "Proxy"] = None, # noqa: RUF013 + ): + """ + Create a Node from the given arguments, then return the Node + wrapped in a Proxy object. + + If kind = 'placeholder', then we're creating a Node that + represents the parameter of a function. If we need to encode + a default parameter, we use the ``args`` tuple. ``args`` is + otherwise empty for ``placeholder`` Nodes. + """ + + args_ = self.create_arg(args) + kwargs_ = self.create_arg(kwargs) + assert isinstance(args_, tuple) + assert isinstance(kwargs_, dict) + + node = self.create_node(kind, target, args_, kwargs_, name, type_expr) + + if not proxy_factory_fn: + proxy = self.proxy(node) + else: + proxy = proxy_factory_fn(node) + + return proxy + + def _find_user_frame(self): + """ + Find the Python stack frame executing the user code during + symbolic tracing. + """ + # We have to do a little dance here. Basically, walk up the callstack and + # record the first frame not in the pytorch source. This is the frame executing + # the user code during tracing. + frame = inspect.currentframe() + + pt_files = [ + "torch/fx/proxy.py", + "torch/fx/_symbolic_trace.py", + "torch/fx/experimental/proxy_tensor.py", + "torch/_ops.py", + "torch/_tensor.py", + "torch/utils/_python_dispatch.py", + "torch/_prims_common/wrappers.py", + "torch/_refs/__init__.py", + "torch/_refs/nn/functional/__init__.py", + "torch/utils/_stats.py", + ] + while frame: + frame = frame.f_back + if frame and all( + not frame.f_code.co_filename.endswith(file) for file in pt_files + ): + break + + if not frame: + return None + + return frame + + @compatibility(is_backward_compatible=True) + def create_arg(self, a: Any) -> Argument: + """ + A method that lowers the objects seen as arguments during symbolic evaluation + into Argument types that can be stored in IR. + + Can be override to support more trace-specific types. + """ + # IMPORTANT: Are you here because you are trying to proxy a new type into + # the graph? Please Please Please contact someone on the PyTorch Compiler team; + # the considerations are subtle. + # + # 1) When you add a new type, all of the downstream consumers and pass writers + # need to handle the new type. torch.fx is intended to be easy to write + # passes for, so we will push back against new types. + # 2) In torch.compile's IR, there are only specific operations that go + # into the graph. In particular, Tensor operations should go into the graph, + # but non-Tensor operations shouldn't. What that means is that constructors + # for new types *SHOULD NOT* become nodes in the FX graph. + handler = _create_arg_bypass.get(type(a)) + if handler is not None: + # this is just a performance optimization and can be removed if needed + # for common types, we have a fast path to avoid isinstance() overhead + # this doesn't remove the checks below since we need to handle subclasses + return handler(self, a) + + if isinstance(a, Proxy): + return a.node # most common arg type goes first + elif hasattr(a, "__fx_create_arg__"): + return a.__fx_create_arg__(self) + # aggregates + elif isinstance(a, tuple): + if hasattr(a, "_fields"): + # NamedTuple constructors don't seem to like getting a generator + # expression as an argument to their constructor, so build this + # intermediate tuple and unpack it into the NamedTuple constructor + args = [self.create_arg(elem) for elem in a] + return type(a)(*args) # type: ignore[arg-type] + return type(a)([self.create_arg(elem) for elem in a]) + elif isinstance(a, list): + return [self.create_arg(elem) for elem in a] + elif isinstance(a, dict): + return _create_arg_dict(self, a) + elif isinstance(a, slice): + return slice( + self.create_arg(a.start), + self.create_arg(a.stop), + self.create_arg(a.step), + ) + + elif isinstance(a, range): + return range( + self.create_arg(a.start), + self.create_arg(a.stop), + self.create_arg(a.step), + ) + + elif isinstance(a, (torch._ops.OpOverload, torch._ops.HigherOrderOperator)): + return a + + elif is_opaque_value_type(type(a)): + return a + + elif is_dataclass(a): + kwargs = { + field.name: self.create_arg(getattr(a, field.name)) + for field in fields(a) + } + return self.create_node("call_function", a.__class__, (), kwargs) + + elif isinstance(a, (*base_types, enum.Enum)) or a is None or a is ...: + return a + + raise NotImplementedError(f"argument of type: {type(a)}") + + @compatibility(is_backward_compatible=True) + def to_bool(self, obj: "Proxy") -> bool: + """Called when a proxy object is being converted to a boolean, such as + when used in control flow. Normally we don't know what to do because + we don't know the value of the proxy, but a custom tracer can attach more + information to the graph node using create_node and can choose to return a value. + """ + raise TraceError( + "symbolically traced variables cannot be used as inputs to control flow" + ) + + @compatibility(is_backward_compatible=True) + def iter(self, obj: "Proxy") -> Iterator: + """Called when a proxy object is being iterated over, such as + when used in control flow. Normally we don't know what to do because + we don't know the value of the proxy, but a custom tracer can attach more + information to the graph node using create_node and can choose to return an iterator. + """ + raise TraceError( + "Proxy object cannot be iterated. This can be " + "attempted when the Proxy is used in a loop or" + " as a *args or **kwargs function argument. " + "See the torch.fx docs on pytorch.org for a " + "more detailed explanation of what types of " + "control flow can be traced, and check out the" + " Proxy docstring for help troubleshooting " + "Proxy iteration errors" + ) + + @compatibility(is_backward_compatible=True) + def keys(self, obj: "Proxy") -> Any: + """Called when a proxy object is has the keys() method called. + This is what happens when ** is called on a proxy. This should return an + iterator it ** is suppose to work in your custom tracer. + """ + return Attribute(obj, "keys")() + + +# used in Proxy object when just appending to the graph while not tracing. +@compatibility(is_backward_compatible=True) +class GraphAppendingTracer(TracerBase): + def __init__(self, graph: Graph): + super().__init__() + self.graph = graph + self.scope = Scope("", None) + self.module_stack = collections.OrderedDict() + self.node_name_to_scope = {} + + +@compatibility(is_backward_compatible=False) +def assert_fn(x): + assert x + + +@compatibility(is_backward_compatible=True) +class TraceError(ValueError): + pass + + +@compatibility(is_backward_compatible=True) +class Proxy: + """ + ``Proxy`` objects are ``Node`` wrappers that flow through the + program during symbolic tracing and record all the operations + (``torch`` function calls, method calls, operators) that they touch + into the growing FX Graph. + + If you're doing graph transforms, you can wrap your own ``Proxy`` + method around a raw ``Node`` so that you can use the overloaded + operators to add additional things to a ``Graph``. + + ``Proxy`` objects cannot be iterated. In other words, the symbolic + tracer will throw an error if a ``Proxy`` is used in a loop or as + an ``*args``/``**kwargs`` function argument. + + There are two main ways around this: + 1. Factor out the untraceable logic into a top-level function and + use ``fx.wrap`` on it. + 2. If the control flow is static (i.e. the loop trip count is + based on some hyperparameter), the code can be kept in its original + position and refactored into something like:: + + for i in range(self.some_hyperparameter): + indexed_item = proxied_value[i] + + For a more detailed description into the Proxy internals, check out + the "Proxy" section in `torch/fx/README.md` + """ + + @compatibility(is_backward_compatible=True) + def __init__(self, node: Node, tracer: "Optional[TracerBase]" = None): + if tracer is None: + # This allows you to create a Proxy object around a raw Node + tracer = GraphAppendingTracer(node.graph) + self.tracer = tracer + self.node = node + + def __repr__(self) -> str: + return f"Proxy({self.node.name})" + + def __getattr__(self, k) -> "Attribute": + # note: not added to the graph yet, if this is a method call + # we peephole optimize to the method invocation + return Attribute(self, k) + + def __getstate__(self) -> dict: + return self.__dict__ + + def __deepcopy__(self, memo) -> dict: + # We have to explicitly override this method, because otherwise deepcopy + # will go to __getattr__(self, "__deepcopy__") and return a + # Attribute(__deepcopy__), and may go into an infinite loop in some cases. + import copy + + new_dict = {} + for k, v in self.__dict__.items(): + try: + new_obj = copy.deepcopy(v, memo) + except Exception: + log.warning( + "Shallow copy %s of Proxy because it cannot be deepcopied. " + "Proxy is created for node %s", + k, + self.node.name, + ) + new_obj = copy.copy(v) + new_dict[k] = new_obj + assert "node" in new_dict + assert "tracer" in new_dict + new_proxy = Proxy(new_dict["node"], new_dict["tracer"]) + for k, v in new_dict.items(): + new_proxy.__dict__[k] = v + return new_proxy + + def __setstate__(self, d): + # This is called when being unpickled/loaded. + self.__dict__ = d + + def __call__(self, *args, **kwargs) -> "Proxy": + return self.tracer.create_proxy( + "call_method", "__call__", (self,) + args, kwargs + ) + + def __iter__(self) -> Iterator["Proxy"]: + frame = inspect.currentframe() + assert frame is not None + calling_frame = frame.f_back + assert calling_frame is not None + inst_list = list(dis.get_instructions(calling_frame.f_code)) + if sys.version_info >= (3, 11): + from bisect import bisect_left + + inst_idx = bisect_left( + inst_list, calling_frame.f_lasti, key=lambda x: x.offset + ) + else: + inst_idx = calling_frame.f_lasti // 2 + inst = inst_list[inst_idx] + if inst.opname == "UNPACK_SEQUENCE": + return (self[i] for i in range(inst.argval)) # type: ignore[index] + + return self.tracer.iter(self) + + def __abs__(self): + return self.tracer.create_proxy("call_function", operator.abs, (self,), {}) + + def __bool__(self) -> bool: + if self.tracer.trace_asserts: + # check if this boolean is used in an assertion, bytecode pattern for assertions + # is pretty stable for Python 3.7--3.9 + frame = inspect.currentframe() + assert frame is not None + calling_frame = frame.f_back + assert calling_frame is not None + insts = list(dis.get_instructions(calling_frame.f_code)) + if sys.version_info >= (3, 11): + from bisect import bisect_left + + cur = bisect_left(insts, calling_frame.f_lasti, key=lambda x: x.offset) + else: + cur = calling_frame.f_lasti // 2 + inst = insts[cur] + + if inst.opname == "POP_JUMP_IF_TRUE": + first = insts[cur + 1] + assert inst.arg is not None + last = insts[inst.arg // 2 - 1] + starts_with_assert = ( + first.opname == "LOAD_GLOBAL" + and first.argval == "AssertionError" + or first.opname == "LOAD_ASSERTION_ERROR" + ) + if starts_with_assert and last.opname == "RAISE_VARARGS": + self.tracer.create_proxy("call_function", assert_fn, (self,), {}) + return True + + return self.tracer.to_bool(self) + + @compatibility(is_backward_compatible=True) + def keys(self): + return self.tracer.keys(self) + + def __len__(self): + raise RuntimeError( + "'len' is not supported in symbolic tracing by default. If you want " + "this call to be recorded, please call torch.fx.wrap('len') at " + "module scope" + ) + + @classmethod + def __torch_function__(cls, orig_method, types, args=None, kwargs=None): + args = args if args else () + kwargs = kwargs if kwargs else {} + + tracers: dict[Any, None] = {} + + def find_tracer(a): + if isinstance(a, cls): + tracers[a.tracer] = None + + map_aggregate(args, find_tracer) + map_aggregate(kwargs, find_tracer) + + if len(tracers) > 1: + raise RuntimeError( + f"Found multiple different tracers {list(tracers.keys())} while " + f"trying to trace operations {orig_method}" + ) + tracer = next(iter(tracers.keys())) + + if isinstance(orig_method, torch._C.ScriptMethod): + args = (orig_method.owner,) + args + return tracer.create_proxy("call_method", orig_method.name, args, kwargs) + if torch.overrides.is_tensor_method_or_property(orig_method): + return tracer.create_proxy( + "call_method", orig_method.__name__, args, kwargs + ) + else: + if isinstance(orig_method, torch._ops.HigherOrderOperator): + # TODO: Define how to symbolically trace HigherOrderOperators + raise RuntimeError("Unable to symbolically trace HigherOrderOperators") + return tracer.create_proxy( + "call_function", + orig_method, + args, + kwargs, + name=tracer.graph._target_to_str(orig_method.__name__), + ) + + +@compatibility(is_backward_compatible=False) +class MetaProxy(Proxy): + """ + A Proxy subclass that propagates metadata (meta['val']) during graph tracing. + """ + + def __init__( + self, node: Node, tracer: "Optional[TracerBase]" = None, fake_mode=None + ): + super().__init__(node, tracer) + self.fake_mode = fake_mode + + def __repr__(self) -> str: + return f"MetaProxy({self.node.name})" + + @classmethod + def __torch_function__(cls, orig_method, types, args=None, kwargs=None): + args = args if args else () + kwargs = kwargs if kwargs else {} + + meta_proxy = None + for arg in args: + if isinstance(arg, MetaProxy): + meta_proxy = arg + break + + assert meta_proxy is not None, ( + "No MetaProxy found in arguments, but one is expected." + ) + + proxy = super().__torch_function__(orig_method, types, args, kwargs) + with meta_proxy.fake_mode: + proxy.node.meta["val"] = orig_method( + *[a.node.meta["val"] if isinstance(a, Proxy) else a for a in args], + **kwargs, + ) + return MetaProxy(proxy.node, proxy.tracer, meta_proxy.fake_mode) + + +@compatibility(is_backward_compatible=True) +class Attribute(Proxy): + @compatibility(is_backward_compatible=True) + def __init__(self, root: Proxy, attr: str): + self.root = root + self.attr = attr + self.tracer = root.tracer + self._node: Optional[Node] = None + + @property + def node(self): + # the node for attributes is added lazily, since most will just be method calls + # which do not rely on the getitem call + if self._node is None: + self._node = self.tracer.create_proxy( + "call_function", getattr, (self.root, self.attr), {} + ).node + return self._node + + def __call__(self, *args, **kwargs): + return self.tracer.create_proxy( + "call_method", self.attr, (self.root,) + args, kwargs + ) + + +@compatibility(is_backward_compatible=False) +class ParameterProxy(Proxy): + """ + A special proxy which lets "shape", "size", "dim", and a few other + attribute accesses pass through to the underlying module parameter object, + so that conditional tests on these attributes will not throw exception during tracing + """ + + def __init__(self, tracer: TracerBase, node: Node, name, param): + super().__init__(node, tracer) + assert isinstance(param, torch.nn.Parameter) + self.param = param + self.name = name + + def __repr__(self) -> str: + return f"ParameterProxy({self.name})" + + @property + def shape(self): + return self.param.shape + + def size(self): + return self.param.size() + + def dim(self): + return self.param.dim() + + @property + def ndim(self): + return self.param.ndim + + def numel(self): + return self.param.numel() + + def nelement(self): + return self.param.nelement() + + +for method in magic_methods: + + def _scope(method): + def impl(*args, **kwargs): + tracer = args[0].tracer + target = getattr(operator, method) + return tracer.create_proxy("call_function", target, args, kwargs) + + impl.__name__ = method + as_magic = f"__{method.strip('_')}__" + setattr(Proxy, as_magic, impl) + + _scope(method) + + +def _define_reflectable(orig_method_name): + method_name = f"__r{orig_method_name.strip('_')}__" + + def impl(self, rhs): + target = getattr(operator, orig_method_name) + return self.tracer.create_proxy("call_function", target, (rhs, self), {}) + + impl.__name__ = method_name + impl.__qualname__ = method_name + setattr(Proxy, method_name, impl) + + +for orig_method_name in reflectable_magic_methods: + _define_reflectable(orig_method_name) + + +def _no_nodes_error(arg): + raise RuntimeError( + "Keys for dictionaries used as an argument cannot contain a " + f"Node. Got key: {arg}" + ) + + +def _create_arg_dict(self, a): + r = {} + for k, v in a.items(): + if not isinstance(k, str): + # Check for invalid dict keys. We do not want a Proxy to appear + # anywhere within the key. Since keys can be collection types, + # we iterate through the key with map_arg + k = self.create_arg(k) + map_arg(k, _no_nodes_error) + r[k] = self.create_arg(v) + return r + + +_create_arg_bypass = { + t: lambda self, a: a + for t in [ + *base_types, + type(None), + type(...), + torch._ops.OpOverload, + torch._ops.HigherOrderOperator, + ] +} +_create_arg_bypass[Proxy] = lambda self, a: a.node +_create_arg_bypass[tuple] = lambda self, a: tuple(self.create_arg(elem) for elem in a) +_create_arg_bypass[list] = lambda self, a: [self.create_arg(elem) for elem in a] +_create_arg_bypass[dict] = _create_arg_dict +_create_arg_bypass[immutable_list] = _create_arg_bypass[list] +_create_arg_bypass[immutable_dict] = _create_arg_bypass[dict] diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/subgraph_rewriter.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/subgraph_rewriter.py new file mode 100644 index 0000000000000000000000000000000000000000..2253da19d36427d4059926b71471744a841c62eb --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/subgraph_rewriter.py @@ -0,0 +1,440 @@ +import copy +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, NamedTuple, Optional, TYPE_CHECKING, Union + +import torch + +from ._compatibility import compatibility +from ._symbolic_trace import symbolic_trace +from .graph import Graph +from .graph_module import GraphModule +from .node import Node + + +if TYPE_CHECKING: + from .passes.utils.matcher_with_name_node_map_utils import InternalMatch + +__all__ = [ + "Match", + "replace_pattern", + "replace_pattern_with_filters", + "ReplacedPatterns", +] + + +@compatibility(is_backward_compatible=True) +class Match(NamedTuple): + # Node from which the match was found + anchor: Node + # Maps nodes in the pattern subgraph to nodes in the larger graph + nodes_map: dict[Node, Node] + + +@compatibility(is_backward_compatible=False) +@dataclass +class ReplacedPatterns: + # Node from which the match was found + anchor: Node + # Maps nodes in the pattern subgraph to nodes in the larger graph + nodes_map: dict[Node, Node] + # List of nodes that were added into the graph + replacements: list[Node] + + +def _replace_attributes(gm: GraphModule, replacement: torch.nn.Module) -> None: + gm.delete_all_unused_submodules() + + if isinstance(replacement, GraphModule): + replacement.graph.lint() + + def try_get_attr(gm: torch.nn.Module, target: str) -> Optional[Any]: + module_path, _, attr_name = target.rpartition(".") + try: + mod: torch.nn.Module = gm.get_submodule(module_path) + except AttributeError: + return None + attr = getattr(mod, attr_name, None) + return attr + + for node in gm.graph.nodes: + if node.op == "call_module" or node.op == "get_attr": + gm_attr = try_get_attr(gm, node.target) + replacement_attr = try_get_attr(replacement, node.target) + + # CASE 1: This target already exists as an attribute in our + # result GraphModule. Whether or not it exists in + # `replacement`, the existing submodule takes precedence. + if gm_attr is not None: + continue + + # CASE 2: The target exists as an attribute in `replacement` + # only, so we need to copy it over. + elif replacement_attr is not None: + new_attr = copy.deepcopy(replacement_attr) + if isinstance(replacement_attr, torch.nn.Module): + gm.add_submodule(node.target, new_attr) + else: + setattr(gm, node.target, new_attr) + + # CASE 3: The target doesn't exist as an attribute in `gm` + # or `replacement` + else: + raise RuntimeError( + 'Attempted to create a "', + node.op, + '" node during subgraph rewriting ' + f"with target {node.target}, but " + "the referenced attribute does not " + "exist in the replacement GraphModule", + ) + + gm.graph.lint() + + +@compatibility(is_backward_compatible=True) +def replace_pattern( + gm: GraphModule, + pattern: Union[Callable, GraphModule], + replacement: Union[Callable, GraphModule], +) -> list[Match]: + """ + Matches all possible non-overlapping sets of operators and their + data dependencies (``pattern``) in the Graph of a GraphModule + (``gm``), then replaces each of these matched subgraphs with another + subgraph (``replacement``). + + Args: + ``gm``: The GraphModule that wraps the Graph to operate on + ``pattern``: The subgraph to match in ``gm`` for replacement + ``replacement``: The subgraph to replace ``pattern`` with + + Returns: + List[Match]: A list of ``Match`` objects representing the places + in the original graph that ``pattern`` was matched to. The list + is empty if there are no matches. ``Match`` is defined as: + + .. code-block:: python + + class Match(NamedTuple): + # Node from which the match was found + anchor: Node + # Maps nodes in the pattern subgraph to nodes in the larger graph + nodes_map: Dict[Node, Node] + + Examples: + + .. code-block:: python + + import torch + from torch.fx import symbolic_trace, subgraph_rewriter + + + class M(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + + def forward(self, x, w1, w2): + m1 = torch.cat([w1, w2]).sum() + m2 = torch.cat([w1, w2]).sum() + return x + torch.max(m1) + torch.max(m2) + + + def pattern(w1, w2): + return torch.cat([w1, w2]) + + + def replacement(w1, w2): + return torch.stack([w1, w2]) + + + traced_module = symbolic_trace(M()) + + subgraph_rewriter.replace_pattern(traced_module, pattern, replacement) + + The above code will first match ``pattern`` in the ``forward`` + method of ``traced_module``. Pattern-matching is done based on + use-def relationships, not node names. For example, if you had + ``p = torch.cat([a, b])`` in ``pattern``, you could match + ``m = torch.cat([a, b])`` in the original ``forward`` function, + despite the variable names being different (``p`` vs ``m``). + + The ``return`` statement in ``pattern`` is matched based on its + value only; it may or may not match to the ``return`` statement in + the larger graph. In other words, the pattern doesn't have to extend + to the end of the larger graph. + + When the pattern is matched, it will be removed from the larger + function and replaced by ``replacement``. If there are multiple + matches for ``pattern`` in the larger function, each non-overlapping + match will be replaced. In the case of a match overlap, the first + found match in the set of overlapping matches will be replaced. + ("First" here being defined as the first in a topological ordering + of the Nodes' use-def relationships. In most cases, the first Node + is the parameter that appears directly after ``self``, while the + last Node is whatever the function returns.) + + One important thing to note is that the parameters of the + ``pattern`` Callable must be used in the Callable itself, + and the parameters of the ``replacement`` Callable must match + the pattern. The first rule is why, in the above code block, the + ``forward`` function has parameters ``x, w1, w2``, but the + ``pattern`` function only has parameters ``w1, w2``. ``pattern`` + doesn't use ``x``, so it shouldn't specify ``x`` as a parameter. + As an example of the second rule, consider replacing + + .. code-block:: python + + def pattern(x, y): + return torch.neg(x) + torch.relu(y) + + with + + .. code-block:: python + + def replacement(x, y): + return torch.relu(x) + + In this case, ``replacement`` needs the same number of parameters + as ``pattern`` (both ``x`` and ``y``), even though the parameter + ``y`` isn't used in ``replacement``. + + After calling ``subgraph_rewriter.replace_pattern``, the generated + Python code looks like this: + + .. code-block:: python + + def forward(self, x, w1, w2): + stack_1 = torch.stack([w1, w2]) + sum_1 = stack_1.sum() + stack_2 = torch.stack([w1, w2]) + sum_2 = stack_2.sum() + max_1 = torch.max(sum_1) + add_1 = x + max_1 + max_2 = torch.max(sum_2) + add_2 = add_1 + max_2 + return add_2 + """ + match_and_replacements = _replace_pattern(gm, pattern, replacement) + return [ + Match(anchor=m.anchor, nodes_map=m.nodes_map) for m in match_and_replacements + ] + + +# Experimental API, not backward compatible +@compatibility(is_backward_compatible=False) +def replace_pattern_with_filters( + gm: GraphModule, + pattern: Union[Callable, Graph, GraphModule], + replacement: Union[Callable, Graph, GraphModule, None] = None, + match_filters: Optional[ + list[Callable[["InternalMatch", Graph, Graph], bool]] + ] = None, + ignore_literals: bool = False, + # Placed at the end to avoid breaking backward compatibility + replacement_callback: Optional[ + Callable[["InternalMatch", Graph, Graph], Graph] + ] = None, + node_name_match: str = "", +) -> list[ReplacedPatterns]: + """ + See replace_pattern for documentation. This function is an overload with an additional match_filter argument. + + Args: + ``match_filters``: A list of functions that take in + (match: InternalMatch, original_graph: Graph, pattern_graph: Graph) and return a boolean indicating + whether the match satisfies the condition. + See matcher_utils.py for definition of InternalMatch. + ``replacement_callback``: A function that takes in a match and returns a + Graph to be used as the replacement. This allows you to construct a + replacement graph based on the match. + ``replacement_callback``: Node name to match. If not empty, it will try to match the node name. + """ + + return _replace_pattern( + gm, + pattern, + replacement, + match_filters, + ignore_literals, + replacement_callback, + node_name_match, + ) + + +def _replace_pattern( + gm: GraphModule, + pattern: Union[Callable, Graph, GraphModule], + replacement: Union[Callable, Graph, GraphModule, None] = None, + match_filters: Optional[ + list[Callable[["InternalMatch", Graph, Graph], bool]] + ] = None, + ignore_literals: bool = False, + # Placed at the end to avoid breaking backward compatibility + replacement_callback: Optional[ + Callable[["InternalMatch", Graph, Graph], Graph] + ] = None, + node_name_match: str = "", +) -> list[ReplacedPatterns]: + from torch.fx.passes.utils.matcher_utils import InternalMatch, SubgraphMatcher + + if match_filters is None: + match_filters = [] + + # Get the graphs for `gm`, `pattern`, `replacement` + original_graph: Graph = gm.graph + + if isinstance(pattern, GraphModule): + pattern_graph = pattern.graph + elif isinstance(pattern, Graph): + pattern_graph = pattern + else: + pattern_graph = symbolic_trace(pattern).graph # type: ignore[arg-type] + + matcher = SubgraphMatcher( + pattern_graph, + match_output=False, + match_placeholder=False, + remove_overlapping_matches=True, + ignore_literals=ignore_literals, + ) + _matches: list[InternalMatch] = matcher.match( + original_graph, node_name_match=node_name_match + ) + + # Filter out matches that don't match the filter + _matches = [ + m + for m in _matches + if all( + match_filter(m, original_graph, pattern_graph) + for match_filter in match_filters + ) + ] + + if isinstance(replacement, GraphModule): + common_replacement_graph = replacement.graph + elif isinstance(replacement, Graph): + common_replacement_graph = replacement + elif callable(replacement): + common_replacement_graph = symbolic_trace(replacement).graph + else: + assert replacement_callback is not None, ( + "Must provide either a replacement GraphModule or a replacement callback" + ) + common_replacement_graph = None # type: ignore[assignment] + + # As we progressively replace nodes, we'll need to keep track of how the match results should change + match_changed_node: dict[Node, Node] = {} + + match_and_replacements = [] + for match in _matches: + if replacement_callback is not None: + replacement_graph = replacement_callback( + match, original_graph, pattern_graph + ) + else: + assert common_replacement_graph is not None, ( + "Must provide either a replacement GraphModule or a replacement callback" + ) + replacement_graph = common_replacement_graph + replacement_placeholders = [ + n for n in replacement_graph.nodes if n.op == "placeholder" + ] + + # Build connecting between replacement graph's input and original graph input producer node + + # Initialize `val_map` with mappings from placeholder nodes in + # `replacement` to their corresponding node in `original_graph` + assert len(match.placeholder_nodes) == len(replacement_placeholders) + val_map: dict[Node, Node] = {} + for rn, gn in zip(replacement_placeholders, match.placeholder_nodes): + if isinstance(gn, Node): + val_map[rn] = match_changed_node.get(gn, gn) + if gn != val_map[rn]: + # Update match.placeholder_nodes and match.nodes_map with the node that replaced gn + gn_ind = match.placeholder_nodes.index(gn) + match.placeholder_nodes[gn_ind] = match_changed_node[gn] + map_key = list(match.nodes_map.keys())[ + list(match.nodes_map.values()).index(gn) + ] + match.nodes_map[map_key] = match_changed_node[gn] + else: + val_map[rn] = gn + + # Copy the replacement graph over + user_nodes: set[Node] = set() + for n in match.returning_nodes: + user_nodes.update(n.users) + + first_user_node = None + if len(user_nodes) == 0: + first_user_node = None + elif len(user_nodes) == 1: + first_user_node = next(iter(user_nodes)) + else: + # If there are multiple user nodes, we need to find the first user node + # in the current execution order of the `original_graph` + for n in original_graph.nodes: + if n in user_nodes: + first_user_node = n + break + + first_next_node = None + if first_user_node is None: + # no users, so we insert the replacement graph before the first next + # node of returning nodes + next_node = None + for n in reversed(original_graph.nodes): + if n in match.returning_nodes: + first_next_node = next_node + break + else: + next_node = n + insert_point = ( + first_user_node if first_user_node is not None else first_next_node + ) + assert insert_point is not None, "The insert point can't be None" + with original_graph.inserting_before(insert_point): + copied_returning_nodes = original_graph.graph_copy( + replacement_graph, val_map + ) + + if isinstance(copied_returning_nodes, Node): + copied_returning_nodes = (copied_returning_nodes,) + + # Get a list of nodes that have been replaced into the graph + replacement_nodes: list[Node] = [ + v for v in val_map.values() if v not in match.placeholder_nodes + ] + + # Hook the output Node of the replacement subgraph in to the + # original Graph at the correct location + assert len(match.returning_nodes) == len(copied_returning_nodes) # type: ignore[arg-type] + for gn, copied_node in zip(match.returning_nodes, copied_returning_nodes): # type: ignore[arg-type] + gn.replace_all_uses_with(copied_node) + match_changed_node[gn] = copied_node + # Remove the original nodes + for node in reversed(pattern_graph.nodes): + if node.op != "placeholder" and node.op != "output": + gn = match.nodes_map[node] + gm.graph.erase_node(gn) + + match_and_replacements.append( + ReplacedPatterns( + anchor=match.anchors[0], + nodes_map=match.nodes_map, + replacements=replacement_nodes, + ) + ) + + # Update the passed-in GraphModule to reflect the new state of + # `original_graph` + gm.recompile() + + # If `replacement` was an nn.Module, we'll need to make sure that + # all the submodules have been copied over correctly + if isinstance(replacement, torch.nn.Module): + _replace_attributes(gm, replacement) + + return match_and_replacements diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/tensor_type.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/tensor_type.py new file mode 100644 index 0000000000000000000000000000000000000000..4f375e461ef288341c5cd22e1e1ec2a851680b4c --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/tensor_type.py @@ -0,0 +1,111 @@ +# mypy: allow-untyped-defs +from torch.fx.experimental.unification import Var # type: ignore[attr-defined] + +from ._compatibility import compatibility + + +@compatibility(is_backward_compatible=False) +class TensorType: + """ + TensorType defines a type for tensors, which consists of a list of dimensions. + Example: + class M(torch.nn.Module): + def forward(self, x:TensorType((1,2,3, Dyn)), y:TensorType((1,2,3, Dyn))): + return torch.add(x, y) + """ + + def __init__(self, dim): + self.__origin__ = TensorType + self.__args__ = dim + + def __repr__(self): + return f"TensorType[{self.__args__}]" + + def __eq__(self, other): + if isinstance(other, self.__class__): + return list(self.__args__) == list(other.__args__) + else: + return False + + @staticmethod + def __class_getitem__(*args): + if len(args) == 1 and isinstance(args[0], tuple): + args = args[0] + return TensorType(tuple(args)) + + +class _DynType: + """ + _DynType defines a type which stands for the absence of type information. + """ + + def __init__(self) -> None: + self.__name__ = "_DynType" + + def __eq__(self, other): + return isinstance(other, self.__class__) + + def __str__(self): + return "Dyn" + + def __repr__(self): + return "Dyn" + + +Dyn = _DynType() + + +@compatibility(is_backward_compatible=False) +def is_consistent(t1, t2): + """ + A binary relation denoted by ~ that determines if t1 is consistent with t2. + The relation is reflexive, symmetric but not transitive. + returns True if t1 and t2 are consistent and False otherwise. + Example: + Dyn ~ TensorType((1,2,3)) + int ~ Dyn + int ~ int + TensorType((1,Dyn,3)) ~ TensorType((1,2,3)) + """ + + if t1 == t2: + return True + + if t1 == Dyn or t2 == Dyn or isinstance(t1, Var) or isinstance(t2, Var): + return True + + if isinstance(t1, TensorType) and isinstance(t2, TensorType): + return len(t1.__args__) == len(t2.__args__) and all( + is_consistent(elem1, elem2) + for elem1, elem2 in zip(t1.__args__, t2.__args__) + ) + else: + return False + + +@compatibility(is_backward_compatible=False) +def is_more_precise(t1, t2): + """ + A binary relation denoted by <= that determines if t1 is more precise than t2. + The relation is reflexive and transitive. + returns True if t1 is more precise than t2 and False otherwise. + Example: + Dyn >= TensorType((1,2,3)) + int >= Dyn + int >= int + TensorType((1,Dyn,3)) <= TensorType((1,2,3)) + """ + if t1 == t2: + return True + + if isinstance(t2, _DynType): + return True + + if isinstance(t1, TensorType) and isinstance(t2, TensorType): + return len(t1.__args__) == len(t2.__args__) and all( + is_more_precise(elem1, elem2) + for elem1, elem2 in zip(t1.__args__, t2.__args__) + ) + + else: + return False diff --git a/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/traceback.py b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/traceback.py new file mode 100644 index 0000000000000000000000000000000000000000..b78ef313f24f5ff67391103468109685c4103ce7 --- /dev/null +++ b/miniconda3/envs/ladir/lib/python3.10/site-packages/torch/fx/traceback.py @@ -0,0 +1,502 @@ +# mypy: allow-untyped-defs +import copy +import logging +import traceback +from contextlib import contextmanager +from enum import Enum +from typing import Any, Optional, Union + +from torch._utils_internal import signpost_event + +from ._compatibility import compatibility +from .graph import Graph +from .graph_module import GraphModule +from .node import Node + + +log = logging.getLogger(__name__) + +__all__ = [ + "annotate", + "annotate_fn", + "preserve_node_meta", + "has_preserved_node_meta", + "set_stack_trace", + "set_grad_fn_seq_nr", + "reset_grad_fn_seq_nr", + "format_stack", + "set_current_meta", + "get_current_meta", + "NodeSource", + "NodeSourceAction", + "get_graph_provenance_json", + "set_current_replay_node", + "get_current_replay_node", +] + +current_meta: dict[str, Any] = {} +current_replay_node: Optional[Node] = None +should_preserve_node_meta = False + +GRADIENT_ACC_SPECIAL_STACK = ( + "Gradient addition node due to multiple use of tensor around:" +) +# ============================================================================= +# FX Metadata Registry for Memory Profiler +# ============================================================================= +# Global in-memory registry for FX metadata +# Maps module_name -> metadata dict containing lineno_map and node_metadata +_FX_METADATA_REGISTRY: dict[str, dict[str, Any]] = {} + + +def _register_fx_metadata(module_name: str, metadata: dict[str, Any]) -> None: + """ + Register FX metadata in the global in-memory registry. + + This is called automatically during graph module compilation to store metadata + for later use by memory profiler augmentation. + + Args: + module_name: The module identifier (content-addressed filename) + metadata: Metadata dict containing lineno_map, node_metadata, and source_code + """ + # TODO: add logging to tlparse + _FX_METADATA_REGISTRY[module_name] = metadata + + +@compatibility(is_backward_compatible=False) +class NodeSourceAction(Enum): + CREATE = "create" + REPLACE = "replace" + + +@compatibility(is_backward_compatible=False) +class NodeSource: + """ + NodeSource is a data structure that contains the provenance information of a node. + If node `a` is created from node `b`, then `a.meta["from_node"]` may contain NodeSource(b). + """ + + class NodeInfo: + def __init__(self, name: str, target: str, graph_id: int): + self.name = name + self.target = target + self.graph_id = graph_id + + pass_name: str + action: list["NodeSourceAction"] + from_node: list["NodeSource"] + node_info: Optional["NodeInfo"] + _dict: Optional[dict[str, Any]] + _action_string: Optional[str] + + def __init__( + self, + node: Optional[Node], + pass_name: str = "", + action: Optional[Union["NodeSourceAction", list["NodeSourceAction"]]] = None, + ): + self.pass_name = pass_name + + if action is None: + action = [] + elif not isinstance(action, list): + action = [action] + for a in action: + assert isinstance(a, NodeSourceAction) + self.action = action + if node: + self.node_info = self.NodeInfo( + name=node.name, target=str(node.target), graph_id=id(node.graph) + ) + self.from_node = ( + copy.deepcopy(node.meta["from_node"]) + if "from_node" in node.meta + else [] + ) + else: + self.node_info = None + self.from_node = [] + + # cache the action string and dict representation for performance. + self._action_string: Optional[str] = None + self._dict: Optional[dict[str, Any]] = None + + @property + def name(self) -> str: + return self.node_info.name if self.node_info else "" + + @property + def target(self) -> str: + return self.node_info.target if self.node_info else "" + + @property + def graph_id(self) -> int: + return self.node_info.graph_id if self.node_info else -1 + + def __repr__(self): + return self.print_readable() + + def _get_action_string(self): + if self._action_string is None: + self._action_string = "+".join([a.name.lower() for a in self.action]) + return self._action_string + + def print_readable(self, indent=0): + if indent > 9: + return "" + result = "" + action_string = self._get_action_string() + result += ( + " " * indent * 4 + + f"(name={self.name}, pass_name={self.pass_name}, action={action_string}, graph_id={self.graph_id})\n" + ) + for item in self.from_node: + result += item.print_readable(indent + 1) + return result + + def to_dict(self) -> dict: + if self._dict is None: + # Convert the object to a dictionary + action_string = self._get_action_string() + self._dict = { + "name": self.name, + "target": self.target, + "graph_id": self.graph_id, + "pass_name": self.pass_name, + "action": action_string, + "from_node": [node.to_dict() for node in self.from_node], + } + + assert self._dict is not None + return self._dict + + def __eq__(self, other: object): + if not isinstance(other, NodeSource): + return False + return self.to_dict() == other.to_dict() + + def __hash__(self): + # Create a hash based on the dictionary representation + # We need to convert the dict to a hashable form + def _make_hashable(obj): + if isinstance(obj, dict): + return tuple(sorted((k, _make_hashable(v)) for k, v in obj.items())) + elif isinstance(obj, list): + return tuple(_make_hashable(item) for item in obj) + else: + return obj + + return hash(_make_hashable(self.to_dict())) + + @classmethod + def _from_dict(cls, d: Optional[dict]) -> Optional["NodeSource"]: + """ + Recursively deserialize from_node metadata from dictionary data. + It is used to deserialize the from_node field from serialized metadata. + Please use constructor NodeSource(node, ...) to create a NodeSource object. + """ + if d is None: + return None + + assert isinstance(d, dict), f"Expected a dict, got {type(d)}" + + # Create a NodeSource object directly without going through the constructor + # to avoid issues with graph ID and node creation + node_source = NodeSource.__new__(NodeSource) + + # Reset the cached properties + node_source._action_string = None + node_source._dict = None + + # Set the basic attributes + node_source.pass_name = d.get("pass_name", "") + + # Parse action string back to NodeSourceAction enum list + action_str = d.get("action", "") + actions = [] + if action_str: + for action_name in action_str.split("+"): + if action_name.upper() == "CREATE": + actions.append(NodeSourceAction.CREATE) + elif action_name.upper() == "REPLACE": + actions.append(NodeSourceAction.REPLACE) + node_source.action = actions + + # Create the NodeInfo object directly + if "name" in d and "target" in d and "graph_id" in d: + node_info = NodeSource.NodeInfo( + d.get("name", ""), d.get("target", ""), d.get("graph_id", -1) + ) + node_source.node_info = node_info + else: + node_source.node_info = None + + # Recursively deserialize nested from_node + if d.get("from_node", None) is not None: + node_source.from_node = [ + result + for fn in d.get("from_node", []) + if (result := cls._from_dict(fn)) is not None + ] + else: + node_source.from_node = [] + return node_source + + +@compatibility(is_backward_compatible=False) +@contextmanager +def preserve_node_meta(enable=True): + global should_preserve_node_meta + global current_meta + saved_should_preserve_node_meta = should_preserve_node_meta + # Shallow copy is OK since fields of current_meta are not mutated + saved_current_meta = current_meta.copy() + try: + should_preserve_node_meta = enable + yield + finally: + should_preserve_node_meta = saved_should_preserve_node_meta + current_meta = saved_current_meta + + +@compatibility(is_backward_compatible=False) +def set_stack_trace(stack: list[str]): + global current_meta + + if should_preserve_node_meta and stack: + current_meta["stack_trace"] = "".join(stack) + + +@compatibility(is_backward_compatible=False) +@contextmanager +def annotate(annotation_dict: dict): + """ + Temporarily adds custom annotations to the current tracing context. + The fx_node produced from this tracing context will have the + custom annotations in node.metadata["custom"] field. + + This context manager allows you to insert arbitrary metadata into the PT2 + tracing system by updating the global `current_meta["custom"]` dictionary. + The annotations are automatically reverted after the context exits. + + Gradient accumulation nodes will not be annotated. + + This is intended for advanced users who need to attach additional metadata to the fx nodes + (e.g., for debugging, analysis, or external tooling) during export tracing. + + Note: + This API is **not backward compatible** and may evolve in future releases. + + Note: + This API is not compatible with fx.symbolic_trace or jit.trace. It's intended + to be used with PT2 family of tracers, e.g. torch.export and dynamo. + + Args: + annotation_dict (dict): A dictionary of custom key-value pairs to inject + into the FX trace metadata. + + Example: + After exiting the context, custom annotations are removed. + + >>> with annotate({"source": "custom_pass", "tag": 42}): + ... pass # Your computation here + """ + + global current_meta + + has_custom = "custom" in current_meta + old_custom = copy.copy(current_meta.get("custom", {})) + + try: + if not has_custom: + current_meta["custom"] = {} + + # Update with all key-value pairs from the input dict + current_meta["custom"].update(annotation_dict) + yield + finally: + if has_custom: + # Restore the original custom dict + current_meta["custom"] = old_custom + else: + del current_meta["custom"] + + +@compatibility(is_backward_compatible=False) +def annotate_fn(annotation_dict: dict): + """ + A decorator that wraps a function with the annotate context manager. + Use this when you want to annotate an entire function instead of a specific code block. + + Note: + This API is **not backward compatible** and may evolve in future releases. + + Note: + This API is not compatible with fx.symbolic_trace or jit.trace. It's intended + to be used with PT2 family of tracers, e.g. torch.export and dynamo. + + Args: + annotation_dict (dict): A dictionary of custom key-value pairs to inject + into the FX trace metadata for all operations in the function. + + Example: + All operations in my_function will have {"pp_stage": 1} in their metadata. + + >>> @annotate_fn({"pp_stage": 1}) + ... def my_function(x): + ... return x + 1 + """ + from functools import wraps + + def decorator(func): + @wraps(func) + def wrapper(*args, **kwargs): + with annotate(annotation_dict): + return func(*args, **kwargs) + + return wrapper + + return decorator + + +@compatibility(is_backward_compatible=False) +def set_grad_fn_seq_nr(seq_nr): + global current_meta + + if should_preserve_node_meta: + # The seq_nr is captured by eager mode in the grad_fn during forward + current_meta["grad_fn_seq_nr"] = current_meta.get("grad_fn_seq_nr", []) + [ + seq_nr + ] + current_meta["in_grad_fn"] = current_meta.get("in_grad_fn", 0) + 1 + + +@compatibility(is_backward_compatible=False) +def reset_grad_fn_seq_nr(): + # NB: reset state properly, this would be helpful towards supporting + # reentrant autograd if we actually wanted to do that. + global current_meta + if should_preserve_node_meta: + current_level = current_meta.get("in_grad_fn", 0) + assert current_level > 0 + if current_level == 1: + del current_meta["in_grad_fn"] + del current_meta["grad_fn_seq_nr"] + else: + current_meta["in_grad_fn"] = current_level - 1 + current_meta["grad_fn_seq_nr"] = current_meta["grad_fn_seq_nr"][:-1] + + +@compatibility(is_backward_compatible=False) +def format_stack() -> list[str]: + if should_preserve_node_meta: + return [current_meta.get("stack_trace", "")] + else: + # fallback to traceback.format_stack() + return traceback.format_list(traceback.extract_stack()[:-1]) + + +@compatibility(is_backward_compatible=False) +def has_preserved_node_meta() -> bool: + return should_preserve_node_meta + + +@compatibility(is_backward_compatible=False) +@contextmanager +def set_current_meta(node, pass_name=""): + global current_meta + if should_preserve_node_meta and node.meta: + saved_meta = current_meta + try: + current_meta = node.meta.copy() + + # Update the "from_node" field in current_meta for provenance tracking. + # Instead of appending, overwrite the "from_node" field because current_meta + # will be assigned to the new node. The new NodeSource(node, ...) will + # include the information from the previous current_meta["from_node"]. + current_meta["from_node"] = [ + NodeSource(node, pass_name, NodeSourceAction.CREATE) + ] + yield + finally: + current_meta = saved_meta + else: + yield + + +@compatibility(is_backward_compatible=False) +def get_current_meta() -> dict[str, Any]: + return current_meta + + +@compatibility(is_backward_compatible=False) +@contextmanager +def set_current_replay_node(node): + """ + Set the currently replay node. If `current_replay_node` is not None, + then we're re-generating the `current_replay_node` in FunctionalTensorMode. + """ + # See [Note] annotation for more details. + global current_replay_node + saved_current_replay_node = current_replay_node + try: + current_replay_node = node + yield + finally: + current_replay_node = saved_current_replay_node + + +@compatibility(is_backward_compatible=False) +def get_current_replay_node(): + """ + Get the currently replay node + """ + return current_replay_node + + +@compatibility(is_backward_compatible=False) +def get_graph_provenance_json(graph: Graph) -> dict[str, Any]: + """ + Given an fx.Graph, return a json that contains the provenance information of each node. + """ + try: + provenance_tracking_json = {} + for node in graph.nodes: + if node.op == "call_function": + provenance_tracking_json[node.name] = ( + [source.to_dict() for source in node.meta["from_node"]] + if "from_node" in node.meta + else [] + ) + return provenance_tracking_json + except Exception as e: + # Since this is just debugging, it should never interfere with regular + # program execution, so we use this try-except to guard against any error + signpost_event( + "inductor", + "provenance_tracking_error", + { + "function": "get_graph_provenance_json", + "error_msg": str(e), + "stack_trace": traceback.format_exc(), + }, + ) + return {} + + +def _get_custom_metadata(gm: GraphModule) -> str: + assert isinstance(gm, GraphModule) + + def helper(gm: GraphModule): + custom_metadata = [] + for node in gm.graph.nodes: + if hasattr(node, "meta") and node.meta.get("custom", None): + custom_metadata.append((node.op, node.name, node.meta["custom"])) + if node.op == "get_attr" and isinstance( + getattr(gm, node.target), GraphModule + ): + custom_metadata.append(helper(getattr(gm, node.target))) + return custom_metadata + + return "\n".join(str(x) for x in helper(gm))