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pytorch/torch/_dynamo/trace_rules.py
Maggie Moss c855f8632e Pyrefly suppressions 7/n (#164913) 
Adds suppressions to pyrefly will typecheck clean: https://github.com/pytorch/pytorch/issues/163283

Almost there!

Test plan:
dmypy restart && python3 scripts/lintrunner.py -a
pyrefly check

step 1: delete lines in the pyrefly.toml file from the project-excludes field
step 2: run pyrefly check
step 3: add suppressions, clean up unused suppressions
before: https://gist.github.com/maggiemoss/4b3bf2037014e116bc00706a16aef199

after:
 INFO 0 errors (6,884 ignored)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164913
Approved by: https://github.com/oulgen
2025-10-08 07:27:17 +00:00

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"""
Tracing rules and policies for TorchDynamo compilation decisions.
This module defines the rules that govern what code TorchDynamo should trace and compile
versus what should be executed eagerly. It contains functions and classes that determine:
- Which modules, functions, and objects should be skipped during tracing
- Which parts of the code should cause graph breaks
- How to handle different Python libraries and third-party packages
- Rules for determining when to inline functions vs calling them eagerly
Key components:
- Skip rules: Functions that return True if an object should be skipped during tracing
- Inlining rules: Policies for when to inline function calls during compilation
- Library-specific handling: Special cases for popular Python packages
- Performance heuristics: Rules that balance compilation overhead vs runtime benefits
These rules are critical for TorchDynamo's ability to automatically determine
compilation boundaries and optimize PyTorch programs effectively.
"""
import abc
import builtins
import copy
import dataclasses
import functools
import importlib
import inspect
import linecache
import operator
import os
import random
import re
import sys
import traceback
import types
import unittest
from collections import defaultdict
from pathlib import Path
from typing import Any, Callable, cast, Optional, Union
import torch
import torch._inductor.test_operators
import torch.distributed
import torch.utils._content_store
from torch._environment import is_fbcode
from torch.utils import _config_module
from . import config
from .resume_execution import TORCH_DYNAMO_RESUME_IN_PREFIX
from .utils import (
getfile,
hashable,
is_annotate_wrapped_function,
is_lru_cache_wrapped_function,
NP_SUPPORTED_MODULES,
unwrap_if_wrapper,
)
from .variables import (
BuiltinVariable,
FunctionalCallVariable,
FunctorchHigherOrderVariable,
LocalGeneratorFunctionVariable,
LocalGeneratorObjectVariable,
NestedUserFunctionVariable,
PolyfilledFunctionVariable,
ReparametrizeModuleCallVariable,
SkipFunctionVariable,
TorchInGraphFunctionVariable,
UserFunctionVariable,
UserMethodVariable,
)
from .variables.base import VariableTracker
np: Optional[types.ModuleType] = None
try:
import numpy as np
except ModuleNotFoundError:
pass
"""
A note on skip/inline rules:
Dynamo consults this file to determine whether function should be inlined or skipped.
A skip applies at the frame boundary, meaning dynamo either triggers a graph break
at the beginning of the frame or attempts to trace/inline the whole frame. When skipping
a frame, recursively called frames are still traced by dynamo unless also skipped.
Skipfiles (skipped at the file level instead of function level) still apply on a
frame-by-frame boundary as dynamo traces, but apply to all functions in that file.
@skip is a helper decorator that can be applied to your function to cause it to be
included here.
Dynamo skip/inline rules & priorities are defined as follows:
* Inline is the default behavior and will be used unless explicitly skipped.
* Dynamo has two SKIPLIST: BUILTIN_SKIPLIST and THIRDPARTY_SKIPLIST.
* BUILTIN_SKIPLIST contains builtin python modules, such as abc, collections, etc.
* THIRDPARTY_SKIPLIST contains common third party libraries, such as numpy, pandas, etc.
* Functions in these two SKIPLISTs are always skipped, except:
* They have explicitly defined rule in `manual_torch_name_rule_map`;
* The corresponding python module has been put into MOD_INLINELIST.
* PyTorch(torch) is in the BUILTIN_SKIPLIST by default, but there are many cases
where we want inline the functions under torch namespace.
We should specify inline for the functions in `manual_torch_name_rule_map` or
put the corresponding python module into MOD_INLINELIST to make dynamo inline them.
* If you call functions under skipped modules/files, Dynamo will wrap these functions
as SkipFunctionVariable. There are a few functions(e.g, collections.OrderedDict) that
we have special handling at SkipFunctionVariable.call_function.
Overall: *_INLINELIST has precedence over *_SKIPLIST has precedence over DEFAULT (inline)
To figure out what the behavior is, check the following list in order:
* `manual_torch_name_rule_map` (Inline if YES)
* MOD_INLINELIST (Inline if YES)
* BUILTIN_SKIPLIST & THIRDPARTY_SKIPLIST (Skip if YES)
* MOD_SKIPLIST (Skip if YES)
* Inline by default
In general, if you want to force inline a function or module, please consider adding
the function's python module to MOD_INLINELIST first.
Use the `manual_torch_name_rule_map` only when there are other functions under the same module that
you don't want to inline them.
"""
"""
Map of function objects to their tracing rules (Dynamo variables).
* TorchInGraphFunctionVariable: The functions should be put into the FX graph or can be constant folded. E.g.,
- torch.add: should be put into the FX graph.
- torch.is_floating_point: constant folded.
* SkipFunctionVariable: The objects should be skipped from tracing.
* UserFunctionVariable: The functions should be inlined.
For developers: If you add/remove a torch level API, it may trigger failures from
test/dynamo/test_trace_rules.py:test_torch_name_rule_map_updated. To fix the failures:
If you are adding a new torch level API or Dynamo implementation:
* Add the name with the corresponding tracing rule to this map
if you are adding a new in graph function or Dynamo implementation for an existing function.
* Remove the object name from test/dynamo/test_trace_rules.ignored_c_binding_in_graph_function_names if it's there.
If you are removing an existing torch level API:
* Remove the entry represented the API from this map or test/dynamo/test_trace_rules.ignored_c_binding_in_graph_function_names
depends on where it is.
"""
manual_torch_name_rule_map: dict[
str,
Union[
type[TorchInGraphFunctionVariable],
type[SkipFunctionVariable],
type[UserFunctionVariable],
],
] = {
"torch.fx.traceback.annotate": UserFunctionVariable,
"torch.onnx.is_in_onnx_export": TorchInGraphFunctionVariable,
"torch.onnx.operators.shape_as_tensor": TorchInGraphFunctionVariable,
"torch.overrides.is_tensor_like": TorchInGraphFunctionVariable,
"torch.jit.is_scripting": TorchInGraphFunctionVariable,
"torch.jit.is_tracing": TorchInGraphFunctionVariable,
"torch.jit.annotate": TorchInGraphFunctionVariable,
"torch.distributed.is_available": TorchInGraphFunctionVariable,
"torch.distributed.is_initialized": TorchInGraphFunctionVariable,
"torch.distributed.get_rank": TorchInGraphFunctionVariable,
"torch.distributed.get_world_size": TorchInGraphFunctionVariable,
"torch.distributed.tensor._api.DTensor#from_local": TorchInGraphFunctionVariable,
"torch.distributed.distributed_c10d._get_group_size_by_name": TorchInGraphFunctionVariable,
"torch.distributed.distributed_c10d._resolve_group_name_by_ranks_and_tag": TorchInGraphFunctionVariable,
"torch.distributed.distributed_c10d._get_group_tag": TorchInGraphFunctionVariable,
"torch.distributed.distributed_c10d.get_process_group_ranks": TorchInGraphFunctionVariable,
"torch._utils.is_compiling": TorchInGraphFunctionVariable,
"torch.fx._symbolic_trace.is_fx_tracing": TorchInGraphFunctionVariable,
"torch.fx._symbolic_trace.is_fx_symbolic_tracing": TorchInGraphFunctionVariable,
"torch._dynamo.external_utils.is_compiling": TorchInGraphFunctionVariable,
"torch._dynamo.utils._disable_side_effect_safety_checks_for_current_subtracer": UserFunctionVariable,
"torch.compiler.is_compiling": TorchInGraphFunctionVariable,
"torch.compiler.is_dynamo_compiling": TorchInGraphFunctionVariable,
"torch.compiler.is_exporting": TorchInGraphFunctionVariable,
"torch._C._to_dlpack": SkipFunctionVariable,
"torch.to_dlpack": SkipFunctionVariable,
# We graph break on RNG state setters or getters like
# `torch.get_rng_state` or `torch.set_rng_state`. These functions
# are not aten operations and therefore they are completely ignored
# by the AOT dispatcher. As a result, the AOT graph does not have
# these setter or getter functions, producing an incorrect graph
# when it comes to rng states.
"torch.default_generator#get_state": SkipFunctionVariable,
"torch._C.Generator#get_state": SkipFunctionVariable,
"torch.get_rng_state": SkipFunctionVariable,
"torch.cuda.get_rng_state": SkipFunctionVariable,
"torch.default_generator#set_state": SkipFunctionVariable,
"torch._C.Generator#set_state": SkipFunctionVariable,
"torch.set_rng_state": SkipFunctionVariable,
"torch.cuda.set_rng_state": SkipFunctionVariable,
# https://github.com/pytorch/pytorch/issues/107187
"torch.manual_seed": SkipFunctionVariable,
# https://github.com/pytorch/pytorch/issues/93501
"torch.nn.utils.rnn.pack_padded_sequence": SkipFunctionVariable,
"torch.nn.Parameter": TorchInGraphFunctionVariable,
"torch.nn.Buffer": TorchInGraphFunctionVariable,
"torch._nested_tensor_from_mask": SkipFunctionVariable,
"torch.nested._internal.nested_tensor.nested_from_padded": TorchInGraphFunctionVariable,
"torch.nested.nested_tensor_from_jagged": UserFunctionVariable,
"torch.nested.nested_tensor_from_padded": UserFunctionVariable,
# torch.fx map utils
"torch.fx.node.map_aggregate": UserFunctionVariable,
"torch.fx.node.map_arg": UserFunctionVariable,
"torch.fx.immutable_collections._no_mutation": UserFunctionVariable,
"torch.fx.immutable_collections._immutable_list_flatten": UserFunctionVariable,
"torch.fx.immutable_collections._immutable_list_unflatten": UserFunctionVariable,
"torch.fx.immutable_collections._immutable_dict_flatten": UserFunctionVariable,
"torch.fx.immutable_collections._immutable_dict_unflatten": UserFunctionVariable,
# symbol operators implemented in Python
"torch.sym_not": TorchInGraphFunctionVariable,
"torch.sym_float": TorchInGraphFunctionVariable,
"torch.sym_int": TorchInGraphFunctionVariable,
"torch.sym_max": TorchInGraphFunctionVariable,
"torch.sym_min": TorchInGraphFunctionVariable,
"torch.sym_sqrt": TorchInGraphFunctionVariable,
"torch.sym_ite": TorchInGraphFunctionVariable,
"torch.sym_sum": TorchInGraphFunctionVariable,
"torch.sym_fresh_size": UserFunctionVariable,
"torch.Tensor#_make_wrapper_subclass": SkipFunctionVariable,
"torch.Tensor#__init__": SkipFunctionVariable,
"torch.Tensor#split": TorchInGraphFunctionVariable,
"torch.cuda.set_device": SkipFunctionVariable,
"torch.cuda.current_device": TorchInGraphFunctionVariable,
"torch._C.autocast_decrement_nesting": SkipFunctionVariable,
"torch._C.autocast_increment_nesting": SkipFunctionVariable,
"torch.autograd.grad": SkipFunctionVariable,
"torch.autograd.backward": SkipFunctionVariable,
"torch._C.clear_autocast_cache": SkipFunctionVariable,
"torch.distributions.constraints.is_dependent": SkipFunctionVariable,
"torch.jit.isinstance": SkipFunctionVariable,
"torch._C.set_anomaly_enabled": SkipFunctionVariable,
"torch._C.set_autocast_cache_enabled": SkipFunctionVariable,
"torch._C.set_autocast_cpu_dtype": SkipFunctionVariable,
"torch._C.set_autocast_cpu_enabled": SkipFunctionVariable,
"torch._C.set_autocast_enabled": SkipFunctionVariable,
"torch._C.set_autocast_gpu_dtype": SkipFunctionVariable,
"torch._C.set_autocast_ipu_dtype": SkipFunctionVariable,
"torch._C.set_autocast_ipu_enabled": SkipFunctionVariable,
"torch._C.set_autocast_xla_dtype": SkipFunctionVariable,
"torch._C.set_autocast_xla_enabled": SkipFunctionVariable,
"torch.resize_as_": SkipFunctionVariable,
"torch._functorch.predispatch._add_batch_dim": TorchInGraphFunctionVariable,
"torch._functorch.predispatch._remove_batch_dim": TorchInGraphFunctionVariable,
"torch.resize_as_sparse_": SkipFunctionVariable,
"torch.get_default_device": TorchInGraphFunctionVariable,
# functorch/vmap
"torch._functorch.vmap._check_int_or_none": UserFunctionVariable,
"torch._functorch.vmap._check_out_dims_is_int_or_int_pytree": UserFunctionVariable,
"torch._functorch.vmap._check_randomness_arg": UserFunctionVariable,
"torch._functorch.vmap._chunked_vmap": UserFunctionVariable,
"torch._functorch.vmap._concat_chunked_outputs": UserFunctionVariable,
"torch._functorch.vmap._create_batched_inputs": UserFunctionVariable,
"torch._functorch.vmap._flat_vmap": UserFunctionVariable,
"torch._functorch.vmap._flatten_chunks_output": UserFunctionVariable,
"torch._functorch.vmap._get_chunked_inputs": UserFunctionVariable,
"torch._functorch.vmap._get_name": UserFunctionVariable,
"torch._functorch.vmap._maybe_remove_batch_dim": UserFunctionVariable,
"torch._functorch.vmap._num_outputs": UserFunctionVariable,
"torch._functorch.vmap._process_batched_inputs": UserFunctionVariable,
"torch._functorch.vmap._unwrap_batched": UserFunctionVariable,
"torch._functorch.vmap._validate_and_get_batch_size": UserFunctionVariable,
"torch._functorch.vmap.doesnt_support_saved_tensors_hooks": UserFunctionVariable,
"torch._functorch.vmap.get_chunk_sizes": UserFunctionVariable,
# lazy_load_decompositions uses a lock that is not supported yet in dynamo
# "torch._functorch.vmap.lazy_load_decompositions": UserFunctionVariable,
"torch._functorch.vmap.restore_vmap": UserFunctionVariable,
"torch._functorch.apis.vmap": UserFunctionVariable,
"torch._functorch.vmap.unwrap_batched": UserFunctionVariable,
"torch._functorch.vmap.vmap_impl": FunctorchHigherOrderVariable,
"torch._functorch.vmap.wrap_batched": UserFunctionVariable,
# functorch/grad
"torch._functorch.eager_transforms.grad_impl": FunctorchHigherOrderVariable,
"torch._functorch.apis.grad_and_value": UserFunctionVariable,
"torch._functorch.eager_transforms._as_tuple": UserFunctionVariable,
"torch._functorch.eager_transforms._check_unique_non_empty": UserFunctionVariable,
"torch._functorch.eager_transforms._create_differentiable": UserFunctionVariable,
"torch._functorch.eager_transforms._slice_argnums": UserFunctionVariable,
"torch._functorch.eager_transforms._undo_create_differentiable": UserFunctionVariable,
"torch._functorch.eager_transforms._validate_and_wrap_argnum": UserFunctionVariable,
"torch._functorch.eager_transforms._validate_and_wrap_argnums": UserFunctionVariable,
"torch._functorch.eager_transforms._wrap_all_tensors": UserFunctionVariable,
"torch._functorch.eager_transforms._wrap_tensor_for_grad": UserFunctionVariable,
# functorch/jacrev
"torch._functorch.eager_transforms.jacrev": FunctorchHigherOrderVariable,
"torch._functorch.eager_transforms.error_if_complex": UserFunctionVariable,
"torch._functorch.eager_transforms._chunked_standard_basis_for_": UserFunctionVariable,
"torch._functorch.eager_transforms._safe_zero_index": UserFunctionVariable,
# functorch/vjp
"torch._functorch.eager_transforms.vjp": FunctorchHigherOrderVariable,
"torch._functorch.eager_transforms._vjp_with_argnums": UserFunctionVariable,
"torch._functorch.eager_transforms.assert_non_empty_tensor_output": UserFunctionVariable,
# functorch/jvp
"torch._functorch.eager_transforms._jvp_with_argnums": UserFunctionVariable,
"torch._functorch.eager_transforms.jvp": FunctorchHigherOrderVariable,
"torch._functorch.eager_transforms._replace_args": UserFunctionVariable,
"torch._functorch.eager_transforms.safe_unpack_dual": UserFunctionVariable,
"torch._functorch.eager_transforms.assert_non_empty_list_of_tensors": UserFunctionVariable,
"torch._functorch.eager_transforms.assert_output_is_tensor_or_tensors": UserFunctionVariable,
"torch.autograd.forward_ad.enter_dual_level": UserFunctionVariable,
"torch.autograd.forward_ad.exit_dual_level": UserFunctionVariable,
"torch.autograd.forward_ad.make_dual": UserFunctionVariable,
"torch.autograd.forward_ad.unpack_dual": UserFunctionVariable,
# functorch/linearize
"torch._functorch.eager_transforms.linearize": FunctorchHigherOrderVariable,
# functorch/jacfwd
"torch._functorch.eager_transforms.jacfwd": FunctorchHigherOrderVariable,
"torch._functorch.eager_transforms._construct_standard_basis_for": UserFunctionVariable,
"torch._functorch.eager_transforms.safe_unflatten": UserFunctionVariable,
# functorch/hessian
"torch._functorch.eager_transforms.hessian": FunctorchHigherOrderVariable,
# functional_call
"torch._functorch.functional_call.functional_call": FunctionalCallVariable,
"torch.nn.utils.stateless._groupby_tensor": TorchInGraphFunctionVariable,
"torch.nn.utils.stateless._reparametrize_module": ReparametrizeModuleCallVariable,
# functorch/deprecated
"torch._functorch.deprecated.jvp": UserFunctionVariable,
"torch._functorch.deprecated.hessian": UserFunctionVariable,
"torch._functorch.deprecated.jacfwd": UserFunctionVariable,
"torch._functorch.deprecated.jacrev": UserFunctionVariable,
"torch._functorch.deprecated.grad": UserFunctionVariable,
"torch._functorch.deprecated.grad_and_value": UserFunctionVariable,
"torch._functorch.deprecated.vjp": UserFunctionVariable,
# functorch/C++ bindings
"torch._C._functorch._wrap_for_grad": TorchInGraphFunctionVariable,
"torch._C._functorch._unwrap_for_grad": TorchInGraphFunctionVariable,
"torch._C._functorch._unwrap_batched": TorchInGraphFunctionVariable,
"torch._C._functorch.current_level": TorchInGraphFunctionVariable,
"torch._C._functorch.maybe_current_level": TorchInGraphFunctionVariable,
"torch._C._functorch.is_batchedtensor": TorchInGraphFunctionVariable,
"torch._C._functorch.peek_interpreter_stack": TorchInGraphFunctionVariable,
"torch._C._functorch.unwrap_if_dead": TorchInGraphFunctionVariable,
"torch._functorch.predispatch._vmap_increment_nesting": TorchInGraphFunctionVariable,
"torch._functorch.predispatch._vmap_decrement_nesting": TorchInGraphFunctionVariable,
# everything else
"torch._functorch.pyfunctorch.coerce_cinterpreter": TorchInGraphFunctionVariable,
"torch._higher_order_ops.triton_kernel_wrap.do_prune_configs": UserFunctionVariable,
"torch._higher_order_ops.foreach_map.foreach_map": UserFunctionVariable,
"torch._constrain_as_size": UserFunctionVariable,
"torch._tensor._convert": UserFunctionVariable,
"torch.jit._unwrap_optional": UserFunctionVariable,
"torch.backends.mha.get_fastpath_enabled": UserFunctionVariable,
"torch._dynamo.dont_skip_tracing": UserFunctionVariable,
"torch._dynamo.mark_static": UserFunctionVariable,
"torch._dynamo.nonstrict_trace": UserFunctionVariable,
"torch._dynamo.patch_dynamo_config": UserFunctionVariable,
"torch._dynamo.error_on_graph_break": UserFunctionVariable,
"torch.fx.experimental.symbolic_shapes.guard_size_oblivious": TorchInGraphFunctionVariable,
"torch.fx.experimental.symbolic_shapes.guard_or_true": TorchInGraphFunctionVariable,
"torch.fx.experimental.symbolic_shapes.guard_or_false": TorchInGraphFunctionVariable,
"torch.fx.experimental.symbolic_shapes.statically_known_true": TorchInGraphFunctionVariable,
"torch.fx.experimental.symbolic_shapes.statically_known_false": TorchInGraphFunctionVariable,
"torch.fx.experimental.symbolic_shapes.sym_and": TorchInGraphFunctionVariable,
"torch.fx.experimental.symbolic_shapes.sym_or": TorchInGraphFunctionVariable,
"torch.fx.experimental.symbolic_shapes.guard_scalar": TorchInGraphFunctionVariable,
"torch.fx.experimental.symbolic_shapes.has_static_value": TorchInGraphFunctionVariable,
"torch.cuda._get_device_properties": TorchInGraphFunctionVariable,
"torch.utils.hooks.BackwardHook": TorchInGraphFunctionVariable,
"torch.set_default_device": UserFunctionVariable,
"torch.sparse_bsc_tensor": SkipFunctionVariable,
"torch.sparse_bsr_tensor": SkipFunctionVariable,
"torch.sparse_csc_tensor": SkipFunctionVariable,
"torch.sparse_csr_tensor": SkipFunctionVariable,
"torch.sparse_compressed_tensor": SkipFunctionVariable,
"torch._C._autograd._unsafe_set_version_counter": TorchInGraphFunctionVariable,
"torch.xpu.get_rng_state": SkipFunctionVariable,
"torch.xpu.set_rng_state": SkipFunctionVariable,
# avoid skipping user defined modules in distributed unit tests
"torch/testing/_internal/common_fsdp.py#forward": UserFunctionVariable,
f"torch/testing/_internal/common_fsdp.py#{TORCH_DYNAMO_RESUME_IN_PREFIX}": UserFunctionVariable,
"torch/testing/_internal/distributed/_tensor/common_dtensor.py#forward": UserFunctionVariable,
f"torch/testing/_internal/distributed/_tensor/common_dtensor.py#{TORCH_DYNAMO_RESUME_IN_PREFIX}": UserFunctionVariable,
"torch/testing/_internal/common_distributed.py#forward": UserFunctionVariable,
f"torch/testing/_internal/common_distributed.py#{TORCH_DYNAMO_RESUME_IN_PREFIX}": UserFunctionVariable,
}
# In graph functions (including constant folding) that are C bindings
torch_c_binding_in_graph_functions = dict.fromkeys(
[
"math.acos",
"math.acosh",
"math.asin",
"math.asinh",
"math.atan",
"math.atan2",
"math.atanh",
"math.ceil",
"math.comb",
"math.copysign",
"math.cos",
"math.cosh",
"math.degrees",
"math.dist",
"math.erf",
"math.erfc",
"math.exp",
"math.expm1",
"math.fabs",
"math.factorial",
"math.floor",
"math.fmod",
"math.frexp",
"math.fsum",
"math.gamma",
"math.gcd",
"math.hypot",
"math.isclose",
"math.isfinite",
"math.isinf",
"math.isnan",
"math.isqrt",
"math.lcm",
"math.ldexp",
"math.lgamma",
"math.log",
"math.log10",
"math.log1p",
"math.log2",
"math.modf",
"math.nextafter",
"math.perm",
"math.pow",
"math.prod",
"math.radians",
"math.remainder",
"math.sin",
"math.sinh",
"math.tan",
"math.tanh",
"math.trunc",
"math.ulp",
"torch._adaptive_avg_pool2d",
"torch._adaptive_avg_pool3d",
"torch._add_batch_dim",
"torch._add_relu_",
"torch._add_relu",
"torch._addmm_activation",
"torch._aminmax",
"torch._amp_foreach_non_finite_check_and_unscale_",
"torch._amp_update_scale_",
"torch._assert_async",
"torch._assert_tensor_metadata",
"torch._batch_norm_impl_index",
"torch._C._accelerator_getAccelerator",
"torch._C._accelerator_getDeviceIndex",
"torch._C._accelerator_getStream",
"torch._C._accelerator_setStream",
"torch._C._accelerator_synchronizeDevice",
"torch._C._activate_gpu_trace",
"torch._C._add_cached_tensor",
"torch._C._add_docstr",
"torch._C._are_functorch_transforms_active",
"torch._C._autograd_init",
"torch._C._awaitable_nowait",
"torch._C._awaitable_wait",
"torch._C._awaitable",
"torch._C._backport_for_mobile_from_buffer_to_buffer",
"torch._C._backport_for_mobile_from_buffer",
"torch._C._backport_for_mobile_to_buffer",
"torch._C._backport_for_mobile",
"torch._C._broadcast_coalesced",
"torch._C._broadcast_out",
"torch._C._broadcast",
"torch._C._c10d_init",
"torch._C._calculate_package_version_based_on_upgraders",
"torch._C._can_use_flash_attention",
"torch._C._can_use_mem_efficient_attention",
"torch._C._can_use_cudnn_attention",
"torch._C._check_onnx_proto",
"torch._C._check_sparse_tensor_invariants",
"torch._C._collect_all",
"torch._C._commit_update",
"torch._C._compile_graph_to_code_table",
"torch._C._construct_CUDA_Tensor_From_Storage_And_Metadata",
"torch._C._construct_storage_from_data_pointer",
"torch._C._conv_determine_backend_memory_format",
"torch._C._cpu._is_avx2_supported",
"torch._C._cpu._is_avx512_supported",
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"torch._C._jit_pass_optimize_frozen_graph",
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"torch.nanmean",
"torch.nanmedian",
"torch.nanquantile",
"torch.nansum",
"torch.narrow_copy",
"torch.narrow",
"torch.native_batch_norm",
"torch.native_channel_shuffle",
"torch.native_dropout",
"torch.native_group_norm",
"torch.native_layer_norm",
"torch.native_norm",
"torch.ne",
"torch.neg_",
"torch.neg",
"torch.negative_",
"torch.negative",
"torch.nextafter",
"torch.nonzero_static",
"torch.nonzero",
"torch.norm_except_dim",
"torch.normal",
"torch.not_equal",
"torch.nuclear_norm",
"torch.numel",
"torch.ones_like",
"torch.ones",
"torch.orgqr",
"torch.ormqr",
"torch.outer",
"torch.pairwise_distance",
"torch.pdist",
"torch.permute_copy",
"torch.permute",
"torch.pinverse",
"torch.pixel_shuffle",
"torch.pixel_unshuffle",
"torch.poisson_nll_loss",
"torch.poisson",
"torch.polar",
"torch.polygamma",
"torch.positive",
"torch.pow",
"torch.prelu",
"torch._print",
"torch.prod",
"torch.promote_types",
"torch.put",
"torch.q_per_channel_axis",
"torch.q_per_channel_scales",
"torch.q_per_channel_zero_points",
"torch.q_scale",
"torch.q_zero_point",
"torch.qr",
"torch.quantile",
"torch.quantize_per_channel",
"torch.quantize_per_tensor_dynamic",
"torch.quantize_per_tensor",
"torch.quantized_batch_norm",
"torch.quantized_gru_cell",
"torch.quantized_lstm_cell",
"torch.quantized_max_pool1d",
"torch.quantized_max_pool2d",
"torch.quantized_max_pool3d",
"torch.quantized_rnn_relu_cell",
"torch.quantized_rnn_tanh_cell",
"torch.rad2deg_",
"torch.rad2deg",
"torch.rand_like",
"torch.rand",
"torch.randint_like",
"torch.randint",
"torch.randn_like",
"torch.randn",
"torch.randperm",
"torch.range",
"torch.ravel",
"torch.real",
"torch.reciprocal_",
"torch.reciprocal",
"torch.relu_",
"torch.relu",
"torch.remainder",
"torch.renorm",
"torch.repeat_interleave",
"torch.reshape",
"torch.resolve_conj",
"torch.resolve_neg",
"torch.result_type",
"torch.rms_norm",
"torch.rnn_relu_cell",
"torch.rnn_relu",
"torch.rnn_tanh_cell",
"torch.rnn_tanh",
"torch.roll",
"torch.rot90",
"torch.round_",
"torch.round",
"torch.row_indices_copy",
"torch.row_stack",
"torch.rrelu_",
"torch.rrelu",
"torch.rsqrt_",
"torch.rsqrt",
"torch.rsub",
"torch.saddmm",
"torch.scalar_tensor",
"torch.scatter_add",
"torch.scatter_reduce",
"torch.scatter",
"torch.searchsorted",
"torch.segment_reduce",
"torch.select_copy",
"torch.select_scatter",
"torch.select",
"torch.selu_",
"torch.selu",
"torch.sgn",
"torch.sigmoid_",
"torch.sigmoid",
"torch.sign",
"torch.signal.windows.windows.sqrt",
"torch.signbit",
"torch.sin_",
"torch.sin",
"torch.sinc_",
"torch.sinc",
"torch.sinh_",
"torch.sinh",
"torch.slice_copy",
"torch.slice_scatter",
"torch.slogdet",
"torch.smm",
"torch.softmax",
"torch.sort",
"torch.split_copy",
"torch.split_with_sizes_copy",
"torch.split_with_sizes",
"torch.spmm",
"torch.sqrt_",
"torch.sqrt",
"torch.square_",
"torch.square",
"torch.squeeze_copy",
"torch.squeeze",
"torch.sspaddmm",
"torch.stack",
"torch.std_mean",
"torch.std",
"torch.sub",
"torch.subtract",
"torch.sum",
"torch.svd",
"torch.swapaxes",
"torch.swapdims",
"torch.sym_constrain_range_for_size",
"torch.sym_constrain_range",
"torch.t_copy",
"torch.t",
"torch.take_along_dim",
"torch.take",
"torch.tan_",
"torch.tan",
"torch.tanh_",
"torch.tanh",
"torch.tensor_split",
"torch.tensor",
"torch.threshold_",
"torch.threshold",
"torch.tile",
"torch.topk",
"torch.trace",
"torch.transpose_copy",
"torch.transpose",
"torch.trapezoid",
"torch.trapz",
"torch.triangular_solve",
"torch.tril_indices",
"torch.tril",
"torch.triplet_margin_loss",
"torch.triu_indices",
"torch.triu",
"torch.true_divide",
"torch.trunc_",
"torch.trunc",
"torch.unbind_copy",
"torch.unbind",
"torch.unflatten",
"torch.unfold_copy",
"torch.unsafe_chunk",
"torch.unsafe_split_with_sizes",
"torch.unsafe_split",
"torch.unsqueeze_copy",
"torch.unsqueeze",
"torch.values_copy",
"torch.vander",
"torch.var_mean",
"torch.var",
"torch.vdot",
"torch.view_as_complex_copy",
"torch.view_as_complex",
"torch.view_as_real_copy",
"torch.view_as_real",
"torch.view_copy",
"torch.vsplit",
"torch.vstack",
"torch.where",
"torch.xlogy_",
"torch.xlogy",
"torch.zero_",
"torch.zeros",
"torch.zeros_like",
"torch._fused_sgd_",
"torch.slice_inverse",
"torch._assert_scalar",
"torch._functional_assert_scalar",
"torch.xpu._get_device_properties",
],
TorchInGraphFunctionVariable,
)
if sys.version_info >= (3, 11):
torch_c_binding_in_graph_functions["math.exp2"] = TorchInGraphFunctionVariable
torch_c_binding_in_graph_functions["math.cbrt"] = TorchInGraphFunctionVariable
# In graph functions (including constant folding) that are not C bindings
# NOTE: [Cacheability of in-graph torch functions]
# Functions in this list have the property that graphs containing them are safe to cache/serialize.
# serialize given only the information in the graph. I.e, either:
# - Your function does not access or close over global state, or
# - Your function closes over global state, but this state is guarded by dynamo, either
# through constant folding or other mechanisms
# If your function needs a custom special handler (via @register on TorchInGraphFunctionVariable),
# or captures global state, please add it to manual_torch_name_rule_map instead
torch_non_c_binding_in_graph_functions = dict.fromkeys(
[
"torch.__future__.get_overwrite_module_params_on_conversion",
"torch.__future__.set_overwrite_module_params_on_conversion",
"torch.__getattr__",
"torch._assert",
"torch._check_index",
"torch._check_is_size",
"torch._check_not_implemented",
"torch._check_tensor_all_with",
"torch._check_tensor_all",
"torch._check_type",
"torch._check_value",
"torch._check_with",
"torch._check",
"torch._compile._disable_dynamo",
"torch._functorch.apis.chunk_vmap",
"torch._functorch.batch_norm_replacement.batch_norm_without_running_stats",
"torch._functorch.batch_norm_replacement.replace_all_batch_norm_modules_",
"torch._functorch.deprecated.combine_state_for_ensemble",
"torch._functorch.deprecated.functionalize",
"torch._functorch.deprecated.get_warning",
"torch._functorch.deprecated.make_functional_with_buffers",
"torch._functorch.deprecated.make_functional",
"torch._functorch.deprecated.setup_docs",
"torch._functorch.deprecated.warn_deprecated",
"torch._functorch.eager_transforms._any_differentiable",
"torch._functorch.eager_transforms._autograd_grad",
"torch._functorch.eager_transforms._set_tensor_requires_grad",
"torch._functorch.eager_transforms._is_differentiable",
"torch._functorch.eager_transforms._maybe_unwrap_functional_tensor",
"torch._functorch.eager_transforms._maybe_wrap_functional_tensor",
"torch._functorch.eager_transforms._unwrap_all_tensors_from_functional",
"torch._functorch.eager_transforms._wrap_all_tensors_to_functional",
"torch._functorch.eager_transforms.assert_flat_tuple_of_tensors",
"torch._functorch.eager_transforms.functionalize",
"torch._functorch.eager_transforms.lazy_dynamo_disable",
"torch._functorch.eager_transforms.noop",
"torch._functorch.utils.enable_single_level_autograd_function",
"torch._functorch.utils.exposed_in",
"torch._functorch.utils.unwrap_dead_wrappers",
"torch._functorch.predispatch.lazy_load_decompositions",
"torch._functorch.predispatch._vmap_increment_nesting",
"torch._functorch.predispatch._vmap_decrement_nesting",
"torch._functorch.predispatch._add_batch_dim",
"torch._functorch.predispatch._remove_batch_dim",
"torch._guards.compile_context",
"torch._guards.detect_fake_mode",
"torch._guards.tracing",
"torch._higher_order_ops.map._has_potential_branch_input_alias",
"torch._higher_order_ops.map._has_potential_branch_input_mutation",
"torch._higher_order_ops.map._stack_pytree",
"torch._higher_order_ops.map._unstack_pytree",
"torch._higher_order_ops.map.create_fw_bw_graph",
"torch._higher_order_ops.map.map_autograd",
"torch._higher_order_ops.map.map_dense",
"torch._higher_order_ops.map.map_fake_tensor_mode",
"torch._higher_order_ops.map.map_functionalize",
"torch._higher_order_ops.map.map_proxy_torch_dispatch_mode",
"torch._higher_order_ops.map.map_wrapper",
"torch._higher_order_ops.map.trace_map",
"torch._higher_order_ops.out_dtype.elementwise_dtypes",
"torch._higher_order_ops.out_dtype.is_int_mm",
"torch._higher_order_ops.out_dtype.out_dtype_dense",
"torch._higher_order_ops.out_dtype.out_dtype_fake_tensor_mode",
"torch._higher_order_ops.out_dtype.out_dtype_fallback",
"torch._higher_order_ops.out_dtype.out_dtype_func",
"torch._higher_order_ops.out_dtype.out_dtype_proxy",
"torch._higher_order_ops.out_dtype.trace_out_dtype",
"torch._higher_order_ops.utils.autograd_not_implemented_inner",
"torch._higher_order_ops.utils.autograd_not_implemented",
"torch._linalg_utils._symeig",
"torch._linalg_utils.basis",
"torch._linalg_utils.bform",
"torch._linalg_utils.eig",
"torch._linalg_utils.get_floating_dtype",
"torch._linalg_utils.is_sparse",
"torch._linalg_utils.lstsq",
"torch._linalg_utils.matmul",
"torch._linalg_utils.matrix_rank",
"torch._linalg_utils.qform",
"torch._linalg_utils.solve",
"torch._linalg_utils.symeig",
"torch._load_global_deps",
"torch._lowrank._svd_lowrank",
"torch._lowrank.get_approximate_basis",
"torch._lowrank.pca_lowrank",
"torch._lowrank.svd_lowrank",
"torch._preload_cuda_deps",
"torch._register_device_module",
"torch._utils._dummy_type",
"torch._utils._flatten_dense_tensors",
"torch._utils._unflatten_dense_tensors",
"torch._weights_only_unpickler._get_allowed_globals",
"torch._weights_only_unpickler.load",
"torch.accelerator.current_accelerator",
"torch.accelerator.current_device_index",
"torch.accelerator.current_stream",
"torch.accelerator.device_count",
"torch.accelerator.is_available",
"torch.accelerator.set_stream",
"torch.accelerator.synchronize",
"torch.align_tensors",
"torch.amp.autocast_mode._enter_autocast",
"torch.amp.autocast_mode._exit_autocast",
"torch.amp.autocast_mode.autocast_decorator",
"torch.amp.autocast_mode.custom_bwd",
"torch.amp.autocast_mode.custom_fwd",
"torch.are_deterministic_algorithms_enabled",
"torch.atleast_1d",
"torch.atleast_2d",
"torch.atleast_3d",
"torch.autograd._calculate_shape",
"torch.autograd._is_checkpoint_valid",
"torch.autograd._profiler_enabled",
"torch.autograd._make_grads",
"torch.autograd._register_py_tensor_class_for_device",
"torch.autograd._tensor_or_tensors_to_tuple",
"torch.autograd.forward_ad._maybe_load_decompositions",
"torch.autograd.function._iter_filter",
"torch.autograd.function._iter_jit_values",
"torch.autograd.function._iter_None_tensors",
"torch.autograd.function._iter_tensors_permissive",
"torch.autograd.function._iter_tensors",
"torch.autograd.function._jit_unwrap_structured",
"torch.autograd.function._map_tensor_data",
"torch.autograd.function._nested_map",
"torch.autograd.function._unflatten",
"torch.autograd.function.once_differentiable",
"torch.autograd.function.traceable",
"torch.autograd.functional._as_tuple_nocheck",
"torch.autograd.functional._as_tuple",
"torch.autograd.functional._autograd_grad",
"torch.autograd.functional._check_requires_grad",
"torch.autograd.functional._construct_standard_basis_for",
"torch.autograd.functional._fill_in_zeros",
"torch.autograd.functional._grad_postprocess",
"torch.autograd.functional._grad_preprocess",
"torch.autograd.functional._jacfwd",
"torch.autograd.functional._tuple_postprocess",
"torch.autograd.functional._validate_v",
"torch.autograd.functional.hessian",
"torch.autograd.functional.hvp",
"torch.autograd.functional.jacobian",
"torch.autograd.functional.jvp",
"torch.autograd.functional.vhp",
"torch.autograd.functional.vjp",
"torch.autograd.grad_mode._enter_inference_mode",
"torch.autograd.grad_mode._exit_inference_mode",
"torch.autograd.graph._get_sid",
"torch.autograd.graph._get_tid",
"torch.autograd.graph.allow_mutation_on_saved_tensors",
"torch.autograd.graph.get_gradient_edge",
"torch.autograd.graph.increment_version",
"torch.autograd.graph.register_multi_grad_hook",
"torch.autograd.variable",
"torch.backends.__allow_nonbracketed_mutation",
"torch.backends.cpu.get_cpu_capability",
"torch.backends.cuda.can_use_efficient_attention",
"torch.backends.cuda.can_use_flash_attention",
"torch.backends.cuda.can_use_cudnn_attention",
"torch.backends.cuda.enable_flash_sdp",
"torch.backends.cuda.enable_math_sdp",
"torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp",
"torch.backends.cuda.enable_mem_efficient_sdp",
"torch.backends.cuda.flash_sdp_enabled",
"torch.backends.cuda.is_built",
"torch.backends.cuda.is_flash_attention_available",
"torch.backends.cuda.math_sdp_enabled",
"torch.backends.cuda.fp16_bf16_reduction_math_sdp_allowed",
"torch.backends.cuda.mem_efficient_sdp_enabled",
"torch.backends.cuda.cudnn_sdp_enabled",
"torch.backends.cuda.enable_cudnn_sdp",
"torch.backends.cuda.preferred_blas_library",
"torch.backends.cuda.preferred_linalg_library",
"torch.backends.cuda.preferred_rocm_fa_library",
"torch.backends.cuda.sdp_kernel",
"torch.backends.cudnn._init",
"torch.backends.cudnn.flags",
"torch.backends.cudnn.is_acceptable",
"torch.backends.cudnn.is_available",
"torch.backends.cudnn.set_flags",
"torch.backends.cudnn.version",
"torch.backends.disable_global_flags",
"torch.backends.flags_frozen",
"torch.backends.mkl.is_available",
"torch.backends.mkldnn.flags",
"torch.backends.mkldnn.is_available",
"torch.backends.mkldnn.set_flags",
"torch.backends.mps._init",
"torch.backends.mps.is_available",
"torch.backends.mps.is_built",
"torch.backends.mps.is_macos13_or_newer",
"torch.backends.openmp.is_available",
"torch.backends.quantized._get_qengine_id",
"torch.backends.quantized._get_qengine_str",
"torch.block_diag",
"torch.broadcast_tensors",
"torch.cartesian_prod",
"torch.cdist",
"torch.chain_matmul",
"torch.compile",
"torch.compiled_with_cxx11_abi",
"torch._C._cpu._is_avx2_supported",
"torch._C._cpu._is_avx512_supported",
"torch._C._cpu._is_avx512_vnni_supported",
"torch._C._cpu._is_avx512_bf16_supported",
"torch._C._cpu._is_amx_tile_supported",
"torch._C._cpu._is_amx_fp16_supported",
"torch.cpu._init_amx",
"torch.cpu.current_device",
"torch.cpu.current_stream",
"torch.cpu.device_count",
"torch.cpu.is_available",
"torch.cpu.set_device",
"torch.cpu.stream",
"torch.cpu.synchronize",
"torch.cuda._check_capability",
"torch.cuda._check_cubins",
"torch.cuda._device_count_amdsmi",
"torch.cuda._device_count_nvml",
"torch.cuda._get_amdsmi_handler",
"torch.cuda._get_amdsmi_device_index",
"torch.cuda._get_device",
"torch.cuda._get_generator",
"torch.cuda._get_nvml_device_index",
"torch.cuda._get_pynvml_handler",
"torch.cuda._get_rng_state_offset",
"torch.cuda._is_compiled",
"torch.cuda._lazy_call",
"torch.cuda._lazy_init",
"torch.cuda._memory_viz._block_extra_legacy",
"torch.cuda._memory_viz._block_extra",
"torch.cuda._memory_viz._format_size",
"torch.cuda._memory_viz._format_viz",
"torch.cuda._memory_viz._frame_filter",
"torch.cuda._memory_viz._frame_fmt",
"torch.cuda._memory_viz._frames_fmt",
"torch.cuda._memory_viz._profile_to_snapshot",
"torch.cuda._memory_viz._report_free",
"torch.cuda._memory_viz._write_blocks",
"torch.cuda._memory_viz.calc_active",
"torch.cuda._memory_viz.compare",
"torch.cuda._memory_viz.format_flamegraph",
"torch.cuda._memory_viz.memory",
"torch.cuda._memory_viz.profile_plot",
"torch.cuda._memory_viz.segment_plot",
"torch.cuda._memory_viz.segments",
"torch.cuda._memory_viz.segsum",
"torch.cuda._memory_viz.trace_plot",
"torch.cuda._memory_viz.trace",
"torch.cuda._nvml_based_avail",
"torch.cuda._parse_visible_devices",
"torch.cuda._raw_device_count_amdsmi",
"torch.cuda._raw_device_count_nvml",
"torch.cuda._raw_device_uuid_amdsmi",
"torch.cuda._raw_device_uuid_nvml",
"torch.cuda._register_triton_kernels",
"torch.cuda._set_rng_state_offset",
"torch.cuda._set_stream_by_id",
"torch.cuda._sleep",
"torch.cuda._transform_uuid_to_ordinals",
"torch.cuda._utils._get_device_index",
"torch.cuda.amp.autocast_mode._cast",
"torch.cuda.amp.autocast_mode.custom_bwd",
"torch.cuda.amp.autocast_mode.custom_fwd",
"torch.cuda.amp.common.amp_definitely_not_available",
"torch.amp.grad_scaler._refresh_per_optimizer_state",
"torch.cuda.can_device_access_peer",
"torch.cuda.check_error",
"torch.cuda.clock_rate",
"torch.cuda.cudart",
"torch.cuda.current_blas_handle",
"torch.cuda.current_stream",
"torch.cuda.default_stream",
"torch.cuda.device_count",
"torch.cuda.device_memory_used",
"torch.cuda.get_arch_list",
"torch.cuda.get_device_capability",
"torch.cuda.get_device_name",
"torch.cuda.get_device_properties",
"torch.cuda.get_gencode_flags",
"torch.cuda.get_sync_debug_mode",
"torch.cuda.graphs.graph_pool_handle",
"torch.cuda.graphs.is_current_stream_capturing",
"torch.cuda.graphs.make_graphed_callables",
"torch.cuda.init",
"torch.cuda.ipc_collect",
"torch.cuda.is_available",
"torch.cuda.is_bf16_supported",
"torch.cuda.is_initialized",
"torch.cuda.jiterator._create_jit_fn",
"torch.cuda.jiterator._create_multi_output_jit_fn",
"torch.cuda.memory_usage",
"torch.cuda.memory._dump_snapshot",
"torch.cuda.memory._free_mutex",
"torch.cuda.memory._get_current_allocator",
"torch.cuda.memory._host_allocator",
"torch.cuda.memory._record_memory_history_impl",
"torch.cuda.memory._record_memory_history_legacy",
"torch.cuda.memory._record_memory_history",
"torch.cuda.memory._save_memory_usage",
"torch.cuda.memory._save_segment_usage",
"torch.cuda.memory._set_allocator_settings",
"torch.cuda.memory._snapshot",
"torch.cuda.memory.caching_allocator_alloc",
"torch.cuda.memory.caching_allocator_delete",
"torch.cuda.memory.caching_allocator_enable",
"torch.cuda.memory.change_current_allocator",
"torch.cuda.memory.empty_cache",
"torch.cuda.memory.get_allocator_backend",
"torch.cuda.memory.get_per_process_memory_fraction",
"torch.cuda.memory.host_memory_stats_as_nested_dict",
"torch.cuda.memory.host_memory_stats",
"torch.cuda.memory.list_gpu_processes",
"torch.cuda.memory.max_memory_allocated",
"torch.cuda.memory.max_memory_cached",
"torch.cuda.memory.max_memory_reserved",
"torch.cuda.memory.mem_get_info",
"torch.cuda.memory.memory_allocated",
"torch.cuda.memory.memory_cached",
"torch.cuda.memory.memory_reserved",
"torch.cuda.memory.memory_snapshot",
"torch.cuda.memory.memory_stats_as_nested_dict",
"torch.cuda.memory.memory_stats",
"torch.cuda.memory.memory_summary",
"torch.cuda.memory.reset_accumulated_host_memory_stats",
"torch.cuda.memory.reset_accumulated_memory_stats",
"torch.cuda.memory.reset_max_memory_allocated",
"torch.cuda.memory.reset_max_memory_cached",
"torch.cuda.memory.reset_peak_host_memory_stats",
"torch.cuda.memory.reset_peak_memory_stats",
"torch.cuda.memory.set_per_process_memory_fraction",
"torch.cuda.nccl._check_sequence_type",
"torch.cuda.nccl.all_gather",
"torch.cuda.nccl.all_reduce",
"torch.cuda.nccl.broadcast",
"torch.cuda.nccl.init_rank",
"torch.cuda.nccl.is_available",
"torch.cuda.nccl.reduce_scatter",
"torch.cuda.nccl.reduce",
"torch.cuda.nccl.unique_id",
"torch.cuda.nccl.version",
"torch.cuda.nvtx.mark",
"torch.cuda.nvtx.range_end",
"torch.cuda.nvtx.range_pop",
"torch.cuda.nvtx.range_push",
"torch.cuda.nvtx.range_start",
"torch.cuda.nvtx.range",
"torch.cuda.power_draw",
"torch.cuda.profiler.init",
"torch.cuda.profiler.profile",
"torch.cuda.profiler.start",
"torch.cuda.profiler.stop",
"torch.cuda.random.get_rng_state_all",
"torch.cuda.random.initial_seed",
"torch.cuda.random.manual_seed_all",
"torch.cuda.random.manual_seed",
"torch.cuda.random.seed_all",
"torch.cuda.random.seed",
"torch.cuda.random.set_rng_state_all",
"torch.cuda.set_stream",
"torch.cuda.set_sync_debug_mode",
"torch.cuda.stream",
"torch.cuda.temperature",
"torch.cuda.utilization",
"torch.einsum",
"torch.functional._check_list_size",
"torch.functional._consecutive_return_counts",
"torch.functional._consecutive_return_inverse_false",
"torch.functional._consecutive_return_inverse_true",
"torch.functional._consecutive_return_inverse",
"torch.functional._consecutive_return_output",
"torch.functional._lu_impl",
"torch.functional._lu_no_infos",
"torch.functional._lu_with_infos",
"torch.functional._meshgrid",
"torch.functional._return_counts",
"torch.functional._return_inverse_false",
"torch.functional._return_inverse_true",
"torch.functional._return_inverse",
"torch.functional._return_output",
"torch.functional._unique_consecutive_impl",
"torch.functional._unique_impl",
"torch.functional._unravel_index",
"torch.functional.broadcast_shapes",
"torch.functional.lu",
"torch.functional.unique",
"torch.functional.unravel_index",
"torch.futures.collect_all",
"torch.futures.wait_all",
"torch.fx.experimental.const_fold.split_const_subgraphs",
"torch.fx.experimental.proxy_tensor.make_fx",
"torch.get_deterministic_debug_mode",
"torch.get_float32_matmul_precision",
"torch.is_deterministic_algorithms_warn_only_enabled",
"torch.is_storage",
"torch.is_tensor",
"torch.is_warn_always_enabled",
"torch.masked._ops._any",
"torch.masked._ops._apply_docstring_templates",
"torch.masked._ops._canonical_dim",
"torch.masked._ops._combine_input_and_mask",
"torch.masked._ops._generate_docstring",
"torch.masked._ops._input_mask",
"torch.masked._ops._output_mask",
"torch.masked._ops._reduction_identity",
"torch.masked._ops._sparse_coo_flatten_indices",
"torch.masked._ops._sparse_coo_scatter_reduction_helper",
"torch.masked._ops._sparse_coo_where",
"torch.masked._ops._sparse_csr_segment_reduction_helper",
"torch.masked._ops._sparse_csr_where",
"torch.masked._ops._std_var",
"torch.masked._ops._where",
"torch.masked._ops.amax",
"torch.masked._ops.amin",
"torch.masked._ops.argmax",
"torch.masked._ops.argmin",
"torch.masked._ops.corresponding_real_dtype",
"torch.masked._ops.cumprod",
"torch.masked._ops.cumsum",
"torch.masked._ops.log_softmax",
"torch.masked._ops.logaddexp",
"torch.masked._ops.logsumexp",
"torch.masked._ops.mean",
"torch.masked._ops.median",
"torch.masked._ops.norm",
"torch.masked._ops.normalize",
"torch.masked._ops.prod",
"torch.masked._ops.softmax",
"torch.masked._ops.softmin",
"torch.masked._ops.std",
"torch.masked._ops.sum",
"torch.masked._ops.var",
"torch.meshgrid",
"torch.mps._get_default_mps_generator",
"torch.mps.current_allocated_memory",
"torch.mps.driver_allocated_memory",
"torch.mps.empty_cache",
"torch.mps.get_rng_state",
"torch.mps.manual_seed",
"torch.mps.profiler.profile",
"torch.mps.profiler.start",
"torch.mps.profiler.stop",
"torch.mps.seed",
"torch.mps.set_per_process_memory_fraction",
"torch.mps.set_rng_state",
"torch.mps.synchronize",
"torch.nested._internal.nested_tensor.buffer_from_jagged",
"torch.nested._internal.nested_tensor.get_tensor_symint",
"torch.nested._internal.nested_tensor.is_expandable_to",
"torch.nested._internal.nested_tensor.jagged_from_list",
"torch.nested._internal.nested_tensor.jagged_from_tensor_and_lengths",
"torch.nested._internal.nested_tensor.nested_view_from_values_offsets",
"torch.nested._internal.nested_tensor.nested_view_from_values_offsets_lengths",
"torch.nested.as_nested_tensor",
"torch.nested.narrow",
"torch.nested.nested_tensor",
"torch.nn._reduction.get_enum",
"torch.nn._reduction.legacy_get_enum",
"torch.nn._reduction.legacy_get_string",
"torch.nn.factory_kwargs",
"torch.nn.functional.adaptive_avg_pool2d",
"torch.nn.functional.adaptive_avg_pool3d",
"torch.nn.functional.adaptive_max_pool1d_with_indices",
"torch.nn.functional.adaptive_max_pool1d",
"torch.nn.functional.adaptive_max_pool2d_with_indices",
"torch.nn.functional.adaptive_max_pool2d",
"torch.nn.functional.adaptive_max_pool3d_with_indices",
"torch.nn.functional.adaptive_max_pool3d",
"torch.nn.functional.affine_grid",
"torch.nn.functional.alpha_dropout",
"torch.nn.functional.assert_int_or_pair",
"torch.nn.functional.batch_norm",
"torch.nn.functional.binary_cross_entropy_with_logits",
"torch.nn.functional.binary_cross_entropy",
"torch.nn.functional.celu",
"torch.nn.functional.cosine_embedding_loss",
"torch.nn.functional.cross_entropy",
"torch.nn.functional.ctc_loss",
"torch.nn.functional.dropout",
"torch.nn.functional.dropout1d",
"torch.nn.functional.dropout2d",
"torch.nn.functional.dropout3d",
"torch.nn.functional.elu",
"torch.nn.functional.embedding_bag",
"torch.nn.functional.embedding",
"torch.nn.functional.feature_alpha_dropout",
"torch.nn.functional.fold",
"torch.nn.functional.fractional_max_pool2d_with_indices",
"torch.nn.functional.fractional_max_pool2d",
"torch.nn.functional.fractional_max_pool3d_with_indices",
"torch.nn.functional.fractional_max_pool3d",
"torch.nn.functional.gaussian_nll_loss",
"torch.nn.functional.glu",
"torch.nn.functional.grid_sample",
"torch.nn.functional.group_norm",
"torch.nn.functional.gumbel_softmax",
"torch.nn.functional.hardsigmoid",
"torch.nn.functional.hardswish",
"torch.nn.functional.hardtanh",
"torch.nn.functional.hinge_embedding_loss",
"torch.nn.functional.huber_loss",
"torch.nn.functional.instance_norm",
"torch.nn.functional.interpolate",
"torch.nn.functional.kl_div",
"torch.nn.functional.l1_loss",
"torch.nn.functional.layer_norm",
"torch.nn.functional.leaky_relu",
"torch.nn.functional.local_response_norm",
"torch.nn.functional.log_softmax",
"torch.nn.functional.lp_pool1d",
"torch.nn.functional.lp_pool2d",
"torch.nn.functional.margin_ranking_loss",
"torch.nn.functional.max_pool1d_with_indices",
"torch.nn.functional.max_pool1d",
"torch.nn.functional.max_pool2d_with_indices",
"torch.nn.functional.max_pool2d",
"torch.nn.functional.max_pool3d_with_indices",
"torch.nn.functional.max_pool3d",
"torch.nn.functional.max_unpool1d",
"torch.nn.functional.max_unpool2d",
"torch.nn.functional.max_unpool3d",
"torch.nn.functional.mish",
"torch.nn.functional.mse_loss",
"torch.nn.functional.multi_head_attention_forward",
"torch.nn.functional.multi_margin_loss",
"torch.nn.functional.multilabel_margin_loss",
"torch.nn.functional.multilabel_soft_margin_loss",
"torch.nn.functional.nll_loss",
"torch.nn.functional.normalize",
"torch.nn.functional.poisson_nll_loss",
"torch.nn.functional.relu",
"torch.nn.functional.relu6",
"torch.nn.functional.rrelu",
"torch.nn.functional.selu",
"torch.nn.functional.sigmoid",
"torch.nn.functional.silu",
"torch.nn.functional.smooth_l1_loss",
"torch.nn.functional.soft_margin_loss",
"torch.nn.functional.softmax",
"torch.nn.functional.softmin",
"torch.nn.functional.softsign",
"torch.nn.functional.tanh",
"torch.nn.functional.tanhshrink",
"torch.nn.functional.triplet_margin_loss",
"torch.nn.functional.unfold",
"torch.nn.functional.upsample_bilinear",
"torch.nn.functional.upsample_nearest",
"torch.nn.functional.upsample",
"torch.nn.grad._pair",
"torch.nn.grad._single",
"torch.nn.grad._triple",
"torch.nn.grad.conv1d_input",
"torch.nn.grad.conv1d_weight",
"torch.nn.grad.conv2d_input",
"torch.nn.grad.conv2d_weight",
"torch.nn.grad.conv3d_input",
"torch.nn.grad.conv3d_weight",
"torch.nn.modules.activation._is_make_fx_tracing",
"torch.nn.modules.utils._list_with_default",
"torch.nn.modules.utils._ntuple",
"torch.nn.modules.utils._quadruple",
"torch.nn.modules.utils._reverse_repeat_tuple",
"torch.nn.modules.utils.consume_prefix_in_state_dict_if_present",
"torch.nn.parameter.is_lazy",
"torch.norm",
"torch.quantization.default_eval_fn",
"torch.random._seed_custom_device",
"torch.random.fork_rng",
"torch.random.initial_seed",
"torch.random.seed",
"torch.return_types.pytree_register_structseq",
"torch.set_default_dtype",
"torch.set_default_tensor_type",
"torch.set_deterministic_debug_mode",
"torch.set_float32_matmul_precision",
"torch.set_warn_always",
"torch.signal.windows.windows._add_docstr",
"torch.signal.windows.windows._window_function_checks",
"torch.signal.windows.windows.bartlett",
"torch.signal.windows.windows.blackman",
"torch.signal.windows.windows.cosine",
"torch.signal.windows.windows.exponential",
"torch.signal.windows.windows.gaussian",
"torch.signal.windows.windows.general_cosine",
"torch.signal.windows.windows.general_hamming",
"torch.signal.windows.windows.hamming",
"torch.signal.windows.windows.hann",
"torch.signal.windows.windows.kaiser",
"torch.signal.windows.windows.merge_dicts",
"torch.signal.windows.windows.nuttall",
"torch.signal.windows.windows.parse_kwargs",
"torch.sparse.semi_structured.to_sparse_semi_structured",
"torch.sparse.sum",
"torch.split",
"torch.stft",
"torch.sym_float",
"torch.sym_int",
"torch.sym_ite",
"torch.sym_max",
"torch.sym_min",
"torch.sym_not",
"torch.tensordot",
"torch.unique_consecutive",
"torch.use_deterministic_algorithms",
"torch.xpu._get_device",
"torch.xpu._get_generator",
"torch.xpu._get_rng_state_offset",
"torch.xpu._is_compiled",
"torch.xpu._lazy_call",
"torch.xpu._lazy_init",
"torch.xpu._set_rng_state_offset",
"torch.xpu._set_stream_by_id",
"torch.xpu._utils._get_device_index",
"torch.xpu.current_device",
"torch.xpu.current_stream",
"torch.xpu.device_count",
"torch.xpu.get_arch_list",
"torch.xpu.get_device_capability",
"torch.xpu.get_device_name",
"torch.xpu.get_device_properties",
"torch.xpu.get_gencode_flags",
"torch.xpu.get_stream_from_external",
"torch.xpu.init",
"torch.xpu.is_available",
"torch.xpu.is_bf16_supported",
"torch.xpu.is_initialized",
"torch.xpu.memory.empty_cache",
"torch.xpu.memory.max_memory_allocated",
"torch.xpu.memory.max_memory_reserved",
"torch.xpu.memory.mem_get_info",
"torch.xpu.memory.memory_allocated",
"torch.xpu.memory.memory_reserved",
"torch.xpu.memory.memory_stats_as_nested_dict",
"torch.xpu.memory.memory_stats",
"torch.xpu.memory.reset_accumulated_memory_stats",
"torch.xpu.memory.reset_peak_memory_stats",
"torch.xpu.random.initial_seed",
"torch.xpu.random.seed_all",
"torch.xpu.random.seed",
"torch.xpu.set_stream",
"torch.xpu.stream",
"torch.xpu.synchronize",
],
TorchInGraphFunctionVariable,
)
torch_name_rule_map = [
manual_torch_name_rule_map,
torch_c_binding_in_graph_functions,
torch_non_c_binding_in_graph_functions,
]
"""
Generate the torch object - Dynamo tracing rule (the wrapping variable) map.
"""
@functools.cache
def get_torch_obj_rule_map() -> dict[Any, type["VariableTracker"]]:
d: dict[Any, type[VariableTracker]] = {}
for m in torch_name_rule_map:
for k, v in m.items(): # type: ignore[attr-defined]
if ".py#" not in k:
obj = load_object(k)
else:
torch_dir = _module_dir(torch)
if torch_dir is None:
continue
obj = torch_dir + k[len("torch/") :]
if obj is not None:
if is_annotate_wrapped_function(obj):
# pyrefly: ignore # missing-attribute
obj = obj.__wrapped__
if is_lru_cache_wrapped_function(obj):
obj = obj.__wrapped__
if obj in d and d[obj] != v:
raise AssertionError(
f"Duplicate torch object {obj} with different rules: {v}, {d[obj]}"
)
else:
d[obj] = v
return d
def _load_obj_from_str(fully_qualified_name: str) -> Any:
module, obj_name = fully_qualified_name.rsplit(".", maxsplit=1)
return getattr(importlib.import_module(module), obj_name)
"""
Load string represented torch objects.
"""
def load_object(name: str) -> Any:
try:
x = name.split("#")
if len(x) == 2:
obj = _load_obj_from_str(x[0])
val = getattr(obj, x[1])
else:
assert len(x) == 1, f"Invalid obj name {name}"
val = _load_obj_from_str(x[0])
val = unwrap_if_wrapper(val)
except (AttributeError, ImportError):
val = None
return val
"""
Get all torch.Tensor methods which are allowed to be in graph functions.
"""
@functools.cache
def get_tensor_method() -> frozenset[Any]:
disallowed_tensor_methods = {"__new__", "_make_wrapper_subclass", "_make_subclass"}
s = set()
for name in dir(torch.Tensor):
method = getattr(torch.Tensor, name)
if (
isinstance(
method,
(
types.MethodDescriptorType,
types.WrapperDescriptorType,
types.BuiltinFunctionType,
),
)
and name not in disallowed_tensor_methods
):
s.add(method)
# mlazos: these are functions which we handle specially in TensorVariable
s.add(torch.Tensor.__contains__) # type: ignore[arg-type]
s.add(torch.Tensor.register_hook) # type: ignore[arg-type]
return frozenset(s)
"""
Return if a torch object is ATen op or torch.Tensor method.
"""
def is_aten_op_or_tensor_method(obj: Any) -> bool:
return obj in get_tensor_method() or isinstance(
obj,
(torch._ops.OpOverloadPacket, torch._ops.OpOverload),
)
class FunctionIdSet:
"""
Track a set of `id()`s of objects which are either allowed or not
allowed to go into the generated FX graph. Use to test for torch.*,
numpy.*, builtins.*, etc.
Support user modification to permit customization of what can be
added to the graph and what will cause a graph break.
"""
function_ids: Optional[set[int]] = None
function_names: Optional[dict[int, str]] = None
def __init__(
self, lazy_initializer: Callable[[], Union[dict[int, str], set[int]]]
) -> None:
self.lazy_initializer = lazy_initializer
def __call__(self) -> set[int]:
if self.function_ids is None:
value = self.lazy_initializer()
if isinstance(value, dict):
self.function_ids = set(value.keys())
self.function_names = value
else:
assert isinstance(value, set)
self.function_ids = value
return self.function_ids
def get_name(self, idx: int, default: str) -> str:
self() # lazy init
assert self.function_names is not None
return self.function_names.get(idx, default)
def add(self, idx: int) -> None:
function_ids = self() # lazy init
function_ids.add(idx)
def remove(self, idx: int) -> None:
function_ids = self()
if idx in function_ids:
function_ids.remove(idx)
def __contains__(self, idx: int) -> bool:
return idx in self()
@FunctionIdSet
def _allowed_callable_ids() -> dict[int, str]:
rv: dict[int, str] = {}
return rv
@FunctionIdSet
def _disallowed_callable_ids() -> dict[int, str]:
rv: dict[int, str] = {}
return rv
@FunctionIdSet
def _nonstrict_trace_callable_ids() -> dict[int, str]:
rv: dict[int, str] = {}
return rv
@FunctionIdSet
def _builtin_function_ids() -> dict[int, str]:
# See also torch/_dynamo/polyfills/loader.py, which removes items in _builtin_function_ids
rv = {
id(v): f"builtins.{k}"
for k, v in builtins.__dict__.items()
if not k.startswith("_") and callable(v)
}
rv.update(
{
id(v): f"operator.{k}"
for k, v in operator.__dict__.items()
if not k.startswith("_") and callable(v)
}
)
rv.update(
{
id(cast): "typing.cast",
id(copy.deepcopy): "copy.deepcopy",
}
)
return rv
@FunctionIdSet
def _polyfilled_function_ids() -> set[int]:
# See also @torch._dynamo.decorators.substitute_in_graph(...), which adds items in _polyfilled_function_ids
return set()
@FunctionIdSet
def _numpy_function_ids() -> dict[int, str]:
unsupported_funcs = {
"seed",
"ranf",
"get_bit_generator",
"RandomState",
"set_bit_generator",
"sample",
}
def is_supported(k: str, v: Any, mod: Any) -> bool:
if not callable(v):
return False
if not getattr(v, "__module__", None):
return True
if v.__module__ == mod.__name__:
return True
if (
v.__module__ == "numpy.random.mtrand"
and mod.__name__ == "numpy.random"
and k not in unsupported_funcs
):
return True
return False
rv = {}
for mod in NP_SUPPORTED_MODULES:
for k, v in mod.__dict__.items():
if is_supported(k, v, mod):
rv[id(v)] = f"{mod.__name__}.{k}"
return rv
@FunctionIdSet
def _builtin_constant_ids() -> dict[int, str]:
"""
Collects constant builtins by eliminating callable items.
"""
rv = {
id(v): f"builtins.{k}"
for k, v in builtins.__dict__.items()
if not k.startswith("_") and not callable(v)
}
return rv
_lazy_module_init: dict[str, list[Callable[[], None]]] = defaultdict(list)
def add_module_init_func(name: str, init_func: Callable[[], None]) -> None:
"""Register a module without eagerly importing it"""
# If the module is already imported, eagerly run init
assert "." not in name, f"Expected a root module name, but got {name}"
assert name not in _lazy_module_init
_lazy_module_init[name].append(init_func)
def _maybe_init_lazy_module(obj: object) -> None:
module = getattr(obj, "__module__", None)
if module is None:
return
base_module = module.split(".")[0]
init_funcs = _lazy_module_init.pop(base_module, None)
if init_funcs is not None:
for fn in init_funcs:
fn()
def is_callable_allowed(obj: Any) -> bool:
_maybe_init_lazy_module(obj)
return id(obj) in _allowed_callable_ids
def is_nonstrict_trace_callable(obj: Any) -> bool:
_maybe_init_lazy_module(obj)
return id(obj) in _nonstrict_trace_callable_ids
def is_callable_disallowed(obj: Any) -> bool:
_maybe_init_lazy_module(obj)
return id(obj) in _disallowed_callable_ids
def is_forbidden(obj: Any) -> bool:
_maybe_init_lazy_module(obj)
return inspect.getattr_static(obj, "_dynamo_forbidden", False)
def is_builtin_callable(obj: Any) -> bool:
# See also torch/_dynamo/polyfills/loader.py, which removes items in _builtin_function_ids
return id(obj) in _builtin_function_ids
def is_builtin_constant(obj: Any) -> bool:
return id(obj) in _builtin_constant_ids
def is_polyfilled_callable(obj: Any) -> bool:
# See also @torch._dynamo.decorators.substitute_in_graph(...), which adds items in _polyfilled_function_ids
return id(obj) in _polyfilled_function_ids
def is_numpy(obj: Any) -> bool:
if np is None:
return False
return isinstance(obj, (np.ndarray, np.generic)) or id(obj) in _numpy_function_ids
def is_numpy_dtype(obj: Any) -> bool:
if np is None:
return False
return isinstance(obj, np.dtype)
def is_numpy_type_info(obj: Any) -> bool:
if np is None:
return False
return isinstance(obj, (np.finfo, np.iinfo))
BUILTIN_SKIPLIST = (
abc,
copy,
random,
traceback,
linecache,
)
# third party libraries skiplist is defined by str, because users may not use these libraries.
# we should use lazy import & skip in the future.
THIRDPARTY_SKIPLIST = (
"fx2trt_oss",
"hypothesis",
"networkx",
"numpy",
"onnx",
"onnxruntime",
"onnx_tf",
"pandas",
"sklearn",
"tabulate",
"tensorflow",
"tensorrt",
"torch2trt",
"tqdm",
"tree",
"tvm",
"xarray",
)
def _as_posix_path(path: str) -> str:
posix_path = Path(os.path.normpath(path)).as_posix()
# os.path.normpath and pathlib.Path remove trailing slash, so we need to add it back
if path.endswith((os.path.sep, "/")):
posix_path += "/"
return posix_path
def _strip_init_py(s: str) -> str:
suffix = "__init__.py"
s = s.removesuffix(suffix)
return _as_posix_path(s)
def _module_dir(m: types.ModuleType) -> Optional[str]:
# Protect against a module not exporting __file__ - this can happen for
# frozen modules, for example.
file = getattr(m, "__file__", None)
return file and _strip_init_py(file)
# These are legacy workarounds, don't add new modules to this list.
# Please use the MOD_INLINELIST instead to force inline functions under particular modules.
#
# NB: The only thing that is different about MOD_INLINELIST and LEGACY_MOD_INLINELIST
# is the behavior of a function f2 in the module when called by a function f1
# in a module in MOD_SKIPLIST (see MOD_SKIPLIST for more details)
#
# LEGACY_MOD_INLINELIST is the same thing as Dynamo's behavior on a module that
# is not in any *_INLINELIST or *_SKIPLIST.
# That being said, we prefer people to add things to MOD_INLINELIST over
# LEGACY_MOD_INLINELIST because it is less likely to break existing tests.
LEGACY_MOD_INLINELIST = {
"torch._dynamo.external_utils",
"torch._export.db.examples",
"torch._export.wrappers",
"torch._functorch.apis",
"torch._functorch.deprecated",
"torch.nn.attention.flex_attention",
"torch.ao.quantization.pt2e.export_utils",
"torch.ao.quantization.pt2e.qat_utils",
"torch.ao.quantization.pt2e.representation.rewrite",
"torch.ao.quantization.pt2e.utils",
"torch.ao.quantization.quantizer.xnnpack_quantizer",
"torch.export.unflatten",
}
if torch.distributed.is_available():
LEGACY_MOD_INLINELIST |= {
"torch.distributed.tensor._api",
"torch.distributed.tensor.device_mesh",
"torch.distributed.device_mesh",
"torch.distributed.algorithms._checkpoint.checkpoint_wrapper",
"torch.distributed.tensor.parallel._data_parallel_utils",
"torch.distributed.tensor.parallel._utils",
"torch.distributed.tensor.parallel.style",
# we have to add replicate to LEGACY_MOD_INLINELIST to ensure
# the forward_hook won't be ignored.
"torch.distributed._composable.replicate",
}
if not config.skip_fsdp_hooks:
LEGACY_MOD_INLINELIST.add("torch.distributed.fsdp._fully_shard")
# Force inline functions under these modules, even they are in *_SKIPLIST.
# We are using python module name instead of file or directory object to avoid circular dependency.
# Please keep this sorted alphabetically.
#
# Btw, it is not "ideal" for something to be in MOD_INLINELIST. If Dynamo
# fully supports a module, then the ideal case is that it is not in
# any *_INLINELIST or *_SKIPLIST: then, the behavior of Dynamo is that
# it will always inline into functions in the module.
MOD_INLINELIST = [
"torch._decomp",
"torch._dynamo._trace_wrapped_higher_order_op",
"torch._dynamo.compiled_autograd",
"torch._dynamo.comptime",
"torch._dynamo.polyfills",
"torch._dynamo.test_case",
"torch._export.non_strict_utils",
"torch._functorch._aot_autograd.subclass_parametrization",
"torch._functorch.autograd_function",
"torch._functorch.eager_transforms",
"torch._functorch.functional_call",
"torch._functorch.pyfunctorch",
"torch._functorch.vmap",
"torch._inductor.test_operators",
"torch._library.autograd",
"torch._library.custom_ops",
"torch._ops",
"torch._prims",
"torch._refs",
"torch._tensor",
"torch.amp.autocast_mode",
"torch.ao.nn",
"torch.autograd.function",
"torch.backends.cuda",
"torch.cuda.amp.autocast_mode",
"torch.distributions",
"torch.export._tree_utils",
"torch.export._unlift",
"torch.export._wrapper_utils",
"torch.fx._pytree",
"torch.fx._symbolic_trace",
"torch.fx.experimental.proxy_tensor",
"torch.fx.passes.shape_prop",
"torch.nn",
"torch.overrides",
"torch.random",
"torch.return_types",
"torch.sparse",
"torch.testing",
"torch.utils._content_store",
"torch.utils._contextlib",
"torch.utils._cxx_pytree",
"torch.utils._device",
"torch.utils._foreach_utils",
"torch.utils._python_dispatch",
"torch.utils._pytree",
"torch.utils.hooks",
]
assert sorted(set(MOD_INLINELIST)) == MOD_INLINELIST
MOD_INLINELIST = set(MOD_INLINELIST)
if torch.distributed.is_available():
MOD_INLINELIST.add("torch.distributed")
if not config.skip_fsdp_hooks:
MOD_INLINELIST.add("torch.distributed.fsdp._fully_shard")
# By default, all functions under these modules are skipped.
# All the other knobs
# (torch_name_rule_map, MOD_INLINELIST, LEGACY_MOD_INLINELIST)
# take precedence over this list; e.g. if a function is in
# MOD_INLINELIST and MOD_SKIPLIST, then it will be inlined.
# See "A note on skip/inline rules" for more details.
#
# The skip is NOT recursive. If a function f1 in a module in MOD_SKIPLIST
# calls out to another function f2 in some other module, then Dynamo's
# behavior (skip/inline) depends on what we've marked f2 as:
# - if f2 is a function in a module in MOD_SKIPLIST, then we skip f2
# - if f2 is a function in a module in MOD_INLINELIST, then we skip f2
# - if f2 is a function in a module in LEGACY_MOD_INLINELIST, then we inline f2
# - if f2 is a function in a module not in any *_LIST, then we inline f2
MOD_SKIPLIST = [
"torch._VF",
"torch.__future__",
"torch.__init__",
"torch._awaits",
"torch._classes",
"torch._compile",
"torch._custom_op",
"torch._custom_ops",
"torch._decomp",
"torch._dispatch",
"torch._dynamo",
"torch._export",
"torch._functorch",
"torch._guards",
"torch._higher_order_ops.effects",
"torch._higher_order_ops.torchbind",
"torch._higher_order_ops.wrap",
"torch._inductor",
"torch._jit_internal",
"torch._lazy",
"torch._library",
"torch._linalg_utils",
"torch._lobpcg",
"torch._logging",
"torch._lowrank",
"torch._meta_registrations",
"torch._namedtensor_internals",
"torch._numpy",
"torch._ops",
"torch._prims",
"torch._prims_common",
"torch._python_dispatcher",
"torch._refs",
"torch._strobelight",
"torch._subclasses",
"torch._tensor",
"torch._tensor_str",
"torch._thread_safe_fork",
"torch._utils",
"torch._utils_internal",
"torch._vmap_internals",
"torch._weights_only_unpickler",
"torch.accelerator",
"torch.amp",
"torch.ao",
"torch.autograd",
"torch.backends",
"torch.compiler",
"torch.contrib",
"torch.cpu",
"torch.cuda",
"torch.distributed",
"torch.distributions",
"torch.export",
"torch.fb",
"torch.fft",
"torch.functional",
"torch.futures",
"torch.fx",
"torch.hub",
"torch.jit",
"torch.library",
"torch.linalg",
"torch.masked",
"torch.monitor",
"torch.mps",
"torch.mtia",
"torch.multiprocessing",
"torch.nested",
"torch.nn",
"torch.onnx",
"torch.overrides",
"torch.package",
"torch.profiler",
"torch.quantization",
"torch.quasirandom",
"torch.random",
"torch.serialization",
"torch.signal",
"torch.sparse",
"torch.special",
"torch.storage",
"torch.testing",
"torch.types",
"torch.utils",
"torch.xpu",
]
assert sorted(set(MOD_SKIPLIST)) == MOD_SKIPLIST
MOD_SKIPLIST = set(MOD_SKIPLIST)
@functools.cache
def get_legacy_mod_inlinelist() -> set[str]:
torch_dir = _module_dir(torch)
if torch_dir is None:
return set()
inlinelist = {
_as_posix_path(torch_dir + m[len("torch.") :].replace(".", "/"))
for m in LEGACY_MOD_INLINELIST
}
return inlinelist
@functools.cache
def get_mod_inlinelist() -> set[str]:
torch_dir = _module_dir(torch)
if torch_dir is None:
return set()
inlinelist = {
_as_posix_path(torch_dir + m[len("torch.") :].replace(".", "/"))
for m in MOD_INLINELIST
}
return inlinelist
@functools.cache
def get_mod_skiplist() -> set[str]:
torch_dir = _module_dir(torch)
if torch_dir is None:
return set()
skiplist = {
_as_posix_path(torch_dir + m[len("torch.") :].replace(".", "/"))
for m in MOD_SKIPLIST
}
return skiplist
# skip some standard python builtin libs
SKIP_DIRS = [
"<frozen importlib",
"<frozen abc",
"<__array_function__ internals>",
_as_posix_path(_config_module.__file__),
"triton/backends",
]
SKIP_DIRS.extend(map(_as_posix_path, filter(None, map(_module_dir, BUILTIN_SKIPLIST))))
SKIP_DIRS_RE = re.compile(r"match nothing^")
# Skip fbcode paths(including torch.package paths) containing
# one of the following strings.
FBCODE_SKIP_DIRS: set[str] = set()
FBCODE_SKIP_DIRS_RE = re.compile(f".*({'|'.join(map(re.escape, FBCODE_SKIP_DIRS))})")
# Remove this after fbcode is fully migrated to tracing through torchrec.
FBCODE_SKIP_TORCHREC_DIRS = {
"torchrec/distributed",
"torchrec/fb/distributed",
"caffe2/torch/fb/sparsenn/pooled_embeddings_modules.py",
}
FBCODE_SKIP_TORCHREC_DIRS_RE = re.compile(
f".*({'|'.join(re.escape(_as_posix_path(d)) for d in FBCODE_SKIP_TORCHREC_DIRS)})"
)
# TODO(yanboliang, anijain2305) - There are a few concerns that we should
# resolve
# 1) Audit if torchrec/distributed is even required in FBCODE_SKIPS_DIR
# 2) To inline just one file but skip others in a directory, we could use
# manual_torch_name_rule_map but this one is hard because FBCODE can add unusual
# names like torch_package.
# So, this is a stop gap solution till then.
FBCODE_INLINE_FILES_IN_SKIPPED_DIRS = {
"torchrec/distributed/types.py",
}
FBCODE_INLINE_FILES_IN_SKIPPED_DIRS_RE = re.compile(
f".*({'|'.join(re.escape(_as_posix_path(d)) for d in FBCODE_INLINE_FILES_IN_SKIPPED_DIRS)})"
)
# torch.optim is a special case,
# we usually want to inline it, but the directory
# structure does not match the module structure
# and we want to skip the functions in optim/lr_scheduler.py
# this has precedence over all other rules in check_file
FORCE_SKIP_FILES = {f"{_module_dir(torch)}optim/lr_scheduler.py"}
def _recompile_re() -> None:
global SKIP_DIRS_RE
SKIP_DIRS_RE = re.compile(
rf"^[^\s<]*({'|'.join(re.escape(_as_posix_path(d)) for d in SKIP_DIRS)})"
)
def add(import_name: str) -> None:
if isinstance(import_name, types.ModuleType):
return add(import_name.__name__)
assert isinstance(import_name, str)
from importlib.util import find_spec
module_spec = find_spec(import_name)
if not module_spec:
return
origin = module_spec.origin
if origin is None:
return
SKIP_DIRS.append(_strip_init_py(origin))
_recompile_re()
@dataclasses.dataclass
class SkipResult:
skipped: bool
reason: Optional[str]
def check_file(filename: Optional[str], is_inlined_call: bool = False) -> SkipResult:
"""Should skip this file?"""
if filename is None:
return SkipResult(True, "filename is None")
filename = _as_posix_path(filename)
if filename in FORCE_SKIP_FILES:
return SkipResult(True, "FORCE_SKIP_FILES")
if any(filename.startswith(d) for d in get_legacy_mod_inlinelist()):
return SkipResult(
False,
"LEGACY_MOD_INLINELIST",
)
if is_inlined_call and is_torch_inline_allowed(filename):
return SkipResult(
False,
"MOD_INLINELIST",
)
if (
is_fbcode()
and FBCODE_SKIP_DIRS
and bool(FBCODE_SKIP_DIRS_RE.match(filename))
and not bool(FBCODE_INLINE_FILES_IN_SKIPPED_DIRS_RE.match(filename))
):
return SkipResult(
True,
"FBCODE_SKIP_DIRS",
)
if (
is_fbcode()
and config.skip_torchrec
and FBCODE_SKIP_TORCHREC_DIRS
and bool(FBCODE_SKIP_TORCHREC_DIRS_RE.match(filename))
and not bool(FBCODE_INLINE_FILES_IN_SKIPPED_DIRS_RE.match(filename))
):
return SkipResult(True, "FBCODE_SKIP_TORCHREC_DIRS")
unittest_dir = _module_dir(unittest)
if (
unittest_dir is not None
and filename.startswith(unittest_dir)
and not torch._dynamo.config.enable_trace_unittest
):
return SkipResult(True, "unittest")
if bool(SKIP_DIRS_RE.match(filename)):
return SkipResult(True, "SKIP_DIRS")
if any(filename.startswith(d) for d in get_mod_skiplist()):
return SkipResult(True, "MOD_SKIPLIST")
return SkipResult(False, "inlined by default")
@dataclasses.dataclass
class FunctionInfo:
py_obj: Optional[object]
name: Optional[str]
filename: str
code: Optional[types.CodeType]
"""
This is the main entry point to determine whether an object (function) should be inlined or skipped.
Let's illustrate the logic with an example:
@torch.compile
def f1(x, y):
......
f2(x, y)
......
def f2(x, y):
......
f3(x, y)
......
def f3(x, y):
......
There are mainly three call sites of check/check_verbose:
* The compile region entrance (like function f1), the corresponding code is located at eval_frame.py.
* When tracing the recursively called functions (like function f2 and f3).
* Dynamo decides inline/skip every time it encounters a new recursively function call, and the call site
is in InliningInstructionTranslator.check_inlineable of symbolic_convert.py.
* If f2 is skipped by Dynamo, when evaluating the frame of f3, Dynamo need the inline/skip check again
and the call site is in catch_errors_wrapper.catch_errors of convert_frame.py.
* For global variables and function arguments, Dynamo needs to decide if they are wrapped as SkipFunctionVariable in builder.py.
`is_inlined_call` is used to indicate if the current function call is inlined (f2 is inlined call if it passes check)
or not (f3 is not inlined call if f2 is skipped). Inside of the `check_verbose` function, there are more rules
to be checked if this `is_inlined_call`.
The reason to have this flag is that if the upper level function call (e.g, f2) is skipped,
we don't want to inline the lower level function call (e.g, f3) by default.
"""
def check_verbose(obj: Any, is_inlined_call: bool = False) -> SkipResult:
if isinstance(
obj,
(
UserFunctionVariable,
UserMethodVariable,
NestedUserFunctionVariable,
LocalGeneratorFunctionVariable,
LocalGeneratorObjectVariable,
),
):
try:
py_obj = obj.get_function()
except NotImplementedError:
py_obj = None
fi = FunctionInfo(py_obj, obj.get_name(), obj.get_filename(), obj.get_code())
elif isinstance(obj, types.CodeType):
fi = FunctionInfo(None, obj.co_name, obj.co_filename, obj)
elif isinstance(obj, (types.FunctionType, types.MethodType)):
filename = getfile(obj)
assert filename is not None
fi = FunctionInfo(
obj,
obj.__name__,
filename,
obj.__code__, # type: ignore[union-attr] # FIXME Add MethodType.__code__ to typeshed
)
else:
filename = getfile(obj)
assert filename is not None
fi = FunctionInfo(obj, None, filename, None)
# Consulte the central trace rules defined in torch._dynamo.trace_rules.
reasons: set[str] = set()
rule = lookup_inner(fi.py_obj, fi.name, fi.filename, is_inlined_call, reasons)
assert rule is not None
if issubclass(
rule,
(
UserFunctionVariable,
LocalGeneratorFunctionVariable,
PolyfilledFunctionVariable,
),
):
return SkipResult(
False,
f"inlined according trace_rules.lookup {reasons.pop()}",
)
elif issubclass(rule, TorchInGraphFunctionVariable):
return SkipResult(
False,
f"registered in torch_obj_rule {reasons.pop()}",
)
else:
assert rule == SkipFunctionVariable, rule
return SkipResult(
True,
f"skipped according trace_rules.lookup {reasons.pop()}",
)
def check(obj: Any, is_inlined_call: bool = False) -> bool:
return check_verbose(obj, is_inlined_call).skipped
# skip common third party libs
for _name in THIRDPARTY_SKIPLIST:
add(_name)
_recompile_re()
def is_torch_inline_allowed(filename: str) -> bool:
return any(filename.startswith(d) for d in get_mod_inlinelist())
@functools.cache
def dynamo_dir() -> Optional[str]:
import torch._dynamo
return _module_dir(torch._dynamo)
def is_torch(filename: str) -> bool:
dynamo_path = dynamo_dir()
if dynamo_path is not None and filename.startswith(dynamo_path):
return False
torch_path = _module_dir(torch)
return torch_path is not None and filename.startswith(torch_path)
"""
Main entry point for looking up the trace rule (the Dynamo variable) for a given callable object.
"""
def lookup_callable(obj: Callable[..., Any]) -> Optional[type[VariableTracker]]:
if not hashable(obj):
return None
# Custom allow/disallow in graph takes precedence over the general lookup.
if is_callable_disallowed(obj):
return SkipFunctionVariable
if is_callable_allowed(obj):
return TorchInGraphFunctionVariable
if is_polyfilled_callable(obj):
return PolyfilledFunctionVariable
if is_builtin_callable(obj):
return BuiltinVariable
return None
"""
Main entry point for looking up the trace rule (the Dynamo variable) for a given function object.
E.g, the lookup result of `torch.sin` is `TorchInGraphFunctionVariable`.
"""
def lookup(obj: Any) -> Optional[type[VariableTracker]]:
return lookup_inner(obj)
# also takes config.dont_skip_tracing into account
def lookup_inner(
obj: Any,
name: Optional[str] = None,
filename: Optional[str] = None,
is_direct_call: bool = True,
reasons: Union[None, set[str]] = None,
) -> Optional[type[VariableTracker]]:
result = _lookup_inner(
obj,
name=name,
filename=filename,
is_direct_call=is_direct_call,
reasons=reasons,
)
# There are still some modules we should absolutely NOT trace into - e.g. most of torch._dynamo,
# as this can result in really weird tracing behaviors.
# Note that if a torch._dynamo function is already not skipped (e.g. functions in external_utils.py),
# then this branch does not apply.
if config.dont_skip_tracing and result is SkipFunctionVariable:
if filename is None:
filename = getfile(obj)
assert filename is not None
filename = _as_posix_path(filename)
torch_dir = _module_dir(torch)
if torch_dir is not None:
dynamo_path = _as_posix_path(torch_dir) + "_dynamo"
if filename.startswith(dynamo_path) and not filename.endswith(
"test_dont_skip_tracing_functions.py"
):
return SkipFunctionVariable
if reasons is not None:
reasons.add(
"Attempted skip but we are ignoring skips due to torch._dynamo.config.dont_skip_tracing"
)
return UserFunctionVariable
return result
def _lookup_inner(
obj: Any,
name: Optional[str] = None,
filename: Optional[str] = None,
is_direct_call: bool = True,
reasons: Optional[set[str]] = None,
) -> Optional[type[VariableTracker]]:
# Step 1: lookup obj's tracing rule in `torch_name_rule_map`.
# The rules defined in `torch_name_rule_map` mainly includes two parts:
# - Manually defined rules for any functions.
# - The list of torch in graph functions.
try:
can_hash = hashable(obj)
except Exception:
can_hash = False
if not can_hash:
if reasons is not None:
reasons.add("obj is not hashable")
return None
if obj is not None:
if is_aten_op_or_tensor_method(obj):
return TorchInGraphFunctionVariable
rule = get_torch_obj_rule_map().get(obj, None)
if rule is not None:
if reasons is not None:
reasons.add("get_torch_obj_rule_map")
return rule
elif name is not None and filename is not None and not is_direct_call:
if name.startswith(TORCH_DYNAMO_RESUME_IN_PREFIX):
rule = get_torch_obj_rule_map().get(
filename + "#" + TORCH_DYNAMO_RESUME_IN_PREFIX, None
)
else:
rule = get_torch_obj_rule_map().get(filename + "#" + name, None)
if rule is not None:
if reasons is not None:
reasons.add("get_torch_obj_rule_map")
return rule
elif name == "<listcomp>":
if reasons is not None:
reasons.add("inlining frame from list comprehension")
return UserFunctionVariable
# Step 2: lookup obj's tracing rule by function name.
if is_direct_call:
if name == "patched_init":
if reasons is not None:
reasons.add("func name is patched_init")
return SkipFunctionVariable
elif name == "__torch_function__" or (
obj and getattr(obj, "__name__", None) == "__torch_function__"
):
if reasons is not None:
reasons.add("func name is __torch_function__")
return UserFunctionVariable
if not is_direct_call:
if name == "__getattr__":
# is_direct_call = False indicates that this is the top-level frame
# being traced (i.e., it is not inlined and not called from
# InliningInstructionTranslator). Tracing __getattr__ at the top
# level is unlikely because we inline it for
# UserDefinedObjectVariable. This scenario occurs only for
# UnspecializedNNModuleVariable, where Dynamo directly calls
# __getattr__ during trace time, generating LOAD_ATTR bytecode
# without going through the underlying __getattr__ data structures.
# When this optimized bytecode is executed, Dynamo is triggered
# again on the __getattr__ call. Therefore, we skip Dynamo tracing
# in this case.
if reasons is not None:
reasons.add(
"Tracing __getattr__ as the top level frame, unsuitable for tracing."
)
return SkipFunctionVariable
# Step 3: lookup obj's tracing rule by filename.
if filename is None:
filename = getfile(obj)
skip_result = check_file(filename, is_direct_call)
if reasons is not None and skip_result.reason is not None:
reasons.add(skip_result.reason)
if skip_result.skipped:
return SkipFunctionVariable
else:
return UserFunctionVariable
def clear_lru_cache() -> None:
torch._dynamo.trace_rules.get_torch_obj_rule_map.cache_clear()
torch._dynamo.trace_rules.get_tensor_method.cache_clear()
torch._dynamo.trace_rules.get_legacy_mod_inlinelist.cache_clear()
torch._dynamo.trace_rules.get_mod_inlinelist.cache_clear()
torch._dynamo.trace_rules.dynamo_dir.cache_clear()
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