# mypy: allow-untyped-defs import _collections_abc import _weakrefset import abc import builtins import collections import contextlib import copy import copyreg import dataclasses import enum import functools import importlib import inspect import linecache import logging import multiprocessing import operator import os import posixpath import random import re import selectors import signal import sys import tempfile import threading import tokenize import traceback import types import typing import unittest import weakref from collections import defaultdict from pathlib import Path from typing import Any, Callable, cast, Dict, List, Optional, Set, Type, Union import torch import torch._inductor.test_operators import torch.distributed import torch.utils._content_store from torch.utils import _config_module from .resume_execution import TORCH_DYNAMO_RESUME_IN_PREFIX from .utils import getfile, hashable, NP_SUPPORTED_MODULES, unwrap_if_wrapper from .variables import ( BuiltinVariable, FunctionalCallVariable, FunctorchHigherOrderVariable, NestedUserFunctionVariable, PolyfilledFunctionVariable, SkipFunctionVariable, TorchInGraphFunctionVariable, UserFunctionVariable, UserMethodVariable, ) np: Optional[types.ModuleType] = None try: import numpy as np except ModuleNotFoundError: pass if typing.TYPE_CHECKING: from .variables.base import VariableTracker """ 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) * 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 = { "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._dynamo.external_utils.is_compiling": TorchInGraphFunctionVariable, "torch.compiler.is_compiling": TorchInGraphFunctionVariable, "torch.compiler.is_dynamo_compiling": TorchInGraphFunctionVariable, "torch.autograd._profiler_enabled": SkipFunctionVariable, "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_from_padded": SkipFunctionVariable, "torch.nested.nested_tensor_from_jagged": 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.Tensor#_make_wrapper_subclass": SkipFunctionVariable, "torch.Tensor#__init__": SkipFunctionVariable, "torch.cuda.set_device": SkipFunctionVariable, "torch.cuda.current_device": SkipFunctionVariable, "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.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, # 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, # everything else "torch._constrain_as_size": UserFunctionVariable, "torch._tensor._convert": UserFunctionVariable, "torch.jit._unwrap_optional": UserFunctionVariable, "torch.backends.mha.get_fastpath_enabled": UserFunctionVariable, "torch._C._functorch._add_batch_dim": TorchInGraphFunctionVariable, "torch._C._functorch._remove_batch_dim": TorchInGraphFunctionVariable, "torch._C._functorch._wrap_for_grad": TorchInGraphFunctionVariable, "torch._C._functorch._unwrap_for_grad": TorchInGraphFunctionVariable, "torch._C._functorch.maybe_current_level": TorchInGraphFunctionVariable, "torch._C._functorch.is_batchedtensor": TorchInGraphFunctionVariable, "torch._dynamo.mark_static": UserFunctionVariable, "torch.fx.experimental.symbolic_shapes.guard_size_oblivious": TorchInGraphFunctionVariable, "torch.cuda._get_device_properties": TorchInGraphFunctionVariable, "torch.utils.hooks.BackwardHook": TorchInGraphFunctionVariable, "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, # 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.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._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", "torch._C._cpu._is_avx512_vnni_supported", "torch._C._cpu._is_avx512_bf16_supported", "torch._C._cpu._is_amx_tile_supported", "torch._C._cpu._init_amx", "torch._C._crash_if_aten_asan", "torch._C._crash_if_csrc_asan", "torch._C._crash_if_csrc_ubsan", "torch._C._crash_if_debug_asserts_fail", "torch._C._crash_if_vptr_ubsan", "torch._C._create_function_from_graph", "torch._C._create_function_from_trace_with_dict", "torch._C._create_function_from_trace", "torch._C._create_graph_by_tracing", "torch._C._create_module_with_type", "torch._C._create_object_with_type", "torch._C._cuda_attach_out_of_memory_observer", "torch._C._cuda_beginAllocateCurrentStreamToPool", "torch._C._cuda_canDeviceAccessPeer", "torch._C._cuda_changeCurrentAllocator", "torch._C._cuda_checkPoolLiveAllocations", "torch._C._cuda_clearCublasWorkspaces", "torch._C._cuda_cudaCachingAllocator_raw_alloc", "torch._C._cuda_cudaCachingAllocator_raw_delete", "torch._C._cuda_cudaCachingAllocator_set_allocator_settings", "torch._C._cuda_cudaHostAllocator", "torch._C._cuda_customAllocator", "torch._C._cuda_emptyCache", "torch._C._cuda_endAllocateCurrentStreamToPool", "torch._C._cuda_exchangeDevice", "torch._C._cuda_get_conv_benchmark_empty_cache", "torch._C._cuda_get_cudnn_benchmark_limit", "torch._C._cuda_get_sync_debug_mode", "torch._C._cuda_getAllocator", "torch._C._cuda_getAllocatorBackend", "torch._C._cuda_getArchFlags", "torch._C._cuda_getCheckpointState", "torch._C._cuda_getCompiledVersion", "torch._C._cuda_getCurrentBlasHandle", "torch._C._cuda_getCurrentRawStream", "torch._C._cuda_getCurrentStream", "torch._C._cuda_getDefaultStream", "torch._C._cuda_getDevice", "torch._C._cuda_getDeviceCount", "torch._C._cuda_hasPrimaryContext", "torch._C._cuda_init", "torch._C._cuda_ipc_collect", "torch._C._cuda_isCurrentStreamCapturing", "torch._C._cuda_isHistoryEnabled", "torch._C._cuda_isInBadFork", "torch._C._cuda_jiterator_compile_and_launch_kernel", "torch._C._cuda_lock_mutex", "torch._C._cuda_maybeExchangeDevice", "torch._C._cuda_memorySnapshot", "torch._C._cuda_memoryStats", "torch._C._cuda_record_memory_history_legacy", "torch._C._cuda_record_memory_history", "torch._C._cuda_releasePool", "torch._C._cuda_resetAccumulatedMemoryStats", "torch._C._cuda_resetPeakMemoryStats", "torch._C._cuda_set_cudnn_benchmark_limit", "torch._C._cuda_set_sync_debug_mode", "torch._C._cuda_setCheckpointPoolState", "torch._C._cuda_setDevice", "torch._C._cuda_setMemoryFraction", "torch._C._cuda_setStream", "torch._C._cuda_sleep", "torch._C._cuda_synchronize", "torch._C._cuda_unlock_mutex", "torch._C._cudnn_set_conv_benchmark_empty_cache", "torch._C._cudnn.getCompileVersion", "torch._C._cudnn.getRuntimeVersion", "torch._C._cudnn.getVersionInt", "torch._C._current_autograd_node", "torch._C._current_graph_task_execution_order", "torch._C._current_graph_task_id", "torch._C._cxx_flags", "torch._C._debug_get_fusion_group_inlining", "torch._C._debug_only_are_vmap_fallback_warnings_enabled", "torch._C._debug_only_display_vmap_fallback_warnings", "torch._C._debug_set_autodiff_subgraph_inlining", "torch._C._debug_set_fusion_group_inlining", "torch._C._demangle", "torch._C._disabled_torch_dispatch_impl", "torch._C._disabled_torch_function_impl", "torch._C._dispatch_call_boxed", "torch._C._dispatch_check_all_invariants", "torch._C._dispatch_check_invariants", "torch._C._dispatch_dump_table", "torch._C._dispatch_dump", "torch._C._dispatch_find_dangling_impls", "torch._C._dispatch_find_schema_or_throw", "torch._C._dispatch_get_all_op_names", "torch._C._dispatch_get_backend_keyset_from_autograd", "torch._C._dispatch_get_registrations_for_dispatch_key", "torch._C._dispatch_has_backend_fallback", "torch._C._dispatch_has_computed_kernel_for_dispatch_key", "torch._C._dispatch_has_kernel_for_any_dispatch_key", "torch._C._dispatch_has_kernel_for_dispatch_key", "torch._C._dispatch_has_kernel", "torch._C._dispatch_is_alias_key", "torch._C._dispatch_is_included_in_alias", "torch._C._dispatch_is_main_interpreter", "torch._C._dispatch_isTensorSubclassLike", "torch._C._dispatch_key_for_device", "torch._C._dispatch_key_name", "torch._C._dispatch_key_parse", "torch._C._dispatch_key_set", "torch._C._dispatch_keys", "torch._C._dispatch_keyset_full_after", "torch._C._dispatch_keyset_full", "torch._C._dispatch_keyset_to_string", "torch._C._dispatch_library", "torch._C._dispatch_num_backends", "torch._C._dispatch_print_registrations_for_dispatch_key", "torch._C._dispatch_pystub", "torch._C._dispatch_set_report_error_callback", "torch._C._dispatch_tls_is_dispatch_key_excluded", "torch._C._dispatch_tls_is_dispatch_key_included", "torch._C._dispatch_tls_local_exclude_set", "torch._C._dispatch_tls_local_include_set", "torch._C._dispatch_tls_set_dispatch_key_excluded", "torch._C._dispatch_tls_set_dispatch_key_included", "torch._C._dist_autograd_init", "torch._C._dump_local_tls_set", "torch._C._dump_upgraders_map", "torch._C._enable_mobile_interface_call_export", "torch._C._enter_dual_level", "torch._C._error_if_any_worker_fails", "torch._C._exit_dual_level", "torch._C._export_operator_list", "torch._C._export_opnames", "torch._C._faulty_agent_init", 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"torch._C._resolve_type", "torch._C._rocm_is_backward_pass", "torch._C._rpc_init", "torch._C._run_emit_module_hook", "torch._C._save_jit_module_to_bytes", "torch._C._save_jit_module", "torch._C._save_mobile_module_to_bytes", "torch._C._save_mobile_module", "torch._C._save_parameters", "torch._C._scatter_out", "torch._C._scatter", "torch._C._select_conv_backend", "torch._C._select_batch_norm_backend", "torch._C._set_autograd_fallback_mode", "torch._C._set_backcompat_broadcast_warn", "torch._C._set_backcompat_keepdim_warn", "torch._C._set_blas_preferred_backend", "torch._C._set_cached_tensors_enabled", "torch._C._set_check_sparse_tensor_invariants", "torch._C._set_conj", "torch._C._set_cublas_allow_bf16_reduced_precision_reduction", "torch._C._set_cublas_allow_fp16_reduced_precision_reduction", "torch._C._set_cublas_allow_tf32", "torch._C._set_cudnn_allow_tf32", "torch._C._set_cudnn_benchmark", "torch._C._set_cudnn_deterministic", "torch._C._set_cudnn_enabled", "torch._C._set_default_dtype", "torch._C._set_default_mobile_cpu_allocator", "torch._C._set_default_tensor_type", "torch._C._set_deterministic_algorithms", "torch._C._set_deterministic_fill_uninitialized_memory", "torch._C._set_dispatch_mode", "torch._C._set_float32_matmul_precision", "torch._C._set_fwd_grad_enabled", "torch._C._set_grad_enabled", "torch._C._set_graph_executor_optimize", "torch._C._set_linalg_preferred_backend", "torch._C._set_meta_in_tls_dispatch_include", "torch._C._set_mkldnn_enabled", "torch._C._set_multithreading_enabled", "torch._C._set_neg", "torch._C._set_nnpack_enabled", "torch._C._set_print_stack_traces_on_fatal_signal", "torch._C._set_qengine", "torch._C._set_sdp_use_flash", "torch._C._set_sdp_use_math", "torch._C._set_math_sdp_allow_fp16_bf16_reduction", "torch._C._set_sdp_use_mem_efficient", "torch._C._set_should_use_format_with_string_table", "torch._C._set_storage_access_error_msg", "torch._C._set_tensor_metadata", "torch._C._set_tracing_state", "torch._C._set_value_trace", "torch._C._set_view_replay_enabled", "torch._C._set_warnAlways", "torch._C._set_worker_pids", "torch._C._set_worker_signal_handlers", "torch._C._should_allow_numbers_as_tensors", "torch._C._show_config", "torch._C._sparse._sparse_addmm", "torch._C._sparse._sparse_log_softmax", "torch._C._sparse._sparse_mm_reduce_impl", "torch._C._sparse._sparse_mm", "torch._C._sparse._sparse_softmax", "torch._C._sparse._spdiags", "torch._C._sparse.sparse_sampled_addmm", "torch._C._special.special_airy_ai", "torch._C._special.special_bessel_j0", "torch._C._special.special_bessel_j1", "torch._C._special.special_bessel_y0", "torch._C._special.special_bessel_y1", "torch._C._special.special_chebyshev_polynomial_t", "torch._C._special.special_chebyshev_polynomial_u", "torch._C._special.special_chebyshev_polynomial_v", "torch._C._special.special_chebyshev_polynomial_w", "torch._C._special.special_digamma", "torch._C._special.special_entr", "torch._C._special.special_erf", "torch._C._special.special_erfc", "torch._C._special.special_erfcx", "torch._C._special.special_erfinv", "torch._C._special.special_exp2", "torch._C._special.special_expit", "torch._C._special.special_expm1", "torch._C._special.special_gammainc", "torch._C._special.special_gammaincc", "torch._C._special.special_gammaln", "torch._C._special.special_hermite_polynomial_h", "torch._C._special.special_hermite_polynomial_he", "torch._C._special.special_i0", "torch._C._special.special_i0e", "torch._C._special.special_i1", "torch._C._special.special_i1e", "torch._C._special.special_laguerre_polynomial_l", "torch._C._special.special_legendre_polynomial_p", "torch._C._special.special_log_ndtr", "torch._C._special.special_log_softmax", "torch._C._special.special_log1p", "torch._C._special.special_logit", "torch._C._special.special_logsumexp", "torch._C._special.special_modified_bessel_i0", "torch._C._special.special_modified_bessel_i1", "torch._C._special.special_modified_bessel_k0", "torch._C._special.special_modified_bessel_k1", "torch._C._special.special_multigammaln", "torch._C._special.special_ndtr", "torch._C._special.special_ndtri", "torch._C._special.special_polygamma", "torch._C._special.special_psi", "torch._C._special.special_round", "torch._C._special.special_scaled_modified_bessel_k0", "torch._C._special.special_scaled_modified_bessel_k1", "torch._C._special.special_shifted_chebyshev_polynomial_t", "torch._C._special.special_shifted_chebyshev_polynomial_u", "torch._C._special.special_shifted_chebyshev_polynomial_v", "torch._C._special.special_shifted_chebyshev_polynomial_w", "torch._C._special.special_sinc", "torch._C._special.special_softmax", "torch._C._special.special_spherical_bessel_j0", "torch._C._special.special_xlog1py", "torch._C._special.special_xlogy", "torch._C._special.special_zeta", "torch._C._stash_obj_in_tls", "torch._C._storage_id", "torch._C._storage_Use_Count", "torch._C._supported_qengines", "torch._C._te.abs", "torch._C._te.acos", "torch._C._te.annotate_input_shapes", "torch._C._te.asin", "torch._C._te.atan", "torch._C._te.atan2", "torch._C._te.ceil", "torch._C._te.Compute", "torch._C._te.Compute2", "torch._C._te.construct_codegen", "torch._C._te.cos", "torch._C._te.cosh", "torch._C._te.erf", "torch._C._te.erfc", "torch._C._te.exp", "torch._C._te.expm1", "torch._C._te.fixup_missing_shape_info", "torch._C._te.floor", "torch._C._te.fmod", "torch._C._te.frac", "torch._C._te.ifThenElse", "torch._C._te.is_graph_compilable", "torch._C._te.isnan", "torch._C._te.lgamma", "torch._C._te.log", "torch._C._te.log10", "torch._C._te.log1p", "torch._C._te.log2", "torch._C._te.lower", "torch._C._te.make_shapes_symbolic", "torch._C._te.pow", "torch._C._te.Reduce", "torch._C._te.remainder", "torch._C._te.remove_graph_output", "torch._C._te.remove_unused_self_argument", "torch._C._te.replace_list_output_with_tuple", "torch._C._te.round", "torch._C._te.rsqrt", "torch._C._te.sigmoid", "torch._C._te.simplify", "torch._C._te.sin", "torch._C._te.sinh", "torch._C._te.sqrt", "torch._C._te.tan", "torch._C._te.tanh", "torch._C._te.trim_graph", "torch._C._te.trunc", "torch._C._tensor_impl_raw_handle", "torch._C._test_only_add_entry_to_op_version_map", "torch._C._test_only_populate_upgraders", "torch._C._test_only_remove_entry_to_op_version_map", "torch._C._test_only_remove_upgraders", "torch._C._to_functionality_key", "torch._C._tracer_set_force_outplace", "torch._C._tracer_set_get_unique_name_fn", "torch._C._tracer_warn_use_python", "torch._C._unset_default_mobile_cpu_allocator", "torch._C._unset_dispatch_mode", "torch._C._valgrind_supported_platform", "torch._C._valgrind_toggle_and_dump_stats", "torch._C._valgrind_toggle", "torch._C._verbose.mkl_set_verbose", "torch._C._verbose.mkldnn_set_verbose", "torch._C._vmapmode_decrement_nesting", "torch._C._vmapmode_increment_nesting", "torch._C._warn_deprecation", "torch._C._warn", "torch._C._will_engine_execute_node", "torch._C._wrap_tensor_impl", "torch._C.fork", "torch._C.get_autocast_cpu_dtype", "torch._C.get_autocast_dtype", "torch._C.get_autocast_gpu_dtype", "torch._C.get_autocast_ipu_dtype", "torch._C.get_autocast_xla_dtype", "torch._C.get_default_dtype", "torch._C.get_num_interop_threads", "torch._C.get_num_threads", "torch._C.import_ir_module_from_buffer", "torch._C.import_ir_module", "torch._C.init_num_threads", "torch._C.is_anomaly_check_nan_enabled", "torch._C.is_anomaly_enabled", "torch._C.is_autocast_cache_enabled", "torch._C.is_autocast_cpu_enabled", "torch._C.is_autocast_enabled", "torch._C.is_autocast_ipu_enabled", "torch._C.is_autocast_xla_enabled", "torch._C.is_grad_enabled", "torch._C.is_inference_mode_enabled", "torch._C.merge_type_from_type_comment", "torch._C.parse_ir", "torch._C.parse_schema", "torch._C.parse_type_comment", "torch._C.read_vitals", "torch._C.set_vital", "torch._C.unify_type_list", "torch._C.vitals_enabled", "torch._C.wait", "torch._cast_Byte", "torch._cast_Char", "torch._cast_Double", "torch._cast_Float", "torch._cast_Half", "torch._cast_Int", "torch._cast_Long", "torch._cast_Short", "torch._choose_qparams_per_tensor", "torch._chunk_cat", "torch._coalesce", "torch._compute_linear_combination", "torch._conj_copy", "torch._conj_physical", "torch._conj", "torch._convert_indices_from_coo_to_csr", "torch._convert_indices_from_csr_to_coo", "torch._convert_weight_to_int4pack", "torch._convolution_mode", "torch._convolution", "torch._copy_from_and_resize", "torch._copy_from", "torch._cslt_compress", "torch._cslt_sparse_mm", "torch._ctc_loss", "torch._cudnn_ctc_loss", "torch._cudnn_init_dropout_state", "torch._cudnn_rnn_flatten_weight", "torch._cudnn_rnn", "torch._cufft_clear_plan_cache", "torch._cufft_get_plan_cache_max_size", "torch._cufft_get_plan_cache_size", "torch._cufft_set_plan_cache_max_size", "torch._cummax_helper", "torch._cummin_helper", "torch._debug_has_internal_overlap", "torch._dim_arange", "torch._dirichlet_grad", "torch._disable_functionalization", "torch._efficientzerotensor", "torch._embedding_bag_forward_only", "torch._embedding_bag", "torch._empty_affine_quantized", "torch._empty_per_channel_affine_quantized", "torch._enable_functionalization", "torch._euclidean_dist", "torch._fake_quantize_learnable_per_channel_affine", "torch._fake_quantize_learnable_per_tensor_affine", "torch._fake_quantize_per_tensor_affine_cachemask_tensor_qparams", "torch._fft_c2c", "torch._fft_c2r", "torch._fft_r2c", "torch._fill_mem_eff_dropout_mask_", "torch._foobar", "torch._foreach_abs_", "torch._foreach_abs", "torch._foreach_acos_", "torch._foreach_acos", "torch._foreach_add_", "torch._foreach_add", "torch._foreach_addcdiv_", "torch._foreach_addcdiv", "torch._foreach_addcmul_", "torch._foreach_addcmul", "torch._foreach_asin_", "torch._foreach_asin", "torch._foreach_atan_", "torch._foreach_atan", "torch._foreach_ceil_", "torch._foreach_ceil", "torch._foreach_clamp_max_", "torch._foreach_clamp_max", "torch._foreach_clamp_min_", "torch._foreach_clamp_min", "torch._foreach_copy_", "torch._foreach_cos_", "torch._foreach_cos", "torch._foreach_cosh_", "torch._foreach_cosh", "torch._foreach_div_", "torch._foreach_div", "torch._foreach_erf_", "torch._foreach_erf", "torch._foreach_erfc_", "torch._foreach_erfc", "torch._foreach_exp_", "torch._foreach_exp", "torch._foreach_expm1_", "torch._foreach_expm1", "torch._foreach_floor_", "torch._foreach_floor", "torch._foreach_frac_", "torch._foreach_frac", "torch._foreach_lerp_", "torch._foreach_lerp", "torch._foreach_lgamma_", "torch._foreach_lgamma", "torch._foreach_log_", "torch._foreach_log", "torch._foreach_log10_", "torch._foreach_log10", "torch._foreach_log1p_", "torch._foreach_log1p", "torch._foreach_log2_", "torch._foreach_log2", "torch._foreach_maximum_", "torch._foreach_maximum", "torch._foreach_minimum_", "torch._foreach_minimum", "torch._foreach_mul_", "torch._foreach_mul", "torch._foreach_neg_", "torch._foreach_neg", "torch._foreach_norm", "torch._foreach_pow_", "torch._foreach_pow", "torch._foreach_reciprocal_", "torch._foreach_reciprocal", "torch._foreach_round_", "torch._foreach_round", "torch._foreach_sigmoid_", "torch._foreach_sigmoid", "torch._foreach_sign_", "torch._foreach_sign", "torch._foreach_sin_", "torch._foreach_sin", "torch._foreach_sinh_", "torch._foreach_sinh", "torch._foreach_sqrt_", "torch._foreach_sqrt", "torch._foreach_sub_", "torch._foreach_sub", "torch._foreach_tan_", "torch._foreach_tan", "torch._foreach_tanh_", "torch._foreach_tanh", "torch._foreach_trunc_", "torch._foreach_trunc", "torch._foreach_zero_", "torch._freeze_functional_tensor", "torch._from_functional_tensor", "torch._functional_assert_async", "torch._functional_sym_constrain_range_for_size", "torch._functional_sym_constrain_range", "torch._functionalize_are_all_mutations_hidden_from_autograd", "torch._functionalize_commit_update", "torch._functionalize_enable_reapply_views", "torch._functionalize_has_data_mutation", "torch._functionalize_has_metadata_mutation", "torch._functionalize_is_multi_output_view", "torch._functionalize_mark_mutation_hidden_from_autograd", "torch._functionalize_replace", "torch._functionalize_sync", "torch._functionalize_was_storage_changed", "torch._fused_adam_", "torch._fused_adamw_", "torch._fused_dropout", "torch._fused_moving_avg_obs_fq_helper", "torch._fused_sdp_choice", "torch._fw_primal_copy", "torch._grid_sampler_2d_cpu_fallback", "torch._has_compatible_shallow_copy_type", "torch._histogramdd_bin_edges", "torch._histogramdd_from_bin_cts", "torch._histogramdd_from_bin_tensors", "torch._index_put_impl_", "torch._indices_copy", "torch._int_mm", "torch._is_all_true", "torch._is_any_true", "torch._is_functional_tensor", "torch._is_zerotensor", "torch._linalg_check_errors", "torch._linalg_det", "torch._linalg_eigh", "torch._linalg_eigvals", "torch._linalg_slogdet", "torch._linalg_solve_ex", "torch._linalg_svd", "torch._log_softmax_backward_data", "torch._log_softmax", "torch._logcumsumexp", "torch._lstm_mps", "torch._lu_with_info", "torch._make_dep_token", "torch._make_dual_copy", "torch._make_dual", "torch._make_per_channel_quantized_tensor", "torch._make_per_tensor_quantized_tensor", "torch._masked_scale", "torch._masked_softmax", "torch._mirror_autograd_meta_to", "torch._mixed_dtypes_linear", "torch._mkldnn_reshape", "torch._mkldnn_transpose_", "torch._mkldnn_transpose", "torch._mps_convolution_transpose", "torch._mps_convolution", "torch._native_batch_norm_legit_no_training", "torch._native_batch_norm_legit", "torch._native_multi_head_attention", "torch._neg_view_copy", "torch._neg_view", "torch._nested_from_padded_and_nested_example", "torch._nested_tensor_from_mask_left_aligned", "torch._nested_tensor_from_tensor_list", "torch._nested_tensor_softmax_with_shape", "torch._nested_view_from_buffer_copy", "torch._nested_view_from_buffer", "torch._nnpack_available", "torch._nnpack_spatial_convolution", "torch._pack_padded_sequence", "torch._pad_packed_sequence", "torch._pin_memory", "torch._prelu_kernel", "torch._propagate_xla_data", "torch._remove_batch_dim", "torch._reshape_alias_copy", "torch._reshape_from_tensor", "torch._resize_output_", "torch._rowwise_prune", "torch._sample_dirichlet", "torch._saturate_weight_to_fp16", "torch._scaled_dot_product_attention_math", "torch._scaled_dot_product_efficient_attention", "torch._scaled_dot_product_flash_attention", "torch._scaled_dot_product_flash_attention_for_cpu", "torch._scaled_dot_product_cudnn_attention", "torch._scaled_mm", "torch._shape_as_tensor", "torch._sobol_engine_draw", "torch._sobol_engine_ff_", "torch._sobol_engine_initialize_state_", "torch._sobol_engine_scramble_", "torch._softmax_backward_data", "torch._softmax", "torch._sparse_broadcast_to_copy", "torch._sparse_broadcast_to", "torch._sparse_csr_prod", "torch._sparse_csr_sum", "torch._sparse_log_softmax_backward_data", "torch._sparse_semi_structured_addmm", "torch._sparse_semi_structured_linear", "torch._sparse_semi_structured_mm", "torch._sparse_softmax_backward_data", "torch._sparse_sparse_matmul", "torch._sparse_sum", "torch._stack", "torch._standard_gamma_grad", "torch._standard_gamma", "torch._test_autograd_multiple_dispatch_view_copy", "torch._test_autograd_multiple_dispatch_view", "torch._test_autograd_multiple_dispatch", "torch._test_check_tensor", "torch._test_functorch_fallback", "torch._test_serialization_subcmul", "torch._to_cpu", "torch._to_functional_tensor", "torch._to_sparse_semi_structured", "torch._transform_bias_rescale_qkv", "torch._transformer_encoder_layer_fwd", "torch._trilinear", "torch._triton_multi_head_attention", "torch._triton_scaled_dot_attention", "torch._unique", "torch._unique2", "torch._unpack_dual", "torch._unsafe_index_put", "torch._unsafe_index", "torch._unsafe_masked_index_put_accumulate", "torch._unsafe_masked_index", "torch._use_cudnn_ctc_loss", "torch._use_cudnn_rnn_flatten_weight", "torch._values_copy", "torch._weight_int4pack_mm", "torch._weight_int8pack_mm", "torch._weight_norm_interface", "torch._weight_norm", "torch.abs_", "torch.abs", "torch.absolute", "torch.acos_", "torch.acos", "torch.acosh_", "torch.acosh", "torch.adaptive_avg_pool1d", "torch.adaptive_max_pool1d", "torch.add", "torch.addbmm", "torch.addcdiv", "torch.addcmul", "torch.addmm", "torch.addmv_", "torch.addmv", "torch.addr", "torch.adjoint", "torch.affine_grid_generator", "torch.alias_copy", "torch.all", "torch.allclose", "torch.alpha_dropout_", "torch.alpha_dropout", "torch.amax", "torch.amin", "torch.aminmax", "torch.angle", "torch.any", "torch.arange", "torch.arccos_", "torch.arccos", "torch.arccosh_", "torch.arccosh", "torch.arcsin_", "torch.arcsin", "torch.arcsinh_", "torch.arcsinh", "torch.arctan_", "torch.arctan", "torch.arctan2", "torch.arctanh_", "torch.arctanh", "torch.argmax", "torch.argmin", "torch.argsort", "torch.argwhere", "torch.as_strided_", "torch.as_strided_copy", "torch.as_strided_scatter", "torch.as_strided", "torch.as_tensor", "torch.asarray", "torch.asin_", "torch.asin", "torch.asinh_", "torch.asinh", "torch.atan_", "torch.atan", "torch.atan2", "torch.atanh_", "torch.atanh", "torch.avg_pool1d", "torch.baddbmm", "torch.bartlett_window", "torch.batch_norm_backward_elemt", "torch.batch_norm_backward_reduce", "torch.batch_norm_elemt", "torch.batch_norm_gather_stats_with_counts", "torch.batch_norm_gather_stats", "torch.batch_norm_stats", "torch.batch_norm_update_stats", "torch.batch_norm", "torch.bernoulli", "torch.bilinear", "torch.binary_cross_entropy_with_logits", "torch.bincount", "torch.binomial", "torch.bitwise_and", "torch.bitwise_left_shift", "torch.bitwise_not", "torch.bitwise_or", "torch.bitwise_right_shift", "torch.bitwise_xor", "torch.blackman_window", "torch.bmm", "torch.broadcast_to", "torch.bucketize", "torch.can_cast", "torch.cat", "torch.ccol_indices_copy", "torch.ceil_", "torch.ceil", "torch.celu_", "torch.celu", "torch.channel_shuffle", "torch.cholesky_inverse", "torch.cholesky_solve", "torch.cholesky", "torch.choose_qparams_optimized", "torch.chunk", "torch.clamp_", "torch.clamp_max_", "torch.clamp_max", "torch.clamp_min_", "torch.clamp_min", "torch.clamp", "torch.clip_", "torch.clip", "torch.clone", "torch.col_indices_copy", "torch.column_stack", "torch.combinations", "torch.complex", "torch.concat", "torch.concatenate", "torch.conj_physical_", "torch.conj_physical", "torch.conj", "torch.constant_pad_nd", "torch.conv_tbc", "torch.conv_transpose1d", "torch.conv_transpose2d", "torch.conv_transpose3d", "torch.conv1d", "torch.conv2d", "torch.conv3d", "torch.convolution", "torch.copysign", "torch.corrcoef", "torch.cos_", "torch.cos", "torch.cosh_", "torch.cosh", "torch.cosine_embedding_loss", "torch.cosine_similarity", "torch.count_nonzero", "torch.cov", "torch.cross", "torch.crow_indices_copy", "torch.ctc_loss", "torch.cudnn_affine_grid_generator", "torch.cudnn_batch_norm", "torch.cudnn_convolution_add_relu", "torch.cudnn_convolution_relu", "torch.cudnn_convolution_transpose", "torch.cudnn_convolution", "torch.cudnn_grid_sampler", "torch.cudnn_is_acceptable", "torch.cummax", "torch.cummin", "torch.cumprod", "torch.cumsum", "torch.cumulative_trapezoid", "torch.deg2rad_", "torch.deg2rad", "torch.dequantize", "torch.det", "torch.detach_", "torch.detach_copy", "torch.detach", "torch.diag_embed", "torch.diag", "torch.diagflat", "torch.diagonal_copy", "torch.diagonal_scatter", "torch.diagonal", "torch.diff", "torch.digamma", "torch.dist", "torch.div", "torch.divide", "torch.dot", "torch.dropout_", "torch.dropout", "torch.dsmm", "torch.dsplit", "torch.dstack", "torch.embedding_bag", "torch.embedding_renorm_", "torch.embedding", "torch.empty_like", "torch.empty_permuted", "torch.empty_quantized", "torch.empty_strided", "torch.empty", "torch.eq", "torch.equal", "torch.erf_", "torch.erf", "torch.erfc_", "torch.erfc", "torch.erfinv", "torch.exp_", "torch.exp", "torch.exp2_", "torch.exp2", "torch.expand_copy", "torch.expm1_", "torch.expm1", "torch.eye", "torch.fake_quantize_per_channel_affine", "torch.fake_quantize_per_tensor_affine", "torch.fbgemm_linear_fp16_weight_fp32_activation", "torch.fbgemm_linear_fp16_weight", "torch.fbgemm_linear_int8_weight_fp32_activation", "torch.fbgemm_linear_int8_weight", "torch.fbgemm_linear_quantize_weight", "torch.fbgemm_pack_gemm_matrix_fp16", "torch.fbgemm_pack_quantized_matrix", "torch.feature_alpha_dropout_", "torch.feature_alpha_dropout", "torch.feature_dropout_", "torch.feature_dropout", "torch.fill_", "torch.fill", "torch.fix_", "torch.fix", "torch.flatten", "torch.flip", "torch.fliplr", "torch.flipud", "torch.float_power", "torch.floor_", "torch.floor_divide", "torch.floor", "torch.fmax", "torch.fmin", "torch.fmod", "torch.frac_", "torch.frac", "torch.frexp", "torch.frobenius_norm", "torch.from_file", "torch.from_numpy", "torch.frombuffer", "torch.full_like", "torch.full", "torch.fused_moving_avg_obs_fake_quant", "torch.gather", "torch.gcd_", "torch.gcd", "torch.ge", "torch.geqrf", "torch.ger", "torch.get_device", "torch.gradient", "torch.greater_equal", "torch.greater", "torch.grid_sampler_2d", "torch.grid_sampler_3d", "torch.grid_sampler", "torch.group_norm", "torch.gru_cell", "torch.gru", "torch.gt", "torch.hamming_window", "torch.hann_window", "torch.hardshrink", "torch.heaviside", "torch.hinge_embedding_loss", "torch.histc", "torch.histogram", "torch.histogramdd", "torch.hsmm", "torch.hsplit", "torch.hspmm", "torch.hstack", "torch.hypot", "torch.i0_", "torch.i0", "torch.igamma", "torch.igammac", "torch.imag", "torch.index_add", "torch.index_copy", "torch.index_fill", "torch.index_put_", "torch.index_put", "torch.index_reduce", "torch.index_select", "torch.indices_copy", "torch.inner", "torch.instance_norm", "torch.int_repr", "torch.inverse", "torch.is_complex", "torch.is_conj", "torch.is_distributed", "torch.is_floating_point", "torch.is_inference", "torch.is_neg", "torch.is_nonzero", "torch.is_same_size", "torch.is_signed", "torch.is_vulkan_available", "torch.isclose", "torch.isfinite", "torch.isin", "torch.isinf", "torch.isnan", "torch.isneginf", "torch.isposinf", "torch.isreal", "torch.istft", "torch.kaiser_window", "torch.kl_div", "torch.kron", "torch.kthvalue", "torch.layer_norm", "torch.lcm_", "torch.lcm", "torch.ldexp_", "torch.ldexp", "torch.le", "torch.lerp", "torch.less_equal", "torch.less", "torch.lgamma", "torch.linspace", "torch.log_", "torch.log_softmax", "torch.log", "torch.log10_", "torch.log10", "torch.log1p_", "torch.log1p", "torch.log2_", "torch.log2", "torch.logaddexp", "torch.logaddexp2", "torch.logcumsumexp", "torch.logdet", "torch.logical_and", "torch.logical_not", "torch.logical_or", "torch.logical_xor", "torch.logit_", "torch.logit", "torch.logspace", "torch.logsumexp", "torch.lstm_cell", "torch.lstm", "torch.lt", "torch.lu_solve", "torch.lu_unpack", "torch.margin_ranking_loss", "torch.masked_fill", "torch.masked_scatter", "torch.masked_select", "torch.matmul", "torch.matrix_exp", "torch.matrix_power", "torch.max_pool1d_with_indices", "torch.max_pool1d", "torch.max_pool2d", "torch.max_pool3d", "torch.max", "torch.maximum", "torch.mean", "torch.median", "torch.min", "torch.minimum", "torch.miopen_batch_norm", "torch.miopen_convolution_add_relu", "torch.miopen_convolution_relu", "torch.miopen_convolution_transpose", "torch.miopen_convolution", "torch.miopen_depthwise_convolution", "torch.miopen_rnn", "torch.mkldnn_adaptive_avg_pool2d", "torch.mkldnn_convolution", "torch.mkldnn_linear_backward_weights", "torch.mkldnn_max_pool2d", "torch.mkldnn_max_pool3d", "torch.mkldnn_rnn_layer", "torch.mm", "torch.mode", "torch.moveaxis", "torch.movedim", "torch.msort", "torch.mul", "torch.multinomial", "torch.multiply", "torch.mv", "torch.mvlgamma", "torch.nan_to_num_", "torch.nan_to_num", "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", ], TorchInGraphFunctionVariable, ) if sys.version_info >= (3, 9): torch_c_binding_in_graph_functions["math.lcm"] = 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 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.autograd_function.custom_function_call_functionalize", "torch._functorch.autograd_function.custom_function_call_grad", "torch._functorch.autograd_function.custom_function_call_vmap_generate_rule", "torch._functorch.autograd_function.custom_function_call_vmap", "torch._functorch.autograd_function.generate_single_level_function", "torch._functorch.autograd_function.get_tangents_in_dims", "torch._functorch.autograd_function.has_overriden_vmap_rule", "torch._functorch.autograd_function.reductify_leaf", "torch._functorch.autograd_function.reductify", "torch._functorch.autograd_function.validate_vmap_returns_tuple_of_two_elements", "torch._functorch.autograd_function.vmapify_autograd_function", "torch._functorch.autograd_function.wrap_outputs_maintaining_identity", "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._vjp_treespec_compare", "torch._functorch.eager_transforms._set_tensor_requires_grad", "torch._functorch.eager_transforms._jvp_treespec_compare", "torch._functorch.eager_transforms._linearize_treespec_compare", "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", 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"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_device", "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.typename", "torch.unique_consecutive", "torch.use_deterministic_algorithms", ], 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.lru_cache(None) 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: obj = _module_dir(torch) + k[len("torch/") :] if obj is not None: 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): 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): 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.lru_cache(None) def get_tensor_method(): s = set() for name in dir(torch.Tensor): method = getattr(torch.Tensor, name) if isinstance( method, (types.MethodDescriptorType, types.WrapperDescriptorType) ): s.add(method) return frozenset(s) """ Return if a torch object is ATen op or torch.Tensor method. """ def is_aten_op_or_tensor_method(obj): 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): self() # lazy init assert self.function_names is not None return self.function_names.get(idx, default) def add(self, idx: int): function_ids = self() # lazy init function_ids.add(idx) def remove(self, idx: int): 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 _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(functools.reduce): "functools.reduce", id(copy.deepcopy): "copy.deepcopy", } ) return rv @FunctionIdSet def _numpy_function_ids() -> Dict[int, str]: rv = {} for mod in NP_SUPPORTED_MODULES: rv.update( { id(v): f"{mod.__name__}.{k}" for k, v in mod.__dict__.items() if callable(v) and (getattr(v, "__module__", None) or mod.__name__) == mod.__name__ } ) 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) -> bool: _maybe_init_lazy_module(obj) return id(obj) in _allowed_callable_ids def is_callable_disallowed(obj) -> bool: _maybe_init_lazy_module(obj) return id(obj) in _disallowed_callable_ids def is_forbidden(obj) -> bool: _maybe_init_lazy_module(obj) return inspect.getattr_static(obj, "_dynamo_forbidden", False) def is_builtin_callable(obj) -> 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) -> bool: return id(obj) in _builtin_constant_ids def is_numpy(obj) -> 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) -> bool: if np is None: return False return isinstance(obj, np.dtype) def is_numpy_type_info(obj) -> bool: if np is None: return False return isinstance(obj, (np.finfo, np.iinfo)) BUILTIN_SKIPLIST = ( abc, collections, contextlib, copy, copyreg, dataclasses, enum, functools, importlib, inspect, linecache, logging, multiprocessing, operator, posixpath, random, re, selectors, signal, tempfile, threading, tokenize, torch, # torch/* is skipped by default unless specified in FUNC_INLINELIST or MOD_INLINELIST traceback, types, typing, unittest, weakref, _collections_abc, _weakrefset, ) # 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", "omegaconf", "onnx", "onnxruntime", "onnx_tf", "pandas", "sklearn", "tabulate", "tensorflow", "tensorrt", "torch2trt", "tqdm", "tree", "tvm", "xarray", ) def _as_posix_path(path): 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): # TODO: Once we require py3.9 use removesuffix instead. suffix = "__init__.py" if s.endswith(suffix): s = s[: -len(suffix)] return _as_posix_path(s) def _module_dir(m: types.ModuleType): # 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. LEGACY_MOD_INLINELIST = { "torch._dynamo.external_utils", "torch._export.db.examples", "torch._export.wrappers", "torch._functorch.apis", "torch._functorch.deprecated", "torch._higher_order_ops.cond", "torch._higher_order_ops.while_loop", "torch._higher_order_ops.associative_scan", "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", "torch.optim", } 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 torch._dynamo.config.skip_fsdp_hooks: LEGACY_MOD_INLINELIST.add("torch.distributed._composable.fsdp") # 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. MOD_INLINELIST = [ "torch._decomp", "torch._dynamo._trace_wrapped_higher_order_op", "torch._dynamo.comptime", "torch._dynamo.polyfills", "torch._functorch.autograd_function", "torch._functorch.eager_transforms", "torch._functorch.functional_call", "torch._functorch.vmap", "torch._higher_order_ops.associative_scan", "torch._higher_order_ops.strict_mode", "torch._higher_order_ops.while_loop", "torch._inductor.test_operators", "torch._library.autograd", "torch._library.custom_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.fx._pytree", "torch.fx._symbolic_trace", "torch.fx.experimental.proxy_tensor", "torch.fx.passes.shape_prop", "torch.nn", "torch.overrides", "torch.random", "torch.sparse", "torch.testing", "torch.utils._content_store", "torch.utils._contextlib", "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 torch._dynamo.config.skip_fsdp_hooks: MOD_INLINELIST.add("torch.distributed._composable.fsdp") @functools.lru_cache(None) def get_legacy_mod_inlinelist(): inlinelist = { _as_posix_path(_module_dir(torch) + m[len("torch.") :].replace(".", "/")) for m in LEGACY_MOD_INLINELIST } return inlinelist @functools.lru_cache(None) def get_mod_inlinelist(): inlinelist = { _as_posix_path(_module_dir(torch) + m[len("torch.") :].replace(".", "/")) for m in MOD_INLINELIST } return inlinelist # skip some standard python builtin libs SKIP_DIRS = [ "", _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^") is_fbcode = importlib.import_module("torch._inductor.config").is_fbcode() # 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", "trochrec/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(): 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): 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, is_inlined_call=False): """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 torch._dynamo.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") if bool(SKIP_DIRS_RE.match(filename)): return SkipResult(True, "SKIP_DIRS") else: 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 correspoinding code is located at eval_frame.py. * When tracing the recursively called functions (like function f2 and f3). * Dynamo decides inline/skip everytime 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, is_inlined_call=False): if isinstance( obj, (UserFunctionVariable, UserMethodVariable, NestedUserFunctionVariable) ): 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)): fi = FunctionInfo( obj, obj.__name__, getfile(obj), obj.__code__ # type: ignore[union-attr] # FIXME Add MethodType.__code__ to typeshed ) else: fi = FunctionInfo(obj, None, getfile(obj), 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) if issubclass(rule, (UserFunctionVariable, PolyfilledFunctionVariable)): return SkipResult( False, f"inlined according trace_rules.lookup {reasons.pop()}", ) else: assert rule == SkipFunctionVariable, rule return SkipResult( True, f"skipped according trace_rules.lookup {reasons.pop()}", ) def check(obj, is_inlined_call=False): 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): return any(filename.startswith(d) for d in get_mod_inlinelist()) @functools.lru_cache(None) def dynamo_dir(): import torch._dynamo return _module_dir(torch._dynamo) def is_torch(filename): if filename.startswith(dynamo_dir()): return False return filename.startswith(_module_dir(torch)) """ Main entry point for looking up the trace rule (the Dynamo variable) for a given callable object. """ def lookup_callable(obj): 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_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): return lookup_inner(obj) def lookup_inner( obj, name=None, filename=None, is_direct_call=True, reasons: Union[None, Set[str]] = None, ): # 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 # 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__": 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: reasons.add(skip_result.reason) if skip_result.skipped: return SkipFunctionVariable else: return UserFunctionVariable def clear_lru_cache(): 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()