| # 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 itertools |
| 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 typing import Any, Callable, cast, Dict, List, Optional, Set, Union |
| |
| np: Optional[types.ModuleType] = None |
| try: |
| import numpy as np |
| except ModuleNotFoundError: |
| pass |
| |
| import torch |
| import torch._inductor.test_operators |
| import torch.distributed |
| import torch.utils._content_store |
| from ..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, |
| FunctorchHigherOrderVariable, |
| NestedUserFunctionVariable, |
| SkipFunctionVariable, |
| TorchInGraphFunctionVariable, |
| UserFunctionVariable, |
| UserMethodVariable, |
| ) |
| |
| |
| 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._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, |
| # 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, |
| # |
| "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_cpu_support_avx2", |
| "torch._C._cpu._is_cpu_support_avx512", |
| "torch._C._cpu._is_cpu_support_avx512_vnni", |
| "torch._C._cpu._is_cpu_support_amx_tile", |
| "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", |
| "torch._C._fft.fft_fft", |
| "torch._C._fft.fft_fft2", |
| "torch._C._fft.fft_fftfreq", |
| "torch._C._fft.fft_fftn", |
| "torch._C._fft.fft_fftshift", |
| "torch._C._fft.fft_hfft", |
| "torch._C._fft.fft_hfft2", |
| "torch._C._fft.fft_hfftn", |
| "torch._C._fft.fft_ifft", |
| "torch._C._fft.fft_ifft2", |
| "torch._C._fft.fft_ifftn", |
| "torch._C._fft.fft_ifftshift", |
| "torch._C._fft.fft_ihfft", |
| "torch._C._fft.fft_ihfft2", |
| "torch._C._fft.fft_ihfftn", |
| "torch._C._fft.fft_irfft", |
| "torch._C._fft.fft_irfft2", |
| "torch._C._fft.fft_irfftn", |
| "torch._C._fft.fft_rfft", |
| "torch._C._fft.fft_rfft2", |
| "torch._C._fft.fft_rfftfreq", |
| "torch._C._fft.fft_rfftn", |
| "torch._C._free_And_Remove_DeleterFn", |
| "torch._C._freeze_module", |
| "torch._C._from_dlpack", |
| "torch._C._functionality_to_backend_keys", |
| "torch._C._functionalization_reapply_views_tls", |
| "torch._C._fuse_to_static_module", |
| "torch._C._gather_out", |
| "torch._C._gather", |
| "torch._C._generate_upgraders_graph", |
| "torch._C._get_autograd_fallback_mode", |
| "torch._C._get_backcompat_broadcast_warn", |
| "torch._C._get_backcompat_keepdim_warn", |
| "torch._C._get_blas_preferred_backend", |
| "torch._C._get_caught_jit_exception_class_name", |
| "torch._C._get_caught_jit_exception_original_msg", |
| "torch._C._get_constant_bool_symnode", |
| "torch._C._get_cpp_backtrace", |
| "torch._C._get_cpu_capability", |
| "torch._C._get_cublas_allow_bf16_reduced_precision_reduction", |
| "torch._C._get_cublas_allow_fp16_reduced_precision_reduction", |
| "torch._C._get_cublas_allow_tf32", |
| "torch._C._get_cudnn_allow_tf32", |
| "torch._C._get_cudnn_benchmark", |
| "torch._C._get_cudnn_deterministic", |
| "torch._C._get_cudnn_enabled", |
| "torch._C._get_custom_class_python_wrapper", |
| "torch._C._get_default_device", |
| "torch._C._get_deterministic_algorithms_warn_only", |
| "torch._C._get_deterministic_algorithms", |
| "torch._C._get_deterministic_fill_uninitialized_memory", |
| "torch._C._get_dispatch_mode", |
| "torch._C._get_dispatch_stack_at", |
| "torch._C._get_file_format", |
| "torch._C._get_flash_sdp_enabled", |
| "torch._C._get_float32_matmul_precision", |
| "torch._C._get_function_stack_at", |
| "torch._C._get_graph_executor_optimize", |
| "torch._C._get_linalg_preferred_backend", |
| "torch._C._get_math_sdp_enabled", |
| "torch._C._get_max_operator_version", |
| "torch._C._get_mem_efficient_sdp_enabled", |
| "torch._C._get_mkldnn_enabled", |
| "torch._C._get_cudnn_sdp_enabled", |
| "torch._C._set_sdp_use_cudnn", |
| "torch._C._get_mobile_model_contained_types_from_buffer", |
| "torch._C._get_mobile_model_contained_types", |
| "torch._C._get_model_bytecode_version_from_buffer", |
| "torch._C._get_model_bytecode_version", |
| "torch._C._get_model_extra_files_from_buffer", |
| "torch._C._get_model_extra_files", |
| "torch._C._get_model_ops_and_info_from_buffer", |
| "torch._C._get_model_ops_and_info", |
| "torch._C._get_module_info_from_flatbuffer", |
| "torch._C._get_nnpack_enabled", |
| "torch._C._get_obj_in_tls", |
| "torch._C._get_operation_overload", |
| "torch._C._get_operator_version_map", |
| "torch._C._get_privateuse1_backend_name", |
| "torch._C._get_qengine", |
| "torch._C._get_schema", |
| "torch._C._get_nested_int", |
| "torch._C._get_tensor_metadata", |
| "torch._C._get_tracing_state", |
| "torch._C._get_upgrader_ranges", |
| "torch._C._get_upgraders_entry_map", |
| "torch._C._get_upgraders_map_size", |
| "torch._C._get_value_trace", |
| "torch._C._get_version_calculator_flag", |
| "torch._C._get_warnAlways", |
| "torch._C._graph_pool_handle", |
| "torch._C._group_tensors_by_device_and_dtype", |
| "torch._C._hack_do_not_use_clone_module_with_class", |
| "torch._C._has_distributed", |
| "torch._C._has_Standard_Deleter", |
| "torch._C._has_storage", |
| "torch._C._has_tensorexpr_cpp_tests", |
| "torch._C._run_tensorexpr_cpp_tests", |
| "torch._C._has_torch_function_unary", |
| "torch._C._has_torch_function_variadic", |
| "torch._C._has_torch_function", |
| "torch._C._import_ir_module_from_package", |
| "torch._C._increment_version", |
| "torch._C._infer_size", |
| "torch._C._init_names", |
| "torch._C._initExtension", |
| "torch._C._is_alias_of", |
| "torch._C._is_any_autocast_enabled", |
| "torch._C._is_cached_tensor", |
| "torch._C._is_fwd_grad_enabled", |
| "torch._C._is_key_in_tls", |
| "torch._C._is_multithreading_enabled", |
| "torch._C._is_torch_function_enabled", |
| "torch._C._is_torch_function_mode_enabled", |
| "torch._C._is_tracing", |
| "torch._C._is_view_replay_enabled", |
| "torch._C._is_xnnpack_enabled", |
| "torch._C._itt.is_available", |
| "torch._C._itt.mark", |
| "torch._C._itt.rangePop", |
| "torch._C._itt.rangePush", |
| "torch._C._ivalue_debug_python_object", |
| "torch._C._ivalue_tags_match", |
| "torch._C._jit_assert_is_instance", |
| "torch._C._jit_can_fuse_on_cpu_legacy", |
| "torch._C._jit_can_fuse_on_cpu", |
| "torch._C._jit_can_fuse_on_gpu", |
| "torch._C._jit_cat_wo_conditionals", |
| "torch._C._jit_check_alias_annotation", |
| "torch._C._jit_clear_class_registry", |
| "torch._C._jit_debug_fuser_num_cached_kernel_specs", |
| "torch._C._jit_debug_module_iterators", |
| "torch._C._jit_decay_packed_param_input_types", |
| "torch._C._jit_decomposition_graph_for_node", |
| "torch._C._jit_differentiate", |
| "torch._C._jit_erase_non_input_shape_information", |
| "torch._C._jit_flatten", |
| "torch._C._jit_fuser_get_fused_kernel_code", |
| "torch._C._jit_get_all_schemas", |
| "torch._C._jit_get_custom_class_schemas", |
| "torch._C._jit_get_emit_hooks", |
| "torch._C._jit_get_inline_everything_mode", |
| "torch._C._jit_get_logging_option", |
| "torch._C._jit_get_num_profiled_runs", |
| "torch._C._jit_get_operation", |
| "torch._C._jit_get_schemas_for_operator", |
| "torch._C._jit_get_te_cuda_pointwise_block_count", |
| "torch._C._jit_get_te_cuda_pointwise_block_size", |
| "torch._C._jit_get_te_cuda_pointwise_loop_levels", |
| "torch._C._jit_get_te_generate_block_code", |
| "torch._C._jit_get_te_must_use_llvm_cpu", |
| "torch._C._jit_get_tracer_state_warn", |
| "torch._C._jit_has_cpp_tests", |
| "torch._C._jit_init", |
| "torch._C._jit_interpret_graph", |
| "torch._C._jit_is_onnx_log_enabled", |
| "torch._C._jit_is_script_object", |
| "torch._C._jit_llga_enabled", |
| "torch._C._jit_nvfuser_can_be_enabled", |
| "torch._C._jit_nvfuser_clear_comparison_callback", |
| "torch._C._jit_nvfuser_enabled", |
| "torch._C._jit_nvfuser_horizontal_mode", |
| "torch._C._jit_nvfuser_set_comparison_callback", |
| "torch._C._jit_nvfuser_single_node_mode", |
| "torch._C._jit_object_is_non_holding", |
| "torch._C._jit_onnx_convert_pattern_from_subblock", |
| "torch._C._jit_onnx_create_full_scope_name", |
| "torch._C._jit_onnx_list_model_parameters", |
| "torch._C._jit_onnx_log", |
| "torch._C._jit_opt_conditionals", |
| "torch._C._jit_override_can_fuse_on_cpu_legacy", |
| "torch._C._jit_override_can_fuse_on_cpu", |
| "torch._C._jit_override_can_fuse_on_gpu", |
| "torch._C._jit_pass_autocast", |
| "torch._C._jit_pass_batch_mm", |
| "torch._C._jit_pass_canonicalize_graph_fuser_ops", |
| "torch._C._jit_pass_canonicalize", |
| "torch._C._jit_pass_complete_shape_analysis", |
| "torch._C._jit_pass_concat_frozen_linear", |
| "torch._C._jit_pass_constant_loop_unrolling", |
| "torch._C._jit_pass_constant_pooling", |
| "torch._C._jit_pass_constant_propagation_immutable_types", |
| "torch._C._jit_pass_constant_propagation", |
| "torch._C._jit_pass_convert_frozen_ops_to_mkldnn", |
| "torch._C._jit_pass_create_autodiff_subgraphs", |
| "torch._C._jit_pass_create_functional_graphs", |
| "torch._C._jit_pass_cse", |
| "torch._C._jit_pass_custom_pattern_based_rewrite_graph", |
| "torch._C._jit_pass_custom_pattern_based_rewrite", |
| "torch._C._jit_pass_dbr_quant_remove_redundant_aliases", |
| "torch._C._jit_pass_dce_allow_deleting_nodes_with_side_effects", |
| "torch._C._jit_pass_dce", |
| "torch._C._jit_pass_decompose_ops", |
| "torch._C._jit_pass_dedup_module_uses", |
| "torch._C._jit_pass_erase_number_types", |
| "torch._C._jit_pass_erase_shape_information", |
| "torch._C._jit_pass_filter_non_tensor_arguments", |
| "torch._C._jit_pass_fixup_onnx_controlflow_node", |
| "torch._C._jit_pass_fold_convbn", |
| "torch._C._jit_pass_fold_frozen_conv_add_or_sub", |
| "torch._C._jit_pass_fold_frozen_conv_bn", |
| "torch._C._jit_pass_fold_frozen_conv_mul_or_div", |
| "torch._C._jit_pass_fold_frozen_linear_bn", |
| "torch._C._jit_pass_fold_prepacking_ops", |
| "torch._C._jit_pass_functional_to_inplace_activation", |
| "torch._C._jit_pass_fuse_add_relu", |
| "torch._C._jit_pass_fuse_addmm", |
| "torch._C._jit_pass_fuse_clamp_w_prepacked_linear_conv", |
| "torch._C._jit_pass_fuse_frozen_conv_add_relu", |
| "torch._C._jit_pass_fuse_linear", |
| "torch._C._jit_pass_fuse_quantized_add_relu", |
| "torch._C._jit_pass_fuse_tensorexprs", |
| "torch._C._jit_pass_fuse", |
| "torch._C._jit_pass_inline_fork_wait", |
| "torch._C._jit_pass_inline_functional_graphs", |
| "torch._C._jit_pass_inline", |
| "torch._C._jit_pass_inplace_to_functional_activation", |
| "torch._C._jit_pass_insert_observer_method_for_ondevice_ptq", |
| "torch._C._jit_pass_insert_observers", |
| "torch._C._jit_pass_insert_prepack_unpack", |
| "torch._C._jit_pass_insert_prepacked_ops", |
| "torch._C._jit_pass_insert_quant_dequant_for_ondevice_ptq", |
| "torch._C._jit_pass_insert_quant_dequant", |
| "torch._C._jit_pass_integer_value_refinement", |
| "torch._C._jit_pass_lint", |
| "torch._C._jit_pass_loop_unrolling", |
| "torch._C._jit_pass_lower_all_tuples", |
| "torch._C._jit_pass_lower_graph", |
| "torch._C._jit_pass_metal_fold_prepacking_ops", |
| "torch._C._jit_pass_metal_fuse_clamp_w_prepacked_conv", |
| "torch._C._jit_pass_metal_insert_prepacked_ops", |
| "torch._C._jit_pass_metal_optimize_for_mobile", |
| "torch._C._jit_pass_onnx_assign_output_shape", |
| "torch._C._jit_pass_onnx_assign_scoped_names_for_node_and_value", |
| "torch._C._jit_pass_onnx_autograd_function_process", |
| "torch._C._jit_pass_onnx_block", |
| "torch._C._jit_pass_onnx_cast_all_constant_to_floating", |
| "torch._C._jit_pass_onnx_clear_scope_records", |
| "torch._C._jit_pass_onnx_constant_fold", |
| "torch._C._jit_pass_onnx_deduplicate_initializers", |
| "torch._C._jit_pass_onnx_eliminate_unused_items", |
| "torch._C._jit_pass_onnx_eval_peephole", |
| "torch._C._jit_pass_onnx_function_extraction", |
| "torch._C._jit_pass_onnx_function_substitution", |
| "torch._C._jit_pass_onnx_graph_shape_type_inference", |
| "torch._C._jit_pass_onnx_lint", |
| "torch._C._jit_pass_onnx_node_shape_type_inference", |
| "torch._C._jit_pass_onnx_peephole", |
| "torch._C._jit_pass_onnx_preprocess_caffe2", |
| "torch._C._jit_pass_onnx_preprocess", |
| "torch._C._jit_pass_onnx_quantization_insert_permutes", |
| "torch._C._jit_pass_onnx_remove_inplace_ops_for_onnx", |
| "torch._C._jit_pass_onnx_remove_print", |
| "torch._C._jit_pass_onnx_scalar_type_analysis", |
| "torch._C._jit_pass_onnx_set_dynamic_input_shape", |
| "torch._C._jit_pass_onnx_track_scope_attributes", |
| "torch._C._jit_pass_onnx_unpack_quantized_weights", |
| "torch._C._jit_pass_onnx", |
| "torch._C._jit_pass_optimize_for_inference", |
| "torch._C._jit_pass_optimize_for_mobile", |
| "torch._C._jit_pass_optimize_frozen_graph", |
| "torch._C._jit_pass_pattern_based_rewrite", |
| "torch._C._jit_pass_peephole_list_idioms", |
| "torch._C._jit_pass_peephole", |
| "torch._C._jit_pass_prepare_division_for_onnx", |
| "torch._C._jit_pass_propagate_device", |
| "torch._C._jit_pass_propagate_dtype", |
| "torch._C._jit_pass_propagate_shapes_on_graph_and_build_compute", |
| "torch._C._jit_pass_propagate_shapes_on_graph", |
| "torch._C._jit_pass_quant_finalize_for_ondevice_ptq", |
| "torch._C._jit_pass_quant_finalize", |
| "torch._C._jit_pass_quant_fusion", |
| "torch._C._jit_pass_refine_integer_values", |
| "torch._C._jit_pass_refine_tuple_types", |
| "torch._C._jit_pass_remove_dropout", |
| "torch._C._jit_pass_remove_expands", |
| "torch._C._jit_pass_remove_inplace_ops", |
| "torch._C._jit_pass_remove_mutation", |
| "torch._C._jit_pass_replace_old_ops_with_upgraders", |
| "torch._C._jit_pass_replicate_dequantize", |
| "torch._C._jit_pass_run_decompositions", |
| "torch._C._jit_pass_specialize_autogradzero", |
| "torch._C._jit_pass_swap_functional_linear", |
| "torch._C._jit_pass_transform_conv1d_to_conv2d", |
| "torch._C._jit_pass_transpose_frozen_linear", |
| "torch._C._jit_pass_vulkan_fold_prepacking_ops", |
| "torch._C._jit_pass_vulkan_fuse_clamp_w_prepacked_conv", |
| "torch._C._jit_pass_vulkan_insert_prepacked_ops", |
| "torch._C._jit_pass_vulkan_optimize_for_mobile", |
| "torch._C._jit_register_decomposition_for_schema", |
| "torch._C._jit_register_shape_compute_graph_for_node", |
| "torch._C._jit_resolve_packet", |
| "torch._C._jit_run_cpp_tests", |
| "torch._C._jit_script_class_compile", |
| "torch._C._jit_script_compile_overload", |
| "torch._C._jit_script_compile", |
| "torch._C._jit_script_interface_compile", |
| "torch._C._jit_set_autocast_mode", |
| "torch._C._jit_set_bailout_depth", |
| "torch._C._jit_set_emit_hooks", |
| "torch._C._jit_set_fusion_strategy", |
| "torch._C._jit_set_inline_everything_mode", |
| "torch._C._jit_set_llga_enabled", |
| "torch._C._jit_set_logging_option", |
| "torch._C._jit_set_logging_stream", |
| "torch._C._jit_set_num_profiled_runs", |
| "torch._C._jit_set_nvfuser_enabled", |
| "torch._C._jit_set_nvfuser_guard_mode", |
| "torch._C._jit_set_nvfuser_horizontal_mode", |
| "torch._C._jit_set_nvfuser_single_node_mode", |
| "torch._C._jit_set_nvfuser_skip_node_kind", |
| "torch._C._jit_set_onnx_log_enabled", |
| "torch._C._jit_set_onnx_log_output_stream", |
| "torch._C._jit_set_profiling_executor", |
| "torch._C._jit_set_profiling_mode", |
| "torch._C._jit_set_symbolic_shapes_test_mode", |
| "torch._C._jit_set_te_cuda_pointwise_block_count", |
| "torch._C._jit_set_te_cuda_pointwise_block_size", |
| "torch._C._jit_set_te_cuda_pointwise_loop_levels", |
| "torch._C._jit_set_te_generate_block_code", |
| "torch._C._jit_set_te_must_use_llvm_cpu", |
| "torch._C._jit_set_texpr_dynamic_shape_enabled", |
| "torch._C._jit_set_texpr_fuser_enabled", |
| "torch._C._jit_set_texpr_reductions_enabled", |
| "torch._C._jit_set_tracer_state_warn", |
| "torch._C._jit_set_utf8_decoding_ignore", |
| "torch._C._jit_shape_compute_graph_for_node", |
| "torch._C._jit_symbolic_shapes_test_mode_enabled", |
| "torch._C._jit_texpr_dynamic_shape_enabled", |
| "torch._C._jit_texpr_fallback_allowed", |
| "torch._C._jit_texpr_fuser_enabled", |
| "torch._C._jit_texpr_reductions_enabled", |
| "torch._C._jit_texpr_set_fallback_allowed", |
| "torch._C._jit_to_backend_selective", |
| "torch._C._jit_to_backend", |
| "torch._C._jit_to_static_module", |
| "torch._C._jit_trace_graph", |
| "torch._C._jit_trace_module", |
| "torch._C._jit_tree_views.FalseLiteral", |
| "torch._C._jit_tree_views.NoneLiteral", |
| "torch._C._jit_tree_views.TrueLiteral", |
| "torch._C._jit_try_infer_type", |
| "torch._C._jit_unflatten", |
| "torch._C._last_executed_optimized_graph", |
| "torch._C._len_torch_dispatch_stack", |
| "torch._C._len_torch_function_stack", |
| "torch._C._linalg._linalg_eigvals", |
| "torch._C._linalg.linalg_cholesky_ex", |
| "torch._C._linalg.linalg_cholesky", |
| "torch._C._linalg.linalg_cond", |
| "torch._C._linalg.linalg_cross", |
| "torch._C._linalg.linalg_det", |
| "torch._C._linalg.linalg_diagonal", |
| "torch._C._linalg.linalg_eig", |
| "torch._C._linalg.linalg_eigh", |
| "torch._C._linalg.linalg_eigvals", |
| "torch._C._linalg.linalg_eigvalsh", |
| "torch._C._linalg.linalg_householder_product", |
| "torch._C._linalg.linalg_inv_ex", |
| "torch._C._linalg.linalg_inv", |
| "torch._C._linalg.linalg_ldl_factor_ex", |
| "torch._C._linalg.linalg_ldl_factor", |
| "torch._C._linalg.linalg_ldl_solve", |
| "torch._C._linalg.linalg_lstsq", |
| "torch._C._linalg.linalg_lu_factor_ex", |
| "torch._C._linalg.linalg_lu_factor", |
| "torch._C._linalg.linalg_lu_solve", |
| "torch._C._linalg.linalg_lu", |
| "torch._C._linalg.linalg_matmul", |
| "torch._C._linalg.linalg_matrix_exp", |
| "torch._C._linalg.linalg_matrix_norm", |
| "torch._C._linalg.linalg_matrix_power", |
| "torch._C._linalg.linalg_matrix_rank", |
| "torch._C._linalg.linalg_multi_dot", |
| "torch._C._linalg.linalg_norm", |
| "torch._C._linalg.linalg_pinv", |
| "torch._C._linalg.linalg_qr", |
| "torch._C._linalg.linalg_slogdet", |
| "torch._C._linalg.linalg_solve_ex", |
| "torch._C._linalg.linalg_solve_triangular", |
| "torch._C._linalg.linalg_solve", |
| "torch._C._linalg.linalg_svd", |
| "torch._C._linalg.linalg_svdvals", |
| "torch._C._linalg.linalg_tensorinv", |
| "torch._C._linalg.linalg_tensorsolve", |
| "torch._C._linalg.linalg_vander", |
| "torch._C._linalg.linalg_vecdot", |
| "torch._C._linalg.linalg_vector_norm", |
| "torch._C._llvm_enabled", |
| "torch._C._load_for_lite_interpreter_from_buffer", |
| "torch._C._load_for_lite_interpreter", |
| "torch._C._load_jit_module_from_bytes", |
| "torch._C._load_jit_module_from_file", |
| "torch._C._load_mobile_module_from_bytes", |
| "torch._C._load_mobile_module_from_file", |
| "torch._C._log_api_usage_metadata", |
| "torch._C._log_api_usage_once", |
| "torch._C._logging_set_logger", |
| "torch._C._meta_in_tls_dispatch_include", |
| "torch._C._mps_acquireEvent", |
| "torch._C._mps_currentAllocatedMemory", |
| "torch._C._mps_deviceSynchronize", |
| "torch._C._mps_driverAllocatedMemory", |
| "torch._C._mps_recommendedMaxMemory", |
| "torch._C._mps_elapsedTimeOfEvents", |
| "torch._C._mps_emptyCache", |
| "torch._C._mps_get_default_generator", |
| "torch._C._mps_is_available", |
| "torch._C._mps_is_in_bad_fork", |
| "torch._C._mps_is_on_macos_13_or_newer", |
| "torch._C._mps_profilerStartTrace", |
| "torch._C._mps_profilerStopTrace", |
| "torch._C._mps_queryEvent", |
| "torch._C._mps_recordEvent", |
| "torch._C._mps_releaseEvent", |
| "torch._C._mps_setMemoryFraction", |
| "torch._C._mps_synchronizeEvent", |
| "torch._C._mps_waitForEvent", |
| "torch._C._multiprocessing_init", |
| "torch._C._nccl_all_gather", |
| "torch._C._nccl_all_reduce", |
| "torch._C._nccl_broadcast", |
| "torch._C._nccl_init_rank", |
| "torch._C._nccl_reduce_scatter", |
| "torch._C._nccl_reduce", |
| "torch._C._nccl_unique_id", |
| "torch._C._nccl_version_suffix", |
| "torch._C._nccl_version", |
| "torch._C._nested.nested_tensor", |
| "torch._C._nested.nested_to_padded_tensor", |
| "torch._C._new_symbolic_shape_symbol", |
| "torch._C._nn_module_to_mobile", |
| "torch._C._nn._conv_depthwise2d", |
| "torch._C._nn._pad_circular", |
| "torch._C._nn._pad_enum", |
| "torch._C._nn._parse_to", |
| "torch._C._nn._test_ambiguous_defaults", |
| "torch._C._nn._test_optional_filled_intlist", |
| "torch._C._nn._test_optional_floatlist", |
| "torch._C._nn._test_optional_intlist", |
| "torch._C._nn._test_string_default", |
| "torch._C._nn._test_warn_in_autograd", |
| "torch._C._nn._upsample_bicubic2d_aa", |
| "torch._C._nn._upsample_bilinear2d_aa", |
| "torch._C._nn._upsample_nearest_exact1d", |
| "torch._C._nn._upsample_nearest_exact2d", |
| "torch._C._nn._upsample_nearest_exact3d", |
| "torch._C._nn.adaptive_avg_pool2d", |
| "torch._C._nn.adaptive_avg_pool3d", |
| "torch._C._nn.adaptive_max_pool2d", |
| "torch._C._nn.adaptive_max_pool3d", |
| "torch._C._nn.avg_pool2d", |
| "torch._C._nn.avg_pool3d", |
| "torch._C._nn.binary_cross_entropy", |
| "torch._C._nn.col2im", |
| "torch._C._nn.conv_depthwise3d", |
| "torch._C._nn.cross_entropy_loss", |
| "torch._C._nn.elu_", |
| "torch._C._nn.elu", |
| "torch._C._nn.flatten_dense_tensors", |
| "torch._C._nn.fractional_max_pool2d", |
| "torch._C._nn.fractional_max_pool3d", |
| "torch._C._nn.gelu_", |
| "torch._C._nn.gelu", |
| "torch._C._nn.glu", |
| "torch._C._nn.hardsigmoid_", |
| "torch._C._nn.hardsigmoid", |
| "torch._C._nn.hardswish_", |
| "torch._C._nn.hardswish", |
| "torch._C._nn.hardtanh_", |
| "torch._C._nn.hardtanh", |
| "torch._C._nn.huber_loss", |
| "torch._C._nn.im2col", |
| "torch._C._nn.l1_loss", |
| "torch._C._nn.leaky_relu_", |
| "torch._C._nn.leaky_relu", |
| "torch._C._nn.linear", |
| "torch._C._nn.log_sigmoid", |
| "torch._C._nn.max_pool2d_with_indices", |
| "torch._C._nn.max_pool3d_with_indices", |
| "torch._C._nn.max_unpool2d", |
| "torch._C._nn.max_unpool3d", |
| "torch._C._nn.mish_", |
| "torch._C._nn.mish", |
| "torch._C._nn.mkldnn_linear", |
| "torch._C._nn.mkldnn_reorder_conv2d_weight", |
| "torch._C._nn.mkldnn_reorder_conv3d_weight", |
| "torch._C._nn.mse_loss", |
| "torch._C._nn.multi_margin_loss", |
| "torch._C._nn.multilabel_margin_loss", |
| "torch._C._nn.nll_loss_nd", |
| "torch._C._nn.nll_loss", |
| "torch._C._nn.nll_loss2d", |
| "torch._C._nn.one_hot", |
| "torch._C._nn.pad_sequence", |
| "torch._C._nn.pad", |
| "torch._C._nn.reflection_pad1d", |
| "torch._C._nn.reflection_pad2d", |
| "torch._C._nn.reflection_pad3d", |
| "torch._C._nn.relu6_", |
| "torch._C._nn.relu6", |
| "torch._C._nn.replication_pad1d", |
| "torch._C._nn.replication_pad2d", |
| "torch._C._nn.replication_pad3d", |
| "torch._C._nn.rrelu_with_noise_", |
| "torch._C._nn.rrelu_with_noise", |
| "torch._C._nn.scaled_dot_product_attention", |
| "torch._C._nn.silu_", |
| "torch._C._nn.silu", |
| "torch._C._nn.slow_conv_dilated2d", |
| "torch._C._nn.slow_conv_dilated3d", |
| "torch._C._nn.slow_conv_transpose2d", |
| "torch._C._nn.slow_conv_transpose3d", |
| "torch._C._nn.slow_conv3d", |
| "torch._C._nn.smooth_l1_loss", |
| "torch._C._nn.soft_margin_loss", |
| "torch._C._nn.softplus", |
| "torch._C._nn.softshrink", |
| "torch._C._nn.thnn_conv2d", |
| "torch._C._nn.unflatten_dense_tensors", |
| "torch._C._nn.upsample_bicubic2d", |
| "torch._C._nn.upsample_bilinear2d", |
| "torch._C._nn.upsample_linear1d", |
| "torch._C._nn.upsample_nearest1d", |
| "torch._C._nn.upsample_nearest2d", |
| "torch._C._nn.upsample_nearest3d", |
| "torch._C._nn.upsample_trilinear3d", |
| "torch._C._non_sym_sizes", |
| "torch._C._overlaps", |
| "torch._C._parallel_info", |
| "torch._C._parse_dispatch_key", |
| "torch._C._parse_source_def", |
| "torch._C._pop_torch_dispatch_stack", |
| "torch._C._pop_torch_function_stack", |
| "torch._C._propagate_and_assign_input_shapes", |
| "torch._C._propagate_shapes", |
| "torch._C._propagate_xla_data", |
| "torch._C._push_on_torch_dispatch_stack", |
| "torch._C._push_on_torch_function_stack", |
| "torch._C._quantize_ondevice_ptq_dynamic", |
| "torch._C._register_py_class_for_device", |
| "torch._C._remove_cached_tensor", |
| "torch._C._remove_worker_pids", |
| "torch._C._rename_privateuse1_backend", |
| "torch._C._replace_", |
| "torch._C._replace_overloaded_method_decl", |
| "torch._C._resolve_type_from_object", |
| "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_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", |
| "torch._functorch.eager_transforms.functionalize", |
| "torch._functorch.eager_transforms.lazy_dynamo_disable", |
| "torch._functorch.eager_transforms.noop", |
| "torch._functorch.functional_call.construct_stacked_leaf", |
| "torch._functorch.functional_call.functional_call", |
| "torch._functorch.functional_call.stack_module_state", |
| "torch._functorch.pyfunctorch.coerce_cinterpreter", |
| "torch._functorch.pyfunctorch.dispatch_functorch", |
| "torch._functorch.pyfunctorch.nested", |
| "torch._functorch.pyfunctorch.retrieve_current_functorch_interpreter", |
| "torch._functorch.pyfunctorch.temporarily_pop_interpreter_stack", |
| "torch._functorch.utils.enable_single_level_autograd_function", |
| "torch._functorch.utils.exposed_in", |
| "torch._functorch.utils.unwrap_dead_wrappers", |
| "torch._functorch.vmap.lazy_load_decompositions", |
| "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._ops._compute_keyset", |
| "torch._ops._get_tensors", |
| "torch._ops._to_flat_tuple", |
| "torch._ops.add_cached_op", |
| "torch._ops.dl_open_guard", |
| "torch._ops.get_cached_ops", |
| "torch._ops.key_extractor", |
| "torch._ops.reset_cached_ops", |
| "torch._ops.resolve_key", |
| "torch._preload_cuda_deps", |
| "torch._register_device_module", |
| "torch._running_with_deploy", |
| "torch._utils._dummy_type", |
| "torch._weights_only_unpickler._get_allowed_globals", |
| "torch._weights_only_unpickler.load", |
| "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._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.enable_mem_efficient_sdp", |
| "torch.backends.cuda.flash_sdp_enabled", |
| "torch.backends.cuda.is_built", |
| "torch.backends.cuda.math_sdp_enabled", |
| "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.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.cpu._is_cpu_support_avx2", |
| "torch.cpu._is_cpu_support_avx512", |
| "torch.cpu._is_cpu_support_avx512_vnni", |
| "torch.cpu._is_cpu_support_amx_tile", |
| "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.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.change_current_allocator", |
| "torch.cuda.memory.empty_cache", |
| "torch.cuda.memory.get_allocator_backend", |
| "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_memory_stats", |
| "torch.cuda.memory.reset_max_memory_allocated", |
| "torch.cuda.memory.reset_max_memory_cached", |
| "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.synchronize", |
| "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_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(): |
| d: Dict[Any, 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]]]): |
| self.lazy_initializer = lazy_initializer |
| |
| def __call__(self): |
| 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): |
| 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]: |
| 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(v): f"functools.{v.__name__}" for v in (itertools.chain, itertools.islice)} |
| ) |
| 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: |
| 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, |
| os, |
| 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 _strip_init_py(s): |
| # TODO: Once we require py3.9 use removesuffix instead. |
| suffix = "__init__.py" |
| if s.endswith(suffix): |
| return s[: -len(suffix)] |
| else: |
| return 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.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.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", |
| } |
| |
| |
| # 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.utils._python_dispatch", |
| "torch._refs", |
| "torch._prims", |
| "torch._decomp", |
| "torch._dynamo._trace_wrapped_higher_order_op", |
| "torch._dynamo.comptime", |
| "torch._dynamo.polyfill", |
| "torch._functorch.vmap", |
| "torch._functorch.autograd_function", |
| "torch._library.custom_ops", |
| "torch._functorch.eager_transforms", |
| "torch._inductor.test_operators", |
| "torch.amp.autocast_mode", |
| "torch.ao.nn", |
| "torch.autograd.function", |
| "torch.backends.cuda", |
| "torch.cuda.amp.autocast_mode", |
| "torch.distributions", |
| "torch.fx._pytree", |
| "torch.fx.passes.shape_prop", |
| "torch.nn", |
| "torch.overrides", |
| "torch.random", |
| "torch.sparse", |
| "torch.testing", |
| "torch.testing._internal.hypothesis_utils", |
| "torch.utils._content_store", |
| "torch.utils._contextlib", |
| "torch.utils._foreach_utils", |
| "torch.utils._pytree", |
| "torch.utils.hooks", |
| "torch._tensor", |
| "torch._higher_order_ops.strict_mode", |
| "torch._higher_order_ops.while_loop", |
| "torch._higher_order_ops.associative_scan", |
| } |
| |
| |
| if torch.distributed.is_available(): |
| MOD_INLINELIST.add("torch.distributed") |
| MOD_INLINELIST.add("torch.distributed._functional_collectives") |
| MOD_INLINELIST.add("torch.distributed._composable.replicate") |
| |
| |
| @functools.lru_cache(None) |
| def get_legacy_mod_inlinelist(): |
| inlinelist = { |
| _module_dir(torch) + m[len("torch.") :].replace(".", "/") |
| for m in LEGACY_MOD_INLINELIST |
| } |
| return inlinelist |
| |
| |
| @functools.lru_cache(None) |
| def get_mod_inlinelist(): |
| inlinelist = { |
| _module_dir(torch) + m[len("torch.") :].replace(".", "/") |
| for m in MOD_INLINELIST |
| } |
| return inlinelist |
| |
| |
| # skip some standard python builtin libs |
| SKIP_DIRS = [ |
| "<frozen importlib", |
| "<__array_function__ internals>", |
| _config_module.__file__, |
| "triton/backends", |
| ] |
| SKIP_DIRS.extend(filter(None, (_module_dir(m) for m in 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() |
| # Remove this after fbcode is fully migrated to tracing through torchrec. |
| if torch._dynamo.config.skip_torchrec: |
| FBCODE_SKIP_DIRS.add("torchrec/distributed") |
| FBCODE_SKIP_DIRS.add("torchrec/fb/distributed") |
| FBCODE_SKIP_DIRS.add("caffe2/torch/fb/sparsenn/pooled_embeddings_modules.py") |
| FBCODE_SKIP_DIRS_RE = re.compile(f".*({'|'.join(map(re.escape, FBCODE_SKIP_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(map(re.escape, 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(map(re.escape, 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") |
| 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 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 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 = torch._dynamo.trace_rules.lookup_inner( |
| fi.py_obj, fi.name, fi.filename, is_inlined_call, reasons |
| ) |
| if rule in [UserFunctionVariable, FunctorchHigherOrderVariable]: |
| 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 |
| |
| |
| """ |
| 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() |
