Spaces:
Running
Running
| 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 .utils import getfile, hashable, NP_SUPPORTED_MODULES, unwrap_if_wrapper | |
| from .variables import ( | |
| BuiltinVariable, | |
| FunctorchHigherOrderVariable, | |
| NestedUserFunctionVariable, | |
| SkipFunctionVariable, | |
| TorchInGraphFunctionVariable, | |
| UserFunctionVariable, | |
| UserMethodVariable, | |
| ) | |
| from .variables.base import VariableTracker | |
| """ | |
| 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.overrides.get_default_nowrap_functions": 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, | |
| # 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, | |
| # 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._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": UserFunctionVariable, | |
| "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": UserFunctionVariable, | |
| "torch._functorch.eager_transforms._vjp_with_argnums": UserFunctionVariable, | |
| "torch._functorch.eager_transforms.assert_non_empty_tensor_output": UserFunctionVariable, | |
| "torch._constrain_as_size": UserFunctionVariable, | |
| "torch._constrain_as_value": 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.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, | |
| } | |
| # 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_cuda_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._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_vnni", | |
| "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_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_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._set_autograd_fallback_mode", | |
| "torch._C._set_backcompat_broadcast_warn", | |
| "torch._C._set_backcompat_keepdim_warn", | |
| "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_dlpack", | |
| "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_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_flush_denormal", | |
| "torch._C.set_num_interop_threads", | |
| "torch._C.set_num_threads", | |
| "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_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_linear", | |
| "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._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.obj", | |
| "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.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._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.grad_and_value", | |
| "torch._functorch.deprecated.hessian", | |
| "torch._functorch.deprecated.jacfwd", | |
| "torch._functorch.deprecated.jacrev", | |
| "torch._functorch.deprecated.jvp", | |
| "torch._functorch.deprecated.make_functional_with_buffers", | |
| "torch._functorch.deprecated.make_functional", | |
| "torch._functorch.deprecated.setup_docs", | |
| "torch._functorch.deprecated.vjp", | |
| "torch._functorch.deprecated.warn_deprecated", | |
| "torch._functorch.eager_transforms._any_differentiable", | |
| "torch._functorch.eager_transforms._autograd_grad", | |
| "torch._functorch.eager_transforms._construct_standard_basis_for", | |
| "torch._functorch.eager_transforms._vjp_treespec_compare", | |
| "torch._functorch.eager_transforms._set_tensor_requires_grad", | |
| "torch._functorch.eager_transforms._is_differentiable", | |
| "torch._functorch.eager_transforms._jvp_with_argnums", | |
| "torch._functorch.eager_transforms._maybe_unwrap_functional_tensor", | |
| "torch._functorch.eager_transforms._maybe_wrap_functional_tensor", | |
| "torch._functorch.eager_transforms._replace_args", | |
| "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.assert_non_empty_list_of_tensors", | |
| "torch._functorch.eager_transforms.assert_output_is_tensor_or_tensors", | |
| "torch._functorch.eager_transforms.functionalize", | |
| "torch._functorch.eager_transforms.hessian", | |
| "torch._functorch.eager_transforms.jacfwd", | |
| "torch._functorch.eager_transforms.jvp", | |
| "torch._functorch.eager_transforms.lazy_dynamo_disable", | |
| "torch._functorch.eager_transforms.linearize", | |
| "torch._functorch.eager_transforms.noop", | |
| "torch._functorch.eager_transforms.safe_unflatten", | |
| "torch._functorch.eager_transforms.safe_unpack_dual", | |
| "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.conjugate", | |
| "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._linalg_utils.transjugate", | |
| "torch._linalg_utils.transpose", | |
| "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.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.backward", | |
| "torch.autograd.forward_ad.enter_dual_level", | |
| "torch.autograd.forward_ad.exit_dual_level", | |
| "torch.autograd.forward_ad.make_dual", | |
| "torch.autograd.forward_ad.unpack_dual", | |
| "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.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_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_vnni", | |
| "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_nvml", | |
| "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_nvml", | |
| "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.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.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_max_pool1d", | |
| "torch.nn.functional._adaptive_max_pool2d", | |
| "torch.nn.functional._adaptive_max_pool3d", | |
| "torch.nn.functional._canonical_mask", | |
| "torch.nn.functional._fractional_max_pool2d", | |
| "torch.nn.functional._fractional_max_pool3d", | |
| "torch.nn.functional._get_softmax_dim", | |
| "torch.nn.functional._in_projection_packed", | |
| "torch.nn.functional._in_projection", | |
| "torch.nn.functional._is_integer", | |
| "torch.nn.functional._max_pool1d", | |
| "torch.nn.functional._max_pool2d", | |
| "torch.nn.functional._max_pool3d", | |
| "torch.nn.functional._mha_shape_check", | |
| "torch.nn.functional._no_grad_embedding_renorm_", | |
| "torch.nn.functional._none_or_dtype", | |
| "torch.nn.functional._threshold", | |
| "torch.nn.functional._unpool_output_size", | |
| "torch.nn.functional._verify_batch_size", | |
| "torch.nn.functional._verify_spatial_size", | |
| "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._arg_requires_grad", | |
| "torch.nn.modules.activation._check_arg_device", | |
| "torch.nn.modules.activation._is_make_fx_tracing", | |
| "torch.nn.modules.container._addindent", | |
| "torch.nn.modules.transformer._detect_is_causal_mask", | |
| "torch.nn.modules.transformer._generate_square_subsequent_mask", | |
| "torch.nn.modules.transformer._get_activation_fn", | |
| "torch.nn.modules.transformer._get_clones", | |
| "torch.nn.modules.transformer._get_seq_len", | |
| "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. | |
| """ | |
| def get_torch_obj_rule_map(): | |
| d: Dict[Any, VariableTracker] = dict() | |
| for m in torch_name_rule_map: | |
| for k, v in m.items(): # type: ignore[attr-defined] | |
| obj = load_object(k) | |
| 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. | |
| """ | |
| 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() | |
| def _allowed_callable_ids() -> Dict[int, str]: | |
| rv: Dict[int, str] = {} | |
| return rv | |
| def _disallowed_callable_ids() -> Dict[int, str]: | |
| rv: Dict[int, str] = {} | |
| return rv | |
| 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 | |
| def _numpy_function_ids() -> Dict[int, str]: | |
| rv = dict() | |
| 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 | |
| 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}" | |
| if name in sys.modules: | |
| init_func() | |
| # Module is not yet imported, delay processing until needed | |
| 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 getattr(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 | |
| """ | |
| 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. | |
| """ | |
| 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._refs", | |
| "torch._prims", | |
| "torch._decomp", | |
| "torch._dynamo._trace_wrapped_higher_order_op", | |
| "torch._dynamo.comptime", | |
| "torch._dynamo.polyfill", | |
| "torch._functorch.vmap", | |
| "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.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", | |
| } | |
| if torch.distributed.is_available(): | |
| MOD_INLINELIST.add("torch.distributed") | |
| MOD_INLINELIST.add("torch.distributed._functional_collectives") | |
| MOD_INLINELIST.add("torch.distributed._composable.replicate") | |
| def get_legacy_mod_inlinelist(): | |
| inlinelist = set() | |
| for m in LEGACY_MOD_INLINELIST: | |
| inlinelist.add(_module_dir(torch) + m[len("torch.") :].replace(".", "/")) | |
| return inlinelist | |
| def get_mod_inlinelist(): | |
| inlinelist = set() | |
| for m in MOD_INLINELIST: | |
| inlinelist.add(_module_dir(torch) + m[len("torch.") :].replace(".", "/")) | |
| return inlinelist | |
| # skip some standard python builtin libs | |
| SKIP_DIRS = [ | |
| "<frozen importlib", | |
| "<__array_function__ internals>", | |
| _config_module.__file__, | |
| ] | |
| 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 = { | |
| "torchrec/distributed", | |
| "torchrec/fb/distributed", | |
| "caffe2/torch/fb/sparsenn/pooled_embeddings_modules.py", | |
| } | |
| FBCODE_SKIP_DIRS_RE = re.compile(f".*({'|'.join(map(re.escape, FBCODE_SKIP_DIRS))})") | |
| def _recompile_re(): | |
| global SKIP_DIRS_RE | |
| SKIP_DIRS_RE = re.compile(f"^({'|'.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 | |
| global SKIP_DIRS_RE | |
| SKIP_DIRS.append(_strip_init_py(origin)) | |
| _recompile_re() | |
| 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 any(filename.startswith(d) for d in get_legacy_mod_inlinelist()): | |
| return SkipResult( | |
| False, | |
| "inlined according trace_rules.LEGACY_MOD_INLINELIST", | |
| ) | |
| if is_inlined_call and is_torch_inline_allowed(filename): | |
| return SkipResult( | |
| False, | |
| "inlined according trace_rules.MOD_INLINELIST", | |
| ) | |
| if is_fbcode and bool(FBCODE_SKIP_DIRS_RE.match(filename)): | |
| return SkipResult( | |
| True, | |
| "skipped according trace_rules.FBCODE_SKIP_DIRS", | |
| ) | |
| if bool(SKIP_DIRS_RE.match(filename)): | |
| return SkipResult(True, "skipped according trace_rules.SKIP_DIRS") | |
| else: | |
| return SkipResult(False, "inlined by default") | |
| 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. | |
| rule = torch._dynamo.trace_rules.lookup_inner( | |
| fi.py_obj, fi.name, fi.filename, is_inlined_call | |
| ) | |
| if rule in [UserFunctionVariable, FunctorchHigherOrderVariable]: | |
| return SkipResult( | |
| False, | |
| "inlined according trace_rules.lookup", | |
| ) | |
| else: | |
| assert rule == SkipFunctionVariable, rule | |
| return SkipResult( | |
| True, | |
| "skipped according trace_rules.lookup", | |
| ) | |
| 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()) | |
| 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): | |
| # 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. | |
| if not hashable(obj): | |
| 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: | |
| return rule | |
| # Step 2: lookup obj's tracing rule by function name. | |
| if is_direct_call: | |
| if name == "patched_init": | |
| return SkipFunctionVariable | |
| elif name == "__torch_function__": | |
| return UserFunctionVariable | |
| # Step 3: lookup obj's tracing rule by filename. | |
| if filename is None: | |
| filename = getfile(obj) | |
| if check_file(filename, is_direct_call).skipped: | |
| return SkipFunctionVariable | |
| else: | |
| return UserFunctionVariable | |