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conf.py
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#
# PyTorch documentation build configuration file, created by
# sphinx-quickstart on Fri Dec 23 13:31:47 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
# import sys
import pkgutil
import re
from os import path
# source code directory, relative to this file, for sphinx-autobuild
# sys.path.insert(0, os.path.abspath('../..'))
import torch
try:
import torchvision # noqa: F401
except ImportError:
import warnings
warnings.warn('unable to load "torchvision" package')
RELEASE = os.environ.get("RELEASE", False)
import pytorch_sphinx_theme
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
needs_sphinx = "3.1.2"
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.autosummary",
"sphinx.ext.doctest",
"sphinx.ext.intersphinx",
"sphinx.ext.todo",
"sphinx.ext.coverage",
"sphinx.ext.napoleon",
"sphinx.ext.viewcode",
"sphinxcontrib.katex",
"sphinx.ext.autosectionlabel",
"sphinx_copybutton",
"sphinx_panels",
"myst_parser",
]
# build the templated autosummary files
autosummary_generate = True
numpydoc_show_class_members = False
# Theme has bootstrap already
panels_add_bootstrap_css = False
# autosectionlabel throws warnings if section names are duplicated.
# The following tells autosectionlabel to not throw a warning for
# duplicated section names that are in different documents.
autosectionlabel_prefix_document = True
# katex options
#
#
katex_prerender = True
napoleon_use_ivar = True
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# TODO: document these and remove them from here.
coverage_ignore_functions = [
# torch
"typename",
# torch.autograd
"register_py_tensor_class_for_device",
"variable",
# torch.cuda
"check_error",
"cudart",
"is_bf16_supported",
# torch.cuda._sanitizer
"zip_arguments",
"zip_by_key",
# torch.distributed.autograd
"is_available",
# torch.distributed.checkpoint.state_dict
"gc_context",
"state_dict",
# torch.distributed.elastic.events
"construct_and_record_rdzv_event",
"record_rdzv_event",
# torch.distributed.elastic.metrics
"initialize_metrics",
# torch.distributed.elastic.rendezvous.registry
"get_rendezvous_handler",
# torch.distributed.launch
"launch",
"main",
"parse_args",
# torch.distributed.rpc
"is_available",
# torch.distributed.run
"config_from_args",
"determine_local_world_size",
"get_args_parser",
"get_rdzv_endpoint",
"get_use_env",
"main",
"parse_args",
"parse_min_max_nnodes",
"run",
"run_script_path",
# torch.distributions.constraints
"is_dependent",
# torch.hub
"import_module",
# torch.jit
"export_opnames",
# torch.jit.unsupported_tensor_ops
"execWrapper",
# torch.onnx
"unregister_custom_op_symbolic",
# torch.ao.quantization
"default_eval_fn",
# torch.backends
"disable_global_flags",
"flags_frozen",
# torch.distributed.algorithms.ddp_comm_hooks
"register_ddp_comm_hook",
# torch.nn
"factory_kwargs",
# torch.nn.parallel
"DistributedDataParallelCPU",
# torch.utils
"set_module",
# torch.utils.model_dump
"burn_in_info",
"get_info_and_burn_skeleton",
"get_inline_skeleton",
"get_model_info",
"get_storage_info",
"hierarchical_pickle",
# torch.amp.autocast_mode
"autocast_decorator",
# torch.ao.nn.quantized.dynamic.modules.rnn
"apply_permutation",
"pack_weight_bias",
# torch.ao.nn.quantized.reference.modules.rnn
"get_quantized_weight",
# torch.ao.ns.fx.graph_matcher
"get_matching_subgraph_pairs",
# torch.ao.ns.fx.graph_passes
"add_loggers_to_model",
"create_a_shadows_b",
# torch.ao.ns.fx.mappings
"add_op_to_sets_of_related_ops",
"get_base_name_for_op",
"get_base_name_to_sets_of_related_ops",
"get_node_type_to_io_type_map",
"get_unmatchable_types_map",
# torch.ao.ns.fx.n_shadows_utils
"create_add_loggers_graph",
"create_n_transformed_and_logged_copies_of_subgraph",
"create_one_transformed_and_logged_copy_of_subgraph",
"create_results_comparison",
"create_submodule_from_subgraph",
"extract_weight_comparison",
"group_results_by_subgraph",
"print_n_shadows_summary",
# torch.ao.ns.fx.pattern_utils
"end_node_matches_reversed_fusion",
"get_reversed_fusions",
"get_type_a_related_to_b",
# torch.ao.ns.fx.utils
"get_arg_indices_of_inputs_to_log",
"get_node_first_input_and_output_type",
"get_node_input_qparams",
"get_normalized_nth_input",
"get_number_of_non_param_args",
"get_target_type_str",
"maybe_add_missing_fqns",
"maybe_dequantize_first_two_tensor_args_and_handle_tuples",
"op_type_supports_shadowing",
"rekey_logger_info_on_node_name_of_model",
"return_first_non_observer_node",
# torch.ao.ns.fx.weight_utils
"extract_weight_from_node",
"get_conv_fun_weight",
"get_conv_mod_weight",
"get_linear_fun_weight",
"get_linear_mod_weight",
"get_lstm_mod_weights",
"get_lstm_weight",
"get_op_to_type_to_weight_extraction_fn",
"get_qconv_fun_weight",
"get_qlinear_fun_weight",
"get_qlstm_weight",
"mod_0_weight_detach",
"mod_weight_bias_0",
"mod_weight_detach",
# torch.ao.pruning.sparsifier.utils
"fqn_to_module",
"get_arg_info_from_tensor_fqn",
"module_contains_param",
"module_to_fqn",
"swap_module",
# torch.ao.quantization.backend_config.executorch
"get_executorch_backend_config",
# torch.ao.quantization.backend_config.fbgemm
"get_fbgemm_backend_config",
# torch.ao.quantization.backend_config.native
"get_native_backend_config",
"get_native_backend_config_dict",
"get_test_only_legacy_native_backend_config",
"get_test_only_legacy_native_backend_config_dict",
# torch.ao.quantization.backend_config.onednn
"get_onednn_backend_config",
# torch.ao.quantization.backend_config.qnnpack
"get_qnnpack_backend_config",
# torch.ao.quantization.backend_config.tensorrt
"get_tensorrt_backend_config",
"get_tensorrt_backend_config_dict",
# torch.ao.quantization.backend_config.utils
"entry_to_pretty_str",
"get_fused_module_classes",
"get_fuser_method_mapping",
"get_fusion_pattern_to_extra_inputs_getter",
"get_fusion_pattern_to_root_node_getter",
"get_module_to_qat_module",
"get_pattern_to_dtype_configs",
"get_pattern_to_input_type_to_index",
"get_qat_module_classes",
"get_root_module_to_quantized_reference_module",
"pattern_to_human_readable",
"remove_boolean_dispatch_from_name",
# torch.ao.quantization.backend_config.x86
"get_x86_backend_config",
# torch.ao.quantization.fuse_modules
"fuse_known_modules",
"fuse_modules_qat",
# torch.ao.quantization.fuser_method_mappings
"fuse_conv_bn",
"fuse_conv_bn_relu",
"fuse_convtranspose_bn",
"fuse_linear_bn",
"get_fuser_method",
"get_fuser_method_new",
# torch.ao.quantization.fx.convert
"convert",
"convert_custom_module",
"convert_standalone_module",
"convert_weighted_module",
# torch.ao.quantization.fx.fuse
"fuse",
# torch.ao.quantization.fx.lower_to_fbgemm
"lower_to_fbgemm",
# torch.ao.quantization.fx.lower_to_qnnpack
"lower_to_qnnpack",
# torch.ao.quantization.fx.pattern_utils
"get_default_fusion_patterns",
"get_default_output_activation_post_process_map",
"get_default_quant_patterns",
# torch.ao.quantization.fx.prepare
"insert_observers_for_model",
"prepare",
"propagate_dtypes_for_known_nodes",
# torch.ao.quantization.fx.utils
"all_node_args_except_first",
"all_node_args_have_no_tensors",
"assert_and_get_unique_device",
"collect_producer_nodes",
"create_getattr_from_value",
"create_node_from_old_node_preserve_meta",
"get_custom_module_class_keys",
"get_linear_prepack_op_for_dtype",
"get_new_attr_name_with_prefix",
"get_non_observable_arg_indexes_and_types",
"get_qconv_prepack_op",
"get_skipped_module_name_and_classes",
"graph_module_from_producer_nodes",
"maybe_get_next_module",
"node_arg_is_bias",
"node_arg_is_weight",
"return_arg_list",
# torch.ao.quantization.pt2e.graph_utils
"find_sequential_partitions",
"get_equivalent_types",
"update_equivalent_types_dict",
# torch.ao.quantization.pt2e.prepare
"prepare",
# torch.ao.quantization.pt2e.representation.rewrite
"reference_representation_rewrite",
# torch.ao.quantization.pt2e.utils
"fold_bn_weights_into_conv_node",
"get_aten_graph_module",
"remove_tensor_overload_for_qdq_ops",
# torch.ao.quantization.qconfig
"get_default_qat_qconfig",
"get_default_qat_qconfig_dict",
"get_default_qconfig",
"get_default_qconfig_dict",
"qconfig_equals",
# torch.ao.quantization.quantization_mappings
"get_default_compare_output_module_list",
"get_default_dynamic_quant_module_mappings",
"get_default_dynamic_sparse_quant_module_mappings",
"get_default_float_to_quantized_operator_mappings",
"get_default_qat_module_mappings",
"get_default_qconfig_propagation_list",
"get_default_static_quant_module_mappings",
"get_default_static_quant_reference_module_mappings",
"get_default_static_sparse_quant_module_mappings",
"get_dynamic_quant_module_class",
"get_embedding_qat_module_mappings",
"get_embedding_static_quant_module_mappings",
"get_quantized_operator",
"get_static_quant_module_class",
"no_observer_set",
# torch.ao.quantization.quantize
"get_default_custom_config_dict",
# torch.ao.quantization.quantize_fx
"attach_preserved_attrs_to_model",
"convert_to_reference_fx",
# torch.ao.quantization.quantize_jit
"convert_dynamic_jit",
"convert_jit",
"fuse_conv_bn_jit",
"prepare_dynamic_jit",
"prepare_jit",
"quantize_dynamic_jit",
"quantize_jit",
"script_qconfig",
"script_qconfig_dict",
# torch.ao.quantization.quantize_pt2e
"convert_pt2e",
"prepare_pt2e",
"prepare_qat_pt2e",
# torch.ao.quantization.quantizer.embedding_quantizer
"get_embedding_operators_config",
# torch.ao.quantization.quantizer.xnnpack_quantizer_utils
"get_bias_qspec",
"get_input_act_qspec",
"get_output_act_qspec",
"get_weight_qspec",
"propagate_annotation",
"register_annotator",
# torch.ao.quantization.utils
"activation_dtype",
"activation_is_dynamically_quantized",
"activation_is_int32_quantized",
"activation_is_int8_quantized",
"activation_is_statically_quantized",
"calculate_qmin_qmax",
"check_min_max_valid",
"check_node",
"determine_qparams",
"get_combined_dict",
"get_fqn_to_example_inputs",
"get_qconfig_dtypes",
"get_qparam_dict",
"get_quant_type",
"get_swapped_custom_module_class",
"getattr_from_fqn",
"has_no_children_ignoring_parametrizations",
"is_per_channel",
"is_per_tensor",
"op_is_int8_dynamically_quantized",
"to_underlying_dtype",
"validate_qmin_qmax",
"weight_dtype",
"weight_is_quantized",
"weight_is_statically_quantized",
# torch.autograd.forward_ad
"enter_dual_level",
"exit_dual_level",
# torch.autograd.function
"once_differentiable",
"traceable",
# torch.autograd.gradcheck
"get_analytical_jacobian",
"get_numerical_jacobian",
"get_numerical_jacobian_wrt_specific_input",
# torch.autograd.graph
"increment_version",
# torch.autograd.profiler
"parse_nvprof_trace",
# torch.backends.cudnn.rnn
"get_cudnn_mode",
"init_dropout_state",
# torch.backends.xeon.run_cpu
"create_args",
# torch.cuda.amp.autocast_mode
"custom_bwd",
"custom_fwd",
# torch.cuda.amp.common
"amp_definitely_not_available",
# torch.cuda.graphs
"graph_pool_handle",
"is_current_stream_capturing",
"make_graphed_callables",
# torch.cuda.memory
"caching_allocator_alloc",
"caching_allocator_delete",
"change_current_allocator",
"empty_cache",
"get_allocator_backend",
"list_gpu_processes",
"max_memory_allocated",
"max_memory_cached",
"max_memory_reserved",
"mem_get_info",
"memory_allocated",
"memory_cached",
"memory_reserved",
"memory_snapshot",
"memory_stats",
"memory_stats_as_nested_dict",
"memory_summary",
"reset_accumulated_memory_stats",
"reset_max_memory_allocated",
"reset_max_memory_cached",
"reset_peak_memory_stats",
"set_per_process_memory_fraction",
# torch.cuda.nccl
"all_gather",
"all_reduce",
"broadcast",
"init_rank",
"reduce",
"reduce_scatter",
"unique_id",
"version",
# torch.cuda.nvtx
"range",
"range_end",
"range_start",
# torch.cuda.profiler
"init",
"profile",
"start",
"stop",
# torch.cuda.random
"get_rng_state",
"get_rng_state_all",
"initial_seed",
"manual_seed",
"manual_seed_all",
"seed",
"seed_all",
"set_rng_state",
"set_rng_state_all",
# torch.distributed.algorithms.ddp_comm_hooks.ddp_zero_hook
"hook_with_zero_step",
"hook_with_zero_step_interleaved",
# torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook
"post_localSGD_hook",
# torch.distributed.algorithms.ddp_comm_hooks.quantization_hooks
"quantization_perchannel_hook",
"quantization_pertensor_hook",
# torch.distributed.algorithms.model_averaging.utils
"average_parameters",
"average_parameters_or_parameter_groups",
"get_params_to_average",
# torch.distributed.checkpoint.default_planner
"create_default_global_load_plan",
"create_default_global_save_plan",
"create_default_local_load_plan",
"create_default_local_save_plan",
# torch.distributed.checkpoint.optimizer
"load_sharded_optimizer_state_dict",
# torch.distributed.checkpoint.planner_helpers
"create_read_items_for_chunk_list",
# torch.distributed.checkpoint.state_dict_loader
"load_state_dict",
# torch.distributed.checkpoint.state_dict_saver
"save_state_dict",
# torch.distributed.checkpoint.utils
"find_state_dict_object",
"find_tensor_shard",
# torch.distributed.collective_utils
"all_gather",
"all_gather_object_enforce_type",
"broadcast",
# torch.distributed.distributed_c10d
"all_gather",
"all_gather_coalesced",
"all_gather_into_tensor",
"all_gather_object",
"all_reduce",
"all_reduce_coalesced",
"all_to_all",
"all_to_all_single",
"barrier",
"batch_isend_irecv",
"broadcast",
"broadcast_object_list",
"destroy_process_group",
"gather",
"gather_object",
"get_backend",
"get_backend_config",
"get_global_rank",
"get_group_rank",
"get_process_group_ranks",
"get_rank",
"get_world_size",
"init_process_group",
"irecv",
"is_backend_available",
"is_gloo_available",
"is_initialized",
"is_mpi_available",
"is_nccl_available",
"is_torchelastic_launched",
"is_ucc_available",
"isend",
"monitored_barrier",
"new_group",
"new_subgroups",
"new_subgroups_by_enumeration",
"recv",
"reduce",
"reduce_scatter",
"reduce_scatter_tensor",
"scatter",
"scatter_object_list",
"send",
"supports_complex",
# torch.distributed.elastic.events.handlers
"get_logging_handler",
# torch.distributed.elastic.metrics.api
"configure",
"getStream",
"get_elapsed_time_ms",
"prof",
"profile",
"publish_metric",
"put_metric",
# torch.distributed.elastic.multiprocessing.api
"get_std_cm",
"to_map",
# torch.distributed.elastic.multiprocessing.errors.handlers
"get_error_handler",
# torch.distributed.elastic.multiprocessing.redirects
"get_libc",
"redirect",
# torch.distributed.elastic.multiprocessing.tail_log
"tail_logfile",
# torch.distributed.elastic.rendezvous.dynamic_rendezvous
"get_method_name",
# torch.distributed.elastic.rendezvous.etcd_rendezvous
"create_rdzv_handler",
# torch.distributed.elastic.rendezvous.etcd_server
"find_free_port",
"stop_etcd",
# torch.distributed.elastic.rendezvous.etcd_store
"cas_delay",
# torch.distributed.elastic.rendezvous.static_tcp_rendezvous
"create_rdzv_handler",
# torch.distributed.elastic.rendezvous.utils
"parse_rendezvous_endpoint",
# torch.distributed.elastic.timer.api
"configure",
"expires",
# torch.distributed.elastic.utils.api
"get_env_variable_or_raise",
"get_socket_with_port",
# torch.distributed.elastic.utils.distributed
"create_c10d_store",
"get_free_port",
"get_socket_with_port",
# torch.distributed.elastic.utils.log_level
"get_log_level",
# torch.distributed.elastic.utils.logging
"get_logger",
# torch.distributed.elastic.utils.store
"barrier",
"get_all",
"synchronize",
# torch.distributed.fsdp.wrap
"always_wrap_policy",
"enable_wrap",
"lambda_auto_wrap_policy",
"size_based_auto_wrap_policy",
"transformer_auto_wrap_policy",
"wrap",
# torch.distributed.nn.functional
"all_gather",
"all_reduce",
"all_to_all",
"all_to_all_single",
"broadcast",
"gather",
"reduce",
"reduce_scatter",
"scatter",
# torch.distributed.nn.jit.instantiator
"get_arg_return_types_from_interface",
"instantiate_non_scriptable_remote_module_template",
"instantiate_scriptable_remote_module_template",
# torch.distributed.nn.jit.templates.remote_module_template
"get_remote_module_template",
# torch.distributed.optim.utils
"as_functional_optim",
"register_functional_optim",
# torch.distributed.pipeline.sync.checkpoint
"checkpoint",
"enable_checkpointing",
"enable_recomputing",
"is_checkpointing",
"is_recomputing",
"restore_rng_states",
"save_rng_states",
# torch.distributed.pipeline.sync.dependency
"fork",
"join",
# torch.distributed.pipeline.sync.microbatch
"check",
"gather",
"scatter",
# torch.distributed.pipeline.sync.phony
"get_phony",
# torch.distributed.pipeline.sync.skip.layout
"inspect_skip_layout",
# torch.distributed.pipeline.sync.skip.tracker
"current_skip_tracker",
"use_skip_tracker",
# torch.distributed.pipeline.sync.stream
"as_cuda",
"current_stream",
"default_stream",
"get_device",
"is_cuda",
"new_stream",
"record_stream",
"use_device",
"use_stream",
"wait_stream",
# torch.distributed.pipeline.sync.utils
"partition_model",
# torch.distributed.pipeline.sync.worker
"create_workers",
"spawn_workers",
"worker",
# torch.distributed.rendezvous
"register_rendezvous_handler",
"rendezvous",
# torch.distributed.rpc.api
"get_worker_info",
"method_factory",
"new_method",
"remote",
"rpc_async",
"rpc_sync",
"shutdown",
# torch.distributed.rpc.backend_registry
"backend_registered",
"construct_rpc_backend_options",
"init_backend",
"register_backend",
# torch.distributed.rpc.internal
"deserialize",
"serialize",
# torch.distributed.tensor.parallel.api
"parallelize_module",
# torch.distributed.tensor.parallel.input_reshard
"input_reshard",
# torch.distributed.tensor.parallel.style
"make_sharded_output_tensor",
# torch.distributions.utils
"broadcast_all",
"clamp_probs",
"logits_to_probs",
"probs_to_logits",
"tril_matrix_to_vec",
"vec_to_tril_matrix",
# torch.functional
"align_tensors",
"atleast_1d",
"atleast_2d",
"atleast_3d",
"block_diag",
"broadcast_shapes",
"broadcast_tensors",
"cartesian_prod",
"cdist",
"chain_matmul",
"einsum",
"lu",
"meshgrid",
"norm",
"split",
"stft",
"tensordot",
"unique",
"unique_consecutive",
"unravel_index",
# torch.fx.annotate
"annotate",
# torch.fx.experimental.accelerator_partitioner
"check_dependency",
"combine_two_partitions",
"get_bfs_level_partition",
"get_device_partition_stats",
"get_device_to_partitions_mapping",
"get_logical_id_to_device",
"get_node_to_partition_mapping",
"reorganize_partitions",
"reset_partition_device",
"set_parents_and_children",
# torch.fx.experimental.const_fold
"get_unique_attr_name_in_module",
"split_const_subgraphs",
# torch.fx.experimental.debug
"set_trace",
# torch.fx.experimental.graph_gradual_typechecker
"adaptiveavgpool2d_check",
"adaptiveavgpool2d_inference_rule",
"add_inference_rule",
"all_eq",
"bn2d_inference_rule",
"broadcast_types",
"calculate_out_dimension",
"conv2d_inference_rule",
"conv_refinement_rule",
"conv_rule",
"element_wise_eq",
"expand_to_tensor_dim",
"first_two_eq",
"flatten_check",
"flatten_inference_rule",
"flatten_refinement_rule",
"get_attr_inference_rule",
"get_greatest_upper_bound",
"get_parameter",
"linear_check",
"linear_inference_rule",
"linear_refinement_rule",
"maxpool2d_check",
"maxpool2d_inference_rule",
"register_algebraic_expressions_inference_rule",
"register_inference_rule",
"register_refinement_rule",
"relu_inference_rule",
"reshape_inference_rule",
"transpose_inference_rule",
# torch.fx.experimental.merge_matmul
"are_nodes_independent",
"may_depend_on",
"merge_matmul",
"split_result_tensors",
# torch.fx.experimental.meta_tracer
"embedding_override",
"functional_relu_override",
"gen_constructor_wrapper",
"nn_layernorm_override",
"proxys_to_metas",
"symbolic_trace",
"torch_abs_override",
"torch_nn_relu_override",
"torch_relu_override",
"torch_where_override",
# torch.fx.experimental.migrate_gradual_types.constraint
"is_algebraic_expression",
"is_bool_expr",
"is_dim",
# torch.fx.experimental.migrate_gradual_types.constraint_generator
"adaptive_inference_rule",
"add_layer_norm_constraints",
"add_linear_constraints",
"arange_inference_rule",
"assert_inference_rule",
"batchnorm_inference_rule",
"bmm_inference_rule",
"broadcasting_inference_rule",
"conv2d_inference_rule",
"cumsum_inference_rule",
"embedding_inference_rule",
"embedding_inference_rule_functional",
"eq_inference_rule",
"equality_inference_rule",
"expand_inference_rule",
"flatten_inference_rule",
"full_inference_rule",
"gen_broadcasting_constraints",
"gen_embedding_rules",
"gen_layer_norm_constraints",
"generate_flatten_constraints",
"get_attr_inference_rule",
"getitem_inference_rule",
"gt_inference_rule",
"index_select_inference_rule",
"layer_norm_functional",
"layer_norm_inference_rule",
"linear_constraints",
"linear_inference_rule",
"lt_inference_rule",
"masked_fill_inference_rule",
"maxpool_inference_rule",
"neq_inference_rule",
"range_check",
"register_inference_rule",
"relu_inference_rule",
"reshape_inference_rule",
"size_inference_rule",
"tensor_inference_rule",
"torch_dim_inference_rule",
"torch_linear_inference_rule",
"transpose_inference_rule",
"type_inference_rule",
"view_inference_rule",
# torch.fx.experimental.migrate_gradual_types.constraint_transformation
"apply_padding",
"broadcast_dim",
"calc_last_two_dims",
"create_equality_constraints_for_broadcasting",
"gen_all_reshape_possibilities",
"gen_broadcasting_constraints",
"gen_consistency_constraints",
"gen_greatest_upper_bound",
"gen_lists_of_dims",
"generate_all_broadcasting_possibilities_no_padding",
"generate_all_int_dyn_dim_possibilities",
"generate_binconstraint_d",
"generate_binconstraint_t",
"generate_broadcasting",
"generate_calc_conv",
"generate_calc_maxpool",
"generate_calc_product",
"generate_conj",
"generate_d_gub",
"generate_disj",
"generate_gub",
"generate_reshape",
"is_dim_div_by_target",
"is_target_div_by_dim",
"no_broadcast_dim_with_index",
"register_transformation_rule",
"transform_constraint",
"transform_get_item",
"transform_get_item_tensor",
"transform_index_select",
"transform_transpose",
"valid_index",
"valid_index_tensor",
# torch.fx.experimental.migrate_gradual_types.transform_to_z3
"evaluate_conditional_with_constraints",
# torch.fx.experimental.migrate_gradual_types.util
"gen_bvar",
"gen_dvar",
"gen_nat_constraints",
"gen_tensor_dims",
"gen_tvar",
# torch.fx.experimental.optimization
"extract_subgraph",
"fuse",
"gen_mkl_autotuner",
"matches_module_pattern",
"modules_to_mkldnn",
"optimize_for_inference",
"remove_dropout",
"replace_node_module",
"reset_modules",
"use_mkl_length",
# torch.fx.experimental.partitioner_utils
"get_comm_latency_between",
"get_extra_size_of",
"get_latency_of_one_partition",
"get_latency_of_partitioned_graph",
"get_partition_to_latency_mapping",
# torch.fx.experimental.proxy_tensor
"decompose",
"disable_autocast_cache",
"disable_proxy_modes_tracing",
"dispatch_trace",
"extract_val",
"fake_signature",
"fetch_sym_proxy",
"fetch_tensor_proxy",
"get_innermost_proxy_mode",
"get_isolated_graphmodule",
"get_proxy_slot",
"get_torch_dispatch_modes",
"has_proxy_slot",
"is_sym_node",
"make_fx",
"maybe_disable_fake_tensor_mode",
"maybe_handle_decomp",
"proxy_call",
"set_meta",
"set_original_aten_op",
"set_proxy_slot",
"snapshot_fake",
"thunkify",
"track_tensor",
"track_tensor_tree",
"wrap_key",
"wrapper_and_args_for_make_fx",
# torch.fx.experimental.recording
"record_shapeenv_event",
"replay_shape_env_events",
"shape_env_check_state_equal",
# torch.fx.experimental.sym_node
"ceil_impl",
"floor_ceil_helper",
"floor_impl",
"method_to_operator",
"sympy_is_channels_last_contiguous_2d",
"sympy_is_channels_last_contiguous_3d",
"sympy_is_channels_last_strides_2d",
"sympy_is_channels_last_strides_3d",
"sympy_is_channels_last_strides_generic",
"sympy_is_contiguous",
"sympy_is_contiguous_generic",
"to_node",
"wrap_node",
"sym_sqrt",
"sym_ite",
# torch.fx.experimental.symbolic_shapes
"bind_symbols",
"cast_symbool_to_symint_guardless",
"constrain_range",
"constrain_unify",
"create_contiguous",
"definitely_false",
"definitely_true",
"error",
"eval_guards",
"eval_is_non_overlapping_and_dense",
"expect_true",
"find_symbol_binding_fx_nodes",
"free_symbols",
"free_unbacked_symbols",
"fx_placeholder_targets",
"fx_placeholder_vals",
"guard_bool",
"guard_float",
"guard_int",
"guard_scalar",
"has_hint",
"has_symbolic_sizes_strides",
"has_free_symbols",
"hint_int",
"is_channels_last_contiguous_2d",
"is_channels_last_contiguous_3d",
"is_channels_last_strides_2d",
"is_channels_last_strides_3d",
"is_concrete_bool",
"is_concrete_int",
"is_contiguous",
"is_non_overlapping_and_dense_indicator",
"is_symbol_binding_fx_node",
"is_symbolic",
"parallel_and",
"parallel_or",
"sym_eq",
# torch.fx.experimental.unification.core
"reify",
# torch.fx.experimental.unification.match
"edge",
"match",
"ordering",
"supercedes",
# torch.fx.experimental.unification.more
"reify_object",
"unifiable",
"unify_object",
# torch.fx.experimental.unification.multipledispatch.conflict
"ambiguities",
"ambiguous",
"consistent",
"edge",
"ordering",
"super_signature",
"supercedes",
# torch.fx.experimental.unification.multipledispatch.core
"dispatch",
"ismethod",
# torch.fx.experimental.unification.multipledispatch.dispatcher
"ambiguity_warn",