# # 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.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", "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.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", "store_timeout", # 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.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.loss "loss_parallel", # 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_object_proxy", "get_innermost_proxy_mode", "get_isolated_graphmodule", "get_proxy_slot", "get_torch_dispatch_modes", "has_proxy_slot", "is_sym_node", "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", "create_contiguous", "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", "is_channels_last_contiguous_2d", "is_channels_last_contiguous_3d", "is_channels_last_strides_2d", "is_channels_last_strides_3d", "is_contiguous", "is_non_overlapping_and_dense_indicator", "is_nested_int", "is_symbol_binding_fx_node", "is_symbolic", # 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", "halt_ordering", "restart_ordering", "source", "str_signature", "variadic_signature_matches", "variadic_signature_matches_iter", "warning_text", # torch.fx.experimental.unification.multipledispatch.utils "expand_tuples", "groupby", "raises", "reverse_dict", # torch.fx.experimental.unification.multipledispatch.variadic "isvariadic", # torch.fx.experimental.unification.unification_tools "assoc", "assoc_in", "dissoc", "first", "get_in", "getter", "groupby", "itemfilter", "itemmap", "keyfilter", "keymap", "merge", "merge_with", "update_in", "valfilter", "valmap", # torch.fx.experimental.unification.utils "freeze", "hashable", "raises", "reverse_dict", "transitive_get", "xfail", # torch.fx.experimental.unification.variable "var", "vars", # torch.fx.experimental.unify_refinements "check_for_type_equality", "convert_eq", "infer_symbolic_types", "infer_symbolic_types_single_pass", "substitute_all_types", "substitute_solution_one_type", "unify_eq", # torch.fx.experimental.validator "bisect", "translation_validation_enabled", "translation_validation_timeout", "z3op", "z3str", # torch.fx.graph_module "reduce_deploy_graph_module", "reduce_graph_module", "reduce_package_graph_module", # torch.fx.node "has_side_effect", "map_aggregate", "map_arg", # torch.fx.operator_schemas "check_for_mutable_operation", "create_type_hint", "get_signature_for_torch_op", "normalize_function", "normalize_module", "type_matches", # torch.fx.passes.annotate_getitem_nodes "annotate_getitem_nodes", # torch.fx.passes.backends.cudagraphs "partition_cudagraphs", # torch.fx.passes.dialect.common.cse_pass "get_CSE_banned_ops", # torch.fx.passes.graph_manipulation "get_size_of_all_nodes", "get_size_of_node", "get_tensor_meta", "replace_target_nodes_with", # torch.fx.passes.infra.pass_manager "pass_result_wrapper", "this_before_that_pass_constraint", # torch.fx.passes.operator_support "any_chain", "chain", "create_op_support", # torch.fx.passes.param_fetch "default_matching", "extract_attrs_for_lowering", "lift_lowering_attrs_to_nodes", # torch.fx.passes.pass_manager "inplace_wrapper", "log_hook", "loop_pass", "these_before_those_pass_constraint", "this_before_that_pass_constraint", # torch.fx.passes.reinplace "reinplace", # torch.fx.passes.split_module "split_module", # torch.fx.passes.split_utils "getattr_recursive", "setattr_recursive", "split_by_tags", # torch.fx.passes.splitter_base "generate_inputs_for_submodules", # torch.fx.passes.tools_common "get_acc_ops_name", "get_node_target", "is_node_output_tensor", "legalize_graph", # torch.fx.passes.utils.common "compare_graphs", "lift_subgraph_as_module", # torch.fx.passes.utils.fuser_utils "erase_nodes", "fuse_as_graphmodule", "fuse_by_partitions", "insert_subgm", "topo_sort", "validate_partition", # torch.fx.passes.utils.source_matcher_utils "check_subgraphs_connected", "get_source_partitions", # torch.fx.proxy "assert_fn", # torch.fx.subgraph_rewriter "replace_pattern", "replace_pattern_with_filters", # torch.fx.tensor_type "is_consistent", "is_more_precise", # torch.fx.traceback "format_stack", "get_current_meta", "has_preserved_node_meta", "preserve_node_meta", "reset_grad_fn_seq_nr", "set_current_meta", "set_grad_fn_seq_nr", "set_stack_trace", # torch.jit.annotations "ann_to_type", "check_fn", "get_enum_value_type", "get_param_names", "get_signature", "get_type_line", "is_function_or_method", "is_tensor", "is_vararg", "parse_type_line", "split_type_line", "try_ann_to_type", "try_real_annotations", # torch.jit.frontend "build_class_def", "build_def", "build_ignore_context_manager", "build_param", "build_param_list", "build_stmts", "build_withitems", "find_before", "get_class_assigns", "get_class_properties", "get_default_args", "get_default_args_for_class", "get_jit_class_def", "get_jit_def", "is_reserved_name", "is_torch_jit_ignore_context_manager", # torch.jit.generate_bytecode "format_bytecode", "generate_upgraders_bytecode", # torch.jit.quantized "apply_permutation", "quantize_linear_modules", "quantize_rnn_cell_modules", "quantize_rnn_modules", # torch.library "define", "get_ctx", "impl", "impl_abstract", # torch.masked.maskedtensor.core "is_masked_tensor", # torch.masked.maskedtensor.creation "as_masked_tensor", "masked_tensor", # torch.multiprocessing.pool "clean_worker", # torch.multiprocessing.reductions "fd_id", "init_reductions", "rebuild_cuda_tensor", "rebuild_meta_tensor", "rebuild_event", "rebuild_nested_tensor", "rebuild_sparse_coo_tensor", "rebuild_sparse_compressed_tensor", "rebuild_storage_empty", "rebuild_storage_fd", "rebuild_storage_filename", "rebuild_tensor", "rebuild_typed_storage", "rebuild_typed_storage_child", "reduce_event", "reduce_storage", "reduce_tensor", "reduce_typed_storage", "reduce_typed_storage_child", "storage_from_cache", # torch.multiprocessing.spawn "start_processes", # torch.nn.functional "adaptive_max_pool1d_with_indices", "adaptive_max_pool2d_with_indices", "adaptive_max_pool3d_with_indices", "assert_int_or_pair", "fractional_max_pool2d_with_indices", "fractional_max_pool3d_with_indices", "max_pool1d_with_indices", "max_pool2d_with_indices", "max_pool3d_with_indices", "multi_head_attention_forward", # torch.nn.grad "conv1d_input", "conv1d_weight", "conv2d_input", "conv2d_weight", "conv3d_input", "conv3d_weight", # torch.nn.init "constant", "dirac", "eye", "kaiming_normal", "kaiming_uniform", "normal", "orthogonal", "sparse", "uniform", "xavier_normal", "xavier_uniform", # torch.nn.modules.rnn "apply_permutation", # torch.nn.modules.utils "consume_prefix_in_state_dict_if_present", # torch.nn.parallel.comm "broadcast", "broadcast_coalesced", "gather", "reduce_add", "reduce_add_coalesced", "scatter", # torch.nn.parallel.data_parallel "data_parallel", # torch.nn.parallel.parallel_apply "get_a_var", "parallel_apply", # torch.nn.parallel.replicate "replicate", # torch.nn.parallel.scatter_gather "gather", "is_namedtuple", "scatter", "scatter_kwargs", # torch.nn.parameter "is_lazy", # torch.nn.utils.clip_grad "clip_grad_norm", "clip_grad_norm_", "clip_grad_value_", # torch.nn.utils.convert_parameters "parameters_to_vector", "vector_to_parameters", # torch.nn.utils.fusion "fuse_conv_bn_eval", "fuse_conv_bn_weights", "fuse_linear_bn_eval", "fuse_linear_bn_weights", # torch.nn.utils.init "skip_init", # torch.nn.utils.memory_format "convert_conv2d_weight_memory_format", # torch.nn.utils.parametrizations "weight_norm", # torch.nn.utils.parametrize "transfer_parametrizations_and_params", "type_before_parametrizations", # torch.nn.utils.rnn "bind", "invert_permutation", # torch.nn.utils.spectral_norm "remove_spectral_norm", "spectral_norm", # torch.nn.utils.weight_norm "remove_weight_norm", "weight_norm", # torch.onnx.operators "reshape_from_tensor_shape", "shape_as_tensor", # torch.onnx.symbolic_caffe2 "add", "avg_pool2d", "cat", "conv2d", "conv2d_relu", "conv_prepack", "dequantize", "linear", "linear_prepack", "max_pool2d", "nchw2nhwc", "nhwc2nchw", "quantize_per_tensor", "register_quantized_ops", "relu", "reshape", "sigmoid", "slice", "upsample_nearest2d", # torch.onnx.symbolic_helper "args_have_same_dtype", "check_training_mode", "dequantize_helper", "is_complex_value", "quantize_helper", "quantized_args", "requantize_bias_helper", # torch.onnx.symbolic_opset10 "dequantize", "div", "embedding_bag", "fake_quantize_per_tensor_affine", "flip", "fmod", "isfinite", "isinf", "nan_to_num", "quantize_per_tensor", "quantized_add", "quantized_add_relu", "quantized_cat", "quantized_conv1d", "quantized_conv1d_relu", "quantized_conv2d", "quantized_conv2d_relu", "quantized_conv3d", "quantized_conv3d_relu", "quantized_conv_transpose1d", "quantized_conv_transpose2d", "quantized_conv_transpose3d", "quantized_group_norm", "quantized_hardswish", "quantized_instance_norm", "quantized_layer_norm", "quantized_leaky_relu", "quantized_linear", "quantized_linear_relu", "quantized_mul", "quantized_sigmoid", "slice", "sort", "topk", # torch.onnx.symbolic_opset11 "Delete", "add", "append", "arange", "argsort", "atleast_1d", "atleast_2d", "atleast_3d", "cat", "chunk", "clamp", "clamp_max", "clamp_min", "constant_pad_nd", "cumsum", "embedding_bag", "embedding_renorm", "flatten", "gather", "hardtanh", "hstack", "im2col", "index", "index_copy", "index_fill", "index_put", "insert", "linalg_det", "linalg_vector_norm", "logdet", "masked_scatter", "masked_select", "mm", "narrow", "normal", "pad", "pixel_shuffle", "pop", "prim_constant_chunk", "reflection_pad", "relu6", "remainder", "replication_pad", "round", "scatter", "select", "size", "sort", "split", "split_with_sizes", "squeeze", "stack", "topk", "unbind", "unique_dim", "unsqueeze", "vstack", # torch.onnx.symbolic_opset12 "argmax", "argmin", "binary_cross_entropy_with_logits", "celu", "cross_entropy_loss", "dropout", "einsum", "ge", "le", "native_dropout", "nll_loss", "nll_loss2d", "nll_loss_nd", "outer", "pow", "tensordot", "unfold", # torch.onnx.symbolic_opset13 "diagonal", "fake_quantize_per_channel_affine", "fake_quantize_per_tensor_affine", "frobenius_norm", "log_softmax", "nonzero_numpy", "quantized_conv1d", "quantized_conv1d_relu", "quantized_conv2d", "quantized_conv2d_relu", "quantized_conv3d", "quantized_conv3d_relu", "quantized_conv_transpose1d", "quantized_conv_transpose2d", "quantized_conv_transpose3d", "quantized_linear", "quantized_linear_relu", "repeat_interleave", "softmax", "split", "split_with_sizes", "tensor_split", "tile", "unbind", "unflatten", "unsafe_chunk", "unsafe_split", "unsafe_split_with_sizes", "where", # torch.onnx.symbolic_opset14 "batch_norm", "hardswish", "quantized_hardswish", "reshape", "scaled_dot_product_attention", "tril", "triu", # torch.onnx.symbolic_opset15 "aten__is_", "aten__isnot_", "bernoulli", "prim_unchecked_cast", # torch.onnx.symbolic_opset16 "grid_sampler", "scatter_add", "scatter_reduce", # torch.onnx.symbolic_opset17 "layer_norm", "stft", # torch.onnx.symbolic_opset18 "col2im", # torch.onnx.symbolic_opset7 "max", "min", # torch.onnx.symbolic_opset8 "addmm", "bmm", "empty", "empty_like", "flatten", "full", "full_like", "gt", "lt", "matmul", "mm", "ones", "ones_like", "prelu", "repeat", "zeros", "zeros_like", # torch.onnx.symbolic_opset9 "abs", "acos", "adaptive_avg_pool1d", "adaptive_avg_pool2d", "adaptive_avg_pool3d", "adaptive_max_pool1d", "adaptive_max_pool2d", "adaptive_max_pool3d", "add", "addcmul", "addmm", "alias", "amax", "amin", "aminmax", "arange", "argmax", "argmin", "as_strided", "as_tensor", "asin", "atan", "atan2", "avg_pool1d", "avg_pool2d", "avg_pool3d", "baddbmm", "batch_norm", "bernoulli", "bitwise_not", "bitwise_or", "bmm", "broadcast_tensors", "broadcast_to", "bucketize", "cat", "cdist", "ceil", "clamp", "clamp_max", "clamp_min", "clone", "constant_pad_nd", "contiguous", "conv1d", "conv2d", "conv3d", "conv_tbc", "conv_transpose1d", "conv_transpose2d", "conv_transpose3d", "convert_element_type", "convolution", "cos", "cosine_similarity", "cross", "cumsum", "detach", "dim", "div", "dot", "dropout", "elu", "embedding", "embedding_bag", "empty", "empty_like", "eq", "erf", "exp", "expand", "expand_as", "eye", "fill", "flatten", "floor", "floor_divide", "floordiv", "frobenius_norm", "full", "full_like", "gather", "ge", "gelu", "get_pool_ceil_padding", "glu", "group_norm", "gru", "gt", "hann_window", "hardshrink", "hardsigmoid", "hardswish", "hardtanh", "index", "index_add", "index_copy", "index_fill", "index_put", "index_select", "instance_norm", "is_floating_point", "is_pinned", "isnan", "item", "kl_div", "layer_norm", "le", "leaky_relu", "lerp", "lift", "linalg_cross", "linalg_matrix_norm", "linalg_norm", "linalg_vector_norm", "linear", "linspace", "log", "log10", "log1p", "log2", "log_sigmoid", "log_softmax", "logical_and", "logical_not", "logical_or", "logical_xor", "logit", "logsumexp", "lstm", "lstm_cell", "lt", "masked_fill", "masked_fill_", "matmul", "max", "max_pool1d", "max_pool1d_with_indices", "max_pool2d", "max_pool2d_with_indices", "max_pool3d", "max_pool3d_with_indices", "maximum", "meshgrid", "min", "minimum", "mish", "mm", "movedim", "mse_loss", "mul", "multinomial", "mv", "narrow", "native_layer_norm", "ne", "neg", "new_empty", "new_full", "new_ones", "new_zeros", "nonzero", "nonzero_numpy", "noop_complex_operators", "norm", "numel", "numpy_T", "one_hot", "ones", "ones_like", "onnx_placeholder", "overload_by_arg_count", "pad", "pairwise_distance", "permute", "pixel_shuffle", "pixel_unshuffle", "pow", "prelu", "prim_constant", "prim_constant_chunk", "prim_constant_split", "prim_data", "prim_device", "prim_dtype", "prim_if", "prim_layout", "prim_list_construct", "prim_list_unpack", "prim_loop", "prim_max", "prim_min", "prim_shape", "prim_tolist", "prim_tuple_construct", "prim_type", "prim_unchecked_cast", "prim_uninitialized", "rand", "rand_like", "randint", "randint_like", "randn", "randn_like", "reciprocal", "reflection_pad", "relu", "relu6", "remainder", "repeat", "repeat_interleave", "replication_pad", "reshape", "reshape_as", "rnn_relu", "rnn_tanh", "roll", "rrelu", "rsqrt", "rsub", "scalar_tensor", "scatter", "scatter_add", "select", "selu", "sigmoid", "sign", "silu", "sin", "size", "slice", "softmax", "softplus", "softshrink", "sort", "split", "split_with_sizes", "sqrt", "square", "squeeze", "stack", "std", "std_mean", "sub", "t", "take", "tan", "tanh", "tanhshrink", "tensor", "threshold", "to", "topk", "transpose", "true_divide", "type_as", "unbind", "unfold", "unsafe_chunk", "unsafe_split", "unsafe_split_with_sizes", "unsqueeze", "unsupported_complex_operators", "unused", "upsample_bilinear2d", "upsample_linear1d", "upsample_nearest1d", "upsample_nearest2d", "upsample_nearest3d", "upsample_trilinear3d", "var", "var_mean", "view", "view_as", "where", "wrap_logical_op_with_cast_to", "wrap_logical_op_with_negation", "zero", "zeros", "zeros_like", # torch.onnx.utils "disable_apex_o2_state_dict_hook", "export", "export_to_pretty_string", "exporter_context", "is_in_onnx_export", "model_signature", "register_custom_op_symbolic", "select_model_mode_for_export", "setup_onnx_logging", "unconvertible_ops", "unpack_quantized_tensor", "warn_on_static_input_change", # torch.onnx.verification "check_export_model_diff", "verify", "verify_aten_graph", # torch.optim.adadelta "adadelta", # torch.optim.adagrad "adagrad", # torch.optim.adam "adam", # torch.optim.adamax "adamax", # torch.optim.adamw "adamw", # torch.optim.asgd "asgd", # torch.optim.nadam "nadam", # torch.optim.optimizer "register_optimizer_step_post_hook", "register_optimizer_step_pre_hook", # torch.optim.radam "radam", # torch.optim.rmsprop "rmsprop", # torch.optim.rprop "rprop", # torch.optim.sgd "sgd", # torch.optim.swa_utils "get_ema_avg_fn", "get_ema_multi_avg_fn", "get_swa_avg_fn", "get_swa_multi_avg_fn", "update_bn", # torch.overrides "enable_reentrant_dispatch", # torch.package.analyze.find_first_use_of_broken_modules "find_first_use_of_broken_modules", # torch.package.analyze.is_from_package "is_from_package", # torch.package.analyze.trace_dependencies "trace_dependencies", # torch.profiler.itt "range", # torch.profiler.profiler "schedule", "supported_activities", "tensorboard_trace_handler", # torch.return_types "pytree_register_structseq", # torch.serialization "check_module_version_greater_or_equal", "default_restore_location", "load", "location_tag", "mkdtemp", "normalize_storage_type", "save", "storage_to_tensor_type", "validate_cuda_device", "validate_hpu_device", # torch.signal.windows.windows "bartlett", "blackman", "cosine", "exponential", "gaussian", "general_cosine", "general_hamming", "hamming", "hann", "kaiser", "nuttall", # torch.sparse.semi_structured "to_sparse_semi_structured", # torch.utils.backend_registration "generate_methods_for_privateuse1_backend", "rename_privateuse1_backend", # torch.utils.benchmark.examples.blas_compare_setup "conda_run", # torch.utils.benchmark.examples.op_benchmark "assert_dicts_equal", # torch.utils.benchmark.op_fuzzers.spectral "power_range", # torch.utils.benchmark.utils.common "ordered_unique", "select_unit", "set_torch_threads", "trim_sigfig", "unit_to_english", # torch.utils.benchmark.utils.compare "optional_min", # torch.utils.benchmark.utils.compile "bench_all", "bench_loop", "benchmark_compile", # torch.utils.benchmark.utils.cpp_jit "compile_callgrind_template", "compile_timeit_template", "get_compat_bindings", # torch.utils.benchmark.utils.fuzzer "dtype_size", "prod", # torch.utils.benchmark.utils.timer "timer", # torch.utils.benchmark.utils.valgrind_wrapper.timer_interface "wrapper_singleton", # torch.utils.bundled_inputs "augment_many_model_functions_with_bundled_inputs", "augment_model_with_bundled_inputs", "bundle_inputs", "bundle_large_tensor", "bundle_randn", # torch.utils.checkpoint "check_backward_validity", "detach_variable", "get_device_states", "noop_context_fn", "set_checkpoint_early_stop", "set_device_states", # torch.utils.collect_env "check_release_file", "get_cachingallocator_config", "get_clang_version", "get_cmake_version", "get_conda_packages", "get_cpu_info", "get_cuda_module_loading_config", "get_cudnn_version", "get_env_info", "get_gcc_version", "get_gpu_info", "get_libc_version", "get_lsb_version", "get_mac_version", "get_nvidia_driver_version", "get_nvidia_smi", "get_os", "get_pip_packages", "get_platform", "get_pretty_env_info", "get_python_platform", "get_running_cuda_version", "get_windows_version", "is_xnnpack_available", "pretty_str", # torch.utils.cpp_backtrace "get_cpp_backtrace", # torch.utils.cpp_extension "check_compiler_is_gcc", "check_compiler_ok_for_platform", "get_cxx_compiler", "get_default_build_root", "library_paths", "remove_extension_h_precompiler_headers", # torch.utils.data.backward_compatibility "worker_init_fn", # torch.utils.data.datapipes.dataframe.dataframe_wrapper "concat", "create_dataframe", "get_columns", "get_df_wrapper", "get_item", "get_len", "is_column", "is_dataframe", "iterate", "set_df_wrapper", # torch.utils.data.datapipes.dataframe.dataframes "disable_capture", "get_val", # torch.utils.data.datapipes.gen_pyi "extract_class_name", "extract_method_name", "find_file_paths", "gen_from_template", "get_method_definitions", "materialize_lines", "parse_datapipe_file", "parse_datapipe_files", "process_signature", "split_outside_bracket", # torch.utils.data.datapipes.map.callable "default_fn", # torch.utils.data.datapipes.utils.common "get_file_binaries_from_pathnames", "get_file_pathnames_from_root", "match_masks", "validate_input_col", "validate_pathname_binary_tuple", # torch.utils.data.datapipes.utils.decoder "audiohandler", "basichandlers", "extension_extract_fn", "handle_extension", "imagehandler", "mathandler", "videohandler", # torch.utils.data.dataset "random_split", # torch.utils.data.graph "traverse", "traverse_dps", # torch.utils.data.graph_settings "apply_random_seed", "apply_sharding", "apply_shuffle_seed", "apply_shuffle_settings", "get_all_graph_pipes", # torch.utils.flop_counter "addmm_flop", "baddbmm_flop", "bmm_flop", "conv_backward_flop", "conv_flop", "conv_flop_count", "convert_num_with_suffix", "get_shape", "get_suffix_str", "mm_flop", "normalize_tuple", "register_flop_formula", "sdpa_backward_flop", "sdpa_backward_flop_count", "sdpa_flop", "sdpa_flop_count", "shape_wrapper", "transpose_shape", # torch.utils.hipify.hipify_python "add_dim3", "compute_stats", "extract_arguments", "file_add_header", "file_specific_replacement", "find_bracket_group", "find_closure_group", "find_parentheses_group", "fix_static_global_kernels", "get_hip_file_path", "hip_header_magic", "hipify", "is_caffe2_gpu_file", "is_cusparse_file", "is_out_of_place", "is_pytorch_file", "is_special_file", "match_extensions", "matched_files_iter", "openf", "preprocess_file_and_save_result", "preprocessor", "processKernelLaunches", "replace_extern_shared", "replace_math_functions", "str2bool", # torch.utils.hooks "unserializable_hook", "warn_if_has_hooks", # torch.utils.jit.log_extract "extract_ir", "load_graph_and_inputs", "make_tensor_from_type", "no_fuser", "time_cpu", "time_cuda", # torch.utils.mkldnn "to_mkldnn", # torch.utils.mobile_optimizer "generate_mobile_module_lints", # torch.utils.tensorboard.summary "audio", "compute_curve", "custom_scalars", "draw_boxes", "half_to_int", "histogram", "histogram_raw", "hparams", "image", "image_boxes", "int_to_half", "make_histogram", "make_image", "make_video", "mesh", "pr_curve", "pr_curve_raw", "scalar", "tensor_proto", "text", "video", # torch.utils.throughput_benchmark "format_time", ] coverage_ignore_classes = [ # torch "FatalError", "QUInt2x4Storage", "Size", "Storage", "Stream", "Tensor", "finfo", "iinfo", "qscheme", "AggregationType", "AliasDb", "AnyType", "Argument", "ArgumentSpec", "AwaitType", "BenchmarkConfig", "BenchmarkExecutionStats", "Block", "BoolType", "BufferDict", "CallStack", "Capsule", "ClassType", "Code", "CompleteArgumentSpec", "ComplexType", "ConcreteModuleType", "ConcreteModuleTypeBuilder", "DeepCopyMemoTable", "DeserializationStorageContext", "DeviceObjType", "DictType", "DispatchKey", "DispatchKeySet", "EnumType", "ExcludeDispatchKeyGuard", "ExecutionPlan", "FileCheck", "FloatType", "FunctionSchema", "Gradient", "Graph", "GraphExecutorState", "IODescriptor", "InferredType", "IntType", "InterfaceType", "ListType", "LockingLogger", "MobileOptimizerType", "ModuleDict", "Node", "NoneType", "NoopLogger", "NumberType", "OperatorInfo", "OptionalType", "ParameterDict", "PyObjectType", "PyTorchFileReader", "PyTorchFileWriter", "RRefType", "ScriptClass", "ScriptClassFunction", "ScriptDict", "ScriptDictIterator", "ScriptDictKeyIterator", "ScriptList", "ScriptListIterator", "ScriptMethod", "ScriptModule", "ScriptModuleSerializer", "ScriptObject", "ScriptObjectProperty", "SerializationStorageContext", "StaticModule", "StringType", "SymIntType", "SymBoolType", "ThroughputBenchmark", "TracingState", "TupleType", "Type", "UnionType", "Use", "Value", # torch.cuda "BFloat16Storage", "BFloat16Tensor", "BoolStorage", "BoolTensor", "ByteStorage", "ByteTensor", "CharStorage", "CharTensor", "ComplexDoubleStorage", "ComplexFloatStorage", "CudaError", "DeferredCudaCallError", "DoubleStorage", "DoubleTensor", "FloatStorage", "FloatTensor", "HalfStorage", "HalfTensor", "IntStorage", "IntTensor", "LongStorage", "LongTensor", "ShortStorage", "ShortTensor", "cudaStatus", # torch.cuda._sanitizer "Access", "AccessType", "Await", "CUDASanitizer", "CUDASanitizerDispatchMode", "CUDASanitizerErrors", "EventHandler", "SynchronizationError", "UnsynchronizedAccessError", # torch.cuda.memory "MemPoolContext", # torch.distributed.elastic.multiprocessing.errors "ChildFailedError", "ProcessFailure", # torch.distributions.constraints "cat", "greater_than", "greater_than_eq", "half_open_interval", "independent", "integer_interval", "interval", "less_than", "multinomial", "stack", # torch.distributions.transforms "AffineTransform", "CatTransform", "ComposeTransform", "CorrCholeskyTransform", "CumulativeDistributionTransform", "ExpTransform", "IndependentTransform", "PowerTransform", "ReshapeTransform", "SigmoidTransform", "SoftmaxTransform", "SoftplusTransform", "StackTransform", "StickBreakingTransform", "TanhTransform", "Transform", # torch.jit "CompilationUnit", "Error", "Future", "ScriptFunction", # torch.onnx "CheckerError", "ExportTypes", # torch.backends "ContextProp", "PropModule", # torch.backends.cuda "cuBLASModule", "cuFFTPlanCache", "cuFFTPlanCacheAttrContextProp", "cuFFTPlanCacheManager", # torch.distributed.algorithms.ddp_comm_hooks "DDPCommHookType", # torch.jit.mobile "LiteScriptModule", # torch.ao.nn.quantized.modules "DeQuantize", "Quantize", # torch.utils.backcompat "Warning", # torch.ao.nn.intrinsic.modules.fused "ConvAdd2d", "ConvAddReLU2d", "LinearBn1d", "LinearLeakyReLU", "LinearTanh", # torch.ao.nn.intrinsic.qat.modules.conv_fused "ConvBnReLU1d", "ConvBnReLU2d", "ConvBnReLU3d", "ConvReLU1d", "ConvReLU2d", "ConvReLU3d", # torch.ao.nn.intrinsic.qat.modules.linear_fused "LinearBn1d", # torch.ao.nn.intrinsic.qat.modules.linear_relu "LinearReLU", # torch.ao.nn.intrinsic.quantized.dynamic.modules.linear_relu "LinearReLU", # torch.ao.nn.intrinsic.quantized.modules.bn_relu "BNReLU2d", "BNReLU3d", # torch.ao.nn.intrinsic.quantized.modules.conv_add "ConvAdd2d", "ConvAddReLU2d", # torch.ao.nn.intrinsic.quantized.modules.conv_relu "ConvReLU1d", "ConvReLU2d", "ConvReLU3d", # torch.ao.nn.intrinsic.quantized.modules.linear_relu "LinearLeakyReLU", "LinearReLU", "LinearTanh", # torch.ao.nn.qat.modules.conv "Conv1d", "Conv2d", "Conv3d", # torch.ao.nn.qat.modules.embedding_ops "Embedding", "EmbeddingBag", # torch.ao.nn.qat.modules.linear "Linear", # torch.ao.nn.quantizable.modules.activation "MultiheadAttention", # torch.ao.nn.quantizable.modules.rnn "LSTM", "LSTMCell", # torch.ao.nn.quantized.dynamic.modules.conv "Conv1d", "Conv2d", "Conv3d", "ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d", # torch.ao.nn.quantized.dynamic.modules.linear "Linear", # torch.ao.nn.quantized.dynamic.modules.rnn "GRU", "GRUCell", "LSTM", "LSTMCell", "PackedParameter", "RNNBase", "RNNCell", "RNNCellBase", # torch.ao.nn.quantized.modules.activation "ELU", "Hardswish", "LeakyReLU", "MultiheadAttention", "PReLU", "ReLU6", "Sigmoid", "Softmax", # torch.ao.nn.quantized.modules.batchnorm "BatchNorm2d", "BatchNorm3d", # torch.ao.nn.quantized.modules.conv "Conv1d", "Conv2d", "Conv3d", "ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d", # torch.ao.nn.quantized.modules.dropout "Dropout", # torch.ao.nn.quantized.modules.embedding_ops "Embedding", "EmbeddingBag", "EmbeddingPackedParams", # torch.ao.nn.quantized.modules.functional_modules "FXFloatFunctional", "FloatFunctional", "QFunctional", # torch.ao.nn.quantized.modules.linear "Linear", "LinearPackedParams", # torch.ao.nn.quantized.modules.normalization "GroupNorm", "InstanceNorm1d", "InstanceNorm2d", "InstanceNorm3d", "LayerNorm", # torch.ao.nn.quantized.modules.rnn "LSTM", # torch.ao.nn.quantized.modules.utils "WeightedQuantizedModule", # torch.ao.nn.quantized.reference.modules.conv "Conv1d", "Conv2d", "Conv3d", "ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d", # torch.ao.nn.quantized.reference.modules.linear "Linear", # torch.ao.nn.quantized.reference.modules.rnn "GRU", "GRUCell", "LSTM", "LSTMCell", "RNNBase", "RNNCell", "RNNCellBase", # torch.ao.nn.quantized.reference.modules.sparse "Embedding", "EmbeddingBag", # torch.ao.nn.quantized.reference.modules.utils "ReferenceQuantizedModule", # torch.ao.nn.sparse.quantized.dynamic.linear "Linear", # torch.ao.nn.sparse.quantized.linear "Linear", "LinearPackedParams", # torch.ao.nn.sparse.quantized.utils "LinearBlockSparsePattern", # torch.ao.ns.fx.graph_matcher "SubgraphTypeRelationship", # torch.ao.ns.fx.n_shadows_utils "OutputProp", # torch.ao.ns.fx.ns_types "NSSingleResultValuesType", "NSSubgraph", # torch.ao.ns.fx.qconfig_multi_mapping "QConfigMultiMapping", # torch.ao.pruning.scheduler.base_scheduler "BaseScheduler", # torch.ao.pruning.scheduler.cubic_scheduler "CubicSL", # torch.ao.pruning.scheduler.lambda_scheduler "LambdaSL", # torch.ao.pruning.sparsifier.base_sparsifier "BaseSparsifier", # torch.ao.pruning.sparsifier.nearly_diagonal_sparsifier "NearlyDiagonalSparsifier", # torch.ao.pruning.sparsifier.utils "FakeSparsity", # torch.ao.pruning.sparsifier.weight_norm_sparsifier "WeightNormSparsifier", # torch.ao.quantization.backend_config.backend_config "BackendConfig", "BackendPatternConfig", "DTypeConfig", # torch.ao.quantization.fake_quantize "FakeQuantize", "FakeQuantizeBase", "FixedQParamsFakeQuantize", "FusedMovingAvgObsFakeQuantize", # torch.ao.quantization.fx.fuse_handler "DefaultFuseHandler", "FuseHandler", # torch.ao.quantization.fx.graph_module "FusedGraphModule", "ObservedGraphModule", "ObservedStandaloneGraphModule", # torch.ao.quantization.fx.quantize_handler "BatchNormQuantizeHandler", "BinaryOpQuantizeHandler", "CatQuantizeHandler", "ConvReluQuantizeHandler", "CopyNodeQuantizeHandler", "CustomModuleQuantizeHandler", "DefaultNodeQuantizeHandler", "EmbeddingQuantizeHandler", "FixedQParamsOpQuantizeHandler", "GeneralTensorShapeOpQuantizeHandler", "LinearReLUQuantizeHandler", "RNNDynamicQuantizeHandler", "StandaloneModuleQuantizeHandler", # torch.ao.quantization.fx.tracer "QuantizationTracer", "ScopeContextManager", # torch.ao.quantization.fx.utils "ObservedGraphModuleAttrs", # torch.ao.quantization.observer "FixedQParamsObserver", "HistogramObserver", "MinMaxObserver", "MovingAverageMinMaxObserver", "MovingAveragePerChannelMinMaxObserver", "NoopObserver", "ObserverBase", "PerChannelMinMaxObserver", "PlaceholderObserver", "RecordingObserver", "ReuseInputObserver", "UniformQuantizationObserverBase", "default_debug_observer", "default_placeholder_observer", "default_reuse_input_observer", # torch.ao.quantization.pt2e.duplicate_dq_pass "DuplicateDQPass", # torch.ao.quantization.pt2e.port_metadata_pass "PortNodeMetaForQDQ", # torch.ao.quantization.qconfig "QConfigDynamic", # torch.ao.quantization.quant_type "QuantType", # torch.ao.quantization.quantizer.composable_quantizer "ComposableQuantizer", # torch.ao.quantization.quantizer.embedding_quantizer "EmbeddingQuantizer", # torch.ao.quantization.quantizer.quantizer "DerivedQuantizationSpec", "FixedQParamsQuantizationSpec", "QuantizationAnnotation", "QuantizationSpec", "QuantizationSpecBase", "SharedQuantizationSpec", # torch.ao.quantization.quantizer.x86_inductor_quantizer "X86InductorQuantizer", # torch.ao.quantization.quantizer.xnnpack_quantizer "XNNPACKQuantizer", # torch.ao.quantization.quantizer.xnnpack_quantizer_utils "OperatorConfig", "QuantizationConfig", # torch.ao.quantization.stubs "DeQuantStub", "QuantStub", "QuantWrapper", # torch.ao.quantization.utils "MatchAllNode", # torch.backends.cudnn.rnn "Unserializable", # torch.amp.grad_scaler "GradScaler", "OptState", # torch.cuda.graphs "CUDAGraph", # torch.cuda.streams "Event", # torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook "PostLocalSGDState", # torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook "PowerSGDState", # torch.distributed.algorithms.join "Join", "JoinHook", "Joinable", # torch.distributed.algorithms.model_averaging.averagers "ModelAverager", "PeriodicModelAverager", # torch.distributed.algorithms.model_averaging.hierarchical_model_averager "HierarchicalModelAverager", # torch.distributed.argparse_util "check_env", "env", # torch.distributed.checkpoint.api "CheckpointException", # torch.distributed.checkpoint.default_planner "DefaultLoadPlanner", "DefaultSavePlanner", # torch.distributed.checkpoint.filesystem "FileSystemReader", "FileSystemWriter", # torch.distributed.checkpoint.metadata "BytesStorageMetadata", "ChunkStorageMetadata", "Metadata", "MetadataIndex", # torch.distributed.checkpoint.planner "LoadItemType", "LoadPlanner", "SavePlanner", "WriteItemType", # torch.distributed.checkpoint.state_dict "DistributedStateDictOptions", # torch.distributed.checkpoint.storage "WriteResult", # torch.distributed.collective_utils "SyncPayload", # torch.distributed.distributed_c10d "AllToAllOptions", "AllreduceCoalescedOptions", "AllreduceOptions", "Backend", "BackendConfig", "BarrierOptions", "BroadcastOptions", "DebugLevel", "GatherOptions", "GroupMember", "ProcessGroup", "ProcessGroupGloo", "ProcessGroupNCCL", "ReduceOptions", "ReduceScatterOptions", "ScatterOptions", "Work", "group", # torch.distributed.elastic.agent.server.api "ElasticAgent", "RunResult", "SimpleElasticAgent", "WorkerSpec", # torch.distributed.elastic.events.api "Event", "RdzvEvent", # torch.distributed.elastic.metrics.api "ConsoleMetricHandler", "MetricData", "MetricHandler", "MetricStream", "MetricsConfig", "NullMetricHandler", # torch.distributed.elastic.multiprocessing.api "MultiprocessContext", "PContext", "RunProcsResult", "SignalException", "Std", "SubprocessContext", "SubprocessHandler", # torch.distributed.elastic.multiprocessing.tail_log "TailLog", # torch.distributed.elastic.rendezvous.api "RendezvousHandler", "RendezvousHandlerRegistry", "RendezvousParameters", # torch.distributed.elastic.rendezvous.dynamic_rendezvous "DynamicRendezvousHandler", "RendezvousSettings", # torch.distributed.elastic.rendezvous.etcd_rendezvous "EtcdRendezvous", "EtcdRendezvousHandler", "EtcdRendezvousRetryImmediately", "EtcdRendezvousRetryableFailure", # torch.distributed.elastic.rendezvous.etcd_server "EtcdServer", # torch.distributed.elastic.rendezvous.static_tcp_rendezvous "StaticTCPRendezvous", # torch.distributed.elastic.timer.api "RequestQueue", "TimerClient", "TimerServer", # torch.distributed.elastic.timer.file_based_local_timer "FileTimerClient", "FileTimerRequest", "FileTimerServer", # torch.distributed.elastic.timer.local_timer "LocalTimerClient", "LocalTimerServer", "MultiprocessingRequestQueue", # torch.distributed.elastic.utils.api "macros", # torch.distributed.elastic.utils.data.cycling_iterator "CyclingIterator", # torch.distributed.elastic.utils.data.elastic_distributed_sampler "ElasticDistributedSampler", # torch.distributed.fsdp.api "StateDictType", # torch.distributed.fsdp.fully_sharded_data_parallel "FullyShardedDataParallel", "OptimStateKeyType", # torch.distributed.fsdp.sharded_grad_scaler "ShardedGradScaler", # torch.distributed.fsdp.wrap "CustomPolicy", "ModuleWrapPolicy", # torch.distributed.launcher.api "LaunchConfig", "elastic_launch", # torch.distributed.optim.optimizer "DistributedOptimizer", # torch.distributed.optim.post_localSGD_optimizer "PostLocalSGDOptimizer", # torch.distributed.optim.zero_redundancy_optimizer "ZeroRedundancyOptimizer", # torch.distributed.rpc.api "AllGatherStates", "RRef", # torch.distributed.rpc.backend_registry "BackendValue", # torch.distributed.rpc.internal "PythonUDF", "RPCExecMode", "RemoteException", # torch.distributed.rpc.rref_proxy "RRefProxy", # torch.distributed.tensor.parallel.fsdp "DTensorExtensions", # torch.distributed.tensor.parallel.style "ParallelStyle", # torch.distributions.logistic_normal "LogisticNormal", # torch.distributions.one_hot_categorical "OneHotCategoricalStraightThrough", # torch.distributions.relaxed_categorical "ExpRelaxedCategorical", # torch.distributions.utils "lazy_property", # torch.export.exported_program "ConstantArgument", "ExportedProgram", # torch.fx.experimental.accelerator_partitioner "DAG", "DAGNode", "PartitionResult", "Partitioner", # torch.fx.experimental.const_fold "FoldedGraphModule", # torch.fx.experimental.graph_gradual_typechecker "Refine", # torch.fx.experimental.meta_tracer "MetaAttribute", "MetaDeviceAttribute", "MetaProxy", "MetaTracer", # torch.fx.experimental.migrate_gradual_types.constraint "ApplyBroadcasting", "BVar", "BinConstraintD", "BinConstraintT", "BinaryConstraint", "CalcConv", "CalcMaxPool", "CalcProduct", "CanReshape", "Conj", "Constraint", "DGreatestUpperBound", "DVar", "Disj", "F", "GetItem", "GetItemTensor", "IndexSelect", "Prod", "T", "TGreatestUpperBound", "TVar", "Transpose", # torch.fx.experimental.migrate_gradual_types.constraint_generator "ConstraintGenerator", # torch.fx.experimental.normalize "NormalizeArgs", "NormalizeOperators", # torch.fx.experimental.optimization "MklSubgraph", "UnionFind", # torch.fx.experimental.partitioner_utils "Device", "Partition", "PartitionLatency", "PartitionMode", "PartitionerConfig", # torch.fx.experimental.proxy_tensor "DecompositionInterpreter", "PreDispatchTorchFunctionMode", "ProxySymDispatchMode", "ProxyTorchDispatchMode", "PythonKeyTracer", # torch.fx.experimental.recording "FakeTensorMeta", "NotEqualError", "ShapeEnvEvent", # torch.fx.experimental.refinement_types "Equality", # torch.fx.experimental.rewriter "AST_Rewriter", "RewritingTracer", # torch.fx.experimental.schema_type_annotation "AnnotateTypesWithSchema", # torch.fx.experimental.sym_node "SymNode", # torch.fx.experimental.symbolic_shapes "Constraint", "ConstraintViolationError", "DynamicDimConstraintPrinter", "GuardOnDataDependentSymNode", "PendingUnbackedSymbolNotFound", "LoggingShapeGuardPrinter", "SymExprPrinter", "RelaxedUnspecConstraint", "RuntimeAssert", "ShapeGuardPrinter", "SymDispatchMode", "SymbolicContext", # torch.fx.experimental.unification.match "Dispatcher", "VarDispatcher", # torch.fx.experimental.unification.multipledispatch.conflict "AmbiguityWarning", # torch.fx.experimental.unification.multipledispatch.dispatcher "Dispatcher", "MDNotImplementedError", "MethodDispatcher", # torch.fx.experimental.unification.multipledispatch.variadic "Variadic", "VariadicSignatureMeta", "VariadicSignatureType", # torch.fx.experimental.unification.variable "Var", # torch.fx.experimental.validator "BisectValidationException", "PopulateValidator", "SympyToZ3", "ValidationException", # torch.fx.graph "PythonCode", # torch.fx.immutable_collections "immutable_dict", "immutable_list", # torch.fx.interpreter "Interpreter", # torch.fx.operator_schemas "ArgsKwargsPair", # torch.fx.passes.backends.cudagraphs "CudaGraphsSupport", # torch.fx.passes.dialect.common.cse_pass "CSEPass", # torch.fx.passes.fake_tensor_prop "FakeTensorProp", # torch.fx.passes.graph_drawer "FxGraphDrawer", # torch.fx.passes.graph_manipulation "size_bytes", # torch.fx.passes.infra.partitioner "CapabilityBasedPartitioner", "Partition", # torch.fx.passes.infra.pass_base "PassBase", "PassResult", # torch.fx.passes.infra.pass_manager "PassManager", # torch.fx.passes.net_min_base "FxNetMinimizerBadModuleError", "FxNetMinimizerResultMismatchError", "FxNetMinimizerRunFuncError", # torch.fx.passes.operator_support "OpSupports", "OperatorSupport", "OperatorSupportBase", # torch.fx.passes.pass_manager "PassManager", # torch.fx.passes.shape_prop "ShapeProp", # torch.fx.passes.split_module "Partition", # torch.fx.passes.split_utils "Component", # torch.fx.passes.splitter_base "FxNetAccNodesFinder", "FxNetSplitterInternalError", "SplitResult", "Subgraph", # torch.fx.passes.tests.test_pass_manager "TestPassManager", # torch.fx.passes.tools_common "FxNetAccFusionsFinder", # torch.fx.passes.utils.common "HolderModule", # torch.fx.passes.utils.matcher_utils "InternalMatch", "SubgraphMatcher", # torch.fx.passes.utils.source_matcher_utils "SourcePartition", # torch.fx.proxy "Attribute", "ParameterProxy", "Proxy", "Scope", "ScopeContextManager", "TraceError", "TracerBase", # torch.fx.subgraph_rewriter "Match", "ReplacedPatterns", # torch.jit.annotations "EvalEnv", "Module", # torch.jit.frontend "Builder", "ExprBuilder", "FrontendError", "FrontendTypeError", "NotSupportedError", "StmtBuilder", "UnsupportedNodeError", "WithItemBuilder", # torch.masked.maskedtensor.core "MaskedTensor", # torch.multiprocessing.pool "Pool", # torch.multiprocessing.queue "ConnectionWrapper", "Queue", "SimpleQueue", # torch.multiprocessing.reductions "SharedCache", # torch.multiprocessing.spawn "ProcessContext", "ProcessException", "ProcessExitedException", "ProcessRaisedException", "SpawnContext", # torch.nn.cpp "ModuleWrapper", "OrderedDictWrapper", # torch.nn.modules.activation "CELU", "ELU", "GELU", "GLU", "Hardshrink", "Hardsigmoid", "Hardswish", "Hardtanh", "LeakyReLU", "LogSigmoid", "LogSoftmax", "Mish", "MultiheadAttention", "PReLU", "RReLU", "ReLU", "ReLU6", "SELU", "SiLU", "Sigmoid", "Softmax", "Softmax2d", "Softmin", "Softplus", "Softshrink", "Softsign", "Tanh", "Tanhshrink", "Threshold", # torch.nn.modules.adaptive "AdaptiveLogSoftmaxWithLoss", # torch.nn.modules.batchnorm "SyncBatchNorm", # torch.nn.modules.channelshuffle "ChannelShuffle", # torch.nn.modules.container "Container", "ModuleList", "ParameterList", "Sequential", # torch.nn.modules.conv "Conv1d", "Conv2d", "Conv3d", "ConvTranspose1d", "ConvTranspose2d", "ConvTranspose3d", # torch.nn.modules.distance "CosineSimilarity", "PairwiseDistance", # torch.nn.modules.dropout "AlphaDropout", "Dropout", "Dropout1d", "Dropout2d", "Dropout3d", "FeatureAlphaDropout", # torch.nn.modules.flatten "Flatten", "Unflatten", # torch.nn.modules.fold "Fold", "Unfold", # torch.nn.modules.linear "Bilinear", "Identity", "LazyLinear", "Linear", "NonDynamicallyQuantizableLinear", # torch.nn.modules.loss "BCELoss", "BCEWithLogitsLoss", "CTCLoss", "CosineEmbeddingLoss", "CrossEntropyLoss", "GaussianNLLLoss", "HingeEmbeddingLoss", "HuberLoss", "KLDivLoss", "L1Loss", "MSELoss", "MarginRankingLoss", "MultiLabelMarginLoss", "MultiLabelSoftMarginLoss", "MultiMarginLoss", "NLLLoss", "NLLLoss2d", "PoissonNLLLoss", "SmoothL1Loss", "SoftMarginLoss", "TripletMarginLoss", "TripletMarginWithDistanceLoss", # torch.nn.modules.module "Module", # torch.nn.modules.normalization "CrossMapLRN2d", "GroupNorm", "LayerNorm", "LocalResponseNorm", # torch.nn.modules.padding "CircularPad1d", "CircularPad2d", "CircularPad3d", "ZeroPad1d", "ZeroPad2d", "ZeroPad3d", # torch.nn.modules.pixelshuffle "PixelShuffle", "PixelUnshuffle", # torch.nn.modules.pooling "AdaptiveAvgPool1d", "AdaptiveAvgPool2d", "AdaptiveAvgPool3d", "AdaptiveMaxPool1d", "AdaptiveMaxPool2d", "AdaptiveMaxPool3d", "AvgPool1d", "AvgPool2d", "AvgPool3d", "FractionalMaxPool2d", "FractionalMaxPool3d", "LPPool1d", "LPPool2d", "LPPool3d", "MaxPool1d", "MaxPool2d", "MaxPool3d", "MaxUnpool1d", "MaxUnpool2d", "MaxUnpool3d", # torch.nn.modules.rnn "GRU", "GRUCell", "LSTM", "LSTMCell", "RNN", "RNNBase", "RNNCell", "RNNCellBase", # torch.nn.modules.sparse "Embedding", "EmbeddingBag", # torch.nn.modules.upsampling "Upsample", # torch.nn.parallel.data_parallel "DataParallel", # torch.nn.parallel.distributed "DistributedDataParallel", # torch.nn.parameter "UninitializedTensorMixin", # torch.nn.utils.parametrize "ParametrizationList", # torch.nn.utils.prune "CustomFromMask", "Identity", "L1Unstructured", "RandomUnstructured", # torch.nn.utils.rnn "PackedSequence", "PackedSequence_", # torch.nn.utils.spectral_norm "SpectralNorm", "SpectralNormLoadStateDictPreHook", "SpectralNormStateDictHook", # torch.nn.utils.weight_norm "WeightNorm", # torch.onnx.errors "OnnxExporterError", "OnnxExporterWarning", "SymbolicValueError", "UnsupportedOperatorError", # torch.onnx.verification "OnnxBackend", "OnnxTestCaseRepro", # torch.optim.adadelta "Adadelta", # torch.optim.adagrad "Adagrad", # torch.optim.adam "Adam", # torch.optim.adamax "Adamax", # torch.optim.adamw "AdamW", # torch.optim.asgd "ASGD", # torch.optim.lbfgs "LBFGS", # torch.optim.lr_scheduler "ChainedScheduler", "ConstantLR", "CosineAnnealingLR", "CosineAnnealingWarmRestarts", "CyclicLR", "ExponentialLR", "LRScheduler", "LambdaLR", "LinearLR", "MultiStepLR", "MultiplicativeLR", "OneCycleLR", "PolynomialLR", "ReduceLROnPlateau", "SequentialLR", "StepLR", # torch.optim.nadam "NAdam", # torch.optim.optimizer "Optimizer", # torch.optim.radam "RAdam", # torch.optim.rmsprop "RMSprop", # torch.optim.rprop "Rprop", # torch.optim.sgd "SGD", # torch.optim.sparse_adam "SparseAdam", # torch.optim.swa_utils "AveragedModel", "SWALR", # torch.overrides "BaseTorchFunctionMode", "TorchFunctionMode", # torch.package.file_structure_representation "Directory", # torch.package.glob_group "GlobGroup", # torch.package.importer "Importer", "ObjMismatchError", "ObjNotFoundError", "OrderedImporter", # torch.package.package_exporter "PackageExporter", "PackagingErrorReason", # torch.package.package_importer "PackageImporter", # torch.profiler.profiler "ExecutionTraceObserver", "profile", # torch.return_types "aminmax", "aminmax_out", "cummax", "cummax_out", "cummin", "cummin_out", "frexp", "frexp_out", "geqrf", "geqrf_out", "histogram", "histogram_out", "histogramdd", "kthvalue", "kthvalue_out", "linalg_cholesky_ex", "linalg_cholesky_ex_out", "linalg_eig", "linalg_eig_out", "linalg_eigh", "linalg_eigh_out", "linalg_inv_ex", "linalg_inv_ex_out", "linalg_ldl_factor", "linalg_ldl_factor_ex", "linalg_ldl_factor_ex_out", "linalg_ldl_factor_out", "linalg_lstsq", "linalg_lstsq_out", "linalg_lu", "linalg_lu_factor", "linalg_lu_factor_ex", "linalg_lu_factor_ex_out", "linalg_lu_factor_out", "linalg_lu_out", "linalg_qr", "linalg_qr_out", "linalg_slogdet", "linalg_slogdet_out", "linalg_solve_ex", "linalg_solve_ex_out", "linalg_svd", "linalg_svd_out", "lu_unpack", "lu_unpack_out", "max", "max_out", "median", "median_out", "min", "min_out", "mode", "mode_out", "nanmedian", "nanmedian_out", "qr", "qr_out", "slogdet", "slogdet_out", "sort", "sort_out", "svd", "svd_out", "topk", "topk_out", "triangular_solve", "triangular_solve_out", # torch.serialization "LoadEndianness", "SourceChangeWarning", # torch.sparse.semi_structured "SparseSemiStructuredTensor", # torch.storage "UntypedStorage", # torch.torch_version "TorchVersion", # torch.types "SymInt", # torch.utils.benchmark.examples.blas_compare_setup "SubEnvSpec", # torch.utils.benchmark.examples.compare "FauxTorch", # torch.utils.benchmark.examples.spectral_ops_fuzz_test "Benchmark", # torch.utils.benchmark.op_fuzzers.binary "BinaryOpFuzzer", # torch.utils.benchmark.op_fuzzers.sparse_binary "BinaryOpSparseFuzzer", # torch.utils.benchmark.op_fuzzers.sparse_unary "UnaryOpSparseFuzzer", # torch.utils.benchmark.op_fuzzers.spectral "SpectralOpFuzzer", # torch.utils.benchmark.op_fuzzers.unary "UnaryOpFuzzer", # torch.utils.benchmark.utils.common "Measurement", "TaskSpec", # torch.utils.benchmark.utils.compare "Colorize", "Compare", "Table", # torch.utils.benchmark.utils.fuzzer "FuzzedParameter", "FuzzedTensor", "Fuzzer", "ParameterAlias", # torch.utils.benchmark.utils.sparse_fuzzer "FuzzedSparseTensor", # torch.utils.benchmark.utils.timer "CPPTimer", "Language", "Timer", # torch.utils.benchmark.utils.valgrind_wrapper.timer_interface "CallgrindStats", "CopyIfCallgrind", "FunctionCount", "FunctionCounts", "GlobalsBridge", "Serialization", # torch.utils.bundled_inputs "InflatableArg", # torch.utils.checkpoint "CheckpointError", "CheckpointFunction", "DefaultDeviceType", # torch.utils.collect_env "SystemEnv", # torch.utils.cpp_extension "BuildExtension", # torch.utils.data.dataloader "DataLoader", # torch.utils.data.datapipes.dataframe.dataframe_wrapper "PandasWrapper", "default_wrapper", # torch.utils.data.datapipes.dataframe.dataframes "Capture", "CaptureA", "CaptureAdd", "CaptureCall", "CaptureControl", "CaptureDataFrame", "CaptureDataFrameWithDataPipeOps", "CaptureF", "CaptureGetAttr", "CaptureGetItem", "CaptureInitial", "CaptureLikeMock", "CaptureMul", "CaptureSetItem", "CaptureSub", "CaptureVariable", "CaptureVariableAssign", "DataFrameTracedOps", "DataFrameTracer", # torch.utils.data.datapipes.dataframe.datapipes "ConcatDataFramesPipe", "DataFramesAsTuplesPipe", "ExampleAggregateAsDataFrames", "FilterDataFramesPipe", "PerRowDataFramesPipe", "ShuffleDataFramesPipe", # torch.utils.data.datapipes.dataframe.structures "DataChunkDF", # torch.utils.data.datapipes.datapipe "DFIterDataPipe", "DataChunk", "IterDataPipe", "MapDataPipe", # torch.utils.data.datapipes.iter.callable "CollatorIterDataPipe", "MapperIterDataPipe", # torch.utils.data.datapipes.iter.combinatorics "SamplerIterDataPipe", "ShufflerIterDataPipe", # torch.utils.data.datapipes.iter.combining "ConcaterIterDataPipe", "DemultiplexerIterDataPipe", "ForkerIterDataPipe", "MultiplexerIterDataPipe", "ZipperIterDataPipe", # torch.utils.data.datapipes.iter.filelister "FileListerIterDataPipe", # torch.utils.data.datapipes.iter.fileopener "FileOpenerIterDataPipe", # torch.utils.data.datapipes.iter.grouping "BatcherIterDataPipe", "GrouperIterDataPipe", "UnBatcherIterDataPipe", # torch.utils.data.datapipes.iter.routeddecoder "RoutedDecoderIterDataPipe", # torch.utils.data.datapipes.iter.selecting "FilterIterDataPipe", # torch.utils.data.datapipes.iter.sharding "SHARDING_PRIORITIES", "ShardingFilterIterDataPipe", # torch.utils.data.datapipes.iter.utils "IterableWrapperIterDataPipe", # torch.utils.data.datapipes.map.callable "MapperMapDataPipe", # torch.utils.data.datapipes.map.combinatorics "ShufflerIterDataPipe", # torch.utils.data.datapipes.map.combining "ConcaterMapDataPipe", "ZipperMapDataPipe", # torch.utils.data.datapipes.map.grouping "BatcherMapDataPipe", # torch.utils.data.datapipes.map.utils "SequenceWrapperMapDataPipe", # torch.utils.data.datapipes.utils.decoder "Decoder", "ImageHandler", "MatHandler", # torch.utils.data.dataset "ConcatDataset", # torch.utils.data.distributed "DistributedSampler", # torch.utils.dlpack "DLDeviceType", # torch.utils.file_baton "FileBaton", # torch.utils.flop_counter "FlopCounterMode", # torch.utils.hipify.hipify_python "CurrentState", "GeneratedFileCleaner", "HipifyResult", "InputError", "Trie", "bcolors", # torch.utils.hooks "BackwardHook", "RemovableHandle", # torch.utils.mkldnn "MkldnnBatchNorm", "MkldnnConv1d", "MkldnnConv2d", "MkldnnConv3d", "MkldnnLinear", "MkldnnPrelu", # torch.utils.mobile_optimizer "LintCode", # torch.utils.show_pickle "DumpUnpickler", "FakeClass", "FakeObject", # torch.utils.tensorboard.writer "FileWriter", "SummaryWriter", # torch.utils.throughput_benchmark "ExecutionStats", # torch.utils.weak "WeakIdKeyDictionary", "WeakIdRef", "WeakTensorKeyDictionary", ] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = ".rst" # The master toctree document. master_doc = "index" # General information about the project. project = "PyTorch" copyright = "2024, PyTorch Contributors" author = "PyTorch Contributors" torch_version = str(torch.__version__) # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. # TODO: change to [:2] at v1.0 version = "main (" + torch_version + " )" # The full version, including alpha/beta/rc tags. # TODO: verify this works as expected release = "main" # Customized html_title here. # Default is " ".join(project, release, "documentation") if not set if RELEASE: # Turn 1.11.0aHASH into 1.11 # Note: the release candidates should no longer have the aHASH suffix, but in any # case we wish to leave only major.minor, even for rc builds. version = ".".join(torch_version.split(".")[:2]) html_title = " ".join((project, version, "documentation")) release = version # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = [] # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True # Disable docstring inheritance autodoc_inherit_docstrings = False # Show type hints in the description autodoc_typehints = "description" # Add parameter types if the parameter is documented in the docstring autodoc_typehints_description_target = "documented_params" # Type aliases for common types # Sphinx type aliases only works with Postponed Evaluation of Annotations # (PEP 563) enabled (via `from __future__ import annotations`), which keeps the # type annotations in string form instead of resolving them to actual types. # However, PEP 563 does not work well with JIT, which uses the type information # to generate the code. Therefore, the following dict does not have any effect # until PEP 563 is supported by JIT and enabled in files. autodoc_type_aliases = { "_size_1_t": "int or tuple[int]", "_size_2_t": "int or tuple[int, int]", "_size_3_t": "int or tuple[int, int, int]", "_size_4_t": "int or tuple[int, int, int, int]", "_size_5_t": "int or tuple[int, int, int, int, int]", "_size_6_t": "int or tuple[int, int, int, int, int, int]", "_size_any_opt_t": "int or None or tuple", "_size_2_opt_t": "int or None or 2-tuple", "_size_3_opt_t": "int or None or 3-tuple", "_ratio_2_t": "float or tuple[float, float]", "_ratio_3_t": "float or tuple[float, float, float]", "_ratio_any_t": "float or tuple", "_tensor_list_t": "Tensor or tuple[Tensor]", } # Enable overriding of function signatures in the first line of the docstring. autodoc_docstring_signature = True # -- katex javascript in header # # def setup(app): # app.add_javascript("https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.js") # -- Options for HTML output ---------------------------------------------- # # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # # # html_theme = "pytorch_sphinx_theme" html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()] # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { "pytorch_project": "docs", "canonical_url": "https://pytorch.org/docs/stable/", "collapse_navigation": False, "display_version": True, "logo_only": True, "analytics_id": "GTM-T8XT4PS", } html_logo = "_static/img/pytorch-logo-dark-unstable.png" if RELEASE: html_logo = "_static/img/pytorch-logo-dark.svg" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] html_css_files = [ "css/jit.css", ] from sphinx.ext.coverage import CoverageBuilder # NB: Due to some duplications of the following modules/functions, we keep # them as expected failures for the time being instead of return 1 ignore_duplicated_modules = { "torch.nn.utils.weight_norm", "torch.nn.utils.spectral_norm", "torch.nn.parallel.data_parallel", "torch.ao.quantization.quantize", } def coverage_post_process(app, exception): if exception is not None: return # Only run this test for the coverage build if not isinstance(app.builder, CoverageBuilder): return if not torch.distributed.is_available(): raise RuntimeError( "The coverage tool cannot run with a version " "of PyTorch that was built with USE_DISTRIBUTED=0 " "as this module's API changes." ) # These are all the modules that have "automodule" in an rst file # These modules are the ones for which coverage is checked # Here, we make sure that no module is missing from that list modules = app.env.domaindata["py"]["modules"] # We go through all the torch submodules and make sure they are # properly tested missing = set() def is_not_internal(modname): split_name = modname.split(".") for name in split_name: if name[0] == "_": return False return True # The walk function does not return the top module if "torch" not in modules: missing.add("torch") for _, modname, ispkg in pkgutil.walk_packages( path=torch.__path__, prefix=torch.__name__ + "." ): if is_not_internal(modname): if modname not in modules and modname not in ignore_duplicated_modules: missing.add(modname) output = [] if missing: mods = ", ".join(missing) output.append( f"\nYou added the following module(s) to the PyTorch namespace '{mods}' " "but they have no corresponding entry in a doc .rst file. You should " "either make sure that the .rst file that contains the module's documentation " "properly contains either '.. automodule:: mod_name' (if you do not want " "the paragraph added by the automodule, you can simply use '.. py:module:: mod_name') " " or make the module private (by appending an '_' at the beginning of its name)." ) # The output file is hard-coded by the coverage tool # Our CI is setup to fail if any line is added to this file output_file = path.join(app.outdir, "python.txt") if output: with open(output_file, "a") as f: for o in output: f.write(o) def process_docstring(app, what_, name, obj, options, lines): """ Custom process to transform docstring lines Remove "Ignore" blocks Args: app (sphinx.application.Sphinx): the Sphinx application object what (str): the type of the object which the docstring belongs to (one of "module", "class", "exception", "function", "method", "attribute") name (str): the fully qualified name of the object obj: the object itself options: the options given to the directive: an object with attributes inherited_members, undoc_members, show_inheritance and noindex that are true if the flag option of same name was given to the auto directive lines (List[str]): the lines of the docstring, see above References: https://www.sphinx-doc.org/en/1.5.1/_modules/sphinx/ext/autodoc.html https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html """ import re remove_directives = [ # Remove all xdoctest directives re.compile(r"\s*>>>\s*#\s*x?doctest:\s*.*"), re.compile(r"\s*>>>\s*#\s*x?doc:\s*.*"), ] filtered_lines = [ line for line in lines if not any(pat.match(line) for pat in remove_directives) ] # Modify the lines inplace lines[:] = filtered_lines # make sure there is a blank line at the end if lines and lines[-1].strip(): lines.append("") # Called automatically by Sphinx, making this `conf.py` an "extension". def setup(app): # NOTE: in Sphinx 1.8+ `html_css_files` is an official configuration value # and can be moved outside of this function (and the setup(app) function # can be deleted). html_css_files = [ "https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css" ] # In Sphinx 1.8 it was renamed to `add_css_file`, 1.7 and prior it is # `add_stylesheet` (deprecated in 1.8). add_css = getattr(app, "add_css_file", app.add_stylesheet) for css_file in html_css_files: add_css(css_file) app.connect("build-finished", coverage_post_process) app.connect("autodoc-process-docstring", process_docstring) # From PyTorch 1.5, we now use autogenerated files to document classes and # functions. This breaks older references since # https://pytorch.org/docs/stable/torch.html#torch.flip # moved to # https://pytorch.org/docs/stable/generated/torch.flip.html # which breaks older links from blog posts, stack overflow answers and more. # To mitigate that, we add an id="torch.flip" in an appropriated place # in torch.html by overriding the visit_reference method of html writers. # Someday this can be removed, once the old links fade away from sphinx.writers import html, html5 def replace(Klass): old_call = Klass.visit_reference def visit_reference(self, node): if "refuri" in node and "generated" in node.get("refuri"): ref = node.get("refuri") ref_anchor = ref.split("#") if len(ref_anchor) > 1: # Only add the id if the node href and the text match, # i.e. the href is "torch.flip#torch.flip" and the content is # "torch.flip" or "flip" since that is a signal the node refers # to autogenerated content anchor = ref_anchor[1] txt = node.parent.astext() if txt == anchor or txt == anchor.split(".")[-1]: self.body.append(f'

') return old_call(self, node) Klass.visit_reference = visit_reference replace(html.HTMLTranslator) replace(html5.HTML5Translator) # -- Options for HTMLHelp output ------------------------------------------ # Output file base name for HTML help builder. htmlhelp_basename = "PyTorchdoc" # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ( master_doc, "pytorch.tex", "PyTorch Documentation", "Torch Contributors", "manual", ), ] # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, "PyTorch", "PyTorch Documentation", [author], 1)] # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, "PyTorch", "PyTorch Documentation", author, "PyTorch", "One line description of project.", "Miscellaneous", ), ] # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { "python": ("https://docs.python.org/3", None), "numpy": ("https://numpy.org/doc/stable", None), } import sphinx.ext.doctest # -- A patch that prevents Sphinx from cross-referencing ivar tags ------- # See http://stackoverflow.com/a/41184353/3343043 from docutils import nodes from sphinx import addnodes from sphinx.util.docfields import TypedField # Without this, doctest adds any example with a `>>>` as a test doctest_test_doctest_blocks = "" doctest_default_flags = sphinx.ext.doctest.doctest.ELLIPSIS doctest_global_setup = """ import torch try: import torchvision except ImportError: torchvision = None """ def patched_make_field(self, types, domain, items, **kw): # `kw` catches `env=None` needed for newer sphinx while maintaining # backwards compatibility when passed along further down! # type: (List, unicode, Tuple) -> nodes.field def handle_item(fieldarg, content): par = nodes.paragraph() par += addnodes.literal_strong("", fieldarg) # Patch: this line added # par.extend(self.make_xrefs(self.rolename, domain, fieldarg, # addnodes.literal_strong)) if fieldarg in types: par += nodes.Text(" (") # NOTE: using .pop() here to prevent a single type node to be # inserted twice into the doctree, which leads to # inconsistencies later when references are resolved fieldtype = types.pop(fieldarg) if len(fieldtype) == 1 and isinstance(fieldtype[0], nodes.Text): typename = fieldtype[0].astext() builtin_types = ["int", "long", "float", "bool", "type"] for builtin_type in builtin_types: pattern = rf"(?>> |\.\.\. " copybutton_prompt_is_regexp = True