| # |
| # 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'<p id="{ref_anchor[1]}"/>') |
| 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"(?<![\w.]){builtin_type}(?![\w.])" |
| repl = f"python:{builtin_type}" |
| typename = re.sub(pattern, repl, typename) |
| par.extend( |
| self.make_xrefs( |
| self.typerolename, |
| domain, |
| typename, |
| addnodes.literal_emphasis, |
| **kw, |
| ) |
| ) |
| else: |
| par += fieldtype |
| par += nodes.Text(")") |
| par += nodes.Text(" -- ") |
| par += content |
| return par |
| |
| fieldname = nodes.field_name("", self.label) |
| if len(items) == 1 and self.can_collapse: |
| fieldarg, content = items[0] |
| bodynode = handle_item(fieldarg, content) |
| else: |
| bodynode = self.list_type() |
| for fieldarg, content in items: |
| bodynode += nodes.list_item("", handle_item(fieldarg, content)) |
| fieldbody = nodes.field_body("", bodynode) |
| return nodes.field("", fieldname, fieldbody) |
| |
| |
| TypedField.make_field = patched_make_field |
| |
| copybutton_prompt_text = r">>> |\.\.\. " |
| copybutton_prompt_is_regexp = True |
