# ${generated_comment} # mypy: disable-error-code="type-arg" # mypy: allow-untyped-defs # ruff: noqa: F401 from collections.abc import Iterable, Iterator, Sequence from enum import Enum, IntEnum from pathlib import Path from types import EllipsisType from typing import ( Any, AnyStr, Callable, Generic, IO, Literal, NamedTuple, overload, SupportsIndex, TypeVar, ) from typing_extensions import ParamSpec, Protocol, runtime_checkable, Self, TypeAlias import numpy import torch from torch import inf, SymInt, Tensor from torch._C import ( _aoti, _cpu, _dynamo, _export, _functorch, _lazy, _lazy_ts_backend, _nn, _onnx, _VariableFunctions, _verbose, ) from torch._prims_common import DeviceLikeType from torch.autograd.graph import Node as _Node from torch.fx.node import Node as FxNode from torch.package import PackageExporter from torch.storage import TypedStorage, UntypedStorage from torch.types import ( _bool, _bytes, _complex, _device, _dispatchkey, _dtype, _float, _int, _layout, _qscheme, _size, _str, _symsize, Device, IntLikeType, Number, Storage, ) from torch.utils._python_dispatch import TorchDispatchMode # This module is defined in torch/csrc/Module.cpp K = TypeVar("K") # noqa: PYI001 T = TypeVar("T") # noqa: PYI001 S = TypeVar("S", bound=torch.Tensor) # noqa: PYI001 P = ParamSpec("P") # noqa: PYI001 R = TypeVar("R", covariant=True) # return value (always covariant) # noqa: PYI001 T_co = TypeVar("T_co", covariant=True) # noqa: PYI001 @runtime_checkable class _NestedSequence(Protocol[T_co]): """A protocol for representing nested sequences. References:: `numpy._typing._NestedSequence` """ def __len__(self, /) -> _int: ... def __getitem__(self, index: _int, /) -> T_co | _NestedSequence[T_co]: ... def __contains__(self, x: object, /) -> _bool: ... def __iter__(self, /) -> Iterator[T_co | _NestedSequence[T_co]]: ... def __reversed__(self, /) -> Iterator[T_co | _NestedSequence[T_co]]: ... def count(self, value: Any, /) -> _int: ... def index(self, value: Any, /) -> _int: ... # Defined in torch/csrc/Device.cpp class device: type: str # THPDevice_type index: _int # THPDevice_index def __get__(self, instance, owner=None) -> device: ... # THPDevice_pynew @overload def __init__(self, device: DeviceLikeType) -> None: ... @overload def __init__(self, type: str, index: _int) -> None: ... # Uncomment if we ever make torch.device a decorator # def __call__(self, func: T) -> T: ... def __enter__(self) -> Self: ... def __exit__(self, exc_type, exc_val, exc_tb) -> None: ... def __reduce__(self) -> tuple[Any, ...]: ... # THPDevice_reduce # Defined in torch/csrc/Stream.cpp class Stream: stream_id: _int # Stream id device_index: _int device_type: _int device: _device # The device of the stream @overload def __new__( cls, device: DeviceLikeType | None = None, *, priority: _int = 0, ) -> Self: ... @overload def __new__( cls, stream_id: _int, device_index: _int, device_type: _int, *, priority: _int = 0, ) -> Self: ... def query(self) -> _bool: ... def synchronize(self) -> None: ... def wait_event(self, event: Event) -> None: ... def wait_stream(self, other: Stream) -> None: ... def record_event(self, event: Event | None = None) -> Event: ... def __hash__(self) -> _int: ... def __eq__(self, other: object) -> _bool: ... def __enter__(self) -> Self: ... def __exit__(self, exc_type, exc_val, exc_tb) -> None: ... # Defined in torch/csrc/Event.cpp class Event: device: _device # The device of the Event event_id: _int # The raw event created by device backend def __new__( cls, device: DeviceLikeType | None = None, *, enable_timing: _bool = False, blocking: _bool = False, interprocess: _bool = False, ) -> Self: ... @classmethod def from_ipc_handle(cls, device: _device, ipc_handle: bytes) -> Event: ... def record(self, stream: Stream | None = None) -> None: ... def wait(self, stream: Stream | None = None) -> None: ... def query(self) -> _bool: ... def elapsed_time(self, other: Event) -> _float: ... def synchronize(self) -> None: ... def ipc_handle(self) -> bytes: ... # Defined in torch/csrc/Size.cpp class Size(tuple[_int, ...]): # TODO: __reduce__ @overload def __getitem__(self: Size, key: SupportsIndex, /) -> _int: ... @overload def __getitem__(self: Size, key: slice, /) -> Size: ... # Note: torch.Size does not support adding non-integer tuples. def __add__(self, other: tuple[_int, ...], /) -> Size: ... # type: ignore[override] def __radd__(self: Size, other: tuple[_int, ...], /) -> Size: ... def __mul__(self, other: SupportsIndex, /) -> Size: ... def __rmul__(self, other: SupportsIndex, /) -> Size: ... def numel(self: Size, /) -> _int: ... # Defined in torch/csrc/Dtype.cpp class dtype: # TODO: __reduce__ is_floating_point: _bool is_complex: _bool is_signed: _bool itemsize: _int def to_real(self) -> dtype: ... def to_complex(self) -> dtype: ... # Defined in torch/csrc/TypeInfo.cpp class iinfo: bits: _int min: _int max: _int dtype: str def __init__(self, dtype: _dtype) -> None: ... class finfo: bits: _int min: _float max: _float eps: _float tiny: _float smallest_normal: _float resolution: _float dtype: str @overload def __init__(self, dtype: _dtype) -> None: ... @overload def __init__(self) -> None: ... ${dtype_class_hints} # Defined in torch/csrc/Layout.cpp class layout: ... # Defined in torch/csrc/utils/disable_torch_function.cpp def DisableTorchFunction(): ... def DisableTorchFunctionSubclass(): ... # Defined in torch/csrc/utils/tensor_layouts.cpp strided: layout = ... sparse_coo: layout = ... sparse_csr: layout = ... sparse_csc: layout = ... sparse_bsr: layout = ... sparse_bsc: layout = ... _mkldnn: layout = ... jagged: layout = ... # Defined in torch/csrc/MemoryFormat.cpp class memory_format: ... # Defined in torch/csrc/utils/tensor_memoryformats.cpp contiguous_format: memory_format = ... channels_last: memory_format = ... channels_last_3d: memory_format = ... preserve_format: memory_format = ... # Defined in torch/csrc/QScheme.cpp class qscheme: ... # Defined in torch/csrc/utils/tensor_qschemes.h per_tensor_affine: qscheme = ... per_channel_affine: qscheme = ... per_tensor_symmetric: qscheme = ... per_channel_symmetric: qscheme = ... per_channel_affine_float_qparams: qscheme = ... # Defined in torch/csrc/autograd/python_function.cpp class _FunctionBase: saved_tensors: tuple[Tensor] _raw_saved_tensors: tuple[Any] next_functions: tuple[tuple[Any, _int], ...] needs_input_grad: tuple[_bool] metadata: dict _materialize_non_diff_grads: _bool # skip adding type hints for the fields that have wrappers defined # in torch/autograd/function.py # Defined in torch/csrc/autograd/python_legacy_variable.cpp class _LegacyVariableBase(Tensor): # inherits from Tensor to appease mypy def __init__( self, data: Tensor | None = ..., requires_grad: _bool | None = ..., volatile: _bool | None = ..., _grad_fn: _FunctionBase | None = ..., ) -> None: ... # Defined in torch/csrc/jit/python/init.cpp class IODescriptor: ... class JITException(Exception): ... class Future(Generic[T]): def __init__(self, devices: list[device]) -> None: ... def done(self) -> _bool: ... def value(self) -> T: ... def wait(self) -> T: ... def add_done_callback(self, callback: Callable) -> None: ... def then(self, callback: Callable) -> Future[T]: ... def set_result(self, result: T) -> None: ... def _set_unwrap_func(self, callback: Callable) -> None: ... class _Await: def __init__(self) -> None: ... def fn(self) -> Callable: ... def args(self) -> tuple[Any, ...]: ... def is_nowait(self) -> _bool: ... def _jit_set_num_profiled_runs(num: _size) -> _size: ... # Defined in torch/csrc/jit/passes/mobile_optimizer_type.h class _MobileOptimizerType: ... CONV_BN_FUSION: _MobileOptimizerType INSERT_FOLD_PREPACK_OPS: _MobileOptimizerType REMOVE_DROPOUT: _MobileOptimizerType FUSE_ADD_RELU: _MobileOptimizerType HOIST_CONV_PACKED_PARAMS: _MobileOptimizerType VULKAN_AUTOMATIC_GPU_TRANSFER: _MobileOptimizerType def fork(*args: Any, **kwargs: Any) -> Future: ... def wait(fut: Future) -> Any: ... def _awaitable(*args: Any, **kwargs: Any) -> _Await: ... def _awaitable_wait(aw: _Await) -> Any: ... def _awaitable_nowait(x: Any) -> _Await: ... def _collect_all(futures: list[Future]) -> Future: ... def _set_print_stack_traces_on_fatal_signal(print: _bool) -> None: ... def unify_type_list(types: list[JitType]) -> JitType: ... def _freeze_module( module: ScriptModule, preserved_attrs: list[str] = ..., freeze_interfaces: _bool = True, preserveParameters: _bool = True, ) -> ScriptModule: ... def _jit_pass_optimize_frozen_graph(Graph, optimize_numerics: _bool = True) -> None: ... def _jit_pass_optimize_for_inference( module: torch.jit.ScriptModule, other_methods: list[str] = ..., ) -> None: ... def _jit_pass_fold_frozen_conv_bn(graph: Graph): ... def _jit_pass_fold_frozen_conv_add_or_sub(graph: Graph): ... def _jit_pass_fold_frozen_conv_mul_or_div(graph: Graph): ... def _jit_pass_fuse_frozen_conv_add_relu(graph: Graph): ... def _jit_pass_concat_frozen_linear(graph: Graph): ... def _jit_pass_convert_frozen_ops_to_mkldnn(graph: Graph): ... def _jit_pass_transpose_frozen_linear(graph: Graph): ... def _jit_pass_remove_dropout(module: torch.jit.ScriptModule): ... def _is_tracing() -> _bool: ... def _jit_init() -> _bool: ... def _jit_flatten(arg: Any) -> tuple[list[Tensor], IODescriptor]: ... def _jit_unflatten(vars: list[Tensor], desc: IODescriptor) -> Any: ... def _jit_get_operation(op_name: str) -> tuple[Callable, list[str]]: ... def _get_operation_overload( op_name: str, op_overload_name: str, ) -> tuple[Callable, Callable, list[Any]]: ... def _get_schema(op_name: str, overload_name: str) -> FunctionSchema: ... def _jit_pass_optimize_for_mobile( module: torch.jit.ScriptModule, optimization_blocklist: set[_MobileOptimizerType], preserved_methods: list[AnyStr], ) -> torch.jit.ScriptModule: ... def _clone_module_with_class( module: torch.jit.ScriptModule, ignored_methods: list[AnyStr], ignored_attributes: list[AnyStr], ) -> torch.jit.ScriptModule: ... def _jit_pass_vulkan_optimize_for_mobile( module: torch.jit.ScriptModule, optimization_blocklist: set[_MobileOptimizerType], preserved_methods: list[AnyStr], ) -> torch.jit.ScriptModule: ... def _jit_pass_metal_optimize_for_mobile( module: torch.jit.ScriptModule, preserved_methods: list[AnyStr], ) -> torch.jit.ScriptModule: ... def _jit_pass_inline(Graph) -> None: ... def _jit_pass_constant_propagation(Graph) -> None: ... def _jit_pass_propagate_shapes_on_graph(Graph) -> None: ... def _jit_register_decomposition_for_schema(schema: FunctionSchema, Graph) -> None: ... def _jit_erase_non_input_shape_information(Graph) -> None: ... def _jit_get_schemas_for_operator(name: str) -> list[FunctionSchema]: ... def _jit_get_all_schemas() -> list[FunctionSchema]: ... def _jit_check_alias_annotation( g: Graph, args: tuple[Any, ...], unqualified_op_name: str, ): ... def _jit_can_fuse_on_cpu() -> _bool: ... def _jit_can_fuse_on_gpu() -> _bool: ... def _jit_can_fuse_on_cpu_legacy() -> _bool: ... def _debug_get_fusion_group_inlining() -> _bool: ... def _debug_set_fusion_group_inlining(enable: _bool): ... def _jit_texpr_fuser_enabled() -> _bool: ... def _jit_nvfuser_enabled() -> _bool: ... def _jit_llga_enabled() -> _bool: ... def _jit_set_llga_enabled(enable: _bool): ... def _llvm_enabled() -> _bool: ... def _jit_override_can_fuse_on_cpu(override: _bool): ... def _jit_override_can_fuse_on_gpu(override: _bool): ... def _jit_override_can_fuse_on_cpu_legacy(override: _bool): ... def _jit_set_symbolic_shapes_test_mode(override: _bool): ... def _jit_symbolic_shapes_test_mode_enabled() -> _bool: ... def _jit_set_texpr_fuser_enabled(enable: _bool): ... def _jit_set_te_must_use_llvm_cpu(use_llvm: _bool): ... def _jit_set_nvfuser_enabled(enable: _bool) -> _bool: ... def _jit_cat_wo_conditionals(optimize_cat: _bool): ... def _jit_opt_conditionals(opt_conds: _bool): ... def _jit_pass_canonicalize(graph: Graph, keep_unique_names: _bool = True): ... def _jit_pass_erase_shape_information(graph: Graph): ... def _jit_pass_fold_convbn(module: torch.jit.ScriptModule): ... def _jit_pass_insert_observers( module: torch.jit.ScriptModule, method_name: str, qconfig_dict: dict[str, Any], inplace: _bool, quant_type: _int, ): ... def _jit_pass_insert_quant_dequant( module: torch.jit.ScriptModule, method_name: str, inplace: _bool, debug: _bool, quant_type: _int, ): ... def _jit_pass_insert_quant_dequant_for_ondevice_ptq( module: torch.jit.ScriptModule, method_name: str, inplace: _bool, debug: _bool, quant_type: _int, ): ... def _jit_pass_quant_finalize( module: torch.jit.ScriptModule, quant_type: _int, preserved_attrs: Sequence[str], ): ... def _jit_pass_quant_finalize_for_ondevice_ptq( module: torch.jit.ScriptModule, quant_type: _int, method_name: str, ): ... def _jit_pass_insert_observer_method_for_ondevice_ptq( module: torch.jit.ScriptModule, method_name: str, qconfig_dict: dict[str, Any], inplace: _bool, quant_type: _int, ): ... def _jit_set_profiling_executor(profiling_flag: _bool) -> _bool: ... def _jit_set_profiling_mode(profiling_flag: _bool) -> _bool: ... def _jit_set_fusion_strategy( strategy: list[tuple[str, _int]], ) -> list[tuple[str, _int]]: ... def _jit_try_infer_type(obj: Any) -> InferredType: ... def _jit_get_trigger_value(trigger_name: str) -> _int: ... # Defined in torch/csrc/jit/python/script_init.cpp ResolutionCallback: TypeAlias = Callable[[str], Callable[..., Any]] # Defined in torch/csrc/jit/python/script_init.cpp # and torch/csrc/jit/python/init.cpp def _maybe_call_torch_function_for_op_packet( op_overload_packet: Any, *args: Any, **kwargs: Any, ) -> Any: ... def _check_schema_allow_fake_script_object( schema: FunctionSchema, *args: Any, **kwargs: Any, ) -> _bool: ... def _create_function_from_graph(qualname: str, graph: Graph) -> ScriptFunction: ... def _debug_set_autodiff_subgraph_inlining(disabled: _bool) -> None: ... def _ivalue_tags_match(lhs: ScriptModule, rhs: ScriptModule) -> _bool: ... def _jit_assert_is_instance(obj: Any, type: JitType): ... def _jit_clear_class_registry() -> None: ... def _jit_set_emit_hooks( ModuleHook: Callable | None, FunctionHook: Callable | None, ) -> None: ... def _jit_get_emit_hooks() -> tuple[Callable, Callable]: ... def _load_for_lite_interpreter( filename: str | Path, map_location: DeviceLikeType | None, ): ... def _load_for_lite_interpreter_from_buffer( buffer: IO[bytes], map_location: DeviceLikeType | None, ): ... def _export_operator_list(module: LiteScriptModule): ... def _quantize_ondevice_ptq_dynamic(module: LiteScriptModule, method_name: str): ... def _get_model_bytecode_version(filename: str | Path) -> _int: ... def _get_model_bytecode_version_from_buffer(buffer: IO[bytes]) -> _int: ... def _backport_for_mobile( filename_input: str | Path, filename_output: str | Path, to_version: _int, ) -> None: ... def _backport_for_mobile_from_buffer( buffer: IO[bytes], filename_output: str | Path, to_version: _int, ) -> None: ... def _backport_for_mobile_to_buffer( filename_input: str | Path, to_version: _int, ) -> bytes: ... def _backport_for_mobile_from_buffer_to_buffer( buffer: IO[bytes], to_version: _int, ) -> bytes: ... def _get_model_ops_and_info(filename: str | Path): ... def _get_model_ops_and_info_from_buffer(buffer: IO[bytes]): ... def _get_mobile_model_contained_types(filename: str | Path): ... def _get_mobile_model_contained_types_from_buffer(buffer: IO[bytes]): ... def _logging_set_logger(logger: LoggerBase) -> LoggerBase: ... def _get_graph_executor_optimize(optimize: _bool | None = None) -> _bool: ... def _set_graph_executor_optimize(optimize: _bool): ... def _export_opnames(module: ScriptModule) -> list[str]: ... def _create_function_from_trace( qualname: str, func: Callable[..., Any], input_tuple: tuple[Any, ...], var_lookup_fn: Callable[[Tensor], str], strict: _bool, force_outplace: _bool, argument_names: list[str], ) -> tuple[Graph, Stack]: ... def _create_function_from_trace_with_dict( qualname: str, func: Callable[..., Any], input_dict: dict[str, Any], var_lookup_fn: Callable[[Tensor], str], strict: _bool, force_outplace: _bool, argument_names: list[str], ) -> tuple[Graph, Stack]: ... def _jit_is_script_object(obj: Any) -> _bool: ... def _last_executed_optimized_graph() -> Graph: ... def parse_type_comment(comment: str) -> Decl: ... def _get_upgraders_map_size() -> _int: ... def _get_upgraders_entry_map() -> dict[str, str]: ... def _dump_upgraders_map() -> dict[str, str]: ... def _test_only_populate_upgraders(content: dict[str, str]) -> None: ... def _test_only_remove_upgraders(content: dict[str, str]) -> None: ... def merge_type_from_type_comment( decl: Decl, type_annotation_decl: Decl, is_method: _bool, ) -> Decl: ... def parse_ir(input: str, parse_tensor_constants: _bool = False) -> Graph: ... def parse_schema(schema: str) -> FunctionSchema: ... def get_device(input: Tensor) -> _int: ... def _resolve_type_from_object( obj: Any, range: SourceRange, rcb: ResolutionCallback, ) -> JitType: ... def _create_module_with_type(ty: JitType) -> ScriptModule: ... def _create_object_with_type(ty: ClassType) -> ScriptObject: ... def _run_emit_module_hook(m: ScriptModule): ... def _replace_overloaded_method_decl( overload_decl: Decl, implementation_def: Def, new_name: str, ) -> Def: ... def _jit_pass_lower_all_tuples(graph: Graph) -> None: ... def _jit_pass_onnx_set_dynamic_input_shape( graph: Graph, dynamic_axes: dict[str, dict[_int, str]], input_names: list[str], ) -> None: ... def _jit_pass_onnx_graph_shape_type_inference( graph: Graph, params_dict: dict[str, IValue], opset_version: _int, ) -> None: ... def _jit_pass_onnx_assign_output_shape( graph: Graph, tensors: list[Tensor], desc: IODescriptor, onnx_shape_inference: _bool, is_script: _bool, opset_version: _int, ) -> None: ... def _jit_pass_onnx_remove_inplace_ops_for_onnx( graph: Graph, module: ScriptModule | None = None, ) -> None: ... def _jit_pass_remove_inplace_ops(graph: Graph) -> None: ... def _jit_pass_canonicalize_graph_fuser_ops(graph: Graph) -> None: ... def _jit_pass_peephole( graph: Graph, disable_shape_peepholes: _bool = False, ) -> None: ... def _jit_pass_onnx_autograd_function_process(graph: Graph) -> None: ... def _jit_pass_fuse_addmm(graph: Graph) -> None: ... def _jit_pass_onnx_preprocess(graph: Graph) -> None: ... def _jit_pass_prepare_division_for_onnx(graph: Graph) -> None: ... def _jit_pass_onnx_remove_print(graph: Graph) -> None: ... def _jit_pass_onnx_preprocess_caffe2(graph: Graph) -> None: ... def _jit_pass_onnx_unpack_quantized_weights( graph: Graph, paramsDict: dict[str, IValue], ) -> dict[str, IValue]: ... def _jit_pass_onnx_quantization_insert_permutes( graph: Graph, paramsDict: dict[str, IValue], ) -> dict[str, IValue]: ... def _jit_pass_custom_pattern_based_rewrite_graph( pattern: str, fused_node_name: str, graph: Graph, ) -> None: ... def _jit_onnx_list_model_parameters( module: ScriptModule, ) -> tuple[ScriptModule, list[IValue]]: ... def _jit_pass_erase_number_types(graph: Graph) -> None: ... def _jit_pass_onnx_lint(graph: Graph) -> None: ... def _jit_pass_onnx( graph: Graph, _jit_pass_onnx: _onnx.OperatorExportTypes, ) -> Graph: ... def _jit_pass_onnx_scalar_type_analysis( graph: Graph, lowprecision_cast: _bool, opset_version: _int, ) -> None: ... def _jit_pass_onnx_peephole( graph: Graph, opset_version: _int, fixed_batch_size: _bool, ) -> None: ... def _jit_pass_dce_allow_deleting_nodes_with_side_effects(graph: Graph) -> None: ... def _jit_pass_onnx_function_substitution(graph: Graph) -> None: ... def _jit_pass_onnx_function_extraction( graph: Graph, module_names: set[str], param_names: list[str], ) -> dict[Node, dict[str, str]]: ... def _jit_pass_onnx_clear_scope_records() -> None: ... def _jit_pass_onnx_track_scope_attributes( graph: Graph, onnx_attrs: dict[str, Any], ) -> None: ... def _jit_is_onnx_log_enabled() -> _bool: ... def _jit_set_onnx_log_enabled(enabled: _bool) -> None: ... def _jit_set_onnx_log_output_stream(stream_name: str) -> None: ... def _jit_onnx_log(*args: Any) -> None: ... def _jit_pass_lower_graph(graph: Graph, m: Module) -> tuple[Graph, list[IValue]]: ... def _jit_pass_inline_fork_wait(graph: Graph) -> None: ... def _jit_pass_onnx_deduplicate_initializers( graph: Graph, params_dict: dict[str, IValue], is_train: _bool, ) -> dict[str, IValue]: ... def _jit_pass_onnx_eval_peephole( graph: Graph, paramsDict: dict[str, IValue], ) -> dict[str, IValue]: ... def _jit_pass_onnx_constant_fold( graph: Graph, paramsDict: dict[str, IValue], opset_version: _int, ) -> dict[str, IValue]: ... def _jit_pass_onnx_eliminate_unused_items( graph: Graph, paramsDict: dict[str, IValue], ) -> dict[str, IValue]: ... def _jit_pass_onnx_cast_all_constant_to_floating(graph: Graph) -> None: ... def _jit_pass_filter_non_tensor_arguments( params: dict[str, IValue], ) -> dict[str, Tensor]: ... def _jit_decay_packed_param_input_types(graph: Graph) -> None: ... def _jit_pass_onnx_node_shape_type_inference( n: Node, paramsDict: dict[str, IValue], opset_version: _int, ) -> None: ... def _jit_onnx_convert_pattern_from_subblock( block: Block, n: Node, env: dict[Value, Value], values_in_env: set[Value], ) -> list[Value]: ... def _jit_pass_onnx_block( old_block: Block, new_block: Block, operator_export_type: _onnx.OperatorExportTypes, env: dict[Value, Value], values_in_env: set[Value], is_sub_block: _bool, ) -> dict[Value, Value]: ... def _jit_pass_onnx_assign_scoped_names_for_node_and_value(graph: Graph) -> None: ... def _jit_pass_fixup_onnx_controlflow_node( n: Node, opset_version: _int, ) -> list[Value]: ... def _jit_onnx_create_full_scope_name(class_name: str, variable_name: str) -> str: ... def _compile_graph_to_code_table(name: str, graph: Graph) -> IValue: ... def _generate_upgraders_graph() -> dict[str, Graph]: ... def _calculate_package_version_based_on_upgraders(val: _bool): ... def _get_version_calculator_flag() -> _bool: ... def _jit_script_interface_compile( name: str, class_def: ClassDef, rcb: ResolutionCallback, is_module: _bool, ): ... def _jit_script_compile_overload( qualname: str, overload_decl: Decl, implementation_def: Def, rcb: ResolutionCallback, implementation_defaults: dict[str, Any], signature: Any, ): ... def _jit_script_compile( qual_name: str, definition: Def, rcb: ResolutionCallback, defaults: dict[str, Any], ): ... def _jit_script_class_compile( qual_name: str, definition: ClassDef, defaults: dict[str, dict[str, Any]], rcb: ResolutionCallback, ): ... def _parse_source_def(src: str) -> Def: ... def import_ir_module( cu: CompilationUnit, filename: str | Path, map_location: DeviceLikeType | None, extra_files: dict[str, Any], ) -> ScriptModule: ... def import_ir_module_from_buffer( cu: CompilationUnit, buffer: IO[bytes], map_location: DeviceLikeType | None, extra_files: dict[str, Any], ) -> ScriptModule: ... def _import_ir_module_from_package( cu: CompilationUnit, reader: PyTorchFileReader, storage_context: DeserializationStorageContext, map_location: DeviceLikeType | None, ts_id: str, ) -> ScriptModule: ... def _assign_output_shapes(graph: Graph, inputs: list[Tensor]) -> Graph: ... def _check_onnx_proto(proto: str) -> None: ... def _propagate_and_assign_input_shapes( graph: Graph, inputs: tuple[Tensor, ...], param_count_list: list[_int], with_grad: _bool, propagate: _bool, ) -> Graph: ... # Defined in torch/csrc/jit/runtime/graph_executor.h class GraphExecutorState: ... # Defined in torch/torch/csrc/jit/ir/alias_analysis.h class AliasDb: ... class _InsertPoint: def __enter__(self) -> None: ... def __exit__(self, *exc_info: object) -> None: ... # Defined in torch/csrc/jit/ir/ir.h class Use: @property def user(self) -> Node: ... @property def offset(self) -> _int: ... def isAfter(self, other: Use) -> _bool: ... # Defined in torch/csrc/jit/ir/ir.h class Value: def type(self) -> JitType: ... def setType(self, t: JitType) -> Value: ... def setTypeAs(self, other: Value) -> Value: ... def inferTypeFrom(self, t: Tensor) -> None: ... def debugName(self) -> str: ... def setDebugName(self, name: str) -> None: ... def unique(self) -> _int: ... def offset(self) -> _int: ... def node(self) -> Node: ... def uses(self) -> list[Use]: ... def replaceAllUsesWith(self, val: Value) -> None: ... def replaceAllUsesAfterNodeWith(self, node: Node, val: Value) -> None: ... def requires_grad(self) -> _bool: ... def requiresGrad(self) -> _bool: ... def copyMetadata(self, other: Value) -> Value: ... def isCompleteTensor(self) -> _bool: ... def toIValue(self) -> IValue: ... # Defined in torch/csrc/jit/ir/ir.h class Block: def inputs(self) -> Iterator[Value]: ... def outputs(self) -> Iterator[Value]: ... def nodes(self) -> Iterator[Node]: ... def paramNode(self) -> Node: ... def returnNode(self) -> Node: ... def owningNode(self) -> Node: ... def registerOutput(self, n: Value) -> _int: ... def addNode(self, name: str, inputs: Sequence[Value]) -> Node: ... # Defined in torch/csrc/jit/ir/ir.h class Node: def __getitem__(self, key: str) -> Any: ... def schema(self) -> str: ... def input(self) -> Value: ... def inputs(self) -> Iterator[Value]: ... def inputsAt(self, idx: _int) -> Value: ... def inputsSize(self) -> _int: ... def output(self) -> Value: ... def outputs(self) -> Iterator[Value]: ... def outputsAt(self, idx: _int) -> Value: ... def outputsSize(self) -> _int: ... def hasMultipleOutputs(self) -> _bool: ... def blocks(self) -> list[Block]: ... def addBlock(self) -> Block: ... def mustBeNone(self) -> _bool: ... def matches(self, pattern: str) -> _bool: ... def kind(self) -> str: ... def kindOf(self, name: str) -> str: ... def addInput(self, name: str) -> Value: ... def replaceInput(self, i: _int, newValue: Value) -> Value: ... def replaceInputWith(self, from_: Value, to: Value) -> None: ... def replaceAllUsesWith(self, n: Node) -> None: ... def insertBefore(self, n: Node) -> Node: ... def insertAfter(self, n: Node) -> Node: ... def isBefore(self, n: Node) -> _bool: ... def isAfter(self, n: Node) -> _bool: ... def moveBefore(self, n: Node) -> None: ... def moveAfter(self, n: Node) -> None: ... def removeInput(self, i: _int) -> None: ... def removeAllInputs(self, i: _int) -> None: ... def hasUses(self) -> _bool: ... def eraseOutput(self, i: _int) -> None: ... def addOutput(self) -> Value: ... def scopeName(self) -> str: ... def isNondeterministic(self) -> _bool: ... def copyAttributes(self, rhs: Node) -> Node: ... def copyMetadata(self, rhs: Node) -> Node: ... def hasAttributes(self) -> _bool: ... def hasAttribute(self, name: str) -> _bool: ... def removeAttribute(self, attr: str) -> Node: ... def namedInput(self, name: str) -> Value: ... def sourceRange(self) -> SourceRange: ... def owningBlock(self) -> Block: ... def findNode(self, kind: str, recurse: _bool = True) -> Node: ... def findAllNodes(self, kind: str, recurse: _bool = True) -> list[Node]: ... def getModuleHierarchy(self) -> str: ... def prev(self) -> Node: ... def destroy(self) -> None: ... def attributeNames(self) -> list[str]: ... # Accessors for attributes as types. def f(self, name: str) -> _float: ... def f_(self, name: str, val: _float) -> Node: ... def fs(self, name: str) -> list[_float]: ... def fs_(self, name: str, val: list[_float]) -> Node: ... def c(self, name: str) -> complex: ... def c_(self, name: str, val: complex) -> Node: ... def s(self, name: str) -> str: ... def s_(self, name: str, val: str) -> Node: ... def ss(self, name: str) -> list[str]: ... def ss_(self, name: str, val: list[str]) -> Node: ... def i(self, name: str) -> _int: ... def i_(self, name: str, val: _int) -> Node: ... # Cannot define "is" like this because it's a reserved keyword in python. # def is(self, name: str) -> List[_int]: ... # def is_(self, name: str, val: List[_int]) -> Node: ... def g(self, name: str) -> Graph: ... def g_(self, name: str, val: Graph) -> Node: ... def gs(self, name: str) -> list[Graph]: ... def gs_(self, name: str, val: list[Graph]) -> Node: ... def ival(self, name: str) -> IValue: ... def ival_(self, name: str, val: IValue) -> Node: ... def t(self, name: str) -> Tensor: ... def t_(self, name: str, val: Tensor) -> Node: ... def ts(self, name: str) -> list[Tensor]: ... def ts_(self, name: str, val: list[Tensor]) -> Node: ... def ty(self, name: str) -> JitType: ... def ty_(self, name: str, val: JitType) -> Node: ... def tys(self, name: str) -> list[JitType]: ... def tys_(self, name: str, val: list[JitType]) -> Node: ... # Defined in torch/torch/csrc/jit/ir/ir.h class Graph: def inputs(self) -> Iterator[Value]: ... def outputs(self) -> Iterator[Value]: ... def nodes(self) -> Iterator[Node]: ... def param_node(self) -> Node: ... def return_node(self) -> Node: ... def addInput(self, name: str = "") -> Value: ... def eraseInput(self, i: _int) -> None: ... def registerOutput(self, n: Value) -> _int: ... def eraseOutput(self, i: _int) -> None: ... def create(self, name: str, args, num_outputs: _int) -> Node: ... def appendNode(self, n: Node) -> Node: ... def prependNode(self, n: Node) -> Node: ... def insertNode(self, n: Node) -> Node: ... def block(self) -> Block: ... def lint(self) -> None: ... def alias_db(self) -> AliasDb: ... def setInsertPoint(self, n: Block | Node) -> None: ... def insert_point_guard(self, n: Block | Node) -> _InsertPoint: ... def insertPoint(self) -> Node: ... def insertGraph(self, callee: Graph, inputs: list[Value]) -> list[Value]: ... def makeMultiOutputIntoTuple(self) -> None: ... def copy(self) -> Graph: ... # Defined in torch/aten/src/ATen/core/alias_info.h class AliasInfo: is_write: _bool before_set: set[str] after_set: set[str] def __init__( self, is_write: _bool, before_set: set[str], after_set: set[str], ) -> None: ... # Defined in torch/aten/src/ATen/core/function_schema.h class Argument: name: str type: JitType default_value: Any | None def has_default_value(self) -> _bool: ... kwarg_only: _bool is_out: _bool alias_info: AliasInfo | None is_write: _bool real_type: JitType def __init__( self, name: str, type: JitType, N: _int | None, defualt_value: Any | None, kwarg_only: _bool, alias_info: AliasInfo | None, ) -> None: ... class FunctionSchema: arguments: list[Argument] returns: list[Argument] name: str overload_name: str is_mutable: _bool def __init__( self, name: str, overload_name: str, arguments: list[Argument], returns: list[Argument], is_vararg: _bool, is_varret: _bool, ) -> None: ... class _UpgraderEntry: bumped_at_version: _int upgrader_name: str old_schema: str def __init__( self, bumped_at_version: _int, upgrader_name: str, old_schema: str, ) -> None: ... class _UpgraderRange: min_version: _int max_version: _int def _get_max_operator_version() -> _int: ... def _get_operator_version_map() -> dict[str, list[_UpgraderEntry]]: ... def _get_upgrader_ranges(name: str) -> list[_UpgraderRange]: ... def _test_only_add_entry_to_op_version(op_name: str, entry: _UpgraderEntry) -> None: ... def _test_only_remove_entry_to_op_version(op_name: str) -> None: ... # Defined in torch/csrc/jit/python/script_init.cpp class ScriptModuleSerializer: def __init__(self, export_writer: PyTorchFileWriter) -> None: ... def serialize(self, model: ScriptModule, script_module_id: _int) -> None: ... def write_files(self) -> None: ... def storage_context(self) -> SerializationStorageContext: ... # Defined in torch/csrc/jit/python/script_init.cpp class SerializationStorageContext: def __init__(self) -> None: ... def has_storage(self, storage: Storage) -> _bool: ... def get_or_add_storage(self, storage: Storage) -> _int: ... # Defined in torch/csrc/jit/python/script_init.cpp class DeserializationStorageContext: def __init__(self) -> None: ... def get_storage(self, name: str, dtype: _dtype) -> Tensor: ... def has_storage(self, name: str) -> _bool: ... def add_storage(self, name: str, tensor: Tensor) -> _int: ... # Defined in torch/csrc/jit/python/script_init.cpp class ConcreteModuleTypeBuilder: def __init__(self, obj: Any) -> None: ... def set_module_dict(self): ... def set_module_list(self): ... def set_parameter_list(self): ... def set_parameter_dict(self): ... def add_attribute( self, name: str, ty: JitType, is_param: _bool, is_buffer: _bool, ): ... def add_module(self, name: str, meta: ConcreteModuleType): ... def add_constant(self, name: str, value: Any): ... def add_overload(self, method_name: str, overloaded_method_names: list[str]): ... def add_builtin_function(self, name: str, symbol_name: str): ... def add_failed_attribute(self, name: str, failure_reason: str): ... def add_function_attribute( self, name: str, ty: JitType, func: Callable[..., Any], ): ... def add_ignored_attribute(self, name: str): ... def add_ignored_attributes(self, names: list[str]): ... def add_forward_hook(self, hook: Callable[..., Any]): ... def add_forward_pre_hook(self, pre_hook: Callable[..., Any]): ... class ConcreteModuleType: def get_constants(self) -> dict[str, Any]: ... def equals(self, other: ConcreteModuleType) -> _bool: ... @staticmethod def from_jit_type(ty: JitType) -> ConcreteModuleType: ... class CallStack: def __init__(self, name: str, range: SourceRange) -> None: ... class ErrorReport: def __init__(self, range: SourceRange) -> None: ... def what(self) -> str: ... @staticmethod def call_stack() -> str: ... class CompilationUnit: def __init__(self, lang: str = ..., _frames_up: _int = ...) -> None: ... def find_function(self, name: str) -> ScriptFunction: ... def __getattr__(self, name: str) -> ScriptFunction: ... def define( self, script: str, rcb: ResolutionCallback = ..., _frames_up: _int = ..., ): ... def get_interface(self, name: str) -> InterfaceType: ... def get_functions(self) -> list[ScriptFunction]: ... def create_function( self, name: str, graph: Graph, shouldMangle: _bool = ..., ) -> ScriptFunction: ... def get_class(self, name: str) -> ClassType: ... class ScriptObject: def setattr(self, name: str, value: Any): ... def _get_method(self, name: str) -> ScriptMethod: ... def _type(self) -> ClassType: ... class ScriptModule(ScriptObject): def _method_names(self) -> list[str]: ... def _get_method(self, name: str) -> ScriptMethod: ... class LiteScriptModule: def __call__(self, *input): ... def find_method(self, method_name: str): ... def forward(self, *input) -> list[str]: ... def run_method(self, method_name: str, *input): ... # NOTE: switch to collections.abc.Callable in python 3.9 class ScriptFunction(Generic[P, R]): def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R: ... def save(self, filename: str, _extra_files: dict[str, bytes]) -> None: ... def save_to_buffer(self, _extra_files: dict[str, bytes]) -> bytes: ... @property def graph(self) -> Graph: ... def inlined_graph(self) -> Graph: ... def schema(self) -> FunctionSchema: ... def code(self) -> str: ... def name(self) -> str: ... @property def qualified_name(self) -> str: ... # NOTE: switch to collections.abc.Callable in python 3.9 class ScriptMethod(Generic[P, R]): graph: Graph def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R: ... @property def owner(self) -> ScriptModule: ... @property def name(self) -> str: ... @property def schema(self) -> FunctionSchema: ... class ScriptDict(Generic[K, T]): def __init__(self, dict: dict[K, T]) -> None: ... def __len__(self) -> _int: ... def __contains__(self, key: K) -> _bool: ... def __getitem__(self, key: K) -> T: ... def __setitem__(self, key: K, value: T) -> None: ... def __delitem__(self, key: K) -> None: ... def __iter__(self) -> Iterator[K]: ... def items(self) -> Iterator[tuple[K, T]]: ... def keys(self) -> Iterator[K]: ... class ScriptList(Generic[T]): def __init__(self, list: list[T]) -> None: ... def __len__(self) -> _int: ... def __contains__(self, item: T) -> _bool: ... @overload def __getitem__(self, idx: _int) -> T: ... @overload def __getitem__(self, idx: slice) -> ScriptList[T]: ... @overload def __setitem__(self, idx: _int, value: T) -> None: ... @overload def __setitem__(self, idx: slice, value: list[T]) -> None: ... def __delitem__(self, idx: _int) -> None: ... def __iter__(self) -> Iterator[T]: ... def count(self, value: T) -> _int: ... def remove(self, value: T) -> None: ... def append(self, value: T) -> None: ... def clear(self) -> None: ... @overload def extend(self, values: list[T]) -> None: ... @overload def extend(self, values: Iterable[T]) -> None: ... @overload def pop(self) -> T: ... @overload def pop(self, idx: _int) -> T: ... class ModuleDict: def __init__(self, mod: ScriptModule) -> None: ... def items(self) -> list[tuple[str, Any]]: ... class ParameterDict: def __init__(self, mod: ScriptModule) -> None: ... class BufferDict: def __init__(self, mod: ScriptModule) -> None: ... # Defined in torch/csrc/jit/api/module.h class Module: ... # Defined in torch/csrc/Module.cpp def _initExtension(shm_manager_path: str) -> None: ... # THPModule_initExtension def _autograd_init() -> _bool: ... # THPAutograd_initExtension def _add_docstr(obj: T, doc_obj: str) -> T: ... # THPModule_addDocStr def _init_names(arg: Sequence[type]) -> None: ... # THPModule_initNames def _has_distributed() -> _bool: ... # THPModule_hasDistributed def _set_default_tensor_type(type) -> None: ... # THPModule_setDefaultTensorType def _set_default_dtype(d: _dtype) -> None: ... # THPModule_setDefaultDtype def _infer_size(arg1: Size, arg2: Size) -> Size: ... # THPModule_inferSize def _crash_if_csrc_asan() -> _int: ... # THPModule_crashIfCsrcASAN def _crash_if_csrc_ubsan() -> _int: ... # THPModule_crashIfCsrcUBSAN def _crash_if_aten_asan() -> _int: ... # THPModule_crashIfATenASAN def _show_config() -> str: ... # THPModule_showConfig def _cxx_flags() -> str: ... # THPModule_cxxFlags def _parallel_info() -> str: ... # THPModule_parallelInfo def _get_cpu_capability() -> str: ... # THPModule_getCpuCapability def _set_backcompat_broadcast_warn( arg: _bool, ) -> None: ... # THPModule_setBackcompatBroadcastWarn def _get_backcompat_broadcast_warn() -> ( _bool ): ... # THPModule_getBackcompatBroadcastWarn def _set_backcompat_keepdim_warn( arg: _bool, ) -> None: ... # THPModule_setBackcompatKeepdimWarn def _get_backcompat_keepdim_warn() -> _bool: ... # THPModule_getBackcompatKeepdimWarn def get_num_thread() -> _int: ... # THPModule_getNumThreads def set_num_threads(nthreads: _int) -> None: ... # THPModule_setNumThreads def get_num_interop_threads() -> _int: ... # THPModule_getNumInteropThreads def set_num_interop_threads( nthreads: _int, ) -> None: ... # THPModule_setNumInteropThreads def _get_cudnn_enabled() -> _bool: ... # THPModule_userEnabledCuDNN def _set_cudnn_enabled(arg: _bool) -> None: ... # THPModule_setUserEnabledCuDNN def _get_flash_sdp_enabled() -> _bool: ... # THPModule_userEnabledFusedSDP def _set_sdp_use_flash(arg: _bool) -> None: ... # THPModule_setSDPUseFlash def _get_mem_efficient_sdp_enabled() -> _bool: ... # THPModule_userEnabledMathSDP def _set_sdp_use_mem_efficient( arg: _bool, ) -> None: ... # THPModule_setSDPUseMemEfficient def _get_math_sdp_enabled() -> _bool: ... # THPModule_userEnabledMathSDP def _set_sdp_use_math(arg: _bool) -> None: ... # THPModule_setSDPUseMath def _get_math_sdp_allow_fp16_bf16_reduction() -> ( _bool ): ... # THPModule_allowFP16BF16ReductionMathSDP def _set_math_sdp_allow_fp16_bf16_reduction( arg: _bool, ) -> None: ... # THPModule_setAllowFP16BF16ReductionMathSDP def _get_overrideable_sdp_enabled() -> ( _bool ): ... # THPModule_userEnabledOverrideableSDP def _set_sdp_use_overrideable( arg: _bool, ) -> None: ... # THPModule_setSDPUseOverrideable def _get_sdp_priority_order() -> list[_int]: ... # THPModule_getSDPPriorityOrder def _set_sdp_priority_order( arg: list[_int], ) -> None: ... # THPModule_setSDPPriorityOrder def _get_cudnn_sdp_enabled() -> _bool: ... # THPModule_userEnabledMathSDP def _set_sdp_use_cudnn(arg: _bool) -> None: ... # THPModule_setSDPUseMath def _get_mkldnn_enabled() -> _bool: ... # THPModule_userEnabledMkldnn def _set_mkldnn_enabled(arg: _bool) -> None: ... # THPModule_setUserEnabledMkldnn def _get_cudnn_benchmark() -> _bool: ... # THPModule_benchmarkCuDNN def _set_cudnn_benchmark(arg: _bool) -> None: ... # THPModule_setBenchmarkCuDNN def _get_cudnn_deterministic() -> _bool: ... # THPModule_deterministicCuDNN def _set_cudnn_deterministic(arg: _bool) -> None: ... # THPModule_setDeterministicCuDNN def _get_mkldnn_deterministic() -> _bool: ... # THPModule_deterministicMkldnn def _set_mkldnn_deterministic( arg: _bool, ) -> None: ... # THPModule_setDeterministicMkldnn def _get_onednn_allow_tf32() -> _bool: ... # THPModule_allowTF32OneDNN def _set_onednn_allow_tf32(arg: _bool) -> None: ... # THPModule_setAllowTF32OneDNN def _get_deterministic_algorithms() -> _bool: ... # THPModule_deterministicAlgorithms def _get_deterministic_algorithms_warn_only() -> ( _bool ): ... # THPModule_deterministicAlgorithmsWarnOnly def _set_deterministic_algorithms( mode: _bool, *, warn_only: _bool = ..., ) -> None: ... # THPModule_setDeterministicAlgorithms def _get_deterministic_fill_uninitialized_memory() -> ( _bool ): ... # THPModule_deterministicFillUninitializedMemory def _set_deterministic_fill_uninitialized_memory( arg: _bool, ) -> None: ... # THPModule_setDeterministicFillUninitializedMemory def _get_nnpack_enabled() -> _bool: ... # THPModule_userEnabledNNPACK def _set_nnpack_enabled(arg: _bool) -> None: ... # THPModule_setUserEnabledNNPACK def _get_warnAlways() -> _bool: ... # THPModule_warnAlways def _set_warnAlways(arg: _bool) -> None: ... # THPModule_setWarnAlways def _get_cudnn_allow_tf32() -> _bool: ... # THPModule_allowTF32CuDNN def _set_cudnn_allow_tf32(arg: _bool) -> None: ... # THPModule_setAllowTF32CuDNN def _get_cublas_allow_tf32() -> _bool: ... # THPModule_allowTF32CuBLAS def _set_cublas_allow_tf32(arg: _bool) -> None: ... # THPModule_setAllowTF32CuBLAS def _get_float32_matmul_precision() -> str: ... # THPModule_float32MatmulPrecision def _set_float32_matmul_precision( arg: str, ) -> None: ... # THPModule_setFloat32MatmulPrecision def _get_cublas_allow_fp16_reduced_precision_reduction() -> ( _bool ): ... # THPModule_allowFP16ReductionCuBLAS def _set_cublas_allow_fp16_reduced_precision_reduction( arg: _bool, ) -> None: ... # THPModule_setAllowFP16ReductionCuBLAS def _get_cublas_allow_bf16_reduced_precision_reduction() -> ( _bool ): ... # THPModule_allowBF16ReductionCuBLAS def _set_cublas_allow_bf16_reduced_precision_reduction( arg: _bool, ) -> None: ... # THPModule_setAllowBF16ReductionCuBLAS def _get_cublas_allow_fp16_accumulation() -> ( _bool ): ... # THPModule_allowFP16AccumulationCuBLAS def _set_cublas_allow_fp16_accumulation( arg: _bool, ) -> None: ... # THPModule_setAllowFP16AccumulationCuBLAS def _get_sm_carveout_experimental() -> _int | None: ... def _set_sm_carveout_experimental(arg: _int | None) -> None: ... def _set_conj(x: Tensor, conj: _bool) -> None: ... def _set_neg(x: Tensor, neg: _bool) -> None: ... def _set_meta_in_tls_dispatch_include(meta_in_tls: _bool) -> None: ... def _meta_in_tls_dispatch_include() -> _bool: ... def _stash_obj_in_tls(key: str, arg: Any) -> None: ... def _get_obj_in_tls(key: str) -> Any: ... def _is_key_in_tls(key: str) -> _bool: ... def _select_batch_norm_backend(*args, **kwargs) -> BatchNormBackend: ... def _select_conv_backend(*args, **kwargs) -> ConvBackend: ... def _conv_determine_backend_memory_format( input: Tensor, weight: Tensor, backend: ConvBackend, ) -> memory_format: ... def _has_storage(x: Tensor) -> _bool: ... def _construct_storage_from_data_pointer( data_ptr: _int, device: torch.device, size: _int, ) -> Storage: ... def _should_allow_numbers_as_tensors(func_name: str) -> _bool: ... def _group_tensors_by_device_and_dtype( nested_tensorlists: list[list[Tensor | None]], with_indices: _bool = False, ) -> dict[ tuple[torch.device, torch.dtype], tuple[list[list[Tensor | None]], list[_int]], ]: ... # NB: There is no Capsule type in typing, see # https://github.com/python/cpython/issues/109562 def _to_dlpack(data: Tensor) -> Any: ... # THPModule_toDLPack def _from_dlpack(data: Any) -> Tensor: ... # THPModule_fromDLPack def _get_cpp_backtrace( frames_to_skip: _int, maximum_number_of_frames: _int, ) -> str: ... # THPModule_getCppBacktrace def set_flush_denormal(arg: _bool) -> _bool: ... # THPModule_setFlushDenormal def get_default_dtype() -> _dtype: ... # THPModule_getDefaultDtype def _get_default_device() -> str: ... # THPModule_getDefaultDevice def _get_qengine() -> _int: ... # THPModule_qEngine def _set_qengine(qengine: _int) -> None: ... # THPModule_setQEngine def _supported_qengines() -> list[_int]: ... # THPModule_supportedQEngines def _is_xnnpack_enabled() -> _bool: ... # THPModule_isEnabledXNNPACK def _check_sparse_tensor_invariants() -> ( _bool ): ... # THPModule_checkSparseTensorInvariants def _set_check_sparse_tensor_invariants( arg: _bool, ) -> None: ... # THPModule_setCheckSparseTensorInvariants def _is_default_mobile_cpu_allocator_set() -> ( _bool ): ... # THPModule_isDefaultMobileCPUAllocatorSet def _set_default_mobile_cpu_allocator() -> ( None ): ... # THPModule_setDefaultMobileCPUAllocator def _unset_default_mobile_cpu_allocator() -> ( None ): ... # THPModule_unsetDefaultMobileCPUAllocator def _is_torch_function_enabled() -> _bool: ... # THPModule_isEnabledTorchFunction def _is_torch_function_all_disabled() -> ( _bool ): ... # THPModule_isAllDisabledTorchFunction def _has_torch_function( args: Iterable[Any], ) -> _bool: ... # THPModule_has_torch_function def _has_torch_function_unary(Any) -> _bool: ... # THPModule_has_torch_function_unary def _has_torch_function_variadic( *args: Any, ) -> _bool: ... # THPModule_has_torch_function_variadic def _vmapmode_increment_nesting() -> _int: ... # THPModule_vmapmode_increment_nesting def _vmapmode_decrement_nesting() -> _int: ... # THPModule_vmapmode_decrement_nesting def _log_api_usage_once(str) -> None: ... # LogAPIUsageOnceFromPython def _log_api_usage_metadata( event: str, metadata_map: dict[str, str], ) -> None: ... # LogAPIUsageMetadataFromPython def _demangle(str) -> str: ... # c10::demangle def _disabled_torch_function_impl( func: Callable, types: Iterable[type], args: tuple, kwargs: dict, ) -> Any: ... # THPModule_disable_torch_function def _disabled_torch_dispatch_impl( func: Callable, types: Iterable[type], args: tuple, kwargs: dict, ) -> Any: ... # THPModule_disable_dispatch_function def _get_linalg_preferred_backend() -> _LinalgBackend: ... def _set_linalg_preferred_backend(arg: _LinalgBackend): ... class _LinalgBackend: Default: _LinalgBackend Cusolver: _LinalgBackend Magma: _LinalgBackend # mypy error: # Detected enum "torch._C.BatchNormBackend" in a type stub with zero # members. There is a chance this is due to a recent change in the semantics # of enum membership. If so, use `member = value` to mark an enum member, # instead of `member: type` class BatchNormBackend(Enum): ... # type: ignore[misc] def _get_blas_preferred_backend() -> _BlasBackend: ... def _set_blas_preferred_backend(arg: _BlasBackend): ... class _BlasBackend: Default: _BlasBackend Cublas: _BlasBackend Cublaslt: _BlasBackend Ck: _BlasBackend def _get_rocm_fa_preferred_backend() -> torch._C._ROCmFABackend: ... def _set_rocm_fa_preferred_backend(arg: torch._C._ROCmFABackend): ... class _ROCmFABackend: Default: _ROCmFABackend AOTriton: _ROCmFABackend Ck: _ROCmFABackend # mypy error: # Error (MYPY) [misc] # Detected enum "torch._C.ConvBackend" in a type stub with zero members. # There is a chance this is due to a recent change in the semantics of enum # membership. If so, use `member = value` to mark an enum member, instead of # `member: type` class ConvBackend(Enum): ... # type: ignore[misc] class Tag(Enum): ${tag_attributes} # Defined in `valgrind.h` and `callgrind.h` respectively. def _valgrind_supported_platform() -> _bool: ... # NVALGRIND def _valgrind_toggle() -> None: ... # CALLGRIND_TOGGLE_COLLECT def _valgrind_toggle_and_dump_stats() -> ( None ): ... # CALLGRIND_TOGGLE_COLLECT and CALLGRIND_DUMP_STATS has_openmp: _bool has_mkl: _bool _has_kleidiai: _bool _has_mps: _bool has_lapack: _bool _has_cuda: _bool _has_magma: _bool _has_xpu: _bool _has_mkldnn: _bool _has_cudnn: _bool _has_cusparselt: _bool has_spectral: _bool _GLIBCXX_USE_CXX11_ABI: _bool default_generator: Generator # Defined in torch/csrc/autograd/init.cpp def _set_grad_enabled(enabled: _bool) -> None: ... def is_grad_enabled() -> _bool: ... def _set_fwd_grad_enabled(enabled: _bool) -> None: ... def _is_fwd_grad_enabled() -> _bool: ... def _any_requires_grad(*args, **kwargs) -> _bool: ... def _any_output_is_alias_to_input_or_output(*args, **kwargs) -> _bool: ... def is_inference_mode_enabled() -> _bool: ... @overload def set_autocast_enabled(device_type: str, enabled: _bool) -> None: ... @overload def set_autocast_enabled(enabled: _bool) -> None: ... @overload def is_autocast_enabled(device_type: str) -> _bool: ... @overload def is_autocast_enabled() -> _bool: ... def set_autocast_dtype(device_type: str, dtype: _dtype) -> None: ... def get_autocast_dtype(device_type: str) -> _dtype: ... def clear_autocast_cache() -> None: ... def set_autocast_cpu_enabled(enabled: _bool) -> None: ... def is_autocast_cpu_enabled() -> _bool: ... def _is_any_autocast_enabled() -> _bool: ... def _is_autocast_available(device_type: str) -> _bool: ... def set_autocast_cpu_dtype(dtype: _dtype) -> None: ... def set_autocast_gpu_dtype(dtype: _dtype) -> None: ... def get_autocast_cpu_dtype() -> _dtype: ... def get_autocast_gpu_dtype() -> _dtype: ... def autocast_increment_nesting() -> _int: ... def autocast_decrement_nesting() -> _int: ... def is_autocast_cache_enabled() -> _bool: ... def set_autocast_cache_enabled(enabled: _bool) -> None: ... def _increment_version(tensors: Iterable[Tensor]) -> None: ... def set_anomaly_enabled(enabled: _bool, check_nan: _bool = True) -> None: ... def is_anomaly_enabled() -> _bool: ... def is_anomaly_check_nan_enabled() -> _bool: ... def _is_multithreading_enabled() -> _bool: ... def _set_multithreading_enabled(enabled: _bool) -> None: ... def _set_view_replay_enabled(enabled: _bool) -> None: ... def _is_view_replay_enabled() -> _bool: ... def _enter_dual_level() -> _int: ... def _exit_dual_level(level: _int) -> None: ... def _make_dual(tensor: Tensor, tangent: Tensor, level: _int) -> Tensor: ... def _unpack_dual(tensor: Tensor, level: _int) -> Tensor: ... def __set_forward_AD_enabled(enabled: _bool) -> None: ... def __is_forward_AD_enabled() -> _bool: ... def _register_default_hooks(pack_hook: Callable, unpack_hook: Callable) -> None: ... def _reset_default_hooks() -> None: ... def _is_torch_function_mode_enabled() -> _bool: ... def _push_on_torch_function_stack(cls: Any) -> None: ... def _pop_torch_function_stack() -> Any: ... def _get_function_stack_at(idx: _int) -> Any: ... def _len_torch_function_stack() -> _int: ... def _set_torch_dispatch_mode(cls: Any) -> None: ... def _push_on_torch_dispatch_stack(cls: TorchDispatchMode) -> None: ... def _pop_torch_dispatch_stack(mode_key: _TorchDispatchModeKey | None = None) -> Any: ... def _get_dispatch_mode(mode_key: _TorchDispatchModeKey | None) -> Any: ... def _unset_dispatch_mode(mode: _TorchDispatchModeKey) -> TorchDispatchMode | None: ... def _set_dispatch_mode(mode: TorchDispatchMode) -> None: ... def _get_dispatch_stack_at(idx: _int) -> Any: ... def _len_torch_dispatch_stack() -> _int: ... def _activate_gpu_trace() -> None: ... class _DisableTorchDispatch: def __init__(self) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _EnableTorchFunction: def __init__(self) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _EnablePythonDispatcher: def __init__(self) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _DisablePythonDispatcher: def __init__(self) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _EnablePreDispatch: def __init__(self) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _DisableFuncTorch: def __init__(self) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _DisableAutocast: def __init__(self) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _InferenceMode: def __init__(self, enabled: _bool) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... def _set_autograd_fallback_mode(mode: str) -> None: ... def _get_autograd_fallback_mode() -> str: ... # Defined in torch/csrc/jit/python/script_init.cpp class LoggerBase: ... class NoopLogger(LoggerBase): ... class LockingLogger(LoggerBase): ... class AggregationType(Enum): SUM = 0 AVG = 1 class FileCheck: def run(self, test_string: str) -> None: ... def check(self, test_string: str) -> FileCheck: ... def check_not(self, test_string: str) -> FileCheck: ... def check_same(self, test_string: str) -> FileCheck: ... def check_next(self, test_string: str) -> FileCheck: ... def check_count( self, test_string: str, count: _int, exactly: _bool = False, ) -> FileCheck: ... def check_dag(self, test_string: str) -> FileCheck: ... def check_source_highlighted(self, test_string: str) -> FileCheck: ... def check_regex(self, test_string: str) -> FileCheck: ... # Defined in torch/csrc/jit/python/init.cpp class PyTorchFileReader: @overload def __init__(self, name: str) -> None: ... @overload def __init__(self, buffer: IO[bytes]) -> None: ... def get_record(self, name: str) -> bytes: ... def get_all_records(self) -> list[str]: ... def serialization_id(self) -> str: ... class PyTorchFileWriter: @overload def __init__( self, name: str, compute_crc32: _bool = True, storage_alignment: _int = 64, ) -> None: ... @overload def __init__( self, buffer: IO[bytes], compute_crc32: _bool = True, storage_alignment: _int = 64, ) -> None: ... def write_record( self, name: str, data: Storage | bytes | _int, size: _int, ) -> None: ... def write_end_of_file(self) -> None: ... def set_min_version(self, version: _int) -> None: ... def get_all_written_records(self) -> list[str]: ... def archive_name(self) -> str: ... def serialization_id(self) -> str: ... def _jit_get_inline_everything_mode() -> _bool: ... def _jit_set_inline_everything_mode(enabled: _bool) -> None: ... def _jit_get_logging_option() -> str: ... def _jit_set_logging_option(option: str) -> None: ... def _jit_set_logging_stream(stream_name: str) -> None: ... def _jit_pass_cse(Graph) -> _bool: ... def _jit_pass_dce(Graph) -> None: ... def _jit_pass_dce_graph(Graph) -> None: ... def _jit_pass_lint(Graph) -> None: ... # Defined in torch/csrc/jit/python/python_custom_class.cpp def _get_custom_class_python_wrapper(name: str, attr: str) -> Any: ... # Defined in torch/csrc/Module.cpp def _rename_privateuse1_backend(backend: str) -> None: ... def _get_privateuse1_backend_name() -> str: ... # Defined in torch/csrc/Generator.cpp class Generator: device: _device def __init__(self, device: DeviceLikeType | None = None) -> None: ... def __reduce__( self, ) -> tuple[type[Generator], tuple[_device], tuple[_int, _int | None, Tensor]]: ... def __setstate__(self, state: tuple[_int, _int | None, Tensor]) -> None: ... def get_state(self) -> Tensor: ... def set_state(self, _new_state: Tensor) -> Generator: ... def clone_state(self) -> Generator: ... def graphsafe_get_state(self) -> Generator: ... def graphsafe_set_state(self, _new_state: Generator) -> Generator: ... def set_offset(self, offset: _int) -> Generator: ... def get_offset(self) -> _int: ... def manual_seed(self, seed: _int) -> Generator: ... def seed(self) -> _int: ... def initial_seed(self) -> _int: ... # Defined in torch/csrc/utils/python_dispatch.cpp class _DispatchOperatorHandle: def schema(self) -> FunctionSchema: ... def debug(self) -> str: ... def redispatch_boxed(self, keyset: DispatchKeySet, *args, **kwargs) -> Any: ... class _DispatchModule: def reset(self) -> None: ... def def_(self, schema: str, alias: str = "") -> _DispatchModule: ... def def_legacy(self, schema: str) -> _DispatchModule: ... def def_name_t_t( self, name: str, dispatch: str, debug: str = "default_def_name_t_t", ) -> _DispatchModule: ... def def_schema_t_t( self, schema: str, dispatch: str, alias: str, debug: str = "default_def_schema_t_t", ) -> _DispatchModule: ... def impl_t_t( self, name: str, dispatch: str, debug: str = "impl_t_t", ) -> _DispatchModule: ... def impl_with_aoti_compile( self, ns: str, op_name_with_overload: str, dispatch: _dispatchkey, ) -> None: ... def impl(self, name: str, dispatch: _dispatchkey, func: Callable) -> None: ... def define(self, schema: str, alias: str = "") -> str: ... def fallback_fallthrough(self, dispatch: str = "") -> _DispatchModule: ... def fallback( self, dispatch: _dispatchkey, func: Callable, with_keyset: _bool = False, ) -> None: ... _after_ADInplaceOrView_keyset: DispatchKeySet _after_autograd_keyset: DispatchKeySet def _dispatch_library( kind: str, name: str, dispatch: str, file: str = "", linenum: Any = 0, ) -> _DispatchModule: ... def _dispatch_dump(name: str) -> str: ... def _dispatch_dump_table(name: str) -> str: ... def _dispatch_check_invariants(name: str) -> None: ... def _dispatch_check_all_invariants() -> None: ... def _dispatch_call_boxed(handle: _DispatchOperatorHandle, *args, **kwargs) -> Any: ... def _dispatch_find_schema_or_throw( name: str, overload_name: str, ) -> _DispatchOperatorHandle: ... def _dispatch_set_report_error_callback( handle: _DispatchOperatorHandle, callback: Callable, ) -> None: ... def _dispatch_has_kernel(name: str) -> _bool: ... def _dispatch_has_kernel_for_dispatch_key( name: str, dispatch: _dispatchkey, ) -> _bool: ... def _dispatch_has_kernel_for_any_dispatch_key( name: str, dispatch_key_set: DispatchKeySet, ) -> _bool: ... def _dispatch_kernel_for_dispatch_key_is_fallthrough( name: str, dispatch: _dispatchkey, ) -> _bool: ... def _dispatch_has_computed_kernel_for_dispatch_key( name: str, dispatch: _dispatchkey, ) -> _bool: ... def _dispatch_find_dangling_impls() -> list[str]: ... def _dispatch_get_all_op_names() -> list[str]: ... def _dispatch_tls_set_dispatch_key_excluded( dispatch: _dispatchkey, val: _bool, ) -> None: ... def _dispatch_tls_is_dispatch_key_excluded(dispatch: _dispatchkey) -> _bool: ... def _dispatch_tls_set_dispatch_key_included( dispatch: _dispatchkey, val: _bool, ) -> None: ... def _dispatch_tls_is_dispatch_key_included(dispatch: _dispatchkey) -> _bool: ... def _dispatch_isTensorSubclassLike(tensor: Tensor) -> _bool: ... def _dispatch_key_name(dispatch: _dispatchkey) -> str: ... def _dispatch_key_for_device(device_type: str) -> str: ... def _parse_dispatch_key(key: str) -> DispatchKey | None: ... def _dispatch_key_parse(dispatch: _dispatchkey) -> DispatchKey: ... def _dispatch_num_backends() -> _int: ... def _dispatch_pystub(name: str, overload: str) -> tuple[str, str] | None: ... def _dispatch_is_alias_key(dispatch: _dispatchkey) -> _bool: ... def _functionality_to_backend_keys(dispatch: _dispatchkey) -> list[DispatchKey]: ... def _functionalization_reapply_views_tls() -> _bool: ... def _only_lift_cpu_tensors() -> _bool: ... def _set_only_lift_cpu_tensors(value: _bool) -> None: ... def _set_throw_on_mutable_data_ptr(tensor: Tensor) -> None: ... def _set_warn_deprecated_on_mutable_data_ptr(tensor: Tensor) -> None: ... class DispatchKey(Enum): ${dispatch_key_hints} class DispatchKeySet: def __init__(self, key: DispatchKey) -> None: ... def __or__(self, other: DispatchKeySet) -> DispatchKeySet: ... def __sub__(self, other: DispatchKeySet) -> DispatchKeySet: ... def __and__(self, other: DispatchKeySet) -> DispatchKeySet: ... def raw_repr(self) -> _int: ... @staticmethod def from_raw_repr(raw: _int) -> DispatchKeySet: ... def highestPriorityTypeId(self) -> DispatchKey: ... def has(self, k: _dispatchkey) -> _bool: ... def add(self, k: _dispatchkey) -> DispatchKeySet: ... def remove(self, k: _dispatchkey) -> DispatchKeySet: ... _dispatch_autogradother_backends: DispatchKeySet _additional_keys_to_prop_for_wrapper_tensors: DispatchKeySet def _dispatch_has_backend_fallback(dispatch: _dispatchkey) -> _bool: ... def _dispatch_keyset_full_after(t: _dispatchkey) -> DispatchKeySet: ... def _dispatch_keyset_full() -> DispatchKeySet: ... def _dispatch_keyset_to_string(keyset: DispatchKeySet) -> str: ... def _dispatch_get_backend_keyset_from_autograd( dispatch: _dispatchkey, ) -> DispatchKeySet: ... def _dispatch_keys(tensor: Tensor) -> DispatchKeySet: ... def _dispatch_tls_local_exclude_set() -> DispatchKeySet: ... def _dispatch_tls_local_include_set() -> DispatchKeySet: ... def _dispatch_is_included_in_alias( dispatch_a: _dispatchkey, dispatch_b: _dispatchkey, ) -> _bool: ... def _propagate_xla_data(a: Tensor, b: Tensor) -> None: ... def _replace_(a: Tensor, b: Tensor) -> None: ... def _commit_update(a: Tensor) -> None: ... class _ExcludeDispatchKeyGuard: def __init__(self, keyset: DispatchKeySet) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _IncludeDispatchKeyGuard: def __init__(self, k: DispatchKey) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _ForceDispatchKeyGuard: def __init__(self, include: DispatchKeySet, exclude: DispatchKeySet) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _PreserveDispatchKeyGuard: def __init__(self) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _AutoDispatchBelowAutograd: def __init__(self) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... class _AutoDispatchBelowADInplaceOrView: def __init__(self) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... def _dispatch_print_registrations_for_dispatch_key(dispatch_key: str = "") -> None: ... def _dispatch_get_registrations_for_dispatch_key( dispatch_key: str = "", ) -> list[str]: ... def _are_functorch_transforms_active() -> _bool: ... # Define in torch/csrc/autograd/init.cpp def _set_python_dispatcher(dispatcher: object) -> None: ... def _get_nested_int(id: _int, coeff: _int) -> SymInt: ... def _get_constant_bool_symnode(val: _bool) -> Any: ... class _TorchDispatchModeKey(Enum): ${torch_dispatch_mode_key_hints} class _SetExcludeDispatchKeyGuard: def __init__(self, k: DispatchKey, enabled: _bool) -> None: ... def __enter__(self): ... def __exit__(self, *exc_info: object) -> None: ... # Defined in torch/csrc/utils/schema_info.h class _SchemaInfo: def __init__(self, schema: FunctionSchema) -> None: ... @overload def is_mutable(self) -> _bool: ... @overload def is_mutable(self, name: str) -> _bool: ... def has_argument(self, name: str) -> _bool: ... # Defined in torch/csrc/utils/init.cpp class BenchmarkConfig: num_calling_threads: _int num_worker_threads: _int num_warmup_iters: _int num_iters: _int profiler_output_path: str class BenchmarkExecutionStats: latency_avg_ms: _float num_iters: _int class ThroughputBenchmark: def __init__(self, module: Any) -> None: ... def add_input(self, *args: Any, **kwargs: Any) -> None: ... def run_once(self, *args: Any, **kwargs: Any) -> Any: ... def benchmark(self, config: BenchmarkConfig) -> BenchmarkExecutionStats: ... # Defined in torch/csrc/Storage.cpp ${legacy_storage_base_hints} # TODO: where ${legacy_class_hints} # Defined in torch/csrc/autograd/python_engine.cpp class _ImperativeEngine: def queue_callback(self, callback: Callable[[], None]) -> None: ... def run_backward(self, *args: Any, **kwargs: Any) -> tuple[Tensor, ...]: ... def is_checkpoint_valid(self) -> _bool: ... # Defined in torch/csrc/autograd/python_variable.cpp class _TensorMeta(type): ... ${index_type_def} # Defined in torch/csrc/autograd/python_variable.cpp class TensorBase(metaclass=_TensorMeta): requires_grad: _bool retains_grad: _bool shape: Size data: Tensor names: list[str] device: _device dtype: _dtype layout: _layout real: Tensor imag: Tensor T: Tensor H: Tensor mT: Tensor mH: Tensor ndim: _int output_nr: _int _version: _int _base: Tensor | None _cdata: _int grad_fn: _Node | None _grad_fn: Any _grad: Tensor | None grad: Tensor | None _backward_hooks: dict[_int, Callable[[Tensor], Tensor | None]] | None nbytes: _int itemsize: _int _has_symbolic_sizes_strides: _bool def _view_func_unsafe( self, new_base: Tensor, symint_visitor_fn: Callable[[_int], _int] | None = None, tensor_visitor_fn: Callable[[Tensor], Tensor] | None = None, ): ... ${tensor_method_hints} _TensorBase = TensorBase # Defined in torch/csrc/multiprocessing/init.cpp def _multiprocessing_init() -> None: ... def _set_thread_name(name: str) -> None: ... def _get_thread_name() -> str: ... # Defined in torch/csrc/Module.cpp def _accelerator_hooks_device_count() -> _int: ... def _accelerator_hooks_set_current_device(device_index: _int) -> None: ... def _accelerator_hooks_get_current_device() -> _int: ... def _accelerator_hooks_exchange_device(device_index: _int) -> _int: ... def _accelerator_hooks_maybe_exchange_device(device_index: _int) -> _int: ... def _get_accelerator(check: _bool = False) -> _device: ... def _storage_Use_Count(storage_ptr: _int) -> _int: ... # Defined in torch/csrc/mtia/Module.cpp def _mtia_init() -> None: ... def _mtia_isBuilt() -> _bool: ... def _mtia_isInBadFork() -> _bool: ... def _mtia_deviceSynchronize() -> None: ... def _mtia_getCurrentStream(device: _int) -> Stream: ... def _mtia_setCurrentStream(stream: Stream) -> None: ... def _mtia_getDefaultStream(device: _int) -> Stream: ... def _mtia_memoryStats(device: _int) -> dict[str, Any]: ... def _mtia_getDeviceCapability(device: _int) -> tuple[_int, _int]: ... def _mtia_getDeviceProperties(device: _int) -> dict[str, Any]: ... def _mtia_emptyCache() -> None: ... def _mtia_recordMemoryHistory( enabled: str | None, stacks: str, max_entries, ) -> None: ... def _mtia_memorySnapshot() -> dict[str, Any]: ... def _mtia_attachOutOfMemoryObserver( observer: Callable[[_int, _int, _int, _int], None], ) -> None: ... def _mtia_getDeviceCount() -> _int: ... def _mtia_resetPeakMemoryStats(device: _int) -> None: ... # Defined in torch/csrc/mps/Module.cpp def _mps_deviceSynchronize() -> None: ... def _mps_get_default_generator() -> Generator: ... def _mps_emptyCache() -> None: ... def _mps_setMemoryFraction(fraction: _float) -> None: ... def _mps_currentAllocatedMemory() -> _int: ... def _mps_driverAllocatedMemory() -> _int: ... def _mps_recommendedMaxMemory() -> _int: ... def _mps_is_available() -> _bool: ... def _mps_is_on_macos_or_newer(major: _int, minor: _int) -> _bool: ... def _mps_profilerStartTrace(mode: str, wait_until_completed: _bool) -> None: ... def _mps_profilerStopTrace() -> None: ... def _mps_acquireEvent(enable_timing: _bool) -> _int: ... def _mps_releaseEvent(event_id: _int) -> None: ... def _mps_recordEvent(event_id: _int) -> None: ... def _mps_waitForEvent(event_id: _int) -> None: ... def _mps_synchronizeEvent(event_id: _int) -> None: ... def _mps_queryEvent(event_id: _int) -> _bool: ... def _mps_elapsedTimeOfEvents(start_event_id: _int, end_event_id: _int) -> _float: ... def _mps_isCaptureEnabled() -> _bool: ... def _mps_isCapturing() -> _bool: ... def _mps_startCapture(name: str) -> None: ... def _mps_stopCapture() -> None: ... # Defined in torch/csrc/cuda/Module.cpp def _cuda_getCurrentStream(device: _int) -> tuple: ... def _cuda_getCurrentRawStream(device: _int) -> _int: ... def _cuda_getDefaultStream(device: _int) -> tuple: ... def _cuda_getStreamFromExternal(data_ptr: _int, device_index: _int) -> tuple: ... def _cuda_getCurrentBlasHandle() -> _int: ... def _cuda_clearCublasWorkspaces() -> None: ... def _cuda_setDevice(device: _int) -> None: ... def _cuda_exchangeDevice(device: _int) -> _int: ... def _cuda_maybeExchangeDevice(device: _int) -> _int: ... def _cuda_getDevice() -> _int: ... def _cuda_getDeviceCount() -> _int: ... def _cuda_set_sync_debug_mode(warn_level: _int | str) -> None: ... def _cuda_get_sync_debug_mode() -> _int: ... def _cuda_sleep(cycles: _int) -> None: ... def _cuda_synchronize() -> None: ... def _cuda_ipc_collect() -> None: ... def _cuda_getArchFlags() -> str | None: ... def _cuda_init() -> None: ... def _cuda_setStream(stream_id: _int, device_index: _int, device_type: _int) -> None: ... def _cuda_getCompiledVersion() -> _int: ... def _cuda_cudaHostAllocator() -> _int: ... def _cuda_cudaCachingAllocator_raw_alloc(size: _int, cuda_stream: _int) -> _int: ... def _cuda_cudaCachingAllocator_raw_delete(ptr: _int) -> None: ... def _cuda_cudaCachingAllocator_enable(val: _bool) -> None: ... def _cuda_cudaCachingAllocator_set_allocator_settings(env: str) -> None: ... def _cuda_beginAllocateToPool(device: _int, mempool_id: tuple[_int, _int]) -> None: ... def _cuda_beginAllocateCurrentThreadToPool( device: _int, mempool_id: tuple[_int, _int], ) -> None: ... def _cuda_endAllocateToPool(device: _int, mempool_id: tuple[_int, _int]) -> None: ... def _cuda_beginAllocateCurrentStreamToPool( device: _int, mempool_id: tuple[_int, _int], ) -> None: ... def _cuda_releasePool(device: _int, mempool_id: tuple[_int, _int]) -> None: ... def _cuda_checkPoolLiveAllocations( device: _int, mempool_id: tuple[_int, _int], expected_live_allocations: set, ) -> _bool: ... def _cuda_setCheckpointPoolState( device: _int, state: _cuda_CUDAAllocator_AllocatorState, stale_storages: list[_int], storages_to_add_deleters_to: list[_int], ) -> None: ... def _cuda_getMemoryFraction(device: _int) -> _float: ... def _cuda_setMemoryFraction(fraction: _float, device: _int) -> None: ... def _cuda_emptyCache() -> None: ... def _cuda_memoryStats(device: _int) -> dict[str, Any]: ... def _cuda_resetAccumulatedMemoryStats(device: _int) -> None: ... def _cuda_resetPeakMemoryStats(device: _int) -> None: ... def _cuda_hostMemoryStats() -> dict[str, Any]: ... def _cuda_resetAccumulatedHostMemoryStats() -> None: ... def _cuda_resetPeakHostMemoryStats() -> None: ... def _cuda_memorySnapshot(mempool_id: tuple[_int, _int] | None) -> dict[str, Any]: ... def _cuda_record_memory_history_legacy( enabled: _bool, record_context: _bool, record_context_cpp: _bool, alloc_trace_max_entries: _int, alloc_trace_record_context: _bool, clear_history: _bool, compile_context: _bool, ) -> None: ... def _cuda_record_memory_history( enabled: str | None, context: str | None, stacks: str, max_entries: _int, clear_history: _bool, compile_context: _bool, ) -> None: ... def _cuda_isHistoryEnabled() -> _bool: ... def _cuda_getAllocatorBackend() -> str: ... class _cuda_CUDAAllocator_AllocatorState: ... def _cuda_getCheckpointState( device: _int, mempool: tuple[_int, _int], ) -> _cuda_CUDAAllocator_AllocatorState: ... def _set_cached_tensors_enabled(enabled: _bool) -> None: ... def _add_cached_tensor(t: Tensor) -> None: ... def _remove_cached_tensor(t: Tensor) -> None: ... def _tensors_data_ptrs_at_indices_equal( tensors: list[Tensor | _int], ptrs: list[_int | None], indices: list[_int], ) -> _bool: ... def _construct_CUDA_Tensor_From_Storage_And_Metadata( metadata: dict, storage: Storage, ) -> Tensor: ... def _set_storage_access_error_msg(t: Tensor, s: str) -> None: ... def _set_storage_data_ptr_access_error_msg(storage_ptr: _int, s: str) -> None: ... def _free_And_Remove_DeleterFn(storage_ptr: _int) -> None: ... def _has_Standard_Deleter(storage_ptr: _int) -> _bool: ... class _cuda_CUDAAllocator: ... def _cuda_customAllocator(alloc_fn: _int, free_fn: _int) -> _cuda_CUDAAllocator: ... def _cuda_changeCurrentAllocator(allocator: _cuda_CUDAAllocator) -> None: ... def _cuda_getAllocator() -> _cuda_CUDAAllocator: ... def _cuda_lock_mutex() -> None: ... def _cuda_unlock_mutex() -> None: ... def _cuda_canDeviceAccessPeer(device: _int, peer_device: _int) -> _bool: ... def _cuda_jiterator_compile_and_launch_kernel( code_string: str, kernel_name: str, return_by_ref: _bool, num_outputs: _int, tensors: tuple, kwargs: dict[str, _int | _float | _bool], ) -> Tensor: ... def _cuda_get_cudnn_benchmark_limit() -> _int: ... def _cuda_set_cudnn_benchmark_limit(arg: _int) -> None: ... def _cuda_get_conv_benchmark_empty_cache() -> _bool: ... def _cudnn_set_conv_benchmark_empty_cache(enable: _bool) -> None: ... def _nccl_version() -> _int: ... def _nccl_version_suffix() -> bytes: ... def _nccl_unique_id() -> bytes: ... def _nccl_init_rank(nranks: _int, comm_id: bytes, rank: _int) -> object: ... def _nccl_reduce( input: Sequence[Tensor], output: Tensor, root: _int, op: _int, streams: Sequence[_CudaStreamBase] | None, comms: Sequence[object] | None, ) -> None: ... def _nccl_all_reduce( input: Sequence[Tensor], output: Sequence[Tensor], op: _int, streams: Sequence[_CudaStreamBase] | None, comms: Sequence[object] | None, ) -> None: ... def _nccl_broadcast( input: Sequence[Tensor], root: _int, streams: Sequence[_CudaStreamBase] | None, comms: Sequence[object] | None, ) -> None: ... def _nccl_all_gather( input: Sequence[Tensor], output: Sequence[Tensor], streams: Sequence[_CudaStreamBase] | None, comms: Sequence[object] | None, ) -> None: ... def _nccl_reduce_scatter( input: Sequence[Tensor], output: Sequence[Tensor], op: _int, streams: Sequence[_CudaStreamBase] | None, comms: Sequence[object] | None, ) -> None: ... def _rocm_is_backward_pass() -> _bool: ... def _cuda_tunableop_enable(val: _bool) -> None: ... def _cuda_tunableop_is_enabled() -> _bool: ... def _cuda_tunableop_tuning_enable(val: _bool) -> None: ... def _cuda_tunableop_tuning_is_enabled() -> _bool: ... def _cuda_tunableop_set_max_tuning_duration(duration: _int) -> None: ... def _cuda_tunableop_get_max_tuning_duration() -> _int: ... def _cuda_tunableop_set_max_tuning_iterations(iterations: _int) -> None: ... def _cuda_tunableop_get_max_tuning_iterations() -> _int: ... def _cuda_tunableop_set_filename( filename: str, insert_device_ordinal: _bool | None, ) -> None: ... def _cuda_tunableop_get_filename() -> str: ... def _cuda_tunableop_write_file(filename: str | None) -> _bool: ... def _cuda_tunableop_read_file(filename: str | None) -> _bool: ... def _cuda_tunableop_write_file_on_exit(val: _bool) -> None: ... def _cuda_tunableop_get_results() -> tuple[str, str, str, _float]: ... def _cuda_tunableop_get_validators() -> tuple[str, str]: ... def _cuda_tunableop_set_rotating_buffer_size(buffer_size: _int) -> None: ... def _cuda_tunableop_get_rotation_buffer_size() -> _int: ... class _CudaDeviceProperties: name: str major: _int minor: _int multi_processor_count: _int total_memory: _int is_integrated: _int is_multi_gpu_board: _int max_threads_per_multi_processor: _int gcnArchName: str warp_size: _int uuid: str L2_cache_size: _int # Functions related to SDPA class _SDPAParams: query: Tensor key: Tensor value: Tensor attn_mask: Tensor | None dropout: _float is_causal: _bool enable_gqa: _bool def __init__( self, query: Tensor, key: Tensor, value: Tensor, attn_mask: Tensor | None, dropout: _float, is_causal: _bool, enable_gqa: _bool, ) -> None: ... class _SDPBackend(Enum): ERROR = -1 MATH = 0 FLASH_ATTENTION = 1 EFFICIENT_ATTENTION = 2 CUDNN_ATTENTION = 3 def _is_flash_attention_available() -> _bool: ... def _can_use_cudnn_attention(params: _SDPAParams, debug: _bool) -> _bool: ... def _can_use_flash_attention(params: _SDPAParams, debug: _bool) -> _bool: ... def _can_use_mem_efficient_attention(params: _SDPAParams, debug: _bool) -> _bool: ... # Defined in torch/csrc/cuda/GdsFile.cpp def _gds_register_buffer(t: Storage) -> None: ... def _gds_deregister_buffer(t: Storage) -> None: ... def _gds_register_handle(fd: _int) -> _int: ... def _gds_deregister_handle(handle: _int) -> None: ... def _gds_load_storage(handle: _int, s: Storage, offset: _int) -> None: ... def _gds_save_storage(handle: _int, s: Storage, offset: _int) -> None: ... # Defined in torch/csrc/cuda/python_comm.cpp def _broadcast(tensor: Tensor, devices: list[_int]) -> list[Tensor]: ... def _broadcast_out(tensor: Tensor, out_tensors: list[Tensor]) -> list[Tensor]: ... def _broadcast_coalesced( tensors: list[Tensor], devices: list[_int], buffer_size: _int, ) -> list[list[Tensor]]: ... def _scatter( tensor: Tensor, devices: list[_int], chunk_sizes: list[_int] | None, dim: _int, streams: list[Stream] | None, ) -> list[Tensor]: ... def _scatter_out( tensor: Tensor, out_tensors: list[Tensor], dim: _int, streams: list[Stream] | None, ) -> list[Tensor]: ... def _gather( tensors: list[Tensor], dim: _int, destination_index: _int | None, ) -> Tensor: ... def _gather_out(tensors: list[Tensor], out_tensor: Tensor, dim: _int) -> Tensor: ... # Defined in torch/csrc/cuda/Stream.cpp class _CudaStreamBase(Stream): stream_id: _int device_index: _int device_type: _int device: _device cuda_stream: _int priority: _int def __new__( cls, priority: _int = 0, stream_id: _int = 0, device_index: _int = 0, stream_ptr: _int = 0, ) -> Self: ... def query(self) -> _bool: ... def synchronize(self) -> None: ... def priority_range(self) -> tuple[_int, _int]: ... # Defined in torch/csrc/cuda/Event.cpp class _CudaEventBase: device: _device cuda_event: _int def __new__( cls, enable_timing: _bool = False, blocking: _bool = False, interprocess: _bool = False, external: _bool = False, ) -> Self: ... @classmethod def from_ipc_handle(cls, device: _device, ipc_handle: bytes) -> _CudaEventBase: ... def record(self, stream: _CudaStreamBase) -> None: ... def wait(self, stream: _CudaStreamBase) -> None: ... def query(self) -> _bool: ... def elapsed_time(self, other: _CudaEventBase) -> _float: ... def synchronize(self) -> None: ... def ipc_handle(self) -> bytes: ... # Defined in torch/csrc/cuda/Graph.cpp class _CUDAGraph: def __new__(cls, keep_graph: _bool = ...) -> Self: ... def capture_begin( self, pool: tuple[_int, _int] | None = ..., capture_error_mode: str = "global", ) -> None: ... def capture_end(self) -> None: ... def instantiate(self) -> None: ... def register_generator_state(self, Generator) -> None: ... def replay(self) -> None: ... def reset(self) -> None: ... def pool(self) -> tuple[_int, _int]: ... def enable_debug_mode(self) -> None: ... def debug_dump(self, debug_path: str) -> None: ... def raw_cuda_graph(self) -> _int: ... # Defined in torch/csrc/cuda/MemPool.cpp class _MemPool: def __init__( self, allocator: _cuda_CUDAAllocator | None = None, is_user_created: _bool = True, use_on_oom: _bool = False, symmetric: _bool = False, ) -> None: ... @property def id(self) -> tuple[_int, _int]: ... @property def is_symmetric(self) -> _bool: ... @property def allocator(self) -> _cuda_CUDAAllocator | None: ... def use_count(self) -> _int: ... def _cuda_isCurrentStreamCapturing() -> _bool: ... def _graph_pool_handle() -> tuple[_int, _int]: ... # Defined in torch/csrc/xpu/Module.cpp def _xpu_setDevice(device: _int) -> None: ... def _xpu_exchangeDevice(device: _int) -> _int: ... def _xpu_maybeExchangeDevice(device: _int) -> _int: ... def _xpu_getDevice() -> _int: ... def _xpu_getDeviceCount() -> _int: ... def _xpu_getArchFlags() -> str | None: ... def _xpu_init() -> None: ... def _xpu_setStream(stream_id: _int, device_index: _int, device_type: _int) -> None: ... def _xpu_getCurrentStream(device: _int) -> tuple: ... def _xpu_getCurrentRawStream(device: _int) -> _int: ... def _xpu_getStreamFromExternal(data_ptr: _int, device_index: _int) -> tuple: ... def _xpu_synchronize(device: _int) -> None: ... def _xpu_emptyCache() -> None: ... def _xpu_memoryStats(device: _int) -> dict[str, Any]: ... def _xpu_resetAccumulatedMemoryStats(device: _int) -> None: ... def _xpu_resetPeakMemoryStats(device: _int) -> None: ... def _xpu_getMemoryInfo(device: _int) -> tuple[_int, _int]: ... class _XpuDeviceProperties: name: str platform_name: str vendor: str driver_version: str version: str max_compute_units: _int gpu_eu_count: _int max_work_group_size: _int max_num_sub_groups: _int sub_group_sizes: list[_int] has_fp16: _bool has_fp64: _bool has_atomic64: _bool has_bfloat16_conversions: _bool has_subgroup_matrix_multiply_accumulate: _bool has_subgroup_matrix_multiply_accumulate_tensor_float32: _bool has_subgroup_2d_block_io: _bool total_memory: _int gpu_subslice_count: _int architecture: _int type: str # Defined in torch/csrc/xpu/Stream.cpp class _XpuStreamBase(Stream): stream_id: _int device_index: _int device_type: _int device: _device sycl_queue: _int priority: _int def __new__( cls, priority: _int = 0, stream_id: _int = 0, device_index: _int = 0, device_type: _int = 0, ) -> Self: ... def query(self) -> _bool: ... def synchronize(self) -> None: ... @staticmethod def priority_range() -> tuple: ... # Defined in torch/csrc/xpu/Event.cpp class _XpuEventBase: device: _device sycl_event: _int def __new__(cls, enable_timing: _bool = False) -> Self: ... def record(self, stream: _XpuEventBase) -> None: ... def wait(self, stream: _XpuStreamBase) -> None: ... def query(self) -> _bool: ... def elapsed_time(self, other: _XpuEventBase) -> _float: ... def synchronize(self) -> None: ... # Defined in torch/csrc/DataLoader.cpp def _set_worker_signal_handlers( *arg: Any, ) -> None: ... # THPModule_setWorkerSignalHandlers def _set_worker_pids( key: _int, child_pids: tuple[_int, ...], ) -> None: ... # THPModule_setWorkerPIDs def _remove_worker_pids(loader_id: _int) -> None: ... # THPModule_removeWorkerPIDs def _error_if_any_worker_fails() -> None: ... # THPModule_errorIfAnyWorkerFails # Defined in torch/csrc/DeviceAccelerator.cpp def _accelerator_getAccelerator() -> _device: ... def _accelerator_setDeviceIndex(device_index: _int) -> None: ... def _accelerator_getDeviceIndex() -> _int: ... def _accelerator_setStream(Stream) -> None: ... def _accelerator_getStream(device_index: _int) -> Stream: ... def _accelerator_synchronizeDevice(device_index: _int) -> None: ... def _accelerator_exchangeDevice(device_index: _int) -> _int: ... def _accelerator_maybeExchangeDevice(device_index: _int) -> _int: ... # Defined in torch/csrc/jit/python/python_tracer.cpp class TracingState: def push_scope(self, scope_name: str) -> None: ... def pop_scope(self) -> None: ... def current_scope(self) -> str: ... def set_graph(self, graph: Graph) -> None: ... def graph(self) -> Graph: ... def _create_graph_by_tracing( func: Callable[..., Any], inputs: Any, var_name_lookup_fn: Callable[[Tensor], str], strict: Any, force_outplace: Any, self: Any = None, argument_names: list[str] = ..., ) -> tuple[Graph, Stack]: ... def _tracer_warn_use_python(): ... def _get_tracing_state() -> TracingState: ... # Defined in torch/csrc/jit/python/python_ir.cpp # Not actually defined in python_ir.cpp, not sure where they are. class IValue: ... Stack: TypeAlias = list[IValue] class JitType: annotation_str: str def isSubtypeOf(self, other: JitType) -> _bool: ... def with_dtype(self, dtype: _dtype) -> JitType: ... def with_sizes(self, sizes: list[_int | None]) -> JitType: ... def kind(self) -> str: ... def scalarType(self) -> str | None: ... def getElementType(self) -> JitType: ... def dtype(self) -> _dtype | None: ... class InferredType: def __init__(self, arg: JitType | str) -> None: ... def type(self) -> JitType: ... def success(self) -> _bool: ... def reason(self) -> str: ... class Type(JitType): def str(self) -> _str: ... def containedTypes(self) -> list[JitType]: ... def dim(self) -> _int | None: ... def undefined(self) -> _bool | None: ... def sizes(self) -> list[_int] | None: ... def symbol_sizes(self) -> list[_int] | None: ... def varyingSizes(self) -> list[_int | None] | None: ... def strides(self) -> list[_int] | None: ... def contiguous(self) -> Self: ... def device(self) -> _device | None: ... def is_interface_type(self) -> _bool: ... def requires_grad(self) -> _bool: ... @property def annotation_string(self) -> _str: ... class AnyType(JitType): @staticmethod def get() -> AnyType: ... class NoneType(JitType): @staticmethod def get() -> NoneType: ... class BoolType(JitType): @staticmethod def get() -> BoolType: ... class FloatType(JitType): @staticmethod def get() -> FloatType: ... class ComplexType(JitType): @staticmethod def get() -> ComplexType: ... class IntType(JitType): @staticmethod def get() -> IntType: ... class SymIntType(JitType): @staticmethod def get() -> SymIntType: ... class SymBoolType(JitType): @staticmethod def get() -> SymBoolType: ... class NumberType(JitType): @staticmethod def get() -> NumberType: ... class StringType(JitType): @staticmethod def get() -> StringType: ... class DeviceObjType(JitType): @staticmethod def get() -> DeviceObjType: ... class _GeneratorType(JitType): @staticmethod def get() -> _GeneratorType: ... class StreamObjType(JitType): @staticmethod def get() -> StreamObjType: ... class ListType(JitType): def __init__(self, a: JitType) -> None: ... def getElementType(self) -> JitType: ... @staticmethod def ofInts() -> ListType: ... @staticmethod def ofTensors() -> ListType: ... @staticmethod def ofFloats() -> ListType: ... @staticmethod def ofComplexDoubles() -> ListType: ... @staticmethod def ofBools() -> ListType: ... @staticmethod def ofStrings() -> ListType: ... class DictType(JitType): def __init__(self, key: JitType, value: JitType) -> None: ... def getKeyType(self) -> JitType: ... def getValueType(self) -> JitType: ... class TupleType(JitType): def __init__(self, a: list[JitType | None]) -> None: ... def elements(self) -> list[JitType]: ... class UnionType(JitType): def __init__(self, a: list[JitType]) -> None: ... class ClassType(JitType): def __init__(self, qualified_name: str) -> None: ... def qualified_name(self) -> str: ... class InterfaceType(JitType): def __init__(self, qualified_name: str) -> None: ... def getMethod(self, name: str) -> FunctionSchema | None: ... def getMethodNames(self) -> list[str]: ... JitTypeT = TypeVar("JitTypeT", bound=JitType) # noqa: PYI001 class OptionalType(JitType, Generic[JitTypeT]): def __init__(self, a: JitTypeT) -> None: ... def getElementType(self) -> JitTypeT: ... @staticmethod def ofTensor() -> OptionalType: ... class FutureType(JitType): def __init__(self, a: JitType) -> None: ... def getElementType(self) -> JitType: ... class AwaitType(JitType): def __init__(self, a: JitType) -> None: ... def getElementType(self) -> JitType: ... class RRefType(JitType): def __init__(self, a: JitType) -> None: ... class EnumType(JitType): def __init__( self, qualified_name: str, value_type: JitType, enum_names_values: list[Any], ) -> None: ... class TensorType(JitType): @classmethod def get(cls) -> TensorType: ... @classmethod def getInferred(cls) -> TensorType: ... def with_sizes(self, other: list[_int | None] | None) -> TensorType: ... def sizes(self) -> list[_int] | None: ... def varyingSizes(self) -> list[_int | None] | None: ... def strides(self) -> list[_int] | None: ... def device(self) -> _device | None: ... def dim(self) -> _int: ... def dtype(self) -> _dtype | None: ... @staticmethod def create_from_tensor(t: Tensor) -> TensorType: ... # Defined in torch/csrc/jit/python/python_tree_views.cpp class SourceRange: ... class TreeView: ... class Ident(TreeView): @property def name(self) -> str: ... class ClassDef(TreeView): ... class Def(TreeView): def name(self) -> Ident: ... class Decl(TreeView): ... # Defined in torch/csrc/distributed/rpc/init.cpp def _rpc_init() -> _bool: ... # Defined in torch/csrc/distributed/autograd/init.cpp def _dist_autograd_init() -> _bool: ... # Defined in torch/csrc/distributed/c10d/init.cpp def _c10d_init() -> _bool: ... # Defined in torch/csrc/distributed/rpc/testing/init.cpp def _faulty_agent_init() -> _bool: ... def _register_py_class_for_device(device: str, cls: Any) -> None: ... # Defined in torch/csrc/Module.cpp def _current_graph_task_id() -> _int: ... def _current_autograd_node() -> _Node: ... def _will_engine_execute_node(node: _Node) -> _bool: ... def _dispatch_key_set(tensor) -> str: ... # Defined in torch/csrc/Exceptions.cpp class AcceleratorError(RuntimeError): ... class OutOfMemoryError(RuntimeError): ... class _DistError(RuntimeError): ... class _DistBackendError(RuntimeError): ... class _DistStoreError(RuntimeError): ... class _DistNetworkError(RuntimeError): ... class _DistQueueEmptyError(_DistStoreError): ... # Defined in torch/csrc/profiler/init.cpp class CapturedTraceback: ... def gather_traceback(python: _bool, script: _bool, cpp: _bool) -> CapturedTraceback: ... def symbolize_tracebacks( tracebacks: list[CapturedTraceback], ) -> list[dict[str, Any]]: ... def _load_mobile_module_from_file(filename: str): ... def _load_mobile_module_from_bytes(bytes_: bytes): ... def _load_jit_module_from_file(filename: str): ... def _load_jit_module_from_bytes(bytes_: bytes): ... def _save_mobile_module(m: LiteScriptModule, filename: str): ... def _save_jit_module(m: ScriptModule, filename: str, extra_files: dict[str, Any]): ... def _save_mobile_module_to_bytes(m: LiteScriptModule) -> bytes: ... def _save_jit_module_to_bytes( m: ScriptModule, extra_files: dict[str, Any], ) -> bytes: ... def _get_module_info_from_flatbuffer(data: bytes): ... def _jit_resolve_packet(op_name: str, *args, **kwargs) -> str: ... def _swap_tensor_impl(t1: Tensor, t2: Tensor): ... def _pickle_save(obj: Any) -> bytes: ... def _pickle_load_obj(bs: bytes) -> Any: ... # Defined in torch/csrc/jit/runtime/static/init.cpp def _jit_to_static_module(graph_or_module: Graph | ScriptModule) -> Any: ... def _fuse_to_static_module( graph_or_module: Graph | ScriptModule, min_size: _int, ) -> Any: ... # Defined in torch/csrc/fx/node.cpp def _fx_map_aggregate(a: Any, fn: Callable[[Any], Any]) -> Any: ... def _fx_map_arg(a: Any, fn: Callable[[Any], Any]) -> Any: ... class _NodeBase: _erased: _bool _prev: FxNode _next: FxNode def __init__( self, graph: Any, name: str, op: str, target: Any, return_type: Any, ) -> None: ... def _update_args_kwargs(self, args: tuple[Any, ...], kwargs: dict[str, Any]): ... class _NodeIter(Iterator[FxNode]): def __init__(self, root: FxNode, reversed: _bool) -> None: ... def __iter__(self) -> Self: ... def __next__(self) -> FxNode: ... # Defined in torch/csrc/inductor/static_cuda_launcher.cpp class _StaticCudaLauncher: @staticmethod def _load_kernel( cubin_file: str, func_name: str, shared_mem_bytes: _int, device: _int, ) -> tuple[_int, _int, _int]: ... @staticmethod def _launch_kernel( func: _int, grid_x: _int, grid_y: _int, grid_z: _int, num_warps: _int, shared_mem_bytes: _int, arg_types: str, args: tuple[Any, ...], stream: _int, ) -> None: ...