diff --git "a/torch/_C/__init__.pyi" "b/torch/_C/__init__.pyi" deleted file mode 100644--- "a/torch/_C/__init__.pyi" +++ /dev/null @@ -1,3533 +0,0 @@ -# @generated from torch/_C/__init__.pyi.in -# mypy: disable-error-code="type-arg" - -import builtins -from enum import Enum, IntEnum -from pathlib import Path -from typing import ( - Any, - AnyStr, - BinaryIO, - Callable, - ContextManager, - Dict, - Generic, - Iterable, - Iterator, - List, - Literal, - NamedTuple, - Optional, - Protocol, - Sequence, - Set, - SupportsIndex, - Tuple, - Type, - TypeVar, - Union, - overload, - runtime_checkable, -) -from typing_extensions import ParamSpec - -import torch -from torch import inf, SymInt, Tensor -from torch.autograd.graph import Node as _Node -from torch.package import PackageExporter -from torch.storage import UntypedStorage, TypedStorage -from torch.types import ( - _bool, - _complex, - _device, - _dispatchkey, - _dtype, - _float, - _int, - _layout, - _qscheme, - _size, - Device, - Number, - Storage, -) - -from torch._prims_common import DeviceLikeType - -# This module is defined in torch/csrc/Module.cpp - -from . import _functorch, _lazy, _lazy_ts_backend, _nn, _onnx, _VariableFunctions, _cpu - -K = TypeVar("K") -T = TypeVar("T") -S = TypeVar("S", bound="torch.Tensor") -P = ParamSpec("P") -ReturnVal = TypeVar("ReturnVal", covariant=True) # return value (always covariant) -_T_co = TypeVar("_T_co", covariant=True) - - -@runtime_checkable -class _NestedSequence(Protocol[_T_co]): - """A protocol for representing nested sequences. - - References:: - `numpy._typing._NestedSequence` - - """ - - def __len__(self, /) -> builtins.int: ... - def __getitem__(self, index: builtins.int, /) -> _T_co | _NestedSequence[_T_co]: ... - def __contains__(self, x: builtins.object, /) -> builtins.bool: ... - def __iter__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]: ... - def __reversed__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]: ... - def count(self, value: Any, /) -> builtins.int: ... - def index(self, value: Any, /) -> builtins.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) -> device: ... - 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 - -# Defined in torch/csrc/Size.cpp -class Size(Tuple[_int, ...]): - # TODO: __reduce__ - - @overload # type: ignore[override] - def __getitem__(self: Size, key: _int) -> _int: ... - @overload - def __getitem__(self: Size, key: slice) -> 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: ... - -float32: dtype = ... -float: dtype = ... -float64: dtype = ... -double: dtype = ... -float16: dtype = ... -bfloat16: dtype = ... -float8_e4m3fn: dtype = ... -float8_e4m3fnuz: dtype = ... -float8_e5m2: dtype = ... -float8_e5m2fnuz: dtype = ... -half: dtype = ... -uint8: dtype = ... -int8: dtype = ... -int16: dtype = ... -short: dtype = ... -int32: dtype = ... -int: dtype = ... -int64: dtype = ... -long: dtype = ... -complex32: dtype = ... -complex64: dtype = ... -chalf: dtype = ... -cfloat: dtype = ... -complex128: dtype = ... -cdouble: dtype = ... -quint8: dtype = ... -qint8: dtype = ... -qint32: dtype = ... -bool: dtype = ... -quint4x2: dtype = ... -quint2x4: dtype = ... -bits1x8: dtype = ... -bits2x4: dtype = ... -bits4x2: dtype = ... -bits8: dtype = ... -bits16: dtype = ... - -# 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: Optional[Tensor] = ..., - requires_grad: Optional[_bool] = ..., - volatile: Optional[_bool] = ..., - _grad_fn: Optional[_FunctionBase] = ..., - ) -> None: ... - -# Defined in torch/csrc/jit/python/init.cpp -class IODescriptor: ... -class JITException: ... - -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 = Callable[[str], Callable[..., Any]] - -# Defined in torch/csrc/jit/python/script_init.cpp -# and torch/csrc/jit/python/init.cpp -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: Optional[Callable], - FunctionHook: Optional[Callable], -) -> None: ... -def _jit_get_emit_hooks() -> Tuple[Callable, Callable]: ... -def _load_for_lite_interpreter( - filename: Union[str, Path], - map_location: Optional[DeviceLikeType], -): ... -def _load_for_lite_interpreter_from_buffer( - buffer: BinaryIO, - map_location: Optional[DeviceLikeType], -): ... -def _export_operator_list(module: LiteScriptModule): ... -def _quantize_ondevice_ptq_dynamic(module: LiteScriptModule, method_name: str): ... -def _get_model_bytecode_version(filename: Union[str, Path]) -> _int: ... -def _get_model_bytecode_version_from_buffer(buffer: BinaryIO) -> _int: ... -def _backport_for_mobile( - filename_input: Union[str, Path], - filename_output: Union[str, Path], - to_version: _int, -) -> None: ... -def _backport_for_mobile_from_buffer( - buffer: BinaryIO, - filename_output: Union[str, Path], - to_version: _int, -) -> None: ... -def _backport_for_mobile_to_buffer( - filename_input: Union[str, Path], - to_version: _int, -) -> bytes: ... -def _backport_for_mobile_from_buffer_to_buffer( - buffer: BinaryIO, - to_version: _int, -) -> bytes: ... -def _get_model_ops_and_info(filename: Union[str, Path]): ... -def _get_model_ops_and_info_from_buffer(buffer: BinaryIO): ... -def _get_mobile_model_contained_types(filename: Union[str, Path]): ... -def _get_mobile_model_contained_types_from_buffer(buffer: BinaryIO): ... -def _logging_set_logger(logger: LoggerBase) -> LoggerBase: ... -def _get_graph_executor_optimize(optimize: Optional[_bool] = 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: Optional[ScriptModule] = 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], - caffe2: _bool, -) -> 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], -) -> List[Value]: ... -def _jit_pass_onnx_block( - old_block: Block, - new_block: Block, - operator_export_type: _onnx.OperatorExportTypes, - env: Dict[Value, 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: Union[str, Path], - map_location: Optional[DeviceLikeType], - extra_files: Dict[str, Any], -) -> ScriptModule: ... -def import_ir_module_from_buffer( - cu: CompilationUnit, - buffer: BinaryIO, - map_location: Optional[DeviceLikeType], - extra_files: Dict[str, Any], -) -> ScriptModule: ... -def _import_ir_module_from_package( - cu: CompilationUnit, - reader: PyTorchFileReader, - storage_context: DeserializationStorageContext, - map_location: Optional[DeviceLikeType], - 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: - def __str__(self) -> str: ... - -class _InsertPoint: - def __enter__(self) -> None: ... - def __exit__(self, *args) -> 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: Union[Block, Node]) -> None: ... - def insert_point_guard(self, n: Union[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] - -# Defined in torch/aten/src/ATen/core/function_schema.h -class Argument: - name: str - type: JitType - default_value: Optional[Any] - def has_default_value(self) -> _bool: ... - kwarg_only: _bool - is_out: _bool - alias_info: Optional[AliasInfo] - -class FunctionSchema: - arguments: List[Argument] - returns: List[Argument] - name: str - overload_name: str - -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): ... - -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): ... - -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, ReturnVal]): - def __call__(self, *args: P.args, **kwargs: P.kwargs) -> ReturnVal: ... - 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, ReturnVal]): - graph: Graph - def __call__(self, *args: P.args, **kwargs: P.kwargs) -> ReturnVal: ... - @property - def owner(self) -> ScriptModule: ... - @property - def name(self) -> str: ... - -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_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_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_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 _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_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[Optional[Tensor]]], with_indices: _bool = False) -> Dict[Tuple[torch.device, str], Tuple[List[List[Optional[Tensor]]], List[_int]]]: ... - -# NB: There is no Capsule type in typing, see -# https://code.activestate.com/lists/python-dev/139675/ -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 _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 _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() -> torch._C._LinalgBackend: ... -def _set_linalg_preferred_backend(arg: torch._C._LinalgBackend): ... - -class _LinalgBackend: - Default: _LinalgBackend - Cusolver: _LinalgBackend - Magma: _LinalgBackend - -class ConvBackend(Enum): ... - -class Tag(Enum): - core: _int = 0 - data_dependent_output: _int = 1 - dynamic_output_shape: _int = 2 - generated: _int = 3 - inplace_view: _int = 4 - nondeterministic_bitwise: _int = 5 - nondeterministic_seeded: _int = 6 - pointwise: _int = 7 - pt2_compliant_tag: _int = 8 - view_copy: _int = 9 - -# 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_mps: _bool -has_lapack: _bool -_has_cuda: _bool -_has_mkldnn: _bool -_has_cudnn: _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 is_inference_mode_enabled() -> _bool: ... -def set_autocast_enabled(enabled: _bool) -> None: ... -def is_autocast_enabled() -> _bool: ... -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 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(tensor: 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 _set_torch_function_mode(cls: Any) -> None: ... -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: Any) -> None: ... -def _pop_torch_dispatch_stack(mode_key: Optional[torch._C._TorchDispatchModeKey] = None) -> Any: ... -def _get_dispatch_mode(mode_key: Optional[torch._C._TorchDispatchModeKey]) -> Any: ... -def _unset_dispatch_mode(mode: torch._C._TorchDispatchModeKey) -> Any: ... -def _set_dispatch_mode(mode: Any) -> None: ... -def _get_dispatch_stack_at(idx: _int) -> Any: ... -def _len_torch_dispatch_stack() -> _int: ... - -class _DisableTorchDispatch: - def __init__(self): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -class _EnableTorchFunction: - def __init__(self): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -class _EnablePythonDispatcher: - def __init__(self): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -class _DisablePythonDispatcher: - def __init__(self): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -class _EnablePreDispatch: - def __init__(self): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -class _DisableFuncTorch: - def __init__(self): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -class _DisableAutocast: - def __init__(self): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -class _InferenceMode: - def __init__(self, enabled: _bool): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -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: BinaryIO) -> None: ... - def get_record(self, name: str) -> bytes: ... - def serialization_id(self) -> str: ... - -class PyTorchFileWriter: - @overload - def __init__(self, name: str) -> None: ... - @overload - def __init__(self, buffer: BinaryIO) -> None: ... - def write_record(self, name: str, data: Union[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_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: Optional[DeviceLikeType] = None) -> None: ... - def get_state(self) -> Tensor: ... - def set_state(self, _new_state: Tensor) -> 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: ... - -class _DispatchModule: - 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(self, name: str, dispatch: str, func: Callable) -> _DispatchModule: ... - def define(self, schema: str, alias: str = "") -> _DispatchModule: ... - def fallback_fallthrough(self, dispatch: str = "") -> _DispatchModule: ... - -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_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) -> Optional[DispatchKey]: ... -def _dispatch_key_parse(dispatch: _dispatchkey) -> DispatchKey: ... -def _dispatch_num_backends() -> _int: ... -def _dispatch_pystub(name: str, overload: str) -> Optional[Tuple[str, str]]: ... -def _dispatch_is_alias_key(dispatch: _dispatchkey) -> _bool: ... -def _functionality_to_backend_keys(dispatch: _dispatchkey) -> List[DispatchKey]: ... -def _functionalization_reapply_views_tls() -> _bool: ... - -class DispatchKey(Enum): - Undefined: DispatchKey = ... - FPGA: DispatchKey = ... - ORT: DispatchKey = ... - Vulkan: DispatchKey = ... - Metal: DispatchKey = ... - MKLDNN: DispatchKey = ... - OpenGL: DispatchKey = ... - OpenCL: DispatchKey = ... - IDEEP: DispatchKey = ... - CustomRNGKeyId: DispatchKey = ... - MkldnnCPU: DispatchKey = ... - Sparse: DispatchKey = ... - SparseCsrCPU: DispatchKey = ... - SparseCsrCUDA: DispatchKey = ... - NestedTensor: DispatchKey = ... - Dense: DispatchKey = ... - Python: DispatchKey = ... - FuncTorchDynamicLayerBackMode: DispatchKey = ... - ZeroTensor: DispatchKey = ... - Conjugate: DispatchKey = ... - Negative: DispatchKey = ... - BackendSelect: DispatchKey = ... - Named: DispatchKey = ... - AutogradOther: DispatchKey = ... - AutogradFunctionality: DispatchKey = ... - AutogradNestedTensor: DispatchKey = ... - Tracer: DispatchKey = ... - Autocast: DispatchKey = ... - Batched: DispatchKey = ... - VmapMode: DispatchKey = ... - FuncTorchGradWrapper: DispatchKey = ... - FuncTorchBatched: DispatchKey = ... - BatchedNestedTensor: DispatchKey = ... - FuncTorchVmapMode: DispatchKey = ... - FuncTorchDynamicLayerFrontMode: DispatchKey = ... - Functionalize: DispatchKey = ... - TESTING_ONLY_GenericWrapper: DispatchKey = ... - TESTING_ONLY_GenericMode: DispatchKey = ... - ADInplaceOrView: DispatchKey = ... - Autograd: DispatchKey = ... - CompositeImplicitAutograd: DispatchKey = ... - CompositeImplicitAutogradNestedTensor: DispatchKey = ... - CompositeExplicitAutograd: DispatchKey = ... - CompositeExplicitAutogradNonFunctional: DispatchKey = ... - FuncTorchBatchedDecomposition: DispatchKey = ... - CPU: DispatchKey = ... - CUDA: DispatchKey = ... - HIP: DispatchKey = ... - XLA: DispatchKey = ... - MTIA: DispatchKey = ... - MPS: DispatchKey = ... - IPU: DispatchKey = ... - XPU: DispatchKey = ... - HPU: DispatchKey = ... - VE: DispatchKey = ... - Lazy: DispatchKey = ... - Meta: DispatchKey = ... - PrivateUse1: DispatchKey = ... - PrivateUse2: DispatchKey = ... - PrivateUse3: DispatchKey = ... - QuantizedCPU: DispatchKey = ... - QuantizedCUDA: DispatchKey = ... - QuantizedHIP: DispatchKey = ... - QuantizedXLA: DispatchKey = ... - QuantizedMTIA: DispatchKey = ... - QuantizedMPS: DispatchKey = ... - QuantizedIPU: DispatchKey = ... - QuantizedXPU: DispatchKey = ... - QuantizedHPU: DispatchKey = ... - QuantizedVE: DispatchKey = ... - QuantizedLazy: DispatchKey = ... - QuantizedMeta: DispatchKey = ... - QuantizedPrivateUse1: DispatchKey = ... - QuantizedPrivateUse2: DispatchKey = ... - QuantizedPrivateUse3: DispatchKey = ... - SparseCPU: DispatchKey = ... - SparseCUDA: DispatchKey = ... - SparseHIP: DispatchKey = ... - SparseXLA: DispatchKey = ... - SparseMTIA: DispatchKey = ... - SparseMPS: DispatchKey = ... - SparseIPU: DispatchKey = ... - SparseXPU: DispatchKey = ... - SparseHPU: DispatchKey = ... - SparseVE: DispatchKey = ... - SparseLazy: DispatchKey = ... - SparseMeta: DispatchKey = ... - SparsePrivateUse1: DispatchKey = ... - SparsePrivateUse2: DispatchKey = ... - SparsePrivateUse3: DispatchKey = ... - NestedTensorCPU: DispatchKey = ... - NestedTensorCUDA: DispatchKey = ... - NestedTensorHIP: DispatchKey = ... - NestedTensorXLA: DispatchKey = ... - NestedTensorMTIA: DispatchKey = ... - NestedTensorMPS: DispatchKey = ... - NestedTensorIPU: DispatchKey = ... - NestedTensorXPU: DispatchKey = ... - NestedTensorHPU: DispatchKey = ... - NestedTensorVE: DispatchKey = ... - NestedTensorLazy: DispatchKey = ... - NestedTensorMeta: DispatchKey = ... - NestedTensorPrivateUse1: DispatchKey = ... - NestedTensorPrivateUse2: DispatchKey = ... - NestedTensorPrivateUse3: DispatchKey = ... - AutogradCPU: DispatchKey = ... - AutogradCUDA: DispatchKey = ... - AutogradHIP: DispatchKey = ... - AutogradXLA: DispatchKey = ... - AutogradMTIA: DispatchKey = ... - AutogradMPS: DispatchKey = ... - AutogradIPU: DispatchKey = ... - AutogradXPU: DispatchKey = ... - AutogradHPU: DispatchKey = ... - AutogradVE: DispatchKey = ... - AutogradLazy: DispatchKey = ... - AutogradMeta: DispatchKey = ... - AutogradPrivateUse1: DispatchKey = ... - AutogradPrivateUse2: DispatchKey = ... - AutogradPrivateUse3: DispatchKey = ... - -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 highestPriorityTypeId(self) -> DispatchKey: ... - def has(self, k: _dispatchkey) -> _bool: ... - def add(self, k: _dispatchkey) -> DispatchKeySet: ... - def remove(self, k: _dispatchkey) -> DispatchKeySet: ... - def __repr__(self) -> str: ... - -_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): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -class _IncludeDispatchKeyGuard: - def __init__(self, k: DispatchKey): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -class _ForceDispatchKeyGuard: - def __init__(self, include: DispatchKeySet, exclude: DispatchKeySet): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -class _AutoDispatchBelowAutograd: - def __init__(self): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -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_singleton_int(id: _int, coeff: _int) -> SymInt: ... - -def _get_constant_bool_symnode(val: _bool) -> Any: ... - -class _TorchDispatchModeKey(Enum): - FAKE: _TorchDispatchModeKey = ... - PROXY: _TorchDispatchModeKey = ... - FUNCTIONAL: _TorchDispatchModeKey = ... - -class _SetExcludeDispatchKeyGuard: - def __init__(self, k: DispatchKey, enabled: _bool): ... - def __enter__(self): ... - def __exit__(self, exc_type, exc_value, traceback): ... - -# 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 -class StorageBase(object): ... - -# TODO: where -class DoubleTensor(Tensor): ... -class FloatTensor(Tensor): ... -class BFloat16Tensor(Tensor): ... -class LongTensor(Tensor): ... -class IntTensor(Tensor): ... -class ShortTensor(Tensor): ... -class HalfTensor(Tensor): ... -class CharTensor(Tensor): ... -class ByteTensor(Tensor): ... -class BoolTensor(Tensor): ... - -# 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): ... - -# 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: Optional[Tensor] - _cdata: _int - grad_fn: Optional[_Node] - _grad_fn: Any - _grad: Optional[Tensor] - grad: Optional[Tensor] - _backward_hooks: Optional[Dict[_int, Callable[[Tensor], Optional[Tensor]]]] - nbytes: _int - itemsize: _int - _has_symbolic_sizes_strides: _bool - def __abs__(self) -> Tensor: ... - def __add__(self, other: Any) -> Tensor: ... - @overload - def __and__(self, other: Tensor) -> Tensor: ... - @overload - def __and__(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def __and__(self, other: Any) -> Tensor: ... - def __bool__(self) -> builtins.bool: ... - def __complex__(self) -> builtins.complex: ... - def __div__(self, other: Any) -> Tensor: ... - def __eq__(self, other: Any) -> Tensor: ... # type: ignore[override] - def __float__(self) -> builtins.float: ... - def __floordiv__(self, other: Any) -> Tensor: ... - def __ge__(self, other: Any) -> Tensor: ... - def __getitem__(self, indices: Union[Union[SupportsIndex, Union[None, _bool, _int, slice, ellipsis, Tensor], _NestedSequence[Union[None, _bool, _int, slice, ellipsis, Tensor]]], tuple[Union[SupportsIndex, Union[None, _bool, _int, slice, ellipsis, Tensor], _NestedSequence[Union[None, _bool, _int, slice, ellipsis, Tensor]]], ...]]) -> Tensor: ... - def __gt__(self, other: Any) -> Tensor: ... - def __iadd__(self, other: Any) -> Tensor: ... - @overload - def __iand__(self, other: Tensor) -> Tensor: ... - @overload - def __iand__(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def __iand__(self, other: Any) -> Tensor: ... - def __idiv__(self, other: Any) -> Tensor: ... - def __ifloordiv__(self, other: Any) -> Tensor: ... - @overload - def __ilshift__(self, other: Tensor) -> Tensor: ... - @overload - def __ilshift__(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def __ilshift__(self, other: Any) -> Tensor: ... - def __imod__(self, other: Any) -> Tensor: ... - def __imul__(self, other: Any) -> Tensor: ... - def __index__(self) -> builtins.int: ... - @overload - def __init__(self, *args: Any, device: Optional[DeviceLikeType] = None) -> None: ... - @overload - def __init__(self, storage: Storage) -> None: ... - @overload - def __init__(self, other: Tensor) -> None: ... - @overload - def __init__(self, size: _size, *, device: Optional[DeviceLikeType] = None) -> None: ... - def __int__(self) -> builtins.int: ... - def __invert__(self) -> Tensor: ... - @overload - def __ior__(self, other: Tensor) -> Tensor: ... - @overload - def __ior__(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def __ior__(self, other: Any) -> Tensor: ... - @overload - def __irshift__(self, other: Tensor) -> Tensor: ... - @overload - def __irshift__(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def __irshift__(self, other: Any) -> Tensor: ... - def __isub__(self, other: Any) -> Tensor: ... - @overload - def __ixor__(self, other: Tensor) -> Tensor: ... - @overload - def __ixor__(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def __ixor__(self, other: Any) -> Tensor: ... - def __le__(self, other: Any) -> Tensor: ... - def __long__(self) -> builtins.int: ... - @overload - def __lshift__(self, other: Tensor) -> Tensor: ... - @overload - def __lshift__(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def __lshift__(self, other: Any) -> Tensor: ... - def __lt__(self, other: Any) -> Tensor: ... - def __matmul__(self, other: Any) -> Tensor: ... - def __mod__(self, other: Any) -> Tensor: ... - def __mul__(self, other: Any) -> Tensor: ... - def __ne__(self, other: Any) -> Tensor: ... # type: ignore[override] - def __neg__(self) -> Tensor: ... - def __new__(self, *args, **kwargs) -> Tensor: ... - def __nonzero__(self) -> builtins.bool: ... - @overload - def __or__(self, other: Tensor) -> Tensor: ... - @overload - def __or__(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def __or__(self, other: Any) -> Tensor: ... - def __pow__(self, other: Any) -> Tensor: ... - def __radd__(self, other: Any) -> Tensor: ... - def __rand__(self, other: Any) -> Tensor: ... - def __rfloordiv__(self, other: Any) -> Tensor: ... - def __rmul__(self, other: Any) -> Tensor: ... - def __ror__(self, other: Any) -> Tensor: ... - def __rpow__(self, other: Any) -> Tensor: ... - @overload - def __rshift__(self, other: Tensor) -> Tensor: ... - @overload - def __rshift__(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def __rshift__(self, other: Any) -> Tensor: ... - def __rsub__(self, other: Any) -> Tensor: ... - def __rtruediv__(self, other: Any) -> Tensor: ... - def __rxor__(self, other: Any) -> Tensor: ... - def __setitem__(self, indices: Union[Union[SupportsIndex, Union[None, _bool, _int, slice, ellipsis, Tensor], _NestedSequence[Union[None, _bool, _int, slice, ellipsis, Tensor]]], tuple[Union[SupportsIndex, Union[None, _bool, _int, slice, ellipsis, Tensor], _NestedSequence[Union[None, _bool, _int, slice, ellipsis, Tensor]]], ...]], val: Union[Tensor, Number]) -> None: ... - def __sub__(self, other: Any) -> Tensor: ... - def __truediv__(self, other: Any) -> Tensor: ... - @overload - def __xor__(self, other: Tensor) -> Tensor: ... - @overload - def __xor__(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def __xor__(self, other: Any) -> Tensor: ... - def _addmm_activation(self, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1, use_gelu: _bool = False) -> Tensor: ... - def _autocast_to_full_precision(self, cuda_enabled: _bool, cpu_enabled: _bool) -> Tensor: ... - def _autocast_to_reduced_precision(self, cuda_enabled: _bool, cpu_enabled: _bool, cuda_dtype: _dtype, cpu_dtype: _dtype) -> Tensor: ... - def _coalesced_(self, coalesced: _bool) -> Tensor: ... - def _conj(self) -> Tensor: ... - def _conj_physical(self) -> Tensor: ... - def _dimI(self) -> _int: ... - def _dimV(self) -> _int: ... - def _indices(self) -> Tensor: ... - def _is_all_true(self) -> Tensor: ... - def _is_any_true(self) -> Tensor: ... - def _is_view(self) -> _bool: ... - def _is_zerotensor(self) -> _bool: ... - @staticmethod - def _make_subclass(cls: Type[S], data: Tensor, require_grad: _bool = False, dispatch_strides: _bool = False, dispatch_device: _bool = False, device_for_backend_keys: Optional[_device] = None) -> S: ... - def _neg_view(self) -> Tensor: ... - def _nested_tensor_size(self) -> Tensor: ... - def _nested_tensor_storage_offsets(self) -> Tensor: ... - def _nested_tensor_strides(self) -> Tensor: ... - def _nnz(self) -> _int: ... - def _sparse_mask_projection(self, mask: Tensor, accumulate_matches: _bool = False) -> Tensor: ... - def _to_dense(self, dtype: Optional[_dtype] = None, masked_grad: Optional[_bool] = None) -> Tensor: ... - @overload - def _to_sparse(self, *, layout: Optional[_layout] = None, blocksize: Optional[Union[_int, _size]] = None, dense_dim: Optional[_int] = None) -> Tensor: ... - @overload - def _to_sparse(self, sparse_dim: _int) -> Tensor: ... - def _to_sparse_bsc(self, blocksize: Union[_int, _size], dense_dim: Optional[_int] = None) -> Tensor: ... - def _to_sparse_bsr(self, blocksize: Union[_int, _size], dense_dim: Optional[_int] = None) -> Tensor: ... - def _to_sparse_csc(self, dense_dim: Optional[_int] = None) -> Tensor: ... - def _to_sparse_csr(self, dense_dim: Optional[_int] = None) -> Tensor: ... - def _values(self) -> Tensor: ... - def abs(self) -> Tensor: ... - def abs_(self) -> Tensor: ... - def absolute(self) -> Tensor: ... - def absolute_(self) -> Tensor: ... - def acos(self) -> Tensor: ... - def acos_(self) -> Tensor: ... - def acosh(self) -> Tensor: ... - def acosh_(self) -> Tensor: ... - def add(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat], *, alpha: Optional[Number] = 1, out: Optional[Tensor] = None) -> Tensor: ... - def add_(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat], *, alpha: Optional[Number] = 1) -> Tensor: ... - def addbmm(self, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - def addbmm_(self, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - def addcdiv(self, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1) -> Tensor: ... - def addcdiv_(self, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1) -> Tensor: ... - def addcmul(self, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1) -> Tensor: ... - def addcmul_(self, tensor1: Tensor, tensor2: Tensor, *, value: Union[Number, _complex] = 1) -> Tensor: ... - def addmm(self, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - def addmm_(self, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - def addmv(self, mat: Tensor, vec: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - def addmv_(self, mat: Tensor, vec: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - def addr(self, vec1: Tensor, vec2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - def addr_(self, vec1: Tensor, vec2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - def adjoint(self) -> Tensor: ... - def align_as(self, other: Tensor) -> Tensor: ... - @overload - def align_to(self, order: Sequence[Union[str, ellipsis, None]], ellipsis_idx: _int) -> Tensor: ... - @overload - def align_to(self, names: Sequence[Union[str, ellipsis, None]]) -> Tensor: ... - @overload - def all(self) -> Tensor: ... - @overload - def all(self, dim: Optional[_size] = None, keepdim: _bool = False) -> Tensor: ... - @overload - def all(self, dim: _int, keepdim: _bool = False) -> Tensor: ... - @overload - def all(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> Tensor: ... - def allclose(self, other: Tensor, rtol: _float = 1e-05, atol: _float = 1e-08, equal_nan: _bool = False) -> _bool: ... - def amax(self, dim: Union[_int, _size] = (), keepdim: _bool = False) -> Tensor: ... - def amin(self, dim: Union[_int, _size] = (), keepdim: _bool = False) -> Tensor: ... - def aminmax(self, *, dim: Optional[_int] = None, keepdim: _bool = False) -> torch.return_types.aminmax: ... - def angle(self) -> Tensor: ... - @overload - def any(self) -> Tensor: ... - @overload - def any(self, dim: Optional[_size] = None, keepdim: _bool = False) -> Tensor: ... - @overload - def any(self, dim: _int, keepdim: _bool = False) -> Tensor: ... - @overload - def any(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> Tensor: ... - def apply_(self, callable: Callable) -> Tensor: ... - def arccos(self) -> Tensor: ... - def arccos_(self) -> Tensor: ... - def arccosh(self) -> Tensor: ... - def arccosh_(self) -> Tensor: ... - def arcsin(self) -> Tensor: ... - def arcsin_(self) -> Tensor: ... - def arcsinh(self) -> Tensor: ... - def arcsinh_(self) -> Tensor: ... - def arctan(self) -> Tensor: ... - def arctan2(self, other: Tensor) -> Tensor: ... - def arctan2_(self, other: Tensor) -> Tensor: ... - def arctan_(self) -> Tensor: ... - def arctanh(self) -> Tensor: ... - def arctanh_(self) -> Tensor: ... - def argmax(self, dim: Optional[_int] = None, keepdim: _bool = False) -> Tensor: ... - def argmin(self, dim: Optional[_int] = None, keepdim: _bool = False) -> Tensor: ... - @overload - def argsort(self, *, stable: _bool, dim: _int = -1, descending: _bool = False) -> Tensor: ... - @overload - def argsort(self, dim: _int = -1, descending: _bool = False) -> Tensor: ... - @overload - def argsort(self, dim: Union[str, ellipsis, None], descending: _bool = False) -> Tensor: ... - def argwhere(self) -> Tensor: ... - def as_strided(self, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: ... - def as_strided_(self, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: ... - def as_strided_scatter(self, src: Tensor, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], storage_offset: Optional[Union[_int, SymInt]] = None) -> Tensor: ... - def as_subclass(self, cls: Type[S]) -> S: ... - def asin(self) -> Tensor: ... - def asin_(self) -> Tensor: ... - def asinh(self) -> Tensor: ... - def asinh_(self) -> Tensor: ... - def atan(self) -> Tensor: ... - def atan2(self, other: Tensor) -> Tensor: ... - def atan2_(self, other: Tensor) -> Tensor: ... - def atan_(self) -> Tensor: ... - def atanh(self) -> Tensor: ... - def atanh_(self) -> Tensor: ... - def baddbmm(self, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - def baddbmm_(self, batch1: Tensor, batch2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - @overload - def bernoulli(self, *, generator: Optional[Generator] = None) -> Tensor: ... - @overload - def bernoulli(self, p: _float, *, generator: Optional[Generator] = None) -> Tensor: ... - @overload - def bernoulli_(self, p: Tensor, *, generator: Optional[Generator] = None) -> Tensor: ... - @overload - def bernoulli_(self, p: _float = 0.5, *, generator: Optional[Generator] = None) -> Tensor: ... - def bfloat16(self) -> Tensor: ... - def bincount(self, weights: Optional[Tensor] = None, minlength: _int = 0) -> Tensor: ... - @overload - def bitwise_and(self, other: Tensor) -> Tensor: ... - @overload - def bitwise_and(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def bitwise_and_(self, other: Tensor) -> Tensor: ... - @overload - def bitwise_and_(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def bitwise_left_shift(self, other: Tensor) -> Tensor: ... - @overload - def bitwise_left_shift(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def bitwise_left_shift_(self, other: Tensor) -> Tensor: ... - @overload - def bitwise_left_shift_(self, other: Union[Number, _complex]) -> Tensor: ... - def bitwise_not(self) -> Tensor: ... - def bitwise_not_(self) -> Tensor: ... - @overload - def bitwise_or(self, other: Tensor) -> Tensor: ... - @overload - def bitwise_or(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def bitwise_or_(self, other: Tensor) -> Tensor: ... - @overload - def bitwise_or_(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def bitwise_right_shift(self, other: Tensor) -> Tensor: ... - @overload - def bitwise_right_shift(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def bitwise_right_shift_(self, other: Tensor) -> Tensor: ... - @overload - def bitwise_right_shift_(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def bitwise_xor(self, other: Tensor) -> Tensor: ... - @overload - def bitwise_xor(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def bitwise_xor_(self, other: Tensor) -> Tensor: ... - @overload - def bitwise_xor_(self, other: Union[Number, _complex]) -> Tensor: ... - def bmm(self, mat2: Tensor) -> Tensor: ... - def bool(self) -> Tensor: ... - @overload - def broadcast_to(self, size: Sequence[Union[_int, SymInt]]) -> Tensor: ... - @overload - def broadcast_to(self, *size: _int) -> Tensor: ... - def byte(self) -> Tensor: ... - def cauchy_(self, median: _float = 0, sigma: _float = 1, *, generator: Optional[Generator] = None) -> Tensor: ... - def ccol_indices(self) -> Tensor: ... - def ceil(self) -> Tensor: ... - def ceil_(self) -> Tensor: ... - def chalf(self, *, memory_format: Optional[memory_format] = None) -> Tensor: ... - def char(self) -> Tensor: ... - def cholesky(self, upper: _bool = False) -> Tensor: ... - def cholesky_inverse(self, upper: _bool = False) -> Tensor: ... - def cholesky_solve(self, input2: Tensor, upper: _bool = False) -> Tensor: ... - def chunk(self, chunks: _int, dim: _int = 0) -> List[Tensor]: ... - @overload - def clamp(self, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: ... - @overload - def clamp(self, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: ... - @overload - def clamp_(self, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: ... - @overload - def clamp_(self, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: ... - @overload - def clamp_max(self, max: Tensor) -> Tensor: ... - @overload - def clamp_max(self, max: Union[Number, _complex]) -> Tensor: ... - @overload - def clamp_max_(self, max: Tensor) -> Tensor: ... - @overload - def clamp_max_(self, max: Union[Number, _complex]) -> Tensor: ... - @overload - def clamp_min(self, min: Tensor) -> Tensor: ... - @overload - def clamp_min(self, min: Union[Number, _complex]) -> Tensor: ... - @overload - def clamp_min_(self, min: Tensor) -> Tensor: ... - @overload - def clamp_min_(self, min: Union[Number, _complex]) -> Tensor: ... - @overload - def clip(self, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: ... - @overload - def clip(self, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: ... - @overload - def clip_(self, min: Optional[Tensor] = None, max: Optional[Tensor] = None) -> Tensor: ... - @overload - def clip_(self, min: Optional[Union[Number, _complex]] = None, max: Optional[Union[Number, _complex]] = None) -> Tensor: ... - def clone(self, *, memory_format: Optional[memory_format] = None) -> Tensor: ... - def coalesce(self) -> Tensor: ... - def col_indices(self) -> Tensor: ... - def conj(self) -> Tensor: ... - def conj_physical(self) -> Tensor: ... - def conj_physical_(self) -> Tensor: ... - def contiguous(self, memory_format=torch.contiguous_format) -> Tensor: ... - def copy_(self, src: Tensor, non_blocking: _bool = False) -> Tensor: ... - @overload - def copysign(self, other: Tensor) -> Tensor: ... - @overload - def copysign(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def copysign_(self, other: Tensor) -> Tensor: ... - @overload - def copysign_(self, other: Union[Number, _complex]) -> Tensor: ... - def corrcoef(self) -> Tensor: ... - def cos(self) -> Tensor: ... - def cos_(self) -> Tensor: ... - def cosh(self) -> Tensor: ... - def cosh_(self) -> Tensor: ... - @overload - def count_nonzero(self, dim: Optional[_int] = None) -> Tensor: ... - @overload - def count_nonzero(self, dim: _size) -> Tensor: ... - @overload - def count_nonzero(self, *dim: _int) -> Tensor: ... - def cov(self, *, correction: _int = 1, fweights: Optional[Tensor] = None, aweights: Optional[Tensor] = None) -> Tensor: ... - def cpu(self) -> Tensor: ... - def cross(self, other: Tensor, dim: Optional[_int] = None) -> Tensor: ... - def crow_indices(self) -> Tensor: ... - def cuda(self, device: Optional[Union[_device, _int, str]] = None, non_blocking: _bool = False) -> Tensor: ... - @overload - def cummax(self, dim: _int) -> torch.return_types.cummax: ... - @overload - def cummax(self, dim: Union[str, ellipsis, None]) -> torch.return_types.cummax: ... - @overload - def cummin(self, dim: _int) -> torch.return_types.cummin: ... - @overload - def cummin(self, dim: Union[str, ellipsis, None]) -> torch.return_types.cummin: ... - @overload - def cumprod(self, dim: _int, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def cumprod(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def cumprod_(self, dim: _int, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def cumprod_(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def cumsum(self, dim: _int, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def cumsum(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def cumsum_(self, dim: _int, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def cumsum_(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: ... - def data_ptr(self) -> _int: ... - def deg2rad(self) -> Tensor: ... - def deg2rad_(self) -> Tensor: ... - def dense_dim(self) -> _int: ... - def dequantize(self) -> Tensor: ... - def det(self) -> Tensor: ... - def detach(self) -> Tensor: ... - def detach_(self) -> Tensor: ... - def diag(self, diagonal: _int = 0) -> Tensor: ... - def diag_embed(self, offset: _int = 0, dim1: _int = -2, dim2: _int = -1) -> Tensor: ... - def diagflat(self, offset: _int = 0) -> Tensor: ... - @overload - def diagonal(self, *, outdim: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None], dim2: Union[str, ellipsis, None], offset: _int = 0) -> Tensor: ... - @overload - def diagonal(self, offset: _int = 0, dim1: _int = 0, dim2: _int = 1) -> Tensor: ... - def diagonal_scatter(self, src: Tensor, offset: _int = 0, dim1: _int = 0, dim2: _int = 1) -> Tensor: ... - def diff(self, n: _int = 1, dim: _int = -1, prepend: Optional[Tensor] = None, append: Optional[Tensor] = None) -> Tensor: ... - def digamma(self) -> Tensor: ... - def digamma_(self) -> Tensor: ... - def dim(self) -> _int: ... - def dist(self, other: Tensor, p: Union[Number, _complex] = 2) -> Tensor: ... - def div(self, other: Union[Tensor, Number], *, rounding_mode: Optional[str] = None) -> Tensor: ... - def div_(self, other: Union[Tensor, Number], *, rounding_mode: Optional[str] = None) -> Tensor: ... - @overload - def divide(self, other: Tensor) -> Tensor: ... - @overload - def divide(self, other: Tensor, *, rounding_mode: Optional[str]) -> Tensor: ... - @overload - def divide(self, other: Union[Number, _complex], *, rounding_mode: Optional[str]) -> Tensor: ... - @overload - def divide(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def divide_(self, other: Tensor) -> Tensor: ... - @overload - def divide_(self, other: Tensor, *, rounding_mode: Optional[str]) -> Tensor: ... - @overload - def divide_(self, other: Union[Number, _complex], *, rounding_mode: Optional[str]) -> Tensor: ... - @overload - def divide_(self, other: Union[Number, _complex]) -> Tensor: ... - def dot(self, tensor: Tensor) -> Tensor: ... - def double(self) -> Tensor: ... - @overload - def dsplit(self, sections: _int) -> List[Tensor]: ... - @overload - def dsplit(self, indices: _size) -> List[Tensor]: ... - @overload - def dsplit(self, *indices: _int) -> List[Tensor]: ... - def element_size(self) -> _int: ... - @overload - def eq(self, other: Tensor) -> Tensor: ... - @overload - def eq(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def eq_(self, other: Tensor) -> Tensor: ... - @overload - def eq_(self, other: Union[Number, _complex]) -> Tensor: ... - def equal(self, other: Tensor) -> _bool: ... - def erf(self) -> Tensor: ... - def erf_(self) -> Tensor: ... - def erfc(self) -> Tensor: ... - def erfc_(self) -> Tensor: ... - def erfinv(self) -> Tensor: ... - def erfinv_(self) -> Tensor: ... - def exp(self) -> Tensor: ... - def exp2(self) -> Tensor: ... - def exp2_(self) -> Tensor: ... - def exp_(self) -> Tensor: ... - @overload - def expand(self, size: Sequence[Union[_int, SymInt]], *, implicit: _bool = False) -> Tensor: ... - @overload - def expand(self, *size: _int, implicit: _bool = False) -> Tensor: ... - def expand_as(self, other: Tensor) -> Tensor: ... - def expm1(self) -> Tensor: ... - def expm1_(self) -> Tensor: ... - def exponential_(self, lambd: _float = 1, *, generator: Optional[Generator] = None) -> Tensor: ... - @overload - def fill_(self, value: Tensor) -> Tensor: ... - @overload - def fill_(self, value: Union[Number, _complex]) -> Tensor: ... - def fill_diagonal_(self, fill_value: Union[Number, _complex], wrap: _bool = False) -> Tensor: ... - def fix(self) -> Tensor: ... - def fix_(self) -> Tensor: ... - @overload - def flatten(self, start_dim: _int = 0, end_dim: _int = -1) -> Tensor: ... - @overload - def flatten(self, start_dim: _int, end_dim: _int, out_dim: Union[str, ellipsis, None]) -> Tensor: ... - @overload - def flatten(self, start_dim: Union[str, ellipsis, None], end_dim: Union[str, ellipsis, None], out_dim: Union[str, ellipsis, None]) -> Tensor: ... - @overload - def flatten(self, dims: Sequence[Union[str, ellipsis, None]], out_dim: Union[str, ellipsis, None]) -> Tensor: ... - @overload - def flip(self, dims: _size) -> Tensor: ... - @overload - def flip(self, *dims: _int) -> Tensor: ... - def fliplr(self) -> Tensor: ... - def flipud(self) -> Tensor: ... - def float(self) -> Tensor: ... - @overload - def float_power(self, exponent: Tensor) -> Tensor: ... - @overload - def float_power(self, exponent: Union[Number, _complex]) -> Tensor: ... - @overload - def float_power_(self, exponent: Tensor) -> Tensor: ... - @overload - def float_power_(self, exponent: Union[Number, _complex]) -> Tensor: ... - def floor(self) -> Tensor: ... - def floor_(self) -> Tensor: ... - def floor_divide(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat], *, out: Optional[Tensor] = None) -> Tensor: ... - def floor_divide_(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat]) -> Tensor: ... - def fmax(self, other: Tensor) -> Tensor: ... - def fmin(self, other: Tensor) -> Tensor: ... - @overload - def fmod(self, other: Tensor) -> Tensor: ... - @overload - def fmod(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def fmod_(self, other: Tensor) -> Tensor: ... - @overload - def fmod_(self, other: Union[Number, _complex]) -> Tensor: ... - def frac(self) -> Tensor: ... - def frac_(self) -> Tensor: ... - def frexp(self) -> torch.return_types.frexp: ... - @overload - def gather(self, dim: _int, index: Tensor, *, sparse_grad: _bool = False) -> Tensor: ... - @overload - def gather(self, dim: Union[str, ellipsis, None], index: Tensor, *, sparse_grad: _bool = False) -> Tensor: ... - def gcd(self, other: Tensor) -> Tensor: ... - def gcd_(self, other: Tensor) -> Tensor: ... - @overload - def ge(self, other: Tensor) -> Tensor: ... - @overload - def ge(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def ge_(self, other: Tensor) -> Tensor: ... - @overload - def ge_(self, other: Union[Number, _complex]) -> Tensor: ... - def geometric_(self, p: _float, *, generator: Optional[Generator] = None) -> Tensor: ... - def geqrf(self) -> torch.return_types.geqrf: ... - def ger(self, vec2: Tensor) -> Tensor: ... - def get_device(self) -> _int: ... - @overload - def greater(self, other: Tensor) -> Tensor: ... - @overload - def greater(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def greater_(self, other: Tensor) -> Tensor: ... - @overload - def greater_(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def greater_equal(self, other: Tensor) -> Tensor: ... - @overload - def greater_equal(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def greater_equal_(self, other: Tensor) -> Tensor: ... - @overload - def greater_equal_(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def gt(self, other: Tensor) -> Tensor: ... - @overload - def gt(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def gt_(self, other: Tensor) -> Tensor: ... - @overload - def gt_(self, other: Union[Number, _complex]) -> Tensor: ... - def half(self) -> Tensor: ... - def hardshrink(self, lambd: Union[Number, _complex] = 0.5) -> Tensor: ... - def has_names(self) -> _bool: ... - def heaviside(self, values: Tensor) -> Tensor: ... - def heaviside_(self, values: Tensor) -> Tensor: ... - def histc(self, bins: _int = 100, min: Union[Number, _complex] = 0, max: Union[Number, _complex] = 0) -> Tensor: ... - @overload - def histogram(self, bins: Tensor, *, weight: Optional[Tensor] = None, density: _bool = False) -> torch.return_types.histogram: ... - @overload - def histogram(self, bins: _int = 100, *, range: Optional[Sequence[_float]] = None, weight: Optional[Tensor] = None, density: _bool = False) -> torch.return_types.histogram: ... - @overload - def hsplit(self, sections: _int) -> List[Tensor]: ... - @overload - def hsplit(self, indices: _size) -> List[Tensor]: ... - @overload - def hsplit(self, *indices: _int) -> List[Tensor]: ... - def hypot(self, other: Tensor) -> Tensor: ... - def hypot_(self, other: Tensor) -> Tensor: ... - def i0(self) -> Tensor: ... - def i0_(self) -> Tensor: ... - def igamma(self, other: Tensor) -> Tensor: ... - def igamma_(self, other: Tensor) -> Tensor: ... - def igammac(self, other: Tensor) -> Tensor: ... - def igammac_(self, other: Tensor) -> Tensor: ... - @overload - def index_add(self, dim: _int, index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: ... - @overload - def index_add(self, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: ... - def index_add_(self, dim: _int, index: Tensor, source: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: ... - @overload - def index_copy(self, dim: _int, index: Tensor, source: Tensor) -> Tensor: ... - @overload - def index_copy(self, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor) -> Tensor: ... - @overload - def index_copy_(self, dim: _int, index: Tensor, source: Tensor) -> Tensor: ... - @overload - def index_copy_(self, dim: Union[str, ellipsis, None], index: Tensor, source: Tensor) -> Tensor: ... - @overload - def index_fill(self, dim: _int, index: Tensor, value: Tensor) -> Tensor: ... - @overload - def index_fill(self, dim: Union[str, ellipsis, None], index: Tensor, value: Tensor) -> Tensor: ... - @overload - def index_fill(self, dim: _int, index: Tensor, value: Union[Number, _complex]) -> Tensor: ... - @overload - def index_fill(self, dim: Union[str, ellipsis, None], index: Tensor, value: Union[Number, _complex]) -> Tensor: ... - @overload - def index_fill_(self, dim: _int, index: Tensor, value: Tensor) -> Tensor: ... - @overload - def index_fill_(self, dim: Union[str, ellipsis, None], index: Tensor, value: Tensor) -> Tensor: ... - @overload - def index_fill_(self, dim: _int, index: Tensor, value: Union[Number, _complex]) -> Tensor: ... - @overload - def index_fill_(self, dim: Union[str, ellipsis, None], index: Tensor, value: Union[Number, _complex]) -> Tensor: ... - def index_put(self, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool = False) -> Tensor: ... - def index_put_(self, indices: Optional[Union[Tuple[Tensor, ...], List[Tensor]]], values: Tensor, accumulate: _bool = False) -> Tensor: ... - def index_reduce(self, dim: _int, index: Tensor, source: Tensor, reduce: str, *, include_self: _bool = True) -> Tensor: ... - def index_reduce_(self, dim: _int, index: Tensor, source: Tensor, reduce: str, *, include_self: _bool = True) -> Tensor: ... - @overload - def index_select(self, dim: _int, index: Tensor) -> Tensor: ... - @overload - def index_select(self, dim: Union[str, ellipsis, None], index: Tensor) -> Tensor: ... - def indices(self) -> Tensor: ... - def inner(self, other: Tensor) -> Tensor: ... - def int(self) -> Tensor: ... - def int_repr(self) -> Tensor: ... - def inverse(self) -> Tensor: ... - def is_coalesced(self) -> _bool: ... - def is_complex(self) -> _bool: ... - def is_conj(self) -> _bool: ... - def is_contiguous(self, memory_format=torch.contiguous_format) -> _bool: ... - is_cpu: _bool - is_cuda: _bool - def is_distributed(self) -> _bool: ... - def is_floating_point(self) -> _bool: ... - def is_inference(self) -> _bool: ... - is_ipu: _bool - is_leaf: _bool - is_meta: _bool - is_mkldnn: _bool - is_mps: _bool - is_mtia: _bool - def is_neg(self) -> _bool: ... - is_nested: _bool - def is_nonzero(self) -> _bool: ... - is_ort: _bool - def is_pinned(self, device: Optional[Optional[DeviceLikeType]] = None) -> _bool: ... - is_quantized: _bool - def is_same_size(self, other: Tensor) -> _bool: ... - def is_set_to(self, tensor: Tensor) -> _bool: ... - def is_signed(self) -> _bool: ... - is_sparse: _bool - is_sparse_csr: _bool - is_vulkan: _bool - def isclose(self, other: Tensor, rtol: _float = 1e-05, atol: _float = 1e-08, equal_nan: _bool = False) -> Tensor: ... - def isfinite(self) -> Tensor: ... - def isinf(self) -> Tensor: ... - def isnan(self) -> Tensor: ... - def isneginf(self) -> Tensor: ... - def isposinf(self) -> Tensor: ... - def isreal(self) -> Tensor: ... - def istft(self, n_fft: _int, hop_length: Optional[_int] = None, win_length: Optional[_int] = None, window: Optional[Tensor] = None, center: _bool = True, normalized: _bool = False, onesided: Optional[_bool] = None, length: Optional[_int] = None, return_complex: _bool = False) -> Tensor: ... - def item(self) -> Number: ... - def kron(self, other: Tensor) -> Tensor: ... - @overload - def kthvalue(self, k: _int, dim: _int = -1, keepdim: _bool = False) -> torch.return_types.kthvalue: ... - @overload - def kthvalue(self, k: _int, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.kthvalue: ... - def lcm(self, other: Tensor) -> Tensor: ... - def lcm_(self, other: Tensor) -> Tensor: ... - def ldexp(self, other: Tensor) -> Tensor: ... - def ldexp_(self, other: Tensor) -> Tensor: ... - @overload - def le(self, other: Tensor) -> Tensor: ... - @overload - def le(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def le_(self, other: Tensor) -> Tensor: ... - @overload - def le_(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def lerp(self, end: Tensor, weight: Tensor) -> Tensor: ... - @overload - def lerp(self, end: Tensor, weight: Union[Number, _complex]) -> Tensor: ... - @overload - def lerp_(self, end: Tensor, weight: Tensor) -> Tensor: ... - @overload - def lerp_(self, end: Tensor, weight: Union[Number, _complex]) -> Tensor: ... - @overload - def less(self, other: Tensor) -> Tensor: ... - @overload - def less(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def less_(self, other: Tensor) -> Tensor: ... - @overload - def less_(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def less_equal(self, other: Tensor) -> Tensor: ... - @overload - def less_equal(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def less_equal_(self, other: Tensor) -> Tensor: ... - @overload - def less_equal_(self, other: Union[Number, _complex]) -> Tensor: ... - def lgamma(self) -> Tensor: ... - def lgamma_(self) -> Tensor: ... - def log(self) -> Tensor: ... - def log10(self) -> Tensor: ... - def log10_(self) -> Tensor: ... - def log1p(self) -> Tensor: ... - def log1p_(self) -> Tensor: ... - def log2(self) -> Tensor: ... - def log2_(self) -> Tensor: ... - def log_(self) -> Tensor: ... - def log_normal_(self, mean: _float = 1, std: _float = 2, *, generator: Optional[Generator] = None) -> Tensor: ... - @overload - def log_softmax(self, dim: _int, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def log_softmax(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: ... - def logaddexp(self, other: Tensor) -> Tensor: ... - def logaddexp2(self, other: Tensor) -> Tensor: ... - @overload - def logcumsumexp(self, dim: _int) -> Tensor: ... - @overload - def logcumsumexp(self, dim: Union[str, ellipsis, None]) -> Tensor: ... - def logdet(self) -> Tensor: ... - def logical_and(self, other: Tensor) -> Tensor: ... - def logical_and_(self, other: Tensor) -> Tensor: ... - def logical_not(self) -> Tensor: ... - def logical_not_(self) -> Tensor: ... - def logical_or(self, other: Tensor) -> Tensor: ... - def logical_or_(self, other: Tensor) -> Tensor: ... - def logical_xor(self, other: Tensor) -> Tensor: ... - def logical_xor_(self, other: Tensor) -> Tensor: ... - def logit(self, eps: Optional[_float] = None) -> Tensor: ... - def logit_(self, eps: Optional[_float] = None) -> Tensor: ... - @overload - def logsumexp(self, dim: Union[_int, _size], keepdim: _bool = False) -> Tensor: ... - @overload - def logsumexp(self, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False) -> Tensor: ... - def long(self) -> Tensor: ... - @overload - def lt(self, other: Tensor) -> Tensor: ... - @overload - def lt(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def lt_(self, other: Tensor) -> Tensor: ... - @overload - def lt_(self, other: Union[Number, _complex]) -> Tensor: ... - def lu_solve(self, LU_data: Tensor, LU_pivots: Tensor) -> Tensor: ... - def map2_(self, x: Tensor, y: Tensor, callable: Callable) -> Tensor: ... - def map_(self, tensor: Tensor, callable: Callable) -> Tensor: ... - @overload - def masked_fill(self, mask: Tensor, value: Tensor) -> Tensor: ... - @overload - def masked_fill(self, mask: Tensor, value: Union[Number, _complex]) -> Tensor: ... - @overload - def masked_fill_(self, mask: Tensor, value: Tensor) -> Tensor: ... - @overload - def masked_fill_(self, mask: Tensor, value: Union[Number, _complex]) -> Tensor: ... - def masked_scatter(self, mask: Tensor, source: Tensor) -> Tensor: ... - def masked_scatter_(self, mask: Tensor, source: Tensor) -> Tensor: ... - def masked_select(self, mask: Tensor) -> Tensor: ... - def matmul(self, other: Tensor) -> Tensor: ... - def matrix_exp(self) -> Tensor: ... - def matrix_power(self, n: _int) -> Tensor: ... - @overload - def max(self) -> Tensor: ... - @overload - def max(self, other: Tensor) -> Tensor: ... - @overload - def max(self, dim: _int, keepdim: _bool = False) -> torch.return_types.max: ... - @overload - def max(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.max: ... - def maximum(self, other: Tensor) -> Tensor: ... - @overload - def mean(self, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def mean(self, dim: Optional[Union[_int, _size]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def mean(self, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def median(self) -> Tensor: ... - @overload - def median(self, dim: _int, keepdim: _bool = False) -> torch.return_types.median: ... - @overload - def median(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.median: ... - @overload - def min(self) -> Tensor: ... - @overload - def min(self, other: Tensor) -> Tensor: ... - @overload - def min(self, dim: _int, keepdim: _bool = False) -> torch.return_types.min: ... - @overload - def min(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.min: ... - def minimum(self, other: Tensor) -> Tensor: ... - def mm(self, mat2: Tensor) -> Tensor: ... - @overload - def mode(self, dim: _int = -1, keepdim: _bool = False) -> torch.return_types.mode: ... - @overload - def mode(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.mode: ... - @overload - def moveaxis(self, source: _int, destination: _int) -> Tensor: ... - @overload - def moveaxis(self, source: _size, destination: _size) -> Tensor: ... - @overload - def movedim(self, source: _int, destination: _int) -> Tensor: ... - @overload - def movedim(self, source: _size, destination: _size) -> Tensor: ... - def msort(self) -> Tensor: ... - def mul(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat], *, out: Optional[Tensor] = None) -> Tensor: ... - def mul_(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat]) -> Tensor: ... - def multinomial(self, num_samples: _int, replacement: _bool = False, *, generator: Optional[Generator] = None) -> Tensor: ... - @overload - def multiply(self, other: Tensor) -> Tensor: ... - @overload - def multiply(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def multiply_(self, other: Tensor) -> Tensor: ... - @overload - def multiply_(self, other: Union[Number, _complex]) -> Tensor: ... - def mv(self, vec: Tensor) -> Tensor: ... - def mvlgamma(self, p: _int) -> Tensor: ... - def mvlgamma_(self, p: _int) -> Tensor: ... - def nan_to_num(self, nan: Optional[_float] = None, posinf: Optional[_float] = None, neginf: Optional[_float] = None) -> Tensor: ... - def nan_to_num_(self, nan: Optional[_float] = None, posinf: Optional[_float] = None, neginf: Optional[_float] = None) -> Tensor: ... - def nanmean(self, dim: Optional[Union[_int, _size]] = None, keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def nanmedian(self) -> Tensor: ... - @overload - def nanmedian(self, dim: _int, keepdim: _bool = False) -> torch.return_types.nanmedian: ... - @overload - def nanmedian(self, dim: Union[str, ellipsis, None], keepdim: _bool = False) -> torch.return_types.nanmedian: ... - @overload - def nanquantile(self, q: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear") -> Tensor: ... - @overload - def nanquantile(self, q: _float, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear") -> Tensor: ... - def nansum(self, dim: Optional[Union[_int, _size]] = None, keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def narrow(self, dim: _int, start: Tensor, length: Union[_int, SymInt]) -> Tensor: ... - @overload - def narrow(self, dim: _int, start: Union[_int, SymInt], length: Union[_int, SymInt]) -> Tensor: ... - def narrow_copy(self, dim: _int, start: Union[_int, SymInt], length: Union[_int, SymInt]) -> Tensor: ... - def ndimension(self) -> _int: ... - @overload - def ne(self, other: Tensor) -> Tensor: ... - @overload - def ne(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def ne_(self, other: Tensor) -> Tensor: ... - @overload - def ne_(self, other: Union[Number, _complex]) -> Tensor: ... - def neg(self) -> Tensor: ... - def neg_(self) -> Tensor: ... - def negative(self) -> Tensor: ... - def negative_(self) -> Tensor: ... - def nelement(self) -> _int: ... - @overload - def new(self, *args: Any, device: Optional[DeviceLikeType] = None) -> Tensor: ... - @overload - def new(self, storage: Storage) -> Tensor: ... - @overload - def new(self, other: Tensor) -> Tensor: ... - @overload - def new(self, size: _size, *, device: Optional[DeviceLikeType] = None) -> Tensor: ... - @overload - def new_empty(self, size: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... - @overload - def new_empty(self, *size: _int, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... - def new_empty_strided(self, size: Sequence[Union[_int, SymInt]], stride: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... - def new_full(self, size: Sequence[Union[_int, SymInt]], fill_value: Union[Number, _complex], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... - @overload - def new_ones(self, size: _size, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: ... - @overload - def new_ones(self, size: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... - @overload - def new_ones(self, *size: _int, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... - def new_tensor(self, data: Any, dtype: Optional[_dtype] = None, device: Optional[DeviceLikeType] = None, requires_grad: _bool = False, pin_memory: _bool = False) -> Tensor: ... - @overload - def new_zeros(self, size: Sequence[Union[_int, SymInt]], *, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... - @overload - def new_zeros(self, *size: _int, dtype: Optional[_dtype] = None, layout: Optional[_layout] = None, device: Optional[Optional[DeviceLikeType]] = None, pin_memory: Optional[_bool] = False, requires_grad: Optional[_bool] = False) -> Tensor: ... - def nextafter(self, other: Tensor) -> Tensor: ... - def nextafter_(self, other: Tensor) -> Tensor: ... - @overload - def nonzero(self, *, as_tuple: Literal[False] = False) -> Tensor: ... - @overload - def nonzero(self, *, as_tuple: Literal[True]) -> Tuple[Tensor, ...]: ... - def nonzero_static(self, *, size: _int, fill_value: _int = -1) -> Tensor: ... - def normal_(self, mean: _float = 0, std: _float = 1, *, generator: Optional[Generator] = None) -> Tensor: ... - @overload - def not_equal(self, other: Tensor) -> Tensor: ... - @overload - def not_equal(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def not_equal_(self, other: Tensor) -> Tensor: ... - @overload - def not_equal_(self, other: Union[Number, _complex]) -> Tensor: ... - def numel(self) -> _int: ... - def numpy(self, *, force: _bool = False) -> Any: ... - def orgqr(self, input2: Tensor) -> Tensor: ... - def ormqr(self, input2: Tensor, input3: Tensor, left: _bool = True, transpose: _bool = False) -> Tensor: ... - def outer(self, vec2: Tensor) -> Tensor: ... - @overload - def permute(self, dims: _size) -> Tensor: ... - @overload - def permute(self, *dims: _int) -> Tensor: ... - def pin_memory(self, device: Optional[Optional[DeviceLikeType]] = None) -> Tensor: ... - def pinverse(self, rcond: _float = 1e-15) -> Tensor: ... - def polygamma(self, n: _int) -> Tensor: ... - def polygamma_(self, n: _int) -> Tensor: ... - def positive(self) -> Tensor: ... - @overload - def pow(self, exponent: Tensor) -> Tensor: ... - @overload - def pow(self, exponent: Union[Number, _complex]) -> Tensor: ... - @overload - def pow_(self, exponent: Tensor) -> Tensor: ... - @overload - def pow_(self, exponent: Union[Number, _complex]) -> Tensor: ... - def prelu(self, weight: Tensor) -> Tensor: ... - @overload - def prod(self, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def prod(self, dim: _int, keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def prod(self, dim: Union[str, ellipsis, None], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... - def put(self, index: Tensor, source: Tensor, accumulate: _bool = False) -> Tensor: ... - def put_(self, index: Tensor, source: Tensor, accumulate: _bool = False) -> Tensor: ... - def q_per_channel_axis(self) -> _int: ... - def q_per_channel_scales(self) -> Tensor: ... - def q_per_channel_zero_points(self) -> Tensor: ... - def q_scale(self) -> _float: ... - def q_zero_point(self) -> _int: ... - def qr(self, some: _bool = True) -> torch.return_types.qr: ... - def qscheme(self) -> _qscheme: ... - @overload - def quantile(self, q: Tensor, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear") -> Tensor: ... - @overload - def quantile(self, q: _float, dim: Optional[_int] = None, keepdim: _bool = False, *, interpolation: str = "linear") -> Tensor: ... - def rad2deg(self) -> Tensor: ... - def rad2deg_(self) -> Tensor: ... - @overload - def random_(self, *, generator: Optional[Generator] = None) -> Tensor: ... - @overload - def random_(self, from_: _int, to: Optional[_int], *, generator: Optional[Generator] = None) -> Tensor: ... - @overload - def random_(self, to: _int, *, generator: Optional[Generator] = None) -> Tensor: ... - def ravel(self) -> Tensor: ... - def reciprocal(self) -> Tensor: ... - def reciprocal_(self) -> Tensor: ... - def record_stream(self, s: Stream) -> None: ... - def refine_names(self, names: Sequence[Union[str, ellipsis, None]]) -> Tensor: ... - def relu(self) -> Tensor: ... - def relu_(self) -> Tensor: ... - @overload - def remainder(self, other: Tensor) -> Tensor: ... - @overload - def remainder(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def remainder_(self, other: Tensor) -> Tensor: ... - @overload - def remainder_(self, other: Union[Number, _complex]) -> Tensor: ... - def rename(self, names: Optional[Sequence[Union[str, ellipsis, None]]]) -> Tensor: ... - def rename_(self, names: Optional[Sequence[Union[str, ellipsis, None]]]) -> Tensor: ... - def renorm(self, p: Union[Number, _complex], dim: _int, maxnorm: Union[Number, _complex]) -> Tensor: ... - def renorm_(self, p: Union[Number, _complex], dim: _int, maxnorm: Union[Number, _complex]) -> Tensor: ... - @overload - def repeat(self, repeats: Sequence[Union[_int, SymInt]]) -> Tensor: ... - @overload - def repeat(self, *repeats: _int) -> Tensor: ... - @overload - def repeat_interleave(self, repeats: Tensor, dim: Optional[_int] = None, *, output_size: Optional[Union[_int, SymInt]] = None) -> Tensor: ... - @overload - def repeat_interleave(self, repeats: Union[_int, SymInt], dim: Optional[_int] = None, *, output_size: Optional[Union[_int, SymInt]] = None) -> Tensor: ... - def requires_grad_(self, mode: _bool = True) -> Tensor: ... - @overload - def reshape(self, shape: Sequence[Union[_int, SymInt]]) -> Tensor: ... - @overload - def reshape(self, *shape: _int) -> Tensor: ... - def reshape_as(self, other: Tensor) -> Tensor: ... - @overload - def resize_(self, size: Sequence[Union[_int, SymInt]], *, memory_format: Optional[memory_format] = None) -> Tensor: ... - @overload - def resize_(self, *size: _int, memory_format: Optional[memory_format] = None) -> Tensor: ... - def resize_as_(self, the_template: Tensor, *, memory_format: Optional[memory_format] = None) -> Tensor: ... - def resize_as_sparse_(self, the_template: Tensor) -> Tensor: ... - def resolve_conj(self) -> Tensor: ... - def resolve_neg(self) -> Tensor: ... - def retain_grad(self) -> None: ... - def roll(self, shifts: Union[Union[_int, SymInt], Sequence[Union[_int, SymInt]]], dims: Union[_int, _size] = ()) -> Tensor: ... - def rot90(self, k: _int = 1, dims: _size = (0,1)) -> Tensor: ... - @overload - def round(self) -> Tensor: ... - @overload - def round(self, *, decimals: _int) -> Tensor: ... - @overload - def round_(self) -> Tensor: ... - @overload - def round_(self, *, decimals: _int) -> Tensor: ... - def row_indices(self) -> Tensor: ... - def rsqrt(self) -> Tensor: ... - def rsqrt_(self) -> Tensor: ... - @overload - def scatter(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: ... - @overload - def scatter(self, dim: _int, index: Tensor, src: Tensor, *, reduce: str) -> Tensor: ... - @overload - def scatter(self, dim: _int, index: Tensor, value: Union[Number, _complex], *, reduce: str) -> Tensor: ... - @overload - def scatter(self, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: ... - @overload - def scatter(self, dim: _int, index: Tensor, value: Union[Number, _complex]) -> Tensor: ... - @overload - def scatter(self, dim: Union[str, ellipsis, None], index: Tensor, value: Union[Number, _complex]) -> Tensor: ... - @overload - def scatter_(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: ... - @overload - def scatter_(self, dim: _int, index: Tensor, src: Tensor, *, reduce: str) -> Tensor: ... - @overload - def scatter_(self, dim: _int, index: Tensor, value: Union[Number, _complex], *, reduce: str) -> Tensor: ... - @overload - def scatter_(self, dim: _int, index: Tensor, value: Union[Number, _complex]) -> Tensor: ... - @overload - def scatter_add(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: ... - @overload - def scatter_add(self, dim: Union[str, ellipsis, None], index: Tensor, src: Tensor) -> Tensor: ... - def scatter_add_(self, dim: _int, index: Tensor, src: Tensor) -> Tensor: ... - def scatter_reduce(self, dim: _int, index: Tensor, src: Tensor, reduce: str, *, include_self: _bool = True) -> Tensor: ... - def scatter_reduce_(self, dim: _int, index: Tensor, src: Tensor, reduce: str, *, include_self: _bool = True) -> Tensor: ... - @overload - def select(self, dim: _int, index: Union[_int, SymInt]) -> Tensor: ... - @overload - def select(self, dim: Union[str, ellipsis, None], index: _int) -> Tensor: ... - def select_scatter(self, src: Tensor, dim: _int, index: Union[_int, SymInt]) -> Tensor: ... - @overload - def set_(self, storage: Union[Storage, TypedStorage, UntypedStorage], offset: _int, size: _size, stride: _size) -> Tensor: ... - @overload - def set_(self, storage: Union[Storage, TypedStorage, UntypedStorage]) -> Tensor: ... - def sgn(self) -> Tensor: ... - def sgn_(self) -> Tensor: ... - def short(self) -> Tensor: ... - def sigmoid(self) -> Tensor: ... - def sigmoid_(self) -> Tensor: ... - def sign(self) -> Tensor: ... - def sign_(self) -> Tensor: ... - def signbit(self) -> Tensor: ... - def sin(self) -> Tensor: ... - def sin_(self) -> Tensor: ... - def sinc(self) -> Tensor: ... - def sinc_(self) -> Tensor: ... - def sinh(self) -> Tensor: ... - def sinh_(self) -> Tensor: ... - @overload - def size(self, dim: None = None) -> Size: ... - @overload - def size(self, dim: _int) -> _int: ... - def slice_scatter(self, src: Tensor, dim: _int = 0, start: Optional[Union[_int, SymInt]] = None, end: Optional[Union[_int, SymInt]] = None, step: Union[_int, SymInt] = 1) -> Tensor: ... - def slogdet(self) -> torch.return_types.slogdet: ... - def smm(self, mat2: Tensor) -> Tensor: ... - @overload - def softmax(self, dim: _int, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def softmax(self, dim: Union[str, ellipsis, None], *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def sort(self, *, stable: Optional[_bool], dim: _int = -1, descending: _bool = False) -> torch.return_types.sort: ... - @overload - def sort(self, dim: _int = -1, descending: _bool = False) -> torch.return_types.sort: ... - @overload - def sort(self, *, stable: Optional[_bool], dim: Union[str, ellipsis, None], descending: _bool = False) -> torch.return_types.sort: ... - @overload - def sort(self, dim: Union[str, ellipsis, None], descending: _bool = False) -> torch.return_types.sort: ... - def sparse_dim(self) -> _int: ... - def sparse_mask(self, mask: Tensor) -> Tensor: ... - def sparse_resize_(self, size: _size, sparse_dim: _int, dense_dim: _int) -> Tensor: ... - def sparse_resize_and_clear_(self, size: _size, sparse_dim: _int, dense_dim: _int) -> Tensor: ... - @overload - def split(self, split_size: _int, dim: _int = 0) -> Sequence[Tensor]: ... - @overload - def split(self, split_size: Tuple[_int, ...], dim: _int = 0) -> Sequence[Tensor]: ... - def split_with_sizes(self, split_sizes: Sequence[Union[_int, SymInt]], dim: _int = 0) -> List[Tensor]: ... - def sqrt(self) -> Tensor: ... - def sqrt_(self) -> Tensor: ... - def square(self) -> Tensor: ... - def square_(self) -> Tensor: ... - @overload - def squeeze(self) -> Tensor: ... - @overload - def squeeze(self, dim: _int) -> Tensor: ... - @overload - def squeeze(self, dim: _size) -> Tensor: ... - @overload - def squeeze(self, *dim: _int) -> Tensor: ... - @overload - def squeeze(self, dim: Union[str, ellipsis, None]) -> Tensor: ... - @overload - def squeeze_(self) -> Tensor: ... - @overload - def squeeze_(self, dim: _int) -> Tensor: ... - @overload - def squeeze_(self, dim: _size) -> Tensor: ... - @overload - def squeeze_(self, *dim: _int) -> Tensor: ... - @overload - def squeeze_(self, dim: Union[str, ellipsis, None]) -> Tensor: ... - def sspaddmm(self, mat1: Tensor, mat2: Tensor, *, beta: Union[Number, _complex] = 1, alpha: Union[Number, _complex] = 1) -> Tensor: ... - @overload - def std(self, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False) -> Tensor: ... - @overload - def std(self, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tensor: ... - @overload - def std(self, unbiased: _bool = True) -> Tensor: ... - @overload - def std(self, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False) -> Tensor: ... - @overload - def std(self, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tensor: ... - def untyped_storage(self) -> UntypedStorage: ... - def storage_offset(self) -> _int: ... - def storage_type(self) -> Storage: ... - @overload - def stride(self, dim: None = None) -> Tuple[_int, ...]: ... - @overload - def stride(self, dim: _int) -> _int: ... - def sub(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat], *, alpha: Optional[Number] = 1, out: Optional[Tensor] = None) -> Tensor: ... - def sub_(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat], *, alpha: Optional[Number] = 1) -> Tensor: ... - @overload - def subtract(self, other: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: ... - @overload - def subtract(self, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: ... - @overload - def subtract_(self, other: Tensor, *, alpha: Union[Number, _complex] = 1) -> Tensor: ... - @overload - def subtract_(self, other: Union[Number, _complex], alpha: Union[Number, _complex] = 1) -> Tensor: ... - @overload - def sum(self, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def sum(self, dim: Optional[Union[_int, _size]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def sum(self, dim: Sequence[Union[str, ellipsis, None]], keepdim: _bool = False, *, dtype: Optional[_dtype] = None) -> Tensor: ... - @overload - def sum_to_size(self, size: Sequence[Union[_int, SymInt]]) -> Tensor: ... - @overload - def sum_to_size(self, *size: _int) -> Tensor: ... - def svd(self, some: _bool = True, compute_uv: _bool = True) -> torch.return_types.svd: ... - def swapaxes(self, axis0: _int, axis1: _int) -> Tensor: ... - def swapaxes_(self, axis0: _int, axis1: _int) -> Tensor: ... - def swapdims(self, dim0: _int, dim1: _int) -> Tensor: ... - def swapdims_(self, dim0: _int, dim1: _int) -> Tensor: ... - def t(self) -> Tensor: ... - def t_(self) -> Tensor: ... - def take(self, index: Tensor) -> Tensor: ... - def take_along_dim(self, indices: Tensor, dim: Optional[_int] = None) -> Tensor: ... - def tan(self) -> Tensor: ... - def tan_(self) -> Tensor: ... - def tanh(self) -> Tensor: ... - def tanh_(self) -> Tensor: ... - @overload - def tensor_split(self, indices: Sequence[Union[_int, SymInt]], dim: _int = 0) -> List[Tensor]: ... - @overload - def tensor_split(self, tensor_indices_or_sections: Tensor, dim: _int = 0) -> List[Tensor]: ... - @overload - def tensor_split(self, sections: Union[_int, SymInt], dim: _int = 0) -> List[Tensor]: ... - @overload - def tile(self, dims: Sequence[Union[_int, SymInt]]) -> Tensor: ... - @overload - def tile(self, *dims: _int) -> Tensor: ... - @overload - def to(self, dtype: _dtype, non_blocking: _bool = False, copy: _bool = False, *, memory_format: Optional[torch.memory_format] = None) -> Tensor: ... - @overload - def to(self, device: Optional[DeviceLikeType] = None, dtype: Optional[_dtype] = None, non_blocking: _bool = False, copy: _bool = False, *, memory_format: Optional[torch.memory_format] = None) -> Tensor: ... - @overload - def to(self, other: Tensor, non_blocking: _bool = False, copy: _bool = False, *, memory_format: Optional[torch.memory_format] = None) -> Tensor: ... - def to_dense(self, dtype: Optional[_dtype] = None, *, masked_grad: Optional[_bool] = None) -> Tensor: ... - def to_mkldnn(self, dtype: Optional[_dtype] = None) -> Tensor: ... - def to_padded_tensor(self, padding: _float, output_size: Optional[Sequence[Union[_int, SymInt]]] = None) -> Tensor: ... - @overload - def to_sparse(self, *, layout: Optional[_layout] = None, blocksize: Optional[Union[_int, _size]] = None, dense_dim: Optional[_int] = None) -> Tensor: ... - @overload - def to_sparse(self, sparse_dim: _int) -> Tensor: ... - def to_sparse_bsc(self, blocksize: Union[_int, _size], dense_dim: Optional[_int] = None) -> Tensor: ... - def to_sparse_bsr(self, blocksize: Union[_int, _size], dense_dim: Optional[_int] = None) -> Tensor: ... - def to_sparse_csc(self, dense_dim: Optional[_int] = None) -> Tensor: ... - def to_sparse_csr(self, dense_dim: Optional[_int] = None) -> Tensor: ... - def tolist(self) -> List: ... - def topk(self, k: Union[_int, SymInt], dim: _int = -1, largest: _bool = True, sorted: _bool = True) -> torch.return_types.topk: ... - def trace(self) -> Tensor: ... - @overload - def transpose(self, dim0: _int, dim1: _int) -> Tensor: ... - @overload - def transpose(self, dim0: Union[str, ellipsis, None], dim1: Union[str, ellipsis, None]) -> Tensor: ... - def transpose_(self, dim0: _int, dim1: _int) -> Tensor: ... - def triangular_solve(self, A: Tensor, upper: _bool = True, transpose: _bool = False, unitriangular: _bool = False) -> torch.return_types.triangular_solve: ... - def tril(self, diagonal: _int = 0) -> Tensor: ... - def tril_(self, diagonal: _int = 0) -> Tensor: ... - def triu(self, diagonal: _int = 0) -> Tensor: ... - def triu_(self, diagonal: _int = 0) -> Tensor: ... - def true_divide(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat], *, out: Optional[Tensor] = None) -> Tensor: ... - def true_divide_(self, other: Union[Tensor, Number, torch.SymInt, torch.SymFloat]) -> Tensor: ... - def trunc(self) -> Tensor: ... - def trunc_(self) -> Tensor: ... - @overload - def type(self, dtype: None = None, non_blocking: _bool = False) -> str: ... - @overload - def type(self, dtype: Union[str, _dtype], non_blocking: _bool = False) -> Tensor: ... - def type_as(self, other: Tensor) -> Tensor: ... - @overload - def unbind(self, dim: _int = 0) -> List[Tensor]: ... - @overload - def unbind(self, dim: Union[str, ellipsis, None]) -> List[Tensor]: ... - @overload - def unflatten(self, dim: Union[str, ellipsis, None], sizes: Sequence[Union[_int, SymInt]], names: Sequence[Union[str, ellipsis, None]]) -> Tensor: ... - @overload - def unflatten(self, dim: _int, sizes: Sequence[Union[_int, SymInt]]) -> Tensor: ... - def unfold(self, dimension: _int, size: _int, step: _int) -> Tensor: ... - def uniform_(self, from_: _float = 0, to: _float = 1, *, generator: Optional[Generator] = None) -> Tensor: ... - def unsafe_chunk(self, chunks: _int, dim: _int = 0) -> List[Tensor]: ... - def unsafe_split(self, split_size: Union[_int, SymInt], dim: _int = 0) -> List[Tensor]: ... - def unsafe_split_with_sizes(self, split_sizes: Sequence[Union[_int, SymInt]], dim: _int = 0) -> List[Tensor]: ... - def unsqueeze(self, dim: _int) -> Tensor: ... - def unsqueeze_(self, dim: _int) -> Tensor: ... - def values(self) -> Tensor: ... - @overload - def var(self, dim: Optional[Union[_int, _size]], unbiased: _bool = True, keepdim: _bool = False) -> Tensor: ... - @overload - def var(self, dim: Optional[Union[_int, _size]] = None, *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tensor: ... - @overload - def var(self, unbiased: _bool = True) -> Tensor: ... - @overload - def var(self, dim: Sequence[Union[str, ellipsis, None]], unbiased: _bool = True, keepdim: _bool = False) -> Tensor: ... - @overload - def var(self, dim: Sequence[Union[str, ellipsis, None]], *, correction: Optional[Union[Number, _complex]] = None, keepdim: _bool = False) -> Tensor: ... - def vdot(self, other: Tensor) -> Tensor: ... - @overload - def view(self, dtype: _dtype) -> Tensor: ... - @overload - def view(self, size: Sequence[Union[_int, SymInt]]) -> Tensor: ... - @overload - def view(self, *size: _int) -> Tensor: ... - def view_as(self, other: Tensor) -> Tensor: ... - @overload - def vsplit(self, sections: _int) -> List[Tensor]: ... - @overload - def vsplit(self, indices: _size) -> List[Tensor]: ... - @overload - def vsplit(self, *indices: _int) -> List[Tensor]: ... - @overload - def where(self, condition: Tensor, other: Tensor) -> Tensor: ... - @overload - def where(self, condition: Tensor, other: Union[Number, _complex]) -> Tensor: ... - @overload - def xlogy(self, other: Tensor) -> Tensor: ... - @overload - def xlogy(self, other: Union[Number, _complex]) -> Tensor: ... - @overload - def xlogy_(self, other: Tensor) -> Tensor: ... - @overload - def xlogy_(self, other: Union[Number, _complex]) -> Tensor: ... - def zero_(self) -> Tensor: ... - -_TensorBase = TensorBase - -# Defined in torch/csrc/multiprocessing/init.cpp -def _multiprocessing_init() -> 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_is_available() -> _bool: ... -def _mps_is_on_macos_13_or_newer(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: ... - - -# 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_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: Union[_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() -> Optional[str]: ... -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_set_allocator_settings(env: str) -> None: ... -def _cuda_beginAllocateCurrentStreamToPool(device: _int, mempool_id: Tuple[_int, _int]) -> None: ... -def _cuda_endAllocateCurrentStreamToPool(device: _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_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_memorySnapshot() -> 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, -) -> None: ... -def _cuda_record_memory_history( - enabled: Optional[str], - context: Optional[str], - stacks: str, - max_entries -) -> None: ... -def _cuda_isHistoryEnabled() -> _bool: ... - -def _cuda_getAllocatorBackend() -> str: ... -class _cuda_CUDAAllocator_AllocatorState: - pass -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 _construct_CUDA_Tensor_From_Storage_And_Metadata(metadata: dict, storage: Storage) -> Tensor: ... -def _storage_Use_Count(storage_ptr: _int) -> _int: ... -def _set_storage_access_error_msg(t: Tensor, 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, Union[_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: Optional[Sequence[_CudaStreamBase]], - comms: Optional[Sequence[object]], -) -> None: ... -def _nccl_all_reduce( - input: Sequence[Tensor], - output: Sequence[Tensor], - op: _int, - streams: Optional[Sequence[_CudaStreamBase]], - comms: Optional[Sequence[object]], -) -> None: ... -def _nccl_broadcast( - input: Sequence[Tensor], - root: _int, - streams: Optional[Sequence[_CudaStreamBase]], - comms: Optional[Sequence[object]], -) -> None: ... -def _nccl_all_gather( - input: Sequence[Tensor], - output: Sequence[Tensor], - streams: Optional[Sequence[_CudaStreamBase]], - comms: Optional[Sequence[object]], -) -> None: ... -def _nccl_reduce_scatter( - input: Sequence[Tensor], - output: Sequence[Tensor], - op: _int, - streams: Optional[Sequence[_CudaStreamBase]], - comms: Optional[Sequence[object]], -) -> None: ... -def _rocm_is_backward_pass() -> _bool: ... - -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 - -# Functions related to SDPA -class _SDPAParams: - query: Tensor - key: Tensor - value: Tensor - attn_mask: Optional[Tensor] - dropout: _float - is_causal: _bool - def __init__( - self, - query: Tensor, - key: Tensor, - value: Tensor, - attn_mask: Optional[Tensor], - dropout: _float, - is_causal: _bool) -> None: ... - -class _SDPBackend(Enum): - ERROR = -1 - MATH = 0 - FLASH_ATTENTION = 1 - EFFICIENT_ATTENTION = 2 - -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/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: Optional[List[_int]], - dim: _int, - streams: Optional[List[Stream]], -) -> List[Tensor]: ... -def _scatter_out( - tensor: Tensor, - out_tensors: List[Tensor], - dim: _int, - streams: Optional[List[Stream]], -) -> List[Tensor]: ... -def _gather( - tensors: List[Tensor], - dim: _int, - destination_index: Optional[_int], -) -> 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__( - self, - priority: _int = 0, - stream_id: _int = 0, - device_index: _int = 0, - stream_ptr: _int = 0, - ) -> _CudaStreamBase: ... - 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, - ) -> _CudaEventBase: ... - @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 capture_begin(self, pool: Optional[Tuple[_int, _int]] = ..., capture_error_mode: str = "global") -> None: ... - def capture_end(self) -> 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 _cuda_isCurrentStreamCapturing() -> _bool: ... -def _graph_pool_handle() -> Tuple[_int, _int]: ... - -# 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/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 = 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[Optional[_int]]) -> JitType: ... - def kind(self) -> str: ... - def scalarType(self) -> Optional[str]: ... - def getElementType(self) -> JitType: ... - def dtype(self) -> Optional[_dtype]: ... - -class InferredType: - def __init__(self, arg: Union[JitType, str]): ... - def type(self) -> JitType: ... - def success(self) -> _bool: ... - def reason(self) -> str: ... - -R = TypeVar("R", bound=JitType) - -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[Optional[JitType]]) -> 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: ... - -class InterfaceType(JitType): - def __init__(self, qualified_name: str) -> None: ... - def getMethod(self, name: str) -> Optional[FunctionSchema]: ... - def getMethodNames(self) -> List[str]: ... - -class OptionalType(JitType, Generic[R]): - def __init__(self, a: JitType) -> None: ... - def getElementType(self) -> JitType: ... - @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: Optional[List[Optional[_int]]]) -> TensorType: ... - def sizes(self) -> Optional[List[_int]]: ... - def varyingSizes(self) -> Optional[List[Optional[_int]]]: ... - def strides(self) -> Optional[List[_int]]: ... - def device(self) -> Optional[_device]: ... - def dim(self) -> _int: ... - def dtype(self) -> Optional[_dtype]: ... - @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: ... -def _activate_cuda_trace() -> None: ... - -# Defined in torch/csrc/Module.cpp -def _current_graph_task_id() -> _int: ... -def _current_autograd_node() -> _Node: ... - -# Defined in torch/csrc/Exceptions.cpp -class _OutOfMemoryError(RuntimeError): ... -class _DistError(RuntimeError): ... -class _DistBackendError(RuntimeError): ... -class _DistStoreError(RuntimeError): ... -class _DistNetworkError(RuntimeError): ... - -# Defined in torch/csrc/profiler/init.cpp -class CapturedTraceback: - pass -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: ...