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56935684c3
------ - [Generic TypeAlias (PEP 585)](https://peps.python.org/pep-0585): e.g. `typing.List[T] -> list[T]`, `typing.Dict[KT, VT] -> dict[KT, VT]`, `typing.Type[T] -> type[T]`. - [Union Type (PEP 604)](https://peps.python.org/pep-0604): e.g. `Union[X, Y] -> X | Y`, `Optional[X] -> X | None`, `Optional[Union[X, Y]] -> X | Y | None`. Note that in `.pyi` stub files, we do not need `from __future__ import annotations`. So this PR does not violate issue #117449: - #117449 Pull Request resolved: https://github.com/pytorch/pytorch/pull/129419 Approved by: https://github.com/ezyang ghstack dependencies: #129375, #129376
84 lines
3.2 KiB
Python
84 lines
3.2 KiB
Python
# mypy: allow-untyped-defs
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from enum import Enum
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from torch import Tensor
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# Defined in torch/csrc/functorch/init.cpp
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def _set_dynamic_layer_keys_included(included: bool) -> None: ...
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def get_unwrapped(tensor: Tensor) -> Tensor: ...
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def is_batchedtensor(tensor: Tensor) -> bool: ...
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def is_functionaltensor(tensor: Tensor) -> bool: ...
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def is_functorch_wrapped_tensor(tensor: Tensor) -> bool: ...
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def is_gradtrackingtensor(tensor: Tensor) -> bool: ...
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def is_legacy_batchedtensor(tensor: Tensor) -> bool: ...
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def maybe_get_bdim(tensor: Tensor) -> int: ...
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def maybe_get_level(tensor: Tensor) -> int: ...
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def maybe_current_level() -> int | None: ...
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def unwrap_if_dead(tensor: Tensor) -> Tensor: ...
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def _unwrap_for_grad(tensor: Tensor, level: int) -> Tensor: ...
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def _wrap_for_grad(tensor: Tensor, level: int) -> Tensor: ...
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def _unwrap_batched(tensor: Tensor, level: int) -> tuple[Tensor, int | None]: ...
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def current_level() -> int: ...
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def count_jvp_interpreters() -> int: ...
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def _add_batch_dim(tensor: Tensor, bdim: int, level: int) -> Tensor: ...
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def set_single_level_autograd_function_allowed(allowed: bool) -> None: ...
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def get_single_level_autograd_function_allowed() -> bool: ...
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def _unwrap_functional_tensor(tensor: Tensor, reapply_views: bool) -> Tensor: ...
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def _wrap_functional_tensor(tensor: Tensor, level: int) -> Tensor: ...
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def _vmap_increment_nesting(batch_size: int, randomness: str) -> int: ...
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def _vmap_decrement_nesting() -> int: ...
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def _grad_increment_nesting() -> int: ...
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def _grad_decrement_nesting() -> int: ...
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def _jvp_increment_nesting() -> int: ...
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def _jvp_decrement_nesting() -> int: ...
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# Defined in aten/src/ATen/functorch/Interpreter.h
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class TransformType(Enum):
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Torch: TransformType = ...
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Vmap: TransformType = ...
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Grad: TransformType = ...
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Jvp: TransformType = ...
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Functionalize: TransformType = ...
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class RandomnessType(Enum):
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Error: TransformType = ...
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Same: TransformType = ...
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Different: TransformType = ...
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class CInterpreter:
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def key(self) -> TransformType: ...
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def level(self) -> int: ...
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class CGradInterpreterPtr:
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def __init__(self, interpreter: CInterpreter) -> None: ...
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def lift(self, Tensor) -> Tensor: ...
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def prevGradMode(self) -> bool: ...
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class CJvpInterpreterPtr:
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def __init__(self, interpreter: CInterpreter) -> None: ...
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def lift(self, Tensor) -> Tensor: ...
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def prevFwdGradMode(self) -> bool: ...
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class CFunctionalizeInterpreterPtr:
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def __init__(self, interpreter: CInterpreter) -> None: ...
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def key(self) -> TransformType: ...
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def level(self) -> int: ...
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def functionalizeAddBackViews(self) -> bool: ...
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class CVmapInterpreterPtr:
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def __init__(self, interpreter: CInterpreter) -> None: ...
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def key(self) -> TransformType: ...
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def level(self) -> int: ...
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def batchSize(self) -> int: ...
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def randomness(self) -> RandomnessType: ...
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class DynamicLayer: ...
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def get_dynamic_layer_stack_depth() -> int: ...
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def get_interpreter_stack() -> list[CInterpreter]: ...
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def peek_interpreter_stack() -> CInterpreter: ...
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def pop_dynamic_layer_stack() -> DynamicLayer: ...
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def pop_dynamic_layer_stack_and_undo_to_depth(int) -> None: ...
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def push_dynamic_layer_stack(dl: DynamicLayer) -> int: ...
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