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https://github.com/zebrajr/pytorch.git
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a408920817
Try to rebase and reland https://github.com/pytorch/pytorch/pull/107062 . One difference compared with previous is to make the DTensor logic same as previously in _clone_input. Pull Request resolved: https://github.com/pytorch/pytorch/pull/107569 Approved by: https://github.com/zou3519
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
from enum import Enum
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from typing import Optional, Tuple
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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 maybe_get_bdim(tensor: Tensor) -> int: ...
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def maybe_get_level(tensor: Tensor) -> int: ...
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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, Optional[int]]: ...
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def current_level() -> 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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# 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): ...
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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): ...
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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): ...
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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): ...
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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 peek_interpreter_stack() -> CInterpreter: ...
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def pop_dynamic_layer_stack() -> DynamicLayer: ...
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def push_dynamic_layer_stack(dl: DynamicLayer) -> int: ...
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