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ffd58293f7
Make Functorch interpreters serializable most of the time, so that we can save the guards on functorch states. ## Test Cases: 0. torch.compile() without functorch layers present. Guard should fail with any layer being pushed. 1. torch.compile() nested in vmap. 2. torch.compile() nested in grad. 3. torch.compile() nested in jvp + vmap 4. torch.compile() nested functionalize 5. torch.compile() nested in vmap + grad Differential Revision: [D74008787](https://our.internmc.facebook.com/intern/diff/D74008787/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/152616 Approved by: https://github.com/zou3519 ghstack dependencies: #152615
87 lines
3.2 KiB
Python
87 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 = ...
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Vmap = ...
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Grad = ...
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Jvp = ...
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Functionalize = ...
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class RandomnessType(Enum):
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Error = ...
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Same = ...
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Different = ...
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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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def serialize(self) -> bytes: ...
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@staticmethod
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def deserialize(bytes) -> CInterpreter: ...
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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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