xhs-note-crawling/python/py/Lib/site-packages/torch/_functorch/utils.py

import contextlib
from collections.abc import Generator
from typing import Any, Union

import torch
from torch._C._functorch import (
    get_single_level_autograd_function_allowed,
    set_single_level_autograd_function_allowed,
    unwrap_if_dead,
)
from torch.utils._exposed_in import exposed_in


__all__ = [
    "exposed_in",
    "argnums_t",
    "enable_single_level_autograd_function",
    "unwrap_dead_wrappers",
]


@contextlib.contextmanager
def enable_single_level_autograd_function() -> Generator[None, None, None]:
    try:
        prev_state = get_single_level_autograd_function_allowed()
        set_single_level_autograd_function_allowed(True)
        yield
    finally:
        set_single_level_autograd_function_allowed(prev_state)


def unwrap_dead_wrappers(args: tuple[Any, ...]) -> tuple[Any, ...]:
    # NB: doesn't use tree_map_only for performance reasons
    result = tuple(
        unwrap_if_dead(arg) if isinstance(arg, torch.Tensor) else arg for arg in args
    )
    return result


argnums_t = Union[int, tuple[int, ...]]