mirror of
https://github.com/zebrajr/pytorch.git
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ghstack-source-id: 859e07e54a
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56835
61 lines
2.4 KiB
Python
61 lines
2.4 KiB
Python
from tools.codegen.utils import S, T, context
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from tools.codegen.model import (NativeFunction, NativeFunctionsGroup, ExternalBackendFunction,
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ExternalBackendFunctionsGroup)
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import tools.codegen.local as local
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import functools
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from typing import TypeVar, Union, Iterator, Callable
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import contextlib
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# Helper functions for defining generators on things in the model
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F = TypeVar(
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'F',
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NativeFunction,
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NativeFunctionsGroup,
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ExternalBackendFunction,
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ExternalBackendFunctionsGroup,
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Union[NativeFunction, NativeFunctionsGroup],
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Union[NativeFunction, ExternalBackendFunction],
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Union[ExternalBackendFunctionsGroup, ExternalBackendFunction],
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Union[NativeFunction, NativeFunctionsGroup, ExternalBackendFunction, ExternalBackendFunctionsGroup]
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)
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@contextlib.contextmanager
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def native_function_manager(g: Union[
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NativeFunctionsGroup, NativeFunction, ExternalBackendFunction, ExternalBackendFunctionsGroup]) -> Iterator[None]:
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if isinstance(g, ExternalBackendFunctionsGroup):
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f = g.primary.native_function
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elif isinstance(g, ExternalBackendFunction):
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f = g.native_function
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elif isinstance(g, NativeFunctionsGroup):
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# By default, we associate all errors with structured native functions
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# with the out variant. In some cases, it might be better to have
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# a more specific place to hang things; if so, use
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# native_function_manager again on the inside
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f = g.out
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else:
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f = g
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with context(f'in native_functions.yaml line {f.loc}:\n {f.func}'):
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with local.parametrize(use_const_ref_for_mutable_tensors=f.use_const_ref_for_mutable_tensors):
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yield
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# Given a function that operates on NativeFunction, wrap it into a new function
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# that sets some appropriate context managers for that native function.
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# YOU MUST WRAP FUNCTIONS IN THIS for calls to api modules to be sound
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# (you will get an error if we try to access the local variables without having
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# set them).
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def with_native_function(func: Callable[[F], T]) -> Callable[[F], T]:
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@functools.wraps(func)
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def wrapper(f: F) -> T:
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with native_function_manager(f):
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return func(f)
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return wrapper
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def method_with_native_function(func: Callable[[S, F], T]) -> Callable[[S, F], T]:
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@functools.wraps(func)
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def wrapper(slf: S, f: F) -> T:
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with native_function_manager(f):
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return func(slf, f)
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return wrapper
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