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https://github.com/zebrajr/pytorch.git
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90e532f3ef
Test Plan: revert-hammer
Differential Revision:
D27708346 (51d0212d0f)
Original commit changeset: 2289edd641f3
fbshipit-source-id: 86711c07db19833b9e772c558e12accba1432499
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
from tools.codegen.model import (Argument, FunctionSchema, Return,
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SelfArgument, TensorOptionsArguments, Type,
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assert_never)
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from tools.codegen.api.types import ArgName, Binding, NamedCType, CType
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from tools.codegen.api import cpp
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import itertools
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from typing import Sequence, List, Union
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# This file describes the translation of JIT schema to the dispatcher
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# API, the *unboxed* calling convention by which invocations through
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# the dispatcher are made. Historically, the dispatcher API matched
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# the C++ API, but with the establishment of the boxed API, we've
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# made changes to the dispatcher API to so that the unboxed API
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# better aligns with the boxed API. The dispatcher API hooks heavily
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# into our template based boxing/unboxing machinery, so changes
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# to this convention will usually need template updates too.
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#
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# Prominent characteristics of the dispatcher API:
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#
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# - dtype, layout, device and pin_memory are represented as separate
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# arguments.
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#
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def name(func: FunctionSchema) -> str:
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return cpp.name(func)
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def argumenttype_type(t: Type, *, mutable: bool, binds: ArgName) -> NamedCType:
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# This is a faux amis. If it makes sense in the future to add
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# more special cases here, or invert things so cpp.argument_type
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# calls this, or just completely inline the function, please do
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# it.
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return cpp.argumenttype_type(t, mutable=mutable, binds=binds)
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def argument_type(a: Argument, *, binds: ArgName) -> NamedCType:
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return argumenttype_type(a.type, mutable=a.is_write, binds=binds)
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def returns_type(rs: Sequence[Return]) -> CType:
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# At present, there is no difference. But there could be!
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return cpp.returns_type(rs)
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def argument(
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a: Union[Argument, TensorOptionsArguments, SelfArgument]
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) -> List[Binding]:
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if isinstance(a, Argument):
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return [Binding(
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nctype=argument_type(a, binds=a.name),
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name=a.name,
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argument=a,
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)]
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elif isinstance(a, SelfArgument):
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return argument(a.argument)
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elif isinstance(a, TensorOptionsArguments):
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return argument(a.dtype) + argument(a.layout) + argument(a.device) + argument(a.pin_memory)
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else:
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assert_never(a)
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def arguments(func: FunctionSchema) -> List[Binding]:
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return [
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r for a in itertools.chain(
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func.arguments.positional,
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func.arguments.kwarg_only,
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func.arguments.out
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) for r in argument(a)
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]
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