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1211bccc65
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/55351 We incorrectly used `Tensor&` to mean "the underlying TensorImpl cannot be changed", as explained in https://github.com/zdevito/ATen/issues/27#issuecomment-330717839 . This diff gets us on the path to fixing this problem: we have an incremental way to fix individual native functions so that we can apply any handwritten fixes a few at a time. It gets the migration started with the `resize` family of native functions. ghstack-source-id: 127092677 Test Plan: fitsships Reviewed By: ezyang Differential Revision: D27583983 fbshipit-source-id: 4eeeec85f5d268e9d0f1645eb9396914a9f9557f
112 lines
4.7 KiB
Python
112 lines
4.7 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, BaseCType, Binding,
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ConstRefCType, NamedCType, CType, MutRefCType, ListCType,
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OptionalCType, tensorT, scalarT, layoutT,
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deviceT, boolT, scalarTypeT)
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from tools.codegen.api import cpp
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from tools.codegen import local
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from typing import Union, Sequence, List, Optional
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# This file describes the translation of JIT schema to the native functions API.
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# This looks a lot like the C++ API (which makes historical sense, because the
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# idea was you wrote native functions to implement functions in the C++ API),
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# but over time we have evolved the C++ API without actually changing our
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# native:: kernels. The intention is to make native API and dispatcher API
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# line up as closely as possible, since this results in the least overhead
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# (no translation is needed from dispatcher API to native API).
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def name(func: FunctionSchema) -> str:
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name = str(func.name.name)
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# TODO: delete this!
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if func.is_out_fn():
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name += '_out'
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if func.name.overload_name:
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name += f'_{func.name.overload_name}'
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return name
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def argumenttype_type(t: Type, *, mutable: bool, binds: ArgName) -> NamedCType:
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if str(t) == 'Tensor?':
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tensor_type: OptionalCType = OptionalCType(BaseCType(tensorT))
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if mutable and not local.use_const_ref_for_mutable_tensors():
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return NamedCType(binds, MutRefCType(tensor_type))
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else:
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return NamedCType(binds, ConstRefCType(tensor_type))
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elif str(t) == 'Tensor?[]':
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return NamedCType(binds, ConstRefCType(ListCType(OptionalCType(BaseCType(tensorT)))))
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elif str(t) == 'Scalar':
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return NamedCType(binds, ConstRefCType(BaseCType(scalarT)))
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elif str(t) == 'Scalar?':
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return NamedCType(binds, ConstRefCType(OptionalCType(BaseCType(scalarT))))
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return cpp.argumenttype_type(t, mutable=mutable, binds=binds)
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def returns_type(rs: Sequence[Return]) -> CType:
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return cpp.returns_type(rs)
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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 argument(a: Union[Argument, SelfArgument, TensorOptionsArguments], *, is_out: bool) -> List[Binding]:
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# Ideally, we NEVER default native functions. However, there are a number
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# of functions that call native:: directly and rely on the defaulting
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# existing. So for BC, we generate defaults for non-out variants (but not
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# for out variants, where it is impossible to generate an appropriate
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# default)
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should_default = not is_out
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if isinstance(a, Argument):
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default: Optional[str] = None
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if should_default and a.default is not None:
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default = cpp.default_expr(a.default, a.type)
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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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default=default,
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argument=a,
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)]
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elif isinstance(a, SelfArgument):
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# Erase SelfArgument from the distinction
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return argument(a.argument, is_out=is_out)
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elif isinstance(a, TensorOptionsArguments):
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default = None
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if should_default:
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default = '{}'
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# TODO: Not sure why the arguments assigned here are for
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# TensorOptionsArguments and not the constituent pieces. It seems
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# to matter
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return [
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Binding(
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nctype=NamedCType('dtype', OptionalCType(BaseCType(scalarTypeT))),
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name='dtype',
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default=default,
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argument=a,
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),
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Binding(
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nctype=NamedCType('layout', OptionalCType(BaseCType(layoutT))),
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name='layout',
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default=default,
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argument=a,
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),
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Binding(
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nctype=NamedCType('device', OptionalCType(BaseCType(deviceT))),
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name='device',
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default=default,
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argument=a,
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),
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Binding(
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nctype=NamedCType('pin_memory', OptionalCType(BaseCType(boolT))),
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name='pin_memory',
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default=default,
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argument=a,
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)]
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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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args: List[Union[Argument, TensorOptionsArguments, SelfArgument]] = []
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args.extend(func.arguments.non_out)
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args.extend(func.arguments.out)
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return [r for arg in args for r in argument(arg, is_out=func.is_out_fn())]
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