mirror of
https://github.com/pytorch/pytorch.git
synced 2025-11-05 00:14:54 +08:00
4534bf5799
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/46090 ghstack-source-id: 114269272 Test Plan: vs base diff: https://www.internalfb.com/intern/fblearner/details/223884639/ Reviewed By: ezyang Differential Revision: D24219942 fbshipit-source-id: 6f338c7c0dd5adfe2fba8b36ccc340032d3faef8
109 lines
4.0 KiB
Python
109 lines
4.0 KiB
Python
from tools.codegen.model import *
|
|
|
|
from tools.codegen.api.types import TensorOptionsArguments, NativeArgument, ThisArgument
|
|
import tools.codegen.api.cpp as cpp
|
|
from tools.codegen import local
|
|
|
|
from typing import Union, Sequence, Tuple
|
|
|
|
# This file describes the translation of JIT schema to the native functions API.
|
|
# This looks a lot like the C++ API (which makes historical sense, because the
|
|
# idea was you wrote native functions to implement functions in the C++ API),
|
|
# but over time we have evolved the C++ API without actually changing our
|
|
# native:: kernels. The intention is to make native API and dispatcher API
|
|
# line up as closely as possible, since this results in the least overhead
|
|
# (no translation is needed from dispatcher API to native API).
|
|
#
|
|
# When a function is not use_c10_dispatcher: full, the dispatcher API actually
|
|
# coincides with the native:: API (e.g., we do as dumb as pass through as
|
|
# possible).
|
|
|
|
def name(func: FunctionSchema) -> str:
|
|
name = str(func.name.name)
|
|
# TODO: delete this!
|
|
if func.is_out_fn():
|
|
name += '_out'
|
|
if func.name.overload_name:
|
|
name += f'_{func.name.overload_name}'
|
|
return name
|
|
|
|
def argumenttype_type(t: Type, *, mutable: bool) -> str:
|
|
if str(t) == 'Tensor?':
|
|
if mutable:
|
|
return 'Tensor &'
|
|
else:
|
|
return 'const Tensor &'
|
|
elif str(t) == 'Tensor?[]':
|
|
return 'TensorList'
|
|
return cpp.argumenttype_type(t, mutable=mutable)
|
|
|
|
def returns_type(rs: Sequence[Return]) -> str:
|
|
return cpp.returns_type(rs)
|
|
|
|
def argument_type(a: Argument) -> str:
|
|
return argumenttype_type(a.type, mutable=a.is_write)
|
|
|
|
def argument(a: Union[Argument, ThisArgument, TensorOptionsArguments]) -> Sequence[NativeArgument]:
|
|
if isinstance(a, Argument):
|
|
return [NativeArgument(
|
|
type=argument_type(a),
|
|
name=a.name,
|
|
default=cpp.default_expr(a.default, a.type) if a.default is not None else None,
|
|
argument=a,
|
|
)]
|
|
elif isinstance(a, ThisArgument):
|
|
# Erase ThisArgument from the distinction
|
|
return [NativeArgument(
|
|
type=argument_type(a.argument),
|
|
name=a.argument.name,
|
|
default=None,
|
|
argument=a.argument,
|
|
)]
|
|
elif isinstance(a, TensorOptionsArguments):
|
|
if local.use_c10_dispatcher() in [UseC10Dispatcher.hacky_wrapper_for_legacy_signatures,
|
|
UseC10Dispatcher.with_codegenerated_unboxing_wrapper]:
|
|
# TODO: expunge this logic entirely
|
|
default = None
|
|
if all(x.default == "None" for x in a.all()):
|
|
default = '{}'
|
|
elif a.dtype.default == "long":
|
|
default = 'at::kLong' # TODO: this is wrong
|
|
return [NativeArgument(
|
|
type='const TensorOptions &',
|
|
name='options',
|
|
default=default,
|
|
argument=a,
|
|
)]
|
|
else:
|
|
assert local.use_c10_dispatcher() == UseC10Dispatcher.full
|
|
return [
|
|
NativeArgument(
|
|
type='c10::optional<ScalarType>',
|
|
name='dtype',
|
|
default='{}',
|
|
argument=a,
|
|
),
|
|
NativeArgument(
|
|
type='c10::optional<Layout>',
|
|
name='layout',
|
|
default='{}',
|
|
argument=a,
|
|
),
|
|
NativeArgument(
|
|
type='c10::optional<Device>',
|
|
name='device',
|
|
default='{}',
|
|
argument=a,
|
|
),
|
|
NativeArgument(
|
|
type='c10::optional<bool>',
|
|
name='pin_memory',
|
|
default='{}',
|
|
argument=a,
|
|
)]
|
|
else:
|
|
assert_never(a)
|
|
|
|
def arguments(func: FunctionSchema) -> Tuple[NativeArgument, ...]:
|
|
return tuple(i for arg in cpp.group_arguments(func, method=False) for i in argument(arg))
|