Traktor/myenv/Lib/site-packages/torchgen/api/native.py
2024-05-26 05:12:46 +02:00

154 lines
5.0 KiB
Python

from typing import List, Optional, Sequence, Union
from torchgen import local
from torchgen.api import cpp
from torchgen.api.types import (
ArgName,
BaseCType,
Binding,
boolT,
ConstRefCType,
CType,
deviceT,
layoutT,
ListCType,
MutRefCType,
NamedCType,
OptionalCType,
scalarT,
scalarTypeT,
tensorT,
)
from torchgen.model import (
Argument,
FunctionSchema,
Return,
SelfArgument,
TensorOptionsArguments,
Type,
)
from torchgen.utils import assert_never
# 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).
#
# NB: this is symint aware, you will get the non-SymInt variant for some
# dispatch entries and SymInt for others.
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, binds: ArgName, symint: bool
) -> NamedCType:
if str(t) == "Tensor?":
tensor_type: OptionalCType = OptionalCType(BaseCType(tensorT))
if mutable and not local.use_const_ref_for_mutable_tensors():
return NamedCType(binds, MutRefCType(tensor_type))
else:
return NamedCType(binds, ConstRefCType(tensor_type))
elif str(t) == "Tensor?[]":
return NamedCType(
binds, ConstRefCType(ListCType(OptionalCType(BaseCType(tensorT))))
)
elif str(t) == "Scalar":
return NamedCType(binds, ConstRefCType(BaseCType(scalarT)))
elif str(t) == "Scalar?":
return NamedCType(binds, ConstRefCType(OptionalCType(BaseCType(scalarT))))
return cpp.argumenttype_type(t, mutable=mutable, binds=binds, symint=symint)
def returns_type(rs: Sequence[Return], *, symint: bool) -> CType:
return cpp.returns_type(rs, symint=symint)
def argument_type(a: Argument, *, binds: ArgName, symint: bool) -> NamedCType:
return argumenttype_type(a.type, mutable=a.is_write, binds=binds, symint=symint)
def argument(
a: Union[Argument, SelfArgument, TensorOptionsArguments],
*,
is_out: bool,
symint: bool,
) -> List[Binding]:
# Ideally, we NEVER default native functions. However, there are a number
# of functions that call native:: directly and rely on the defaulting
# existing. So for BC, we generate defaults for non-out variants (but not
# for out variants, where it is impossible to generate an appropriate
# default)
should_default = not is_out
if isinstance(a, Argument):
default: Optional[str] = None
if should_default and a.default is not None:
default = cpp.default_expr(a.default, a.type, symint=symint)
return [
Binding(
nctype=argument_type(a, binds=a.name, symint=symint),
name=a.name,
default=default,
argument=a,
)
]
elif isinstance(a, SelfArgument):
# Erase SelfArgument from the distinction
return argument(a.argument, is_out=is_out, symint=symint)
elif isinstance(a, TensorOptionsArguments):
default = None
if should_default:
default = "{}"
# TODO: Not sure why the arguments assigned here are for
# TensorOptionsArguments and not the constituent pieces. It seems
# to matter
return [
Binding(
nctype=NamedCType("dtype", OptionalCType(BaseCType(scalarTypeT))),
name="dtype",
default=default,
argument=a,
),
Binding(
nctype=NamedCType("layout", OptionalCType(BaseCType(layoutT))),
name="layout",
default=default,
argument=a,
),
Binding(
nctype=NamedCType("device", OptionalCType(BaseCType(deviceT))),
name="device",
default=default,
argument=a,
),
Binding(
nctype=NamedCType("pin_memory", OptionalCType(BaseCType(boolT))),
name="pin_memory",
default=default,
argument=a,
),
]
else:
assert_never(a)
def arguments(func: FunctionSchema, *, symint: bool) -> List[Binding]:
args: List[Union[Argument, TensorOptionsArguments, SelfArgument]] = []
args.extend(func.arguments.non_out)
args.extend(func.arguments.out)
return [
r for arg in args for r in argument(arg, symint=symint, is_out=func.is_out_fn())
]