pytorch/tools/codegen/api/native.py

from tools.codegen.model import (Argument, FunctionSchema, Return,
                                 SelfArgument, TensorOptionsArguments, Type,
                                 assert_never)

from tools.codegen.api.types import (ArgName, BaseCType, Binding,
                                     ConstRefCType, NamedCType, CType, MutRefCType, ListCType,
                                     OptionalCType, tensorT, scalarT, layoutT,
                                     deviceT, boolT, scalarTypeT)
from tools.codegen.api import cpp

from typing import Union, Sequence, List, Optional

# 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).

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) -> NamedCType:
    if str(t) == 'Tensor?':
        tensor_type: OptionalCType = OptionalCType(BaseCType(tensorT))
        if mutable:
            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)

def returns_type(rs: Sequence[Return]) -> CType:
    return cpp.returns_type(rs)

def argument_type(a: Argument, *, binds: ArgName) -> NamedCType:
    return argumenttype_type(a.type, mutable=a.is_write, binds=binds)

def argument(a: Union[Argument, SelfArgument, TensorOptionsArguments], *, is_out: 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)
        return [Binding(
            nctype=argument_type(a, binds=a.name),
            name=a.name,
            default=default,
            argument=a,
        )]
    elif isinstance(a, SelfArgument):
        # Erase SelfArgument from the distinction
        return argument(a.argument, is_out=is_out)
    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) -> 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, is_out=func.is_out_fn())]