gaussian-splatting-my/envs/gaussian_splatting/Lib/site-packages/torchgen/api/dispatcher.py

from torchgen.model import (
    Argument,
    FunctionSchema,
    Return,
    SelfArgument,
    TensorOptionsArguments,
    Type,
)

from torchgen.api.types import ArgName, Binding, NamedCType, CType
from torchgen.api import cpp
from torchgen.utils import concatMap, assert_never

import itertools
from typing import Sequence, List, Union

# This file describes the translation of JIT schema to the dispatcher
# API, the *unboxed* calling convention by which invocations through
# the dispatcher are made.  Historically, the dispatcher API matched
# the C++ API, but with the establishment of the boxed API, we've
# made changes to the dispatcher API to so that the unboxed API
# better aligns with the boxed API.  The dispatcher API hooks heavily
# into our template based boxing/unboxing machinery, so changes
# to this convention will usually need template updates too.
#
# Prominent characteristics of the dispatcher API:
#
#   - dtype, layout, device and pin_memory are represented as separate
#     arguments.
#


def name(func: FunctionSchema) -> str:
    return cpp.name(func)


def argumenttype_type(
    t: Type, *, mutable: bool, binds: ArgName, remove_non_owning_ref_types: bool = False
) -> NamedCType:
    # This is a faux amis.  If it makes sense in the future to add
    # more special cases here, or invert things so cpp.argument_type
    # calls this, or just completely inline the function, please do
    # it.
    return cpp.argumenttype_type(
        t,
        mutable=mutable,
        binds=binds,
        remove_non_owning_ref_types=remove_non_owning_ref_types,
    )


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


def returns_type(rs: Sequence[Return]) -> CType:
    # At present, there is no difference. But there could be!
    return cpp.returns_type(rs)


def jit_arguments(func: FunctionSchema) -> List[Argument]:
    def to_argument(
        a: Union[Argument, TensorOptionsArguments, SelfArgument]
    ) -> List[Argument]:
        if isinstance(a, Argument):
            return [a]
        elif isinstance(a, SelfArgument):
            return [a.argument]
        elif isinstance(a, TensorOptionsArguments):
            return [a.dtype, a.layout, a.device, a.pin_memory]
        else:
            assert_never(a)

    return list(
        concatMap(
            to_argument,
            itertools.chain(
                func.arguments.positional, func.arguments.kwarg_only, func.arguments.out
            ),
        )
    )


def argument(a: Argument, *, remove_non_owning_ref_types: bool = False) -> Binding:
    return Binding(
        nctype=argument_type(
            a, binds=a.name, remove_non_owning_ref_types=remove_non_owning_ref_types
        ),
        name=a.name,
        argument=a,
    )


def arguments(func: FunctionSchema) -> List[Binding]:
    return [argument(a) for a in jit_arguments(func)]