build/torch25-cxx11-cu118-x86_64-linux/moe/utils/custom_ops.py

from typing import Callable, Optional

import torch
from torch.library import Library

from .._ops import ops
from ..platforms import current_platform


# Some backends use pytorch version < 2.4.0 which doesn't
# support `torch.library.custom_op`.
def supports_custom_op() -> bool:
    return hasattr(torch.library, "custom_op")


# TODO: we probably need a shorthand for the ops name.
vllm_lib = Library(ops.__name__.split(".")[-1], "FRAGMENT")


def direct_register_custom_op(
    op_name: str,
    op_func: Callable,
    mutates_args: list[str],
    fake_impl: Optional[Callable] = None,
    target_lib: Optional[Library] = None,
    dispatch_key: str = "CUDA",
):
    """
    `torch.library.custom_op` can have significant overhead because it
    needs to consider complicated dispatching logic. This function
    directly registers a custom op and dispatches it to the CUDA backend.
    See https://gist.github.com/youkaichao/ecbea9ec9fc79a45d2adce1784d7a9a5
    for more details.

    By default, the custom op is registered to the vLLM library. If you
    want to register it to a different library, you can pass the library
    object to the `target_lib` argument.

    IMPORTANT: the lifetime of the operator is tied to the lifetime of the
    library object. If you want to bind the operator to a different library,
    make sure the library object is alive when the operator is used.
    """
    if not supports_custom_op():
        from ..platforms import current_platform

        assert not current_platform.is_cuda_alike(), (
            "cuda platform needs torch>=2.4 to support custom op, "
            "chances are you are using an old version of pytorch "
            "or a custom build of pytorch. It is recommended to "
            "use vLLM in a fresh new environment and let it install "
            "the required dependencies."
        )
        return

    import torch.library

    if hasattr(torch.library, "infer_schema"):
        schema_str = torch.library.infer_schema(op_func, mutates_args=mutates_args)
    else:
        # for pytorch 2.4
        import torch._custom_op.impl

        schema_str = torch._custom_op.impl.infer_schema(op_func, mutates_args)
    my_lib = target_lib or vllm_lib
    my_lib.define(op_name + schema_str)
    my_lib.impl(op_name, op_func, dispatch_key=dispatch_key)
    if fake_impl is not None:
        my_lib._register_fake(op_name, fake_impl)