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chore: import upstream snapshot with attribution
2026-07-13 12:32:31 +08:00

62 lines
2.3 KiB
Python

# Copyright (c) 2026 LightSeek Foundation
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from __future__ import annotations
import importlib
from collections.abc import Callable
import torch
import torch.library
from torch.library import Library
tokenspeed_lib = Library("tokenspeed", "FRAGMENT")
def direct_register_custom_op(
op_name: str,
op_func: Callable,
mutates_args: list[str],
fake_impl: Callable | None = None,
target_lib: Library | None = None,
) -> None:
"""Register a low-overhead torch custom op in the TokenSpeed namespace."""
target = target_lib or tokenspeed_lib
lib_name = getattr(getattr(target, "m", None), "name", "tokenspeed")
try:
if hasattr(torch.ops, lib_name) and hasattr(
getattr(torch.ops, lib_name), op_name
):
return
except (AttributeError, RuntimeError):
pass
if hasattr(torch.library, "infer_schema"):
schema_str = torch.library.infer_schema(op_func, mutates_args=mutates_args)
else:
custom_op_impl = importlib.import_module("torch._custom_op.impl")
schema_str = custom_op_impl.infer_schema(op_func, mutates_args)
target.define(op_name + schema_str)
target.impl(op_name, op_func, "CUDA")
if fake_impl is not None:
target._register_fake(op_name, fake_impl)