xc-llm-ascend/vllm_ascend/patch/worker/patch_unquantized_gemm.py

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import torch
import vllm.model_executor.layers.utils
from vllm.utils.torch_utils import direct_register_custom_op


def unquantized_gemm(
    x: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor | None = None,
) -> torch.Tensor:
    return torch.nn.functional.linear(x, weight, bias)


def unquantized_gemm_fake(
    x: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor | None = None,
) -> torch.Tensor:
    output_shape = (x.shape[0], weight.shape[0])
    return torch.empty(output_shape, dtype=x.dtype, device=x.device)


direct_register_custom_op(
    op_name="unquantized_gemm",
    op_func=unquantized_gemm,
    fake_impl=unquantized_gemm_fake,
    mutates_args=[],
    dispatch_key="PrivateUse1",
)


def default_unquantized_gemm(
    layer: torch.nn.Module,
    x: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor | None = None,
) -> torch.Tensor:
    if x.device.type == "npu":
        return torch.ops.vllm.unquantized_gemm(x, weight, bias)
    else:
        return torch.nn.functional.linear(x, weight, bias)


vllm.model_executor.layers.utils.default_unquantized_gemm = default_unquantized_gemm