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Refactor the ops PyTorch adapter，cleanup for csrc/torch_binding.cpp (#6732)

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### What this PR does / why we need it?
Refactor the ops PyTorch adapter，cleanup for csrc/torch_binding.cpp,
more details see
https://github.com/vllm-project/vllm-ascend/issues/6486

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
install the new package to test the new modification, here is the
result:


- vLLM version: v0.15.0
- vLLM main:
9562912cea

---------

Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Signed-off-by: luomin2005 <luomin2005@huawei.com>
Co-authored-by: liziyu <56102866+liziyu179@users.noreply.github.com>
Co-authored-by: wangxiaoteng <wangxiaoteng@huawei.com>
This commit is contained in:
luomin2005
2026-02-24 09:12:43 +08:00
committed by GitHub
parent f0caeeadcb
commit f41eeeb11e
15 changed files with 1037 additions and 735 deletions
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54
csrc/batch_matmul_transpose/batch_matmul_transpose_torch_adpt.h Normal file
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/*
* Copyright (c) Huawei Technologies Co., Ltd. 2026. All rights reserved.
*
* 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.
*/
#ifndef BATCH_MATMUL_TRANSPOSE_TORCH_ADPT_H
#define BATCH_MATMUL_TRANSPOSE_TORCH_ADPT_H
#include "op_host/batch_matmul_transpose.h"
namespace vllm_ascend {
void batch_matmul_transpose(const at::Tensor &tensor_a, const at::Tensor &tensor_b, at::Tensor &tensor_c,
c10::optional<c10::string_view> format_mode,
c10::optional<c10::string_view> quant_mode)
{
auto [tiling_tensor, block_dim] = bmm_trans::batch_matmul_transpose_tiling(
tensor_a,
tensor_b,
tensor_c,
format_mode,
quant_mode
);
void *gm_a = tensor_a.data_ptr();
void *gm_b = tensor_b.data_ptr();
void *gm_c = tensor_c.data_ptr();
void *gm_tiling_data = tiling_tensor.data_ptr();
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("batch_matmul_transpose");
cmd.SetCustomHandler([stream, gm_a, gm_b, gm_c, gm_tiling_data,
block_dim]() -> int {
batch_matmul_transpose_impl(stream, gm_a, gm_b, gm_c, gm_tiling_data,
block_dim);
return 0;
});
cmd.Run();
return;
}
}
#endif