Refactor the ops PyTorch adapter,cleanup for csrc/torch_binding.cpp (#6732)
### 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:
@@ -0,0 +1,40 @@
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/*
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* Copyright (c) Huawei Technologies Co., Ltd. 2026. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef APPLY_TOP_K_TOP_P_CUSTOM_TORCH_ADPT_H
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#define APPLY_TOP_K_TOP_P_CUSTOM_TORCH_ADPT_H
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namespace vllm_ascend {
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at::Tensor npu_apply_top_k_top_p(
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const at::Tensor& logits,
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const c10::optional<at::Tensor>& p,
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const c10::optional<at::Tensor>& k)
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{
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TORCH_CHECK(p.has_value() || k.has_value(),
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"apply_top_k_top_p: p and k cannot be None at the same time.");
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at::Tensor out = at::empty_like(logits);
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EXEC_NPU_CMD(
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aclnnApplyTopKTopPCustom,
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logits,
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p,
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k,
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out);
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return out;
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}
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}
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#endif
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