f41eeeb11e
### 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>
50 lines
1.8 KiB
C++
50 lines
1.8 KiB
C++
/*
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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 DISPATCH_LAYOUT_TORCH_ADPT_H
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#define DISPATCH_LAYOUT_TORCH_ADPT_H
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namespace vllm_ascend {
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std::tuple<at::Tensor, at::Tensor, at::Tensor> get_dispatch_layout(const at::Tensor& topk_idx, int64_t num_experts,
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int64_t num_ranks) {
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TORCH_BIND_ASSERT(topk_idx.dim() == 2);
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TORCH_BIND_ASSERT(topk_idx.is_contiguous());
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TORCH_BIND_ASSERT(num_experts > 0);
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const int num_tokens = topk_idx.size(0);
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const int num_topk = topk_idx.size(1);
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auto device = topk_idx.device();
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auto num_tokens_per_expert = at::zeros({num_experts}, at::dtype(at::kInt).device(device));
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auto num_tokens_per_rank = at::zeros({num_ranks}, at::dtype(at::kInt).device(device));
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auto is_token_in_rank = at::zeros({num_tokens, num_ranks}, at::dtype(at::kInt).device(device));
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EXEC_NPU_CMD(aclnnDispatchLayout,
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topk_idx,
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num_tokens,
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num_ranks,
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num_experts,
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num_topk,
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num_tokens_per_rank,
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num_tokens_per_expert,
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is_token_in_rank);
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auto is_token_in_rank_bool = is_token_in_rank.to(at::kBool);
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return std::make_tuple(num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank_bool);
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}
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}
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#endif |