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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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74
csrc/moe_gating_top_k/moe_gating_top_k_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 MOE_GATING_TOP_K_TORCH_ADPT_H
#define MOE_GATING_TOP_K_TORCH_ADPT_H
namespace vllm_ascend {
std::tuple<at::Tensor, at::Tensor, at::Tensor> moe_gating_top_k(
const at::Tensor& x,
int64_t k,
int64_t k_group,
int64_t group_count,
int64_t group_select_mode,
int64_t renorm,
int64_t norm_type,
bool out_flag,
double routed_scaling_factor,
double eps,
const c10::optional<at::Tensor>& bias_opt
)
{
TORCH_CHECK(x.dim() == 2, "The x should be 2D");
TORCH_CHECK(
x.scalar_type() == at::kHalf || x.scalar_type() == at::kFloat || x.scalar_type() == at::kBFloat16,
"float16、float32 or bfloat16 tensor expected but got a tensor with dtype: ",
x.scalar_type());
auto x_size = x.sizes();
auto rows = x_size[0];
auto expert_num = x_size[1];
const at::Tensor &bias = c10::value_or_else(bias_opt, [] { return at::Tensor(); });
if (bias.defined()) {
TORCH_CHECK(x.scalar_type() == bias.scalar_type(), "The dtype of x and bias should be same");
TORCH_CHECK(bias.dim() == 1, "The bias should be 1D");
auto bias_size = bias.sizes();
TORCH_CHECK(bias_size[0] == expert_num, "The bias first dim should be same as x second dim");
}
at::Tensor y = at::empty({rows, k}, x.options());
at::Tensor expert_idx = at::empty({rows, k}, x.options().dtype(at::kInt));
at::Tensor out = at::empty({rows, expert_num}, x.options().dtype(at::kFloat));
EXEC_NPU_CMD(aclnnMoeGatingTopK,
x,
bias,
k,
k_group,
group_count,
group_select_mode,
renorm,
norm_type,
out_flag,
routed_scaling_factor,
eps,
y,
expert_idx,
out
);
return std::tuple<at::Tensor, at::Tensor, at::Tensor>(y,expert_idx,out);
}
}
#endif