a813eadd2d
### What this PR does / why we need it? Currently, we are usinge2b31243c0/vllm/model_executor/layers/conv.py (L219-L232)for convolution computation, which is used in patch embedding for VL models. After profiling, we find that this linear method will take about **6.87 ms**, which is much slower than just using `F.conv3d()`. In `F.conv3d()`, it will call aclnn `BatchMatMulV2` with optimization on Ascend NPU, which only take about **2.50 ms** and is **2.7x faster** than linear method. - vLLM version: v0.16.0 - vLLM main:15d76f74e2--------- Signed-off-by: shen-shanshan <467638484@qq.com>
33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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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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import torch
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from vllm.model_executor.layers.conv import Conv2dLayer, Conv3dLayer
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class AscendConv2dLayer(Conv2dLayer):
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def forward_oot(self, x: torch.Tensor) -> torch.Tensor:
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# Use aclnn BatchMatMulV2 for better performance on Ascend NPU.
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return self._forward_conv(x)
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class AscendConv3dLayer(Conv3dLayer):
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def forward_oot(self, x: torch.Tensor) -> torch.Tensor:
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# Use aclnn BatchMatMulV2 for better performance on Ascend NPU.
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return self._forward_conv(x)
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