[Performance] Use forward_native for Conv3dLayer and add UT (#8375)

What this PR does / why we need it?
switch Ascend conv3d forward_oot to use forward_native and  add ut

Does this PR introduce any user-facing change?
No

How was this patch tested?
by CI

---------

Signed-off-by: zouyizhou <zouyizhou@huawei.com>
This commit is contained in:
zyz111222
2026-04-20 17:20:40 +08:00
committed by GitHub
parent c124e8df07
commit dd7e08c6db
3 changed files with 64 additions and 0 deletions

View File

@@ -0,0 +1,27 @@
#
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# 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.
#
import torch
from vllm_ascend.ops.conv import AscendConv3dLayer
class AscendConv3dLayer310(AscendConv3dLayer):
def forward_oot(self, x: torch.Tensor) -> torch.Tensor:
# 310P should avoid the aclnn BatchMatMulV2 Conv3D path used by
# AscendConv3dLayer and keep vLLM's native Conv3d dispatch behavior.
return super().forward_native(x)