[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>
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35
tests/ut/_310p/ops/test_conv_310.py
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35
tests/ut/_310p/ops/test_conv_310.py
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from unittest.mock import MagicMock, patch
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import pytest
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import torch
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from vllm.config import set_current_vllm_config
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from vllm.model_executor.layers.conv import Conv3dLayer
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from vllm_ascend._310p.ops.conv import AscendConv3dLayer310
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@pytest.fixture(autouse=True)
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def default_vllm_config():
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mock_config = MagicMock()
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mock_config.compilation_config.custom_ops = ["all"]
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with set_current_vllm_config(mock_config):
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yield mock_config
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def test_conv3d_310_forward_oot_uses_forward_native():
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layer = AscendConv3dLayer310(
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in_channels=2,
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out_channels=4,
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kernel_size=(2, 2, 2),
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stride=(2, 2, 2),
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bias=True,
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params_dtype=torch.float32,
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)
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x = torch.randn(1, 2, 4, 4, 4, dtype=torch.float32)
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expected = torch.randn(1, 4, 2, 2, 2, dtype=torch.float32)
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with patch.object(Conv3dLayer, "forward_native", autospec=True, return_value=expected) as mock_forward_native:
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out = layer.forward_oot(x)
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mock_forward_native.assert_called_once_with(layer, x)
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assert out is expected
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27
vllm_ascend/_310p/ops/conv.py
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vllm_ascend/_310p/ops/conv.py
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#
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# Copyright (c) 2026 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_ascend.ops.conv import AscendConv3dLayer
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class AscendConv3dLayer310(AscendConv3dLayer):
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def forward_oot(self, x: torch.Tensor) -> torch.Tensor:
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# 310P should avoid the aclnn BatchMatMulV2 Conv3D path used by
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# AscendConv3dLayer and keep vLLM's native Conv3d dispatch behavior.
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return super().forward_native(x)
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@@ -662,6 +662,7 @@ def register_ascend_customop(vllm_config: VllmConfig | None = None):
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if is_310p():
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from vllm_ascend._310p.fused_moe.fused_moe import AscendFusedMoE310, AscendSharedFusedMoE310
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from vllm_ascend._310p.ops.activation import AscendSiluAndMul310
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from vllm_ascend._310p.ops.conv import AscendConv3dLayer310
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from vllm_ascend._310p.ops.layernorm import (
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AscendGemmaRMSNorm310,
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AscendRMSNorm310,
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@@ -686,6 +687,7 @@ def register_ascend_customop(vllm_config: VllmConfig | None = None):
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"ParallelLMHead": AscendParallelLMHead310,
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"VocabParallelEmbedding": AscendVocabParallelEmbedding310,
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"MMEncoderAttention": AscendMMEncoderAttention310,
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"Conv3dLayer": AscendConv3dLayer310,
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}
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)
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