574fe407eb
### What this PR does / why we need it?
We'll refator `CustomOp` in vllm-ascend from this pr on.
Use function `CustomOp.register_oot` to achieve the customop registery,
taking `AscendQuickGELU` as an example:
```python
from vllm_ascend.ops.activation import AscendQuickGELU
CustomOp.register_oot(_decorated_op_cls=AscendQuickGELU, name="QuickGELU")
```
This is a quick adapt for `CustomOp.register_oot` mechanism from vllm
0.9.2. For further step, we can remove inherit from `QuickGELU` can
write our own `QuickGELU` at all.
Part of https://github.com/vllm-project/vllm-ascend/pull/1647
- vLLM version: v0.9.2
- vLLM main:
8dfb45ca33
---------
Signed-off-by: MengqingCao <cmq0113@163.com>
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
#
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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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# This file is a part of the vllm-ascend project.
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#
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from unittest.mock import patch
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import pytest
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import torch
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from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
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@pytest.fixture
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def dummy_tensor():
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return torch.randn(4, 8, dtype=torch.float16)
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@patch("torch_npu.npu_fast_gelu", side_effect=lambda x: x + 1)
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def test_QuickGELU_forward(mock_gelu, dummy_tensor):
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layer = QuickGELU()
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out = layer.forward(dummy_tensor)
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expected_out = dummy_tensor + 1
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assert torch.allclose(out, expected_out)
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mock_gelu.assert_called_once()
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@pytest.mark.parametrize("is_310p_return", [True, False])
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@patch("torch_npu.npu_swiglu", side_effect=lambda x: x + 1)
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def test_SiluAndMul_forward(mock_swiglu, is_310p_return, dummy_tensor):
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with patch("vllm_ascend.utils.is_310p", return_value=is_310p_return):
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layer = SiluAndMul()
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out = layer.forward(dummy_tensor)
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if is_310p_return:
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expected_arg = dummy_tensor.to(torch.float32)
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else:
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expected_arg = dummy_tensor
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# assert mock_swiglu.call_count == 1
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mock_swiglu.assert_called_once()
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actual_arg = mock_swiglu.call_args[0][0]
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assert torch.allclose(
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actual_arg,
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expected_arg), "npu_swiglu called with unexpected input"
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expected_out = dummy_tensor + 1
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assert torch.allclose(out, expected_out)
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