[1/N][CustomOp] Register activation customop instead of overwrite forward_oot (#1841)

### 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>

tests/ut/ops/test_activation.py

@@ -0,0 +1,61 @@
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+#
+
+from unittest.mock import patch
+
+import pytest
+import torch
+from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
+
+
+@pytest.fixture
+def dummy_tensor():
+    return torch.randn(4, 8, dtype=torch.float16)
+
+
+@patch("torch_npu.npu_fast_gelu", side_effect=lambda x: x + 1)
+def test_QuickGELU_forward(mock_gelu, dummy_tensor):
+    layer = QuickGELU()
+    out = layer.forward(dummy_tensor)
+
+    expected_out = dummy_tensor + 1
+    assert torch.allclose(out, expected_out)
+
+    mock_gelu.assert_called_once()
+
+
+@pytest.mark.parametrize("is_310p_return", [True, False])
+@patch("torch_npu.npu_swiglu", side_effect=lambda x: x + 1)
+def test_SiluAndMul_forward(mock_swiglu, is_310p_return, dummy_tensor):
+
+    with patch("vllm_ascend.utils.is_310p", return_value=is_310p_return):
+        layer = SiluAndMul()
+        out = layer.forward(dummy_tensor)
+
+        if is_310p_return:
+            expected_arg = dummy_tensor.to(torch.float32)
+        else:
+            expected_arg = dummy_tensor
+
+        # assert mock_swiglu.call_count == 1
+        mock_swiglu.assert_called_once()
+
+        actual_arg = mock_swiglu.call_args[0][0]
+        assert torch.allclose(
+            actual_arg,
+            expected_arg), "npu_swiglu called with unexpected input"
+
+        expected_out = dummy_tensor + 1
+        assert torch.allclose(out, expected_out)