[310P]: refactoring for 310p kvcache and some ops class (#6117)

### What this PR does / why we need it?
* Refactor the LayerNorm and activation operator classes to decouple the
310P device implementation from the main branch.
* Refactor `mm_encoder_attention` on 310P to use the
`torch_npu._npu_flash_attention_unpad` operator.
* Refactor the QKV inputs in the prefill stage of `attention_v1` on 310P
so they are no longer padded to 16× alignment.
* Refactor `model_runner` on 310P to align the KV-cache initialization
logic with the mainline implementation.

### Does this PR introduce _any_ user-facing change?
NO

### How was this patch tested?
use the e2e tests.

- vLLM version: v0.13.0
- vLLM main:
d68209402d

---------

Signed-off-by: Tflowers-0129 <2906339855@qq.com>

tests/ut/ops/test_activation.py

@@ -21,6 +21,7 @@ from vllm.config import set_current_vllm_config
 from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
 
 from vllm_ascend.utils import AscendDeviceType
+from vllm_ascend.utils import is_310p as is_310p_hw
 
 
 @pytest.fixture
@@ -51,18 +52,26 @@ def test_QuickGELU_forward(mock_gelu, dummy_tensor, default_vllm_config):
     mock_gelu.assert_called_once()
 
 
+@pytest.mark.skipif(is_310p_hw(), reason="310P operator classes have already been refactored.")
 @pytest.mark.parametrize("is_310p", [True, False])
 @patch("torch_npu.npu_swiglu", side_effect=lambda x: x + 1)
 @patch("torch.ops.vllm.maybe_wait_prefetch_done", side_effect=lambda x: None)
-@patch("torch.ops.vllm.maybe_prefetch_mlp_down_proj",
-       side_effect=lambda x: None)
-def test_SiluAndMul_forward(mock_maybe_prefetch_mlp_down_proj,
-                            mock_maybe_wait_prefetch_done, mock_swiglu,
-                            is_310p, dummy_tensor, default_vllm_config):
+@patch("torch.ops.vllm.maybe_prefetch_mlp_down_proj", side_effect=lambda x: None)
+def test_SiluAndMul_forward(
+    mock_maybe_prefetch_mlp_down_proj,
+    mock_maybe_wait_prefetch_done,
+    mock_swiglu,
+    is_310p,
+    dummy_tensor,
+    default_vllm_config,
+):
+    if is_310p and (not is_310p_hw()):
+        pytest.skip("Pseudo-310P param case is not valid on non-310P CI after refactor.")
 
-    with patch("vllm_ascend.utils.get_ascend_device_type",
-               return_value=AscendDeviceType._310P
-               if is_310p else AscendDeviceType.A3):
+    with patch(
+        "vllm_ascend.utils.get_ascend_device_type",
+        return_value=AscendDeviceType._310P if is_310p else AscendDeviceType.A3,
+    ):
         layer = SiluAndMul()
         out = layer.forward(dummy_tensor)
 
@@ -81,9 +90,7 @@ def test_SiluAndMul_forward(mock_maybe_prefetch_mlp_down_proj,
         mock_maybe_wait_prefetch_done.assert_called_once()
 
         actual_arg = mock_swiglu.call_args[0][0]
-        assert torch.allclose(
-            actual_arg,
-            expected_arg), "npu_swiglu called with unexpected input"
+        assert torch.allclose(actual_arg, expected_arg), "npu_swiglu called with unexpected input"
 
         expected_out = dummy_tensor + 1
         assert torch.allclose(out, expected_out)