[Bugfix][310p] the new A5 mmencoder op donot support 310p (#7518)

### What this PR does / why we need it?

Because the new A5 MMEncoder operator was merged, the 310P can no longer
run any VL models. This PR fixes that issue. details at #7046

### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
e2e
- vLLM version: v0.17.0
- vLLM main:
8b6325758c

---------

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

tests/ut/_310p/ops/test_mm_encoder_attention_310.py

@@ -0,0 +1,81 @@
+#
+# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# 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.
+
+from unittest import mock
+
+import torch
+
+from vllm_ascend import utils
+from vllm_ascend._310p.ops.mm_encoder_attention import AscendMMEncoderAttention310
+
+
+def test_register_customop_overrides_mm_encoder_attention_for_310p():
+    original_registered = utils._ASCEND_CUSTOMOP_IS_REIGISTERED
+    try:
+        utils._ASCEND_CUSTOMOP_IS_REIGISTERED = False
+        with (
+            mock.patch("vllm.model_executor.custom_op.CustomOp.register_oot"),
+            mock.patch("vllm_ascend.utils.is_310p", return_value=True),
+        ):
+            utils.register_ascend_customop()
+
+        assert utils.REGISTERED_ASCEND_OPS["MMEncoderAttention"] is AscendMMEncoderAttention310
+    finally:
+        utils._ASCEND_CUSTOMOP_IS_REIGISTERED = original_registered
+
+
+def test_mm_encoder_attention_310_forward_oot_with_padding():
+    layer = AscendMMEncoderAttention310.__new__(AscendMMEncoderAttention310)
+    layer.num_heads = 4
+    layer.num_kv_heads = 2
+    layer.head_size = 80
+    layer.enable_pad = True
+    layer.scale_value = layer.head_size**-0.5
+
+    bsz, q_len, kv_len = 2, 3, 3
+    query = torch.randn(bsz, q_len, layer.num_heads, layer.head_size)
+    key = torch.randn(bsz, kv_len, layer.num_kv_heads, layer.head_size)
+    value = torch.randn(bsz, kv_len, layer.num_kv_heads, layer.head_size)
+
+    capture = {}
+
+    def fake_flash_attention_unpad(*, query, key, value, seq_len, scale_value, num_heads, num_kv_heads, out):
+        capture["query_shape"] = query.shape
+        capture["key_shape"] = key.shape
+        capture["value_shape"] = value.shape
+        capture["seq_len"] = seq_len
+        capture["scale_value"] = scale_value
+        capture["num_heads"] = num_heads
+        capture["num_kv_heads"] = num_kv_heads
+        out.copy_(query + 1.0)
+
+    with mock.patch(
+        "vllm_ascend._310p.ops.mm_encoder_attention.torch_npu._npu_flash_attention_unpad",
+        side_effect=fake_flash_attention_unpad,
+        create=True,
+    ):
+        out = layer.forward_oot(query, key, value)
+
+    assert capture["query_shape"] == (bsz * q_len, layer.num_heads, 128)
+    assert capture["key_shape"] == (bsz * kv_len, layer.num_heads, 128)
+    assert capture["value_shape"] == (bsz * kv_len, layer.num_heads, 128)
+    assert capture["seq_len"].device.type == "cpu"
+    torch.testing.assert_close(capture["seq_len"], torch.tensor([q_len, q_len], dtype=torch.int32))
+    assert capture["num_heads"] == layer.num_heads
+    assert capture["num_kv_heads"] == layer.num_kv_heads
+
+    assert out.shape == query.shape
+    torch.testing.assert_close(out, query + 1.0)
+