[CI]Add e2e test for 310p (#1879)

### What this PR does / why we need it?
Add e2e test for 310p:
trigger conditions：tag, labels(ready-for-test, e2e-310p-test), schedule
image: m.daocloud.io/quay.io/ascend/cann:8.1.rc1-310p-ubuntu22.04-py3.10
runner: linux-aarch64-310p-1, linux-aarch64-310p-4
model: IntervitensInc/pangu-pro-moe-model, Qwen/Qwen3-0.6B-Base,
Qwen/Qwen2.5-7B-Instruct

- vLLM version: v0.10.0
- vLLM main:
b917da442b

Signed-off-by: hfadzxy <starmoon_zhang@163.com>

tests/e2e/singlecard/test_offline_inference_310p.py

@@ -0,0 +1,44 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# 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.
+import pytest
+import vllm  # noqa: F401
+
+import vllm_ascend  # noqa: F401
+from tests.e2e.conftest import VllmRunner
+
+MODELS = ["Qwen/Qwen3-0.6B-Base", "Qwen/Qwen2.5-7B-Instruct"]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["float16"])
+@pytest.mark.parametrize("max_tokens", [5])
+def test_models(model: str, dtype: str, max_tokens: int) -> None:
+    example_prompts = [
+        "Hello, my name is",
+        "The future of AI is",
+    ]
+
+    with VllmRunner(model,
+                    tensor_parallel_size=1,
+                    dtype=dtype,
+                    max_model_len=2048,
+                    enforce_eager=True,
+                    compilation_config={
+                        "custom_ops":
+                        ["none", "+rms_norm", "+rotary_embedding"]
+                    }) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)