[TEST]Add initial multi modal cases of Qwen2.5-VL-32B-Instruct for nightly test (#3707)

### What this PR does / why we need it?
This PR adds the initial multi modal model for nightly test, including 2
cases for Qwen2.5-vl-32b acc/perf test on A3, we need test them daily.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running the test

vLLM version: v0.11.0rc3
vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: wangyu31577 <wangyu31577@hundsun.com>
Co-authored-by: wangyu31577 <wangyu31577@hundsun.com>

tests/e2e/nightly/models/test_qwen2_5_vl_32b.py

@@ -0,0 +1,110 @@
+# 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.
+#
+from typing import Any
+
+import openai
+import pytest
+from vllm.utils import get_open_port
+
+from tests.e2e.conftest import RemoteOpenAIServer
+from tools.aisbench import run_aisbench_cases
+from tools.send_mm_request import send_image_request
+
+MODELS = [
+    "Qwen/Qwen2.5-VL-32B-Instruct",
+]
+
+TENSOR_PARALLELS = [4]
+
+prompts = [
+    "San Francisco is a",
+]
+
+api_keyword_args = {
+    "max_tokens": 10,
+}
+
+aisbench_cases = [{
+    "case_type": "accuracy",
+    "dataset_path": "vllm-ascend/textvqa-lite",
+    "request_conf": "vllm_api_stream_chat",
+    "dataset_conf": "textvqa/textvqa_gen_base64",
+    "max_out_len": 2048,
+    "batch_size": 128,
+    "baseline": 76,
+    "temperature": 0,
+    "top_k": -1,
+    "top_p": 1,
+    "repetition_penalty": 1,
+    "threshold": 5
+}, {
+    "case_type": "performance",
+    "dataset_path": "vllm-ascend/textvqa-perf-1080p",
+    "request_conf": "vllm_api_stream_chat",
+    "dataset_conf": "textvqa/textvqa_gen_base64",
+    "num_prompts": 512,
+    "max_out_len": 256,
+    "batch_size": 128,
+    "temperature": 0,
+    "top_k": -1,
+    "top_p": 1,
+    "repetition_penalty": 1,
+    "request_rate": 0,
+    "baseline": 1,
+    "threshold": 0.97
+}]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("tp_size", TENSOR_PARALLELS)
+async def test_models(model: str, tp_size: int) -> None:
+    port = get_open_port()
+    env_dict = {
+        "TASK_QUEUE_ENABLE": "1",
+        "VLLM_ASCEND_ENABLE_NZ": "0",
+        "HCCL_OP_EXPANSION_MODE": "AIV"
+    }
+    server_args = [
+        "--no-enable-prefix-caching", "--disable-mm-preprocessor-cache",
+        "--tensor-parallel-size",
+        str(tp_size), "--port",
+        str(port), "--max-model-len", "30000", "--max-num-batched-tokens",
+        "40000", "--max-num-seqs", "400", "--trust-remote-code",
+        "--gpu-memory-utilization", "0.8", "--additional-config",
+        '{"ascend_scheduler_config":{"enabled":false}}'
+    ]
+    request_keyword_args: dict[str, Any] = {
+        **api_keyword_args,
+    }
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            env_dict=env_dict,
+                            auto_port=False) as server:
+        client = server.get_async_client()
+        batch = await client.completions.create(
+            model=model,
+            prompt=prompts,
+            **request_keyword_args,
+        )
+        choices: list[openai.types.CompletionChoice] = batch.choices
+        assert choices[0].text, "empty response"
+        print(choices)
+        send_image_request(model, server)
+        # aisbench test
+        run_aisbench_cases(model, port, aisbench_cases)