[CI] Add accuracy ci for DP and EP and TP and ETP (#1140)

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

Add accuracy ci for DP and EP and TP

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.9.2
- vLLM main:
35514b682a

---------

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

tests/e2e/long_term/accuracy/accuracy_singlecard.py

@@ -0,0 +1,115 @@
+#
+# 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.
+# Adapted from vllm-project/blob/main/tests/entrypoints/llm/test_accuracy.py
+#
+
+import gc
+import multiprocessing
+import sys
+from multiprocessing import Queue
+
+import lm_eval
+import pytest
+import torch
+
+# pre-trained model path on Hugging Face.
+MODEL_NAME = ["Qwen/Qwen2.5-0.5B-Instruct", "Qwen/Qwen2.5-VL-3B-Instruct"]
+# Benchmark configuration mapping models to evaluation tasks:
+# - Text model: GSM8K (grade school math reasoning)
+# - Vision-language model: MMMU Art & Design validation (multimodal understanding)
+TASK = {
+    "Qwen/Qwen2.5-0.5B-Instruct": "gsm8k",
+    "Qwen/Qwen2.5-VL-3B-Instruct": "mmmu_val_art_and_design"
+}
+# Answer validation requiring format consistency.
+FILTER = {
+    "Qwen/Qwen2.5-0.5B-Instruct": "exact_match,strict-match",
+    "Qwen/Qwen2.5-VL-3B-Instruct": "acc,none"
+}
+# 3% relative tolerance for numerical accuracy.
+RTOL = 0.03
+# Baseline accuracy after VLLM optimization.
+EXPECTED_VALUE = {
+    "Qwen/Qwen2.5-0.5B-Instruct": 0.316,
+    "Qwen/Qwen2.5-VL-3B-Instruct": 0.566
+}
+# Maximum context length configuration for each model.
+MAX_MODEL_LEN = {
+    "Qwen/Qwen2.5-0.5B-Instruct": 4096,
+    "Qwen/Qwen2.5-VL-3B-Instruct": 8192
+}
+# Model types distinguishing text-only and vision-language models.
+MODEL_TYPE = {
+    "Qwen/Qwen2.5-0.5B-Instruct": "vllm",
+    "Qwen/Qwen2.5-VL-3B-Instruct": "vllm-vlm"
+}
+# wrap prompts in a chat-style template.
+APPLY_CHAT_TEMPLATE = {"vllm": False, "vllm-vlm": True}
+# Few-shot examples handling as multi-turn dialogues.
+FEWSHOT_AS_MULTITURN = {"vllm": False, "vllm-vlm": True}
+# batch_size
+BATCH_SIZE = {
+    "Qwen/Qwen2.5-0.5B-Instruct": "auto",
+    "Qwen/Qwen2.5-VL-3B-Instruct": 1
+}
+
+multiprocessing.set_start_method("spawn", force=True)
+
+
+def run_test(queue, model, max_model_len, model_type):
+    try:
+        if model_type == "vllm-vlm":
+            model_args = (f"pretrained={model},max_model_len={max_model_len},"
+                          "tensor_parallel_size=1,dtype=auto,max_images=2")
+        else:
+            model_args = (f"pretrained={model},max_model_len={max_model_len},"
+                          "tensor_parallel_size=1,dtype=auto")
+        results = lm_eval.simple_evaluate(
+            model=model_type,
+            model_args=model_args,
+            tasks=TASK[model],
+            batch_size=BATCH_SIZE[model],
+            apply_chat_template=APPLY_CHAT_TEMPLATE[model_type],
+            fewshot_as_multiturn=FEWSHOT_AS_MULTITURN[model_type],
+        )
+        result = results["results"][TASK[model]][FILTER[model]]
+        print("result:", result)
+        queue.put(result)
+    except Exception as e:
+        queue.put(e)
+        sys.exit(1)
+    finally:
+        gc.collect()
+        torch.npu.empty_cache()
+
+
+@pytest.mark.parametrize("model", MODEL_NAME)
+def test_lm_eval_accuracy(monkeypatch: pytest.MonkeyPatch, model):
+    with monkeypatch.context():
+        result_queue: Queue[float] = multiprocessing.Queue()
+        p = multiprocessing.Process(target=run_test,
+                                    args=(result_queue, model,
+                                          MAX_MODEL_LEN[model],
+                                          MODEL_TYPE[model]))
+        p.start()
+        p.join()
+        result = result_queue.get()
+        if isinstance(result, Exception):
+            pytest.fail(f"Subprocess failed with exception: {str(result)}")
+        print(result)
+        assert (EXPECTED_VALUE[model] - RTOL < result < EXPECTED_VALUE[model] + RTOL), \
+            f"Expected: {EXPECTED_VALUE[model]}±{RTOL} | Measured: {result}"