[CI/UT][Refactor] move e2e spec decode and deepseek acc test to per pr (#1136)

### What this PR does / why we need it?
1. run deepseek acc ut per pr --- multicard CI time increased by 9 min
2. run spec decode e2e test on v1 per pr --- singlecard CI time
increased by 3 min (partly is disabled due to not work now)
~~3. align the output of whether dbo is enabled or not~~
    The generated results with and without dbo cannot be aligned.

https://github.com/vllm-project/vllm-ascend/actions/runs/15822900528/job/44600029405?pr=1136
4. skip V0 mtp test due to failure in
https://github.com/vllm-project/vllm-ascend/actions/runs/16012172833/job/45171988816
5. fix some version conflicts
### How was this patch tested?
CI passed with new added test.

---------

Signed-off-by: MengqingCao <cmq0113@163.com>

tests/e2e/multicard/test_deepseek_v2_lite_tp2_accuracy.py

@@ -0,0 +1,71 @@
+#
+# 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
+from multiprocessing import Queue
+
+import lm_eval
+import pytest
+import torch
+
+# pre-trained model path on Hugging Face.
+MODELS = ["deepseek-ai/DeepSeek-V2-Lite"]
+# Math reasoning benchmark (Grade School Math 8K).
+TASK = "gsm8k"
+# Answer validation requiring format consistency.
+FILTER = "exact_match,strict-match"
+# 3% relative tolerance for numerical accuracy.
+RTOL = 0.03
+# Baseline accuracy after VLLM optimization.
+EXPECTED_VALUE = 0.3843821076573162
+
+
+def run_test(model_name, queue, more_args=None):
+    model_args = f"pretrained={model_name},max_model_len=4096,trust_remote_code=True,tensor_parallel_size=4,enforce_eager=True"
+    if more_args is not None:
+        model_args = f"{model_args},{more_args}"
+    results = lm_eval.simple_evaluate(
+        model="vllm",
+        model_args=model_args,
+        tasks=TASK,
+        batch_size="auto",
+    )
+    result = results["results"][TASK][FILTER]
+    print(100 * "*", "\nThe accuracy test result:", result)
+    queue.put(result)
+    del results
+    torch.npu.empty_cache()
+    gc.collect()
+
+
+@pytest.mark.parametrize("model", MODELS)
+def test_lm_eval_accuracy(model, monkeypatch: pytest.MonkeyPatch):
+    with monkeypatch.context():
+        result_queue: Queue[float] = multiprocessing.Queue()
+        p = multiprocessing.Process(target=run_test,
+                                    args=(
+                                        model,
+                                        result_queue,
+                                    ))
+        p.start()
+        p.join()
+        result = result_queue.get()
+        assert (EXPECTED_VALUE - RTOL < result < EXPECTED_VALUE + RTOL), \
+            f"Expected: {EXPECTED_VALUE}±{RTOL} | Measured: {result}"

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -73,21 +73,6 @@ def test_models_distributed_DeepSeek_multistream_moe():
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
-def test_models_distributed_DeepSeek():
-    example_prompts = [
-        "Hello, my name is",
-    ]
-    dtype = "half"
-    max_tokens = 5
-    with VllmRunner(
-            "deepseek-ai/DeepSeek-V2-Lite",
-            dtype=dtype,
-            tensor_parallel_size=4,
-            distributed_executor_backend="mp",
-    ) as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)
-
-
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE": "1"})
 def test_models_distributed_topk() -> None:
     example_prompts = [