[Test] Add e2e test and accuracy test for Qwen3-Next-80B-A3B-Instruct (#3450)

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

Add e2e test and accuracy test for Qwen3-Next-80B-A3B-Instruct

### How was this patch tested?
accuracy test:
https://github.com/vllm-project/vllm-ascend/actions/runs/18771221544/job/53556027634?pr=3450
ci test:
https://github.com/vllm-project/vllm-ascend/actions/runs/18771221530/job/53556027614?pr=3450
<img width="1703" height="562" alt="image"
src="https://github.com/user-attachments/assets/973b6cfa-8240-41e3-893a-5024ff8d0693"
/>



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

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

tests/e2e/multicard/test_qwen3_next.py

@@ -0,0 +1,38 @@
+#
+# 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/tests/basic_correctness/test_basic_correctness.py
+#
+"""Compare the short outputs of HF and vLLM when using greedy sampling.
+
+Run `pytest tests/e2e/multicard/test_qwen3_next.py`.
+"""
+
+from tests.e2e.conftest import VllmRunner
+
+
+def test_models_distributed_Qwen3_NEXT_TP4():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+    with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
+                    tensor_parallel_size=4,
+                    max_model_len=4096,
+                    gpu_memory_utilization=0.7,
+                    distributed_executor_backend="mp",
+                    enforce_eager=True) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)