[CI] cleanup single/multi-card test (#5623)

1. speed up e2e light test.
2. create `2-cards` and `4-cards` folder in multicard
3. move ops to nightly
4. run test in Alphabetical Order

- vLLM version: v0.13.0
- vLLM main:
8be6432bda

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

tests/e2e/multicard/2-cards/test_qwen3_moe.py

@@ -0,0 +1,126 @@
+#
+# 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
+#
+import json
+import os
+from unittest.mock import patch
+
+import openai
+import pytest
+from modelscope import snapshot_download  # type: ignore
+from vllm.utils.network_utils import get_open_port
+
+from tests.e2e.conftest import RemoteOpenAIServer, VllmRunner
+
+
+@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
+def test_qwen3_moe_distributed_mp_tp2_ep():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+    with VllmRunner(
+            "Qwen/Qwen3-30B-A3B",
+            tensor_parallel_size=2,
+            enable_expert_parallel=True,
+            cudagraph_capture_sizes=[1, 2, 4, 8],
+            distributed_executor_backend="mp",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+def test_qwen3_moe_w8a8_distributed_tp2():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+    with VllmRunner(
+            snapshot_download("vllm-ascend/Qwen3-30B-A3B-W8A8"),
+            max_model_len=8192,
+            tensor_parallel_size=2,
+            cudagraph_capture_sizes=[1, 2, 4, 8],
+            quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+def test_qwen3_moe_distributed_aiv_tp2():
+    os.environ['HCCL_OP_EXPANSION_MODE'] = 'AIV'
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    dtype = "auto"
+    max_tokens = 5
+    with VllmRunner(
+            "Qwen/Qwen3-30B-A3B",
+            dtype=dtype,
+            tensor_parallel_size=2,
+            cudagraph_capture_sizes=[1, 2, 4, 8],
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+@pytest.mark.asyncio
+async def test_qwen3_moe_w8a8_distributed_tp2_ep_dynamic_eplb():
+    model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
+    port = get_open_port()
+    server_args = [
+        "--max_model_len", "8192", "--tensor_parallel_size", "2",
+        "--enable_expert_parallel", "--quantization", "ascend", "--port",
+        str(port), "--enforce_eager"
+    ]
+    env_dict = {"HCCL_BUFFSIZE": "1024"}
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            auto_port=False,
+                            env_dict=env_dict) as server:
+        client = server.get_async_client()
+        batch = await client.completions.create(model=model,
+                                                prompt="What is deeplearning?",
+                                                max_tokens=300,
+                                                temperature=0,
+                                                top_p=1.0,
+                                                n=1)
+        gt_choices: list[openai.types.CompletionChoice] = batch.choices
+
+    # dynamic eplb test
+    # Since pytest runs as a daemon, it conflicts with the dynamic eplb manager
+    # during initialization in offline mode, so the online mode is used instead.
+    env_dict.update({"DYNAMIC_EPLB": "true"})
+    additional_config = {
+        "dynamic_eplb": True,
+        "num_iterations_eplb_update": 100,
+        "num_wait_worker_iterations": 20
+    }
+    server_args.extend(["--additional-config", json.dumps(additional_config)])
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            auto_port=False,
+                            env_dict=env_dict) as server:
+        client = server.get_async_client()
+        batch = await client.completions.create(model=model,
+                                                prompt="What is deeplearning?",
+                                                max_tokens=300,
+                                                temperature=0,
+                                                top_p=1.0,
+                                                n=1)
+        eplb_choices: list[openai.types.CompletionChoice] = batch.choices
+    assert gt_choices[0].text == eplb_choices[
+        0].text, f"{gt_choices[0].text=} \n {eplb_choices[0].text=}"