[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_full_graph_mode.py

@@ -0,0 +1,114 @@
+#
+# 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 os
+
+from vllm import SamplingParams
+
+from tests.e2e.conftest import VllmRunner
+from tests.e2e.model_utils import check_outputs_equal
+
+
+def test_qwen3_moe_full_decode_only_tp2():
+    if 'HCCL_OP_EXPANSION_MODE' in os.environ:
+        del os.environ['HCCL_OP_EXPANSION_MODE']
+    prompts = [
+        "Hello, my name is", "The president of the United States is",
+        "The capital of France is", "The future of AI is"
+    ]
+    model = "Qwen/Qwen3-30B-A3B"
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+    with VllmRunner(model,
+                    max_model_len=1024,
+                    tensor_parallel_size=2,
+                    compilation_config={
+                        "cudagraph_mode": "FULL_DECODE_ONLY",
+                        "cudagraph_capture_sizes": [4, 8, 24, 48, 60]
+                    }) as runner:
+        vllm_fullgraph_outputs = runner.model.generate(prompts,
+                                                       sampling_params)
+
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            cudagraph_capture_sizes=[4, 8, 24, 48, 60],
+            tensor_parallel_size=2,
+    ) as runner:
+        vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
+
+    vllm_fullgraph_outputs_list = []
+    for output in vllm_fullgraph_outputs:
+        vllm_fullgraph_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    vllm_eager_outputs_list = []
+    for output in vllm_eager_outputs:
+        vllm_eager_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_eager_outputs_list,
+        outputs_1_lst=vllm_fullgraph_outputs_list,
+        name_0="vllm_eager_outputs",
+        name_1="vllm_fullgraph_outputs",
+    )
+
+
+def test_qwen3_moe_full_graph_tp2():
+    if 'HCCL_OP_EXPANSION_MODE' in os.environ:
+        del os.environ['HCCL_OP_EXPANSION_MODE']
+    prompts = [
+        "Hello, my name is", "The president of the United States is",
+        "The capital of France is", "The future of AI is"
+    ]
+    model = "Qwen/Qwen3-30B-A3B"
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+    with VllmRunner(model,
+                    max_model_len=1024,
+                    tensor_parallel_size=2,
+                    compilation_config={
+                        "cudagraph_mode": "FULL",
+                        "cudagraph_capture_sizes": [4, 8, 24, 48, 60]
+                    }) as runner:
+        vllm_fullgraph_outputs = runner.model.generate(prompts,
+                                                       sampling_params)
+
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            cudagraph_capture_sizes=[4, 8, 24, 48, 60],
+            tensor_parallel_size=2,
+    ) as runner:
+        vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
+
+    vllm_fullgraph_outputs_list = []
+    for output in vllm_fullgraph_outputs:
+        vllm_fullgraph_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    vllm_eager_outputs_list = []
+    for output in vllm_eager_outputs:
+        vllm_eager_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_eager_outputs_list,
+        outputs_1_lst=vllm_fullgraph_outputs_list,
+        name_0="vllm_eager_outputs",
+        name_1="vllm_fullgraph_outputs",
+    )