[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_shared_expert_dp.py

@@ -0,0 +1,93 @@
+import os
+
+import pytest
+from vllm import SamplingParams
+
+from tests.e2e.conftest import VllmRunner
+from tests.e2e.model_utils import check_outputs_equal
+
+MODELS = [
+    "deepseek-ai/DeepSeek-V2-Lite",
+]
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+
+@pytest.mark.parametrize("model", MODELS)
+def test_deepseek_v2_lite_enable_shared_expert_dp_tp2(model: str) -> None:
+
+    if 'HCCL_OP_EXPANSION_MODE' in os.environ:
+        del os.environ['HCCL_OP_EXPANSION_MODE']
+
+    prompts = [
+        "Hello, my name is", "The capital of the United States is",
+        "The capital of France is", "The future of AI is"
+    ]
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            enforce_eager=True,
+            tensor_parallel_size=2,
+            enable_expert_parallel=True,
+    ) as runner:
+        vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
+
+    os.environ["VLLM_ASCEND_ENABLE_FLASHCOMM1"] = "1"
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            enforce_eager=True,
+            tensor_parallel_size=2,
+            enable_expert_parallel=True,
+            additional_config={
+                "enable_shared_expert_dp": True,
+            },
+    ) as runner:
+        shared_expert_dp_eager_outputs = runner.model.generate(
+            prompts, sampling_params)
+
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            tensor_parallel_size=2,
+            enable_expert_parallel=True,
+            compilation_config={
+                "cudagraph_capture_sizes": [1, 4, 8, 16],
+                "cudagraph_mode": "FULL_DECODE_ONLY",
+            },
+            additional_config={
+                "enable_shared_expert_dp": True,
+            },
+    ) as runner:
+        shared_expert_dp_aclgraph_outputs = runner.model.generate(
+            prompts, sampling_params)
+
+    vllm_eager_outputs_list = []
+    for output in vllm_eager_outputs:
+        vllm_eager_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    shared_expert_dp_eager_outputs_list = []
+    for output in shared_expert_dp_eager_outputs:
+        shared_expert_dp_eager_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    shared_expert_dp_aclgraph_outputs_list = []
+    for output in shared_expert_dp_aclgraph_outputs:
+        shared_expert_dp_aclgraph_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_eager_outputs_list,
+        outputs_1_lst=shared_expert_dp_eager_outputs_list,
+        name_0="vllm_eager_outputs",
+        name_1="shared_expert_dp_eager_outputs",
+    )
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_eager_outputs_list,
+        outputs_1_lst=shared_expert_dp_aclgraph_outputs_list,
+        name_0="vllm_eager_outputs",
+        name_1="shared_expert_dp_aclgraph_outputs",
+    )