[Test][Misc] Refactor and consolidate Qwen3.5 and Qwen3-Next tests (vllm-project#11190)

### What this PR does / why we need it?
[Test][Misc] Refactor and consolidate Qwen3.5 and Qwen3-Next tests
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?

- vLLM version: v0.23.0
- vLLM main:
vllm-project/vllm@dc68bd8

Signed-off-by: Wangbei25 <wangbei41@huawie.com>
Co-authored-by: Wangbei25 <wangbei41@huawie.com>

Author: Wangbei25

tests/e2e/pull_request/four_card/test_qwen3_5.py

@@ -39,51 +39,6 @@ def test_qwen3_5_27b_distributed_mp_tp4():
         del vllm_model
 
 
-def test_qwen3_5_35b_distributed_mp_tp4():
-    example_prompts = [
-        "Hello, my name is",
-    ] * 4
-    max_tokens = 5
-    with VllmRunner(
-        "Qwen/Qwen3.5-35B-A3B",
-        tensor_parallel_size=4,
-        cudagraph_capture_sizes=[1, 2, 4, 8],
-        max_model_len=4096,
-        gpu_memory_utilization=0.90,
-        distributed_executor_backend="mp",
-    ) as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)
-        del vllm_model
-
-
-def test_qwen3_5_35b_distributed_mp_tp4_full_decode_only_mtp3():
-    example_prompts = [
-        "Hello, my name is",
-        "The president of the United States is",
-        "The capital of France is",
-        "The future of AI is",
-    ]
-
-    max_tokens = 20
-    with VllmRunner(
-        "Qwen/Qwen3.5-35B-A3B",
-        tensor_parallel_size=4,
-        max_model_len=4096,
-        gpu_memory_utilization=0.90,
-        distributed_executor_backend="mp",
-        compilation_config={
-            "cudagraph_mode": "FULL_DECODE_ONLY",
-            "cudagraph_capture_sizes": [4, 8, 12, 16],
-        },
-        speculative_config={
-            "method": "qwen3_5_mtp",
-            "num_speculative_tokens": 3,
-        },
-    ) as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)
-        del vllm_model
-
-
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
 def test_qwen3_5_35b_distributed_mp_tp4_full_decode_only_mtp3_flashcomm():
     example_prompts = [

tests/e2e/pull_request/four_card/test_qwen3_next.py

@@ -22,23 +22,6 @@
 from tests.e2e.conftest import VllmRunner
 
 
-def test_qwen3_next_distributed_mp_tp4():
-    example_prompts = [
-        "Hello, my name is",
-    ] * 4
-    max_tokens = 5
-    with VllmRunner(
-        "Qwen/Qwen3-Next-80B-A3B-Instruct",
-        tensor_parallel_size=4,
-        cudagraph_capture_sizes=[1, 2, 4, 8],
-        max_model_len=4096,
-        gpu_memory_utilization=0.8,
-        distributed_executor_backend="mp",
-    ) as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)
-        del vllm_model
-
-
 def test_qwen3_next_distributed_mp_full_decode_only_tp4():
     example_prompts = [
         "Hello, my name is",
@@ -50,47 +33,28 @@ def test_qwen3_next_distributed_mp_full_decode_only_tp4():
         max_model_len=4096,
         gpu_memory_utilization=0.8,
         distributed_executor_backend="mp",
-        compilation_config={"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes": [1, 8, 24, 48, 60]},
+        compilation_config={"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes": [1, 2, 4]},
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
         del vllm_model
 
 
-# TODO: will conduct accuracy verification after the subsequent version becomes stable
-@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
-def test_qwen3_next_w8a8dynamic_distributed_tp4_ep():
-    example_prompts = [
-        "Hello, my name is",
-    ]
-    max_tokens = 5
-    with VllmRunner(
-        "vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8",
-        max_model_len=4096,
-        tensor_parallel_size=4,
-        gpu_memory_utilization=0.4,
-        max_num_seqs=1,
-        enable_expert_parallel=True,
-        cudagraph_capture_sizes=[1, 2, 4, 8],
-        quantization="ascend",
-    ) as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)
-
-
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
 @patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
-def test_qwen3_next_distributed_mp_flash_comm_tp4():
+def test_qwen3_next_w8a8dynamic_distributed_mp_flash_comm_tp4():
     example_prompts = [
         "Hello, my name is",
     ] * 4
     max_tokens = 5
     with VllmRunner(
-        "Qwen/Qwen3-Next-80B-A3B-Instruct",
+        "vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8",
         tensor_parallel_size=4,
         max_model_len=4096,
         gpu_memory_utilization=0.7,
         distributed_executor_backend="mp",
         enable_expert_parallel=True,
         enforce_eager=True,
+        quantization="ascend",
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
         del vllm_model
@@ -109,7 +73,7 @@ def test_qwen3_next_distributed_mp_graph_mode_tp4():
         gpu_memory_utilization=0.8,
         distributed_executor_backend="mp",
         enable_expert_parallel=True,
-        cudagraph_capture_sizes=[1, 2, 8],
+        cudagraph_capture_sizes=[1, 2, 4],
         enforce_eager=False,
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)