[CI]refactor: standardize test case naming convention (#5243)

### What this PR does / why we need it?
- Standardize test case naming in `vllm-ascend/tests/e2e/multicard/` to
follow the `<model>_<feature>_<distributed>` convention.

- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: root <root@LAPTOP-VQKDDVMG.localdomain>
Co-authored-by: root <root@LAPTOP-VQKDDVMG.localdomain>

.github/workflows/_e2e_test.yaml

@@ -209,13 +209,13 @@ jobs:
           #pytest -sv --durations=0 tests/e2e/multicard/test_ilama_lora_tp2.py
 
           # To avoid oom, we need to run the test in a single process.
-          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_multistream_moe
+          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_deepseek_multistream_moe_tp2
-          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC
+          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_qwen3_w4a8_dynamic_tp2
-          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_sp_for_qwen3_moe
+          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_qwen3_moe_sp_tp2
-          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_fc2_for_qwen3_moe
+          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_qwen3_moe_fc2_tp2
-          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen_Dense_with_flashcomm_v1
+          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_qwen3_dense_fc1_tp2
-          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen_Dense_with_prefetch_mlp_weight
+          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_qwen3_dense_prefetch_mlp_weight_tp2
-          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_deepseek_w4a8_accuracy
+          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_deepseek_w4a8_accuracy_tp2
 
           pytest -sv --durations=0 tests/e2e/multicard/test_prefix_caching.py
           pytest -sv --durations=0 tests/e2e/multicard/test_pipeline_parallel.py
@@ -288,8 +288,8 @@ jobs:
         env:
           VLLM_WORKER_MULTIPROC_METHOD: spawn
         run: |
-          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_multistream_moe
+          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_deepseek_multistream_moe_tp2
-          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Kimi_K2_Thinking_W4A16
+          pytest -sv --durations=0 tests/e2e/multicard/test_offline_inference_distributed.py::test_kimi_k2_thinking_w4a16_tp4
           pytest -sv --durations=0 tests/e2e/multicard/test_data_parallel_tp2.py
           pytest -sv --durations=0 tests/e2e/multicard/long_sequence/test_basic.py
           pytest -sv --durations=0 tests/e2e/multicard/long_sequence/test_accuracy.py

tests/e2e/multicard/test_aclgraph_capture_replay.py

@@ -134,7 +134,7 @@ def _run_worker_process(
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("max_tokens", [4, 36])
 @patch.dict(os.environ, {"ASCEND_RT_VISIBLE_DEVICES": "0,1"})
-def test_aclgraph_capture_replay_metrics_dp2(
+def test_models_aclgraph_capture_replay_metrics_dp2(
     model: str,
     max_tokens: int,
     monkeypatch: pytest.MonkeyPatch,

tests/e2e/multicard/test_data_parallel.py

@@ -38,7 +38,7 @@ MODELS = [
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("max_tokens", [32])
 @patch.dict(os.environ, {"ASCEND_RT_VISIBLE_DEVICES": "0,1"})
-def test_qwen_inference_dp2(model, max_tokens):
+def test_qwen3_inference_dp2(model, max_tokens):
     moe_models = ["Qwen/Qwen3-30B-A3B", "vllm-ascend/Qwen3-30B-A3B-W8A8"]
     quantization_models = ["vllm-ascend/Qwen3-30B-A3B-W8A8"]
     script = "examples/offline_data_parallel.py"

tests/e2e/multicard/test_data_parallel_tp2.py

@@ -15,7 +15,7 @@ MODELS = ["Qwen/Qwen3-0.6B"]
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("max_tokens", [32])
 @patch.dict(os.environ, {"ASCEND_RT_VISIBLE_DEVICES": "0,1,2,3"})
-def test_qwen_inference_dp2_tp2(model, max_tokens):
+def test_qwen3_inference_dp2_tp2(model, max_tokens):
     script = "examples/offline_data_parallel.py"
 
     env = os.environ.copy()

tests/e2e/multicard/test_external_launcher.py

@@ -37,7 +37,7 @@ DEVICE_NAME = torch_npu.npu.get_device_name(0)[:10]
 
 @pytest.mark.parametrize("model", MODELS)
 @patch.dict(os.environ, {"HCCL_BUFFSIZE": "500"})
-def test_qwen_external_launcher(model):
+def test_qwen3_external_launcher(model):
     script = Path(
         __file__
     ).parent.parent.parent.parent / "examples" / "offline_external_launcher.py"
@@ -78,7 +78,7 @@ def test_qwen_external_launcher(model):
 
 
 @pytest.mark.parametrize("model", MOE_MODELS)
-def test_qwen_moe_external_launcher_ep(model):
+def test_qwen3_moe_external_launcher_ep_tp2(model):
     script = Path(
         __file__
     ).parent.parent.parent.parent / "examples" / "offline_external_launcher.py"
@@ -109,7 +109,7 @@ def test_qwen_moe_external_launcher_ep(model):
 
 
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "0"})
-def test_qwen_external_launcher_with_sleepmode():
+def test_qwen3_external_launcher_with_sleepmode():
     script = Path(
         __file__
     ).parent.parent.parent.parent / "examples" / "offline_external_launcher.py"
@@ -154,7 +154,7 @@ def test_qwen_external_launcher_with_sleepmode():
 
 
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "0"})
-def test_qwen_external_launcher_with_sleepmode_level2():
+def test_qwen3_external_launcher_with_sleepmode_level2():
     script = Path(
         __file__
     ).parent.parent.parent.parent / "examples" / "offline_external_launcher.py"
@@ -210,7 +210,7 @@ def test_qwen_external_launcher_with_sleepmode_level2():
     "VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE": "1",
     "HCCL_BUFFSIZE": "500"
 })
-def test_qwen_external_launcher_with_matmul_allreduce(model):
+def test_qwen3_external_launcher_with_matmul_allreduce(model):
     script = Path(
         __file__
     ).parent.parent.parent.parent / "examples" / "offline_external_launcher.py"

tests/e2e/multicard/test_full_graph_mode.py

@@ -29,7 +29,7 @@ from tests.e2e.conftest import VllmRunner
 from tests.e2e.model_utils import check_outputs_equal
 
 
-def test_qwen_moe_with_full_decode_only():
+def test_qwen3_moe_full_decode_only_tp2():
     if 'HCCL_OP_EXPANSION_MODE' in os.environ:
         del os.environ['HCCL_OP_EXPANSION_MODE']
     prompts = [
@@ -75,7 +75,7 @@ def test_qwen_moe_with_full_decode_only():
     )
 
 
-def test_qwen_moe_with_full():
+def test_qwen3_moe_full_graph_tp2():
     if 'HCCL_OP_EXPANSION_MODE' in os.environ:
         del os.environ['HCCL_OP_EXPANSION_MODE']
     prompts = [

tests/e2e/multicard/test_fused_moe_allgather_ep.py

@@ -41,7 +41,7 @@ from tests.e2e.conftest import VllmRunner
         "TASK_QUEUE_ENABLE": "1",
         "VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP": "1"
     })
-def test_deepseek_moe_fused_allgather_ep():
+def test_deepseek_v3_moe_fused_allgather_ep_tp2():
     example_prompts = ["Hello, my name is"]
     sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
 
@@ -62,7 +62,7 @@ def test_deepseek_moe_fused_allgather_ep():
     "VLLM_WORKER_MULTIPROC_METHOD": "spawn",
     "TASK_QUEUE_ENABLE": "1"
 })
-def test_deepseek_moe_fused_alltoall_ep():
+def test_deepseek_v3_moe_fused_alltoall_ep_tp2():
     example_prompts = ["Hello, my name is"]
     sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
 

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -34,7 +34,7 @@ os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
 QWEN_DENSE_MODELS = [
-    "vllm-ascend/Qwen3-8B-W8A8",
+    "vllm-ascend/Qwen3-0.6B-W8A8",
 ]
 
 QWEN_W4A8_MODELS = [
@@ -50,7 +50,7 @@ KIMI_W4A16_MODELS = [
 ]
 
 
-def test_models_distributed_DeepSeek_multistream_moe():
+def test_deepseek_multistream_moe_tp2():
     example_prompts = [
         "Hello, my name is",
     ]
@@ -70,7 +70,7 @@ def test_models_distributed_DeepSeek_multistream_moe():
 
 
 @pytest.mark.parametrize("model", QWEN_W4A8_MODELS)
-def test_models_distributed_Qwen3_W4A8DYNAMIC(model):
+def test_qwen3_w4a8_dynamic_tp2(model):
     prompts = [
         "Hello, my name is",
     ]
@@ -85,7 +85,7 @@ def test_models_distributed_Qwen3_W4A8DYNAMIC(model):
         vllm_model.generate_greedy(prompts, max_tokens)
 
 
-def test_sp_for_qwen3_moe() -> None:
+def test_qwen3_moe_sp_tp2() -> None:
     example_prompts = [
         "Hello, my name is",
     ]
@@ -108,7 +108,7 @@ def test_sp_for_qwen3_moe() -> None:
 
 @pytest.mark.parametrize("model", DEEPSEEK_W4A8_MODELS)
 @patch.dict(os.environ, {"HCCL_BUFFSIZE": "2048"})
-def test_deepseek_w4a8_accuracy(model):
+def test_deepseek_w4a8_accuracy_tp2(model):
     prompts = [
         "Hello, my name is", "The president of the United States is",
         "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs"
@@ -140,7 +140,7 @@ def test_deepseek_w4a8_accuracy(model):
 
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
 @patch.dict(os.environ, {"VLLM_ASCEND_FLASHCOMM2_PARALLEL_SIZE": "1"})
-def test_fc2_for_qwen3_moe() -> None:
+def test_qwen3_moe_fc2_tp2() -> None:
     example_prompts = [
         "Hello, my name is",
     ]
@@ -159,7 +159,7 @@ def test_fc2_for_qwen3_moe() -> None:
 
 
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
-def test_models_distributed_deepseek_v2_lite_with_flashcomm_v1() -> None:
+def test_deepseek_v2_lite_fc1_tp2() -> None:
     example_prompts = [
         "test" * 1001,
     ]
@@ -180,7 +180,7 @@ def test_models_distributed_deepseek_v2_lite_with_flashcomm_v1() -> None:
 @pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE": "1"})
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
-def test_models_distributed_Qwen_Dense_with_flashcomm_v1(model):
+def test_qwen3_dense_fc1_tp2(model):
     example_prompts = [
         "Hello, my name is",
     ]
@@ -200,7 +200,7 @@ def test_models_distributed_Qwen_Dense_with_flashcomm_v1(model):
 @pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE": "1"})
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_PREFETCH_MLP": "1"})
-def test_models_distributed_Qwen_Dense_with_prefetch_mlp_weight(model):
+def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
     example_prompts = [
         "Hello, my name is",
     ]
@@ -218,7 +218,7 @@ def test_models_distributed_Qwen_Dense_with_prefetch_mlp_weight(model):
 
 
 @pytest.mark.parametrize("model", KIMI_W4A16_MODELS)
-def test_models_distributed_Kimi_K2_Thinking_W4A16(model):
+def test_kimi_k2_thinking_w4a16_tp4(model):
     example_prompts = [
         "Hello, my name is",
     ]

tests/e2e/multicard/test_offline_weight_load.py

@@ -31,7 +31,7 @@ MODELS = ["Qwen/Qwen3-30B-A3B"]
 
 @pytest.mark.parametrize("model", MODELS)
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "0"})
-def test_qwen_offline_weight_load_and_sleepmode(model):
+def test_qwen3_offline_load_and_sleepmode_tp2(model):
     script = Path(
         __file__
     ).parent.parent.parent.parent / "examples" / "offline_external_launcher.py"

tests/e2e/multicard/test_pipeline_parallel.py

@@ -44,4 +44,4 @@ def test_models_pp2(model: str, tp_size: int, pp_size: int,
                     pipeline_parallel_size=pp_size,
                     distributed_executor_backend=distributed_executor_backend,
                     gpu_memory_utilization=0.7) as vllm_model:
-        vllm_model.generate_greedy(prompts, 64)
+        vllm_model.generate_greedy(prompts, 64)