[CI] refect e2e test (#4799)

### What this PR does / why we need it?
This PR updates the CI configuration and adjusts a set of end-to-end
(e2e) tests under tests/e2e/multicard, in order to refactor the test
suite and ensure compatibility with current codebase and CI workflows.

1. tests/e2e/multicard/test_prefix_caching.py: change model to Qwen3-8B
and rename the test case
2. tests/e2e/multicard/test_quantization.py: rename the test case
3. tests/e2e/multicard/test_qwen3_moe.py: remove duplicate test and
rename test cases
4. tests/e2e/multicard/test_qwen3_next.py: rename test cases and change
the W8A8 pruning model to the W8A8 model and remove the eager parameter
5. tests/e2e/multicard/test_shared_expert_dp.py: rename test case and
remove the eager parameter
6. tests/e2e/multicard/test_single_request_aclgraph.py: rename test case
and change Qwen3-30B to Qwen3-0.6B
7. tests/e2e/multicard/test_torchair_graph_mode.py: delete test cases
about torchair

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: hfadzxy <starmoon_zhang@163.com>

.github/workflows/_e2e_test.yaml

@@ -197,8 +197,8 @@ jobs:
           pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen_Dense_with_flashcomm_v1
           pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen_Dense_with_prefetch_mlp_weight
 
-          pytest -sv tests/e2e/multicard/test_pipeline_parallel.py
           pytest -sv tests/e2e/multicard/test_prefix_caching.py
+          pytest -sv tests/e2e/multicard/test_pipeline_parallel.py
           pytest -sv tests/e2e/multicard/test_qwen3_moe.py
           pytest -sv tests/e2e/multicard/test_offline_weight_load.py
 
@@ -266,9 +266,8 @@ jobs:
           pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_multistream_moe
           pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_W4A8DYNAMIC
           pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Kimi_K2_Thinking_W4A16
-          # pytest -sv tests/e2e/multicard/test_qwen3_moe.py::test_models_distributed_Qwen3_MOE_TP2_WITH_EP
-          # pytest -sv tests/e2e/multicard/test_qwen3_moe.py::test_models_distributed_Qwen3_MOE_W8A8_WITH_EP
           pytest -sv tests/e2e/multicard/test_data_parallel_tp2.py
+
       - name: Install Ascend toolkit & triton_ascend (for Qwen3-Next-80B-A3B-Instruct)
         shell: bash -l {0}
         run: |

tests/e2e/multicard/test_prefix_caching.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Compare the with and without prefix caching on V1 scheduler or AscendScheduler."""
+"""Compare the with and without prefix caching."""
 
 import pytest
 
@@ -9,7 +9,7 @@ from tests.e2e.model_utils import check_outputs_equal
 
 MODELS = [
     # for MHA
-    "Qwen/Qwen3-8B-Base",
+    "Qwen/Qwen3-8B",
     # for MLA
     "deepseek-ai/DeepSeek-V2-Lite-Chat"
 ]
@@ -60,9 +60,8 @@ INPUT_PROMPTS = [
 
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("max_tokens", [50])
-def test_prefix_cache_with_v1_scheduler(model: str, max_tokens: int) -> None:
+def test_models_prefix_cache_tp2(model: str, max_tokens: int) -> None:
     with VllmRunner(model,
-                    enforce_eager=False,
                     max_model_len=2048,
                     tensor_parallel_size=2,
                     gpu_memory_utilization=0.7) as vllm_model:
@@ -71,7 +70,6 @@ def test_prefix_cache_with_v1_scheduler(model: str, max_tokens: int) -> None:
 
     with VllmRunner(model,
                     enable_prefix_caching=False,
-                    enforce_eager=False,
                     max_model_len=2048,
                     tensor_parallel_size=2,
                     gpu_memory_utilization=0.7) as vllm_model:

tests/e2e/multicard/test_quantization.py

@@ -25,16 +25,17 @@ from modelscope import snapshot_download  # type: ignore
 from tests.e2e.conftest import VllmRunner
 
 
-def test_models_distributed_quantized_W8A8():
+def test_qwen2_5_w8a8_external_quantized_tp2():
     example_prompts = [
         "The president of the United States is",
     ]
     max_tokens = 5
-    with VllmRunner(snapshot_download("neuralmagic/Qwen2.5-3B-quantized.w8a8"),
+    with VllmRunner(
+            snapshot_download("neuralmagic/Qwen2.5-3B-quantized.w8a8"),
             tensor_parallel_size=2,
             max_model_len=4096,
             gpu_memory_utilization=0.8,
-                    enforce_eager=False) as vllm_model:
+    ) as vllm_model:
         vllm_output = vllm_model.generate_greedy(example_prompts, max_tokens)
 
     golden_results = [

tests/e2e/multicard/test_qwen3_moe.py

@@ -29,21 +29,8 @@ from modelscope import snapshot_download  # type: ignore
 from tests.e2e.conftest import VllmRunner
 
 
-def test_models_distributed_Qwen3_MOE_TP2():
-    example_prompts = [
-        "Hello, my name is",
-    ]
-    max_tokens = 5
-    with VllmRunner(
-            "Qwen/Qwen3-30B-A3B",
-            tensor_parallel_size=2,
-            distributed_executor_backend="mp",
-    ) as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)
-
-
 @patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
-def test_models_distributed_Qwen3_MOE_TP2_WITH_EP():
+def test_qwen3_moe_distributed_mp_tp2_ep():
     example_prompts = [
         "Hello, my name is",
     ]
@@ -53,12 +40,11 @@ def test_models_distributed_Qwen3_MOE_TP2_WITH_EP():
             tensor_parallel_size=2,
             enable_expert_parallel=True,
             distributed_executor_backend="mp",
-            enforce_eager=False,
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
-def test_models_distributed_Qwen3_MOE_W8A8():
+def test_qwen3_moe_w8a8_distributed_tp2():
     example_prompts = [
         "Hello, my name is",
     ]
@@ -73,7 +59,7 @@ def test_models_distributed_Qwen3_MOE_W8A8():
 
 
 @patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
-def test_models_distributed_Qwen3_MOE_W8A8_WITH_EP():
+def test_qwen3_moe_w8a8_distributed_tp2_ep():
     example_prompts = [
         "Hello, my name is",
     ]
@@ -88,7 +74,7 @@ def test_models_distributed_Qwen3_MOE_W8A8_WITH_EP():
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
-def test_models_distributed_Qwen3_MOE_TP2_WITH_ACLGRAPH_AIV():
+def test_qwen3_moe_distributed_aiv_tp2():
     os.environ['HCCL_OP_EXPANSION_MODE'] = 'AIV'
     example_prompts = [
         "Hello, my name is",
@@ -99,23 +85,5 @@ def test_models_distributed_Qwen3_MOE_TP2_WITH_ACLGRAPH_AIV():
             "Qwen/Qwen3-30B-A3B",
             dtype=dtype,
             tensor_parallel_size=2,
-            enforce_eager=False,
-    ) as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)
-
-
-def test_models_distributed_Qwen3_MOE_TP2_WITH_ACLGRAPH():
-    if 'HCCL_OP_EXPANSION_MODE' in os.environ:
-        del os.environ['HCCL_OP_EXPANSION_MODE']
-    example_prompts = [
-        "Hello, my name is",
-    ]
-    dtype = "auto"
-    max_tokens = 5
-    with VllmRunner(
-            "Qwen/Qwen3-30B-A3B",
-            dtype=dtype,
-            tensor_parallel_size=2,
-            enforce_eager=False,
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)

tests/e2e/multicard/test_qwen3_next.py

@@ -29,7 +29,7 @@ from modelscope import snapshot_download  # type: ignore
 from tests.e2e.conftest import VllmRunner
 
 
-def test_models_distributed_Qwen3_NEXT_TP4():
+def test_qwen3_next_distributed_mp_tp4():
     example_prompts = [
         "Hello, my name is",
     ] * 4
@@ -38,13 +38,12 @@ def test_models_distributed_Qwen3_NEXT_TP4():
                     tensor_parallel_size=4,
                     max_model_len=4096,
                     gpu_memory_utilization=0.8,
-                    distributed_executor_backend="mp",
+                    distributed_executor_backend="mp") as vllm_model:
-                    enforce_eager=True) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
         del vllm_model
 
 
-def test_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
+def test_qwen3_next_distributed_mp_full_decode_only_tp4():
     example_prompts = [
         "Hello, my name is",
     ] * 4
@@ -54,7 +53,6 @@ def test_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
                     max_model_len=4096,
                     gpu_memory_utilization=0.8,
                     distributed_executor_backend="mp",
-                    enforce_eager=False,
                     compilation_config={
                         "cudagraph_mode": "FULL_DECODE_ONLY",
                         "cudagraph_capture_sizes": [1, 8, 24, 48, 60]
@@ -64,7 +62,7 @@ def test_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
 
 
 # TODO: Fix the accuary of batch chunked prefill
-def test_models_distributed_Qwen3_NEXT_MTP_TP4_SIMILARITY():
+def test_qwen3_next_distributed_mp_eager_mtp_similarity_tp4():
     example_prompts = ["Hello, my name is"]
     max_tokens = 20
 
@@ -110,16 +108,15 @@ def test_models_distributed_Qwen3_NEXT_MTP_TP4_SIMILARITY():
 
 # TODO: will conduct accuracy verification after the subsequent version becomes stable
 @patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
-def test_models_distributed_Qwen3_NEXT_W8A8DYNAMIC_WITH_EP():
+def test_qwen3_next_w8a8dynamic_distributed_tp4_ep():
     example_prompts = [
         "Hello, my name is",
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download(
+            snapshot_download("vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8"),
-                "vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8-Pruning"),
             max_model_len=4096,
-            tensor_parallel_size=2,
+            tensor_parallel_size=4,
             gpu_memory_utilization=0.4,
             max_num_seqs=1,
             enable_expert_parallel=True,

tests/e2e/multicard/test_shared_expert_dp.py

@@ -7,13 +7,13 @@ from tests.e2e.conftest import VllmRunner
 from tests.e2e.model_utils import check_outputs_equal
 
 MODELS = [
-    "vllm-ascend/DeepSeek-V2-Lite",
+    "deepseek-ai/DeepSeek-V2-Lite",
 ]
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
 
 @pytest.mark.parametrize("model", MODELS)
-def test_models_with_enable_shared_expert_dp(model: str) -> None:
+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']
@@ -51,7 +51,7 @@ def test_models_with_enable_shared_expert_dp(model: str) -> None:
             model,
             max_model_len=1024,
             tensor_parallel_size=2,
-            enforce_eager=False,
+            enable_expert_parallel=True,
             compilation_config={
                 "cudagraph_capture_sizes": [1, 4, 8, 16],
                 "cudagraph_mode": "FULL_DECODE_ONLY",

tests/e2e/multicard/test_single_request_aclgraph.py

@@ -23,7 +23,7 @@ from vllm.utils.network_utils import get_open_port
 
 from tests.e2e.conftest import RemoteOpenAIServer
 
-MODELS = ["Qwen/Qwen3-30B-A3B", "vllm-ascend/DeepSeek-V2-Lite-W8A8"]
+MODELS = ["Qwen/Qwen3-0.6B", "vllm-ascend/DeepSeek-V2-Lite-W8A8"]
 
 DATA_PARALLELS = [2]
 
@@ -39,7 +39,8 @@ api_keyword_args = {
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dp_size", DATA_PARALLELS)
-async def test_single_request_aclgraph(model: str, dp_size: int) -> None:
+async def test_models_single_request_aclgraph_dp2(model: str,
+                                                  dp_size: int) -> None:
     port = get_open_port()
     env_dict = {
         "TASK_QUEUE_ENABLE": "1",