ACLgraph enable: Test cases revisions for all features (#3388)

### What this PR does / why we need it?
This PR revise the test cases of various features on the warehouse which
add the enablement of aclgraph to the test cases.

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?
ut

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: lilinsiman <lilinsiman@gmail.com>

tests/e2e/models/configs/DeepSeek-V2-Lite.yaml

@@ -14,4 +14,4 @@ gpu_memory_utilization: 0.7
 apply_chat_template: False
 fewshot_as_multiturn: False
 trust_remote_code: True
-enforce_eager: True
+enforce_eager: False

tests/e2e/multicard/test_data_parallel.py

@@ -52,8 +52,8 @@ def test_data_parallel_inference(model, max_tokens):
         "--node-rank",
         "0",
         "--trust-remote-code",
-        "--enforce-eager",
     ]
+
     if model == "Qwen/Qwen3-30B-A3B":
         cmd.append("--enable-expert-parallel")
 

tests/e2e/multicard/test_expert_parallel.py

@@ -21,7 +21,7 @@ def test_e2e_ep_correctness(model_name):
             additional_config={"ascend_scheduler_config": {
                 "enabled": True
             }},
-            enforce_eager=True) as vllm_model:
+            enforce_eager=False) as vllm_model:
         tp_output = vllm_model.generate_greedy(example_prompts, max_tokens)
 
     with VllmRunner(
@@ -31,7 +31,7 @@ def test_e2e_ep_correctness(model_name):
             additional_config={"ascend_scheduler_config": {
                 "enabled": True
             }},
-            enforce_eager=True) as vllm_model:
+            enforce_eager=False) as vllm_model:
         ep_output = vllm_model.generate_greedy(example_prompts, max_tokens)
 
     check_outputs_equal(

tests/e2e/multicard/test_ilama_lora_tp2.py

@@ -16,7 +16,7 @@ def test_ilama_lora_tp2(distributed_executor_backend, ilama_lora_files):
                     max_num_seqs=16,
                     tensor_parallel_size=2,
                     distributed_executor_backend=distributed_executor_backend,
-                    enforce_eager=True) as vllm_model:
+                    enforce_eager=False) as vllm_model:
         output = do_sample(vllm_model.model, ilama_lora_files, lora_id=2)
 
     for i in range(len(EXPECTED_LORA_OUTPUT)):

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -52,7 +52,7 @@ def test_models_distributed_QwQ():
             dtype=dtype,
             tensor_parallel_size=2,
             distributed_executor_backend="mp",
-            enforce_eager=True,
+            enforce_eager=False,
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
@@ -163,11 +163,10 @@ def test_sp_for_qwen3_moe() -> None:
         vllm_model.generate(example_prompts, sampling_params)
 
 
-@pytest.mark.parametrize("enforce_eager", [True, False])
 @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, enforce_eager):
+def test_models_distributed_Qwen_Dense_with_flashcomm_v1(model):
     example_prompts = [
         "Hello, my name is",
     ]
@@ -176,7 +175,7 @@ def test_models_distributed_Qwen_Dense_with_flashcomm_v1(model, enforce_eager):
     with VllmRunner(
             snapshot_download(model),
             max_model_len=8192,
-            enforce_eager=enforce_eager,
+            enforce_eager=False,
             dtype="auto",
             tensor_parallel_size=2,
             quantization="ascend",
@@ -184,12 +183,10 @@ def test_models_distributed_Qwen_Dense_with_flashcomm_v1(model, enforce_eager):
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
-@pytest.mark.parametrize("enforce_eager", [True, False])
 @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(
+def test_models_distributed_Qwen_Dense_with_prefetch_mlp_weight(model):
-        model, enforce_eager):
     example_prompts = [
         "Hello, my name is",
     ]
@@ -198,7 +195,7 @@ def test_models_distributed_Qwen_Dense_with_prefetch_mlp_weight(
     with VllmRunner(
             snapshot_download(model),
             max_model_len=8192,
-            enforce_eager=enforce_eager,
+            enforce_eager=False,
             dtype="auto",
             tensor_parallel_size=2,
             quantization="ascend",

tests/e2e/multicard/test_prefix_caching.py

@@ -62,7 +62,7 @@ INPUT_PROMPTS = [
 @pytest.mark.parametrize("max_tokens", [50])
 def test_prefix_cache_with_v1_scheduler(model: str, max_tokens: int) -> None:
     with VllmRunner(model,
-                    enforce_eager=True,
+                    enforce_eager=False,
                     max_model_len=2048,
                     tensor_parallel_size=2,
                     gpu_memory_utilization=0.7) as vllm_model:
@@ -71,7 +71,7 @@ def test_prefix_cache_with_v1_scheduler(model: str, max_tokens: int) -> None:
 
     with VllmRunner(model,
                     enable_prefix_caching=False,
-                    enforce_eager=True,
+                    enforce_eager=False,
                     max_model_len=2048,
                     tensor_parallel_size=2,
                     gpu_memory_utilization=0.7) as vllm_model:
@@ -96,7 +96,7 @@ def test_prefix_cache_with_ascend_scheduler(model: str,
                             'enabled': True,
                         },
                     },
-                    enforce_eager=True,
+                    enforce_eager=False,
                     max_model_len=2048,
                     tensor_parallel_size=2,
                     gpu_memory_utilization=0.7) as vllm_model:
@@ -109,7 +109,7 @@ def test_prefix_cache_with_ascend_scheduler(model: str,
                             'enable_prefix_caching': True,
                         },
                     },
-                    enforce_eager=True,
+                    enforce_eager=False,
                     max_model_len=2048,
                     tensor_parallel_size=2,
                     gpu_memory_utilization=0.7) as vllm_model:

tests/e2e/multicard/test_weight_loader.py

@@ -33,47 +33,7 @@ DEVICE_NAME = torch_npu.npu.get_device_name(0)[:10]
 
 
 @pytest.mark.parametrize("model", MOE_MODELS)
-def test_external_launcher_eager(model):
+def test_external_launcher(model):
-    script = script = "/usr/local/python3.11.13/bin/python3.11/__w/vllm-ascend/tests/examples/test_weight_loader.py"
-    env = os.environ.copy()
-    # TODO: Change to 2 when ci machine has 4 cards
-    cmd = [
-        sys.executable,
-        str(script),
-        "--model",
-        model,
-        "--tp-size",
-        "2",
-        "--proc-per-node",
-        "2",
-        "--trust-remote-code",
-        "--enforce-eager",
-        "--enable-expert-parallel",
-        "--enable-sleep-mode",
-        "--model-weight-gib",
-        "20",
-    ]
-
-    print(f"Running subprocess: {' '.join(cmd)}")
-    proc = subprocess.run(
-        cmd,
-        env=env,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT,
-        timeout=600,
-    )
-    output = proc.stdout.decode()
-
-    print(output)
-
-    assert "TP RANKS: [0]" in output
-    assert "TP RANKS: [1]" in output
-    assert "Generated text:" in output
-    assert proc.returncode == 0
-
-
-@pytest.mark.parametrize("model", MOE_MODELS)
-def test_external_launcher_aclgraph(model):
     script = "/usr/local/python3.11.13/bin/python3.11/__w/vllm-ascend/tests/examples/test_weight_loader.py"
     env = os.environ.copy()
     # TODO: Change to 2 when ci machine has 4 cards
@@ -147,42 +107,3 @@ def test_external_launcher_dense(model):
     assert "TP RANKS: [1]" in output
     assert "Generated text:" in output
     assert proc.returncode == 0
-
-
-@pytest.mark.parametrize("model", MODELS)
-def test_external_launcher_dense_eager(model):
-    script = "/usr/local/python3.11.13/bin/python3.11/__w/vllm-ascend/tests/examples/test_weight_loader.py"
-    env = os.environ.copy()
-    # TODO: Change to 2 when ci machine has 4 cards
-    cmd = [
-        sys.executable,
-        str(script),
-        "--model",
-        model,
-        "--tp-size",
-        "2",
-        "--proc-per-node",
-        "2",
-        "--trust-remote-code",
-        "--enforce-eager",
-        "--enable-sleep-mode",
-        "--model-weight-gib",
-        "20",
-    ]
-
-    print(f"Running subprocess: {' '.join(cmd)}")
-    proc = subprocess.run(
-        cmd,
-        env=env,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT,
-        timeout=600,
-    )
-    output = proc.stdout.decode()
-
-    print(output)
-
-    assert "TP RANKS: [0]" in output
-    assert "TP RANKS: [1]" in output
-    assert "Generated text:" in output
-    assert proc.returncode == 0

tests/e2e/pd_disaggreate/run_edge_case_test.sh

@@ -73,7 +73,6 @@ run_tests_for_model() {
   BASE_CMD="ASCEND_RT_VISIBLE_DEVICES=0 VLLM_ASCEND_LLMDD_RPC_PORT=5559 vllm serve $model_name \
   --port $PREFILL_PORT \
   --seed 1024 \
-  --enforce-eager \
   --disable-log-requests \
   --gpu-memory-utilization 0.8 \
   --kv-transfer-config '{\"kv_connector\":\"LLMDataDistCMgrConnector\",\"kv_role\":\"kv_producer\",\"kv_buffer_device\":\"npu\",\"kv_parallel_size\":\"1\",\"kv_port\":\"20001\",\"engine_id\":\"0\",\"kv_connector_module_path\":\"vllm_ascend.distributed.llmdatadist_c_mgr_connector\"}'"
@@ -93,7 +92,6 @@ run_tests_for_model() {
   BASE_CMD="ASCEND_RT_VISIBLE_DEVICES=1 VLLM_ASCEND_LLMDD_RPC_PORT=6000 vllm serve $model_name \
   --port $DECODE_PORT \
   --seed 1024 \
-  --enforce-eager \
   --disable-log-requests \
   --gpu-memory-utilization 0.8 \
   --kv-transfer-config '{\"kv_connector\":\"LLMDataDistCMgrConnector\",\"kv_role\":\"kv_consumer\",\"kv_buffer_device\":\"npu\",\"kv_parallel_size\":\"1\",\"kv_port\":\"20001\",\"engine_id\":\"0\",\"kv_connector_module_path\":\"vllm_ascend.distributed.llmdatadist_c_mgr_connector\"}'"

tests/e2e/pd_disaggreate/setup_pd.sh

@@ -66,7 +66,6 @@ function run_prefill_instance() {
   --served-model-name Deepseek \
   --max-model-len 2000 \
   --trust-remote-code \
-  --enforce-eager \
   --kv-transfer-config "$KV_CONFIG"
 }
 
@@ -120,7 +119,6 @@ function run_decode_instance() {
     --max-num-batched-tokens 2000 \
     --trust-remote-code \
     --gpu-memory-utilization 0.9 \
-    --enforce-eager \
     --kv-transfer-config "$KV_CONFIG"
 }
 

tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py

@@ -71,7 +71,7 @@ def test_ngram_correctness(
     should be the same when using ngram speculative decoding.
     '''
     pytest.skip("Not current support for the test.")
-    ref_llm = LLM(model=model_name, max_model_len=1024, enforce_eager=True)
+    ref_llm = LLM(model=model_name, max_model_len=1024, enforce_eager=False)
     ref_outputs = ref_llm.chat(test_prompts, sampling_config)
     del ref_llm
     with VllmRunner(model_name,
@@ -82,7 +82,7 @@ def test_ngram_correctness(
                         "num_speculative_tokens": 3,
                     },
                     max_model_len=1024,
-                    enforce_eager=True) as runner:
+                    enforce_eager=False) as runner:
         spec_outputs = runner.model.chat(test_prompts, sampling_config)
     matches = 0
     misses = 0
@@ -111,7 +111,7 @@ def test_eagle_correctness(
     should be the same when using eagle speculative decoding.
     '''
 
-    ref_llm = LLM(model=model_name, max_model_len=2048, enforce_eager=True)
+    ref_llm = LLM(model=model_name, max_model_len=2048, enforce_eager=False)
     ref_outputs = ref_llm.chat(test_prompts, sampling_config)
     del ref_llm
 
@@ -129,7 +129,7 @@ def test_eagle_correctness(
                 "max_model_len": 128,
             },
             max_model_len=128,
-            enforce_eager=True,
+            enforce_eager=False,
     ) as runner:
         spec_outputs = runner.model.chat(test_prompts, sampling_config)
 

tests/e2e/singlecard/test_ascend_scheduler.py

@@ -9,7 +9,8 @@ from tests.e2e.model_utils import check_outputs_equal
 MODEL = "Qwen/Qwen3-0.6B"
 
 
-def test_concurrent_partial_prefill():
+@pytest.mark.parametrize("enforce_eager", [True, False])
+def test_concurrent_partial_prefill(enforce_eager):
     with VllmRunner(MODEL,
                     additional_config={
                         'ascend_scheduler_config': {
@@ -18,7 +19,7 @@ def test_concurrent_partial_prefill():
                     },
                     max_num_seqs=3,
                     max_num_batched_tokens=2048,
-                    enforce_eager=True,
+                    enforce_eager=enforce_eager,
                     max_model_len=2048,
                     gpu_memory_utilization=0.7) as vllm_model:
         outputs = vllm_model.model.generate(["Hello my name is Robert and I"] *
@@ -28,7 +29,8 @@ def test_concurrent_partial_prefill():
             assert len(output.outputs) == 1
 
 
-def test_prefix_cache_stats_is_recorded():
+@pytest.mark.parametrize("enforce_eager", [True, False])
+def test_prefix_cache_stats_is_recorded(enforce_eager):
     with VllmRunner(MODEL,
                     additional_config={
                         'ascend_scheduler_config': {
@@ -37,7 +39,7 @@ def test_prefix_cache_stats_is_recorded():
                     },
                     max_num_seqs=3,
                     max_num_batched_tokens=2048,
-                    enforce_eager=True,
+                    enforce_eager=enforce_eager,
                     max_model_len=2048,
                     gpu_memory_utilization=0.7) as vllm_model:
         # 17 tokens will make sure first 16 tokens are cached in a block

tests/e2e/singlecard/test_camem.py

@@ -74,7 +74,7 @@ def test_end_to_end():
     sampling_params = SamplingParams(temperature=0, max_tokens=10)
 
     with VllmRunner("Qwen/Qwen3-0.6B",
-                    enforce_eager=True,
+                    enforce_eager=False,
                     enable_sleep_mode=True) as runner:
 
         output = runner.model.generate(prompt, sampling_params)

tests/e2e/singlecard/test_chunked.py

@@ -43,12 +43,13 @@ def test_models(
         temperature=0.0,
     )
 
-    with VllmRunner(model, long_prefill_token_threshold=20,
+    with VllmRunner(model,
-                    enforce_eager=True) as vllm_model:
+                    long_prefill_token_threshold=20,
+                    enforce_eager=False) as vllm_model:
         output1 = vllm_model.generate(prompts, sampling_params)
 
     with VllmRunner(model,
-                    enforce_eager=True,
+                    enforce_eager=False,
                     additional_config={
                         'ascend_scheduler_config': {
                             'enabled': True

tests/e2e/singlecard/test_embedding.py

@@ -29,7 +29,7 @@ def test_embed_models_correctness():
     with VllmRunner(
             model_name,
             task="embed",
-            enforce_eager=True,
+            enforce_eager=False,
     ) as vllm_runner:
         vllm_outputs = vllm_runner.encode(queries)
 

tests/e2e/singlecard/test_ilama_lora.py

@@ -51,7 +51,7 @@ def test_ilama_lora(ilama_lora_files):
                     max_loras=4,
                     max_model_len=1024,
                     max_num_seqs=16,
-                    enforce_eager=True) as vllm_model:
+                    enforce_eager=False) as vllm_model:
 
         output1 = do_sample(vllm_model.model, ilama_lora_files, lora_id=1)
         for i in range(len(EXPECTED_LORA_OUTPUT)):

tests/e2e/singlecard/test_quantization.py

@@ -28,7 +28,7 @@ def test_quant_W8A8():
     with VllmRunner(
             snapshot_download("vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8"),
             max_model_len=8192,
-            enforce_eager=True,
+            enforce_eager=False,
             gpu_memory_utilization=0.7,
             quantization="ascend",
     ) as vllm_model:

tests/e2e/singlecard/test_vlm.py

@@ -46,7 +46,7 @@ def test_multimodal_vl(prompt_template):
                         "max_pixels": 1280 * 28 * 28,
                         "fps": 1,
                     },
-                    enforce_eager=True) as vllm_model:
+                    enforce_eager=False) as vllm_model:
         outputs = vllm_model.generate_greedy(prompts=prompts,
                                              images=images,
                                              max_tokens=64)