Refactor e2e CI (#2276)

Refactor E2E CI to make it clear and faster
1. remove some uesless e2e test
2. remove some uesless function
3. Make sure all test runs with VLLMRunner to avoid oom error
4. Make sure all ops test end with torch.empty_cache to avoid oom error
5. run the test one by one to avoid resource limit error


- vLLM version: v0.10.1.1
- vLLM main:
a344a5aa0a

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -23,18 +23,12 @@ Run `pytest tests/test_offline_inference.py`.
 import os
 from unittest.mock import patch
 
-import pytest
 from modelscope import snapshot_download  # type: ignore
 from vllm import SamplingParams
-from vllm.model_executor.models.registry import ModelRegistry
 
 from tests.e2e.conftest import VllmRunner
 
 os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
-DEEPSEEK_W4A8_MODELS = [
-    "vllm-ascend/DeepSeek-V3-W4A8-Pruing",
-    "vllm-ascend/DeepSeek-R1-w4a8-pruning"
-]
 
 
 def test_models_distributed_QwQ():
@@ -48,6 +42,7 @@ def test_models_distributed_QwQ():
             dtype=dtype,
             tensor_parallel_size=2,
             distributed_executor_backend="mp",
+            enforce_eager=True,
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
@@ -73,35 +68,10 @@ def test_models_distributed_DeepSeek_multistream_moe():
                 },
                 "refresh": True,
             },
-            enforce_eager=False,
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
-@pytest.mark.skip(
-    reason=
-    "deepseek dbo dose not consider the support on half precision float, will enable this ut after we actually support it"
-)
-@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DBO": "1"})
-def test_models_distributed_DeepSeekV3_dbo():
-    example_prompts = ["The president of the United States is"] * 41
-    dtype = "half"
-    sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
-    with VllmRunner(
-            "vllm-ascend/DeepSeek-V3-Pruning",
-            dtype=dtype,
-            tensor_parallel_size=2,
-            distributed_executor_backend="mp",
-    ) as vllm_model:
-        model_arch = 'DeepseekV3ForCausalLM'
-        registed_models = ModelRegistry.models
-        assert registed_models[
-            model_arch].module_name == "vllm_ascend.models.deepseek_dbo"
-        assert registed_models[
-            model_arch].class_name == "CustomDeepseekDBOForCausalLM"
-        vllm_model.generate(example_prompts, sampling_params)
-
-
 def test_models_distributed_pangu():
     example_prompts = [
         "Hello, my name is",
@@ -118,28 +88,6 @@ def test_models_distributed_pangu():
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
-@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_TOPK_TOPP_OPTIMIZATION": "1"})
-def test_models_distributed_topk() -> None:
-    example_prompts = [
-        "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.",
-        "Briefly describe the major milestones in the development of artificial intelligence from 1950 to 2020.",
-        "Compare and contrast artificial intelligence with human intelligence in terms of processing information.",
-    ]
-    dtype = "half"
-    sampling_params = SamplingParams(max_tokens=5,
-                                     temperature=0.0,
-                                     top_k=50,
-                                     top_p=0.9)
-
-    with VllmRunner(
-            "deepseek-ai/DeepSeek-V2-Lite",
-            dtype=dtype,
-            tensor_parallel_size=2,
-            distributed_executor_backend="mp",
-    ) as vllm_model:
-        vllm_model.generate(example_prompts, sampling_params)
-
-
 def test_models_distributed_Qwen3_W8A8():
     example_prompts = [
         "Hello, my name is",
@@ -172,15 +120,14 @@ def test_models_distributed_Qwen3_W4A8DYNAMIC():
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
-@pytest.mark.parametrize("model", DEEPSEEK_W4A8_MODELS)
 @patch.dict(os.environ, {"VLLM_ASCEND_MLA_PA": "1"})
-def test_models_distributed_DeepSeek_W4A8DYNAMIC(model):
+def test_models_distributed_DeepSeek_W4A8DYNAMIC():
     prompts = [
         "Hello, my name is",
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download(model),
+            snapshot_download("vllm-ascend/DeepSeek-V3-W4A8-Pruing"),
             dtype="auto",
             tensor_parallel_size=2,
             quantization="ascend",
@@ -207,16 +154,15 @@ def test_sp_for_qwen3_moe() -> None:
                                      top_k=50,
                                      top_p=0.9)
 
-    with VllmRunner(
-            snapshot_download("Qwen/Qwen3-30B-A3B"),
-            dtype="auto",
-            tensor_parallel_size=2,
-            distributed_executor_backend="mp",
-            compilation_config={
-                "pass_config": {
-                    "enable_sequence_parallelism": True
-                }
-            },
-            enable_expert_parallel=True,
-    ) as vllm_model:
+    with VllmRunner(snapshot_download("Qwen/Qwen3-30B-A3B"),
+                    dtype="auto",
+                    tensor_parallel_size=2,
+                    distributed_executor_backend="mp",
+                    compilation_config={
+                        "pass_config": {
+                            "enable_sequence_parallelism": True
+                        }
+                    },
+                    enable_expert_parallel=True,
+                    enforce_eager=True) as vllm_model:
         vllm_model.generate(example_prompts, sampling_params)