[CI] Add 310p e2e test back (#5797)

This PR add 310 e2e test back to ensure the related PR will be tested on
310.
1. for light e2e, we'll run 310p test if the changed files are located
in `vllm_ascend/_310p`
2. for full e2e, we'll always run 310p test
3. for main2main test, we'll stop run 310p test

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

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

tests/e2e/310p/test_offline_inference_310p.py

@@ -15,58 +15,61 @@
 # limitations under the License.
 # This file is a part of the vllm-ascend project.
 import pytest
-import vllm  # noqa: F401
-from vllm import SamplingParams
+from vllm.assets.image import ImageAsset
 
-import vllm_ascend  # noqa: F401
 from tests.e2e.conftest import VllmRunner
 
-MODELS = ["Qwen/Qwen3-0.6B", "Qwen/Qwen2.5-7B-Instruct"]
 
-
-@pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["float16"])
 @pytest.mark.parametrize("max_tokens", [5])
-def test_models(model: str, dtype: str, max_tokens: int) -> None:
+def test_llm_models(dtype: str, max_tokens: int) -> None:
     example_prompts = [
         "Hello, my name is",
         "The future of AI is",
     ]
 
-    with VllmRunner(model,
+    with VllmRunner("Qwen/Qwen3-0.6B",
                     tensor_parallel_size=1,
                     dtype=dtype,
                     max_model_len=2048,
-                    enforce_eager=True,
-                    compilation_config={
-                        "custom_ops":
-                        ["none", "+rms_norm", "+rotary_embedding"]
-                    }) as vllm_model:
+                    enforce_eager=True) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
-VL_MODELS = ["Qwen/Qwen2.5-VL-3B-Instruct"]
+def test_multimodal_vl():
+    image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
 
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["float16"])
-def test_vl_model_with_samples(model: str, dtype: str) -> None:
-    example_prompts = [
-        "Hello, my name is",
-        "The future of AI is",
+    img_questions = [
+        "What is the content of this image?",
+        "Describe the content of this image in detail.",
+        "What's in the image?",
+        "Where is this image taken?",
     ]
 
-    with VllmRunner(model,
-                    tensor_parallel_size=1,
-                    dtype=dtype,
-                    max_model_len=2048,
+    images = [image] * len(img_questions)
+    placeholder = "<|image_pad|>"
+    prompts = [
+        ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+         f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+         f"{q}<|im_end|>\n<|im_start|>assistant\n") for q in img_questions
+    ]
+
+    with VllmRunner("Qwen/Qwen2.5-VL-3B-Instruct",
+                    mm_processor_kwargs={
+                        "min_pixels": 28 * 28,
+                        "max_pixels": 1280 * 28 * 28,
+                        "fps": 1,
+                    },
+                    max_model_len=8192,
                     enforce_eager=True,
-                    compilation_config={
-                        "custom_ops":
-                        ["none", "+rms_norm", "+rotary_embedding"]
-                    }) as vllm_model:
-        sampling_params = SamplingParams(max_tokens=100,
-                                         top_p=0.95,
-                                         top_k=50,
-                                         temperature=0.6)
-        vllm_model.generate(example_prompts, sampling_params)
+                    limit_mm_per_prompt={"image": 1}) as vllm_model:
+        outputs = vllm_model.generate_greedy(
+            prompts=prompts,
+            images=images,
+            max_tokens=64,
+        )
+
+        assert len(outputs) == len(prompts)
+
+        for _, output_str in outputs:
+            assert output_str, "Generated output should not be empty."

tests/e2e/310p/test_offline_inference_parallel_310p.py

@@ -0,0 +1,20 @@
+import pytest
+
+from tests.e2e.conftest import VllmRunner
+
+
+@pytest.mark.parametrize("dtype", ["float16"])
+@pytest.mark.parametrize("max_tokens", [5])
+@pytest.skip("310p does not support parallel inference now. Fix me")
+def test_models(dtype: str, max_tokens: int) -> None:
+    example_prompts = [
+        "Hello, my name is",
+        "The future of AI is",
+    ]
+
+    with VllmRunner("Qwen/Qwen3-0.6B",
+                    tensor_parallel_size=4,
+                    dtype=dtype,
+                    max_model_len=2048,
+                    enforce_eager=True) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)