add qwen2.5-vl

Signed-off-by: wangli <wangli858794774@gmail.com>

Author: Potabk

tests/conftest.py

@@ -21,6 +21,7 @@
 from typing import List, Optional, Tuple, TypeVar, Union
 
 import numpy as np
+import contextlib
 import pytest
 import torch
 from PIL import Image
@@ -34,7 +35,7 @@
 from vllm.sampling_params import BeamSearchParams
 from vllm.utils import is_list_of
 
-from tests.model_utils import (TokensTextLogprobs,
+from tests.model_utils import (PROMPT_TEMPLATES, TokensTextLogprobs,
                                TokensTextLogprobsPromptLogprobs)
 
 logger = init_logger(__name__)
@@ -51,6 +52,8 @@
 def cleanup_dist_env_and_memory():
     destroy_model_parallel()
     destroy_distributed_environment()
+    with contextlib.suppress(AssertionError):
+        torch.distributed.destroy_process_group()
     gc.collect()
     torch.npu.empty_cache()
 
@@ -340,3 +343,8 @@ def __exit__(self, exc_type, exc_value, traceback):
 @pytest.fixture(scope="session")
 def vllm_runner():
     return VllmRunner
+
+
+@pytest.fixture(params=list(PROMPT_TEMPLATES.keys()))
+def prompt_template(request):
+    return PROMPT_TEMPLATES[request.param]

tests/model_utils.py

@@ -18,7 +18,7 @@
 #
 
 import warnings
-from typing import Dict, List, Optional, Sequence, Tuple, Union
+from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 import torch
 from vllm.config import ModelConfig, TaskOption
@@ -301,3 +301,16 @@ def build_model_context(model_name: str,
         limit_mm_per_prompt=limit_mm_per_prompt,
     )
     return InputContext(model_config)
+
+
+def qwen_prompt(questions: List[str]) -> List[str]:
+    placeholder = "<|image_pad|>"
+    return [("<|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 questions]
+
+
+# Map of prompt templates for different models.
+PROMPT_TEMPLATES: dict[str, Callable] = {
+    "qwen2.5vl": qwen_prompt,
+}

tests/multicard/test_offline_inference_distributed.py

@@ -25,6 +25,7 @@
 import pytest
 import vllm  # noqa: F401
 from conftest import VllmRunner
+from vllm.assets.image import ImageAsset
 
 os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
 
@@ -50,6 +51,34 @@ def test_models_distributed(model: str,
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
+@pytest.mark.parametrize("model", ["Qwen/Qwen2.5-VL-32B-Instruct"])
+@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "1",
+                    reason="qwen2.5_vl is not supported on v1")
+def test_multimodal(model: str, prompt_template, vllm_runner):
+    image = ImageAsset("cherry_blossom") \
+        .pil_image.convert("RGB")
+    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?",
+    ]
+    images = [image] * len(img_questions)
+    prompts = prompt_template(img_questions)
+    with vllm_runner(model,
+                     max_model_len=4096,
+                     tensor_parallel_size=4,
+                     distributed_executor_backend="mp",
+                     mm_processor_kwargs={
+                         "min_pixels": 28 * 28,
+                         "max_pixels": 1280 * 28 * 28,
+                         "fps": 1,
+                     }) as vllm_model:
+        vllm_model.generate_greedy(prompts=prompts,
+                                   images=images,
+                                   max_tokens=64)
+
+
 if __name__ == "__main__":
     import pytest
     pytest.main([__file__])

tests/ops/test_rotary_embedding.py

@@ -202,3 +202,4 @@ def test_rotary_embedding_quant_with_leading_dim(
                                ref_key,
                                atol=DEFAULT_ATOL,
                                rtol=DEFAULT_RTOL)
+    torch.npu.empty_cache()

tests/singlecard/test_offline_inference.py

@@ -25,12 +25,13 @@
 import pytest
 import vllm  # noqa: F401
 from conftest import VllmRunner
+from vllm.assets.image import ImageAsset
 
 import vllm_ascend  # noqa: F401
 
-MODELS = [
-    "Qwen/Qwen2.5-0.5B-Instruct",
-]
+MODELS = ["Qwen/Qwen2.5-0.5B-Instruct"]
+MULTIMODALITY_MODELS = ["Qwen/Qwen2.5-VL-3B-Instruct"]
+
 os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
 
 
@@ -53,6 +54,32 @@ def test_models(model: str, dtype: str, max_tokens: int) -> None:
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
+@pytest.mark.parametrize("model", MULTIMODALITY_MODELS)
+@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "1",
+                    reason="qwen2.5_vl is not supported on v1")
+def test_multimodal(model: str, prompt_template, vllm_runner):
+    image = ImageAsset("cherry_blossom") \
+        .pil_image.convert("RGB")
+    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?",
+    ]
+    images = [image] * len(img_questions)
+    prompts = prompt_template(img_questions)
+    with vllm_runner(model,
+                     max_model_len=4096,
+                     mm_processor_kwargs={
+                         "min_pixels": 28 * 28,
+                         "max_pixels": 1280 * 28 * 28,
+                         "fps": 1,
+                     }) as vllm_model:
+        vllm_model.generate_greedy(prompts=prompts,
+                                   images=images,
+                                   max_tokens=64)
+
+
 if __name__ == "__main__":
     import pytest
     pytest.main([__file__])