[Model][VLM] Add multi-video support for LLaVA-Onevision (vllm-project#8905)

Co-authored-by: litianjian <[email protected]>
Co-authored-by: DarkLight1337 <[email protected]>
Signed-off-by: LeiWang1999 <[email protected]>

Author: 3 people

tests/models/decoder_only/vision_language/test_llava_onevision.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Tuple, Type, overload
+from typing import List, Optional, Tuple, Type
 
 import pytest
 from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer,
@@ -9,9 +9,8 @@
 from vllm.sequence import SampleLogprobs
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
 
-from ....conftest import (VIDEO_ASSETS, HfRunner, PromptImageInput, VllmRunner,
-                          _VideoAssets)
-from ....utils import large_gpu_test
+from ....conftest import (VIDEO_ASSETS, HfRunner, PromptImageInput,
+                          PromptVideoInput, VllmRunner)
 from ...utils import check_logprobs_close
 
 # Video test
@@ -20,7 +19,7 @@
     "<|im_start|>user\n<video>\nwhy is this video funny?<|im_end|>\n<|im_start|>assistant\n"  # noqa: E501
 })
 
-models = ["llava-hf/llava-onevision-qwen2-7b-ov-hf"]
+models = ["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"]
 
 
 def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
@@ -47,50 +46,16 @@ def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
     return hf_output_ids, hf_output_str, out_logprobs
 
 
-@overload
-def run_video_test(
-    hf_runner: Type[HfRunner],
-    vllm_runner: Type[VllmRunner],
-    video_assets: _VideoAssets,
-    model: str,
-    *,
-    size_factors: List[float],
-    dtype: str,
-    max_tokens: int,
-    num_logprobs: int,
-    num_frames: int,
-    tensor_parallel_size: int,
-    distributed_executor_backend: Optional[str] = None,
-):
-    ...
-
-
-@overload
-def run_video_test(
-    hf_runner: Type[HfRunner],
-    vllm_runner: Type[VllmRunner],
-    video_assets: _VideoAssets,
-    model: str,
-    *,
-    sizes: List[Tuple[int, int]],
-    dtype: str,
-    max_tokens: int,
-    num_logprobs: int,
-    num_frames: int,
-    tensor_parallel_size: int,
-    distributed_executor_backend: Optional[str] = None,
-):
-    ...
+# Video test
+_LIMIT_VIDEO_PER_PROMPT = 4
 
 
 def run_video_test(
     hf_runner: Type[HfRunner],
     vllm_runner: Type[VllmRunner],
-    video_assets: _VideoAssets,
+    inputs: List[Tuple[List[str], PromptVideoInput]],
     model: str,
     *,
-    size_factors: Optional[List[float]] = None,
-    sizes: Optional[List[Tuple[int, int]]] = None,
     dtype: str,
     max_tokens: int,
     num_logprobs: int,
@@ -99,38 +64,20 @@ def run_video_test(
     distributed_executor_backend: Optional[str] = None,
 ):
     torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype]
-
-    videos = [
-        sample_frames_from_video(asset.np_ndarrays, num_frames)
-        for asset in video_assets
-    ]
-
-    if size_factors is not None:
-        inputs_per_video = [(
-            [prompt for _ in size_factors],
-            [rescale_video_size(video, factor) for factor in size_factors],
-        ) for video, prompt in zip(videos, HF_VIDEO_PROMPTS)]
-    elif sizes is not None:
-        inputs_per_video = [(
-            [prompt for _ in sizes],
-            [resize_video(video, size) for size in sizes],
-        ) for video, prompt in zip(videos, HF_VIDEO_PROMPTS)]
-    else:
-        raise ValueError("You must provide either `size_factors` or `sizes`")
-
-    # max_model_len should be greater than image_feature_size
     with vllm_runner(model,
                      dtype=dtype,
-                     max_model_len=4096,
+                     max_model_len=16384,
                      tensor_parallel_size=tensor_parallel_size,
                      distributed_executor_backend=distributed_executor_backend,
-                     enforce_eager=True) as vllm_model:
-        vllm_outputs_per_video = [
+                     enforce_eager=True,
+                     limit_mm_per_prompt={"video": _LIMIT_VIDEO_PER_PROMPT
+                                          }) as vllm_model:
+        vllm_outputs_per_input = [
             vllm_model.generate_greedy_logprobs(prompts,
                                                 max_tokens,
                                                 num_logprobs=num_logprobs,
                                                 videos=videos)
-            for prompts, videos in inputs_per_video
+            for prompts, videos in inputs
         ]
 
     def process(hf_inputs: BatchEncoding):
@@ -142,16 +89,16 @@ def process(hf_inputs: BatchEncoding):
                    dtype=dtype,
                    postprocess_inputs=process,
                    auto_cls=AutoModelForVision2Seq) as hf_model:
-        hf_outputs_per_video = [
+        hf_outputs_per_input = [
             hf_model.generate_greedy_logprobs_limit(prompts,
                                                     max_tokens,
                                                     num_logprobs=num_logprobs,
                                                     videos=videos)
-            for prompts, videos in inputs_per_video
+            for prompts, videos in inputs
         ]
 
-    for hf_outputs, vllm_outputs in zip(hf_outputs_per_video,
-                                        vllm_outputs_per_video):
+    for hf_outputs, vllm_outputs in zip(hf_outputs_per_input,
+                                        vllm_outputs_per_input):
         # TODO: Check whether using original CLIPVisionModel can improve
         # consistency against HF
         check_logprobs_close(
@@ -165,74 +112,51 @@ def process(hf_inputs: BatchEncoding):
         )
 
 
-@large_gpu_test(min_gb=48)
 @pytest.mark.parametrize("model", models)
-@pytest.mark.parametrize(
-    "size_factors",
-    [
-        # No video
-        [],
-        # Single-scale
-        [1.0],
-        # Single-scale, batched
-        [1.0, 1.0, 1.0],
-        # Multi-scale
-        [0.25, 0.5, 1.0],
-    ],
-)
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [128])
 @pytest.mark.parametrize("num_logprobs", [5])
 @pytest.mark.parametrize("num_frames", [16])
-def test_models(hf_runner, vllm_runner, video_assets, model, size_factors,
-                dtype, max_tokens, num_logprobs, num_frames) -> None:
-    """Inference result should be the same between hf and vllm.
-
-    All the image fixtures for the test is under tests/videos.
-    For huggingface runner, we provide the np.ndarray as input.
-    For vllm runner, we provide MultiModalDataDict objects 
-    and corresponding MultiModalConfig as input.
-    Note, the text input is also adjusted to abide by vllm contract.
-    The text output is sanitized to be able to compare with hf.
-    """
+def test_models_multiple_video_inputs(hf_runner, vllm_runner, video_assets,
+                                      model, dtype, max_tokens, num_logprobs,
+                                      num_frames) -> None:
+    video = sample_frames_from_video(video_assets[0].np_ndarrays, num_frames)
+    inputs = [(
+        [
+            "<|im_start|>user <video><video>\nDescribe 2 videos. \
+                <|im_end|><|im_start|>assistant\n",
+            "<|im_start|>user <video><video>\nDescribe 2 videos. \
+                <|im_end|><|im_start|>assistant\n",
+            "<|im_start|>user <video><video><video><video>\nDescribe 4 videos. \
+                <|im_end|><|im_start|>assistant\n",
+            "<|im_start|>user <video>\nwhy is this video funny? \
+                <|im_end|><|im_start|>assistant\n",
+        ],
+        [
+            [video, video],
+            # Images with different sizes and aspect-ratios
+            [
+                rescale_video_size(video, 0.1),
+                video,
+            ],
+            [
+                video,
+                rescale_video_size(video, 0.25),
+                resize_video(video, (183, 488)),
+                resize_video(video, (488, 183))
+            ],
+            video,
+        ])]
     run_video_test(
         hf_runner,
         vllm_runner,
-        video_assets,
+        inputs,
         model,
-        size_factors=size_factors,
         dtype=dtype,
         max_tokens=max_tokens,
         num_logprobs=num_logprobs,
-        num_frames=num_frames,
         tensor_parallel_size=1,
-    )
-
-
-@large_gpu_test(min_gb=48)
-@pytest.mark.parametrize("model", models)
-@pytest.mark.parametrize(
-    "sizes",
-    [[(1669, 2560), (2560, 1669), (183, 488), (488, 183)]],
-)
-@pytest.mark.parametrize("dtype", ["half"])
-@pytest.mark.parametrize("max_tokens", [128])
-@pytest.mark.parametrize("num_logprobs", [5])
-@pytest.mark.parametrize("num_frames", [16])
-def test_models_fixed_sizes(hf_runner, vllm_runner, video_assets, model, sizes,
-                            dtype, max_tokens, num_logprobs,
-                            num_frames) -> None:
-    run_video_test(
-        hf_runner,
-        vllm_runner,
-        video_assets,
-        model,
-        sizes=sizes,
-        dtype=dtype,
-        max_tokens=max_tokens,
-        num_logprobs=num_logprobs,
         num_frames=num_frames,
-        tensor_parallel_size=1,
     )
 
 
@@ -303,7 +227,6 @@ def process(hf_inputs: BatchEncoding):
         )
 
 
-@large_gpu_test(min_gb=48)
 @pytest.mark.parametrize("model", models)
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [128])

vllm/model_executor/models/clip.py

@@ -88,6 +88,7 @@ def dummy_image_for_clip(
 def dummy_video_for_clip(
     hf_config: CLIPVisionConfig,
     num_frames: int,
+    num_videos: int = 1,
     *,
     image_width_override: Optional[int] = None,
     image_height_override: Optional[int] = None,
@@ -99,7 +100,8 @@ def dummy_video_for_clip(
         image_height_override=image_height_override)
     np_frame = np.array(pil_frame["image"])
     mm_data_per_video = np.repeat([np_frame], num_frames, axis=0)
-    mm_data = {"video": mm_data_per_video}
+    video_data = [mm_data_per_video] * num_videos
+    mm_data = {"video": video_data}
     return mm_data