[Misc] Move some multimodal utils to modality-specific modules (#11494)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/models/decoder_only/vision_language/test_awq.py

@@ -3,7 +3,7 @@ from typing import List, Optional, Type
 import pytest
 import torch
 
-from vllm.multimodal.utils import rescale_image_size
+from vllm.multimodal.image import rescale_image_size
 
 from ....conftest import IMAGE_ASSETS, VllmRunner, _ImageAssets
 from ...utils import check_logprobs_close

tests/models/decoder_only/vision_language/test_h2ovl.py

@@ -8,7 +8,7 @@ from transformers import AutoConfig
 # Import the functions to test
 from vllm.model_executor.models.h2ovl import (calculate_num_blocks,
                                               image_to_pixel_values_wrapper)
-from vllm.multimodal.utils import rescale_image_size
+from vllm.multimodal.image import rescale_image_size
 
 models = [
     "h2oai/h2ovl-mississippi-800m",  # Replace with your actual model names

tests/models/decoder_only/vision_language/test_phi3v.py

@@ -5,7 +5,7 @@ from typing import List, Optional, Tuple, Type
 import pytest
 from transformers import AutoTokenizer
 
-from vllm.multimodal.utils import rescale_image_size
+from vllm.multimodal.image import rescale_image_size
 from vllm.platforms import current_platform
 from vllm.sequence import SampleLogprobs
 

tests/models/decoder_only/vision_language/test_qwen2_vl.py

@@ -6,8 +6,8 @@ import torch
 from PIL import Image
 
 from vllm.entrypoints.llm import LLM
-from vllm.multimodal.utils import (rescale_image_size, rescale_video_size,
-                                   sample_frames_from_video)
+from vllm.multimodal.image import rescale_image_size
+from vllm.multimodal.video import rescale_video_size, sample_frames_from_video
 
 from ....conftest import (IMAGE_ASSETS, VIDEO_ASSETS, PromptImageInput,
                           PromptVideoInput, VllmRunner)

tests/models/decoder_only/vision_language/vlm_utils/builders.py

@@ -5,8 +5,9 @@ from typing import Callable, Iterable, List, Optional, Tuple, Union
 
 import torch
 
-from vllm.multimodal.utils import (rescale_image_size, rescale_video_size,
-                                   resize_video, sample_frames_from_video)
+from vllm.multimodal.image import rescale_image_size
+from vllm.multimodal.video import (rescale_video_size, resize_video,
+                                   sample_frames_from_video)
 
 from .....conftest import _ImageAssets, _VideoAssets
 from .types import (SINGLE_IMAGE_BASE_PROMPTS, TEST_IMG_PLACEHOLDER,

tests/models/decoder_only/vision_language/vlm_utils/custom_inputs.py

 """Custom input builders for edge-cases in different models."""
 from typing import Callable
 
-from vllm.multimodal.utils import (rescale_image_size, rescale_video_size,
-                                   resize_video, sample_frames_from_video)
+from vllm.multimodal.image import rescale_image_size
+from vllm.multimodal.video import (rescale_video_size, resize_video,
+                                   sample_frames_from_video)
 
 from .....conftest import IMAGE_ASSETS, VIDEO_ASSETS
 from .builders import build_multi_image_inputs, build_single_image_inputs

tests/models/encoder_decoder/vision_language/test_mllama.py

@@ -6,7 +6,7 @@ from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer,
 
 from vllm.attention.selector import (_Backend, _cached_get_attn_backend,
                                      global_force_attn_backend_context_manager)
-from vllm.multimodal.utils import rescale_image_size
+from vllm.multimodal.image import rescale_image_size
 from vllm.sequence import SampleLogprobs
 
 from ....conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner,

tests/multimodal/test_mapper.py

@@ -6,7 +6,7 @@ from transformers import LlavaNextImageProcessor
 
 from vllm.config import ModelConfig
 from vllm.multimodal import MultiModalRegistry
-from vllm.multimodal.utils import rescale_image_size
+from vllm.multimodal.image import rescale_image_size
 
 
 @pytest.fixture

vllm/assets/video.py

@@ -7,7 +7,7 @@ import numpy.typing as npt
 from huggingface_hub import hf_hub_download
 from PIL import Image
 
-from vllm.multimodal.utils import (sample_frames_from_video,
+from vllm.multimodal.video import (sample_frames_from_video,
                                    try_import_video_packages)
 
 from .base import get_cache_dir

vllm/multimodal/audio.py

+from typing import Any
+
 import numpy as np
 import numpy.typing as npt
 
@@ -26,6 +28,16 @@ class AudioPlugin(MultiModalPlugin):
             "There is no default maximum multimodal tokens")
 
 
+def try_import_audio_packages() -> tuple[Any, Any]:
+    try:
+        import librosa
+        import soundfile
+    except ImportError as exc:
+        raise ImportError(
+            "Please install vllm[audio] for audio support.") from exc
+    return librosa, soundfile
+
+
 def resample_audio(
     audio: npt.NDArray[np.floating],
     *,

vllm/multimodal/image.py

@@ -84,3 +84,15 @@ class ImagePlugin(MultiModalPlugin):
 
     def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
         return 3000
+
+
+def rescale_image_size(image: Image.Image,
+                       size_factor: float,
+                       transpose: int = -1) -> Image.Image:
+    """Rescale the dimensions of an image by a constant factor."""
+    new_width = int(image.width * size_factor)
+    new_height = int(image.height * size_factor)
+    image = image.resize((new_width, new_height))
+    if transpose >= 0:
+        image = image.transpose(Image.Transpose(transpose))
+    return image

vllm/multimodal/utils.py

@@ -2,7 +2,7 @@ import base64
 import os
 from functools import lru_cache
 from io import BytesIO
-from typing import Any, List, Optional, Tuple, TypeVar, Union
+from typing import List, Optional, Tuple, TypeVar, Union
 
 import numpy as np
 import numpy.typing as npt
@@ -14,7 +14,9 @@ from vllm.connections import global_http_connection
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer, get_tokenizer
 
+from .audio import try_import_audio_packages
 from .inputs import MultiModalDataDict, PlaceholderRange
+from .video import try_import_video_packages
 
 logger = init_logger(__name__)
 
@@ -198,16 +200,6 @@ async def async_fetch_video(video_url: str,
     return video
 
 
-def try_import_audio_packages() -> Tuple[Any, Any]:
-    try:
-        import librosa
-        import soundfile
-    except ImportError as exc:
-        raise ImportError(
-            "Please install vllm[audio] for audio support.") from exc
-    return librosa, soundfile
-
-
 def fetch_audio(audio_url: str) -> Tuple[np.ndarray, Union[int, float]]:
     """
     Load audio from a URL.
@@ -324,60 +316,6 @@ def load_image_from_base64(image: Union[bytes, str]) -> Image.Image:
     return _load_image_from_bytes(base64.b64decode(image))
 
 
-def rescale_image_size(image: Image.Image,
-                       size_factor: float,
-                       transpose: int = -1) -> Image.Image:
-    """Rescale the dimensions of an image by a constant factor."""
-    new_width = int(image.width * size_factor)
-    new_height = int(image.height * size_factor)
-    image = image.resize((new_width, new_height))
-    if transpose >= 0:
-        image = image.transpose(Image.Transpose(transpose))
-    return image
-
-
-def try_import_video_packages():
-    try:
-        import cv2
-        import decord
-    except ImportError as exc:
-        raise ImportError(
-            "Please install vllm[video] for video support.") from exc
-    return cv2, decord
-
-
-def resize_video(frames: npt.NDArray, size: Tuple[int, int]) -> npt.NDArray:
-    cv2, _ = try_import_video_packages()
-
-    num_frames, _, _, channels = frames.shape
-    new_height, new_width = size
-    resized_frames = np.empty((num_frames, new_height, new_width, channels),
-                              dtype=frames.dtype)
-    for i, frame in enumerate(frames):
-        resized_frame = cv2.resize(frame, (new_width, new_height))
-        resized_frames[i] = resized_frame
-    return resized_frames
-
-
-def rescale_video_size(frames: npt.NDArray, size_factor: float) -> npt.NDArray:
-    _, height, width, _ = frames.shape
-    new_height = int(height * size_factor)
-    new_width = int(width * size_factor)
-
-    return resize_video(frames, (new_height, new_width))
-
-
-def sample_frames_from_video(frames: npt.NDArray,
-                             num_frames: int) -> npt.NDArray:
-    total_frames = frames.shape[0]
-    if num_frames == -1:
-        return frames
-    else:
-        frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
-        sampled_frames = frames[frame_indices, ...]
-        return sampled_frames
-
-
 def encode_video_base64(frames: npt.NDArray) -> str:
     base64_frames = []
     frames_list = [frames[i] for i in range(frames.shape[0])]

vllm/multimodal/video.py

@@ -2,6 +2,7 @@ from functools import lru_cache
 from typing import TYPE_CHECKING, Any, Dict, Optional
 
 import numpy as np
+import numpy.typing as npt
 
 from vllm.inputs.registry import InputContext
 from vllm.logger import init_logger
@@ -75,3 +76,45 @@ class VideoPlugin(ImagePlugin):
 
     def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
         return 4096
+
+
+def try_import_video_packages() -> tuple[Any, Any]:
+    try:
+        import cv2
+        import decord
+    except ImportError as exc:
+        raise ImportError(
+            "Please install vllm[video] for video support.") from exc
+    return cv2, decord
+
+
+def resize_video(frames: npt.NDArray, size: tuple[int, int]) -> npt.NDArray:
+    cv2, _ = try_import_video_packages()
+
+    num_frames, _, _, channels = frames.shape
+    new_height, new_width = size
+    resized_frames = np.empty((num_frames, new_height, new_width, channels),
+                              dtype=frames.dtype)
+    for i, frame in enumerate(frames):
+        resized_frame = cv2.resize(frame, (new_width, new_height))
+        resized_frames[i] = resized_frame
+    return resized_frames
+
+
+def rescale_video_size(frames: npt.NDArray, size_factor: float) -> npt.NDArray:
+    _, height, width, _ = frames.shape
+    new_height = int(height * size_factor)
+    new_width = int(width * size_factor)
+
+    return resize_video(frames, (new_height, new_width))
+
+
+def sample_frames_from_video(frames: npt.NDArray,
+                             num_frames: int) -> npt.NDArray:
+    total_frames = frames.shape[0]
+    if num_frames == -1:
+        return frames
+
+    frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
+    sampled_frames = frames[frame_indices, ...]
+    return sampled_frames