[Bugfix] Fix max image feature size for Llava-one-vision

tests/models/multimodal/processing/test_llava_next.py

@@ -13,6 +13,67 @@
 from ...utils import build_model_context
 
 
+def _validate_image_max_tokens_one(
+    processor: BaseMultiModalProcessor,
+    max_tokens: int,
+    failed_size_excs: list[tuple[ImageSize, Exception]],
+    image_size: ImageSize,
+) -> None:
+    info = processor.info
+    feature_size = info.get_num_image_tokens(image_width=image_size.width,
+                                             image_height=image_size.height)
+
+    try:
+        assert feature_size <= max_tokens, f"{feature_size} <= {max_tokens}"
+    except Exception as exc:
+        failed_size_excs.append((image_size, exc))
+
+
+@pytest.mark.skip("This test takes around 5 minutes to run. "
+                  "Comment this out to run it manually.")
+@pytest.mark.parametrize("model_id", ["llava-hf/llava-v1.6-mistral-7b-hf"])
+def test_processor_max_tokens(model_id):
+    ctx = build_model_context(
+        model_name=model_id,
+        tokenizer_name=model_id,
+        mm_processor_kwargs=None,
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(
+        ctx.model_config,
+        tokenizer=cached_get_tokenizer(ctx.model_config.tokenizer),
+    )
+    info = processor.info
+
+    seen_aspect_ratios = set[float]()
+    image_sizes = list[ImageSize]()
+
+    # The aspect ratio of the grid layout is between 1 and 2
+    # NOTE: Assumes that feature size calculation is the same if we
+    # swap the width and height of the image
+    for w, h in itertools.product(range(32, 4096), repeat=2):
+        aspect_ratio = w / h
+        if 1 <= aspect_ratio <= 2 and aspect_ratio not in seen_aspect_ratios:
+            image_sizes.append(ImageSize(w, h))
+            seen_aspect_ratios.add(aspect_ratio)
+
+    failed_size_excs = list[tuple[ImageSize, Exception]]()
+
+    validate_one = partial(
+        _validate_image_max_tokens_one,
+        processor,
+        info.get_max_image_tokens(),  # type: ignore
+        failed_size_excs,
+    )
+    pqdm(image_sizes, validate_one, n_jobs=8, desc="Validating image sizes")
+
+    if failed_size_excs:
+        msg = "Found failing image sizes:" \
+            + "\n========\n".join(f"[{size}]\n{exc}"
+                                  for size, exc in failed_size_excs)
+        raise AssertionError(msg)
+
+
 def _validate_image_prompt_replacements_one(
     processor: BaseMultiModalProcessor,
     num_imgs: int,

tests/models/multimodal/processing/test_llava_onevision.py

@@ -13,6 +13,68 @@
 from ...utils import build_model_context
 
 
+def _validate_image_max_tokens_one(
+    processor: BaseMultiModalProcessor,
+    max_tokens: int,
+    failed_size_excs: list[tuple[ImageSize, Exception]],
+    image_size: ImageSize,
+) -> None:
+    info = processor.info
+    feature_size = info.get_num_image_tokens(image_width=image_size.width,
+                                             image_height=image_size.height)
+
+    try:
+        assert feature_size <= max_tokens, f"{feature_size} <= {max_tokens}"
+    except Exception as exc:
+        failed_size_excs.append((image_size, exc))
+
+
+@pytest.mark.skip("This test takes around 5 minutes to run. "
+                  "Comment this out to run it manually.")
+@pytest.mark.parametrize("model_id",
+                         ["llava-hf/llava-onevision-qwen2-0.5b-ov-hf"])
+def test_processor_max_tokens(model_id):
+    ctx = build_model_context(
+        model_name=model_id,
+        tokenizer_name=model_id,
+        mm_processor_kwargs=None,
+        limit_mm_per_prompt={"image": 1},
+    )
+    processor = MULTIMODAL_REGISTRY.create_processor(
+        ctx.model_config,
+        tokenizer=cached_get_tokenizer(ctx.model_config.tokenizer),
+    )
+    info = processor.info
+
+    seen_aspect_ratios = set[float]()
+    image_sizes = list[ImageSize]()
+
+    # The aspect ratio of the grid layout is between 1 and 6
+    # NOTE: Assumes that feature size calculation is the same if we
+    # swap the width and height of the image
+    for w, h in itertools.product(range(32, 4096), repeat=2):
+        aspect_ratio = w / h
+        if 1 <= aspect_ratio <= 6 and aspect_ratio not in seen_aspect_ratios:
+            image_sizes.append(ImageSize(w, h))
+            seen_aspect_ratios.add(aspect_ratio)
+
+    failed_size_excs = list[tuple[ImageSize, Exception]]()
+
+    validate_one = partial(
+        _validate_image_max_tokens_one,
+        processor,
+        info.get_max_image_tokens(),  # type: ignore
+        failed_size_excs,
+    )
+    pqdm(image_sizes, validate_one, n_jobs=8, desc="Validating image sizes")
+
+    if failed_size_excs:
+        msg = "Found failing image sizes:" \
+            + "\n========\n".join(f"[{size}]\n{exc}"
+                                  for size, exc in failed_size_excs)
+        raise AssertionError(msg)
+
+
 def _validate_image_prompt_replacements_one(
     processor: BaseMultiModalProcessor,
     num_imgs: int,

vllm/model_executor/models/llava_onevision.py

@@ -19,8 +19,8 @@
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
                                     NestedTensors)
-from vllm.multimodal.parse import (MultiModalDataItems, VideoEmbeddingItems,
-                                   VideoProcessorItems)
+from vllm.multimodal.parse import (ImageSize, MultiModalDataItems,
+                                   VideoEmbeddingItems, VideoProcessorItems)
 from vllm.multimodal.processing import PromptReplacement
 from vllm.multimodal.profiling import ProcessorInputs
 from vllm.sequence import IntermediateTensors
@@ -145,6 +145,10 @@ def _get_num_unpadded_features(
 
         return (unpadded_features, newline_features)
 
+    def get_image_size_with_most_features(self) -> ImageSize:
+        # NOTE: This hardcoded value is found via processor tests
+        return ImageSize(width=1153, height=944)
+
     def _get_num_frame_tokens(
         self,
         *,