[Model] Add min_pixels / max_pixels to Qwen2VL as mm_processor_kwargs

examples/offline_inference_vision_language.py

@@ -267,6 +267,11 @@ def run_qwen2_vl(question: str, modality: str):
         model=model_name,
         max_model_len=8192,
         max_num_seqs=5,
+        # Note - mm_processor_kwargs can also be passed to generate/chat calls
+        mm_processor_kwargs={
+            "min_pixels": 28 * 28,
+            "max_pixels": 1280 * 28 * 28,
+        },
     )
 
     prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"

tests/models/decoder_only/vision_language/test_qwen2_vl.py

@@ -0,0 +1,160 @@
+from typing import Any, Dict, Tuple
+
+import pytest
+import torch
+from PIL.Image import Image
+from transformers import AutoTokenizer
+
+from vllm.inputs import InputContext, token_inputs
+from vllm.multimodal import MultiModalRegistry
+
+from ....conftest import _ImageAssets
+from ...utils import build_model_context
+
+MODEL = "Qwen/Qwen2-VL-2B-Instruct"
+MIN_PIXELS = "min_pixels"
+MAX_PIXELS = "max_pixels"
+
+
+# Fixtures lazy import to avoid initializing CUDA during test collection
+# NOTE: Qwen2vl supports multiple input modalities, so it registers multiple
+# input mappers.
+@pytest.fixture()
+def image_input_mapper_for_qwen2_vl():
+    from vllm.model_executor.models.qwen2_vl import (
+        image_input_mapper_for_qwen2_vl)
+    return image_input_mapper_for_qwen2_vl
+
+
+@pytest.fixture()
+def input_processor_for_qwen2_vl():
+    from vllm.model_executor.models.qwen2_vl import (
+        input_processor_for_qwen2_vl)
+    return input_processor_for_qwen2_vl
+
+
+@pytest.fixture()
+def qwen2_vl_context() -> InputContext:
+    return build_model_context(model_name=MODEL)
+
+
+@pytest.fixture()
+def get_max_qwen2_vl_image_tokens():
+    from vllm.model_executor.models.qwen2_vl import (
+        get_max_qwen2_vl_image_tokens)
+    return get_max_qwen2_vl_image_tokens
+
+
+@pytest.fixture()
+def dummy_data_for_qwen2_vl():
+    from vllm.model_executor.models.qwen2_vl import dummy_data_for_qwen2_vl
+    return dummy_data_for_qwen2_vl
+
+
+@pytest.mark.parametrize("mm_processor_kwargs,expected_max_tokens", [
+    ({}, 1225),
+    ({
+        MIN_PIXELS: 64**2,
+        MAX_PIXELS: 512**2
+    }, 324),
+])
+def test_qwen2_vl_max_image_tokens(get_max_qwen2_vl_image_tokens,
+                                   qwen2_vl_context: InputContext,
+                                   mm_processor_kwargs: Dict[str, Any],
+                                   expected_max_tokens: int):
+    """Ensure that the max token calc handles min/max pixels properly."""
+    actual_max_tokens = get_max_qwen2_vl_image_tokens(qwen2_vl_context,
+                                                      **mm_processor_kwargs)
+    assert actual_max_tokens == expected_max_tokens
+
+
+@pytest.mark.parametrize("mm_processor_kwargs,token_count,img_size", [
+    [{}, 1225, (980, 980)],
+    [{
+        MIN_PIXELS: 64**2,
+        MAX_PIXELS: 512**2
+    }, 324, (504, 504)],
+])
+def test_qwen2_vl_dummy_data(dummy_data_for_qwen2_vl,
+                             qwen2_vl_context: InputContext,
+                             mm_processor_kwargs: Dict[str, Any],
+                             token_count: int, img_size: Tuple[int, int]):
+    """Ensure that the dummy data handles min/max pixels properly."""
+    seq_len = 3000
+    hf_config = qwen2_vl_context.get_hf_config()
+    image_token_id = hf_config.image_token_id
+
+    # NOTE: video value is required, but isn't actually used
+    # when making the dummy data except for error handling currently
+    seq_data, mm_data = dummy_data_for_qwen2_vl(qwen2_vl_context, seq_len, {
+        "image": 1,
+        "video": 0
+    }, **mm_processor_kwargs)
+
+    # Ensure we have the right number of placeholders for min/max pixel values
+    assert seq_data.get_token_ids().count(image_token_id) == token_count
+
+    # Ensure the images were resized correctly
+    image = mm_data["image"]
+    assert isinstance(image, Image)
+    assert image.size == img_size
+
+
+@pytest.mark.parametrize("mm_processor_kwargs,num_placeholders", [
+    ({}, 1426),
+    ({
+        MIN_PIXELS: 64**2,
+        MAX_PIXELS: 512**2
+    }, 330),
+])
+def test_input_processor(input_processor_for_qwen2_vl,
+                         qwen2_vl_context: InputContext,
+                         image_assets: _ImageAssets, num_placeholders: int,
+                         mm_processor_kwargs: Dict[str, Any]):
+    """Ensure that the image processor handles min/max pixels properly."""
+    tokenizer = AutoTokenizer.from_pretrained(MODEL)
+    prompt = "<|vision_start|><|image_pad|><|vision_end|>"
+
+    image = image_assets[0].pil_image
+    hf_config = qwen2_vl_context.get_hf_config()
+    image_token_id = hf_config.image_token_id
+
+    inputs = token_inputs(prompt_token_ids=tokenizer.encode(prompt),
+                          prompt=prompt,
+                          multi_modal_data={"image": [image]})
+
+    processed_inputs = input_processor_for_qwen2_vl(qwen2_vl_context, inputs,
+                                                    **mm_processor_kwargs)
+    assert processed_inputs["prompt_token_ids"].count(
+        image_token_id) == num_placeholders
+    assert len(processed_inputs["multi_modal_data"]["image"]) == 1
+
+
+@pytest.mark.parametrize("mm_processor_kwargs,pixels_shape", [
+    ({}, [5704, 1176]),
+    ({
+        MIN_PIXELS: 64**2,
+        MAX_PIXELS: 512**2
+    }, [1320, 1176]),
+])
+def test_image_mapper_override(qwen2_vl_context: InputContext,
+                               image_assets: _ImageAssets,
+                               mm_processor_kwargs: Dict[str, Any],
+                               pixels_shape: Tuple[int, int]):
+    """Ensure that the image mapper handles min/max pixels properly."""
+    mm_registry = MultiModalRegistry()
+    mm_registry.init_mm_limits_per_prompt(qwen2_vl_context.model_config)
+
+    image = image_assets[0].pil_image
+
+    mapped_output = mm_registry.map_input(
+        qwen2_vl_context.model_config,
+        {"image": image},
+        mm_processor_kwargs=mm_processor_kwargs,
+    )
+
+    # Dimension 0 of pixel values should match the product of image_grid_thw
+    actual_pixels_shape = mapped_output["pixel_values"].shape
+    assert list(actual_pixels_shape) == pixels_shape
+    assert actual_pixels_shape[0] == torch.prod(
+        mapped_output["image_grid_thw"])

vllm/model_executor/models/qwen2_vl.py

@@ -549,6 +549,9 @@ def mm_input_mapper_for_qwen2_vl(
     ctx: InputContext,
     data: MultiModalData[object],
     data_type_key: str,
+    *,
+    min_pixels: Optional[int] = None,
+    max_pixels: Optional[int] = None,
 ) -> MultiModalInputs:
     """Input mapper for Qwen2-VL."""
     if data_type_key == "image" and isinstance(data, dict):
@@ -557,8 +560,19 @@ def mm_input_mapper_for_qwen2_vl(
             "image_grid_thw": data.get("image_grid_thw"),
         })
     model_config = ctx.model_config
+    # Handle mm processor kwargs; we pass these at creation time
+    # because preprocess() in transformers doesn't expose them
+    mm_processor_kwargs = {}
+    if min_pixels:
+        mm_processor_kwargs["min_pixels"] = min_pixels
+    if max_pixels:
+        mm_processor_kwargs["max_pixels"] = max_pixels
+
     image_processor = cached_get_image_processor(
-        model_config.model, trust_remote_code=model_config.trust_remote_code)
+        model_config.model,
+        trust_remote_code=model_config.trust_remote_code,
+        **mm_processor_kwargs,
+    )
     if image_processor is None:
         raise RuntimeError("No HuggingFace processor is available "
                            "to process the image object")
@@ -631,25 +645,36 @@ def _get_max_image_info(
     image_processor,
     data_type_key: str = "image",
     mm_count: int = 1,
+    min_pixels: Optional[int] = None,
+    max_pixels: Optional[int] = None,
 ):
+    # Limit min / max pixels unless they're explicitly provided
+    if min_pixels is None:
+        min_pixels = max(image_processor.min_pixels, 28 * 28)
+    if max_pixels is None:
+        max_pixels = min(image_processor.max_pixels, 1280 * 28 * 28)
+
     return _get_vision_info(
         image_processor,
         height=9999999,
         width=9999999,
-
-        # Limit min / max pixels.
-        min_pixels=max(image_processor.min_pixels, 28 * 28),
-        max_pixels=min(image_processor.max_pixels, 1280 * 28 * 28),
+        min_pixels=min_pixels,
+        max_pixels=max_pixels,
         data_type_key=data_type_key,
         mm_count=mm_count,
     )
 
 
-def get_max_qwen2_vl_mm_tokens(ctx: InputContext, data_type_key: str) -> int:
+def get_max_qwen2_vl_mm_tokens(ctx: InputContext,
+                               data_type_key: str,
+                               *,
+                               min_pixels=None,
+                               max_pixels=None) -> int:
     image_processor = cached_get_image_processor(ctx.model_config.model)
     max_resized_height, max_resized_width, max_llm_image_tokens = \
         _get_max_image_info(image_processor, data_type_key=data_type_key,
-                            mm_count=1)
+                            mm_count=1, min_pixels=min_pixels,
+                            max_pixels=max_pixels)
     return max_llm_image_tokens
 
 
@@ -660,14 +685,20 @@ def get_max_qwen2_vl_mm_tokens(ctx: InputContext, data_type_key: str) -> int:
 
 
 def dummy_data_for_qwen2_vl(
-    ctx: InputContext, seq_len: int, mm_counts: Mapping[str, int]
+    ctx: InputContext,
+    seq_len: int,
+    mm_counts: Mapping[str, int],
+    *,
+    min_pixels: Optional[int] = None,
+    max_pixels: Optional[int] = None
 ) -> Tuple[SequenceData, Optional[MultiModalDataDict]]:
     image_processor = cached_get_image_processor(ctx.model_config.model)
 
     num_images = mm_counts["image"]
     max_resized_height, max_resized_width, max_llm_image_tokens = \
         _get_max_image_info(image_processor, data_type_key="image",
-                            mm_count=num_images)
+                            mm_count=num_images, min_pixels=min_pixels,
+                            max_pixels=max_pixels)
     if seq_len - max_llm_image_tokens - 2 < 0:
         raise RuntimeError(
             f"Qwen2-VL cannot process {num_images} images in a prompt, "
@@ -678,10 +709,11 @@ def dummy_data_for_qwen2_vl(
     num_videos = mm_counts["video"]
     max_resized_height, max_resized_width, max_llm_video_tokens = \
         _get_max_image_info(image_processor, data_type_key="video",
-                            mm_count=num_videos)
+                            mm_count=num_videos, min_pixels=min_pixels,
+                            max_pixels=max_pixels)
     if seq_len - max_llm_video_tokens - 2 < 0:
         raise RuntimeError(
-            f"Qwen2-VL cannot process {num_images} videos in a prompt, "
+            f"Qwen2-VL cannot process {num_videos} videos in a prompt, "
             "please increase max_model_len or reduce video limit by "
             "--limit-mm-per-prompt.")
 
@@ -706,6 +738,8 @@ def _get_llm_num_vision_tokens(
     mm_inputs: list,
     data_type_key: str,
     image_processor,
+    min_pixels: int,
+    max_pixels: int,
 ):
     """Get number of vision tokens of multimodal inputs.
 
@@ -715,12 +749,13 @@ def _get_llm_num_vision_tokens(
     image = to_numpy_array(mm_inputs[0])
     input_data_format = infer_channel_dimension_format(image)
     height, width = get_image_size(image, channel_dim=input_data_format)
+
     _, _, llm_num_vision_tokens = _get_vision_info(
         image_processor,
         height=height,
         width=width,
-        min_pixels=image_processor.min_pixels,
-        max_pixels=image_processor.max_pixels,
+        min_pixels=min_pixels,
+        max_pixels=max_pixels,
         do_resize=image_processor.do_resize,
         data_type_key=data_type_key,
         mm_count=len(mm_inputs),
@@ -730,7 +765,8 @@ def _get_llm_num_vision_tokens(
 
 def _expand_pad_tokens(inputs: list, token_id: int, make_batched_fn: Callable,
                        data_type_key: str, image_processor: Any,
-                       prompt_token_ids: List[int]) -> List[int]:
+                       prompt_token_ids: List[int], min_pixels: Optional[int],
+                       max_pixels: Optional[int]) -> List[int]:
     """
     Expand pad tokens for multi-modal inputs (e.g., images or videos).
 
@@ -741,6 +777,8 @@ def _expand_pad_tokens(inputs: list, token_id: int, make_batched_fn: Callable,
         data_type_key (str): The type of the multi-modal input.
         image_processor (Any): The image processor used to process the inputs.
         prompt_token_ids (List[int]): The list of token IDs in the prompt.
+        min_pixels (int): min pixels to used for img processing
+        max_pixels (int): max pixels to be used for img processing
 
     Returns:
         List[int]: The list of token IDs for the multi-modal inputs.
@@ -757,6 +795,8 @@ def _expand_pad_tokens(inputs: list, token_id: int, make_batched_fn: Callable,
             [data] if data_type_key == "image" else data,
             data_type_key=data_type_key,
             image_processor=image_processor,
+            min_pixels=min_pixels,
+            max_pixels=max_pixels,
         )
         if cnt == 0:
             end_idx = indices[cnt]
@@ -773,6 +813,9 @@ def _expand_pad_tokens(inputs: list, token_id: int, make_batched_fn: Callable,
 def input_processor_for_qwen2_vl(
     ctx: InputContext,
     inputs: DecoderOnlyInputs,
+    *,
+    min_pixels: Optional[int] = None,
+    max_pixels: Optional[int] = None,
 ) -> DecoderOnlyInputs:
     multi_modal_data = inputs.get("multi_modal_data", None)
     if multi_modal_data is None:
@@ -783,6 +826,10 @@ def input_processor_for_qwen2_vl(
 
     processor = cached_get_processor(ctx.model_config.model)
     image_processor = processor.image_processor
+    # Apply processor kwarg overrides for image processor options
+    min_pixels = min_pixels if min_pixels else image_processor.min_pixels
+    max_pixels = max_pixels if max_pixels else image_processor.max_pixels
+
     hf_config = ctx.get_hf_config(Qwen2VLConfig)
 
     # To avoid redundant processing of vision objects (resize, rescale, etc.),
@@ -830,16 +877,22 @@ def input_processor_for_qwen2_vl(
         else:
             prompt_token_ids = _expand_pad_tokens(image_inputs,
                                                   hf_config.image_token_id,
-                                                  make_batched_images, "image",
+                                                  make_batched_images,
+                                                  "image",
                                                   image_processor,
-                                                  prompt_token_ids)
+                                                  prompt_token_ids,
+                                                  min_pixels=min_pixels,
+                                                  max_pixels=max_pixels)
 
     if video_inputs is not None:
         prompt_token_ids = _expand_pad_tokens(video_inputs,
                                               hf_config.video_token_id,
-                                              make_batched_videos, "video",
+                                              make_batched_videos,
+                                              "video",
                                               image_processor,
-                                              prompt_token_ids)
+                                              prompt_token_ids,
+                                              min_pixels=min_pixels,
+                                              max_pixels=max_pixels)
 
     return token_inputs(
         prompt_token_ids=prompt_token_ids,