[Refactor] Multimodal data processing for VLM (#6659)

Signed-off-by: Xinyuan Tong <justinning0323@outlook.com>

docs/backend/vlm_query.ipynb

@@ -132,7 +132,7 @@
     "\n",
     "mm_item = dict(\n",
     "    modality=\"IMAGE\",\n",
-    "    image_grid_thws=processed_prompt[\"image_grid_thw\"],\n",
+    "    image_grid_thw=processed_prompt[\"image_grid_thw\"],\n",
     "    precomputed_features=precomputed_features,\n",
     ")\n",
     "out = llm.generate(input_ids=input_ids, image_data=[mm_item])\n",

python/sglang/srt/managers/multimodal_processors/base_processor.py

@@ -5,7 +5,8 @@ import multiprocessing as mp
 import os
 import re
 from abc import ABC, abstractmethod
-from typing import Any, List, Optional, Tuple, Union
+from enum import Enum
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -16,16 +17,24 @@ from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
 from sglang.srt.utils import encode_video, load_audio, load_image
 
 
+class MultimodalInputFormat(Enum):
+    """Enum for different multimodal input formats."""
+
+    RAW_IMAGES = "raw_images"
+    PRECOMPUTED_FEATURES = "precomputed_features"
+    PIXEL_VALUES = "pixel_values"
+
+
 @dataclasses.dataclass
 class BaseMultiModalProcessorOutput:
     # input_text, with each frame of video/image represented with a image_token
     input_text: str
 
     # frames loaded from image and video, in given order
-    images: Optional[list[Union[Image.Image, MultimodalDataItem]]] = None
+    images: Optional[list[Union[Image.Image, dict]]] = None
 
     # audios
-    audios: Optional[list[Union[np.ndarray, MultimodalDataItem]]] = None
+    audios: Optional[list[Union[np.ndarray, dict]]] = None
 
     def normalize(self):
         for field_name in ["images", "audios"]:
@@ -170,8 +179,6 @@ class BaseMultimodalProcessor(ABC):
     ):
         """Static method that can be pickled for multiprocessing"""
         if isinstance(data, dict):
-            return MultimodalDataItem.from_dict(data)
-        if isinstance(data, MultimodalDataItem):
             return data
         try:
             if is_audio:
@@ -370,29 +377,180 @@ class BaseMultimodalProcessor(ABC):
 
         return list(zip(indices_start.tolist(), indices_end.tolist()))
 
-    def mm_inputs_are_preprocessed(self, mm_inputs: Optional[list]):
-        """Returns true if all images are preprocessed, false if all are not, and error otherwise."""
-        if not mm_inputs:
-            return True
-        ret = any(isinstance(mm_input, MultimodalDataItem) for mm_input in mm_inputs)
-        if ret and not all(
-            isinstance(mm_input, MultimodalDataItem) for mm_input in mm_inputs
-        ):
-            raise ValueError(
-                "Unsupported: mixture of multimodal inputs where some but not all are preprocessed."
-            )
-        return ret
-
     @staticmethod
     def _extract_processor_features(
-        items: List[Any], attr_name: str
+        items: List[dict], attr_name: str
     ) -> Optional[torch.Tensor]:
         """
         Helper function to concat extracted attributes from processor output.
         """
-        values = [
-            getattr(item, attr_name)
-            for item in items
-            if getattr(item, attr_name) is not None
-        ]
-        return torch.concat(values) if values else None
+        values = [value for item in items if (value := item.get(attr_name)) is not None]
+        return torch.cat(values) if values else None
+
+    # When we assume that all the items have the same attributes
+    def _extract_processor_features_from_all_attributes(
+        self, items: List[dict]
+    ) -> dict:
+        values = {}
+        # Verify all items have the same keys
+        first_keys = set(items[0].keys())
+        for item in items[1:]:
+            if set(item.keys()) != first_keys:
+                raise ValueError(
+                    f"All items must have the same attributes. "
+                    f"First item has {first_keys}, but found {set(item.keys())}"
+                )
+
+        # Process each attribute
+        for k, v in items[0].items():
+            if isinstance(v, list):
+                values[k] = self._extract_processor_features(items, k)
+            else:
+                # Verify all items have the same value for non-list attributes
+                for item in items[1:]:
+                    if item[k] != v:
+                        raise ValueError(
+                            f"All items must have the same value for attribute {k}. "
+                            f"First item has {v}, but found {item[k]}"
+                        )
+                values[k] = v
+        return values
+
+    def process_and_combine_mm_data(
+        self, base_output: BaseMultiModalProcessorOutput
+    ) -> Tuple[Optional[MultimodalDataItem], torch.Tensor]:
+        """
+        Process multimodal data and return the combined multimodal item and input_ids.
+        Handles all three input formats at the same abstraction level.
+
+        Returns:
+            Tuple of (combined_mm_item, input_ids)
+        """
+
+        def tokenize_text(input_text: str) -> torch.Tensor:
+            """Tokenize input text."""
+            return self._processor.tokenizer(
+                input_text,
+                return_tensors="pt",
+                add_special_tokens=True,
+            ).input_ids.flatten()
+
+        def categorize_mm_inputs(mm_inputs: List) -> MultimodalInputFormat:
+            """Categorize multimodal inputs and validate consistency."""
+            try:
+                has_image = False
+                has_pixel_values = False
+                has_precomputed_features = False
+
+                for mm_input in mm_inputs:
+                    if isinstance(mm_input, Image.Image):
+                        has_image = True
+                    elif isinstance(mm_input, dict):
+                        if mm_input.get("precomputed_features", None) is not None:
+                            has_precomputed_features = True
+                        elif mm_input.get("pixel_values", None) is not None:
+                            has_pixel_values = True
+                        else:
+                            raise ValueError(
+                                f"Invalid multimodal input: {mm_input}, expected dict with pixel_values or precomputed_features"
+                            )
+                    else:
+                        raise ValueError(
+                            f"Invalid multimodal input: {mm_input}, expected Image.Image or dict"
+                        )
+
+                # Validate format consistency
+                format_count = sum(
+                    [has_image, has_pixel_values, has_precomputed_features]
+                )
+                if format_count > 1:
+                    raise ValueError(
+                        "Unsupported: mixture of multimodal input formats. "
+                        f"Found formats: image={has_image}, pixel_values={has_pixel_values}, "
+                        f"precomputed_features={has_precomputed_features}"
+                    )
+
+                if has_image:
+                    return MultimodalInputFormat.RAW_IMAGES
+                elif has_precomputed_features:
+                    return MultimodalInputFormat.PRECOMPUTED_FEATURES
+                elif has_pixel_values:
+                    return MultimodalInputFormat.PIXEL_VALUES
+                else:
+                    raise ValueError("No valid multimodal input format found")
+            except Exception as e:
+                raise ValueError(f"Failed to categorize inputs: {e}")
+
+        def process_raw_images(
+            base_output: BaseMultiModalProcessorOutput,
+        ) -> Tuple[MultimodalDataItem, torch.Tensor]:
+            """Process raw Image.Image objects using transformers processor."""
+            ret = self.process_mm_data(
+                input_text=base_output.input_text,
+                images=base_output.images,
+            )
+            combined_mm_item = MultimodalDataItem(modality=Modality.IMAGE)
+
+            # Copy all fields from processor output except input_ids
+            for key, value in ret.items():
+                if key != "input_ids" and hasattr(combined_mm_item, key):
+                    setattr(combined_mm_item, key, value)
+
+            input_ids = ret["input_ids"].flatten()
+            return combined_mm_item, input_ids
+
+        def process_precomputed_features(
+            base_output: BaseMultiModalProcessorOutput,
+        ) -> Tuple[MultimodalDataItem, torch.Tensor]:
+            """Process inputs with precomputed features."""
+            combined_mm_item = MultimodalDataItem(modality=Modality.IMAGE)
+            combined_mm_item.precomputed_features = self._extract_processor_features(
+                base_output.images, "precomputed_features"
+            )
+            input_ids = tokenize_text(base_output.input_text)
+            return combined_mm_item, input_ids
+
+        def process_pixel_values(
+            base_output: BaseMultiModalProcessorOutput,
+        ) -> Tuple[MultimodalDataItem, torch.Tensor]:
+            """Process inputs with pixel values."""
+            values = self._extract_processor_features_from_all_attributes(
+                base_output.images
+            )
+            combined_mm_item = MultimodalDataItem.from_dict(values)
+            input_ids = tokenize_text(base_output.input_text)
+            return combined_mm_item, input_ids
+
+        def finalize_mm_item(
+            combined_mm_item: MultimodalDataItem, input_ids: torch.Tensor
+        ) -> MultimodalDataItem:
+            """Apply common post-processing to the multimodal item."""
+            combined_mm_item.image_offsets = self.get_mm_items_offset(
+                input_ids=input_ids,
+                mm_token_id=self.IM_TOKEN_ID,
+            )
+            return combined_mm_item
+
+        # Main logic
+        mm_inputs = base_output.images
+        if not mm_inputs:
+            # Return text-only case
+            input_ids = tokenize_text(base_output.input_text)
+            return None, input_ids
+
+        # Categorize input formats
+        input_format = categorize_mm_inputs(mm_inputs)
+
+        # Process based on format
+        if input_format == MultimodalInputFormat.RAW_IMAGES:
+            combined_mm_item, input_ids = process_raw_images(base_output)
+        elif input_format == MultimodalInputFormat.PRECOMPUTED_FEATURES:
+            combined_mm_item, input_ids = process_precomputed_features(base_output)
+        elif input_format == MultimodalInputFormat.PIXEL_VALUES:
+            combined_mm_item, input_ids = process_pixel_values(base_output)
+        else:
+            raise ValueError(f"Unknown input format: {input_format}")
+
+        # Finalize with common processing
+        combined_mm_item = finalize_mm_item(combined_mm_item, input_ids)
+        return combined_mm_item, input_ids

python/sglang/srt/managers/multimodal_processors/gemma3.py

@@ -27,6 +27,7 @@ class Gemma3SGLangImageProcessor(SGLangBaseProcessor):
         )
         self.IM_START_TOKEN_ID = hf_config.boi_token_index
         self.IM_END_TOKEN_ID = hf_config.eoi_token_index
+        self.IM_TOKEN_ID = hf_config.image_token_index
 
     async def process_mm_data_async(
         self,
@@ -42,49 +43,21 @@ class Gemma3SGLangImageProcessor(SGLangBaseProcessor):
         if isinstance(image_data, str):
             image_data = [image_data]
 
-        image_token = self.IMAGE_TOKEN
-        image_token_regex = self.IMAGE_TOKEN_REGEX
         base_output = self.load_mm_data(
             prompt=input_text,
             image_data=image_data,
             multimodal_tokens=MultimodalSpecialTokens(
-                image_token=image_token, image_token_regex=image_token_regex
+                image_token=self.IMAGE_TOKEN, image_token_regex=self.IMAGE_TOKEN_REGEX
             ),
             max_req_input_len=max_req_input_len,
             discard_alpha_channel=True,
         )
 
-        images_are_preprocessed = self.mm_inputs_are_preprocessed(base_output.images)
-        ret = self.process_mm_data(
-            input_text=base_output.input_text,
-            images=None if images_are_preprocessed else base_output.images,
-        )
-
-        items = []
-        input_ids = ret["input_ids"].flatten()
-        image_offsets = self.get_mm_items_offset(
-            input_ids=input_ids,
-            mm_token_id=self.hf_config.image_token_index,
-        )
-        for i, image in enumerate(base_output.images):
-            if images_are_preprocessed:
-                pixel_values = image.pixel_values
-                precomputed_features = image.precomputed_features
-            else:
-                pixel_values = ret["pixel_values"][i]
-                precomputed_features = None
-
-            item = MultimodalDataItem(
-                pixel_values=pixel_values,
-                precomputed_features=precomputed_features,
-                modality=Modality.IMAGE,
-                image_offsets=image_offsets[i],
-            )
-            items += [item]
+        combined_mm_item, input_ids = self.process_and_combine_mm_data(base_output)
 
         return {
-            "mm_items": items,
             "input_ids": input_ids.tolist(),
+            "mm_items": [combined_mm_item] if combined_mm_item is not None else [],
             "im_start_id": self.IM_START_TOKEN_ID,
             "im_end_id": self.IM_END_TOKEN_ID,
         }

python/sglang/srt/managers/multimodal_processors/kimi_vl.py

@@ -21,7 +21,7 @@ class KimiVLImageProcessor(SGLangBaseProcessor):
         super().__init__(hf_config, server_args, _processor)
         self.IMAGE_TOKEN = "<|media_pad|>"
         self.IMAGE_TOKEN_REGEX = re.compile(r"(?:<\|media_pad\|>)+")
-        self.im_token_id = _processor.tokenizer.convert_tokens_to_ids(self.IMAGE_TOKEN)
+        self.IM_TOKEN_ID = _processor.tokenizer.convert_tokens_to_ids(self.IMAGE_TOKEN)
 
     async def process_mm_data_async(
         self,
@@ -46,48 +46,10 @@ class KimiVLImageProcessor(SGLangBaseProcessor):
             max_req_input_len=max_req_input_len,
         )
 
-        images_are_preprocessed = self.mm_inputs_are_preprocessed(base_output.images)
-        if not images_are_preprocessed:
-            ret = self.process_mm_data(
-                input_text=base_output.input_text,
-                images=base_output.images,
-            )
-            input_ids = ret["input_ids"].flatten()
-            image_grid_thws = ret["image_grid_hws"]
-            pixel_values = ret["pixel_values"]
-            precomputed_features = None
-        else:
-            input_ids = self._processor.tokenizer(
-                base_output.input_text,
-                return_tensors="pt",
-                add_special_tokens=True,
-            ).input_ids.flatten()
-
-            image_grid_thws = self._extract_processor_features(
-                base_output.images, "image_grid_thws"
-            )
-            precomputed_features = self._extract_processor_features(
-                base_output.images, "precomputed_features"
-            )
-            pixel_values = self._extract_processor_features(
-                base_output.images, "pixel_values"
-            )
-
-        image_offsets = self.get_mm_items_offset(
-            input_ids=input_ids,
-            mm_token_id=self.im_token_id,
-        )
+        combined_mm_item, input_ids = self.process_and_combine_mm_data(base_output)
 
         return {
             "input_ids": input_ids.tolist(),
-            "mm_items": [
-                MultimodalDataItem(
-                    pixel_values=pixel_values,
-                    image_grid_thws=image_grid_thws,
-                    precomputed_features=precomputed_features,
-                    modality=Modality.IMAGE,
-                    image_offsets=image_offsets,
-                )
-            ],
-            "im_token_id": self.im_token_id,
+            "mm_items": [combined_mm_item] if combined_mm_item is not None else [],
+            "im_token_id": self.IM_TOKEN_ID,
         }

python/sglang/srt/managers/multimodal_processors/qwen_vl.py

@@ -32,8 +32,8 @@ class Qwen2_5VLImageProcessor(SGLangBaseProcessor):
         )
         self.IM_START_TOKEN_ID = hf_config.vision_start_token_id
         self.IM_END_TOKEN_ID = hf_config.vision_end_token_id
-        self.image_token_id = hf_config.image_token_id
-        self.video_token_id = hf_config.video_token_id
+        self.IM_TOKEN_ID = hf_config.image_token_id
+        self.VIDEO_TOKEN_ID = hf_config.video_token_id
         self.vision_start_token_id = hf_config.vision_start_token_id
         self.vision_end_token_id = hf_config.vision_end_token_id
         self.NUM_TOKEN_PER_FRAME = 770
@@ -125,72 +125,45 @@ class Qwen2_5VLImageProcessor(SGLangBaseProcessor):
         async def resize_image_async(image):
             return resize_image(image)
 
-        images_are_preprocessed = self.mm_inputs_are_preprocessed(base_output.images)
-        if base_output.images and not images_are_preprocessed:
+        # Qwen-specific: resize images if they are raw Image objects
+        if base_output.images and isinstance(base_output.images[0], Image.Image):
             resize_tasks = [resize_image_async(image) for image in base_output.images]
             base_output.images = await asyncio.gather(*resize_tasks)
 
-        ret = self.process_mm_data(
-            input_text=base_output.input_text,
-            images=None if images_are_preprocessed else base_output.images,
-        )
-        input_ids = ret["input_ids"].flatten().tolist()
-        image_offsets = self.get_mm_items_offset(
-            input_ids=ret["input_ids"].flatten(), mm_token_id=self.image_token_id
-        )
-        image_grid_thw = None
         video_grid_thw = None  # TODO
-        items = []
 
-        if base_output.images:
-            if images_are_preprocessed:
-                image_grid_thw = self._extract_processor_features(
-                    base_output.images, "image_grid_thws"
-                )
-                precomputed_features = self._extract_processor_features(
-                    base_output.images, "precomputed_features"
-                )
-                pixel_values = self._extract_processor_features(
-                    base_output.images, "pixel_values"
-                )
-            else:
-                image_grid_thw = ret["image_grid_thw"]
-                pixel_values = ret["pixel_values"]
-                precomputed_features = None
-            items += [
-                MultimodalDataItem(
-                    pixel_values=pixel_values,
-                    image_grid_thws=image_grid_thw,
-                    video_grid_thws=video_grid_thw,
-                    precomputed_features=precomputed_features,
-                    image_offsets=image_offsets,
-                    modality=Modality.IMAGE,
-                )
-            ]
+        combined_mm_item, input_ids = self.process_and_combine_mm_data(base_output)
+
+        if combined_mm_item is None:
+            # Note(Xinyuan): This is the case where image loading fails.
+            return None
+
+        video_grid_thw = None  # TODO
+        second_per_grid_ts = getattr(combined_mm_item, "second_per_grid_ts", None)
 
         mrope_positions, mrope_position_delta = MRotaryEmbedding.get_rope_index(
             spatial_merge_size=self.hf_config.vision_config.spatial_merge_size,
-            image_token_id=self.image_token_id,
-            video_token_id=self.video_token_id,
+            image_token_id=self.IM_TOKEN_ID,
+            video_token_id=self.VIDEO_TOKEN_ID,
             vision_start_token_id=self.vision_start_token_id,
             model_type=self.hf_config.model_type,
             tokens_per_second=getattr(
                 self.hf_config.vision_config, "tokens_per_second", None
             ),
-            input_ids=torch.tensor(input_ids).unsqueeze(0),
-            image_grid_thw=image_grid_thw,
+            input_ids=input_ids.unsqueeze(0),
+            image_grid_thw=combined_mm_item.image_grid_thw,
             video_grid_thw=video_grid_thw,
-            second_per_grid_ts=ret.get("second_per_grid_ts", None),
+            second_per_grid_ts=second_per_grid_ts,
         )
         mrope_positions = mrope_positions.squeeze(1)
 
         return {
-            "input_ids": input_ids,
-            "mm_items": items,
+            "input_ids": input_ids.tolist(),
+            "mm_items": [combined_mm_item],
             "im_start_id": self.IM_START_TOKEN_ID,
             "im_end_id": self.IM_END_TOKEN_ID,
-            "im_token_id": self.image_token_id,
-            "video_token_id": self.video_token_id,
+            "im_token_id": self.IM_TOKEN_ID,
+            "video_token_id": self.VIDEO_TOKEN_ID,
             "mrope_positions": mrope_positions,
             "mrope_position_delta": mrope_position_delta,
         }

python/sglang/srt/managers/schedule_batch.py

@@ -188,7 +188,7 @@ class MultimodalDataItem:
     # the real data, pixel_values or audio_features
     # data: Union[List[torch.Tensor], List[np.ndarray]]
     pixel_values: Union[torch.Tensor, np.ndarray] = None
-    image_grid_thws: Union[torch.Tensor, np.ndarray] = None
+    image_grid_thw: Union[torch.Tensor, np.ndarray] = None
     video_grid_thws: Union[torch.Tensor, np.ndarray] = None
 
     image_emb_mask: Optional[torch.Tensor] = None
@@ -198,6 +198,9 @@ class MultimodalDataItem:
     # [num_images, (n, w, h)]
     tgt_size: Tuple[int, int] = None
 
+    # kimi-vl related
+    image_grid_hws: Optional[List[torch.Tensor]] = None
+
     audio_features: Union[torch.Tensor, np.ndarray] = None
     audio_feature_lens: Optional[List[torch.Tensor]] = None
     audio_offsets: Optional[List[Tuple[int, int]]] = None

python/sglang/srt/models/gemma3_mm.py

@@ -286,14 +286,26 @@ class Gemma3ForConditionalGeneration(PreTrainedModel):
         all_pixel_values = flatten_nested_list([item.pixel_values for item in items])
         vision_outputs_list = []
 
-        for pixel_value in all_pixel_values:
-            # Add batch dimension for single image processing
-            pixel_value_batch = pixel_value.unsqueeze(0)
-            pixel_value_batch = pixel_value_batch.to(device=self.vision_tower.device)
-            pixel_value_batch = pixel_value_batch.to(dtype=self.language_model.dtype())
-
-            vision_output = self.vision_tower(pixel_values=pixel_value_batch)
-            vision_outputs_list.append(vision_output)
+        for pixel_values_batch in all_pixel_values:
+            # Normalize input shape to [batch_size, channels, height, width]
+            if pixel_values_batch.dim() == 5:
+                pixel_values_batch = pixel_values_batch.squeeze(0)
+            elif pixel_values_batch.dim() == 3:
+                pixel_values_batch = pixel_values_batch.unsqueeze(0)
+            elif pixel_values_batch.dim() != 4:
+                raise ValueError(
+                    f"Unexpected pixel_values shape: {pixel_values_batch.shape}"
+                )
+
+            # Process each image in the batch
+            batch_size = pixel_values_batch.shape[0]
+            for i in range(batch_size):
+                pixel_value = pixel_values_batch[i : i + 1]  # Keep batch dimension as 1
+                pixel_value = pixel_value.to(
+                    device=self.vision_tower.device, dtype=self.language_model.dtype()
+                )
+                vision_output = self.vision_tower(pixel_values=pixel_value)
+                vision_outputs_list.append(vision_output)
 
         # Concatenate all vision outputs
         vision_outputs = torch.cat(vision_outputs_list, dim=0)

python/sglang/srt/models/kimi_vl.py

@@ -144,10 +144,10 @@ class KimiVLForConditionalGeneration(nn.Module):
             .type(self.vision_tower.dtype)
             .to(self.vision_tower.device)
         )
-        image_grid_thws = torch.concat(
-            [item.image_grid_thws for item in items], dim=0
-        ).to(self.vision_tower.device)
-        image_features = self.vision_tower(pixel_values, image_grid_thws)
+        image_grid_hws = torch.cat([item.image_grid_hws for item in items], dim=0).to(
+            self.vision_tower.device
+        )
+        image_features = self.vision_tower(pixel_values, image_grid_hws)
         assert isinstance(image_features, list)
         # lengths = [x.shape[0] for x in image_features]
         res = self.multi_modal_projector(torch.cat(image_features))  # .split(lengths)

python/sglang/srt/models/qwen2_5_vl.py

@@ -503,10 +503,10 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
         pixel_values = torch.cat([item.pixel_values for item in items], dim=0).type(
             self.visual.dtype
         )
-        image_grid_thws = torch.concat([item.image_grid_thws for item in items], dim=0)
+        image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
         assert pixel_values.dim() == 2, pixel_values.dim()
-        assert image_grid_thws.dim() == 2, image_grid_thws.dim()
-        image_embeds = self.visual(pixel_values, grid_thw=image_grid_thws)
+        assert image_grid_thw.dim() == 2, image_grid_thw.dim()
+        image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
         return image_embeds
 
     def _process_video_input(self, video_input: Qwen2VLVideoInputs) -> torch.Tensor:

python/sglang/srt/models/qwen2_vl.py

@@ -490,10 +490,10 @@ class Qwen2VLForConditionalGeneration(nn.Module):
         pixel_values = torch.cat([item.pixel_values for item in items], dim=0).type(
             self.visual.dtype
         )
-        image_grid_thws = torch.concat([item.image_grid_thws for item in items], dim=0)
+        image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
         assert pixel_values.dim() == 2, pixel_values.dim()
-        assert image_grid_thws.dim() == 2, image_grid_thws.dim()
-        image_embeds = self.visual(pixel_values, grid_thw=image_grid_thws)
+        assert image_grid_thw.dim() == 2, image_grid_thw.dim()
+        image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
         return image_embeds
 
     def _process_video_input(self, video_input: Qwen2VLVideoInputs) -> torch.Tensor:

test/srt/test_vlm_input_format.py

@@ -156,7 +156,7 @@ class TestQwenVLUnderstandsImage(VLMInputTestBase, unittest.IsolatedAsyncioTestC
     def _pixel_values_image_data(self, processor_output):
         return dict(
             modality="IMAGE",
-            image_grid_thws=processor_output["image_grid_thw"],
+            image_grid_thw=processor_output["image_grid_thw"],
             pixel_values=processor_output["pixel_values"],
         )
 
@@ -207,8 +207,8 @@ class TestKimiVLImageUnderstandsImage(
     def _pixel_values_image_data(self, processor_output):
         return dict(
             modality="IMAGE",
-            image_grid_thws=processor_output["image_grid_hws"],
             pixel_values=processor_output["pixel_values"],
+            image_grid_hws=processor_output["image_grid_hws"],
         )