[Bugfix] Ensure correctness of Cohere2Vision processing (#23245)

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

tests/models/multimodal/processing/test_common.py

@@ -268,6 +268,7 @@ def _test_processing_correctness_one(
     "CohereForAI/aya-vision-8b",
     "Salesforce/blip2-opt-2.7b",
     "facebook/chameleon-7b",
+    "CohereLabs/command-a-vision-07-2025",
     "deepseek-ai/deepseek-vl2-tiny",
     "microsoft/Florence-2-base",
     "adept/fuyu-8b",

vllm/model_executor/models/aya_vision.py

@@ -250,8 +250,7 @@ class AyaVisionMultiModalProcessor(
         image_processor = hf_processor.image_processor
 
         def get_replacement(item_idx: int):
-            images: ImageProcessorItems = mm_items.get("image",
-                                                       ImageProcessorItems)
+            images = mm_items.get_items("image", ImageProcessorItems)
             image_size: ImageSize = images.get_image_size(item_idx)
             num_patches = self.info.get_num_patches(
                 image_width=image_size.width,

vllm/model_executor/models/cohere2_vision.py

@@ -10,6 +10,8 @@ import torch
 from torch import nn
 from transformers import BatchFeature, PretrainedConfig
 from transformers.models.cohere2_vision import Cohere2VisionConfig
+from transformers.models.cohere2_vision.image_processing_cohere2_vision_fast import (  # noqa: E501
+    get_optimal_tiled_canvas)
 from transformers.models.cohere2_vision.processing_cohere2_vision import (
     Cohere2VisionProcessor)
 
@@ -150,14 +152,46 @@ class Cohere2VisionProcessingInfo(BaseProcessingInfo):
         max_patches = image_processor.max_patches
         return ImageSize(height=height * max_patches, width=width)
 
-    def get_num_patches(self, image_width: int, image_height: int) -> int:
+    def get_num_patches(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+        processor: Optional[Cohere2VisionProcessor],
+    ) -> int:
         """
         Calculate the number of image patches for a given image.
         Uses the HF processor to determine the actual number of patches.
         """
-        return self.get_hf_processor(
-        ).image_processor.get_number_of_image_patches(image_height,
-                                                      image_width, {})
+        if processor is None:
+            processor = self.get_hf_processor()
+
+        image_processor = processor.image_processor
+
+        # The current implementation of get_number_of_image_patches
+        # is incorrect, so we patch it here.
+        # return image_processor.get_number_of_image_patches(image_height,
+        #                                                    image_width, {})
+
+        min_patches = image_processor.min_patches
+        max_patches = image_processor.max_patches
+        patch_size = image_processor.size
+        crop_to_patches = image_processor.crop_to_patches
+
+        if not crop_to_patches:
+            return 1
+
+        num_columns, num_rows = get_optimal_tiled_canvas(
+            (image_height, image_width),
+            (patch_size["height"], patch_size["width"]),
+            min_patches,
+            max_patches,
+        )
+        num_patches = num_columns * num_rows
+        if num_patches > 1:
+            num_patches += 1  # Thumbnail image
+
+        return num_patches
 
 
 class Cohere2VisionDummyInputsBuilder(
@@ -208,6 +242,8 @@ class Cohere2VisionMultiModalProcessor(
         # Ensure num_patches is available for proper tensor splitting
         if "num_patches" not in processed_outputs and (
                 images := mm_data.get("images")) is not None:
+            hf_processor = self.info.get_hf_processor(**mm_kwargs)
+
             # Fallback calculation if HF processor didn't provide num_patches
             parsed_images = self._get_data_parser().parse_mm_data({
                 "image":
@@ -217,8 +253,9 @@ class Cohere2VisionMultiModalProcessor(
             num_patches = [
                 self.info.get_num_patches(
                     image_width=parsed_images.get_image_size(i).width,
-                    image_height=parsed_images.get_image_size(i).height)
-                for i in range(len(parsed_images))
+                    image_height=parsed_images.get_image_size(i).height,
+                    processor=hf_processor,
+                ) for i in range(len(parsed_images))
             ]
             processed_outputs["num_patches"] = torch.tensor(num_patches)
 
@@ -245,25 +282,25 @@ class Cohere2VisionMultiModalProcessor(
     ) -> Sequence[PromptUpdate]:
         hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
         image_token = hf_processor.image_token
+        img_tokens_per_tile = int(hf_processor.patch_size**2)
         img_line_break_token = hf_processor.img_line_break_token
         boi_token = hf_processor.boi_token
         eoi_token = hf_processor.eoi_token
 
         def get_replacement(item_idx: int):
-            images: ImageProcessorItems = mm_items.get("image",
-                                                       ImageProcessorItems)
+            images = mm_items.get_items("image", ImageProcessorItems)
             image_size: ImageSize = images.get_image_size(item_idx)
 
-            num_patches = self.info.get_num_patches(image_size.height,
-                                                    image_size.width)
-            img_tokens_per_tile = int(hf_processor.patch_size**2)
-            single_tile_tokens = image_token * img_tokens_per_tile + \
-                img_line_break_token
-            img_string = f"{boi_token}\
-                {single_tile_tokens * num_patches}\
-                {eoi_token}"
+            num_patches = self.info.get_num_patches(
+                image_width=image_size.width,
+                image_height=image_size.height,
+                processor=hf_processor,
+            )
+            patch_tokens = (image_token * img_tokens_per_tile +
+                            img_line_break_token)
+            repl = f"{boi_token}{patch_tokens * num_patches}{eoi_token}"
 
-            return PromptUpdateDetails.select_text(img_string, image_token)
+            return PromptUpdateDetails.select_text(repl, image_token)
 
         return [
             PromptReplacement(