[Bugfix] Fix input processor for InternVL2 model (#7164)

Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

tests/models/test_internvl.py

@@ -5,6 +5,7 @@ import pytest
 import torch
 from huggingface_hub import snapshot_download
 from PIL.Image import Image
+from transformers import AutoConfig
 
 from vllm.model_executor.models.internvl import (IMG_CONTEXT, IMG_END,
                                                  IMG_START,
@@ -26,10 +27,15 @@ HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
 
 # we use snapshot_download to prevent conflicts between
 # dynamic_module and trust_remote_code for hf_runner
+DOWNLOAD_PATTERN = ["*.json", "*.py", "*.safetensors", "*.txt", "*.model"]
 models = [
-    snapshot_download("OpenGVLab/InternVL2-1B"),
+    snapshot_download("OpenGVLab/InternVL2-1B",
-    snapshot_download("OpenGVLab/InternVL2-2B"),
+                      allow_patterns=DOWNLOAD_PATTERN),
-    # snapshot_download("OpenGVLab/InternVL2-4B"),  # broken
+    snapshot_download("OpenGVLab/InternVL2-2B",
+                      allow_patterns=DOWNLOAD_PATTERN),
+    # Broken due to outdated implementation of Phi-3
+    # See: https://huggingface.co/OpenGVLab/InternVL2-4B/discussions/3
+    # snapshot_download("OpenGVLab/InternVL2-4B"),
 ]
 
 
@@ -41,8 +47,17 @@ class InternVLProcessor:
         self.tokenizer = hf_runner.tokenizer
         self.dtype = hf_runner.model.dtype
 
+        self.config = AutoConfig.from_pretrained(hf_runner.model_name)
+        self.vision_config = self.config.vision_config
+        self.use_thumbnail = self.config.use_thumbnail
+        self.min_num = self.config.min_dynamic_patch
+        self.max_num = self.config.max_dynamic_patch
+        self.image_size = self.vision_config.image_size
+
     def __call__(self, text: str, images: Image, **kwargs):
-        pixel_values = image_to_pixel_values(images).to(self.dtype)
+        pixel_values = image_to_pixel_values(images, self.image_size,
+                                             self.min_num, self.max_num,
+                                             self.use_thumbnail).to(self.dtype)
         num_patches_list = [pixel_values.shape[0]]
         for num_patches in num_patches_list:
             context_tokens = IMG_CONTEXT * self.num_image_token * num_patches

vllm/model_executor/models/internvl.py

@@ -38,9 +38,6 @@ IMG_CONTEXT = '<IMG_CONTEXT>'
 IMAGENET_MEAN = (0.485, 0.456, 0.406)
 IMAGENET_STD = (0.229, 0.224, 0.225)
 
-MAX_IMAGE_FEATURE_SIZE_WIDTH = 3000
-MAX_IMAGE_FEATURE_SIZE_HEIGHT = 500
-
 
 class InternVLImagePixelInputs(TypedDict):
     type: Literal["pixel_values"]
@@ -84,11 +81,9 @@ def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height,
     return best_ratio
 
 
-def calculate_num_blocks(orig_width: int,
+def calculate_num_blocks(orig_width: int, orig_height: int, min_num: int,
-                         orig_height: int,
+                         max_num: int,
-                         min_num=1,
+                         image_size: int) -> Tuple[int, int, int]:
-                         max_num=6,
-                         image_size=448):
     aspect_ratio = orig_width / orig_height
 
     # calculate the existing image aspect ratio
@@ -110,11 +105,9 @@ def calculate_num_blocks(orig_width: int,
 
 
 # adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
-def dynamic_preprocess(image,
+def dynamic_preprocess(image: Image.Image, min_num: int, max_num: int,
-                       min_num=1,
+                       image_size: int,
-                       max_num=6,
+                       use_thumbnail: int) -> List[Image.Image]:
-                       image_size=448,
-                       use_thumbnail=False):
     orig_width, orig_height = image.size
 
     blocks, target_width, target_height = calculate_num_blocks(
@@ -138,12 +131,14 @@ def dynamic_preprocess(image,
 
 
 # adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
-def image_to_pixel_values(image: Image.Image, input_size=448, max_num=6):
+def image_to_pixel_values(image: Image.Image, input_size: int, min_num: int,
+                          max_num: int, use_thumbnail: bool) -> torch.Tensor:
     transform = build_transform(input_size=input_size)
     images = dynamic_preprocess(image,
+                                min_num=min_num,
+                                max_num=max_num,
                                 image_size=input_size,
-                                use_thumbnail=True,
+                                use_thumbnail=use_thumbnail)
-                                max_num=max_num)
     pixel_values = [transform(image) for image in images]
     pixel_values = torch.stack(pixel_values)
     return pixel_values
@@ -159,12 +154,18 @@ def get_internvl_num_patches(image_size: int, patch_size: int,
 def get_max_internvl_image_tokens(ctx: InputContext):
     hf_config = ctx.get_hf_config(PretrainedConfig)
     vision_config = hf_config.vision_config
+
+    use_thumbnail = hf_config.use_thumbnail
+    max_dynamic_patch = hf_config.max_dynamic_patch
+    if use_thumbnail:
+        max_dynamic_patch += 1
+    downsample_ratio = hf_config.downsample_ratio
+
     image_size = vision_config.image_size
     patch_size = vision_config.patch_size
-    downsample_ratio = hf_config.downsample_ratio
     num_patches = get_internvl_num_patches(image_size, patch_size,
                                            downsample_ratio)
-    return num_patches * 7
+    return num_patches * max_dynamic_patch
 
 
 def input_processor_for_internvl(ctx: InputContext, llm_inputs: LLMInputs):
@@ -176,21 +177,27 @@ def input_processor_for_internvl(ctx: InputContext, llm_inputs: LLMInputs):
     hf_config = ctx.get_hf_config(PretrainedConfig)
     vision_config = hf_config.vision_config
 
-    image_data = multi_modal_data["image"]
-    if isinstance(image_data, Image.Image):
-        width, height = image_data.size
-        num_blocks, _, _ = calculate_num_blocks(width, height)
-    elif isinstance(image_data, torch.Tensor):
-        raise NotImplementedError("Embeddings input is not supported yet")
-    else:
-        raise TypeError(f"Invalid image type: {type(image_data)}")
-
     image_size = vision_config.image_size
     patch_size = vision_config.patch_size
     downsample_ratio = hf_config.downsample_ratio
     num_patches = get_internvl_num_patches(image_size, patch_size,
                                            downsample_ratio)
 
+    image_data = multi_modal_data["image"]
+    if isinstance(image_data, Image.Image):
+        width, height = image_data.size
+        min_num = hf_config.min_dynamic_patch
+        max_num = hf_config.max_dynamic_patch
+        num_blocks, _, _ = calculate_num_blocks(width, height, min_num,
+                                                max_num, image_size)
+        # add thumbnail image if num_blocks > 1
+        if hf_config.use_thumbnail and num_blocks > 1:
+            num_blocks += 1
+    elif isinstance(image_data, torch.Tensor):
+        raise NotImplementedError("Embeddings input is not supported yet")
+    else:
+        raise TypeError(f"Invalid image type: {type(image_data)}")
+
     tokenizer = cached_get_tokenizer(model_config.tokenizer,
                                      trust_remote_code=True)
 
@@ -198,8 +205,7 @@ def input_processor_for_internvl(ctx: InputContext, llm_inputs: LLMInputs):
     prompt_token_ids = llm_inputs["prompt_token_ids"]
     if prompt is None:
         prompt = tokenizer.decode(prompt_token_ids)
-    image_prompt = IMG_START + IMG_CONTEXT * (num_blocks +
+    image_prompt = IMG_START + IMG_CONTEXT * num_blocks * num_patches + IMG_END
-                                              1) * num_patches + IMG_END
     new_prompt = prompt.replace('<image>', image_prompt, 1)
     new_prompt_token_ids = tokenizer.encode(new_prompt)
 
@@ -209,8 +215,19 @@ def input_processor_for_internvl(ctx: InputContext, llm_inputs: LLMInputs):
 
 
 def input_mapper_for_internvl(ctx: InputContext, data: object):
+    hf_config = ctx.get_hf_config(PretrainedConfig)
+
+    use_thumbnail = hf_config.use_thumbnail
+    min_num = hf_config.min_dynamic_patch
+    max_num = hf_config.max_dynamic_patch
+    image_size = hf_config.vision_config.image_size
+
     if isinstance(data, Image.Image):
-        data = image_to_pixel_values(data)
+        data = image_to_pixel_values(data,
+                                     image_size,
+                                     min_num,
+                                     max_num,
+                                     use_thumbnail=use_thumbnail)
     model_config = ctx.model_config
     tokenizer = cached_get_tokenizer(model_config.tokenizer,
                                      trust_remote_code=True)
@@ -240,10 +257,17 @@ def dummy_data_for_internvl(ctx: InputContext, seq_len: int):
                                         add_special_tokens=False)[0],
         image_feature_size_override=image_feature_size,
     )
+
+    image_size = vision_config.image_size
+    min_num = hf_config.min_dynamic_patch
+    max_num = hf_config.max_dynamic_patch
+    max_image_width = max_num * image_size
+    max_image_height = min_num * image_size
+
     mm_data = dummy_image_for_clip(
         vision_config,
-        image_width_override=MAX_IMAGE_FEATURE_SIZE_WIDTH,
+        image_width_override=max_image_width,
-        image_height_override=MAX_IMAGE_FEATURE_SIZE_HEIGHT,
+        image_height_override=max_image_height,
     )
 
     return seq_data, mm_data