Add: num_additional_image_tokens to models

src/transformers/models/llava/modeling_llava.py

@@ -240,6 +240,7 @@ def __init__(self, config: LlavaConfig):
         self.vocab_size = config.text_config.vocab_size
         self.language_model = AutoModelForCausalLM.from_config(config.text_config)
         self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+
         self.post_init()
 
     def get_input_embeddings(self):

src/transformers/models/llava/processing_llava.py

@@ -157,7 +157,9 @@ def __call__(
             # Replace the image token with the expanded image token sequence
             pixel_values = image_inputs["pixel_values"]
             height, width = get_image_size(to_numpy_array(pixel_values[0]))
-            num_image_tokens = (height // self.patch_size) * (width // self.patch_size) + 1
+            num_image_tokens = (height // self.patch_size) * (
+                width // self.patch_size
+            ) + self.num_additional_image_tokens
             if self.vision_feature_select_strategy == "default":
                 num_image_tokens -= 1
 

src/transformers/models/llava_next/processing_llava_next.py

@@ -155,6 +155,9 @@ def __call__(
             for sample in text:
                 while self.image_token in sample:
                     image_size = next(image_sizes)
+                    if not isinstance(image_size, (list, tuple)):
+                        # cast to list to avoid numerical precision errors when calculating unpadding
+                        image_size = image_size.tolist()
                     orig_height, orig_width = image_size
                     num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
                     if self.vision_feature_select_strategy == "default":

src/transformers/models/llava_next_video/processing_llava_next_video.py

@@ -180,6 +180,9 @@ def __call__(
             for sample in text:
                 while self.image_token in sample:
                     image_size = next(image_sizes)
+                    if not isinstance(image_size, (list, tuple)):
+                        # cast to list to avoid numerical precision errors when calculating unpadding
+                        image_size = image_size.tolist()
                     orig_height, orig_width = image_size
                     num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
                     if self.vision_feature_select_strategy == "default":
@@ -193,6 +196,8 @@ def __call__(
             one_video = to_numpy_array(videos_inputs.get("pixel_values_videos")[0])
             height, width = get_image_size(one_video[0])
             num_frames = one_video.shape[0]  # frame dim is always after batch dim
+
+            # no `self.num_additional_image_tokens` added because video always has a default feature selection strategy
             num_image_tokens = (height // self.patch_size) * (width // self.patch_size)
             num_video_tokens = num_image_tokens // 4 * num_frames  # divide by 4 needed for avg pooling layer
             prompt_strings = []

src/transformers/models/video_llava/processing_video_llava.py

@@ -179,7 +179,7 @@ def __call__(
             ) + self.num_additional_image_tokens
             num_video_tokens = num_image_tokens * num_frames
             if self.vision_feature_select_strategy == "default":
-                num_image_tokens -= self.num_additional_image_tokens
+                num_image_tokens -= 1
 
             prompt_strings = []
             for sample in text:

src/transformers/models/vipllava/modeling_vipllava.py

@@ -243,6 +243,7 @@ def __init__(self, config: VipLlavaConfig):
         self.vocab_size = config.text_config.vocab_size
         self.language_model = AutoModelForCausalLM.from_config(config.text_config)
         self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
+
         self.post_init()
 
     def get_input_embeddings(self):