Repository: huggingface/transformers
Pull request: SmolVLM and InternVL: Ensure pixel values are converted to the correct dtype for fp16/bf16

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Changed files:
filename: src/transformers/models/internvl/modeling_internvl.py
patch: @@ -604,6 +604,7 @@ def get_image_features(
             if vision_feature_select_strategy is not None
             else self.config.vision_feature_select_strategy
         )
+        pixel_values = pixel_values.to(dtype=self.dtype)  # fp16 compatibility
 
         downsample_ratio = self.config.downsample_ratio
         if vision_feature_layer == -1:

filename: src/transformers/models/internvl/modular_internvl.py
patch: @@ -544,6 +544,7 @@ def get_image_features(
             if vision_feature_select_strategy is not None
             else self.config.vision_feature_select_strategy
         )
+        pixel_values = pixel_values.to(dtype=self.dtype)  # fp16 compatibility
 
         downsample_ratio = self.config.downsample_ratio
         if vision_feature_layer == -1:

filename: src/transformers/models/smolvlm/modeling_smolvlm.py
patch: @@ -661,6 +661,7 @@ def get_image_features(self, pixel_values: torch.FloatTensor, pixel_attention_ma
                 The attention mask indicating padded regions in the image.
         """
         batch_size, num_images, num_channels, height, width = pixel_values.shape
+        pixel_values = pixel_values.to(dtype=self.dtype)  # fp16 compatibility
         pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:])
 
         # Remove padding images - padding images are full 0.

filename: src/transformers/models/smolvlm/modular_smolvlm.py
patch: @@ -205,6 +205,7 @@ def get_image_features(self, pixel_values: torch.FloatTensor, pixel_attention_ma
                 The attention mask indicating padded regions in the image.
         """
         batch_size, num_images, num_channels, height, width = pixel_values.shape
+        pixel_values = pixel_values.to(dtype=self.dtype)  # fp16 compatibility
         pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:])
 
         # Remove padding images - padding images are full 0.