[Bugfix] Fix LLaVA-NeXT

vllm/model_executor/models/llava_next.py

@@ -216,6 +216,30 @@ def _parse_and_validate_image_input(
 
         return None
 
+    def _select_image_features(self, image_features: torch.Tensor, *,
+                               strategy: str) -> torch.Tensor:
+        # Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421  # noqa
+        if strategy == "default":
+            return image_features[:, 1:]
+        elif strategy == "full":
+            return image_features
+
+        raise ValueError(f"Unexpected select feature strategy: {strategy}")
+
+    def _image_pixels_to_features(self, vision_tower: CLIPVisionModel,
+                                  pixel_values: torch.Tensor) -> torch.Tensor:
+        # TODO(xwjiang): Maybe port minimal CLIPVisionModel over.
+        image_outputs = vision_tower(pixel_values.to(vision_tower.device),
+                                     output_hidden_states=True)
+
+        image_features = image_outputs.hidden_states[
+            self.config.vision_feature_layer]
+
+        return self._select_image_features(
+            image_features,
+            strategy=self.config.vision_feature_select_strategy,
+        )
+
     def _merge_image_patch_embeddings(self, image_size: torch.Tensor,
                                       patch_embeddings: torch.Tensor, *,
                                       strategy: str) -> torch.Tensor:

vllm/multimodal/utils.py

@@ -77,7 +77,7 @@ def get_full_image_text_prompt(image_prompt: str, text_prompt: str,
     """Combine image and text prompts for vision language model depending on
     the model architecture."""
 
-    if config.hf_config.model_type == "llava":
+    if config.hf_config.model_type in ("llava", "llava_next"):
         full_prompt = f"{image_prompt}\n{text_prompt}"
     else:
         raise ValueError(