[VLM] Merged multi-modal processor for LLaVA-NeXT (#11682)

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

vllm/model_executor/models/siglip.py

@@ -28,6 +28,8 @@ from vllm.multimodal.utils import (cached_get_tokenizer,
                                    resolve_visual_encoder_outputs)
 from vllm.sequence import SequenceData
 
+from .vision import VisionEncoderInfo
+
 
 def get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
     # Since interpolation is applied, the image size need not be divisible
@@ -156,6 +158,29 @@ def input_processor_for_siglip(
                         multi_modal_placeholders={"image": ranges})
 
 
+class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]):
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        return get_siglip_image_feature_size(self.vision_config)
+
+    def get_max_image_tokens(self) -> int:
+        return get_max_siglip_image_tokens(self.vision_config)
+
+    def get_num_patches(self) -> int:
+        return get_siglip_patch_grid_length(
+            image_size=self.vision_config.image_size,
+            patch_size=self.vision_config.patch_size,
+        )
+
+    def get_image_size(self) -> int:
+        return self.vision_config.image_size
+
+
 # Adapted from https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/models/siglip/modeling_siglip.py#L249 # noqa
 class SiglipVisionEmbeddings(nn.Module):