[VLM] Cleanup siglip legacy code and fix broken paligemma multimodal processor

vllm/model_executor/models/paligemma.py

@@ -24,9 +24,10 @@
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsMultiModal, SupportsPP
-from .siglip import SiglipVisionModel, get_max_siglip_image_tokens
+from .siglip import SiglipVisionModel
 from .utils import (AutoWeightsLoader, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
+from .vision import get_vision_encoder_info
 
 logger = init_logger(__name__)
 
@@ -67,6 +68,9 @@ class PaliGemmaProcessingInfo(BaseProcessingInfo):
     def get_hf_config(self):
         return self.ctx.get_hf_config(PaliGemmaConfig)
 
+    def get_vision_encoder_info(self):
+        return get_vision_encoder_info(self.get_hf_config())
+
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
         return {"image": 1}
 
@@ -78,9 +82,8 @@ def get_mm_max_tokens_per_item(
         return {"image": self.get_num_image_tokens()}
 
     def get_num_image_tokens(self) -> int:
-        hf_config = self.get_hf_config()
-        vision_config = hf_config.vision_config
-        return get_max_siglip_image_tokens(vision_config)
+        vision_encoder_info = self.get_vision_encoder_info()
+        return vision_encoder_info.get_max_image_tokens()
 
 
 class PaliGemmaDummyInputsBuilder(
@@ -173,8 +176,10 @@ def apply(
         prompt: Union[str, list[int]],
         mm_data: MultiModalDataDict,
         hf_processor_mm_kwargs: Mapping[str, object],
+        return_mm_hashes: bool = False,
     ) -> MultiModalInputs:
-        mm_inputs = super().apply(prompt, mm_data, hf_processor_mm_kwargs)
+        mm_inputs = super().apply(prompt, mm_data, hf_processor_mm_kwargs,
+                                  return_mm_hashes)
         prompt_token_ids = mm_inputs["prompt_token_ids"]
 
         tokenizer = self.info.get_tokenizer()

vllm/model_executor/models/siglip.py

@@ -6,7 +6,6 @@
 from typing import Iterable, Optional, Set, Tuple, Union
 
 import torch
-from PIL import Image
 from torch import nn
 from transformers import SiglipVisionConfig
 
@@ -20,74 +19,10 @@
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
-from vllm.multimodal.utils import consecutive_placeholder_ranges
-from vllm.sequence import SequenceData
 
 from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
 
 
-def get_siglip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
-    # Since interpolation is applied, the image size need not be divisible
-    # assert image_size % patch_size == 0
-    return image_size // patch_size
-
-
-def get_siglip_num_patches(*, image_size: int, patch_size: int) -> int:
-    grid_length = get_siglip_patch_grid_length(image_size=image_size,
-                                               patch_size=patch_size)
-    return grid_length * grid_length
-
-
-def get_siglip_image_feature_size(hf_config: SiglipVisionConfig) -> int:
-    return get_siglip_num_patches(image_size=hf_config.image_size,
-                                  patch_size=hf_config.patch_size)
-
-
-def get_max_siglip_image_tokens(hf_config: SiglipVisionConfig) -> int:
-    return get_siglip_image_feature_size(hf_config)
-
-
-def dummy_seq_data_for_siglip(
-    hf_config: SiglipVisionConfig,
-    seq_len: int,
-    num_images: int,
-    *,
-    image_token_id: int,
-    image_feature_size_override: Optional[int] = None,
-    mm_key: str = "image",
-):
-    if image_feature_size_override is None:
-        image_feature_size = get_siglip_image_feature_size(hf_config)
-    else:
-        image_feature_size = image_feature_size_override
-
-    return SequenceData.from_prompt_token_counts(
-        (image_token_id, image_feature_size * num_images),
-        (0, seq_len - image_feature_size * num_images),
-    ), {
-        mm_key:
-        consecutive_placeholder_ranges(num_items=num_images,
-                                       item_size=image_feature_size)
-    }
-
-
-def dummy_image_for_siglip(
-    hf_config: SiglipVisionConfig,
-    num_images: int,
-    *,
-    image_width_override: Optional[int] = None,
-    image_height_override: Optional[int] = None,
-):
-    width = height = hf_config.image_size
-    if image_width_override is not None:
-        width = image_width_override
-    if image_height_override is not None:
-        height = image_height_override
-
-    image = Image.new("RGB", (width, height), color=0)
-    return {"image": image if num_images == 1 else [image] * num_images}
-
-
 class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]):
 
     def get_num_image_tokens(
@@ -96,10 +31,10 @@ def get_num_image_tokens(
         image_width: int,
         image_height: int,
     ) -> int:
-        return get_siglip_image_feature_size(self.vision_config)
+        return self.get_patch_grid_length()**2
 
     def get_max_image_tokens(self) -> int:
-        return get_max_siglip_image_tokens(self.vision_config)
+        return self.get_patch_grid_length()**2
 
     def get_image_size(self) -> int:
         return self.vision_config.image_size
@@ -108,10 +43,8 @@ def get_patch_size(self) -> int:
         return self.vision_config.patch_size
 
     def get_patch_grid_length(self) -> int:
-        return get_siglip_patch_grid_length(
-            image_size=self.vision_config.image_size,
-            patch_size=self.vision_config.patch_size,
-        )
+        image_size, patch_size = self.get_image_size(), self.get_patch_size()
+        return image_size // patch_size
 
 
 # Adapted from https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/models/siglip/modeling_siglip.py#L249 # noqa