[VLM] Remove input processor from clip and siglip (#13165)

vllm/model_executor/models/siglip.py

@@ -3,18 +3,15 @@
 within a vision language model."""
 
 import math
-from typing import Iterable, List, Optional, Set, Tuple, Union
+from typing import Iterable, Optional, Set, Tuple, Union
 
-import numpy as np
 import torch
 from PIL import Image
 from torch import nn
 from transformers import SiglipVisionConfig
 
 from vllm.attention.layer import MultiHeadAttention
-from vllm.config import ModelConfig
 from vllm.distributed import divide, get_tensor_model_parallel_world_size
-from vllm.inputs import DecoderOnlyInputs, token_inputs
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                QKVParallelLinear,
@@ -23,9 +20,7 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 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 (cached_get_tokenizer,
-                                   consecutive_placeholder_ranges,
-                                   repeat_and_pad_placeholder_tokens)
+from vllm.multimodal.utils import consecutive_placeholder_ranges
 from vllm.sequence import SequenceData
 
 from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
@@ -93,71 +88,6 @@ def dummy_image_for_siglip(
     return {"image": image if num_images == 1 else [image] * num_images}
 
 
-def dummy_video_for_siglip(
-    hf_config: SiglipVisionConfig,
-    num_frames: int,
-    num_videos: int = 1,
-    *,
-    image_width_override: Optional[int] = None,
-    image_height_override: Optional[int] = None,
-):
-    pil_frame = dummy_image_for_siglip(
-        hf_config,
-        num_images=1,
-        image_width_override=image_width_override,
-        image_height_override=image_height_override)
-    np_frame = np.array(pil_frame["image"])
-    mm_data_per_video = np.repeat([np_frame], num_frames, axis=0)
-    video_data = [mm_data_per_video] * num_videos
-    mm_data = {"video": video_data}
-    return mm_data
-
-
-def input_processor_for_siglip(
-    model_config: ModelConfig,
-    hf_config: SiglipVisionConfig,
-    inputs: DecoderOnlyInputs,
-    *,
-    image_token_id: int,
-    image_feature_size_override: Optional[Union[int, List[int]]] = None,
-):
-    multi_modal_data = inputs.get("multi_modal_data")
-    if multi_modal_data is None or "image" not in multi_modal_data:
-        return inputs
-
-    if "multi_modal_placeholders" in inputs and "image" in inputs[
-            "multi_modal_placeholders"]:
-        # The inputs already have placeholders.
-        return inputs
-
-    tokenizer = cached_get_tokenizer(model_config.tokenizer)
-
-    if image_feature_size_override is None:
-        image_data = multi_modal_data["image"]
-        if isinstance(image_data, Image.Image):
-            image_feature_size = get_siglip_image_feature_size(hf_config)
-        elif isinstance(image_data, torch.Tensor):
-            num_images, image_feature_size, hidden_size = image_data.shape
-        else:
-            raise TypeError(f"Invalid image type: {type(image_data)}")
-    else:
-        image_feature_size = image_feature_size_override
-
-    new_prompt, new_token_ids, ranges = repeat_and_pad_placeholder_tokens(
-        tokenizer,
-        inputs.get("prompt"),
-        inputs["prompt_token_ids"],
-        placeholder_token_id=image_token_id,
-        repeat_count=image_feature_size,
-    )
-
-    # NOTE: Create a defensive copy of the original inputs
-    return token_inputs(prompt_token_ids=new_token_ids,
-                        prompt=new_prompt,
-                        multi_modal_data=multi_modal_data,
-                        multi_modal_placeholders={"image": ranges})
-
-
 class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]):
 
     def get_num_image_tokens(