[VLM] Remove input processor from clip and siglip

vllm/model_executor/models/clip.py

@@ -1,156 +1,24 @@
 # SPDX-License-Identifier: Apache-2.0
 """Minimal implementation of CLIPVisionModel intended to be only used
 within a vision language model."""
-from typing import Iterable, List, Optional, Set, Tuple, Union
+from typing import Iterable, Optional, Set, Tuple, Union
 
-import numpy as np
 import torch
 import torch.nn as nn
-from PIL import Image
 from transformers import CLIPVisionConfig
 
 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,
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
 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.sequence import SequenceData
 
 from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
 
 
-def get_clip_patch_grid_length(*, image_size: int, patch_size: int) -> int:
-    assert image_size % patch_size == 0
-    return image_size // patch_size
-
-
-def get_clip_num_patches(*, image_size: int, patch_size: int) -> int:
-    grid_length = get_clip_patch_grid_length(image_size=image_size,
-                                             patch_size=patch_size)
-    return grid_length * grid_length
-
-
-def get_clip_image_feature_size(hf_config: CLIPVisionConfig) -> int:
-    return get_clip_num_patches(image_size=hf_config.image_size,
-                                patch_size=hf_config.patch_size) + 1
-
-
-def get_max_clip_image_tokens(hf_config: CLIPVisionConfig) -> int:
-    return get_clip_image_feature_size(hf_config)
-
-
-def dummy_seq_data_for_clip(hf_config: CLIPVisionConfig,
-                            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_clip_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_clip(
-    hf_config: CLIPVisionConfig,
-    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}
-
-
-def dummy_video_for_clip(
-    hf_config: CLIPVisionConfig,
-    num_frames: int,
-    num_videos: int = 1,
-    *,
-    image_width_override: Optional[int] = None,
-    image_height_override: Optional[int] = None,
-):
-    pil_frame = dummy_image_for_clip(
-        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_clip(
-    model_config: ModelConfig,
-    hf_config: CLIPVisionConfig,
-    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_clip_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 CLIPEncoderInfo(VisionEncoderInfo[CLIPVisionConfig]):
 
     def get_num_image_tokens(
@@ -159,10 +27,10 @@ def get_num_image_tokens(
         image_width: int,
         image_height: int,
     ) -> int:
-        return get_clip_image_feature_size(self.vision_config)
+        return self.get_patch_grid_length()**2 + 1
 
     def get_max_image_tokens(self) -> int:
-        return get_max_clip_image_tokens(self.vision_config)
+        return self.get_patch_grid_length()**2 + 1
 
     def get_image_size(self) -> int:
         return self.vision_config.image_size
@@ -171,10 +39,9 @@ def get_patch_size(self) -> int:
         return self.vision_config.patch_size
 
     def get_patch_grid_length(self) -> int:
-        return get_clip_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()
+        assert image_size % patch_size == 0
+        return image_size // patch_size
 
 
 # Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py#L164 # noqa
@@ -186,6 +53,7 @@ def __init__(self, config: CLIPVisionConfig):
         self.embed_dim = config.hidden_size
         self.image_size = config.image_size
         self.patch_size = config.patch_size
+        assert self.image_size % self.patch_size == 0
 
         self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
 
@@ -197,8 +65,7 @@ def __init__(self, config: CLIPVisionConfig):
             bias=False,
         )
 
-        self.num_patches = get_clip_num_patches(image_size=self.image_size,
-                                                patch_size=self.patch_size)
+        self.num_patches = (self.image_size // self.patch_size)**2
         self.num_positions = self.num_patches + 1
         self.position_embedding = nn.Embedding(self.num_positions,
                                                self.embed_dim)

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.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(