[1/2] Move InternVL-based processors (#37260)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
603
vllm/transformers_utils/processors/internvl.py
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603
vllm/transformers_utils/processors/internvl.py
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@@ -0,0 +1,603 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# adapted from https://huggingface.co/OpenGVLab/InternVL2-4B/blob/main/modeling_internvl_chat.py
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# --------------------------------------------------------
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# InternVL
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# Copyright (c) 2023 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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# --------------------------------------------------------
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from abc import ABC, abstractmethod
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from typing import Any, TypeVar
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import numpy.typing as npt
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import torch
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import torchvision.transforms as T
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from PIL import Image
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from transformers import BatchFeature, PretrainedConfig, TensorType
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from vllm.multimodal.image import convert_image_mode
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from vllm.multimodal.processing import PromptUpdateDetails
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from vllm.tokenizers import TokenizerLike
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_T = TypeVar("_T")
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IMG_START = "<img>"
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IMG_END = "</img>"
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IMG_CONTEXT = "<IMG_CONTEXT>"
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IMAGENET_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_STD = (0.229, 0.224, 0.225)
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# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
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def build_transform(input_size: int):
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MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
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transform = T.Compose(
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[
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T.Lambda(lambda img: convert_image_mode(img, "RGB")),
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T.Resize(
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(input_size, input_size), interpolation=T.InterpolationMode.BICUBIC
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),
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T.ToTensor(),
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T.Normalize(mean=MEAN, std=STD),
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]
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)
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return transform
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# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
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def find_closest_aspect_ratio(
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aspect_ratio: float,
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target_ratios: list[tuple[int, int]],
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*,
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width: int,
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height: int,
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image_size: int,
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) -> tuple[int, int]:
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best_ratio_diff = float("inf")
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best_ratio = (1, 1)
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area = width * height
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for ratio in target_ratios:
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target_aspect_ratio = ratio[0] / ratio[1]
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ratio_diff = abs(aspect_ratio - target_aspect_ratio)
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if ratio_diff < best_ratio_diff:
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best_ratio_diff = ratio_diff
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best_ratio = ratio
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elif ratio_diff == best_ratio_diff:
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if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
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best_ratio = ratio
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return best_ratio
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def resolve_internvl_min_max_num(
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*,
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min_dynamic_patch: int,
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max_dynamic_patch: int,
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dynamic_image_size: bool,
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use_thumbnail: bool,
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) -> tuple[int, int]:
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min_dynamic_patch = min_dynamic_patch if dynamic_image_size else 1
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max_dynamic_patch = max_dynamic_patch if dynamic_image_size else 1
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if use_thumbnail and max_dynamic_patch != 1:
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max_dynamic_patch += 1
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return min_dynamic_patch, max_dynamic_patch
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def get_internvl_target_ratios(
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min_num: int,
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max_num: int,
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) -> list[tuple[int, int]]:
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target_ratios = {
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(i, j)
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for n in range(min_num, max_num + 1)
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for i in range(1, n + 1)
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for j in range(1, n + 1)
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if min_num <= i * j <= max_num
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}
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return sorted(target_ratios, key=lambda x: x[0] * x[1])
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def calculate_internvl_targets(
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*,
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orig_width: int,
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orig_height: int,
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target_ratios: list[tuple[int, int]],
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image_size: int,
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use_thumbnail: bool,
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) -> tuple[int, int, int]:
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aspect_ratio = orig_width / orig_height
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# find the closest aspect ratio to the target
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target_aspect_ratio = find_closest_aspect_ratio(
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aspect_ratio,
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target_ratios,
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width=orig_width,
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height=orig_height,
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image_size=image_size,
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)
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# calculate the target width and height
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target_width = image_size * target_aspect_ratio[0]
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target_height = image_size * target_aspect_ratio[1]
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blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
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# add thumbnail image if num_blocks != 1
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if use_thumbnail and blocks != 1:
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blocks += 1
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return blocks, target_width, target_height
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# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
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def dynamic_preprocess_internvl(
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image: Image.Image,
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*,
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target_ratios: list[tuple[int, int]],
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image_size: int,
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use_thumbnail: bool,
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) -> list[Image.Image]:
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orig_width, orig_height = image.size
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# calculate the number of blocks without thumbnail
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blocks, target_width, target_height = calculate_internvl_targets(
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orig_width=orig_width,
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orig_height=orig_height,
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target_ratios=target_ratios,
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image_size=image_size,
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use_thumbnail=False,
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)
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# resize the image
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resized_img = image.resize((target_width, target_height))
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processed_images = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size,
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)
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# split the image
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split_img = resized_img.crop(box)
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processed_images.append(split_img)
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assert len(processed_images) == blocks
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if use_thumbnail and len(processed_images) != 1:
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thumbnail_img = image.resize((image_size, image_size))
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processed_images.append(thumbnail_img)
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return processed_images
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# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
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def image_to_pixel_values_internvl(
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image: Image.Image,
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*,
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input_size: int,
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min_num: int,
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max_num: int,
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use_thumbnail: bool,
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) -> torch.Tensor:
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target_ratios = get_internvl_target_ratios(min_num, max_num)
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transform = build_transform(input_size=input_size)
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images = dynamic_preprocess_internvl(
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image,
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target_ratios=target_ratios,
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image_size=input_size,
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use_thumbnail=use_thumbnail,
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)
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pixel_values = torch.stack([transform(image) for image in images])
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return pixel_values
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# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
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def video_to_pixel_values_internvl(
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video: npt.NDArray,
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*,
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input_size: int,
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min_num: int,
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max_num: int,
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use_thumbnail: bool,
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) -> torch.Tensor:
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target_ratios = get_internvl_target_ratios(min_num, max_num)
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transform = build_transform(input_size=input_size)
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frames_list = list[Image.Image]()
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for frame in video:
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pil_frame = dynamic_preprocess_internvl(
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Image.fromarray(frame, mode="RGB"),
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target_ratios=target_ratios,
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image_size=input_size,
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use_thumbnail=use_thumbnail,
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)
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assert len(pil_frame) == 1
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frames_list.extend(pil_frame)
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pixel_values = torch.stack([transform(image) for image in frames_list])
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return pixel_values
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class BaseInternVLProcessor(ABC):
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"""
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This model doesn't define its own HF processor,
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so we implement our own one here.
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The code to insert image tokens is based on:
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https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/modeling_internvl_chat.py#L252
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"""
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def __init__(
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self,
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config: PretrainedConfig,
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tokenizer: TokenizerLike,
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*,
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min_dynamic_patch: int | None = None,
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max_dynamic_patch: int | None = None,
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dynamic_image_size: bool | None = None,
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) -> None:
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super().__init__()
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self.config = config
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self.tokenizer = tokenizer
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image_size: int = config.vision_config.image_size
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patch_size: int = config.vision_config.patch_size
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if min_dynamic_patch is None:
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min_dynamic_patch = config.min_dynamic_patch
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assert isinstance(min_dynamic_patch, int)
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if max_dynamic_patch is None:
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max_dynamic_patch = config.max_dynamic_patch
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assert isinstance(max_dynamic_patch, int)
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if dynamic_image_size is None:
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dynamic_image_size = config.dynamic_image_size
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assert isinstance(dynamic_image_size, bool)
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self.num_image_token = int(
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(image_size // patch_size) ** 2 * (config.downsample_ratio**2)
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)
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self.image_size = image_size
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self.min_dynamic_patch = min_dynamic_patch
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self.max_dynamic_patch = max_dynamic_patch
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self.dynamic_image_size = dynamic_image_size
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self.use_thumbnail: bool = config.use_thumbnail
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@property
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@abstractmethod
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def image_token_id(self) -> int:
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raise NotImplementedError
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@abstractmethod
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def get_image_repl(
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self,
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feature_size: int,
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num_patches: int | None,
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) -> PromptUpdateDetails[str]:
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raise NotImplementedError
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def resolve_min_max_num(
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self,
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*,
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min_dynamic_patch: int | None = None,
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max_dynamic_patch: int | None = None,
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dynamic_image_size: bool | None = None,
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use_thumbnail: bool | None = None,
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) -> tuple[int, int]:
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min_dynamic_patch = (
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self.min_dynamic_patch if min_dynamic_patch is None else min_dynamic_patch
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)
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max_dynamic_patch = (
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self.max_dynamic_patch if max_dynamic_patch is None else max_dynamic_patch
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)
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dynamic_image_size = (
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self.dynamic_image_size
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if dynamic_image_size is None
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else dynamic_image_size
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)
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use_thumbnail = self.use_thumbnail if use_thumbnail is None else use_thumbnail
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return resolve_internvl_min_max_num(
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min_dynamic_patch=min_dynamic_patch,
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max_dynamic_patch=max_dynamic_patch,
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dynamic_image_size=dynamic_image_size,
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use_thumbnail=use_thumbnail,
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)
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def resolve_target_ratios(
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self,
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*,
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min_dynamic_patch: int | None = None,
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max_dynamic_patch: int | None = None,
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dynamic_image_size: bool | None = None,
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use_thumbnail: bool | None = None,
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) -> list[tuple[int, int]]:
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min_num, max_num = self.resolve_min_max_num(
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min_dynamic_patch=min_dynamic_patch,
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max_dynamic_patch=max_dynamic_patch,
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dynamic_image_size=dynamic_image_size,
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use_thumbnail=use_thumbnail,
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)
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return get_internvl_target_ratios(min_num, max_num)
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def get_num_image_tokens(
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self,
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*,
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image_width: int,
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image_height: int,
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) -> int:
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target_ratios = self.resolve_target_ratios(
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use_thumbnail=False, # Applied in calculate_targets
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)
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num_patches, _, _ = calculate_internvl_targets(
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orig_width=image_width,
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orig_height=image_height,
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image_size=self.image_size,
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target_ratios=target_ratios,
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use_thumbnail=self.use_thumbnail,
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)
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return num_patches * self.num_image_token
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def _images_to_pixel_values_lst(
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self,
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images: list[Image.Image],
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min_dynamic_patch: int | None = None,
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max_dynamic_patch: int | None = None,
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dynamic_image_size: bool | None = None,
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) -> list[torch.Tensor]:
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min_num, max_num = self.resolve_min_max_num(
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min_dynamic_patch=min_dynamic_patch,
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max_dynamic_patch=max_dynamic_patch,
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dynamic_image_size=dynamic_image_size,
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use_thumbnail=False, # Applied in image_to_pixel_values
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)
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return [
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image_to_pixel_values_internvl(
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image,
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input_size=self.image_size,
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min_num=min_num,
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max_num=max_num,
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use_thumbnail=self.use_thumbnail,
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)
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for image in images
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]
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def _preprocess_image(
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self,
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text: list[str],
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images: list[Image.Image],
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min_dynamic_patch: int | None = None,
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max_dynamic_patch: int | None = None,
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dynamic_image_size: bool | None = None,
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) -> tuple[list[str], dict[str, torch.Tensor]]:
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if len(images) == 0:
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image_inputs = {}
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else:
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pixel_values_lst = self._images_to_pixel_values_lst(
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images,
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min_dynamic_patch=min_dynamic_patch,
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max_dynamic_patch=max_dynamic_patch,
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dynamic_image_size=dynamic_image_size,
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)
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image_inputs = {
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"pixel_values_flat": torch.cat(pixel_values_lst),
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"image_num_patches": torch.tensor(
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[len(item) for item in pixel_values_lst]
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),
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}
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for pixel_values in pixel_values_lst:
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num_patches = pixel_values.shape[0]
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feature_size = num_patches * self.num_image_token
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image_repl = self.get_image_repl(feature_size, num_patches)
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text = [t.replace("<image>", image_repl.full, 1) for t in text]
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return text, image_inputs
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def _make_batch_input(self, input_item: _T | list[_T] | None = None) -> list[_T]:
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if input_item is None:
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input_item = []
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if not isinstance(input_item, list):
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input_item = [input_item]
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return input_item
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def __call__(
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self,
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text: str | list[str] | None = None,
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images: Image.Image | list[Image.Image] | None = None,
|
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*,
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min_dynamic_patch: int | None = None,
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max_dynamic_patch: int | None = None,
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dynamic_image_size: bool | None = None,
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return_tensors: str | TensorType | None = None,
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**kwargs,
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) -> BatchFeature:
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text = self._make_batch_input(text)
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images = self._make_batch_input(images)
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text, image_inputs = self._preprocess_image(
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text=text,
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images=images,
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min_dynamic_patch=min_dynamic_patch,
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max_dynamic_patch=max_dynamic_patch,
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dynamic_image_size=dynamic_image_size,
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)
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text_inputs = self.tokenizer(text)
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combined_outputs = {**text_inputs, **image_inputs}
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return BatchFeature(combined_outputs, tensor_type=return_tensors)
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class InternVLProcessor(BaseInternVLProcessor):
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"""
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HF Processor for InternVLChatModel with extended video processing logic.
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Code for video processing is adapted from video example:
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https://huggingface.co/OpenGVLab/InternVL3-1B#inference-with-transformers
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"""
|
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def __init__(
|
||||
self,
|
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config: PretrainedConfig,
|
||||
tokenizer: TokenizerLike,
|
||||
*,
|
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min_dynamic_patch: int | None = None,
|
||||
max_dynamic_patch: int | None = None,
|
||||
dynamic_image_size: bool | None = None,
|
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video_token: str | None = None,
|
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) -> None:
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super().__init__(
|
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config=config,
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tokenizer=tokenizer,
|
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min_dynamic_patch=min_dynamic_patch,
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max_dynamic_patch=max_dynamic_patch,
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dynamic_image_size=dynamic_image_size,
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)
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# add extra video token for video processing
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self.video_token = video_token
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@property
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def image_token_id(self) -> int:
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return self.tokenizer.get_vocab()[IMG_CONTEXT]
|
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@property
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def video_token_id(self) -> int | None:
|
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if self.video_token is None:
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return None
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return self.tokenizer.get_vocab().get(self.video_token, None)
|
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@property
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def supports_video(self) -> bool:
|
||||
return self.video_token_id is not None
|
||||
|
||||
def _videos_to_pixel_values_lst(
|
||||
self,
|
||||
videos: list[npt.NDArray],
|
||||
dynamic_image_size: bool | None = None,
|
||||
) -> list[torch.Tensor]:
|
||||
min_num, max_num = self.resolve_min_max_num(
|
||||
min_dynamic_patch=1,
|
||||
max_dynamic_patch=1,
|
||||
dynamic_image_size=dynamic_image_size,
|
||||
use_thumbnail=False, # Applied in image_to_pixel_values
|
||||
)
|
||||
|
||||
return [
|
||||
video_to_pixel_values_internvl(
|
||||
video,
|
||||
input_size=self.image_size,
|
||||
min_num=min_num,
|
||||
max_num=max_num,
|
||||
use_thumbnail=False,
|
||||
)
|
||||
for video in videos
|
||||
]
|
||||
|
||||
def _preprocess_video(
|
||||
self,
|
||||
text: list[str],
|
||||
videos: list[npt.NDArray],
|
||||
dynamic_image_size: bool | None = None,
|
||||
) -> tuple[list[str], dict[str, Any]]:
|
||||
if len(videos) == 0 or not self.supports_video:
|
||||
return text, {}
|
||||
|
||||
video_token = self.video_token
|
||||
assert video_token is not None
|
||||
|
||||
pixel_values_lst_video = self._videos_to_pixel_values_lst(
|
||||
videos,
|
||||
dynamic_image_size=dynamic_image_size,
|
||||
)
|
||||
video_inputs = {
|
||||
"pixel_values_flat_video": torch.cat(pixel_values_lst_video),
|
||||
"video_num_patches": torch.tensor(
|
||||
[len(item) for item in pixel_values_lst_video]
|
||||
),
|
||||
}
|
||||
|
||||
for pixel_values in pixel_values_lst_video:
|
||||
num_patches = pixel_values.shape[0]
|
||||
|
||||
video_repl = self.get_video_repl(
|
||||
self.num_image_token, num_patches, video_token
|
||||
)
|
||||
text = [t.replace("<video>", video_repl.full, 1) for t in text]
|
||||
return text, video_inputs
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
text: str | list[str] | None = None,
|
||||
images: Image.Image | list[Image.Image] | None = None,
|
||||
videos: npt.NDArray | list[npt.NDArray] | None = None,
|
||||
*,
|
||||
min_dynamic_patch: int | None = None,
|
||||
max_dynamic_patch: int | None = None,
|
||||
dynamic_image_size: bool | None = None,
|
||||
return_tensors: str | TensorType | None = None,
|
||||
**kwargs,
|
||||
) -> BatchFeature:
|
||||
text = self._make_batch_input(text)
|
||||
images = self._make_batch_input(images)
|
||||
videos = self._make_batch_input(videos)
|
||||
|
||||
text, image_inputs = self._preprocess_image(
|
||||
text=text,
|
||||
images=images,
|
||||
min_dynamic_patch=min_dynamic_patch,
|
||||
max_dynamic_patch=max_dynamic_patch,
|
||||
dynamic_image_size=dynamic_image_size,
|
||||
)
|
||||
|
||||
text, video_inputs = self._preprocess_video(
|
||||
text=text,
|
||||
videos=videos,
|
||||
dynamic_image_size=dynamic_image_size,
|
||||
)
|
||||
|
||||
text_inputs = self.tokenizer(text)
|
||||
|
||||
combined_outputs = {**text_inputs, **image_inputs, **video_inputs}
|
||||
|
||||
return BatchFeature(combined_outputs, tensor_type=return_tensors)
|
||||
|
||||
def get_image_repl(
|
||||
self,
|
||||
feature_size: int,
|
||||
num_patches: int | None,
|
||||
) -> PromptUpdateDetails[str]:
|
||||
repl_features = IMG_CONTEXT * feature_size
|
||||
repl_full = IMG_START + repl_features + IMG_END
|
||||
|
||||
return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
|
||||
|
||||
def get_video_repl(
|
||||
self,
|
||||
feature_size: int,
|
||||
num_patches: int | None,
|
||||
video_context_token: str = IMG_CONTEXT,
|
||||
) -> PromptUpdateDetails[str]:
|
||||
if num_patches is None:
|
||||
raise NotImplementedError("Embedding inputs are not supported")
|
||||
|
||||
repl_features = video_context_token * self.num_image_token
|
||||
repl_features_with_sep = IMG_START + repl_features + IMG_END
|
||||
# num_patches is equal to num_frames
|
||||
repl_full = "".join(
|
||||
[f"Frame{i + 1}: {repl_features_with_sep}" for i in range(num_patches)]
|
||||
)
|
||||
|
||||
return PromptUpdateDetails.select_text(repl_full, video_context_token)
|
||||
Reference in New Issue
Block a user