Chugging Data

A library to help w/ efficient training for multi-modal data. Initially focused on image & document + text tasks.

chug currently leverages webdataset and Hugging Face datasets.

webdataset tar files and dataset pipelines are preferred for scalable pretraining.

Hugging Face datasets are supported and work great for exploration, validation, and fine-tune use cases.

chug provides on the fly PDF decoding and rendering via either pypdfium2 (https://github.com/pypdfium2-team/pypdfium2) as a default, or fitz/pymupdf (https://github.com/pymupdf/PyMuPDF) if your use case is okay with their AGPL-3.0 license. fitz support must be manually enabled. The pdf handling is implemented at the webdataset level, so you can plug it in to other webdataset pipelines. This enables large scale sharded streaming of native .pdf files without needing to pre-render to .png/.tiff, etc.

Design

Submodule Hierarchy

The library has been designed so that functions, classes at different levels can be used independently.

If one wants to build a loader & pipeline with JSON/YAML serializable configs, use the top-level chug.create_loader() in chug/loader.py. Depending on dataset sources, one can easily switch this between webdataset, HF datasets (in the future, other sources).

Bypassing the highest level, one can also call build_pipeline_* methods in task_pipeline and then call create_loader_wds with a full array of args for wds only use cases.

If one doesn't want to use chug loaders and pipelines at all, image, text, and wds (especially decoder) functionality may be useful in other projects.

Library modules (highest to lowest level)

The dependencies of modules within the library are intended to follow the hierarchy below. e.g. doc depends on wds, but wds should never depend on doc.

app
|
loader (chug/loader.py)
|
task_pipeline
|
doc
|
wds, hfds, image, text
|
common

Submodules

common

Configs, structures (dataclasses) for general use across the library

wds

Webdataset (wds for short) specific code. Extensions and alterations of webdataset functionality to fit covered use case and improve robustness.

All data pipelines in chug currently leverage wds pipelines, even when not using wds datasets.

Document oriented decoding (pdf decoder) is present in chug/wds/decode.py, it can be used with any webdataset pipeline as a decoder. e.g. wds.decode(chug.wds.DecodeDoc('pill'), 'pill')

hfds

Hugging Face datasets support. A minimal wrapper that allows datasets to be used with chug processing pipelines.

The processing pipelines remain webdataset based when using datasets, they are invoked by a custom collate class.

image

Image processing, torchvision and albumentations based transform building code. A mix of generic image (imagenet, simclr) transforms and document specific transforms, including an implementation of albumentations based nougat transforms.

text

Text processing, tokenization code.

doc

Document processing code. Currently focused on processors that apply image/pdf decoders and process document OCR or VQA annotations.

task_pipeline

Task specific pipelines, where dataset formats meet modelling needs.

Inputs to task pipelines are sample dictionaries based on the dataset form, they are decoded and then processed into outputs that match model input requirements.

Task specific pipelines that handle the data <--> model input interface are inserted into an encompassing data pipeline which handles shard lists, shuffle, wrapping, distributed worker, splitting, batching, etc.

chug.loader

This lone top-level file includes the main factory methods for creating loaders w/ associated pipelines from config dataclasses.

app

Most applications using chug will exist outside of the lib in training libraries, etc. Some builtin utility / exploration apps will be included here.

Concepts

WIP

TODOs

Nearish

• Cleanup and refinement, codebase will change
• Documentation & unit-tests
• Support reading of info .json/.yaml files for automatic shard info resolution for webdatasets (like timm)
• Support unified preprocessor functions for combined image + text tokenization (img+text token interleaving, etc.)

Longish

• Increase range of task pipelines for other tasks, modelling needs
• Support additional modalities & targets (video, audio, detection/dense pixel targets, image/video/audio targets)
• Explore alternatives to .tar shards (array_record, arrow, etc)

Usage / Examples

Document Reading, Training w/ IDL

import chug
img_cfg = chug.ImageInputCfg(size=(1024, 768), transform_type='doc_better')
img_fn = chug.create_image_preprocessor(input_cfg=img_cfg, is_training=True)
txt_fn = chug.create_text_preprocessor(
    'naver-clova-ix/donut-base',
    prompt_end_token='<s_idl>',
    task_start_token='<s_idl>',  # NOTE needs to be added to tokenizer
)

task_cfg = chug.DataTaskDocReadCfg(
    image_process_fn=img_fn,
    text_process_fn=txt_fn,
    page_sampling='random',
    error_handler='dump_and_reraise',
)
data_cfg = chug.DataCfg(
    source='pipe:curl -s -f -L https://huggingface.co/datasets/pixparse/IDL-wds/resolve/main/idl-train-0{0000..2999}.tar',
    batch_size=8,
    num_samples=3144726,
    format='wds',
)
lb = chug.create_loader(
    data_cfg,
    task_cfg,
    is_training=True,
)
ii = iter(lb)
sample = next(ii)

Document Reading, Exploring IDL

import chug
task_cfg = chug.DataTaskDocReadCfg(page_sampling='all')
data_cfg = chug.DataCfg(
    source='pixparse/IDL-wds',
    split='train',
    batch_size=None,
    format='hfids',
    num_workers=0,    
)
lb = chug.create_loader(
    data_cfg,
    task_cfg,
)
ii = iter(lb)
sample = next(ii)

Document Reading, Training with PDFA

import chug
img_cfg = chug.ImageInputCfg(size=(1024, 768), transform_type='doc_nougat')
img_fn = chug.create_image_preprocessor(input_cfg=img_cfg, is_training=True)
txt_fn = chug.create_text_preprocessor(
    'naver-clova-ix/donut-base',
    prompt_end_token='<s_pdfa>',
    task_start_token='<s_pdfa>',  # NOTE needs to be added to tokenizer
)

task_cfg = chug.DataTaskDocReadCfg(
    image_process_fn=img_fn,
    text_process_fn=txt_fn,
    page_sampling='random',
)
data_cfg = chug.DataCfg(
    source='pipe:curl -s -f -L https://huggingface.co/datasets/pixparse/pdfa-english-train/resolve/main/pdfa-eng-train-{000000..005000}.tar',
    batch_size=8,
    num_samples=1000000,  # FIXME replace with actual
    format='wds',   
)
lb = chug.create_loader(
    data_cfg,
    task_cfg,
    is_training=True,
)
ii = iter(lb)
sample = next(ii)

Document Reading, Exploring PDFA

import chug

task_cfg = chug.DataTaskDocReadCfg(
    page_sampling='all',
)
data_cfg = chug.DataCfg(
    source='pixparse/pdfa-eng-wds',
    split='train',
    batch_size=None,
    format='hfids',
    num_workers=0,
)
lb = chug.create_loader(
    data_cfg,
    task_cfg,
)
ii = iter(lb)
sample = next(ii)

Image + Text

Training

import chug
import transformers
from functools import partial
img_cfg = chug.ImageInputCfg(size=(512, 512), transform_type='image_timm')
img_fn = chug.create_image_preprocessor(input_cfg=img_cfg, is_training=True)
tokenizer = transformers.AutoTokenizer.from_pretrained('laion/CLIP-ViT-H-14-laion2B-s32B-b79K')
txt_fn = partial(chug.tokenize, max_length=1000, tokenizer=tokenizer)
task_cfg = chug.DataTaskImageTextCfg(
    image_process_fn=img_fn,
    text_process_fn=txt_fn,
)
data_cfg = chug.DataCfg(
    source='pipe:curl -s -f -L https://huggingface.co/datasets/pixparse/cc12m-wds/resolve/main/cc12m-train-{0000..2175}.tar',
    batch_size=8,
    num_samples=10000000,  # FIXME replace with actual
    format='wds',   
)
lb = chug.create_loader(
    data_cfg,
    task_cfg,
    is_training=True,
)
ii = iter(lb)
sample = next(ii)

Document VQA

Training, Fine-tuning

import chug
from chug.task_pipeline import create_task_pipeline
img_cfg = chug.ImageInputCfg(size=(1024, 768), transform_type='doc_basic')
img_fn = chug.create_image_preprocessor(img_cfg, is_training=True)
txt_fn = chug.create_text_preprocessor(
    'naver-clova-ix/donut-base-finetuned-docvqa',
    prompt_end_token='<s_answer>',
    task_start_token='<s_docvqa>',
)

task_cfg = chug.DataTaskDocVqaCfg(
    image_process_fn=img_fn,
    text_process_fn=txt_fn,
)
data_cfg = chug.DataCfg(
    source='pipe:curl -s -f -L https://huggingface.co/datasets/pixparse/docvqa-wds/resolve/main/docvqa-train-{000..383}.tar',
    batch_size=8,
    format='wds',
    num_samples=39463,
)
lb = chug.create_loader(
    data_cfg,
    task_cfg,
    is_training=True,
)
ii = iter(lb)
sample = next(ii)

Exploration

import chug
from chug.task_pipeline import create_task_pipeline
task_cfg = chug.DataTaskDocVqaCfg(
    question_prefix='Question: ',
    question_suffix='',
    answer_prefix='Answer: ',
    answer_suffix=''
)
data_cfg = chug.DataCfg(
    source='pixparse/docvqa-single-page-questions',
    split='validation',
    batch_size=None,
    format='hfids',
    num_workers=0,
)
lb = chug.create_loader(
    data_cfg,
    task_cfg
)
ii = iter(lb)
sample = next(ii)