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.
This library is still a WIP, consider this an alpha release (pre announcement). Major features should be working, the library has been tested with several PDF datasets we will shortly make public. However, do expect there will still be breaking changes, lots of improvements, etc.
pip install --pre chug will install the current dev version.
- 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)
- Option to output bbox annotations for lines (or word + word output) for tasks that leverage layout
- Unified preprocessor functions for combined image + text tokenization (img+text token interleaving, etc.)
- Image token (patch) packing ala NaViT. Online bin packing based algorithms integrated with image preprocessing and pipeline.
- 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)
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.
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
Configs, structures (dataclasses) for general use across the library
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')
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 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 processing, tokenization code.
Document processing code. Currently focused on processors that apply image/pdf decoders and process document OCR or VQA annotations.
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.
This lone top-level file includes the main factory methods for creating loaders w/ associated pipelines from config dataclasses.
Most applications using chug will exist outside of the lib in training libraries, etc. Some builtin utility / exploration apps will be included here.
WIP
Datasets that work well with this library can be found on the Hugging Face Hub under the pixparse organization (https://huggingface.co/pixparse).
We'll add links to other noteworthy datasets that can be used as we become aware of them.
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)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)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)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)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=10968539,
format='wds',
)
lb = chug.create_loader(
data_cfg,
task_cfg,
is_training=True,
)
ii = iter(lb)
sample = next(ii)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)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)chug evolve from the webdataset datapipeline used successfully in the OpenCLIP project. Thanks to all the contributors in that project. Future work will likely involve closing the loop and leveraging chug in OpenCLIP for increased capability.
The image/document augmentations in chug rely on a number of external influences. Our document oriented doc_better torchvision augmentations are influenced by nougat, and the doc_nougat is a direct adaptation of the albumentations + cv2 document pipeline in nougat. Several image augmentations leverage existing work in the timm library.
Also, big thanks to the maintainers of webdataset and Hugging Face datasets.
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