&nbspDocumentation &nbsp| &nbspPaper&nbsp｜&nbsp Blog &nbsp |&nbsp ModelScope &nbsp

What is OFASys?

OFASys is a multi-modal multi-task learning system designed to make multi-modal tasks declarative, modular and task-scalable. With OFASys, it is easy to:

• Rapidly introduce new multi-modal tasks/datasets by defining a declarative one-line instruction.
• Develop new or reuse existing modality-specific components.
• Jointly train multiple multi-modal tasks together without manual processing of multi-modal data collating.

For now, OFASys supports 7 modalities and more than 20 classes of multi-modal tasks, including:

• Text: for tasks like Natural language Understanding, Text Summarization and Text Infilling.
• Image: for tasks like Image Classification, Visual Entailment, Image Captioning, Visual Question Answering, Text-to-Image Generation and Image Infilling.
• Box: for tasks like Visual Grounding, Grounded Caption, Object Detection
• Video: for tasks like Video Classification, Video Captioning and Video Question Answering.
• Audio: for tasks like Automatic Speech Recognition, and Text to Speech.
• Structural Language: for tasks like Text-to-SQL, Table-to-Text, Table question answering, and Sudoku.
• Motion: for tasks like Text-to-Motion.

News

• 2022.12.23 v0.1.0-patch1:
  • Refactored and released diffusion-based Text-to-Motion task (v0.1), see doc for usage.
  • Refactored TextPreprocess: BOS and EOS no longer required when writing an instruction.
  • Added DatabasePreprocess for the Text-to-SQL task.

Requirements

• PyTorch version >= 1.8.0
• Python version >= 3.7
• Torchaudio >= 0.8.0

Installation

Install with pip

Through the pip installation, users can experience the basic multi-task training and inference functions of OFASys.

pip install http://ofasys.oss-cn-zhangjiakou.aliyuncs.com/pkg/ofasys-0.1.0-py3-none-any.whl

Test your installation.

python -c "import ofasys"

Using the audio feature in OFASys requires the soundfile library to be installed. In the Ubuntu OS, run the following command:

sudo apt-get update
sudo apt-get install libsndfile1

Install with Source (Optional)

Users can install OFASys from the source code to customize their training tasks and full functions.

git clone https://github.com/OFA-Sys/OFASys.git
cd OFASys
python setup.py develop

Getting Started

The documents contains more instructions for getting started.

Training One Model for All Tasks

Define the Tasks

OFASys can co-train multiple multi-modal tasks flexibly.

from ofasys import Task, Trainer, GeneralistModel
task1 = Task(
     name='caption',
     instruction='[IMAGE:image_url] what does the image describe? -> [TEXT:caption]',
     micro_batch_size=4,
 )
task2 = Task(
     name='text_infilling',
     instruction='what is the complete text of " [TEXT:sentence,mask_ratio=0.3] "? -> [TEXT:sentence]',
     micro_batch_size=2,
 )

In the simplest scenario, you only need to specify an instruction to define your task and a task name as an identifier.

Set the Dataset

The Task can use a regular Pytorch Dataloader which can be constructed by Huggingface Dataset or a customized Pytorch Dataset.

from datasets import load_dataset
task1.add_dataset(load_dataset('TheFusion21/PokemonCards')['train'], 'train')
task2.add_dataset(load_dataset('glue', 'cola')['train'], 'train')

Create a Generalist Model and Train All Tasks Together

The GeneralistModel of OFASys (OFA+) is capable of handling multiple modalities including: TEXT, IMAGE, AUDIO, VIDEO, MOTION, BOX, PHONE.

The OFASys Trainer “mixes” multiple Tasks with any dataset and abstracts away all the engineering complexity needed for scale.

model = GeneralistModel()
trainer = Trainer()
trainer.fit(model=model, tasks=[task1, task2])

The complete script is available at scripts/trainer_api.py.

Infer Multiple Multi-modal Tasks with One Checkpoint

OFASys can infer multiple multi-modal tasks using just One checkpoint.

from ofasys import OFASys
model = OFASys.from_pretrained('multitask.pt')

OFASys enables multi-task multi-modal inference through the instruction alone. The multitask checkpoint can be download at here. Let's go through a couple of examples!

Image Captioning

instruction = '[IMAGE:img] what does the image describe?  -> [TEXT:cap]'
data = {'img': "./COCO_val2014_000000222628.jpg"}
output = model.inference(instruction, data=data)
print(output.text)
# "a man and woman sitting in front of a laptop computer"

Visual Grounding

instruction = '[IMAGE:img] which region does the text " [TEXT:cap] " describe? -> [BOX:patch_boxes]'
data = {'img': "https://www.2008php.com/2014_Website_appreciate/2015-06-22/20150622131649.jpg", "cap": "hand"}
output = model.inference(instruction, data=data)
output.save_box("output.jpg")

Text Summarization

instruction = 'what is the summary of article " [TEXT:src] "? -> [TEXT:tgt]'
data = {'src': "poland 's main opposition party tuesday endorsed president lech walesa in an upcoming "
        "presidential run-off election after a reformed communist won the first round of voting ."}
output = model.inference(instruction, data=data)
print(output.text)
# "polish opposition endorses walesa in presidential run-off"

Table-to-Text Generation

instruction = 'structured knowledge: " [STRUCT:database,uncased] "  . how to describe the tripleset ? -> [TEXT:tgt]'
data = {
     'database': [['Atlanta', 'OFFICIAL_POPULATION', '5,457,831'],
                  ['[TABLECONTEXT]', 'METROPOLITAN_AREA', 'Atlanta'],
                  ['5,457,831', 'YEAR', '2012'],
                  ['[TABLECONTEXT]', '[TITLE]', 'List of metropolitan areas by population'],
                  ['Atlanta', 'COUNTRY', 'United States'],
     ]
 }
output = model.inference(instruction, data=data, beam_size=1)
print(output.text)
# "atlanta, united states has a population of 5,457,831 in 2012."

Text-to-SQL Generation

instruction = ' " [TEXT:src] " ; structured knowledge: " [STRUCT:database,max_length=876] " . generating sql code. -> [TEXT:tgt]'
database = [
             ['concert_singer'],
             ['stadium', 'stadium_id , location , name , capacity , highest , lowest , average'],
             ['singer', 'singer_id , name , country , song_name , song_release_year , age , is_male'],
             ['concert', 'concert_id , concert_name , theme , stadium_id , year'],
             ['singer_in_concert', 'concert_id , singer_id']
 ]
data = [
     {'src': 'What are the names, countries, and ages for every singer in descending order of age?', 'database': database},
     {'src': 'What are all distinct countries where singers above age 20 are from?', 'database': database},
     {'src': 'Show the name and the release year of the song by the youngest singer.', 'database': database}
 ]
output = model.inference(instruction, data=data)
print('\n'.join([o.text for o in output]))
# "select name, country, age from singer order by age desc"
# "select distinct country from singer where age > 20"
# "select song_name, song_release_year from singer order by age limit 1"

Video Captioning

instruction = '[VIDEO:video] what does the video describe? -> [TEXT:cap]'
data = {'video': './video7021.mp4'}
output = model.inference(instruction, data=data)
print(output.text)
# "a baseball player is hitting a ball"

Speech-to-Text Generation

instruction = '[AUDIO:wav] what is the text corresponding to the voice? -> [TEXT:text,preprocess=text_phone]'
data = {'wav': './1272-128104-0001.flac'}
output = model.inference(instruction, data=data)
print(output.text)
# "nor is mister klohs manner less interesting than his manner"

Text-to-Image Generation

instruction = 'what is the complete image? caption: [TEXT:text]"? -> [IMAGE,preprocess=image_vqgan,adaptor=image_vqgan]'
data = {'text': "a city with tall buildings and a large green park."}
output = model.inference(instruction, data=data)
output[0].save_image('0.png')

Text-to-Motion Generation

model = OFASys.from_pretrained('single_task_motion.pt')
instruction = 'motion capture: [TEXT:text] -> [MOTION:bvh_frames,preprocess=motion_6d,adaptor=motion_6d]'
guided_prompts = [
    {'text': 'run then jump'},  # # The positive prompt.
    {'text': ''},  # The negative prompt, or an empty string for classifier-free guidance.
]
# This API requires the positive and negative prompts be in the same batch, so please ensure batch_size % 2 == 0.
output = model.inference(instruction, data=guided_prompts, guidance_weight=3.0, batch_size=2)
output[0].save_as_gif('run_then_jump__guided.gif')

The checkpoint of the single motion task and more motion cases can be found at here.

Learn More

Section	Description
Documentation	Full API documentation and tutorials
Quick tour	Usage in 15 minutes, including training and inference
How to define a task	How to define a task using the instruction
Task summary	Tasks supported by OFASys

Getting Involved

Feel free to submit Github issues or pull requests. Welcome to contribute to our project!

To contact us, never hestitate to send an email to [email protected] or [email protected]!

Citation

Please cite our paper if you find it helpful :)

@article{bai2022ofasys,
  author    = {
      Jinze Bai and 
      Rui Men and 
      Hao Yang and 
      Xuancheng Ren and 
      Kai Dang and 
      Yichang Zhang and 
      Xiaohuan Zhou and 
      Peng Wang and 
      Sinan Tan and 
      An Yang and 
      Zeyu Cui and 
      Yu Han and 
      Shuai Bai and 
      Wenbin Ge and 
      Jianxin Ma and 
      Junyang Lin and 
      Jingren Zhou and 
      Chang Zhou},
  title     = {OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models},
  journal   = {CoRR},
  volume    = {abs/2212.04408},
  year      = {2022}
}