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O S L O

Open Source framework for Large-scale transformer Optimization

GitHub release Apache 2.0 Docs Issues



What's New:

What is OSLO about?

OSLO is a framework that provides various GPU based optimization technologies for large-scale modeling. 3D Parallelism and Kernel Fusion which could be useful when training a large model like EleutherAI/gpt-j-6B are the key features. OSLO makes these technologies easy-to-use by magical compatibility with Hugging Face Transformers that is being considered as a de facto standard in 2021. Currently, the architectures such as GPT2, GPTNeo, and GPTJ are supported, but we plan to support more soon.

Installation

OSLO can be easily installed using the pip package manager. All the dependencies such as torch, transformers, dacite, ninja and pybind11 should be installed automatically with the following command. Be careful that the 'core' is in the PyPI project name.

pip install oslo-core

Some of features rely on the C++ language. So we provide an option, CPP_AVAILABLE, to decide whether or not you install them.

  • If the C++ is available:
CPP_AVAILABLE=1 pip install oslo-core
  • If the C++ is not available:
CPP_AVAILABLE=0 pip install oslo-core

Note that the default value of CPP_AVAILABLE is 0 in Windows and 1 in Linux.

Key Features

import deepspeed
from oslo import GPTJForCausalLM

# 1. 3D Parallelism
model = GPTJForCausalLM.from_pretrained_with_parallel(
    "EleutherAI/gpt-j-6B", tensor_parallel_size=2, pipeline_parallel_size=2,
)

# 2. Kernel Fusion
model = model.fuse()

# 3. DeepSpeed Support
engines = deepspeed.initialize(
    model=model.gpu_modules(), model_parameters=model.gpu_parameters(), ...,
)

# 4. Data Processing
from oslo import (
    DatasetPreprocessor,
    DatasetBlender,
    DatasetForCausalLM,
    ...
)

# 5. Deployment Launcher
model = GPTJForCausalLM.from_pretrained_with_parallel(..., deployment=True)

OSLO offers the following features.

  • 3D Parallelism: The state-of-the-art technique for training a large-scale model with multiple GPUs.
  • Kernel Fusion: A GPU optimization method to increase training and inference speed.
  • DeepSpeed Support: We support DeepSpeed which provides ZeRO data parallelism.
  • Data Processing: Various utilities for efficient large-scale data processing.
  • Deployment Launcher: A launcher for easily deploying a parallelized model to the web server.

See USAGE.md to learn how to use them.

Administrative Notes

Citing OSLO

If you find our work useful, please consider citing:

@misc{oslo,
  author       = {Ko, Hyunwoong and Kim, Soohwan and Park, Kyubyong},
  title        = {OSLO: Open Source framework for Large-scale transformer Optimization},
  howpublished = {\url{https://github.com/tunib-ai/oslo}},
  year         = {2021},
}

Licensing

The Code of the OSLO project is licensed under the terms of the Apache License 2.0.

Copyright 2021 TUNiB Inc. http://www.tunib.ai All Rights Reserved.

Acknowledgements

The OSLO project is built with GPU support from the AICA (Artificial Intelligence Industry Cluster Agency).