๐ค MegaBlocks
MegaBlocks is a light-weight library for mixture-of-experts (MoE) training. The core of the system is efficient "dropless-MoE" (dMoE, paper) and standard MoE layers.
MegaBlocks is built on top of Megatron-LM, where we support data, expert and pipeline parallel training of MoEs. We're working on extending more frameworks to support MegaBlocks.
๐ Performance
MegaBlocks dMoEs outperform MoEs trained with Tutel by up to 40% compared to Tutel's best performing capacity_factor
configuration. MegaBlocks dMoEs use a reformulation of MoEs in terms of block-sparse operations, which allows us to avoid token dropping without sacrificing hardware efficiency. In addition to being faster, MegaBlocks simplifies MoE training by removing the capacity_factor
hyperparameter alltogether. Compared to dense Transformers trained with Megatron-LM, MegaBlocks dMoEs can accelerate training by as much as 2.4x. Check out our paper for more details!
๐๏ธ Installation
Note: this assumes you have numpy
and torch
installed
Training models with Megatron-LM: We recommend using NGC's nvcr.io/nvidia/pytorch:23.01-py3
PyTorch container. The Dockerfile builds on this image with additional dependencies. To build the image, run docker build . -t megablocks-dev
and then bash docker.sh
to launch the container. Once inside the container, install MegaBlocks with pip install .
. See Usage for instructions on training MoEs with MegaBlocks + Megatron-LM.
Using MegaBlocks in other packages: To install the MegaBlocks package for use in other frameworks, run pip install megablocks
.
๐ Usage
We provide scripts for pre-training Transformer MoE and dMoE language models under the top-level directory. The quickest way to get started is to use one of the experiment launch scripts. These scripts require a dataset in Megatron-LM's format, which can be created by following their instructions.
โ๏ธ Citation
@article{megablocks,
title={{MegaBlocks: Efficient Sparse Training with Mixture-of-Experts}},
author={Trevor Gale and Deepak Narayanan and Cliff Young and Matei Zaharia},
journal={Proceedings of Machine Learning and Systems},
volume={5},
year={2023}
}