507 citations · 817 across the 20 of their papers we have counts for
4 papers · 2 filters
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training
Shenggui Li, Hongxin Liu, Zhengda Bian +5
The success of Transformer models has pushed the deep learning model scale to billions of parameters. Due to the limited memory resource of a single GPU, However, the best practice…
PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
Jiarui Fang, Zilin Zhu, Shenggui Li +4
The pre-trained model (PTM) is revolutionizing Artificial Intelligence (AI) technology. However, the hardware requirement of PTM training is prohibitively high, making it a game fo…
Sequence Parallelism: Long Sequence Training from System Perspective
Shenggui Li, Fuzhao Xue, Chaitanya Baranwal +2
Transformer achieves promising results on various tasks. However, self-attention suffers from quadratic memory requirements with respect to the sequence length. Existing work focus…
An Efficient 2D Method for Training Super-Large Deep Learning Models
Qifan Xu, Shenggui Li, Chaoyu Gong +1
Huge neural network models have shown unprecedented performance in real-world applications. However, due to memory constraints, model parallelism must be utilized to host large mod…