Summary of Distributed Sign Momentum with Local Steps For Training Transformers, by Shuhua Yu et al.
Distributed Sign Momentum with Local Steps for Training Transformers
by Shuhua Yu, Ding Zhou, Cong Xie, An Xu, Zhi Zhang, Xin Liu, Soummya Kar
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates a novel communication-efficient distributed sign momentum method for training large-scale Transformer models in scenarios where communicating at every step is prohibitive. The proposed method allows for various base optimizers and uses sign momentum in the global step, generating momentum from differences accumulated during local steps. The authors provide a general convergence analysis that specializes to an O(1/√T) rate for specific instances. They also show an optimal O(1/T^(1/4)) rate for nonconvex smooth cost functions using stochastic gradient descent as the base optimizer. Empirical results demonstrate significant improvement compared to other distributed methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to train big computer models that use Transformers. Training these models is very resource-intensive and takes a lot of time and energy. The authors are trying to make it faster by allowing parts of the model to learn on their own before sharing information with other parts. They create a method that makes this work efficiently and show that it can be used for different types of models and tasks. The results are very promising, showing significant improvements over other methods. |
Keywords
» Artificial intelligence » Stochastic gradient descent » Transformer
