Summary of Asynchronous Local-sgd Training For Language Modeling, by Bo Liu et al.
Asynchronous Local-SGD Training for Language Modeling
by Bo Liu, Rachita Chhaparia, Arthur Douillard, Satyen Kale, Andrei A. Rusu, Jiajun Shen, Arthur Szlam, Marc’Aurelio Ranzato
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 asynchronous local stochastic gradient descent (Local-SGD) for training language models. Local-SGD is an approach where each device performs multiple SGD updates before communicating with others. The study examines how hardware heterogeneity, model size, number of workers, and optimizer impact learning performance. Despite updating the global parameters more frequently, asynchronous Local-SGD takes longer to converge than its synchronous counterpart. To address this challenge, the authors propose a novel method that utilizes delayed Nesterov momentum updates and adjusts local training steps based on computation speed. The approach is evaluated with models up to 150M parameters on the C4 dataset and achieves performance comparable to synchronous Local-SGD in terms of perplexity per update step, while significantly outperforming it in terms of wall clock time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a way to train language models using computers that aren’t all the same. It’s called asynchronous local stochastic gradient descent (Local-SGD). The researchers want to know how different things affect how well this method works, like how fast the computers are and what size the model is. They find that when they use this method, it takes longer to get good results than if they used a different way. To solve this problem, they come up with a new approach that helps the computers work together better. This approach does just as well as the other way in terms of getting good results quickly, and is much faster overall. |
Keywords
* Artificial intelligence * Perplexity * Stochastic gradient descent
