Summary of Loco: Low-bit Communication Adaptor For Large-scale Model Training, by Xingyu Xie et al.
LoCo: Low-Bit Communication Adaptor for Large-scale Model Training
by Xingyu Xie, Zhijie Lin, Kim-Chuan Toh, Pan Zhou
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 proposes a novel approach called the Low-bit Communication Adaptor (LoCo) that efficiently trains large-scale models while maintaining training quality. LoCo compensates gradients on local GPU nodes before compression, ensuring efficient synchronization without compromising training quality. This is achieved by designing a moving average of historical compensation errors to stably estimate concurrent compression error and then adopting it to compensate for the concurrent gradient compression. The mechanism allows compatibility with general optimizers like Adam and sharding strategies like FSDP. Experimental results show that LoCo significantly improves communication efficiency, improving training speed by 14% to 40% without performance degradation on large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoCo is a new way to make big models work faster. Right now, it takes a long time to train these models because computers have to send lots of information to each other. LoCo makes this process faster and more efficient by adjusting the information before sending it. This means that computers can talk to each other faster without losing any important details. The paper shows that this works really well with different types of optimizers and even helps big language models learn better. |
