Summary of Comera: Computing- and Memory-efficient Training Via Rank-adaptive Tensor Optimization, by Zi Yang et al.
CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization
by Zi Yang, Ziyue Liu, Samridhi Choudhary, Xinfeng Xie, Cao Gao, Siegfried Kunzmann, Zheng Zhang
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents CoMERA, a novel method for training large AI models like LLMs and DLRMs efficiently. The high cost of training these models has become a barrier to entry, limiting it to big tech companies, while also raising environmental concerns. To address this issue, the authors propose a Computing- and Memory-Efficient (CoMERA) training method that achieves both high compression ratios and excellent accuracy. CoMERA employs a multi-objective optimization formulation and optimized numerical computations to reduce run-time overhead on GPUs, resulting in a 2-3 times speedup per training epoch compared to standard training. The authors also demonstrate that CoMERA outperforms recent methods like GaLore in terms of memory and computing efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make big AI models more affordable for everyone by creating a way to train them faster and using less computer power. Right now, only the biggest tech companies can afford to train these models because it takes so much time and computer power. This is bad news for the environment too. The authors of this paper came up with a new method called CoMERA that makes training these models cheaper and more efficient. They used special mathematical tricks and optimized their code to make it run faster on computers. This means that smaller companies or even individuals might be able to train their own AI models now. |
Keywords
» Artificial intelligence » Optimization
