Summary of 4-bit Shampoo For Memory-efficient Network Training, by Sike Wang et al.
4-bit Shampoo for Memory-Efficient Network Training
by Sike Wang, Pan Zhou, Jia Li, Hua Huang
First submitted to arxiv on: 28 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 proposes the first 4-bit second-order optimizers, specifically a 4-bit version of the Shampoo optimizer, which maintains performance similar to its 32-bit counterpart. The key challenge is addressing the reduced precision of the preconditioner and its inverse root, which restricts the size of models that can be trained. The authors show that quantizing the eigenvector matrix of the preconditioner is more effective than quantizing the preconditioner itself. They also find that linear square quantization outperforms dynamic tree quantization when quantizing second-order optimizer states. Evaluation on image classification and natural language modeling tasks demonstrates that 4-bit Shampoo achieves comparable performance to its 32-bit counterpart while reducing memory usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for computers to train bigger models using less memory. They do this by developing a new way of doing something called optimization, which is used in machine learning. The problem they’re trying to solve is that when you try to use these big models, the computer runs out of memory because the calculations are too complex. To fix this, they developed a way to reduce the amount of information the computer needs to store and process while still getting good results. They tested their new method on image recognition and natural language processing tasks and found that it worked just as well as the original method but used much less memory. |
Keywords
» Artificial intelligence » Image classification » Machine learning » Natural language processing » Optimization » Precision » Quantization
