Summary of Coat: Compressing Optimizer States and Activation For Memory-efficient Fp8 Training, by Haocheng Xi et al.
COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training
by Haocheng Xi, Han Cai, Ligeng Zhu, Yao Lu, Kurt Keutzer, Jianfei Chen, Song Han
First submitted to arxiv on: 25 Oct 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 The abstract introduces COAT, a novel framework for efficient FP8 training of large models. By aligning optimizer state distributions with the FP8 representation range and optimizing activation memory using mixed-granularity quantization strategies, COAT reduces memory footprint by 1.54x compared to BF16 while achieving nearly lossless performance across tasks such as language model pretraining and fine-tuning, and vision language model training. Additionally, COAT achieves a 1.43x speedup compared to BF16, performing on par with or surpassing TransformerEngine’s speedup. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary COAT is a new way to train big models using less memory and faster processing. It helps reduce the amount of memory needed for training by spreading out the information in a more efficient way. This allows for larger models to be trained on fewer computers, making it easier to work with large datasets. The code for COAT is available online. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Pretraining » Quantization
