Summary of To Fp8 and Back Again: Quantifying the Effects Of Reducing Precision on Llm Training Stability, by Joonhyung Lee et al.
To FP8 and Back Again: Quantifying the Effects of Reducing Precision on LLM Training Stability
by Joonhyung Lee, Jeongin Bae, Byeongwook Kim, Se Jung Kwon, Dongsoo Lee
First submitted to arxiv on: 29 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 The paper explores the use of reduced-precision floating-point representations to accelerate large language model (LLM) pretraining. It discusses the BrainFloat16 (BF16) precision, which has become the de facto standard for LLM training, and the introduction of FP8 in recent processors. The authors argue that reduced-precision training schemes must have similar training stability and hyperparameter sensitivities to their higher-precision counterparts to be cost-effective. However, they find that current methods are not robust enough to replace higher-precision counterparts. To address this issue, the paper proposes new evaluation techniques and a new metric for quantifying loss landscape sharpness in autoregressive language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to make training large language models faster and cheaper. It talks about using less precise numbers, like BrainFloat16 (BF16) or even FP8, instead of the usual 32-bit floating-point numbers. The authors think that if these reduced-precision methods are stable and don’t change too much with different hyperparameters, they could be a cost-effective way to train language models. But they find that current methods aren’t good enough to use as alternatives. To help fix this problem, the paper introduces new ways to evaluate these reduced-precision methods and measures how well they work. |
Keywords
» Artificial intelligence » Autoregressive » Hyperparameter » Large language model » Precision » Pretraining
