To FP8 and Back Again: Quantifying the Effects of Reducing Precision on LLM Training Stability

• URL: http://arxiv.org/abs/2405.18710v1
• Date: Wed, 29 May 2024 02:42:23 GMT
• Title: To FP8 and Back Again: Quantifying the Effects of Reducing Precision on LLM Training Stability
• Authors: Joonhyung Lee, Jeongin Bae, Byeongwook Kim, Se Jung Kwon, Dongsoo Lee,
• Abstract summary: BrainFloat16 (BF16) precision has become the de facto standard for large language model pretraining. However, prior experience with FP16, which was found to be less stable than BF16, raises concerns as to whether FP8 can be a cost-effective option for LLM training. We propose new evaluation techniques and a new metric for quantifying loss landscape sharpness in autoregressive language models.
• Score: 7.115739465137031
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The massive computational costs associated with large language model (LLM) pretraining have spurred great interest in reduced-precision floating-point representations to accelerate the process. As a result, the BrainFloat16 (BF16) precision has become the de facto standard for LLM training, with hardware support included in recent accelerators. This trend has gone even further in the latest processors, where FP8 has recently been introduced. However, prior experience with FP16, which was found to be less stable than BF16, raises concerns as to whether FP8, with even fewer bits than FP16, can be a cost-effective option for LLM training. We argue that reduced-precision training schemes must have similar training stability and hyperparameter sensitivities to their higher-precision counterparts in order to be cost-effective. However, we find that currently available methods for FP8 training are not robust enough to allow their use as economical replacements. This prompts us to investigate the stability of reduced-precision LLM training in terms of robustness across random seeds and learning rates. To this end, we propose new evaluation techniques and a new metric for quantifying loss landscape sharpness in autoregressive language models. By simulating incremental bit reductions in floating-point representations, we analyze the relationship between representational power and training stability with the intent of aiding future research into the field.

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