Memory Efficient Mixed-Precision Optimizers

Basile Lewandowski; Atli Kosson

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arXiv · 2309.12381

Memory Efficient Mixed-Precision Optimizers

Basile Lewandowski·Atli Kosson

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Abstract

Traditional optimization methods rely on the use of single-precision floating point arithmetic, which can be costly in terms of memory size and computing power. However, mixed precision optimization techniques leverage the use of both single and half-precision floating point arithmetic to reduce memory requirements while maintaining model accuracy. We provide here an algorithm to further reduce memory usage during the training of a model by getting rid of the floating point copy of the parameters, virtually keeping only half-precision numbers. We also explore the benefits of getting rid of the gradient's value by executing the optimizer step during the back-propagation. In practice, we achieve up to 25% lower peak memory use and 15% faster training while maintaining the same level of accuracy.

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Basile Lewandowski, Atli Kosson. 2023-09-21. Memory Efficient Mixed-Precision Optimizers. https://arxiv.org/abs/2309.12381

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