Adam-mini: Use Fewer Learning Rates To Gain More
Authors: name: Yushun Zhang, name: Congliang Chen, name: Ziniu Li, name: Tian Ding, name: Chenwei Wu, name: Yinyu Ye, name: Zhi-Quan Luo, name: Ruoyu Sun

We propose Adam-mini, an optimizer that achieves on-par or better performance
than AdamW with 45% to 50% less memory footprint. Adam-mini reduces memory by
cutting down the learning rate resources in Adam (i.e., 1/v). We find
that geq 90% of these learning rates in v could be harmlessly removed if
we (1) carefully partition the parameters into blocks following our proposed
principle on Hessian structure; (2) assign a single but good learning rate to
each parameter block. We further find that, for each of these parameter blocks,
there exists a single high-quality learning rate that can outperform Adam,
provided that sufficient resources are available to search it out. We then
provide one cost-effective way to find good learning rates and propose
Adam-mini. Empirically, we verify that Adam-mini performs on par or better than
AdamW on various language models sized from 125M to 7B for pre-training,
supervised fine-tuning, and RLHF. The reduced memory footprint of Adam-mini
also alleviates communication overheads among GPUs and CPUs, thereby increasing
throughput. For instance, Adam-mini achieves 49.6% higher throughput than AdamW
when pre-training Llama2-7B on 2times A800-80GB GPUs, which saves 33%
wall-clock time for pre-training.