APOLLO: SGD-like Memory, AdamW-level Performance
Authors: name: Hanqing Zhu; name: Zhenyu Zhang; name: Wenyan Cong; name: Xi Liu; name: Sem Park; name: Vikas Chandra; name: Bo Long; name: David Z. Pan; name: Zhangyang Wang; name: Jinwon Lee
Published: 2024-12-06 18:55:34

Abstract: Large language models (LLMs) are notoriously memory-intensive during
training, particularly with the popular AdamW optimizer. This memory burden
necessitates using more or higher-end GPUs or reducing batch sizes, limiting
training scalability and throughput. To address this, various memory-efficient
optimizers have been proposed to reduce optimizer memory usage. However, they
face critical challenges: (i) reliance on costly SVD operations; (ii)
significant performance trade-offs compared to AdamW; and (iii) still
substantial optimizer memory overhead to maintain competitive performance.
  In this work, we identify that AdamW's learning rate adaptation rule can be
effectively coarsened as a structured learning rate update. Based on this
insight, we propose Approximated Gradient Scaling for Memory-Efficient LLM
Optimization (APOLLO), which approximates learning rate scaling using an
auxiliary low-rank optimizer state based on pure random projection. This
structured learning rate update rule makes APOLLO highly tolerant to further
memory reductions while delivering comparable pre-training performance. Even
its rank-1 variant, APOLLO-Mini, achieves superior pre-training performance
compared to AdamW with SGD-level memory costs.
  Extensive experiments demonstrate that the APOLLO series performs on-par with
or better than AdamW, while achieving greater memory savings by nearly
eliminating the optimization states of AdamW. These savings provide significant
system-level benefits: (1) Enhanced Throughput: 3x throughput on an 8xA100-80GB
setup compared to AdamW by supporting 4x larger batch sizes. (2) Improved Model
Scalability: Pre-training LLaMA-13B with naive DDP on A100-80GB GPUs without
system-level optimizations. (3) Low-End GPU Friendly Pre-training: Pre-training
LLaMA-7B on a single GPU using less than 12 GB of memory with weight
quantization.