Flora: Low-Rank Adapters Are Secretly Gradient Compressors

Despite large neural networks demonstrating remarkable abilities to complete different tasks, they require excessive memory usage to store the optimization states for training. To alleviate this, the low-rank adaptation (LoRA) is proposed to reduce the optimization states by training fewer parameters. However, LoRA restricts overall weight update matrices to be low-rank, limiting the model performance. In this work, we investigate the dynamics of LoRA and identify that it can be approximated by a random projection. Based on this observation, we …

cs.ai cs.lg flora gradient lora low low-rank adaptation memory networks neural networks optimization parameters reduce stat.ml store tasks training update usage