DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation

Training deep neural networks typically involves substantial computational costs during both forward and backward propagation. The conventional layer dropping techniques drop certain layers during training for reducing the computations burden. However, dropping layers during forward propagation adversely affects the training process by degrading accuracy. In this paper, we propose Dropping Backward Propagation (DropBP), a novel approach designed to reduce computational costs while maintaining accuracy. DropBP randomly drops layers during the backward propagation, which does not deviate forward propagation. Moreover, DropBP calculates the sensitivity of each layer to assign appropriate drop rate, thereby stabilizing the training process. DropBP is designed to enhance the efficiency of the training process with backpropagation, thereby enabling the acceleration of both full fine-tuning and parameter-efficient fine-tuning using backpropagation. Specifically, utilizing DropBP in QLoRA reduces training time by 44%, increases the convergence speed to the identical loss level by 1.5$\times$, and enables training with a 6.2$\times$ larger sequence length on a single NVIDIA-A100 80GiB GPU in LLaMA2-70B. The code is available at https://github.com/WooSunghyeon/dropbp.