Gradient Weight-normalized Low-rank Projection for Efficient LLM Training (2412.19616v1)

Abstract: LLMs have shown remarkable performance across various tasks, but the escalating demands on computational resources pose significant challenges, particularly in the extensive utilization of full fine-tuning for downstream tasks. To address this, parameter-efficient fine-tuning (PEFT) methods have been developed, but they often underperform compared to full fine-tuning and struggle with memory efficiency. In this work, we introduce Gradient Weight-Normalized Low-Rank Projection (GradNormLoRP), a novel approach that enhances both parameter and memory efficiency while maintaining comparable performance to full fine-tuning. GradNormLoRP normalizes the weight matrix to improve gradient conditioning, facilitating better convergence during optimization. Additionally, it applies low-rank approximations to the weight and gradient matrices, significantly reducing memory usage during training. Extensive experiments demonstrate that our 8-bit GradNormLoRP reduces optimizer memory usage by up to 89.5% and enables the pre-training of large LLMs, such as LLaMA 7B, on consumer-level GPUs like the NVIDIA RTX 4090, without additional inference costs. Moreover, GradNormLoRP outperforms existing low-rank methods in fine-tuning tasks. For instance, when fine-tuning the RoBERTa model on all GLUE tasks with a rank of 8, GradNormLoRP achieves an average score of 80.65, surpassing LoRA's score of 79.23. These results underscore GradNormLoRP as a promising alternative for efficient LLM pre-training and fine-tuning. Source code and Appendix: https://github.com/Jhhuangkay/Gradient-Weight-normalized-Low-rank-Projection-for-Efficient-LLM-Training

Gradient Weight-normalized Low-rank Projection for Efficient LLM Training: An Overview

The paper "Gradient Weight-normalized Low-rank Projection for Efficient LLM Training" introduces GradNormLoRP, an innovative methodology designed to address the inefficiencies in fine-tuning LLMs. As LLMs continue to expand in scale, the computational requirements for full fine-tuning become increasingly prohibitive, creating a demand for parameter-efficient fine-tuning (PEFT) methodologies. Although existing PEFT approaches offer some relief, they typically lag in performance and can be memory intensive. The GradNormLoRP approach seeks to reconcile these issues by combining gradient weight normalization with low-rank projections.

Key Contributions

1. Innovative Approach: GradNormLoRP introduces a novel blend of techniques aimed at enhancing both parameter and memory efficiency in LLM fine-tuning. It utilizes gradient weight normalization to improve gradient conditioning, which facilitates smoother convergence during optimization. Moreover, the method applies low-rank approximations to weight and gradient matrices, resulting in significant reductions in memory usage.
2. Memory Efficiency: One of the notable achievements of GradNormLoRP is its ability to substantially cut down on memory consumption. The paper reports that the 8-bit version of GradNormLoRP is capable of reducing memory usage by up to 89.5% in optimizer states. Such reductions allow for pre-training significantly large models like LLaMA 7B on consumer GPUs, such as the NVIDIA RTX 4090, without the necessity of complex memory management strategies in inference.
3. Enhanced Performance: The experimental results showcase that GradNormLoRP not only matches but in some cases, exceeds the performance of full fine-tuning and other PEFT methods. For instance, when fine-tuning models like RoBERTa on the GLUE benchmark with a rank of 8, GradNormLoRP achieves an average score of 80.65, outperforming LoRA's score of 79.23. This indicates the method's robustness in maintaining model performance while improving efficiency.

Implications and Future Directions

The implications of GradNormLoRP are twofold. Practically, it offers a scalable solution to the growing demands of LLM training, especially for institutions lacking extensive computational resources. Theoretically, it challenges the current paradigms of model training by demonstrating that efficiency gains can be achieved without compromising performance, thereby influencing future PEFT research directions.

In future developments, one can anticipate further refinement of low-rank approximation techniques to better capture the essential features within LLMs. Additionally, exploring the integration of GradNormLoRP with emerging architectures or hybrid model training frameworks could provide further insights or improvements. As AI continues to evolve, tools that enhance the efficiency of training large-scale models will be crucial, and GradNormLoRP stands as a promising candidate in this arena.