ResLoRA: Identity Residual Mapping in Low-Rank Adaption (2402.18039v1)

Abstract: As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune LLMs. However, updating the weights of LoRA blocks effectively and expeditiously is challenging due to the long calculation path in the original model. To address this, we propose ResLoRA, an improved framework of LoRA. By adding residual paths during training and using merging approaches to eliminate these extra paths during inference, our method can achieve better results in fewer training steps without any extra trainable parameters or inference cost compared to LoRA. The experiments on NLG, NLU, and text-to-image tasks demonstrate the effectiveness of our method. To the best of our knowledge, ResLoRA is the first work that combines the residual path with LoRA. The code of our method is available at https://github.com/microsoft/LMOps/tree/main/reslora .

• The paper introduces ResLoRA, a framework that integrates residual paths with LoRA to expedite gradient flow and enhance performance.
• It proposes three distinct residual structures that are merged post-training, retaining the parameter efficiency during inference.
• Experimental results demonstrate performance gains between 1% and 20% across tasks in NLP and text-to-image generation.

Enhancing Low-Rank Adaptation with Residual Paths: Introducing ResLoRA

Introduction to Low-Rank Adaptation Methods

LLMs have taken center stage in the field of NLP and beyond, thanks to their unparalleled prowess in handling a myriad of tasks. As beneficial as they are, the fine-tuning process of these behemoths often incurs prohibitive costs, given their sprawling parameter spaces. Low-Rank Adaptation (LoRA) emerged as a promising solution, pinpointing a method that tweaks only a fraction of the model's parameters, thereby offering a more cost-effective way to adapt LLMs to specific tasks. The essence of LoRA lies in its employment of two matrices that operate in tandem with the model's linear layers, which, after training, merge seamlessly, imposing no additional computational demands during inference.

ResLoRA: Bridging Residual Learning with LoRA

Despite the efficacy of LoRA, its potential isn't fully unleashed due to the hampering long backpropagation paths, which often impede the swift convergence and overall performance enhancement. Addressing this shortcoming, we introduce ResLoRA, an innovative framework that integrates the residual learning concept from ResNet into the LoRA method. This integration involves the introduction of residual paths in the LoRA blocks during the training phase, which are meticulously merged back, ensuring a plain structure synonymous with the original LoRA blocks during inference. This approach not only retains the parameter efficiency of LoRA but also significantly boosts model performance and training efficiency.

Methodological Insights and Achievements

ResLoRA advocates for several key advancements. First, it proposes three types of residual structures within LoRA blocks, namely input-shortcut, block-shortcut, and middle-shortcut, each addressing the gradient flow in distinct yet coherent manners. These structures ensure that the gradient backpropagation is expedited, thus overcoming the inherent limitations of the traditional LoRA framework. Second, it devises novel merging strategies aimed at reabsorbing the introduced residual paths back into the original LoRA configuration without accruing additional inference costs. Experimental revelations underscore the model's performance leaps, with improvements ranging from 1% to 20% across various tasks including Natural Language Generation (NLG), Natural Language Understanding (NLU), and even text-to-image generations, showcasing the framework's versatility and robustness.

Theoretical Foundations and Practical Implications

The mathematical underpinning provided for ResLoRA not only rationalizes the observed performance enhancements but also lays the groundwork for future explorations in merging deep learning architectures with parameter-efficient tuning methods. The implications of this research extend far into the practical domain, offering a viable pathway to harnessing the power of LLMs in resource-constrained settings without compromising on model performance or adaptability.

Future Directions and Conclusion

Despite its considerable contributions, ResLoRA is not without its limitations, most notably the additional computational overhead during training and the slight accuracy trade-off resulting from the merge operations. These limitations, however, open avenues for further research into optimizing the merging mechanisms and exploring the integration of ResLoRA with other LoRA variants and models. In its essence, ResLoRA paves a novel pathway in the landscape of PEFT methods, offering both a superior alternative and a foundational basis for future advancements in the fine-tuning of large-scale models efficiently and effectively.