We investigate parameter-efficient fine-tuning (PEFT) methods that can provide good accuracy under limited computational and memory budgets in the context of large language models (LLMs). We present a new PEFT method called Robust Adaptation (RoSA) inspired by robust principal component analysis that jointly trains $\textit{low-rank}$ and $\textit{highly-sparse}$ components on top of a set of fixed pretrained weights to efficiently approximate the performance of a full-fine-tuning (FFT) solution. Across a series of challenging generative tasks such as grade-school math and SQL query generation, which require fine-tuning for good performance, we show that RoSA outperforms LoRA, pure sparse fine-tuning, and alternative hybrid methods at the same parameter budget, and can even recover the performance of FFT on some tasks. We provide system support for RoSA to complement the training algorithm, specifically in the form of sparse GPU kernels which enable memory- and computationally-efficient training, and show that it is also compatible with low-precision base weights, resulting in the first joint representation combining quantization, low-rank and sparse approximations. Our code is accessible at https://github.com/IST-DASLab/RoSA.

翻译：我们研究在大语言模型（LLM）的有限计算和内存预算下，能够提供良好精度的参数高效微调（PEFT）方法。受鲁棒主成分分析启发，我们提出一种新的参数高效微调方法——鲁棒自适应（RoSA），该方法在固定预训练权重基础上联合训练$\textit{低秩}$和$\textit{高度稀疏}$分量，以高效逼近全微调（FFT）解决方案的性能。在一系列具有挑战性的生成任务（如小学数学和SQL查询生成，这些任务需要微调以获得良好性能）中，我们证明RoSA在相同参数预算下优于LoRA、纯稀疏微调及替代混合方法，甚至在某些任务上能恢复全微调的性能。我们为RoSA提供了系统支持以补充训练算法，具体表现为能够实现内存高效和计算高效训练的稀疏GPU内核，并证明其与低精度基权重兼容，从而首次实现量化、低秩和稀疏逼近的联合表示。我们的代码可在https://github.com/IST-DASLab/RoSA获取。