Dynamic Low-Rank Sparse Adaptation for Large Language Models

Applying Low-Rank Adaptation (LoRA) to fine-tune the sparse LLMs offers an intuitive approach to counter this predicament, while it holds shortcomings include: 1) The inability to integrate LoRA weights into sparse LLMs post-training, and 2) Insufficient performance recovery at high sparsity ratios. In this paper, we introduce dynamic Low-rank Sparse A daptation (LoSA), a novel method that seamlessly integrates low-rank adaptation into LLM sparsity within a unified framework, thereby enhancing the performance of sparse LLMs without increasing the inference latency. In particular, LoSA dynamically sparsifies the LoRA outcomes based on the corresponding sparse weights during fine-tuning, thus guaranteeing that the LoRA module can be integrated into the sparse LLMs post-training. Besides, LoSA leverages Representation Mutual Information (RMI) as an indicator to determine the importance of layers, thereby efficiently determining the layer-wise sparsity rates during fine-tuning. Predicated on this, LoSA adjusts the rank of the LoRA module based on the variability in layer-wise reconstruction errors, allocating an appropriate fine-tuning for each layer to reduce the output discrepancies between dense and sparse LLMs. Extensive experiments tell that LoSA can efficiently boost the efficacy of sparse LLMs within a few hours, without introducing any additional inferential burden. For example, LoSA reduced the perplexity of sparse LLaMA-2-7B by 68.73 and increased zero-shot accuracy by 16.32%, achieving a 2.60 speedup on CPU and 2.23 speedup on GPU, requiring only 45 minutes of fine-tuning on a single NVIDIA A100 80GB GPU. The development of large language models (LLMs) (Zhang et al., 2022; Touvron et al., 2023a;b) has marked substantial advancements in the field of natural language processing (Achiam et al., 2023). As the scale of these models increases, they demonstrate enhanced capabilities in understanding and generating across diverse contexts (Kaplan et al., 2020; Brown et al., 2020). Nevertheless, the exponential growth in model size presents formidable challenges for deployment and inference, primarily due to escalated computational demands and latency (Zhu et al., 2023). To mitigate these issues, a variety of model compression strategies have been developed. Additionally, LoRA weights cannot be merged into the sparse LLM weights. Moreover, LoSA dynamically determines the layer-wise sparsity rates based on representation mutual information and allocates the ranks of the low-rank adaptation according to the reconstruction errors of the sparse LLM. Among the diverse array of model compression techniques, sparsity emerges as a prominent method for diminishing both the size and computational demands of LLMs (Li et al., 2023b; Lu et al., 2024; Frantar & Alistarh, 2023; Sun et al., 2023).