SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs
Mohammad Mozaffari Amir Yazdanbakhsh Zhao Zhang Maryam Mehri Dehnavi
We propose SLoPe, a Double-Pruned **S**parse Plus **L**azy L**o**w-rank Adapter **P**r**e**training method for LLMs that improves the accuracy of sparse LLMs while accelerating their pretraining and inference and reducing their memory footprint. Sparse pretraining of LLMs reduces the accuracy of the model, to overcome this, prior work uses dense models during fine-tuning. SLoPe improves the accuracy of sparsely pretrained models by adding low-rank adapters in the final 1% iterations of pretraining without adding significant overheads to the model pretraining and inference. In addition, SLoPe uses a double-pruned backward pass formulation that prunes the transposed weight matrix using N:M sparsity structures to enable an accelerated sparse backward pass. SLoPe accelerates the training and inference of models with billions of parameters up to 1.25× and 1.54× respectively (OPT-33B and OPT-66B) while reducing their memory usage by up to 0.63× and 0.61× for training and inference respectively.
sparse training, low rank adapter, LLM, optimization
We propose SLoPe, a double-pruned sparse plus low-rank adapter method improves sparse LLM accuracy while accelerating pretraining and inference, offering up to 1.25x and 1.54x training and inference speedup with reduced memory usage.