  We propose SLoPe, a Double-Pruned Sparse Plus Lazy Low-rank Adapter
Pretraining 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 $1.14\times$ and $1.34\times$ respectively (OPT-33B and OPT-66B) while
reducing their memory usage by up to $0.77\times$ and $0.51\times$ for training
and inference respectively.
