  Traditional pruning methods are known to be challenging to work in Large
Language Models (LLMs) for Generative AI because of their unaffordable training
process and large computational demands. For the first time, we introduce the
information entropy of hidden state features into a pruning metric design,
namely E-Sparse, to improve the accuracy of N:M sparsity on LLM. E-Sparse
employs the information richness to leverage the channel importance, and
further incorporates several novel techniques to put it into effect: (1) it
introduces information entropy to enhance the significance of parameter weights
and input feature norms as a novel pruning metric, and performs N:M sparsity
without modifying the remaining weights. (2) it designs global naive shuffle
and local block shuffle to quickly optimize the information distribution and
adequately cope with the impact of N:M sparsity on LLMs' accuracy. E-Sparse is
implemented as a Sparse-GEMM on FasterTransformer and runs on NVIDIA Ampere
GPUs. Extensive experiments on the LLaMA family and OPT models show that
E-Sparse can significantly speed up the model inference over the dense model
(up to 1.53X) and obtain significant memory saving (up to 43.52%), with
acceptable accuracy loss.
