Not All Experts are Equal: Efficient Expert Pruning and Skipping for
  Mixture-of-Experts Large Language Models
Xudong Lu Qi Liu Yuhui Xu Aojun Zhou Siyuan Huang Bo Zhang Junchi Yan Hongsheng Li
A pivotal advancement in the progress of large language models (LLMs) is the
emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs,
MoE LLMs can achieve higher performance with fewer parameters, but it is still
hard to deploy them due to their immense parameter sizes. Different from
previous weight pruning methods that rely on specifically designed hardware,
this paper mainly aims to enhance the deployment efficiency of MoE LLMs by
introducing plug-and-play expert-level sparsification techniques. Specifically,
we propose, for the first time to our best knowledge, post-training approaches
for task-agnostic and task-specific expert pruning and skipping of MoE LLMs,
tailored to improve deployment efficiency while maintaining model performance
across a wide range of tasks. Extensive experiments show that our proposed
methods can simultaneously reduce model sizes and increase the inference speed,
while maintaining satisfactory performance. Data and code will be available at
https://github.com/Lucky-Lance/Expert_Sparsity.