The remarkable performance of large language models (LLMs) in various language tasks has attracted considerable attention. However, the ever-increasing size of these models presents growing challenges for deployment and inference. Structured pruning, an effective model compression technique, is gaining increasing attention due to its ability to enhance inference efficiency. Nevertheless, most previous optimization-based structured pruning methods sacrifice the uniform structure across layers for greater flexibility to maintain performance. The heterogeneous structure hinders the effective utilization of off-the-shelf inference acceleration techniques and impedes efficient configuration for continued training. To address this issue, we propose a novel masking learning paradigm based on minimax optimization to obtain the uniform pruned structure by optimizing the masks under sparsity regularization. Extensive experimental results demonstrate that our method can maintain high performance while ensuring the uniformity of the pruned model structure, thereby outperforming existing SOTA methods.

本研究针对大语言模型（LLM）日益增长的模型规模所带来的部署与推理挑战，提出了一种基于最小最大优化的掩膜学习新范式，以获得均匀的剪枝结构。该方法在保持高性能的同时，确保了剪枝模型结构的均匀性，超越了现有的最新技术水平。

MaskPrune：基于掩膜的层级均匀结构大语言模型剪枝