Structured pruning of modern large language models (LLMs) has emerged as a
way of decreasing their high computational needs. Width pruning reduces the
size of projection weight matrices (e.g., by removing attention heads) while
maintaining the number of layers. Depth pruning, in contrast, removes entire
layers or blocks, while keeping the size of the remaining weights unchanged.
Most current research focuses on either width-only or a blend of width and
depth pruning, with little comparative analysis between the two units (width
vs. depth) concerning their impact on LLM inference efficiency. In this work,
we show that a simple depth pruning approach can compete with recent width
pruning methods in terms of zero-shot task performance. Our pruning method
boosts inference speeds, especially under memory-constrained conditions that
require limited batch sizes for running LLMs, where width pruning is
ineffective. We hope this work can help deploy LLMs on local and edge devices.

通过深度剪枝方法，我们展示出其在零样本任务性能方面可以与最近的宽度剪枝方法竞争，并且在内存受限情况下的推理速度提升尤为明显，希望这项工作能够帮助在本地和边缘设备上部署大型语言模型。