LoRAShear: Efficient Large Language Model Structured Pruning and
  Knowledge Recovery
Authors: name: Tianyi Chen, name: Tianyu Ding, name: Badal Yadav, name: Ilya Zharkov, name: Luming Liang

Large Language Models (LLMs) have transformed the landscape of artificial
intelligence, while their enormous size presents significant challenges in
terms of computational costs. We introduce LoRAShear, a novel efficient
approach to structurally prune LLMs and recover knowledge. Given general LLMs,
LoRAShear first creates the dependency graphs to discover minimally removal
structures and analyze the knowledge distribution. It then proceeds progressive
structured pruning on LoRA adaptors and enables inherent knowledge transfer to
better preserve the information in the redundant structures. To recover the
lost knowledge during pruning, LoRAShear meticulously studies and proposes a
dynamic fine-tuning schemes with dynamic data adaptors to effectively narrow
down the performance gap to the full models. Numerical results demonstrate that
by only using one GPU within a couple of GPU days, LoRAShear effectively
reduced footprint of LLMs by 20% with only 1.0% performance degradation and
significantly outperforms state-of-the-arts. The source code will be available
at https://github.com/microsoft/lorashear.