Compressing Large-Scale Transformer-Based Models Case Study

This document is a case study focused on the compression of large-scale transformer-based models, specifically BERT. It outlines the necessity for model compression due to the rapidly increasing size of pre-trained language models, which poses challenges for deployment and inference, especially with high-performance GPUs. The study details various methods of model compression, including quantization, pruning, knowledge distillation, matrix decomposition, and dynamic inference acceleration. Each method is described in terms of its application and effectiveness in reducing model size while maintaining performance. The document also references recent research in the field, highlighting advancements in pruning techniques and the effectiveness of combining multiple compression methods to achieve significant reductions in model size with minimal accuracy loss. Practical advice is provided for implementing these compression techniques, emphasizing the importance of selecting appropriate hardware and settings for optimal results.