Q8BERT: Quantized 8bit BERT

Ofir Zafrir, Guy Boudoukh, Peter Izsak, Moshe Wasserblat. Arxiv 2019 – 179 citations

[Paper]    
Training Techniques Transformer GPT Fine-Tuning Reinforcement Learning BERT Efficiency and Optimization Model Architecture Quantization

Recently, pre-trained Transformer based language models such as BERT and GPT, have shown great improvement in many Natural Language Processing (NLP) tasks. However, these models contain a large amount of parameters. The emergence of even larger and more accurate models such as GPT2 and Megatron, suggest a trend of large pre-trained Transformer models. However, using these large models in production environments is a complex task requiring a large amount of compute, memory and power resources. In this work we show how to perform quantization-aware training during the fine-tuning phase of BERT in order to compress BERT by \(4\times\) with minimal accuracy loss. Furthermore, the produced quantized model can accelerate inference speed if it is optimized for 8bit Integer supporting hardware.

Similar Work