Extreme Compression for Pre-trained Transformers Made Simple and Efficient

• URL: http://arxiv.org/abs/2206.01859v1
• Date: Sat, 4 Jun 2022 00:19:45 GMT
• Title: Extreme Compression for Pre-trained Transformers Made Simple and Efficient
• Authors: Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He
• Abstract summary: Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices. We propose a simple yet effective compression pipeline for extreme compression, named XTC.
• Score: 31.719905773863566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices. However, to preserve the accuracy for such aggressive compression schemes, cutting-edge methods usually introduce complicated compression pipelines, e.g., multi-stage expensive knowledge distillation with extensive hyperparameter tuning. Also, they oftentimes focus less on smaller transformer models that have already been heavily compressed via knowledge distillation and lack a systematic study to show the effectiveness of their methods. In this paper, we perform a very comprehensive systematic study to measure the impact of many key hyperparameters and training strategies from previous works. As a result, we find out that previous baselines for ultra-low bit precision quantization are significantly under-trained. Based on our study, we propose a simple yet effective compression pipeline for extreme compression, named XTC. XTC demonstrates that (1) we can skip the pre-training knowledge distillation to obtain a 5-layer BERT while achieving better performance than previous state-of-the-art methods, e.g., the 6-layer TinyBERT; (2) extreme quantization plus layer reduction is able to reduce the model size by 50x, resulting in new state-of-the-art results on GLUE tasks.

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