Block-wise Bit-Compression of Transformer-based Models

• URL: http://arxiv.org/abs/2303.09184v2
• Date: Sat, 1 Apr 2023 12:50:29 GMT
• Title: Block-wise Bit-Compression of Transformer-based Models
• Authors: Gaochen Dong, Wei Chen
• Abstract summary: We propose BBCT, a method of block-wise bit-compression for transformer without retraining. Our benchmark test results on General Language Understanding Evaluation (GLUE) show that BBCT can achieve less than 1% accuracy drop in most tasks.
• Score: 9.77519365079468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the popularity of the recent Transformer-based models represented by BERT, GPT-3 and ChatGPT, there has been state-of-the-art performance in a range of natural language processing tasks. However, the massive computations, huge memory footprint, and thus high latency of Transformer-based models is an inevitable challenge for the cloud with high real-time requirement. To tackle the issue, we propose BBCT, a method of block-wise bit-compression for transformer without retraining. Our method achieves more fine-grained compression of the whole transformer, including embedding, matrix multiplication, GELU, softmax, layer normalization, and all the intermediate results. As a case, we compress an efficient BERT with the method of BBCT. Our benchmark test results on General Language Understanding Evaluation (GLUE) show that BBCT can achieve less than 1% accuracy drop in most tasks.

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