Blockwise Compression of Transformer-based Models without Retraining

Transformer-based models, represented by GPT-3, ChatGPT, and GPT-4, have recently attracted increasing interest, research enthusiasm, and business demand. However, their massive computation resources and huge memory footprint are inevitable challenges. To tackle this issue, we propose BCT, a framework of blockwise compression for transformers without retraining, to lower deployment thresholds. BCT 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 model with BCT and evaluate it on several General Language Understanding Evaluation (GLUE) datasets. The results show that BCT can achieve a less than 0.90% accuracy drop in most tasks.

Block-wise Bit-Compression of Transformer-based Models

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.