PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
This addresses the computational bottleneck for deploying large language models like BERT in real-time applications, offering a significant improvement over prior methods.
The paper tackles the problem of slow inference time in BERT models by proposing PoWER-BERT, which eliminates redundant word-vectors to accelerate inference while maintaining accuracy, achieving up to 4.5x speedup on BERT and 6.8x on ALBERT with less than 1% accuracy loss.
We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pertaining to word-vectors (intermediate encoder outputs) and eliminating the redundant vectors. b) determining which word-vectors to eliminate by developing a strategy for measuring their significance, based on the self-attention mechanism. c) learning how many word-vectors to eliminate by augmenting the BERT model and the loss function. Experiments on the standard GLUE benchmark shows that PoWER-BERT achieves up to 4.5x reduction in inference time over BERT with <1% loss in accuracy. We show that PoWER-BERT offers significantly better trade-off between accuracy and inference time compared to prior methods. We demonstrate that our method attains up to 6.8x reduction in inference time with <1% loss in accuracy when applied over ALBERT, a highly compressed version of BERT. The code for PoWER-BERT is publicly available at https://github.com/IBM/PoWER-BERT.