RomeBERT: Robust Training of Multi-Exit BERT

TL;DR

RomeBERT introduces a robust training method for multi-exit BERT models, improving early exit performance and reducing training time through gradient regularized self-distillation and joint training, outperforming previous methods on GLUE tasks.

Contribution

The paper proposes RomeBERT, a novel training approach that enhances early exit performance and simplifies training for multi-exit BERT models using gradient regularized self-distillation.

Findings

RomeBERT outperforms DeeBERT on GLUE datasets.

It achieves better early exit accuracy.

Training time is reduced due to joint training strategy.

Abstract

BERT has achieved superior performances on Natural Language Understanding (NLU) tasks. However, BERT possesses a large number of parameters and demands certain resources to deploy. For acceleration, Dynamic Early Exiting for BERT (DeeBERT) has been proposed recently, which incorporates multiple exits and adopts a dynamic early-exit mechanism to ensure efficient inference. While obtaining an efficiency-performance tradeoff, the performances of early exits in multi-exit BERT are significantly worse than late exits. In this paper, we leverage gradient regularized self-distillation for RObust training of Multi-Exit BERT (RomeBERT), which can effectively solve the performance imbalance problem between early and late exits. Moreover, the proposed RomeBERT adopts a one-stage joint training strategy for multi-exits and the BERT backbone while DeeBERT needs two stages that require more training time. Extensive experiments on GLUE datasets are performed to demonstrate the superiority of our approach. Our code is available at https://github.com/romebert/RomeBERT.