On the Transformer Growth for Progressive BERT Training
This work addresses the computational expense of training large language models for researchers and practitioners, offering an incremental improvement over existing progressive methods.
The paper tackles the high cost of large-scale language model pre-training by studying progressive training for BERT, finding that compound scaling across dimensions like depth and width speeds up pre-training by 73.6% for base and 82.2% for large models while maintaining performance.
Due to the excessive cost of large-scale language model pre-training, considerable efforts have been made to train BERT progressively -- start from an inferior but low-cost model and gradually grow the model to increase the computational complexity. Our objective is to advance the understanding of Transformer growth and discover principles that guide progressive training. First, we find that similar to network architecture search, Transformer growth also favors compound scaling. Specifically, while existing methods only conduct network growth in a single dimension, we observe that it is beneficial to use compound growth operators and balance multiple dimensions (e.g., depth, width, and input length of the model). Moreover, we explore alternative growth operators in each dimension via controlled comparison to give operator selection practical guidance. In light of our analyses, the proposed method speeds up BERT pre-training by 73.6% and 82.2% for the base and large models respectively, while achieving comparable performances