Large language models have become increasingly difficult to train because of the growing computation time and cost.
In this work, we present SRU++, a highly-efficient architecture that combines fast recurrence and attention for sequence modeling.
SRU++ exhibits strong modeling capacity and training efficiency.
On standard language modeling tasks such asenwik8,Wiki-103andbillion worddatasets, our model obtains better bits-per-character and perplexity while using 3x-10x less training cost compared to top-performing Transformer models.
For instance, our model achieves a state-of-the-art result on theenwik8dataset using 1.6 days of training on an 8-GPU machine.
We further demonstrate that SRU++ requires minimal attention for near state-of-the-art performance.
Our results suggest jointly leveraging fast recurrence with little attention as a promising direction for accelerating model training and inference.111Our code, experimental setup and models are available athttps://github.com/asappresearch/sru.