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Overparameterized transformer networks have obtained state of the art resultsin various natural language processing tasks, such as machine translation,language modeling, and question answering. These models contain hundreds ofmillions of parameters, necessitating a large amount of computation and makingthem prone to overfitting. In this work, we explore LayerDrop, a form ofstructured dropout, which has a regularization effect during training andallows for efficient pruning at inference time. In particular, we show that itis possible to select sub-networks of any depth from one large network withouthaving to finetune them and with limited impact on performance. We demonstratethe effectiveness of our approach by improving the state of the art on machinetranslation, language modeling, summarization, question answering, and languageunderstanding benchmarks. Moreover, we show that our approach leads to smallBERT-like models of higher quality compared to training from scratch or usingdistillation.

Reducing Transformer Depth on Demand with Structured Dropout