Optimal Subarchitecture Extraction For BERT

We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by applying recent breakthroughs in algorithms for neural architecture search. This optimal subset, which we refer to as "Bort", is demonstrably smaller, having an effective (that is, not counting the embedding layer) size of $5.5%$ the original BERT-large architecture, and $16%$ of the net size. Bort is also able to be pretrained in $288$ GPU hours, which is $1.2%$ of the time required to pretrain the highest-performing BERT parametric architectural variant, RoBERTa-large (Liu et al., 2019), and about $33%$ of that of the world-record, in GPU hours, required to train BERT-large on the same hardware. It is also $7.9$x faster on a CPU, as well as being better performing than other compressed variants of the architecture, and some of the non-compressed variants: it obtains performance improvements of between $0.3%$ and $31%$, absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks.