Optimal Subarchitecture Extraction For BERT

Understand

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.