  Recent works have widely adopted large language model pretraining for source
code, suggested source code-specific pretraining objectives and investigated
the applicability of various Transformer-based language model architectures for
source code. This work investigates another important aspect of such models,
namely the effect of different subtokenization options, and aims at identifying
most effective and length-efficient subtokenizations, taking into account code
specifics. We propose subtokenziation that reduces average length by 17%
without downstream performance drop, and show that a carefully chosen
subtokenization may improve quality by 0.5-2%, possibly with some length
increase.
