  Recent studies have shown that code language models at scale demonstrate
significant performance gains on downstream tasks, i.e., code generation.
However, most of the existing works on code representation learning train
models at a hundred million parameter scale using very limited pretraining
corpora. In this work, we fuel code representation learning with a vast amount
of code data via a two-stage pretraining scheme. We first train the encoders
via a mix that leverages both randomness in masking language modeling and the
structure aspect of programming language. We then enhance the representations
via contrastive learning with hard negative and hard positive constructed in an
unsupervised manner. We establish an off-the-shelf encoder model that
persistently outperforms the existing models on a wide variety of downstream
tasks by large margins. To comprehend the factors contributing to successful
code representation learning, we conduct detailed ablations and share our
findings on (i) a customized and effective token-level denoising scheme for
source code; (ii) the importance of hard negatives and hard positives; (iii)
how the proposed bimodal contrastive learning boost the cross-lingual semantic
search performance; and (iv) how the pretraining schemes decide the downstream
task performance scales with the model size.
