Efficient Transformer Knowledge Distillation: A Performance Review
Nathan Brown Ashton Williamson Tahj Anderson Logan Lawrence
As pretrained transformer language models continue to achieve
state-of-the-art performance, the Natural Language Processing community has
pushed for advances in model compression and efficient attention mechanisms to
address high computational requirements and limited input sequence length.
Despite these separate efforts, no investigation has been done into the
intersection of these two fields. In this work, we provide an evaluation of
model compression via knowledge distillation on efficient attention
transformers. We provide cost-performance trade-offs for the compression of
state-of-the-art efficient attention architectures and the gains made in
performance in comparison to their full attention counterparts. Furthermore, we
introduce a new long-context Named Entity Recognition dataset, GONERD, to train
and test the performance of NER models on long sequences. We find that
distilled efficient attention transformers can preserve a significant amount of
original model performance, preserving up to 98.6% across short-context tasks
(GLUE, SQUAD, CoNLL-2003), up to 94.6% across long-context
Question-and-Answering tasks (HotpotQA, TriviaQA), and up to 98.8% on
long-context Named Entity Recognition (GONERD), while decreasing inference
times by up to 57.8%. We find that, for most models on most tasks, performing
knowledge distillation is an effective method to yield high-performing
efficient attention models with low costs.