  Large-scale transformer models have become the de-facto architectures for
various machine learning applications, e.g., CV and NLP. However, those large
models also introduce prohibitive training costs. To mitigate this issue, we
propose a novel random and layerwise token dropping method (random-LTD), which
skips the computation of a subset of the input tokens at all middle layers.
Particularly, random-LTD achieves considerable speedups and comparable accuracy
as the standard training baseline. Compared to other token dropping methods,
random-LTD does not require (1) any importance score-based metrics, (2) any
special token treatment (e.g., [CLS]), and (3) many layers in full sequence
length training except the first and the last layers. Besides, a new LayerToken
learning rate schedule is proposed for pretraining problems that resolve the
heavy tuning requirement for our proposed training mechanism. Finally, we
demonstrate that random-LTD can be applied to broader applications, including
GPT and BERT pretraining as well as ViT and GPT finetuning tasks. Our results
show that random-LTD can save about 33.3% theoretical compute cost and 25.6%
wall-clock training time while achieving similar zero-shot evaluations on
GPT-31.3B as compared to baseline.
