  State of the art sequence-to-sequence models for large scale tasks perform a
fixed number of computations for each input sequence regardless of whether it
is easy or hard to process. In this paper, we train Transformer models which
can make output predictions at different stages of the network and we
investigate different ways to predict how much computation is required for a
particular sequence. Unlike dynamic computation in Universal Transformers,
which applies the same set of layers iteratively, we apply different layers at
every step to adjust both the amount of computation as well as the model
capacity. On IWSLT German-English translation our approach matches the accuracy
of a well tuned baseline Transformer while using less than a quarter of the
decoder layers.
