Learning to Skip the Middle Layers of Transformers
Authors: name: Tim Lawson, name: Laurence Aitchison

Conditional computation is a popular strategy to make Transformers more
efficient. Existing methods often target individual modules (e.g.,
mixture-of-experts layers) or skip layers independently of one another.
However, interpretability research has demonstrated that the middle layers of
Transformers exhibit greater redundancy, and that early layers aggregate
information into token positions. Guided by these insights, we propose a novel
architecture that dynamically skips a variable number of layers from the middle
outward. In particular, a learned gating mechanism determines whether to bypass
a symmetric span of central blocks based on the input, and a gated attention
mechanism prevents subsequent tokens from attending to skipped token positions.
Residual norms are controlled with a 'sandwich' or 'perilayernorm' scheme and
gate sparsity with an adaptive regularization loss. We had aimed to reduce
compute requirements for 'simpler' tokens and potentially foster an emergent
multi-level representational hierarchy but, at the scales investigated, our
approach does not achieve improvements in the trade-off between validation
cross-entropy and estimated FLOPs compared to dense baselines with fewer
layers. We release our code at https://github.com/tim-lawson/skip-middle.