  Traditional (unstructured) pruning methods for a Transformer model focus on
regularizing the individual weights by penalizing them toward zero. In this
work, we explore spectral-normalized identity priors (SNIP), a structured
pruning approach that penalizes an entire residual module in a Transformer
model toward an identity mapping. Our method identifies and discards
unimportant non-linear mappings in the residual connections by applying a
thresholding operator on the function norm. It is applicable to any structured
module, including a single attention head, an entire attention block, or a
feed-forward subnetwork. Furthermore, we introduce spectral normalization to
stabilize the distribution of the post-activation values of the Transformer
layers, further improving the pruning effectiveness of the proposed
methodology. We conduct experiments with BERT on 5 GLUE benchmark tasks to
demonstrate that SNIP achieves effective pruning results while maintaining
comparable performance. Specifically, we improve the performance over the
state-of-the-art by 0.5 to 1.0% on average at 50% compression ratio.
