  Pruning is an effective way to reduce the huge inference cost of Transformer
models. However, prior work on pruning Transformers requires retraining the
models. This can add high training cost and high complexity to model
deployment, making it difficult to use in many practical situations. To address
this, we propose a fast post-training pruning framework for Transformers that
does not require any retraining. Given a resource constraint and a sample
dataset, our framework automatically prunes the Transformer model using
structured sparsity methods. To retain high accuracy without retraining, we
introduce three novel techniques: (i) a lightweight mask search algorithm that
finds which heads and filters to prune based on the Fisher information; (ii)
mask rearrangement that complements the search algorithm; and (iii) mask tuning
that reconstructs the output activations for each layer. We apply our method to
BERT-base and DistilBERT, and we evaluate its effectiveness on GLUE and SQuAD
benchmarks. Our framework achieves up to 2.0x reduction in FLOPs and 1.56x
speedup in inference latency, while maintaining < 1% loss in accuracy.
Importantly, our framework prunes Transformers in less than 3 minutes on a
single GPU, which is over two orders of magnitude faster than existing pruning
approaches that retrain the models.
