  Neural network sparsity has attracted many research interests due to its
similarity to biological schemes and high energy efficiency. However, existing
methods depend on long-time training or fine-tuning, which prevents large-scale
applications. Recently, some works focusing on post-training sparsity (PTS)
have emerged. They get rid of the high training cost but usually suffer from
distinct accuracy degradation due to neglect of the reasonable sparsity rate at
each layer. Previous methods for finding sparsity rates mainly focus on the
training-aware scenario, which usually fails to converge stably under the PTS
setting with limited data and much less training cost. In this paper, we
propose a fast and controllable post-training sparsity (FCPTS) framework. By
incorporating a differentiable bridge function and a controllable optimization
objective, our method allows for rapid and accurate sparsity allocation
learning in minutes, with the added assurance of convergence to a predetermined
global sparsity rate. Equipped with these techniques, we can surpass the
state-of-the-art methods by a large margin, e.g., over 30\% improvement for
ResNet-50 on ImageNet under the sparsity rate of 80\%. Our plug-and-play code
and supplementary materials are open-sourced at
https://github.com/ModelTC/FCPTS.
