  Pruning is an effective method to reduce the memory footprint and
computational cost associated with large natural language processing models.
However, current pruning algorithms either only focus on one pruning category,
e.g., structured pruning and unstructured, or need extensive hyperparameter
tuning in order to get reasonable accuracy performance. To address these
challenges, we propose LEArnable Pruning (LEAP), an effective method to
gradually prune the model based on thresholds learned by gradient descent.
Different than previous learnable pruning methods, which utilize $L_0$ or $L_1$
penalty to indirectly affect the final pruning ratio, LEAP introduces a novel
regularization function, that directly interacts with the preset target pruning
ratio. Moreover, in order to reduce hyperparameter tuning, a novel adaptive
regularization coefficient is deployed to control the regularization penalty
adaptively. With the new regularization term and its associated adaptive
regularization coefficient, LEAP is able to be applied for different pruning
granularity, including unstructured pruning, structured pruning, and hybrid
pruning, with minimal hyperparameter tuning. We apply LEAP for BERT models on
QQP/MNLI/SQuAD for different pruning settings. Our result shows that for all
datasets, pruning granularity, and pruning ratios, LEAP achieves on-par or
better results as compared to previous heavily hand-tuned methods.
