Pruning Pre-trained Language Models with Principled Importance and Self-regularization

Siyu Ren; Kenny Q. Zhu

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arXiv · 2305.12394

Pruning Pre-trained Language Models with Principled Importance and Self-regularization

Siyu Ren·Kenny Q. Zhu

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Abstract

Iterative pruning is one of the most effective compression methods for pre-trained language models. We discovered that finding the optimal pruning decision is an equality-constrained 0-1 Integer Linear Programming problem. The solution to this optimization problem leads to a principled importance criterion which we use to rank parameters during iterative model pruning. To mitigate the poor generalization at high sparsity levels, we propose a self-regularization scheme where model prediction is regularized by the latest checkpoint with increasing sparsity throughout pruning. Our experiments on natural language understanding, question-answering, named entity recognition, and data-to-text generation with various Transformer-based PLMs show the effectiveness of the approach at various sparsity levels.

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Siyu Ren, Kenny Q. Zhu. 2023-05-21. Pruning Pre-trained Language Models with Principled Importance and Self-regularization. https://arxiv.org/abs/2305.12394

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