GMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods

Eldar Kurtic; Dan Alistarh

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arXiv · 2210.06384

GMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods

Eldar Kurtic·Dan Alistarh

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Abstract

We revisit the performance of the classic gradual magnitude pruning (GMP) baseline for large language models, focusing on the classic BERT benchmark on various popular tasks. Despite existing evidence in the literature that GMP performs poorly, we show that a simple and general variant, which we call GMP*, can match and sometimes outperform more complex state-of-the-art methods. Our results provide a simple yet strong baseline for future work, highlight the importance of parameter tuning for baselines, and even improve the performance of the state-of-the-art second-order pruning method in this setting.

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BibTeX RIS

Eldar Kurtic, Dan Alistarh. 2022-10-12. GMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods. https://arxiv.org/abs/2210.06384

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