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.
@article{arxiv.2210.06384,
title = {GMP*: Well-Tuned Gradual Magnitude Pruning Can Outperform Most BERT-Pruning Methods},
author = {Eldar Kurtic and Dan Alistarh},
journal= {arXiv preprint arXiv:2210.06384},
year = {2022}
}