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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.
A simple and effective method for removal of hidden units and weights
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Distilling the knowledge in a neural network
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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First quora dataset release: Question pairs
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman. 2017 · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta. 2017 · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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The state of sparsity in deep neural networks
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The lottery ticket hypothesis for pre-trained bert networks
Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, and Michael Carbin. 2020 · 2020
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Inducing and exploiting activation sparsity for fast inference on deep neural networks
Mark Kurtz, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, Sage Moore, Bill Nell, Nir Shavit, and Dan Alistarh. 2020 · 2020
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Movement pruning: Adaptive sparsity by fine-tuning
Victor Sanh, Thomas Wolf, and Alexander Rush. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
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Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste. 2021 · 2021
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Block pruning for faster transformers
François Lagunas, Ella Charlaix, Victor Sanh, and Alexander Rush. 2021 · 2021
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf. 2021 · 2021
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Rethinking network pruning–under the pre-train and fine-tune paradigm
Dongkuan Xu, Ian EH Yen, Jinxi Zhao, and Zhibin Xiao. 2021 · 2021
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A survey of quantization methods for efficient neural network inference
Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer. 2021 · 2021
Cited alongside, same era.
Prune once for all: Sparse pre-trained language models
Ofir Zafrir, Ariel Larey, Guy Boudoukh, Haihao Shen, and Moshe Wasserblat. 2021 · 2021
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The optimal bert surgeon: Scalable and accurate second-order pruning for large language models
Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, and Dan Alistarh. 2022 · 2022
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