How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark

• URL: http://arxiv.org/abs/2312.13547v1
• Date: Thu, 21 Dec 2023 03:11:30 GMT
• Title: How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark
• Authors: Eldar Kurtic, Torsten Hoefler, Dan Alistarh
• Abstract summary: We revisit the question of accurate BERT-pruning during fine-tuning on downstream datasets. We propose a set of general guidelines for successful pruning, even on the challenging SMC benchmark.
• Score: 60.72725673114168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pruning large language models (LLMs) from the BERT family has emerged as a standard compression benchmark, and several pruning methods have been proposed for this task. The recent ``Sparsity May Cry'' (SMC) benchmark put into question the validity of all existing methods, exhibiting a more complex setup where many known pruning methods appear to fail. We revisit the question of accurate BERT-pruning during fine-tuning on downstream datasets, and propose a set of general guidelines for successful pruning, even on the challenging SMC benchmark. First, we perform a cost-vs-benefits analysis of pruning model components, such as the embeddings and the classification head; second, we provide a simple-yet-general way of scaling training, sparsification and learning rate schedules relative to the desired target sparsity; finally, we investigate the importance of proper parametrization for Knowledge Distillation in the context of LLMs. Our simple insights lead to state-of-the-art results, both on classic BERT-pruning benchmarks, as well as on the SMC benchmark, showing that even classic gradual magnitude pruning (GMP) can yield competitive results, with the right approach.

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