24494

AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman
University of Wisconsin-Madison
arXiv:2102.01386 [cs.LG], (2 Feb 2021)

@misc{liu2021autofreeze,

   title={AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning},

   author={Yuhan Liu and Saurabh Agarwal and Shivaram Venkataraman},

   year={2021},

   eprint={2102.01386},

   archivePrefix={arXiv},

   primaryClass={cs.LG}

}

With the rapid adoption of machine learning (ML), a number of domains now use the approach of fine-tuning models pre-trained on a large corpus of data. However, our experiments show that even fine-tuning on models like BERT can take many hours when using GPUs. While prior work proposes limiting the number of layers that are fine-tuned, e.g., freezing all layers but the last layer, we find that such static approaches lead to reduced accuracy. We propose, AutoFreeze, a system that uses an adaptive approach to choose which layers are trained and show how this can accelerate model fine-tuning while preserving accuracy. We also develop mechanisms to enable efficient caching of intermediate activations which can reduce the forward computation time when performing fine-tuning. Our evaluation on fourNLP tasks shows that AutoFreeze, with caching enabled, can improve fine-tuning performance by up to 2.55x.
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