this post was submitted on 19 Jun 2023
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Singularity | Artificial Intelligence (ai), Technology & Futurology

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Revolutionizing AI Efficiency: Meta AI’s New Approach, READ, Cuts Memory Consumption by 56% and GPU Use by 84% (24.05.2023 paper) (www.marktechpost.com)
submitted 1 year ago* (last edited 1 year ago) by [email protected] to c/[email protected]
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Abstract:

Fine-tuning large-scale Transformers has led to the explosion of many AI applications across Natural Language Processing and Computer Vision tasks. However, fine-tuning all pre-trained model parameters becomes impractical as the model size and number of tasks increase. Parameter-efficient transfer learning (PETL) methods aim to address these challenges. While effective in reducing the number of trainable parameters, PETL methods still require significant energy and computational resources to fine-tune. In this paper, we introduce REcurrent ADaption (READ) -- a lightweight and memory-efficient fine-tuning method -- to overcome the limitations of the current PETL approaches. Specifically, READ inserts a small RNN network alongside the backbone model so that the model does not have to back-propagate through the large backbone network. Through comprehensive empirical evaluation of the GLUE benchmark, we demonstrate READ can achieve a 56% reduction in the training memory consumption and an 84% reduction in the GPU energy usage while retraining high model quality compared to full-tuning. Additionally, the model size of READ does not grow with the backbone model size, making it a highly scalable solution for fine-tuning large Transformers.

Paper: https://arxiv.org/abs/2305.15348

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