Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation

Published: 16 Jan 2024, Last Modified: 05 Mar 2024ICLR 2024 posterEveryoneRevisionsBibTeX
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Keywords: Green AI, Large Language Models, Fine-Tuning, Adaptive Backpropagation
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TL;DR: This paper presents a new technique of minimizing the FLOPs of LLM fine-tuning with respect to the Green AI requirements, by selecting the best trainable portions of the model based on their backpropagation costs.
Abstract: Fine-tuning is essential to adapting pre-trained large language models to downstream applications. With the increasing popularity of LLM-enabled applications, fine-tuning has been performed intensively worldwide, incurring a tremendous amount of computing costs that correspond to big carbon footprint and environmental impact. Mitigating such environmental impact directly correlates to reducing the fine-tuning FLOPs. Existing fine-tuning schemes focus on either saving memory or reducing the overhead of computing weight updates, but cannot achieve sufficient FLOPs reduction due to their ignorance of the training cost in backpropagation. To address this limitation, in this paper we present GreenTrainer, a new technique that minimizes the FLOPs of LLM fine-tuning via adaptive backpropagation, which adaptively selects the most appropriate set of LLM tensors for fine-tuning based on their importance and backpropagation cost in training. Experiment results show that GreenTrainer can save up to 64\% training FLOPs compared to full fine-tuning, without any noticeable accuracy loss. Compared to the existing schemes such as Prefix Tuning and LoRA, GreenTrainer can achieve up to 4\% improvement of model accuracy, with on-par FLOPs reduction.
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Primary Area: generative models
Submission Number: 2162