Sparsity-Accelerated Training for Large Language Models

• URL: http://arxiv.org/abs/2406.01392v2
• Date: Thu, 6 Jun 2024 16:38:34 GMT
• Title: Sparsity-Accelerated Training for Large Language Models
• Authors: Da Ma, Lu Chen, Pengyu Wang, Hongshen Xu, Hanqi Li, Liangtai Sun, Su Zhu, Shuai Fan, Kai Yu,
• Abstract summary: Large language models (LLMs) have demonstrated proficiency across various natural language processing (NLP) tasks. LLMs often require additional training, such as continual pre-training and supervised fine-tuning. This paper proposes leveraging emphsparsity in pre-trained LLMs to expedite this training process.
• Score: 20.86225596276327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated proficiency across various natural language processing (NLP) tasks but often require additional training, such as continual pre-training and supervised fine-tuning. However, the costs associated with this, primarily due to their large parameter count, remain high. This paper proposes leveraging \emph{sparsity} in pre-trained LLMs to expedite this training process. By observing sparsity in activated neurons during forward iterations, we identify the potential for computational speed-ups by excluding inactive neurons. We address associated challenges by extending existing neuron importance evaluation metrics and introducing a ladder omission rate scheduler. Our experiments on Llama-2 demonstrate that Sparsity-Accelerated Training (SAT) achieves comparable or superior performance to standard training while significantly accelerating the process. Specifically, SAT achieves a $45\%$ throughput improvement in continual pre-training and saves $38\%$ training time in supervised fine-tuning in practice. It offers a simple, hardware-agnostic, and easily deployable framework for additional LLM training. Our code is available at https://github.com/OpenDFM/SAT.

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