在人工智能领域不断探索与创新的前沿，学术与技术的交流与传播变得尤为重要。AIxiv专栏作为机器之心发布学术、技术内容的平台，自成立以来，已报道了2000多篇内容，涵盖了全球各大高校与企业的顶级实验室的研究成果，为学术交流与传播搭建了有力的桥梁。如果您有卓越的研究成果或见解想要分享，欢迎投稿或寻求报道合作。
近期，AIxiv专栏关注到一项由南开大学统计与数据科学学院研二硕士生杨雨辰与指导老师徐君副教授共同完成的研究成果，这项工作为提高微调大型Transformer模型的效率提供了新思路。在深度学习领域，大型Transformer模型因其强大的表征能力而被广泛应用于多个领域，微调成为将这些预训练大模型应用于特定任务的关键步骤。然而，随着模型规模的不断扩大，微调过程中对显存的需求也急剧增加，这成为了限制模型应用的瓶颈。
为解决这一挑战，研究团队提出了“LowMemoryBP”（低内存反向传播）方法，旨在提升反向传播过程中的显存使用效率。通过这一创新，团队不仅减少了显存的消耗，还避免了使用梯度检查点时导致的训练速度下降。梯度检查点是一种传统策略，通过牺牲部分训练速度来降低显存需求，但“LowMemoryBP”方法在不牺牲训练速度的同时，实现了显存的高效管理，为大规模模型的微调提供了更为优化的解决方案。
徐君老师团队在计算机视觉、生成式AI和高效机器学习领域有着深厚的积累，其研究成果在顶级会议和期刊上屡获认可，谷歌学术引用超过4700次。这一新方法不仅展示了团队在人工智能领域持续创新的能力，也为未来模型微调提供了新的可能性，有望在实际应用中实现更高效的资源利用，加速人工智能技术的发展与普及。
通过AIxiv专栏的报道，这一研究成果不仅为学术界提供了宝贵的参考，也为工业界在大规模模型应用中面临的问题提供了解决方案，促进了人工智能技术的进一步发展和应用。未来，期待更多类似的研究，共同推动人工智能技术向着更加高效、可持续的方向迈进。
英语如下：
News Title: “LowMemoryBP: Enhancing Large Transformer Model Tuning with Efficiency and Memory Optimization”
Keywords: ICML 2024, LowMemoryBP, Transformer models
News Content: In the ever-evolving landscape of artificial intelligence (AI) research and innovation, the exchange and dissemination of academic and technical insights have become paramount. AIxiv, a platform for publishing academic and technical content by Machine Intelligence, has reported over 2000 pieces since its inception, covering groundbreaking research from top laboratories at universities and companies worldwide. If you have exceptional research findings or insights to share, we welcome your contributions or inquiries about collaboration.
Recently, AIxiv has spotlighted a study by Yang Yuchen, a second-year master’s student at the School of Statistics and Data Science, Nankai University, and his mentor, Associate Professor Xu Jun. This research introduces a novel approach to optimizing the efficiency of fine-tuning large Transformer models, a critical step in adapting pre-trained large models to specific tasks in the deep learning domain. However, as model sizes grow, the demand for memory increases during the fine-tuning phase, which has become a bottleneck in model application.
To tackle this challenge, the research team proposed the “LowMemoryBP” (Low Memory Backpropagation) method, aiming to improve memory utilization during the backpropagation process. This innovation not only reduces memory consumption but also avoids the training speed reduction associated with gradient checkpoints, a traditional strategy that sacrifices some training speed to lower memory demands. Unlike gradient checkpoints, the “LowMemoryBP” method optimizes memory management without compromising training speed, providing a more efficient solution for the fine-tuning of large-scale models.
The team, led by Professor Xu Jun, has a strong foundation in computer vision, generative AI, and efficient machine learning, with their work being widely recognized in top conferences and journals, with over 4700 Google Scholar citations. This new method not only showcases the team’s continuous innovation in the AI domain but also opens new possibilities for future model fine-tuning, promising more efficient resource utilization in practical applications and accelerating the advancement and dissemination of AI technologies.
Through AIxiv’s reporting, this study provides valuable references for the academic community and solutions to challenges faced by the industry in the application of large-scale models, thereby fostering further development and application of AI technologies. In the future, we anticipate more research like this, collectively pushing AI technologies towards more efficient and sustainable advancements.
【来源】https://www.jiqizhixin.com/articles/2024-07-12-5
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