正文：
在人工智能领域，大模型的训练一直是学术界和工业界的热点话题。随着模型的不断扩大，系统工程问题成为了训练过程中的一个重要挑战。华为诺亚实验室的研究团队近日发表了一项新成果，提出了一种名为ExCP的极致压缩检查点技术，能够将模型存储开销降低70倍，为人工智能的研究和应用带来了新的可能。
这项技术的主要创新点在于，它利用了训练过程中的检查点残差信息，通过时间序列上的信息稀疏性实现更高的剪枝比例。此外，ExCP方法还将优化器和权重联合起来进行压缩，从而实现了整体的高压缩率。通过这种方法，华为诺亚实验室不仅大幅降低了存储开销，还提高了训练过程中的效率。
ExCP方法的核心算法包括计算权重残差、联合压缩和非均匀量化等步骤。这种方法不仅适用于大语言模型，也适用于视觉模型等其他类型的模型。通过这种压缩技术，研究人员和企业在训练大型模型时可以节省大量的存储资源，从而降低成本，提高效率。
这项技术的成功开源，意味着更多的研究人员和开发者可以利用这一方法来优化他们的模型训练过程。这对于推动人工智能技术的进步和应用具有重要意义。随着人工智能技术的不断发展，未来我们将看到更多的创新成果，为各行各业带来新的变革。
英语如下：
News Title: “Huawei Noah’s Ark Lab Unveils a 70x Compression Method to Aid Large Model Training”
Keywords: Large Models, Compression Technology, Huawei Noah’s Ark Lab
News Content:
Title: Huawei Noah’s Ark Lab Announces New Method to Significantly Reduce Storage Costs for Large Model Training
In the field of artificial intelligence, the training of large models has long been a hot topic both in academia and industry. As models continue to grow in size, system engineering issues have become a significant challenge during the training process. Recently, the research team at Huawei Noah’s Ark Lab published a new breakthrough, introducing a novel extreme compression checkpoint technique named ExCP, which can reduce the storage costs of models by 70 times, opening up new possibilities for AI research and applications.
The main innovation of this technology lies in its use of checkpoint residual information during the training process, leveraging temporal information sparsity to achieve a higher pruning ratio. Additionally, the ExCP method compresses both the optimizer and weights together, achieving a high overall compression rate. With this method, Huawei Noah’s Ark Lab not only significantly reduces storage costs but also enhances the efficiency of the training process.
The core algorithms of the ExCP method include steps for calculating weight residuals, joint compression, and non-uniform quantization. This method is not only applicable to large language models but also suitable for visual models and other types of models. With this compression technology, researchers and companies can save vast amounts of storage resources when training large models, thereby reducing costs and enhancing efficiency.
The successful open-sourcing of this technology means that more researchers and developers can utilize this method to optimize their model training processes. This is of great significance for advancing the progress and applications of artificial intelligence technology. As artificial intelligence technology continues to develop, we will see more innovative achievements that bring about new changes in various industries.
【来源】https://www.jiqizhixin.com/articles/2024-08-05-3
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