随着人工智能技术的不断发展，大型语言模型的训练已经成为人工智能领域的一个重要挑战。近日，Meta官方披露了在16384块H100 80GB训练集群上进行Llama3 405B训练的故障率，显示了大型训练系统面临的软硬件故障问题。据统计，短短54天内，Llama3模型平均每三小时就会崩溃一次。
为了解决这一问题，字节跳动豆包大模型团队与香港大学联合提出了ByteCheckpoint系统。ByteCheckpoint是一个PyTorch原生的、兼容多个训练框架的Checkpointing系统，能够在保证训练进度和提高训练效率的同时，有效克服训练故障。与现有的方法相比，ByteCheckpoint在Checkpoint保存和加载上的性能都有显著提升，用户接口极简，且支持Checkpoint自动重新切分，大大降低了用户的使用成本。
ByteCheckpoint的出现，为解决大模型训练中的Checkpoint技术挑战提供了新的解决方案。该系统利用了Checkpoint保存过程中的GPU到CPU内存拷贝的独立性，以及不同训练进程的并行处理潜力，减少了额外I/O开销。此外，ByteCheckpoint的自动重新切分功能简化了Checkpoint迁移和评估的过程，提高了系统的易用性。
这一成果的公开，对于人工智能领域的技术进步具有重要意义，有助于推动大模型训练效率的提升，加速人工智能技术的应用和发展。
英语如下：
Title: “ByteCheckpoint Enhances Efficiency in Large Model Training”
Keywords: Efficiency Boost, Checkpoint, Failure Rate
Content:
As artificial intelligence technology continues to evolve, the training of large language models has become a significant challenge in the field. Recently, Meta officially disclosed the failure rate of training Llama3 405B on a 16384-GPU H100 80GB training cluster, highlighting the hardware and software faults faced by large training systems. According to statistics, the Llama3 model collapsed every three hours on average within just 54 days.
To address this issue, the ByteDance Baobao Large Model Team, in collaboration with the Hong Kong University, proposed the ByteCheckpoint system. ByteCheckpoint is a native PyTorch checkpointing system compatible with multiple training frameworks that effectively overcomes training failures while ensuring training progress and efficiency. Compared to existing methods, ByteCheckpoint significantly improves performance in checkpoint saving and loading, offers a highly simplified user interface, and supports automatic checkpoint resplitting, significantly reducing user operational costs.
The emergence of ByteCheckpoint provides a new solution to the checkpoint technology challenges in large model training. The system leverages the independence of GPU-to-CPU memory copying during checkpoint saving and the parallel processing potential of different training processes, reducing additional I/O overhead. Furthermore, ByteCheckpoint’s automatic resplitting feature simplifies the checkpoint migration and evaluation process, enhancing system usability.
This achievement’s public disclosure is of great significance for the progress of artificial intelligence technology, helping to boost the efficiency of large model training and accelerate the application and development of artificial intelligence technology.
【来源】https://www.jiqizhixin.com/articles/2024-08-08-7
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