This July 2024 paper introduces ByteCheckpoint, a novel PyTorch-native system designed for Large Language Model (LLM) development. This system addresses critical challenges in LLM training, particularly the high I/O costs associated with saving and loading checkpoints, and the complexities of checkpoint resharding across different parallel configurations and training frameworks. ByteCheckpoint achieves this through a data/metadata disaggregated storage architecture and asynchronous tensor merging, enabling automatic online resharding and multi-framework support. The paper highlights ByteCheckpoint's significant performance improvements in reducing checkpoint saving and loading times compared to existing methods, making LLM development more efficient and robust. The authors detail various I/O performance optimization techniques.
Source:
https://arxiv.org/html/2407.20143v1
