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About this paper
Ao Sun, Weilin Zhao, Xu Han, Cheng Yang, Xinrong Zhang, Zhiyuan Liu, Chuan Shi, Maosong Sun
Researchers from Beijing University of Posts and Telecommunications and Tsinghua University developed Seq1F1B, a pipeline parallelism method that partitions long sequences into sub-sequences to significantly improve memory efficiency and training throughput for large language models. The approach enables training a 30B parameter LLM on 64K token sequences using 64 NVIDIA A100 GPUs, a task previously limited by memory constraints.
Key insights
- Decomposing micro-batches into causally-dependent sub-sequences, rather than processing entire micro-batches, drastically reduces peak memory consumption and pipeline idle time for long sequences.
- A specialized, partially ordered queue is essential to correctly manage the dependencies of sub-sequence execution in a causal language model setting, enabling both memory efficiency and correct gradient computation.
- Balancing the computational workload across these sub-sequences based on estimated Floating Point Operations (FLOPs) is critical for maximizing the efficiency gains of sequence-level partitioning.
Summary by alphaXiv.
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