4 papers · 1 filter
StateFlow: Sequence Pipeline Parallelism for Long-Context Modeling with Linear Recurrence
Wenxuan Zhao, Yingfa Chen, Xu Han +7
Long-context training is increasingly important for large language models, and linear attention and state space models have become popular for improving long-context efficiency. Ho…
InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training
Shiju Wang, Yujie Wang, Ao Sun +5
Long context training is crucial for LLM's context extension. Existing schemes, such as sequence parallelism, incur substantial communication overhead. Pipeline parallelism (PP) re…
BurstEngine: an Efficient Distributed Framework for Training Transformers on Extremely Long Sequences of over 1M Tokens
Ao Sun, Weilin Zhao, Xu Han +4
Existing methods for training LLMs on long-sequence data, such as Tensor Parallelism and Context Parallelism, exhibit low Model FLOPs Utilization as sequence lengths and number of…
Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training
Ao Sun, Weilin Zhao, Xu Han +5
The emergence of large language models (LLMs) relies heavily on distributed training strategies, among which pipeline parallelism plays a crucial role. As LLMs' training sequence l…