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About this paper
William Brandon, Aniruddha Nrusimha, Kevin Qian, Zachary Ankner, Tian Jin, Zhiye Song, Jonathan Ragan-Kelley
Striped Attention enhances Ring Attention for causal transformer models by addressing a workload imbalance, leading to more efficient training of large language models with very long sequence contexts. The method achieves up to 1.65x end-to-end throughput improvements for training models with sequence lengths up to 786k tokens.
Key insights
- Ring Attention's contiguous sharding for causal attention leads to a workload imbalance where some devices are idle or compute only masked data while others process full workloads, limiting overall throughput to the slowest device.
- Permuting the input sequence into 'stripes' across devices effectively balances the causal attention workload, ensuring all devices process a proportional amount of necessary (unmasked) computations.
- By correctly applying the causal mask relative to the original sequence indices, Striped Attention can achieve a balanced, triangular workload on each device throughout all attention iterations.
Summary by alphaXiv.
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