# Ring Attention with Blockwise Transformers for Near-Infinite Context

**Authors:** Hao Liu, Matei Zaharia, Pieter Abbeel  
**arXiv:** [2310.01889](https://arxiv.org/abs/2310.01889)  
**Date:** 2023-10-03  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.01889v1/

## Key points

- Transformers are popular for AI models but have high memory demands
- Ring Attention is a novel approach to address this limitation
- Ring Attention uses blockwise computation of self-attention to distribute long sequences across multiple devices
- It allows for overlapping communication of key-value blocks with blockwise attention computation
- Enables processing longer input sequences while maintaining memory efficiency
- Eliminates memory constraints, allowing near-infinite context processing
- Extensive experiments show effectiveness in enabling large sequence input sizes and improving performance

## Summary

In recent years, Transformers have emerged as the go-to architecture for cutting-edge AI models due to their exceptional performance in various applications. However, one major limitation of Transformers is their high memory demands. This hinders their ability to effectively handle long sequences and poses challenges for tasks that involve extended dependencies over a long period. To address this issue, a novel approach called Ring Attention has been introduced. Ring Attention utilizes blockwise computation of self-attention to distribute long sequences across multiple devices. This innovative method also allows for the overlapping of communication of key-value blocks with the computation of blockwise attention. By implementing Ring Attention, longer input sequences can be processed while maintaining memory efficiency. This breakthrough enables the training and inference of sequences that are significantly longer than what was previously possible with memory-efficient Transformers. The key advantage of Ring Attention is its ability to eliminate the memory constraints imposed by individual devices, effectively allowing for near-infinite context processing. Extensive experiments conducted on language modeling tasks have demonstrated the effectiveness of Ring Attention in enabling large sequence input sizes and ultimately improving overall performance. Authored by Hao Liu, Matei Zaharia, and Pieter Abbeel, the paper titled "Ring Attention with Blockwise Transformers for Near-Infinite Context" presents a game-changing solution to the limitations faced by traditional Transformers when dealing with long sequences. With its innovative approach and promising results in language modeling tasks, Ring Attention stands out as a significant advancement in the field of AI architecture design.