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ring-attention

Ring Attention leverages blockwise computation of self-attention on multiple GPUs and enables training and inference of sequences that would be too long for a single devices.

This repository contains notebooks, experiments and a collection of links to papers and other material related to Ring Attention.

Reserach / Material

• Paper: Ring Attention with Blockwise Transformers for Near-Infinite Context

  • code: lhao499/ring-attention

• Paper: World Model on Million-Length Video And Language With RingAttention

  • code: LargeWorldModel/LWM,
  • project site: largeworldmodel.github.io
  • models: HF/LargeWorldModel

• Paper: Striped Attention: Faster Ring Attention for Causal Transformers, code: exists-forall/striped_attention

• Paper (2022): 4D parallelism: Sequence Parallelism: Long Sequence Training from System Perspective

• related: Flash-Decoding for long-context inference (together.ai blog)

• Paper: Online normalizer calculation for softmax (NVIDIA, 2018)

• ELI5: FlashAttention by Aleksa Gordić

• LWM model in ollama: https://ollama.com/ifioravanti/lwm

• Phil Wang's (lucidrain) pytorch impl: lucidrains/ring-attention-pytorch

• Zilin Zhu's nice zhuzilin/ring-flash-attention implementation

Notebooks

• Incremental Softmax (to understand the algorithm in 'high-level' pytorch)
• Naive flash-attn (to understand the algorithm in 'high-level' pytorch)

Development References

• NVIDIA Collective Communication Library (NCCL) Documentation
• PyTorch Distributed Overview
• Distributed communication package - torch.distributed (send(), recv(), broadcast(), etc.)

How to contribute

Contact us on the GPU MODE discord server: https://discord.gg/gpumode, PRs are welcome (please create an issue first).