Distributing FlashAttention: Solving memory Bottlenecks of Context Windows

The post discusses memory bottlenecks in training large language models (LLMs) with extended context windows. It introduces FlashAttention as a technique to optimize memory and computation for long sequences in transformer models. The post explains how Kvax, an open-source implementation based on JAX, facilitates efficient distributed training by combining FlashAttention with parallelism techniques. It highlights key optimizations in FlashAttention, such as fused kernels and tiling, and demonstrates how these innovations can enable training of models with extremely long sequences.