Ring Attention with Blockwise Transformers for Near-Infinite Context

Published: 28 Oct 2023, Last Modified: 26 Nov 2023Instruction Workshop @ NeurIPS 2023EveryoneRevisionsBibTeX
Keywords: Language Model, Large Context, Reinforcement Learning
TL;DR: We present an efficient method of computing the standard transformer architecture, enabling effective processing of long contextual information.
Abstract: Transformers have emerged as the architecture of choice for many state-of-the-art AI models, showcasing exceptional performance across a wide range of AI applications. However, the memory demands imposed by Transformers limit their ability to handle long sequences, thereby creating challenges for tasks involving extended sequences or long-term dependencies. We present a distinct approach, Ring Attention, which leverages blockwise computation of self-attention to distribute long sequences across multiple devices while overlapping the communication of key-value blocks with the computation of blockwise attention. Ring Attention enables training and inference of sequences that are up to device count times longer than those of prior memory-efficient Transformers, effectively eliminating the memory constraints imposed by individual devices. Extensive experiments on language modeling tasks demonstrate the effectiveness of Ring Attention in allowing large sequence input size and improving performance.
Submission Number: 25