Ring Attention with Blockwise Transformers for Near-Infinite Context

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 posing challenges in utilizing videos, actions, and other long-form sequences and modalities in complex environments. We present a novel approach, Ring Attention with Blockwise Transformers (Ring Attention), which leverages blockwise computation of self-attention and feedforward to distribute long sequences across multiple devices while fully overlapping the communication of key-value blocks with the computation of blockwise attention. Our approach enables training and inference of sequences that are up to device count times longer than those achievable by prior memory-efficient Transformers, without resorting to approximations or incurring additional communication and computation overheads. Extensive experiments on language modeling and reinforcement learning tasks demonstrate the effectiveness of our approach in allowing millions of tokens context size and improving performance.

• The paper presents a novel Ring Attention method that distributes sequence computation across devices to enable near-infinite context lengths.
• It achieves a 512-fold increase in supported tokens—over 16 million on TPUv4-1024—compared to prior memory-efficient Transformers.
• This advancement facilitates processing of long videos, expansive code repositories, and detailed scientific data without memory bottlenecks.

Ring Attention with Blockwise Transformers for Near-Infinite Context

This paper presents a novel technique, Ring Attention with Blockwise Transformers (Ring Attention), that addresses the memory challenges associated with Transformers, particularly when dealing with long sequences. The importance of this work lies in its ability to extend the context length of models significantly, achieving near-infinite context sizes by leveraging distributed computing across multiple devices.

Approach and Methodology

The proposed Ring Attention technique operates by distributing the sequence dimension across multiple devices, thus leveraging blockwise computation of self-attention and feedforward layers. This method ensures efficient parallelization without the need for approximations. Key to this approach is the ring topology, where devices exchange key-value blocks in a rotating fashion while concurrently computing blockwise operations. By overlapping communication with computation, Ring Attention achieves substantial memory savings and enables sequence processing that scales linearly with the number of devices. This capability allows the model to handle context sizes that were previously unmanageable with traditional Transformers, surpassing millions of tokens.

Experimental Results

The paper provides extensive experimental validation of the technique's effectiveness. For example, using TPUv4-1024, the Ring Attention model supports context sizes exceeding 16 million tokens, a 512-fold increase compared to prior methods with memory-efficient Transformers. These results were consistently observed across different hardware setups, including various configurations of A100 GPUs and TPUs, and with models of varying sizes such as 3B, 7B, 13B, and 30B parameters.

Practical Implications

The practical applications of Ring Attention are numerous and significant. By eliminating the context memory bottleneck, models can now process long videos, large code repositories, or detailed scientific data without compromising on sequence length. This broadens the scope for Transformers in sectors like video-audio-language modeling, complex trial-and-error reinforcement learning, and scientific computation.

Theoretical Implications

Theoretically, this approach challenges the conventional scaling laws associated with memory constraints in Transformers. It also highlights how overlapping communication and computation can potentially redefine efficiency in distributed deep learning systems. Ring Attention exemplifies how memory-efficient designs can pave the way for more scalable AI systems.

Future Directions

Future research directions could explore the integration of Ring Attention with various forms of parallelism, such as data or tensor parallelism, for even larger models. Furthermore, examining its applicability to more diverse tasks, beyond natural language processing and reinforcement learning, could extend the utility of Transformers across other domains. There is also potential for optimizing the network bandwidth utilization, further enhancing scaling efficiency.

Conclusion

In sum, the Ring Attention approach offers a compelling solution to the memory constraints that limit the scalability of Transformers. Its ability to facilitate training and inference over tremendously longer context sequences, without adding overhead, marks a substantial step forward. As the challenges in AI continue to grow in complexity and scale, innovations like Ring Attention will be crucial in advancing the capabilities of AI models.