Summary Ring Attention with Blockwise Transformers for Long Sequences arxiv.org
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One Line
Ring Attention is a solution that overcomes memory limitations in Transformers by distributing long sequences across devices, allowing for processing of longer inputs and removing memory constraints.
Slides
Slide Presentation (12 slides)
Key Points
- Transformers have exceptional performance but struggle with handling long sequences and memory demands.
- Ring Attention is a proposed approach that allows for the processing of longer input sequences while maintaining memory efficiency.
- Ring Attention enables training and inference of sequences that are device count times longer than previous memory-efficient Transformers.
- Large context Transformers are essential for various AI challenges, and Ring Attention allows for training sequences that exceed 100 million in length.
- Ring Attention reduces the memory requirements of Transformers and eliminates the memory constraints imposed by individual devices.
- The approach involves distributing sequence dimensions across multiple devices and overlapping communication with computation.
- Extensive experiments demonstrate the effectiveness of Ring Attention in enabling larger sequence input sizes and improving performance.
Summaries
22 word summary
Ring Attention addresses memory limitations of Transformers by distributing long sequences across devices, enabling processing of longer inputs and eliminating memory constraints.
81 word summary
Ring Attention is proposed as a solution to the memory limitations of Transformers when handling long sequences. It distributes long sequences across multiple devices using blockwise computation of self-attention, allowing for processing longer input sequences while maintaining memory efficiency. Experimental results show that Ring Attention enables training and inference of sequences that are device count times longer than previous memory-efficient Transformers. It also eliminates memory constraints imposed by individual devices, enabling sequences with lengths that scale with the number of devices.
139 word summary
The authors propose Ring Attention as a solution to the memory limitations of Transformers when handling long sequences. Transformers are widely used in AI models but struggle with tasks involving extended sequences or long-term dependencies due to their memory demands. Ring Attention addresses this challenge by distributing long sequences across multiple devices using blockwise computation of self-attention. This allows for processing longer input sequences while maintaining memory efficiency. Experimental results on language modeling tasks show that Ring Attention enables training and inference of sequences that are device count times longer than previous memory-efficient Transformers and allows for training of sequences exceeding 100 million in length without approximations to attention. It also improves performance by accommodating larger sequence input sizes. Ring Attention eliminates memory constraints imposed by individual devices, enabling sequences with lengths that scale with the number of devices.
415 word summary
The authors of this paper propose a new approach called Ring Attention to address the memory constraints of Transformers, which limit their ability to handle long sequences. The authors explain that Transformers have become popular in AI models due to their exceptional performance, but their memory demands pose challenges for tasks involving extended sequences or long-term dependencies.
To overcome these challenges, the authors introduce Ring Attention, which leverages blockwise computation of self-attention to distribute long sequences across multiple devices. This allows for the processing of longer input sequences while maintaining memory efficiency. The authors conducted experiments on language modeling tasks to evaluate the effectiveness of Ring Attention and found that it enables training and inference of sequences that are device count times longer than previous memory-efficient Transformers. It also allows for the training of sequences that exceed 100 million in length without approximations to attention. Additionally, Ring Attention improves performance by allowing for larger sequence input sizes.
The authors highlight the significance of reducing memory cost in Transformers, as large context Transformers are essential for various AI challenges. They compare the maximum context length of different Transformer architectures and demonstrate that Ring Attention allows for training 512 times longer sequences than baselines.
To tackle the challenge of memory overhead in attention computation, the authors propose the Ring Attention approach. They explain that by performing self-attention and feedforward network computations in a blockwise fashion, they can distribute sequence dimensions across multiple devices and enable concurrent computation and communication. By overlapping communication with computation, each device only requires memory proportional to the block size, effectively eliminating the memory constraints imposed by individual devices.
The authors evaluate the effectiveness of Ring Attention on language modeling benchmarks and find that it reduces the memory requirements of Transformers. It allows for training sequences that are device count times longer than previous memory-efficient models and enables training of sequences exceeding 100 million in length without approximations to attention. Importantly, Ring Attention eliminates the memory constraints imposed by individual devices, enabling sequences with lengths that scale with the number of devices.
In conclusion, the authors propose Ring Attention as a memory-efficient approach to address the memory constraints of Transformers. Their approach allows for the scaling of context length with the number of devices, improving performance and eliminating the memory bottleneck. The experiments conducted demonstrate the effectiveness of Ring Attention in allowing for larger sequence input sizes and highlight its significance in enabling the training and inference of sequences with near-infinite context size.
638 word summary
Transformers have become the architecture of choice for many AI models due to their exceptional performance. However, the memory demands of Transformers limit their ability to handle long sequences, creating challenges for tasks involving extended sequences or long-term dependencies. To address this issue, the authors propose a distinct approach called Ring Attention. Ring Attention 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. This allows for the processing of longer input sequences while maintaining memory efficiency, effectively eliminating the memory constraints imposed by individual devices.
The authors conduct extensive experiments on language modeling tasks to evaluate the effectiveness of Ring Attention. The results demonstrate that Ring Attention enables training and inference of sequences that are device count times longer than those of prior memory-efficient Transformers. It allows for the training of sequences that exceed 100 million in length without making approximations to attention. The experiments also show that Ring Attention improves performance by allowing for larger sequence input sizes.
The proposed approach addresses the challenge of scaling up the context length of Transformers. The inherent architecture design of Transformers, specifically self-attention, has a memory cost quadratic in the input sequence length, making it difficult to scale to longer input sequences. Large context Transformers are essential for various AI challenges, such as processing books, images, videos, and scientific experiment data. The authors compare the maximum context length of different Transformer architectures and demonstrate that Ring Attention allows for training 512 times longer sequences than baselines.
The authors highlight the significance of reducing memory cost in Transformers. One line of research focuses on reducing the memory overhead of attention computation by computing attention in a blockwise manner without materializing the full matrix. However, a significant challenge remains in storing the output of each layer due to self-attention's inherent nature. Failing to store the outputs increases computational costs and renders it impractical for longer sequences. The authors provide insights into the memory demand of Transformers and emphasize that even processing 100 million tokens with a batch size of 1 requires over 1000GB of memory, exceeding the capacity of contemporary GPUs and TPUs.
To tackle this challenge, the authors propose the Ring Attention approach. They observe that by performing self-attention and feedforward network computations in a blockwise fashion, they can distribute sequence dimensions across multiple devices and enable concurrent computation and communication. The authors explain the mechanism of Ring Attention, which involves a conceptual ring formed by host devices. During the inner loop of blockwise attention computation, each device sends a copy of its key-value blocks to the next device while receiving key-value blocks from the previous one. By overlapping communication with computation, each device only requires memory proportional to the block size, effectively eliminating the memory constraints imposed by individual devices.
The authors evaluate the effectiveness of Ring Attention on language modeling benchmarks. The experiments demonstrate that Ring Attention reduces the memory requirements of Transformers and enables training sequences that are device count times longer than prior memory-efficient state-of-the-art models. It also allows for the training of sequences that exceed 100 million in length without making approximations to attention. Importantly, Ring Attention eliminates the memory constraints imposed by individual devices, enabling sequences with lengths that scale in proportion to the number of devices.
In conclusion, the authors propose a memory-efficient approach called Ring Attention to address the memory constraints of Transformers. Their approach allows for the scaling of context length with the number of devices while maintaining performance, effectively eliminating the memory bottleneck imposed by individual devices. Extensive experiments demonstrate the effectiveness of Ring Attention in allowing for larger sequence input sizes and improving performance. The authors emphasize the significance of their approach in enabling the training and inference of sequences with near-infinite context size.