Which mechanism optimizes memory usage and computation managing attention in models?

Paged attention is a mechanism designed to optimize memory usage and computation for managing attention in language models. It effectively reduces the amount of memory required by only keeping relevant parts of the input data in the attention mechanism at any given time. This is particularly important in deep learning models, where the attention mechanism can become a bottleneck due to the quadratic scaling of memory usage with the length of the input sequence.

In paged attention, segments or "pages" of the input are used instead of trying to attend to the entire input sequence all at once. This approach allows the model to selectively focus on specific portions of the input, which not only minimizes memory consumption but also enables faster computation. By leveraging this segmented approach, models can handle longer sequences without overwhelming computational resources, making it a practical solution for scaling attention mechanisms effectively.

Other mechanisms, while they may also aim to improve attention management, do not specifically target optimizations related to memory and computation in the way that paged attention does.