flash attention

Flash attention is a type of attention mechanism used in deep learning models, such as Transformers. Unlike traditional attention mechanisms that attend to all the input elements, flash attention only attends to a random subset of the input elements, making it more efficient and faster to compute.

In flash attention, the subset of input elements are selected randomly at each time step, and the attention weights are calculated only for this subset. This makes the attention mechanism more dynamic and allows the model to focus on different parts of the input sequence at different time steps.

Flash attention has been shown to be effective in improving the performance of deep learning models on tasks such as language modeling and machine translation.