window attention blocks

Window attention blocks are a type of attention mechanism used in neural networks, particularly in natural language processing tasks. They are similar to the standard self-attention mechanism used in transformer networks, but with a more limited scope.

In window attention blocks, the attention mechanism only considers a fixed-size "window" of nearby tokens or features, rather than attending to all tokens in the sequence. This can help reduce the computational complexity of the model while still allowing it to capture important relationships between nearby tokens.

Window attention blocks are used in a variety of NLP tasks, such as language modeling, machine translation, and text classification. They have been shown to achieve state-of-the-art performance on several benchmark datasets.