Global and Sliding Window Attention

Global and Sliding Window Attention is an attention pattern for attention-based models. It is motivated by the fact that non-sparse attention in the original Transformer formulation has a self-attention component with $O\left(n^{2}\right)$ time and memory complexity where $n$ is the input sequence length and thus, is not efficient to scale to long inputs.

Since windowed and dilated attention patterns are not flexible enough to learn task-specific representations, the authors of the Longformer add “global attention” on few pre-selected input locations. This attention is operation symmetric: that is, a token with a global attention attends to all tokens across the sequence, and all tokens in the sequence attend to it. The Figure to the right shows an example of a sliding window attention with global attention at a few tokens at custom locations. For the example of classification, global attention is used for the [CLS] token, while in the example of Question Answering, global attention is provided on all question tokens.