The implementation of attention in vision transformers and language transformers differs primarily in the type of input that the attention mechanism is applied to.

In language transformers, attention is typically applied to a sequence of word embeddings that represent a sentence or a document. The query vector is derived from the current position in the sequence, and the key and value vectors are derived from all positions in the sequence. The attention mechanism allows the model to attend to different parts of the input sequence, and has been shown to be particularly effective for tasks such as machine translation and language modeling.

In contrast, in vision transformers, attention is typically applied to a grid of patches that represent an image. The query vector is derived from each patch, and the key and value vectors are derived from all patches in the grid. The attention mechanism allows the model to attend to different regions of the image, and has been shown to be effective for tasks such as image classification and object detection.

One key difference in the implementation of attention in vision transformers is the use of positional embeddings, which provide the model with information about the spatial relationships between patches in the grid. These positional embeddings are typically added to the patch embeddings before they are fed into the attention mechanism, allowing the model to attend to both the content and the position of the patches.

Overall, while the basic idea of attention is the same in both vision transformers and language transformers, the specific implementation details differ based on the nature of the input data and the task at hand.
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