The implementation of attention in vision transformers and language transformers is different due to the nature of the input data and the structure of the models.

In language transformers, the input data consists of sequences of tokens, such as words or subwords, which are processed by the model one at a time. The attention mechanism in language transformers is designed to attend to different parts of the input sequence in order to extract meaningful relationships between the tokens. The input sequence is typically divided into multiple segments or "heads", and each head computes a set of attention weights based on the relationships between the tokens in that segment. The outputs of the different heads are then concatenated and linearly transformed to produce the final attention output.

In vision transformers, the input data consists of images represented as a sequence of 2D patches, which are flattened and processed by the model. The attention mechanism in vision transformers is designed to attend to different parts of the image in order to extract features that are relevant for the task at hand, such as object detection or image classification. Unlike language transformers, the attention mechanism in vision transformers typically does not use multiple segments or heads. Instead, it uses a single attention matrix to compute attention weights between all pairs of patches in the image, which are then used to produce a weighted sum of the patches. The output of the attention mechanism is then typically fed into a feedforward neural network for further processing.

Overall, while the underlying principles of attention are similar in language and vision transformers, the specific implementations are tailored to the unique characteristics of the input data and the structure of the models.
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