multihead masked attention mechanism

Multi-head masked attention mechanism is a type of attention mechanism used in deep learning models, particularly in transformer-based models like BERT and GPT. It is a variant of the standard attention mechanism used in sequence-to-sequence models.

In multi-head masked attention, the input sequence is split into multiple sub-sequences and each sub-sequence is processed independently using the standard attention mechanism. The output of each sub-sequence is then concatenated and passed through a linear layer to produce the final output.

The "masked" part of the mechanism refers to the fact that during training, some of the input tokens are randomly masked, meaning that they are ignored during the attention calculation. This is done to prevent the model from simply memorizing the input sequence and instead forces it to learn more robust representations.

Overall, multi-head masked attention allows the model to attend to multiple parts of the input sequence simultaneously while also incorporating the concept of masking for improved performance.