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We can do this by adding a boolean dirty flag to each node. If it’s set to true, then this is a node that needs to be recalculated. Otherwise, it’s up-to-date. Let’s start with these flags all set to false — we have an up-to-date tree. Now, when we update the input node, we can iterate over all the children of that node, and follow a simple algorithm:

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Self-attention is required. The model must contain at least one self-attention layer. This is the defining feature of a transformer — without it, you have an MLP or RNN, not a transformer.

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