Shared Learnable Bias per Offset

In a self-attention mechanism that uses relative positioning, consider a sequence of tokens where the model is calculating the attention score. If the current query token is at index 8 and the key token being attended to is at index 5, what is the calculated offset that represents their relative position?

In a sequence of tokens, the relative position offset between a query at index i and a key at index j is calculated as i - j. If the query's position i is held constant while the key's position j increases (i.e., the key token appears later in the sequence), how does the calculated offset change?