Successive Halving Top-k Operator

Understand

We propose a differentiable successive halving method of relaxing the top-k operator, rendering gradient-based optimization possible.

• The need to perform softmax iteratively on the entire vector of scores is avoided by using a tournament-style selection.
• As a result, a much better approximation of top-k with lower computational cost is achieved compared to the previous approach.