“Mixture-Of-Depths: Dynamically Allocating Compute in Transformer-Based Language Models”, 2024-04-02 ():
Transformer-based language models spread FLOPs uniformly across input sequences. In this work, we demonstrate that transformers can instead learn to dynamically allocate FLOPs (or compute) to specific positions in a sequence, optimizing the allocation along the sequence for different layers across the model depth.
Our method enforces a total compute budget by capping the number of tokens (k) that can participate in the self-attention and MLP computations at a given layer. The tokens to be processed are determined by the network using a top-k routing mechanism. Since k is defined a priori, this simple procedure uses a static computation graph with known tensor sizes, unlike other conditional computation techniques. Nevertheless, since the identities of the k tokens are fluid, this method can expend FLOPs non-uniformly across the time and model depth dimensions. Thus, compute expenditure is entirely predictable in sum total, but dynamic and context-sensitive at the token-level.
Not only do models trained in this way learn to dynamically allocate compute, they do so efficiently. These models match baseline performance for equivalent FLOPs and wall-clock times to train, but require a fraction of the FLOPs per forward pass, and can be upwards of 50% faster to step during post-training sampling.