The computational cost of transformer models makes them inefficient in low-latency or low-power applications. While techniques such as quantization or linear attention can reduce the computational load, they may incur a reduction in accuracy. In addition, globally reducing the cost for all inputs may be sub-optimal.
We observe that for each layer, the full width of the layer may be needed only for a small subset of tokens inside a batch and that the “effective” width needed to process a token can vary from layer to layer.
Motivated by this observation, we introduce theAdaptive Computation Module(ACM) , a generic module that dynamically adapts its computational load to match the estimated difficulty of the input on a per-token basis.
An ACM consists of a sequence oflearnersthat progressively refine the output of their preceding counterparts. An additional gating mechanism determines the optimal number of learners to execute for each token.
We also describe a distillation technique to replace any pre-trained model with an “ACMized” variant. The distillation phase is designed to be highly parallelizable across layers while being simple to plug-and-play into existing networks.
Our evaluation of transformer models in computer vision and speech recognition demonstrates that substituting layers with ACMs significantly reduces inference costs without degrading the downstream accuracy for a wide interval of user-defined budgets.