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While vision transformers have achieved impressive results, effectively andefficiently accelerating these models can further boost performances. In thiswork, we propose a dense/sparse training framework to obtain a unified model,enabling weight sharing across various token densities. Thus one model offers arange of accuracy and throughput tradeoffs for different applications. Besides,we introduce adaptive token pruning to optimize the patch token sparsity basedon the input image. In addition, we investigate knowledge distillation toenhance token selection capability in early transformer modules. Sparseadaptive image Transformer (SaiT) offers varying levels of model accelerationby merely changing the token sparsity on the fly. Specifically, SaiT reducesthe computation complexity (FLOPs) by 39% - 43% and increases the throughput by67% - 91% with less than 0.5% accuracy loss for various vision transformermodels. Meanwhile, the same model also provides the zero accuracy drop optionby skipping the sparsification step. SaiT achieves better accuracy andcomputation tradeoffs than state-of-the-art transformer and convolutionalmodels.

SaiT: Sparse Vision Transformers through Adaptive Token Pruning