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Network quantization significantly reduces model inference complexity and hasbeen widely used in real-world deployments. However, most existing quantizationmethods have been developed mainly on Convolutional Neural Networks (CNNs), andsuffer severe degradation when applied to fully quantized vision transformers.In this work, we demonstrate that many of these difficulties arise because ofserious inter-channel variation in LayerNorm inputs, and present, Power-of-TwoFactor (PTF), a systematic method to reduce the performance degradation andinference complexity of fully quantized vision transformers. In addition,observing an extreme non-uniform distribution in attention maps, we proposeLog-Int-Softmax (LIS) to sustain that and simplify inference by using 4-bitquantization and the BitShift operator. Comprehensive experiments on varioustransformer-based architectures and benchmarks show that our Fully QuantizedVision Transformer (FQ-ViT) outperforms previous works while even using lowerbit-width on attention maps. For instance, we reach 84.89% top-1 accuracy withViT-L on ImageNet and 50.8 mAP with Cascade Mask R-CNN (Swin-S) on COCO. To ourknowledge, we are the first to achieve lossless accuracy degradation (~1%) onfully quantized vision transformers. The code is available athttps://github.com/megvii-research/FQ-ViT.

FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer