ORQ-ViT: Outlier resilient Post Training Quantization for vision transformers via outlier decomposition

Post-training quantization (PTQ) is critical for deploying Vision Transformers (ViTs) on resource-constrained devices. However, outliers clustered in activation channels tend to dominate the quantization range and induce significant accuracy degradation. To address the outlier challenge, this paper proposes an outlier resilient PTQ method through outlier decomposition, namely ORQ-ViT. Its core idea is to decompose outliers clustered in outlier channels into isolated outliers, so that they can be easily excluded, thus alleviating their adverse impact. Specifically, we decompose activations along the patch token dimension. Since there are very few outlier channels, decomposed rows after outlier decomposition usually cover several isolated outliers, which can be easily identified and filtered. We further design an adaptive quantization range determination strategy during quantization parameters initialization to prevent outliers from serving as boundary values of the quantization range. ORQ-ViT can improve quantization levels utilization to generate activations with higher quantization resolution, thereby achieving higher accuracy. Additionally, ORQ-ViT supports pure integer matrix multiplications to ensure the inference efficiency of quantized ViTs on edge hardware. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy across various ViT variants under multiple low bit-width scenarios. For image classification, the top-1 accuracy of ORQ-ViT outperforms that of the SOTA methods by an average of 2.26% at W4A4. Even for object detection and instance segmentation, ORQ-ViT also delivers highly competitive results. We also evaluate the inference efficiency of pure integer matrix multiplications and the results show that our method can achieve up to 2.1× speedup.