Patch Similarity Aware Data-Free Quantization for Vision Transformers

This repository contains the official PyTorch implementation for the ECCV 2022 paper "Patch Similarity Aware Data-Free Quantization for Vision Transformers". To the best of our knowledge, this is the first work on data-free quantization for vision transformers. Below are instructions for reproducing the results.

Installation

• To install PSAQ-ViT and develop locally:

git clone https://github.com/zkkli/PSAQ-ViT.git
cd PSAQ-ViT

Quantization

• You can quantize and evaluate a single model using the following command:

python test_quant.py [--model] [--dataset] [--w_bit] [--a_bit] [--mode]

optional arguments:
--model: Model architecture, the choises can be: 
         deit_tiny, deit_small, deit_base, swin_tiny, and swin_small.
--dataset: Path to ImageNet dataset.
--w_bit: Bit-precision of weights, default=8.
--a_bit: Bit-precision of activation, default=8.
--mode: Mode of calibration data,
        0: Generated fake data (PSAQ-ViT)
        1: Gaussian noise
        2: Real data

• Example: Quantize DeiT-B with generated fake data (PSAQ-ViT).

python test_quant.py --model deit_base --dataset <YOUR_DATA_DIR> --mode 0

• Example: Quantize DeiT-B with Gaussian noise.

python test_quant.py --model deit_base --dataset <YOUR_DATA_DIR> --mode 1

• Example: Quantize DeiT-B with Real data.

python test_quant.py --model deit_base --dataset <YOUR_DATA_DIR> --mode 2

Results

Below are the experimental results of our proposed PSAQ-ViT that you should get on ImageNet dataset using an RTX 3090 GPU.

Model	Prec.	Top-1(%)	Prec.	Top-1(%)
DeiT-T (72.21)	W4/A8	65.57	W8/A8	71.56
DeiT-S (79.85)	W4/A8	73.23	W8/A8	76.92
DeiT-B (81.85)	W4/A8	77.05	W8/A8	79.10
Swin-T (81.35)	W4/A8	71.79	W8/A8	75.35
Swin-S (83.20)	W4/A8	75.14	W8/A8	76.64

Citation

We appreciate it if you would please cite the following paper if you found the implementation useful for your work:

@inproceedings{li2022psaqvit,
  title={Patch Similarity Aware Data-Free Quantization for Vision Transformers},
  author={Li, Zhikai and Ma, Liping and Chen, Mengjuan and Xiao, Junrui and Gu, Qingyi},
  booktitle={European Conference on Computer Vision},
  pages={154--170},
  year={2022}
}