Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective (CVPR 2023) (Link)

Overview

Prerequisites

• Linux
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Requirements

• MQBench
• pyyaml
• easydict

Data preparation

• ImageNet

Add the imagenet path to the "data.path" in the config file.

Run MRECG

• Run the following command to quantize the model using the MRECG algorithm, all yaml files can be found in ./config. You can change the bit width, batchsize, pre-trained model path and other quantization parameters in the yaml file.

  python ptq_main.py --config configs/qdrop/mbv2_2_4.yaml

Results

Method\Model	W/A	Res18	Res50	MBV2×1.0	MBV2×0.75	MBV2×0.5	MBV2×0.35	Reg600M
Full Prec.	32/32	71.01	76.63	72.20	69.95	64.60	60.08	73.52
Pretrained	-	link	link	link	link	link	link	link
Ours+BRECQ	4/4	69.06	74.84	68.56	64.55	55.26	50.67	-
Ours+BRECQ	2/4	65.61	70.04	58.49	52.50	41.16	35.46	-
Ours+BRECQ	3/3	65.64	70.68	57.14	50.21	35.11	30.26	-
Ours+BRECQ	2/2	52.02	43.72	13.84	9.46	3.43	3.22	-
Ours+QDROP	4/4	69.46	75.35	68.84	64.39	55.64	50.94	71.22
Ours+QDROP	2/4	66.18	70.53	57.85	53.71	40.09	35.85	65.16
Ours+QDROP	3/3	66.30	71.92	58.40	51.78	38.43	32.96	66.08
Ours+QDROP	2/2	54.46	56.82	14.44	11.40	4.18	3.09	43.67

Due to the presence of random numbers in the experiment, the actual model accuracy may be slightly high or low.

Acknowledgements

Our code relies on the MQBench package.

Reference

@article{ma2023solving,
  title={Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective},
  author={Yuexiao Ma and Huixia Li and Xiawu Zheng and Xuefeng Xiao and Rui Wang and Shilei Wen and Xin Pan and Fei Chao and Rongrong Ji},
  journal={arXiv preprint arXiv:2303.11906},
  year={2023}
}