DSG: Diverse Sample Generation — Pushing the Limit of Generative Data-Free Quantization

IEEE TPAMI 45(10):11689–11706 · 2023

Haotong Qin, Yifu Ding, Xiangguo Zhang, Jiakai Wang, Xianglong Liu, Jiwen Lu

Published paper | arXiv | Citation

Diverse Sample Generation (DSG) improves data-free quantization by diversifying synthetic calibration/training samples derived from a pretrained model. It uses Slack Distribution Alignment (SDA), Layerwise Sample Enhancement (LSE), and Sample Correlation Inhibition (SCI) to reduce distribution- and sample-level homogenization.

ImageNet top-1 accuracy (%) from Table 3 of the journal manuscript. W/A denotes weight/activation precision. DSG¹ and DSG² use the paper's different PTQ calibration settings; the original repository results below correspond to DSG¹.

These are data-free PTQ accuracy results. They do not measure training cost, inference speed, or runtime memory. The QAT results require optimization of the quantized model and are separate from PTQ.

• Synthetic-data diversity is important for quantization, and exact BN-statistics matching can cause homogenization (Sections 3.2–3.3).
• SDA relaxes the distribution constraint; LSE diversifies the influence of BN layers; SCI discourages correlated samples (Section 3.4; Table 1).
• DSG improves the evaluated data-free PTQ settings across several ImageNet architectures, particularly at W4A4 (Table 3).
• The scheme also benefits the evaluated data-free QAT pipelines (Table 4), which should be compared separately from PTQ.
• Results depend on calibration and quantization choices; the paper studies additional calibration, DFQ, and AdaRound settings (Tables 5–7).

Scope: “data-free” means real training data are not accessed for quantization. The method still needs a pretrained full-precision network and its BN statistics, synthetic-data generation, and real labeled validation data to measure accuracy. This repository accompanies the TPAMI journal extension; the CVPR 2021 precursor is a distinct publication with a different title/author order.

python==3.7
pytorch==1.2.0
torchvision==0.4.0
# other dependencies in requirements.txt

(1) Clone this repo:

git clone https://github.com/htqin/DSG.git

(2) Choose one quantization scheme:

cd DSG/quantization_aware_training  # if choosing QAT
cd DSG/post_training_quantization  # if choosing PTQ

(3) Install pytorch and other dependencies:

pip install -r requirements.txt

For Post-Training Quantization (PTQ):

Set the data_path in run script. For example:

data_path = "/path/to/imagenet/val"

For Quantization-Aware Training (QAT):

Set the dataPath in "imagenet_resnet.hocon" if using ImageNet dataset. For example:

dataPath = "/path/to/imagenet"

For PTQ:

python3 uniform_test.py [--dataset] [--data_path] [--model] [--batch_size] [--test_batch_size] [--w_bit] [--a_bit]

optional arguments:
--dataset                   name of dataset (default: imagenet)
--data_path									path of dataset (default: /path/to/imagenet/val)
--model                     model to be quantized (default: resnet18)
--batch_size                batch size of distilled data (default: 64)
--test_batch_size           batch size of test data (default: 512)
--w_bit											bit-width of weight (default: 4)
--a_bit											bit-width of activation (default: 4)

For QAT:

python3 main.py [--conf_path] [--model] [--id]

optional arguments:
--conf_path 					 			configure file path (default: imagenet_resnet.hocon)
--model				  						model to be quantized (default :resnet18)
--id 												experiment id (default: 0)

The following are the results of our DSG. We also provide models quantized by DSG-PTQ for the lowest bit-width for all network architectures and their results for easy evaluation, where parenthesized values are subsequent repository-reported results, separate from the manuscript results. These records have not been rerun in this documentation update.

* DSG-PTQ denotes DSG-PTQ^1 in manuscript

The original code is borrowed from ZeroQ and GDFQ.

Please cite the published paper below. Open paper versions are linked at the top of this README.

@article{qin2023diverse,
  title = {Diverse Sample Generation: Pushing the Limit of Generative Data-Free Quantization},
  author = {Haotong Qin and Yifu Ding and Xiangguo Zhang and Jiakai Wang and Xianglong Liu and Jiwen Lu},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2023},
  volume = {45},
  number = {10},
  pages = {11689--11706},
  doi = {10.1109/TPAMI.2023.3272925},
  url = {https://doi.org/10.1109/TPAMI.2023.3272925}
}