BRECQ

Pytorch implementation of BRECQ, ICLR 2021

@article{li2021brecq,
  title={BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction},
  author={Li, Yuhang and Gong, Ruihao and Tan, Xu and Yang, Yang and Hu, Peng and Zhang, Qi and Yu, Fengwei and Wang, Wei and Gu, Shi},
  journal={arXiv preprint arXiv:2102.05426},
  year={2021}
}

Update (Jul 30): Add Multi-GPU Reconstruction

We release the code for multi-GPU reconstruction.

Note that this cannot be simply performed with torch.nn.DataParallel or DDP. To synchorize the gradients, activation scale, etc., we have to manully call torch.distributed.allreduce.

The first step is to initialize the distributed envrionment, and then use distributed sampler for data loading.

Please use main_imagenet_dist for multi-GPU reconstruction. With this, you can reconstruct larger models and use more data samples!

python -m main_imagenet_dist **KWARGS_FOR_RECON

Pretrained models

We provide all the pretrained models and they can be accessed via torch.hub

For example: use res18 = torch.hub.load('yhhhli/BRECQ', model='resnet18', pretrained=True) to get the pretrained ResNet-18 model.

If you encounter URLError when downloading the pretrained network, it's probably a network failure. An alternative way is to use wget to manually download the file, then move it to ~/.cache/torch/checkpoints, where the load_state_dict_from_url function will check before downloading it.

For example:

wget https://github.com/yhhhli/BRECQ/releases/download/v1.0/resnet50_imagenet.pth.tar 
mv resnet50_imagenet.pth.tar ~/.cache/torch/checkpoints

Usage

python main_imagenet.py --data_path PATN/TO/DATA --arch resnet18 --n_bits_w 2 --channel_wise --n_bits_a 4 --act_quant --test_before_calibration

You can get the following output:

Quantized accuracy before brecq: 0.13599999248981476
Weight quantization accuracy: 66.32799530029297
Full quantization (W2A4) accuracy: 65.21199798583984

MobileNetV2 Quantization:

python main_imagenet.py --data_path PATN/TO/DATA --arch mobilenetv2 --n_bits_w 2 --channel_wise --weight 0.1

Results: Weight quantization accuracy: 59.48799896240234