Competitive Collaborative Quantization (CCQ)

This repo is the PyTorch implementation of our DAC'20 paper on Learning to Quantize Deep Neural Networks: A Competitive-Collaborative Approach

Competitive Collaborative Quantization (CCQ) uses online learning to learn bit-precision for different layers within a neural network using a competitive-collaborative approach during training delivering mixed precision interger inference model. The implementation is essentially a wrapper on IntelLabs distiller. For requirements please refer to the reuirements.txt file. To install all the dependencies:

$ pip install -r requirements.txt

To invoke CCQ

$ cd examples/classifier_compression/
$ python ccq.py --model resnet18 --qsteps 10 --gpus 0 --name resq

1. This implementation is for resnet18
2. --qsteps-> determines how many quantization steps to perform
3. --name -> provides a name of to the run, The name argument is important. Ideally, inside the examples/classifier_compression/base/ should contatin the pretrained or 8-bit uniformly quantized model and a csv file containing the PACT clipping values per layer.

    ----|->base|
               |->resq0_8b_uniform|
                                  |->resq0_checkpoint.pth.tar
                                  |->resq0_8b_uniform.log
               |->pact_track.csv
    ----|->yaml_dir|
                   |base_q_schedule.yaml 

4. The pact_track.csv contains the learned clipping values of each layers during pretraining.
5. The --name argument should match with the pretrained model prefix resq. This is important
6. The base_q_schedule.yaml contains the per-layer bit configuration. The provided file is for resnet18. For other networks, similar file should be used. In this particular case, a pretrained int8 uniformly quantized model was used which reflects on the file as well.

Citation

If you found the repository helpful, please cite using the following:

@INPROCEEDINGS{9218576,
  author={Khan, Md Fahim Faysal and Kamani, Mohammad Mahdi and Mahdavi, Mehrdad and Narayanan, Vijaykrishnan},
  booktitle={2020 57th ACM/IEEE Design Automation Conference (DAC)}, 
  title={Learning to Quantize Deep Neural Networks: A Competitive-Collaborative Approach}, 
  year={2020},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/DAC18072.2020.9218576}}

Acknowledgement

This work was supported in part by C-BRIC (Center for Braininspired Computing), one of six centers in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA.