qtorch-plus 0.1.0

Low-Precision Arithmetic Simulation in Pytorch - Extension for Posit and customized number formats

QPytorch+: Extending Qpytorch for Posit format and more

Author: minhhn2910@github, himeshi@github

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Install

Install in developer mode:

git clone https://github.com/minhhn2910/QPyTorch.git
cd QPyTorch
pip install -e ./

Simple test if c-extension is working correctly :

python test.py

Important: if there are errors when running test.py, please export the environment variables indicating build directory and/or CUDA_HOME, otherwise we will have permission problem in multi-user-server.

export TORCH_EXTENSIONS_DIR=/[your-home-folder]/torch_extension
export CUDA_HOME=/[your cuda instalation directory e.g. /usr/local/cuda-10.2] 
python test.py

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Functionality:

• Support Posit Format with round to nearest mode.
• Scaling of value before & after conversion to/from posit is supported (Exponent bias when the scale is a power of 2).
  For example: value x -> x*scale -> Posit(x*scale) -> x
• Support Tanh approximation with Posit and correction of error:
  When x is in a posit format with es = 0 => Sigmoid(x) = (x XOR 0x8000) >> 2 => PositTanh(x) = 2 · Sigmoid(2x) − 1
• More number formats (Table lookup, log2 system ..., and new rounding modes will be supported on new versions).

Currently under development and update to support more number formats and schemes.

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Demo and tutorial:

• Approximate Tanh Function with Posit is presented at examples/tutorial/test_posit_func.ipynb
• Most functionalities can be tested by using the notebooks in posit tutorials: ./examples/tutorial/
• Notebook demo training Cifar10 with vanilla Posit 8 bit: examples/tutorial/CIFAR10_Posit_Training_Example.ipynb
• Demo of DCGAN Cifar10 training with Posit 8 bit: Google Colab Link
• Demo of DCGAN Lsun inference using Posit 6 bit and Approximate Tanh : Google Colab Link
• Demo of applying posit 6 bits & 8 bits to ALBERT for Question Answering Task: GoogleColab Demo

If you find this repo useful, please cite our paper(s) listed below. The below also explain the terms and usage of Posit's enhancements (exponent bias and tanh function).

@inproceedings{ho2021posit,
  title={Posit Arithmetic for the Training and Deployment of Generative Adversarial Networks},
  author={Ho, Nhut-Minh and Nguyen, Duy-Thanh and De Silva, Himeshi and Gustafson, John L and Wong, Weng-Fai and Chang, Ik Joon},
  booktitle={2021 Design, Automation \& Test in Europe Conference \& Exhibition (DATE)},
  pages={1350--1355},
  year={2021},
  organization={IEEE}
}

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The original Qpytorch package which supports floating point and fixed point:

The original README file is in REAME.original.md

Credit to the Qpytorch team and their original publication

@misc{zhang2019qpytorch,
    title={QPyTorch: A Low-Precision Arithmetic Simulation Framework},
    author={Tianyi Zhang and Zhiqiu Lin and Guandao Yang and Christopher De Sa},
    year={2019},
    eprint={1910.04540},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Qpytorch Team

• Tianyi Zhang
• Zhiqiu Lin
• Guandao Yang
• Christopher De Sa