qtorch-posit 0.1.1

Low-Precision Arithmetic Simulation in Pytorch - Extension for Posit

Extended version of QPytorch

Author: minhhn2910@github, himeshi@github

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

Functionality:

Currently under development

The below is the original README file

QPyTorch

News:

• Updated to version 0.2.0:
  • Bug fixed: previously in our floating point quantization, numbers that are closer to 0 than the smallest representable positive number are rounded to the smallest rep positive number. Now we round to 0 or the smallest representable number based on which one is the nearest.
  • Different Behavior: To be consistent with PyTorch Issue #17443, we round to nearest even now.
  • We migrate to PyTorch 1.5.0. There are several changes in the C++ API of PyTorch. This new version is not backward-compatible with older PyTorch.
  • Note: if you are using CUDA 10.1, please install CUDA 10.1 Update 1 (or later version). There is a bug in the first version of CUDA 10.1 which leads to compilation errors.
  • Note: previous users, please remove the cache in the pytorch extension directory. For example, you can run this command rm -rf /tmp/torch_extensions/quant_cuda /tmp/torch_extensions/quant_cpu if you are using the default directory for pytorch extensions.

Overview

QPyTorch is a low-precision arithmetic simulation package in PyTorch. It is designed to support researches on low-precision machine learning, especially for researches in low-precision training. A more comprehensive write-up can be found here.

Notably, QPyTorch supports quantizing different numbers in the training process with customized low-precision formats. This eases the process of investigating different precision settings and developing new deep learning architectures. More concretely, QPyTorch implements fused kernels for quantization and integrates smoothly with existing PyTorch kernels (e.g. matrix multiplication, convolution).

Recent researches can be reimplemented easily through QPyTorch. We offer an example replication of WAGE in a downstream repo WAGE. We also provide a list of working examples under Examples.

Note: QPyTorch relies on PyTorch functions for the underlying computation, such as matrix multiplication. This means that the actual computation is done in single precision. Therefore, QPyTorch is not intended to be used to study the numerical behavior of different accumulation strategies.

Note: QPyTorch, as of now, have a different rounding mode with PyTorch. QPyTorch does round-away-from-zero while PyTorch does round-to-nearest-even. This will create a discrepancy between the PyTorch half-precision tensor and QPyTorch's simulation of half-precision numbers.

if you find this repo useful please cite

@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}
}

Installation

requirements:

• Python >= 3.6
• PyTorch >= 1.5.0
• GCC >= 4.9 on linux
• CUDA >= 10.1 on linux

Install other requirements by:

pip install -r requirements.txt

Install QPyTorch through pip:

pip install qtorch

For more details about compiler requirements, please refer to PyTorch extension tutorial.

Documentation

See our readthedocs page.

Tutorials

• An overview of QPyTorch's features
• CIFAR-10 Low-Precision Training Tutorial

Examples

• Low-Precision VGGs and ResNets using fixed point, block floating point on CIFAR and ImageNet. lp_train
• Reproduction of WAGE in QPyTorch. WAGE
• Implementation (simulation) of 8-bit Floating Point Training in QPyTorch. IBM8

Team

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