qtorch 0.2.0
Low-Precision Arithmetic Simulation in Pytorch
License: MIT
Requires Python: >=3.6

# QPyTorch
[![Downloads](https://pepy.tech/badge/qtorch)](https://pepy.tech/project/qtorch) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

#### 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 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](https://github.com/pytorch/pytorch/pull/17443),
  we round the 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 error.
  - *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_cuda` if 
  you are using the default directory for pytorch extensions.


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. 

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](https://arxiv.org/abs/1802.04680) in a downstream
repo [WAGE](https://github.com/Tiiiger/QPyTorch/blob/master/examples/WAGE). We also provide a list
of working examples under [Examples](#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.

## Installation

requirements:

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

Install other requirements by:
```bash
pip install -r requirements.txt
```

Install QPyTorch through pip:
```bash
pip install qtorch
```

For more details about compiler requirements, 
please refer to [PyTorch extension tutorial](https://pytorch.org/tutorials/advanced/cpp_extension.html).

## Documentation
See our [readthedocs](https://qpytorch.readthedocs.io/en/latest/) page.

## Tutorials
- [An overview of QPyTorch's features](https://github.com/Tiiiger/QPyTorch/blob/master/examples/tutorial/Functionality_Overview.ipynb)
- [CIFAR-10 Low-Precision Training Tutorial](https://github.com/Tiiiger/QPyTorch/blob/master/examples/tutorial/CIFAR10_Low_Precision_Training_Example.ipynb)

## Examples
- Low-Precision VGGs and ResNets using fixed point, block floating point on CIFAR and ImageNet. [lp_train](https://github.com/Tiiiger/QPyTorch/blob/master/examples/lp_train)
- Reproduction of WAGE in QPyTorch. [WAGE](https://github.com/Tiiiger/QPyTorch/blob/master/examples/WAGE)
- Implementation (simulation) of 8-bit Floating Point Training in QPyTorch. [IBM8](https://github.com/Tiiiger/QPyTorch/blob/master/examples/IBM8)

## Team
* [Tianyi Zhang](https://scholar.google.com/citations?user=OI0HSa0AAAAJ&hl=en)
* Zhiqiu Lin
* [Guandao Yang](http://www.guandaoyang.com/)
* [Christopher De Sa](http://www.cs.cornell.edu/~cdesa/)