Metadata-Version: 2.1
Name: qtorch
Version: 0.2.0
Summary: Low-Precision Arithmetic Simulation in Pytorch
Home-page: UNKNOWN
Author: Tianyi Zhang, Zhiqiu Lin, Guandao Yang, Christopher De Sa
Author-email: tz58@cornell.edu
License: MIT
Project-URL: Documentation, https://qpytorch.readthedocs.io
Project-URL: Source, https://github.com/Tiiiger/QPyTorch/graphs/contributors
Description: # 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/)
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
