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Operator to X - optox

Goal

Write operators only once and use it everywhere.

Concept

Write an operator in C++/CUDA and generate wrappers to different languages such as Python and machine learning libraries such as Tensorflow or Pytorch.

optox provides a tensor interface to ease data transfer between host tensors optox::HTensor and device tensors optox::DTensor of any floating type and number of dimensions. Using this interface, an operator is only written once in C++/CUDA and wrappers for Python, Tensorflow 2.x and Pytorch expose the functionality to a higher level application (e.g. iterative reconstruction, custom deep learning reconstruction, ...).

Overview

The source files are organized as follows:

.
+-- src             : `optox` library source files
|   +-- tensor      : header only implementation of `HTensor` and `DTensor`
|   +-- operators   : actual implementation of operator functionality
+-- python          : python wrappers 
+-- pytorch         : pytorch wrappers
+-- tensorflow      : tensorflow wrappers

Install instructions

First setup the following environment variables:

  • COMPUTE_CAPABILITY with the compute capability of your CUDA-enabled GPU see here
  • CUDA_ROOT_DIR to point to the NVidia CUDA toolkit (typically /usr/local/cuda)
  • CUDA_SDK_ROOT_DIR to point to the NVidia CUDA examples (typically /usr/local/cuda/samples)

Note that the CUDA version used to build the optox library should match the version required by Tensorflow and/or Pytorch. Thus, we recommend building both deep learning frameworks from source.

Install dependencies using anaconda:

  • conda install pybind11 for Python wrappers

[Update July 2021] We provide an anaconda environment with Tensorflow 2.4, Pytorch 1.9, Cuda 11.1. The environment optox can be created via

conda env create -f environment.yml

To build the basic optox library perform the following steps:

mkdir build
cd build
cmake .. 
make install

CUDA sync free build

Use CMAKE_BUILD_TYPE=Release to avoid the device synchronization after each CUDA call. Then, no CUDA errors are generated but runtime is strongly reduced.

Python wrappers

To build the Python wrappers optox requires pybind11 which can be installed in an anaconda environment by conda install pybind11. To also build Python wrappers substitute the cmake command by:

cmake .. -DWITH_PYTHON=ON

Pytorch wrappers

To build it, the pytorch package must be installed.

cmake .. -DWITH_PYTORCH=ON

Tensorflow wrappers

To build it, the tensorflow package must be installed.

cmake .. -DWITH_TENSORFLOW=ON

Note that multiple combinations are supported.

Testing

Python

To perform an adjointness test of the nabla operator using the Python wrappers execute

python -m unittest optopy.nabla

If successful the output should be

(optox) ∂ python -m unittest optopy.nabla 
dtype: <class 'numpy.float64'> dim: 2 diff: 6.661338147750939e-16
.dtype: <class 'numpy.float64'> dim: 3 diff: 2.842170943040401e-14
.dtype: <class 'numpy.float32'> dim: 2 diff: 2.86102294921875e-06
.dtype: <class 'numpy.float32'> dim: 3 diff: 7.62939453125e-06
.
----------------------------------------------------------------------
Ran 4 tests in 1.099s

OK

Pytorch

To perform a gradient test of the activations operators using the Pytorch wrappers execute

python -m unittest optoth.activations.act

If successful the output should be

(optox) ∂ python -m unittest optoth.activations.act 
grad_x: -3616.3090656 num_grad_x -3616.3090955 success: True
grad_w: 7232.6181312 num_grad_w 7232.6181312 success: True
.grad_x: 535.2185935 num_grad_x 535.2185935 success: True
grad_w: 2236.8791233 num_grad_w 2236.8791233 success: True
.grad_x: -215.0009414 num_grad_x -215.0009432 success: True
grad_w: 430.0018828 num_grad_w 430.0018828 success: True
.
----------------------------------------------------------------------
Ran 3 tests in 2.263s

OK

Tensorflow

To perform an adjointness test of the nabla operators using the Tensorflow wrappers execute

python -m unittest optotf.nabla

If successful the output should be

(optox) ∂ python -m unittest optotf.nabla
...
dtype: <dtype: 'float64'> dim: 2 diff: 1.0658141036401503e-14
.dtype: <dtype: 'float32'> dim: 2 diff: 0.0
.
----------------------------------------------------------------------
Ran 2 tests in 1.490s


OK

Keras support

The keras layers can be found in optotf.keras.xxx.

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