Benchmark the fortran to pytorch coupling library

Recent results using MiMA showed a moderate slowdown when using our Fortran to PyTorch direct coupling library. This repository contains code to investigate that surprising result.

Requirements

You will need:

CMake >= 3.14
Python
a virtual environment with PyTorch and NumPy installed

Build instructions

Create a build directory and run cmake in the usual way:

mkdir build
cd build
cmake ..

You can specify cmake options like -DCMAKE_BUILD_TYPE=RelWithDebInfo or -DCMAKE_Fortran_COMPILER=ifort if you need.

Then, run make and the program(s) should build.

Profiling build type

There is now a custom build type for cmake you can set with -DCMAKE_BUILD_TYPE=Profile. This will add the necessary options for instrumented profiling, in the gprof style.

Running instructions

You'll need the Python virtual environment loaded. Run the program:

benchmarker_forpy <path-to-model-dir> <python-module-to-load> <N>

Where <path-to-model-dir> is the path to where the PyTorch model resides, <python-module-to-load> is the Python file to load. It should export initialize and compute_reshape_drag methods. See the Wavenet 2 model provided. <N> is the number of times to run the inference.

Profiling using `-DCMAKE_BUILD_TYPE=Profile`

Run cmake with the -DCMAKE_BUILD_TYPE=Profile option and make the code. Then, after you run it, a gmon.out file will be created in the current directory. To process this file you must do:

gprof <path-to-benchmarker_forpy> <path-to-gmon.out>

By default this gets you the default flat profile followed by the call graph. Check the other options to gprof.