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Example 2 - SimpleNet

This example provides a simple but complete demonstration of how to use the library.

The aim is to demonstrate the most basic features of coupling before worrying about multi-dimension tensors, row- vs. column-major, multiple inputs etc. that will be covered in later examples.

Description

A Python file simplenet.py is provided that defines a very simple PyTorch 'net' that takes an input vector of length 5 and applies a single Linear layer to multiply it by 2. Running this file as a script will write the model out in PyTorch's .pt file format.

The pt2ts tool is used to convert the model file to the TorchScript format.

A series of files simplenet_infer_<LANG> then bind from other languages to run the TorchScript model in inference mode.

Dependencies

To run this example requires:

  • CMake or Make
  • Fortran compiler
  • FTorch (installed as described in main package)
  • Python 3

Running

To run this example install FTorch as described in the main documentation. Then from this directory create a virtual environment and install the necessary Python modules:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

You can check that everything is working by running simplenet.py:

python3 simplenet.py

This defines the net and runs it with an input tensor [0.0, 1.0, 2.0, 3.0, 4.0] to produce the result:

Model output: tensor([[0., 2., 4., 6., 8.]])

You should find that a PyTorch model file saved_simplenet_model_cpu.pt is created.

To convert the SimpleNet model to TorchScript run the pt2ts script:

pt2ts simplenet.py SimpleNet saved_simplenet_model_cpu.pt

Without any further arguments, this will overwrite the saved_simplenet_model_cpu.pt model file so you will receive a warning.

You can check that everything is working by running the simplenet_infer_python.py script:

python3 simplenet_infer_python.py

This reads the model in from the TorchScript file and runs it with an input tensor [0.0, 1.0, 2.0, 3.0, 4.0] to produce the following output:

Model parameters:
Parameter: _fwd_seq.0.weight
tensor([[2., 0., 0., 0., 0.],
        [0., 2., 0., 0., 0.],
        [0., 0., 2., 0., 0.],
        [0., 0., 0., 2., 0.],
        [0., 0., 0., 0., 2.]])
Model output: tensor([[0., 2., 4., 6., 8.]])

First, the model parameters are printed (the weights of the linear layer), followed by the output of the model.

At this point we no longer require Python, so can deactivate the virtual environment:

deactivate

To call the saved SimpleNet model from Fortran we need to compile the simplenet_infer files. This can be done using the included CMakeLists.txt as follows:

mkdir build
cd build
cmake .. -DCMAKE_PREFIX_PATH=<path/to/your/installation/of/library/> -DCMAKE_BUILD_TYPE=Release
cmake --build .

(Note that the Fortran compiler can be chosen explicitly with the -DCMAKE_Fortran_COMPILER flag, and should match the compiler that was used to locally build FTorch.)

To run the compiled code calling the saved SimpleNet TorchScript from Fortran run the executable with an argument of the saved model file:

./simplenet_infer_fortran ../saved_simplenet_model_cpu.pt

This runs the model with the array [0.0, 1.0, 2.0, 3.0, 4.0] should produce the following output:

 Model parameters:
_fwd_seq.0.weight:
 2  0  0  0  0
 0  2  0  0  0
 0  0  2  0  0
 0  0  0  2  0
 0  0  0  0  2
[ CPUFloatType{5,5} ]
 Model output:
   0.00000000       2.00000000       4.00000000       6.00000000       8.00000000

Again, the model parameters are printed first (the weights of the linear layer), followed by the output of the model, which is consistent with the earlier Python results. The CPUFloatType{5,5} indicates that the weight tensor is a 5x5 matrix of 32-bit floats stored on the CPU.

Alternatively we can use make, instead of CMake, with the included Makefile. However, to do this you will need to modify Makefile to link to and include your installation of FTorch as described in the main documentation. Also check that the compiler is the same as the one you built the Library with.

make
./simplenet_infer_fortran saved_simplenet_model_cpu.pt

You will also likely need to add the location of the dynamic library files (.so or .dylib files) that we will link against at runtime to your LD_LIBRARY_PATH:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:</path/to/library/installation>/lib

or DYLD_LIBRARY_PATH on Mac:

export DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:</path/to/library/installation>/lib

Further options

To explore the functionalities of this model:

  • Try saving the model through tracing rather than scripting by modifying pt2ts.py
  • Consider adapting the model definition in simplenet.py to function differently and then adapt the rest of the code to successfully couple your new model.