# ML/PYTORCH

The overall goal of this machine learning tutorial is to accelerate computationally expensive point-wise kernels/routines within an AMReX simulation. This tutorial demonstrates how to interface a pre-trained PyTorch machine learning model to an AMReX simulation by querying inputs from and supplying outputs to an AMReX MultiFab. PyTorch is a commonly used machine learning package with a C++ API library called LibTorch. Located in the directory amrex-tutorials/ExampleCodes/ML/PYTORCH, this example uses a machine learning model to solve a radioactive beta decay problem. Here we use a 1-input, 2-output model to illustrate the interface between the PyTorch model and a MultiFab.

## Beta Decay Reaction

In this example, the machine learning model is a regression model pre-trained to solve a two-component ODE system describing beta decay.

$\frac{\partial X_0}{\partial t} = -X_0$
$\frac{\partial X_1}{\partial t} = X_0$
$X_0(0) = 1; ~~~ X_1(0) = 0$

In the context of the pytorch model, the input is a time step dt and output is the two-component solution of the ODE system at time t = dt.

## Pre-trained Model

The TorchScript model that is included in this example is located at ML/PYTORCH/Exec/model.pt. If you wish to change the model, edit the model_file parameter in inputs.

## Running an AMReX application with a PyTorch model

To begin, we initialize a MultiFab full of data representing different dt values, then copy this data into a PyTorch tensor, then call the pre-trained model to compute the outputs, and finally load the result back into a MultiFab. The model can be evaluated on the CPU or GPU.

Below is a step-by-step guide to successfully run an AMReX program that uses a PyTorch model. It will require the model to have been saved as a TorchScript. In this example the TorchScript file is model.pt. For more information on TorchScript, please see their intro tutorial.

1. Before compiling, either a CPU or CUDA version of LibTorch (PyTorch C++ library) must be downloaded into ML/PYTORCH/. To download the CPU-only version of libtorch and rename it to libtorch_cpu:

wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.0.1%2Bcpu.zip
unzip libtorch-cxx11-abi-shared-with-deps-2.0.1+cpu.zip
mv libtorch libtorch_cpu


Similarly, the CUDA 11.8 version of libtorch can be downloaded and renamed to libtorch_cuda:

wget https://download.pytorch.org/libtorch/cu118/libtorch-cxx11-abi-shared-with-deps-2.0.1%2Bcu118.zip
unzip libtorch-cxx11-abi-shared-with-deps-2.0.1+cu118.zip
mv libtorch libtorch_cuda


2. Go to ML/PYTORCH/Exec to compile the executable. Run make and optionally USE_CUDA=TRUE and it should result in an executable named, e.g., main2d.gnu.MPI.CUDA.ex
3. Then you can run the example, e.g., ./main2d.gnu.MPI.CUDA.ex inputs or mpiexec -n 4 ./main2d.gnu.MPI.ex inputs. There will be two plotfiles, plt_inputs (containing dt) and plt_outputs (containing X_0 and X_1 at the final time).