L4CasADi enables using PyTorch models and functions in a CasADi graph while supporting CasADis code generation capabilities. The only requirement on the PyTorch model is to be traceable and differentiable.
If you use this framework please cite our paper
@article{salzmann2023neural,
title={Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms},
author={Salzmann, Tim and Kaufmann, Elia and Arrizabalaga, Jon and Pavone, Marco and Scaramuzza, Davide and Ryll, Markus},
journal={IEEE Robotics and Automation Letters},
doi={10.1109/LRA.2023.3246839},
year={2023}
}
Tested on Ubuntu 20.04 and MacOS.
Python 3.9 or higher. gcc, make and cmake.
CPU only installation of PyTorch==2.0.0. CPU versions >2.0.0 might work too.
Install L4CasADi via pip install .
CUDA installation of PyTorch==2.0.0.
Install L4CasADi via USE_CUDA=TRUE CUDACXX=<PATH_TO_nvcc> pip install .
On MacOS with M1 chip you will have to compile tera_renderer from source
and place the binary in l4casadi/template_generation/bin. For other platforms it will be downloaded automatically.
Please note that only casadi.MX symbolic variables are supported as input.
Multi-input multi-output functions can be realized by concatenating the symbolic inputs when passing to the model and splitting them inside the PyTorch function.
To use GPU (CUDA) simply pass device="cuda" to the L4CasADi constructor.
An example of solving a simple NLP with torch system model can be found in examples/simple_nlp.py.
If your PyTorch model expects a batch dimension as first dimension (which most models do) you should pass
model_expects_batch_dim=True to the L4CasADi constructor. The MX input to the L4CasADi component is then expected
to be a vector of shape [X, 1]. L4CasADi will add a batch dimension of 1 automatically such that the input to the
underlying PyTorch model is of shape [1, X].
To use this framework with Acados:
- Follow the installation instructions.
- Install the Python Interface.
- Ensure that
LD_LIBRARY_PATHis set correctly (DYLD_LIBRARY_PATHon MacOS). - Ensure that
ACADOS_SOURCE_DIRis set correctly.
An example of how a PyTorch model can be used as dynamics model in the Acados framework for Model Predictive Control can be found in examples/acados.py
To use L4CasADi with Acados you will have to set model_external_shared_lib_dir and model_external_shared_lib_name
in the AcadosOcp.solver_options accordingly.
ocp.solver_options.model_external_shared_lib_dir = l4c_model.shared_lib_dir
ocp.solver_options.model_external_shared_lib_name = l4c_model.name
Note that PyTorch builds the graph on first execution. Thus, the first call(s) to the CasADi function will be slow. You can warm up to the execution graph by calling the generated CasADi function one or multiple times before using it.
Further development of this framework will be prioritized by popular demand. If a feature is important to your work please get in contact or create a pull request.
Possible upcoming features include:
- Explicit multi input, multi output functions.