Learning 4 CasADi Framework

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}
}

Installation

Requirements

Independently if you install from source or via pip you will need to meet the following requirements:

• Working PyTorch installation in your python environment.
  python -c "import torch; print(torch.__version__)"

Pip Install

• Ensure all build dependencies are installed

setuptools>=68.1
scikit-build>=0.17
cmake>=3.27
ninja>=1.11
torch>=2.0

• Run
  pip install l4casadi --no-build-isolation

From Source

• Clone the repository
  git clone https://github.com/Tim-Salzmann/l4casadi.git

• All build dependencies installed via
  pip install -r requirements_build.txt

• Build from source
  pip install . --no-build-isolation

The --no-build-isolation flag is required for L4CasADi to find and link against the installed PyTorch.

GPU (CUDA) Installation

Install L4CasADi via CUDACXX=<PATH_TO_nvcc> pip install l4casadi --no-build-isolation or CUDACXX=<PATH_TO_nvcc> pip install l4casadi --no-build-isolation to build from source.

Mac M1 - ARM

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.

Example

l4casadi/examples/readme.py

Lines 28 to 40 in 23e0738

	l4c_model = l4c.L4CasADi(pyTorch_model, model_expects_batch_dim=True, device='cpu') # device='cuda' for GPU
	x_sym = cs.MX.sym('x', 2, 1)
	y_sym = l4c_model(x_sym)
	f = cs.Function('y', [x_sym], [y_sym])
	df = cs.Function('dy', [x_sym], [cs.jacobian(y_sym, x_sym)])
	ddf = cs.Function('ddy', [x_sym], [cs.hessian(y_sym, x_sym)[0]])
	x = cs.DM([[0.], [2.]])
	print(l4c_model(x))
	print(f(x))
	print(df(x))
	print(ddf(x))

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.

Batch Dimension

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].

Integration with Acados

To use this framework with Acados:

• Follow the installation instructions.
• Install the Python Interface.
• Ensure that LD_LIBRARY_PATH is set correctly (DYLD_LIBRARY_PATHon MacOS).
• Ensure that ACADOS_SOURCE_DIR is 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

l4casadi/examples/acados.py

Lines 156 to 160 in 421de6e

	ocp.solver_options.model_external_shared_lib_dir = self.external_shared_lib_dir
	if COST == 'LINEAR_LS':
	ocp.solver_options.model_external_shared_lib_name = self.external_shared_lib_name
	else:
	ocp.solver_options.model_external_shared_lib_name = self.external_shared_lib_name + ' -l' + l4c_y_expr.name

Warm Up

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.

Roadmap

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.