zonasw / torchdiffeq

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

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PyTorch Implementation of Differentiable ODE Solvers

This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpropagation through all solvers is supported using the adjoint method. For usage of ODE solvers in deep learning applications, see [1].

As the solvers are implemented in PyTorch, algorithms in this repository are fully supported to run on the GPU.

Discrete-depth network Continuous-depth network


git clone https://github.com/rtqichen/torchdiffeq.git
cd torchdiffeq
pip install -e .


Examples are placed in the examples directory.

We encourage those who are interested in using this library to take a look at examples/ode_demo.py for understanding how to use torchdiffeq to fit a simple spiral ODE.

ODE Demo

Basic usage

This library provides one main interface odeint which contains general-purpose algorithms for solving initial value problems (IVP), with gradients implemented for all main arguments. An initial value problem consists of an ODE and an initial value,

dy/dt = f(t, y)    y(t_0) = y_0.

The goal of an ODE solver is to find a continuous trajectory satisfying the ODE that passes through the initial condition.

To solve an IVP using the default solver:

from torchdiffeq import odeint

odeint(func, y0, t)

where func is any callable implementing the ordinary differential equation f(t, x), y0 is an any-D Tensor or a tuple of any-D Tensors representing the initial values, and t is a 1-D Tensor containing the evaluation points. The initial time is taken to be t[0].

Backpropagation through odeint goes through the internals of the solver, but this is not supported for all solvers. Instead, we encourage the use of the adjoint method explained in [1], which will allow solving with as many steps as necessary due to O(1) memory usage.

To use the adjoint method:

from torchdiffeq import odeint_adjoint as odeint

odeint(func, y0, t)

odeint_adjoint simply wraps around odeint, but will use only O(1) memory in exchange for solving an adjoint ODE in the backward call.

The biggest gotcha is that func must be a nn.Module when using the adjoint method. This is used to collect parameters of the differential equation.

Keyword Arguments

  • rtol Relative tolerance.
  • atol Absolute tolerance.
  • method One of the solvers listed below.

List of ODE Solvers:


  • dopri5 Runge-Kutta 4(5) [default].
  • adams Adaptive-order implicit Adams.


  • euler Euler method.
  • midpoint Midpoint method.
  • rk4 Fourth-order Runge-Kutta with 3/8 rule.
  • explicit_adams Explicit Adams.
  • fixed_adams Implicit Adams.


[1] Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud. "Neural Ordinary Differential Equations." Advances in Neural Processing Information Systems. 2018. [arxiv]

If you found this library useful in your research, please consider citing

  title={Neural Ordinary Differential Equations},
  author={Chen, Ricky T. Q. and Rubanova, Yulia and Bettencourt, Jesse and Duvenaud, David},
  journal={Advances in Neural Information Processing Systems},


Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

License:MIT License


Language:Python 100.0%