"Hey, that's not an ODE": Faster ODE Adjoints via Seminorms
(ICML 2021)
[arXiv]

One simple-to-implement trick dramatically improves the speed at which Neural ODEs and Neural CDEs can be trained. (As much as doubling the speed.)

Backpropagation through a Neural ODE/CDE can be performed via the "adjoint method", which involves solving another differential equation backwards in time. However it turns out that default numerical solvers are unnecessarily stringent when solving the adjoint equation, and take too many steps, that are too small.

Tweaking things slightly reduces the number of function evaluations on the backward pass by as much as 62%. (Exact number will be problem-dependent, of course.)

torchdiffeq now supports this feature natively!

---

Summary:

If you're using torchdiffeq (at least version 0.1.0) then replace

import torchdiffeq

func = ...
y0 = ...
t = ...
torchdiffeq.odeint_adjoint(func=func, y0=y0, t=t)

with

import torchdiffeq

def rms_norm(tensor):
    return tensor.pow(2).mean().sqrt()

def make_norm(state):
    state_size = state.numel()
    def norm(aug_state):
        y = aug_state[1:1 + state_size]
        adj_y = aug_state[1 + state_size:1 + 2 * state_size]
        return max(rms_norm(y), rms_norm(adj_y))
    return norm

func = ...    
y0 = ...
t = ...
torchdiffeq.odeint_adjoint(func=func, y0=y0, t=t, 
                           adjoint_options=dict(norm=make_norm(y0)))

That's it.

Reproducing experiments

The code for the Neural CDE and Symplectic ODE-Net experiments is available.

Requirements

PyTorch >= 1.6
torchdiffeq >= 0.1.0
torchcde >= 0.1.0
torchaudio >= 0.6.0
sklearn >= 0.23.1
gym >= 0.17.2
tqdm >= 4.47.0

In summary:

conda install pytorch torchaudio -c pytorch
pip install torchdiffeq scikit-learn gym tqdm
pip install git+https://github.com/patrick-kidger/torchcde.git

Neural CDEs

python
>>> import speech_commands
>>> device = 'cuda'
>>> norm = False  # don't use our trick
>>> norm = True   # use our trick
>>> rtol = 1e-3
>>> atol = 1e-5
>>> results = speech_commands.main(device, norm, rtol, atol)
>>> print(results.keys())  # inspect results object
>>> print(results.test_metrics.accuracy)  # query results object

Symplectic ODE-Net

python
>>> import acrobot
>>> device = 'cuda'
>>> norm = False  # don't use our trick
>>> norm = True   # use our trick
>>> results = acrobot.main(device, norm)
>>> print(results.keys())  # inspect results object
>>> print(results.test_metrics.loss)  # query results object

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Citation

@article{kidger2021hey,
    author={Kidger, Patrick and Chen, Ricky T. Q. and Lyons, Terry},
    title={{``Hey, that's not an ODE'': Faster ODE Adjoints via Seminorms}},
    year={2021},
    journal={International Conference on Machine Learning}
}