October 19, 2020

350 words 2 mins read

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

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language Python
size (curr.) 4029 kB
stars (curr.) 573
created 2020-07-06

# PyTorch Implementation of Differentiable SDE Solvers

This library provides stochastic differential equation (SDE) solvers with GPU support and efficient backpropagation.

## Installation

``````pip install git+https://github.com/google-research/torchsde.git
``````

Requirements: Python >=3.6 and PyTorch >=1.6.0.

Available here.

## Examples

### Quick example

``````import torch
import torchsde

batch_size, state_size, brownian_size = 32, 3, 2
t_size = 20

class SDE(torch.nn.Module):
noise_type = 'general'
sde_type = 'ito'

def __init__(self):
super().__init__()
self.mu = torch.nn.Linear(state_size,
state_size)
self.sigma = torch.nn.Linear(state_size,
state_size * brownian_size)

def f(self, t, y):
return self.mu(y)  # shape (batch_size, state_size)

def g(self, t, y):
return self.sigma(y).view(batch_size,
state_size,
brownian_size)

sde = SDE()
y0 = torch.full((batch_size, state_size), 0.1)
ts = torch.linspace(0, 1, t_size)
# Initial state y0, the SDE is solved over the interval [ts[0], ts[-1]].
# ys will have shape (t_size, batch_size, state_size)
ys = torchsde.sdeint(sde, y0, ts)
``````

### Notebook

`examples/demo.ipynb` gives a short guide on how to solve SDEs, including subtle points such as fixing the randomness in the solver and the choice of noise types.

### Latent SDE

`examples/latent_sde.py` learns a latent stochastic differential equation, as in Section 5 of [1]. The example fits an SDE to data, whilst regularizing it to be like an Ornstein-Uhlenbeck prior process. The model can be loosely viewed as a variational autoencoder with its prior and approximate posterior being SDEs. This example can be run via

``````python -m examples.latent_sde --train-dir <TRAIN_DIR>
``````

The program outputs figures to the path specified by `<TRAIN_DIR>`. Training should stabilize after 500 iterations with the default hyperparameters.

## Citation

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

``````@article{li2020scalable,
title={Scalable gradients for stochastic differential equations},
author={Li, Xuechen and Wong, Ting-Kam Leonard and Chen, Ricky T. Q. and Duvenaud, David},
journal={International Conference on Artificial Intelligence and Statistics},
year={2020}
}
``````

## References

[1] Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud. “Scalable Gradients for Stochastic Differential Equations.” International Conference on Artificial Intelligence and Statistics. 2020. [arXiv]

This is a research project, not an official Google product.