torchsde 0.2.6
SDE solvers and stochastic adjoint sensitivity analysis in PyTorch.
Requires Python: >=3.8

# PyTorch Implementation of Differentiable SDE Solvers ![Python package](https://github.com/google-research/torchsde/workflows/Python%20package/badge.svg?branch=dev)
This library provides [stochastic differential equation (SDE)](https://en.wikipedia.org/wiki/Stochastic_differential_equation) solvers with GPU support and efficient backpropagation.

---
<p align="center">
  <img width="600" height="450" src="./assets/latent_sde.gif">
</p>

## Installation
```shell script
pip install torchsde
```

**Requirements:** Python >=3.8 and PyTorch >=1.6.0.

## Documentation
Available [here](./DOCUMENTATION.md).

## Examples
### Quick example
```python
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)

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

    # Diffusion
    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`](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`](examples/latent_sde.py) learns a *latent stochastic differential equation*, as in Section 5 of [\[1\]](https://arxiv.org/pdf/2001.01328.pdf).
The example fits an SDE to data, whilst regularizing it to be like an [Ornstein-Uhlenbeck](https://en.wikipedia.org/wiki/Ornstein%E2%80%93Uhlenbeck_process) prior process.
The model can be loosely viewed as a [variational autoencoder](https://en.wikipedia.org/wiki/Autoencoder#Variational_autoencoder_(VAE)) with its prior and approximate posterior being SDEs. This example can be run via
```shell script
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.

### Neural SDEs as GANs
[`examples/sde_gan.py`](examples/sde_gan.py) learns an SDE as a GAN, as in [\[2\]](https://arxiv.org/abs/2102.03657), [\[3\]](https://arxiv.org/abs/2105.13493). The example trains an SDE as the generator of a GAN, whilst using a [neural CDE](https://github.com/patrick-kidger/NeuralCDE) [\[4\]](https://arxiv.org/abs/2005.08926) as the discriminator. This example can be run via

```shell script
python -m examples.sde_gan
```

## Citation

If you found this codebase useful in your research, please consider citing either or both of:

```
@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}
}
```

```
@article{kidger2021neuralsde,
  title={Neural {SDE}s as {I}nfinite-{D}imensional {GAN}s},
  author={Kidger, Patrick and Foster, James and Li, Xuechen and Oberhauser, Harald and Lyons, Terry},
  journal={International Conference on Machine Learning},
  year={2021}
}
```

## 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]](https://arxiv.org/pdf/2001.01328.pdf)

\[2\] Patrick Kidger, James Foster, Xuechen Li, Harald Oberhauser, Terry Lyons. "Neural SDEs as Infinite-Dimensional GANs". *International Conference on Machine Learning* 2021. [[arXiv]](https://arxiv.org/abs/2102.03657)

\[3\] Patrick Kidger, James Foster, Xuechen Li, Terry Lyons. "Efficient and Accurate Gradients for Neural SDEs". 2021. [[arXiv]](https://arxiv.org/abs/2105.13493)

\[4\] Patrick Kidger, James Morrill, James Foster, Terry Lyons, "Neural Controlled Differential Equations for Irregular Time Series". *Neural Information Processing Systems* 2020. [[arXiv]](https://arxiv.org/abs/2005.08926)

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