torch-harmonics 0.8.0
Differentiable signal processing on the sphere for PyTorch.
Requires Python: >=3.9

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# torch-harmonics

[**Overview**](#overview) | [**Installation**](#installation) | [**More information**](#more-about-torch-harmonics) | [**Getting started**](#getting-started) | [**Contributors**](#contributors) | [**Cite us**](#cite-us) | [**References**](#references)

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## Overview

torch-harmonics implements differentiable signal processing on the sphere. This includes differentiable implementations of the spherical harmonic transforms, vector spherical harmonic transforms and discrete-continuous convolutions on the sphere. The package was originally implemented to enable Spherical Fourier Neural Operators (SFNO) [1].

The SHT algorithm uses quadrature rules to compute the projection onto the associated Legendre polynomials and FFTs for the projection onto the harmonic basis. This algorithm tends to outperform others with better asymptotic scaling for most practical purposes [2].

torch-harmonics uses PyTorch primitives to implement these operations, making it fully differentiable. Moreover, the quadrature can be distributed onto multiple ranks making it spatially distributed.

torch-harmonics has been used to implement a variety of differentiable PDE solvers which generated the animations below. Moreover, it has enabled the development of Spherical Fourier Neural Operators  [1].

<div align="center">
<table border="0" cellspacing="0" cellpadding="0">
    <tr>
        <td><img src="https://media.githubusercontent.com/media/NVIDIA/torch-harmonics/main/images/sfno.gif"  width="240"></td>
        <td><img src="https://media.githubusercontent.com/media/NVIDIA/torch-harmonics/main/images/zonal_jet.gif"  width="240"></td>
        <td><img src="https://media.githubusercontent.com/media/NVIDIA/torch-harmonics/main/images/allen-cahn.gif"  width="240"></td>
    </tr>
<!--     <tr>
        <td style="text-align:center; border-style : hidden!important;">Shallow Water Eqns.</td>
        <td style="text-align:center; border-style : hidden!important;">Ginzburg-Landau Eqn.</td>
        <td style="text-align:center; border-style : hidden!important;">Allen-Cahn Eqn.</td>
    </tr>  -->
</table>
</div>


## Installation
A simple installation can be directly done from PyPI:

```bash
pip install torch-harmonics
```
If you are planning to use spherical convolutions, we recommend building the corresponding custom CUDA kernels. To enforce this, you can set the `FORCE_CUDA_EXTENSION` flag. You may also want to set appropriate architectures with the `TORCH_CUDA_ARCH_LIST` flag. Finally, make sure to disable build isolation via the `--no-build-isolation` flag to ensure that the custom kernels are built with the existing torch installation.
```bash
export FORCE_CUDA_EXTENSION=1
export TORCH_CUDA_ARCH_LIST="7.0 7.2 7.5 8.0 8.6 8.7 9.0+PTX"
pip install --no-build-isolation torch-harmonics
```
:warning: Please note that the custom CUDA extensions currently only support CUDA architectures >= 7.0.

If you want to actively develop torch-harmonics, we recommend building it in your environment from github:

```bash
git clone git@github.com:NVIDIA/torch-harmonics.git
cd torch-harmonics
pip install -e .
```

Alternatively, use the Dockerfile to build your custom container after cloning:

```bash
git clone git@github.com:NVIDIA/torch-harmonics.git
cd torch-harmonics
docker build . -t torch_harmonics
docker run --gpus all -it --rm --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 torch_harmonics
```

## More about torch-harmonics

### Spherical harmonics

The [spherical harmonics](https://en.wikipedia.org/wiki/Spherical_harmonics) are special functions defined on the two-dimensional sphere $S^2$ (embedded in three dimensions). They form an orthonormal basis of the space of square-integrable functions defined on the sphere $L^2(S^2)$ and are comparable to the harmonic functions defined on a circle/torus. The spherical harmonics are defined as

$$
Y_l^m(\theta, \lambda) = \sqrt{\frac{(2l + 1)}{4 \pi} \frac{(l - m)!}{(l + m)!}} P_l^m(\cos \theta)  \exp(im\lambda),
$$

where $\theta$ and $\lambda$ are colatitude and longitude respectively, and $P_l^m$ the normalized, [associated Legendre polynomials](https://en.wikipedia.org/wiki/Associated_Legendre_polynomials).

<div align="center">
<img src="https://media.githubusercontent.com/media/NVIDIA/torch-harmonics/main/images/spherical_harmonics.gif" width="432">
<br>
Spherical harmonics up to degree 5
</div>

### Spherical harmonic transform

The spherical harmonic transform (SHT)

$$
f_l^m = \int_{S^2}  \overline{Y_{l}^{m}}(\theta, \lambda) f(\theta, \lambda) \mathrm{d} \mu(\theta, \lambda)
$$

realizes the projection of a signal $f(\theta, \lambda)$ on $S^2$ onto the spherical harmonics basis. The SHT generalizes the Fourier transform on the sphere. Conversely, a truncated series expansion of a function $f$ can be written in terms of spherical harmonics as

$$
f (\theta, \lambda) = \sum_{m=-M}^{M} \exp(im\lambda) \sum_{l=|m|}^{M} \hat f_l^m  P_l^m (\cos \theta),
$$

where $\hat{f}_l^m$, are the expansion coefficients associated to the mode $m$, $n$.

The implementation of the SHT follows the algorithm as presented in [2]. A direct spherical harmonic transform can be accomplished by a Fourier transform

$$
\hat f^m(\theta) = \frac{1}{2 \pi} \int_{0}^{2\pi} f(\theta, \lambda) \exp(-im\lambda) \mathrm{d} \lambda
$$

in longitude and a Legendre transform

$$
\hat f_l^m = \frac{1}{2} \int^{\pi}_0 \hat f^{m} (\theta) P_l^m (\cos \theta) \sin \theta \mathrm{d} \theta
$$

in latitude.

### Discrete Legendre transform

The second integral, which computed the projection onto the Legendre polynomials is realized with quadrature. On the Gaussian grid, we use Gaussian quadrature in the $\cos \theta$ domain. The integral

$$
\hat f_l^m = \frac{1}{2} \int_{-1}^1 \hat{f}^m(\arccos x) P_l^m (x) \mathrm{d} x
$$

is obtained with the substitution $x = \cos \theta$ and then approximated by the sum

$$
\hat f_l^m = \sum_{j=1}^{N_\theta}  \hat{f}^m(\arccos x_j) P_l^m(x_j) w_j.
$$

Here, $x_j \in [-1,1]$ are the quadrature nodes with the respective quadrature weights $w_j$.

### Discrete-continuous convolutions on the sphere

torch-harmonics now provides local discrete-continuous (DISCO) convolutions as outlined in [5] on the sphere. These are use in local neural operators [2] to generalize convolutions to structured and unstructured meshes on the sphere.

### Spherical (neighborhood) attention

torch-harmonics introducers spherical attention mechanisms which correctly generalize the attention mechanism to the sphere. The use of quadrature rules makes the resulting operations approximately equivariant and equivariant in the continuous limit. Moreover, neighborhood attention is correctly generalized onto the sphere by using the geodesic distance to determine the size of the neighborhood.

## Getting started

The main functionality of `torch_harmonics` is provided in the form of `torch.nn.Modules` for composability. A minimum example is given by:

```python
import torch
import torch_harmonics as th

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

nlat = 512
nlon = 2*nlat
batch_size = 32
signal = torch.randn(batch_size, nlat, nlon, device=device)

# transform data on an equiangular grid
sht = th.RealSHT(nlat, nlon, grid="equiangular").to(device)

coeffs = sht(signal)
```

To enable scalable model-parallelism, `torch-harmonics` implements a distributed variant of the SHT located in `torch_harmonics.distributed`.

Detailed usage of torch-harmonics, alongside helpful analysis provided in a series of notebooks:

1. [Getting started](./notebooks/getting_started.ipynb)
2. [Quadrature](./notebooks/quadrature.ipynb)
3. [Visualizing the spherical harmonics](./notebooks/plot_spherical_harmonics.ipynb)
4. [Spectral fitting vs. SHT](./notebooks/gradient_analysis.ipynb)
5. [Conditioning of the Gramian](./notebooks/conditioning_sht.ipynb)
6. [Solving the Helmholtz equation](./notebooks/helmholtz.ipynb)
7. [Solving the shallow water equations](./notebooks/shallow_water_equations.ipynb)
8. [Training Spherical Fourier Neural Operators (SFNO)](./notebooks/train_sfno.ipynb)
9. [Resampling signals on the sphere](./notebooks/resample_sphere.ipynb)

## Examples and reproducibility

The `examples` folder contains training scripts for three distinct tasks:

* [solution of the shallow water equations on the rotating sphere](./examples/shallow_water_equations/train.py)
* [depth estimation on the sphere](./examples/depth/train.py)
* [semantic segmentation on the sphere](./examples/segmentation/train.py)

Results from the papers can generally be reproduced by running `python train.py`. In the case of some older results the number of epochs and learning-rate may need to be adjusted by passing the corresponding command line argument.

## Remarks on automatic mixed precision (AMP) support

Note that torch-harmonics uses Fourier transforms from `torch.fft` which in turn uses kernels from the optimized `cuFFT` library. This library supports fourier transforms of `float32` and `float64` (i.e. `single` and `double` precision) tensors for all input sizes. For `float16` (i.e. `half` precision) and `bfloat16` inputs however, the dimensions which are transformed are restricted to powers of two. Since data is converted to one of these reduced precision floating point formats when `torch.autocast` is used, torch-harmonics will issue an error when the input shapes are not powers of two. For these cases, we recommend disabling autocast for the harmonics transform specifically:

```python
import torch
import torch_harmonics as th

sht = th.RealSHT(512, 1024, grid="equiangular").cuda()

with torch.autocast(device_type="cuda", enabled = True):
   # do some AMP converted math here
   x = some_math(x)
   # convert tensor to float32
   x = x.to(torch.float32)
   # now disable autocast specifically for the transform,
   # making sure that the tensors are not converted
   # back to reduced precision internally
   with torch.autocast(device_type="cuda", enabled = False):
      xt = sht(x)

   # continue operating on the transformed tensor
   xt = some_more_math(xt)
```

Depending on the problem, it might be beneficial to upcast data to `float64` instead of `float32` precision for numerical stability.

## Contributors

[Boris Bonev](https://bonevbs.github.io) (bbonev@nvidia.com), [Thorsten Kurth](https://github.com/azrael417) (tkurth@nvidia.com), [Max Rietmann](https://github.com/rietmann-nv), [Mauro Bisson](https://scholar.google.com/citations?hl=en&user=f0JE-0gAAAAJ), [Andrea Paris](https://github.com/apaaris), [Alberto Carpentieri](https://github.com/albertocarpentieri), [Massimiliano Fatica](https://scholar.google.com/citations?user=Deaq4uUAAAAJ&hl=en), [Nikola Kovachki](https://kovachki.github.io), [Jean Kossaifi](http://jeankossaifi.com), [Christian Hundt](https://github.com/gravitino)

## Cite us

If you use `torch-harmonics` in an academic paper, please cite [1]

```bibtex
@misc{bonev2023spherical,
      title={Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere},
      author={Boris Bonev and Thorsten Kurth and Christian Hundt and Jaideep Pathak and Maximilian Baust and Karthik Kashinath and Anima Anandkumar},
      year={2023},
      eprint={2306.03838},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```

## References

<a id="1">[1]</a>
Bonev B., Kurth T., Hundt C., Pathak, J., Baust M., Kashinath K., Anandkumar A.;
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere;
International Conference on Machine Learning, 2023. [arxiv link](https://arxiv.org/abs/2306.03838)

<a id="1">[2]</a>
Liu-Schiaffini M., Berner J., Bonev B., Kurth T., Azizzadenesheli K., Anandkumar A.;
Neural Operators with Localized Integral and Differential Kernels;
International Conference on Machine Learning, 2024. [arxiv link](https://arxiv.org/abs/2402.16845)

<a id="1">[3]</a>
Schaeffer N.;
Efficient spherical harmonic transforms aimed at pseudospectral numerical simulations;
G3: Geochemistry, Geophysics, Geosystems, 2013.

<a id="1">[4]</a>
Wang B., Wang L., Xie Z.;
Accurate calculation of spherical and vector spherical harmonic expansions via spectral element grids;
Adv Comput Math, 2018.

<a id="1">[5]</a>
Ocampo, Price, McEwen, Scalable and equivariant spherical CNNs by discrete-continuous (DISCO) convolutions, ICLR (2023), arXiv:2209.13603

<a id="1">[6]</a>
Bonev B., Rietmann M., Paris A., Carpentieri A., Kurth T.; Attention on the Sphere; [arxiv link](https://arxiv.org/abs/2505.11157)