Update README.md (#20)

* Update README.md

adding autocast remark to readme

* Update README.md

small typo fix

README.md

@@ -194,6 +194,33 @@ Detailed usage of torch-harmonics, alongside helpful analysis provided in a seri
 7. [Solving the shallow water equations](./notebooks/shallow_water_equations.ipynb)
 8. [Training Spherical Fourier Neural Operators](./notebooks/train_sfno.ipynb)
 
+## 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.cuda.amp.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.cuda.amp.autocast(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.cuda.amp.autocast(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), [Christian Hundt](https://github.com/gravitino) (chundt@nvidia.com), [Nikola Kovachki](https://kovachki.github.io) (nkovachki@nvidia.com), [Jean Kossaifi](http://jeankossaifi.com) (jkossaifi@nvidia.com)