`half` and `complex32` support for `torch.fft.{fft, fft2, fftn, hfft, hfft2, hfftn, ifft, ifft2, ifftn, ihfft, ihfft2, ihfftn, irfft, irfft2, irfftn, rfft, rfft2, rfftn}`
* We only add support for `CUDA` as `cuFFT` supports these precision.
* We still error out on `CPU` and `ROCm` as their respective backends don't support this precision
For `cuFFT` following are the constraints for these precisions
* Minimum GPU architecture is SM_53
* Sizes are restricted to powers of two only
* Strides on the real part of real-to-complex and complex-to-real transforms are not supported
* More than one GPU is not supported
* Transforms spanning more than 4 billion elements are not supported
Ref: https://docs.nvidia.com/cuda/cufft/#half-precision-transforms
TODO:
* [x] Update docs about the restrictions
* [x] Check the correct way to check for `hip` device. (seems like `device.is_cuda()` is true for hip as well) (Thanks @peterbell10 )
Ref for second point in TODO:e424e7d214/aten/src/ATen/native/SpectralOps.cpp (L31)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74857
Approved by: https://github.com/anjali411, https://github.com/peterbell10
Summary:
Apparently normal reST doctests aren't run in CI, because of this line in the `conf.py`:
ac86e0a0e5/docs/source/conf.py (L366)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57290
Reviewed By: astaff
Differential Revision: D28118198
Pulled By: mruberry
fbshipit-source-id: 7af621c4fef4e5d37e0fc62b9fd4382cc1698d89
Summary:
Ref https://github.com/pytorch/pytorch/issues/42175
This removes the 4 deprecated spectral functions: `torch.{fft,rfft,ifft,irfft}`. `torch.fft` is also now imported by by default.
The actual `at::native` functions are still used in `torch.stft` so can't be full removed yet. But will once https://github.com/pytorch/pytorch/issues/47601 has been merged.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48594
Reviewed By: heitorschueroff
Differential Revision: D25298929
Pulled By: mruberry
fbshipit-source-id: e36737fe8192fcd16f7e6310f8b49de478e63bf0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45164
This PR implements `fft2`, `ifft2`, `rfft2` and `irfft2`. These are the last functions required for `torch.fft` to match `numpy.fft`. If you look at either NumPy or SciPy you'll see that the 2-dimensional variants are identical to `*fftn` in every way, except for the default value of `axes`. In fact you can even use `fft2` to do general n-dimensional transforms.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D24363639
Pulled By: mruberry
fbshipit-source-id: 95191b51a0f0b8e8e301b2c20672ed4304d02a57
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44550
Part of the `torch.fft` work (gh-42175).
This adds n-dimensional transforms: `fftn`, `ifftn`, `rfftn` and `irfftn`.
This is aiming for correctness first, with the implementation on top of the existing `_fft_with_size` restrictions. I plan to follow up later with a more efficient rewrite that makes `_fft_with_size` work with arbitrary numbers of dimensions.
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D23846032
Pulled By: mruberry
fbshipit-source-id: e6950aa8be438ec5cb95fb10bd7b8bc9ffb7d824
Summary:
This PR creates a new namespace, torch.fft (torch::fft) and puts a single function, fft, in it. This function is analogous to is a simplified version of NumPy's [numpy.fft.fft](https://numpy.org/doc/1.18/reference/generated/numpy.fft.fft.html?highlight=fft#numpy.fft.fft) that accepts no optional arguments. It is intended to demonstrate how to add and document functions in the namespace, and is not intended to deprecate the existing torch.fft function.
Adding this namespace was complicated by the existence of the torch.fft function in Python. Creating a torch.fft Python module makes this name ambiguous: does it refer to a function or module? If the JIT didn't exist, a solution to this problem would have been to make torch.fft refer to a callable class that mimicked both the function and module. The JIT, however, cannot understand this pattern. As a workaround it's required to explicitly `import torch.fft` to access the torch.fft.fft function in Python:
```
import torch.fft
t = torch.randn(128, dtype=torch.cdouble)
torch.fft.fft(t)
```
See https://github.com/pytorch/pytorch/issues/42175 for future work. Another possible future PR is to get the JIT to understand torch.fft as a callable class so it need not be imported explicitly to be used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41911
Reviewed By: glaringlee
Differential Revision: D22941894
Pulled By: mruberry
fbshipit-source-id: c8e0b44cbe90d21e998ca3832cf3a533f28dbe8d
