Tracking issue for tasks related to the torch.fft namespace, analogous to NumPy's numpy.fft namespace and SciPy's scipy.fft namespace.

PyTorch already has fft functions (fft, ifft, rfft , irfft, stft, istft), but they're inconsistent with NumPy and don't accept complex tensor inputs. The torch.fft namespace should be consistent with NumPy and SciPy where possible, plus provide a path towards removing PyTorch's existing fft functions in the 1.8 release (deprecating them in 1.7).

While adding the torch.fft namespace infrastructure and deprecating PyTorch's current fft-related functions are the top priorities, PyTorch is also missing many helpful functions, listed below, which should (eventually) be added to the new namespace, too.

Tasks:

• Write doc preamble to fft module
• Write blogpost about the torch.fft module
• Write tutorial
• Create forum group

Completed:

• Fix erronenous stft() warning (see comment below) (fixed in Use new FFT operators in stft #47601)
• Remove torch.fft() (and torch.Tensor.fft() and torch.rfft() and torch.Tensor.rfft()) and import the torch.fft module by default (Remove deprecated spectral ops from torch namespace #48594)
• Review fft architecture for (1) functions used are in ATen and (2) functions actually support complex values (so complex-specific kernels can be used) (New FFT operators in Improve torch.fft n-dimensional transforms #46911 use complex throughout)
• Rewrite _fft_with_size to handle transforming arbitrary dimensions without transposing/cloning (Improve torch.fft n-dimensional transforms #46911 still requires cloning, but this is required for best performance)
• Review operator performance
  • Related issues: CUDA irfft may be doing unnecessary cloning of input #38413
• Add OpInfo based tests
• Implement torch function overrides for the fft module
• Add support for out arguments
• Implement torch.fft.fft2()
• Implement torch.fft.iff2()
• Implement torch.fft.rfft2()
• Implement torch.fft.irfft2()
• Investigate gradgrad and ensure gradgradcheck passes
• stft crash (stft does not consistently check window device #30865)
• Implement torch.fft.fft()
• Implement torch.fft.ifft()
• Implement torch.fft.rfft()
• Implement torch.fft.irfft()
• Update torch.stft() and torch.istft() to handle complex tensors (like librosa does) and document librosa-compat (not SciPy)
• Create test_spectral_ops.py (Creates spectral ops test suite #42157)
• Create torch.fft namespace (Adds fft namespace #41911)
• Implement torch.fft.fftn()
• Implement torch.fft.ifftn()
• Implement torch.fft.fftfreq()
• Implement torch.fft.rfftfreq()
• Implement torch.fft.fftshift()
• Implement torch.fft.ifftshift()
• Deprecate torch.fft()
• Deprecate torch.ifft()
• Deprecate torch.rfft()
• Deprecate torch.irfft()
• Deprecate torch.stft() and torch.istft() returning non-complex tensors mimicking complex tensors
• pytorch istft runs slower than torchaudio istft especially at higher n_fft #42213, pytorch istft runs slower than torchaudio istft

cc @ezyang @gchanan @zou3519 @anjali411 @dylanbespalko @mruberry @rgommers @peterbell10