Support for np.fft.fft, np.fft.ifft etc.

Feature request

It would be amazing if numba would support the FFT pack of numpy. Personally, I would be interested in

   np.fft.fft
   np.fft.ifft
   np.fft.rfft
   np.fft.irfft

Obviously, there are many applications in signal processing that could benefit from this.

Are there any plans for that?

[Sbozzolo]

I stumbled upon this thread having the same problem everyone else has: I want to use fft’s routines in my numba functions. What I found is the following (maybe useful for others). If the fft is only a small part of the function, object mode can be used as a good workaround. A sketch of code would look like:

@njit
def myfunc(input)
     .....
     .....
     with objmode(out='complex128[:]'):
            out = np.fft.fft(in)
            # Maybe also out = np.fft.fftshift(out)
     .....
     # heavy computations
     return ...

In my specific case, this is still significantly faster than a pure implementation. Of course, it would be great to have first-class support for these functions.

[stuartarchibald]

Thanks for the request. This would be great to have, the reason it hasn’t been done is that it would rely upon:

1. Some package, e.g. SciPy providing a consistent means of access. Numba uses scipy.linalg.cython_blas and scipy.linalg.cython_lapack as a means of obtaining the addresses of BLAS and LAPACK functions so as to implement numpy.linalg.*, similar would be needed for FFT functions.
2. There would need to be a “consistent” interface (or subset of an interface) for accessing and using the functions that do the FFT work (e.g. FFTW vs MKL FFT vs … something else). This would likely be more of a challenge to overcome than exposing a raw binding. From memory, the interfaces are quite involved and to get good performance require things like the caching of plans.