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FFT Implementations

Existing packages

The following packages extend the functionality provided by AbstractFFTs:

  • FFTW.jl: Bindings for the FFTW library. This also used to be part of Base Julia.
  • FastTransforms.jl: Pure-Julia implementation of FFT, with support for arbitrary AbstractFloat types.

Defining a new implementation

To define a new FFT implementation in your own module, you should

  • Define a new subtype (e.g. MyPlan) of AbstractFFTs.Plan{T} for FFTs and related transforms on arrays of T. This must have a pinv::Plan field, initially undefined when a MyPlan is created, that is used for caching the inverse plan.

  • Define a new method AbstractFFTs.plan_fft(x, region; kws...) that returns a MyPlan for at least some types of x and some set of dimensions region. The region (or a copy thereof) should be accessible via fftdims(p::MyPlan) (which defaults to p.region), and the input size size(x) should be accessible via size(p::MyPlan).

  • Define a method of LinearAlgebra.mul!(y, p::MyPlan, x) that computes the transform p of x and stores the result in y.

  • Define a method of *(p::MyPlan, x), which can simply call your mul! method. This is not defined generically in this package due to subtleties that arise for in-place and real-input FFTs.

  • If the inverse transform is implemented, you should also define plan_inv(p::MyPlan), which should construct the inverse plan to p, and plan_bfft(x, region; kws...) for an unnormalized inverse ("backwards") transform of x. Implementations only need to provide the unnormalized backwards FFT, similar to FFTW, and we do the scaling generically to get the inverse FFT.

  • You can also define similar methods of plan_rfft and plan_brfft for real-input FFTs.

  • To support adjoints in a new plan, define the trait AbstractFFTs.AdjointStyle. AbstractFFTs implements the following adjoint styles: AbstractFFTs.FFTAdjointStyle, AbstractFFTs.RFFTAdjointStyle, AbstractFFTs.IRFFTAdjointStyle, and AbstractFFTs.UnitaryAdjointStyle. To define a new adjoint style, define the methods AbstractFFTs.adjoint_mul and AbstractFFTs.output_size.

The normalization convention for your FFT should be that it computes y_k = \sum_j x_j \exp(-2\pi i j k/n) for a transform of length n, and the "backwards" (unnormalized inverse) transform computes the same thing but with \exp(+2\pi i jk/n).