Merge branch 'master' of https://github.com/JuliaMath/NFFT.jl

.buildkite/pipeline.yml

@@ -0,0 +1,37 @@
+steps:
+  - label: "Nvidia GPUs -- NFFT.jl"
+    plugins:
+      - JuliaCI/julia#v1:
+          version: "1.10"
+    agents:
+      queue: "juliagpu"
+      cuda: "*"
+    command: |
+      julia --color=yes --project -e '
+      using Pkg
+      Pkg.add("TestEnv")
+      using TestEnv
+      TestEnv.activate();
+      Pkg.add("CUDA")
+      Pkg.instantiate()
+      include("test/gpu/cuda.jl")'
+    timeout_in_minutes: 30
+
+  - label: "AMD GPUs -- NFFT.jl"
+    plugins:
+      - JuliaCI/julia#v1:
+          version: "1.10"
+    agents:
+      queue: "juliagpu"
+      rocm: "*"
+      rocmgpu: "*"
+    command: |
+      julia --color=yes --project -e '
+      using Pkg
+      Pkg.add("TestEnv")
+      using TestEnv
+      TestEnv.activate();
+      Pkg.add("AMDGPU")
+      Pkg.instantiate()
+      include("test/gpu/rocm.jl")'
+    timeout_in_minutes: 30

.github/workflows/ci.yml

@@ -15,7 +15,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.6' # Replace this with the minimum Julia version that your package supports. E.g. if your package requires Julia 1.5 or higher, change this to '1.5'.
+          - '1.9' # Replace this with the minimum Julia version that your package supports. E.g. if your package requires Julia 1.5 or higher, change this to '1.5'.
           - '1' # Leave this line unchanged. '1' will automatically expand to the latest stable 1.x release of Julia.
           # - 'nightly'
         os:

CuNFFT/Project.toml

@@ -1,7 +1,7 @@
 name = "CuNFFT"
 uuid = "a9291f20-7f4c-4d50-b30d-4e07b13252e1"
 author = ["Tobias Knopp <[email protected]>"]
-version = "0.3.7"
+version = "0.3.8"
 
 [deps]
 AbstractFFTs = "621f4979-c628-5d54-868e-fcf4e3e8185c" 

CuNFFT/src/CuNFFT.jl

@@ -8,6 +8,13 @@ using AbstractFFTs
 using CUDA
 using CUDA.CUSPARSE
 
+function __init__()
+  @warn """Please be aware that CuNFFT.jl has been deprecated by a package extension in NFFT.jl.
+  As a result, no additional packages are required to run an NFFT on a GPU.
+
+  To remove this warning when continuing to use CuNFFT.jl, please pin CuNFFT.jl to version 0.3.7."""
+end
+
 include("implementation.jl")
 
 end

Project.toml

@@ -1,7 +1,7 @@
 name = "NFFT"
 uuid = "efe261a4-0d2b-5849-be55-fc731d526b0d"
 authors = ["Tobias Knopp <[email protected]>"]
-version = "0.13.3"
+version = "0.13.4"
 
 [deps]
 AbstractNFFTs = "7f219486-4aa7-41d6-80a7-e08ef20ceed7"
@@ -18,31 +18,40 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 
 [compat]
+Adapt = "3, 4"
 AbstractNFFTs = "0.8"
 BasicInterpolators = "0.6.5, 0.7"
 DataFrames = "1.3.1, 1.4.1"
 FFTW = "1.5"
 FINUFFT = "3.0.1"
 FLoops = "0.2"
+GPUArrays = "8, 9, 10"
+JLArrays = "0.1.2"
 Reexport = "1.0"
 PrecompileTools = "1"
 SpecialFunctions = "0.8, 0.10, 1, 2"
-julia = "1.6"
+julia = "1.9"
 #StaticArrays = "1.4"
 Ducc0 = "0.1"
 
 [extras]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
-CuNFFT = "a9291f20-7f4c-4d50-b30d-4e07b13252e1"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 FINUFFT = "d8beea63-0952-562e-9c6a-8e8ef7364055"
 NFFT3 = "53104703-03e8-40a5-ab01-812303a44cae"
 NFFTTools = "7424e34d-94f7-41d6-98a0-85abaf1b6c91"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Ducc0 = "47ec601d-2729-4ac9-bed9-2b3ab5fca9ff"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+JLArrays = "27aeb0d3-9eb9-45fb-866b-73c2ecf80fcb"
 
+[weakdeps]
+Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+GPUArrays = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7"
 
 [targets]
-test = ["Test", "BenchmarkTools", "FINUFFT", "NFFT3", "CuNFFT", "Zygote",
-        "NFFTTools", "DataFrames", "Ducc0"]  # "NFFTTools"  "CuNFFT"
+test = ["Test", "JLArrays", "BenchmarkTools", "FINUFFT", "NFFT3", "Zygote",
+        "NFFTTools", "DataFrames", "Ducc0"]  # "NFFTTools"
+
+[extensions]
+NFFTGPUArraysExt = ["Adapt", "GPUArrays"]

ext/NFFTGPUArraysExt/NFFTGPUArraysExt.jl

@@ -0,0 +1,9 @@
+module NFFTGPUArraysExt
+
+using NFFT, NFFT.AbstractNFFTs
+using NFFT.SparseArrays, NFFT.LinearAlgebra, NFFT.FFTW
+using GPUArrays, Adapt
+
+include("implementation.jl")
+
+end

ext/NFFTGPUArraysExt/implementation.jl

@@ -0,0 +1,128 @@
+mutable struct GPU_NFFTPlan{T,D, arrTc <: AbstractGPUArray{Complex{T}, D}, vecI <: AbstractGPUVector{Int32}, FP, BP, INV, SM} <: AbstractNFFTPlan{T,D,1} 
+  N::NTuple{D,Int64}
+  NOut::NTuple{1,Int64}
+  J::Int64
+  k::Matrix{T}
+  Ñ::NTuple{D,Int64}
+  dims::UnitRange{Int64}
+  params::NFFTParams{T}
+  forwardFFT::FP
+  backwardFFT::BP
+  tmpVec::arrTc
+  tmpVecHat::arrTc
+  deconvolveIdx::vecI
+  windowLinInterp::Vector{T}
+  windowHatInvLUT::INV
+  B::SM
+end
+
+function AbstractNFFTs.plan_nfft(arr::Type{<:AbstractGPUArray}, k::Matrix{T}, N::NTuple{D,Int}, rest...;
+  timing::Union{Nothing,TimingStats} = nothing, kargs...) where {T,D}
+  t = @elapsed begin
+    p = GPU_NFFTPlan(arr, k, N, rest...; kargs...)
+  end
+  if timing != nothing
+    timing.pre = t
+  end
+  return p
+end
+
+function GPU_NFFTPlan(arr, k::Matrix{T}, N::NTuple{D,Int}; dims::Union{Integer,UnitRange{Int64}}=1:D,
+                 fftflags=nothing, kwargs...) where {T,D}
+
+    if dims != 1:D
+      error("GPU NFFT does not work along directions right now!")
+    end
+
+    params, N, NOut, J, Ñ, dims_ = NFFT.initParams(k, N, dims; kwargs...)
+    params.storeDeconvolutionIdx = true # GPU_NFFT only works this way
+    params.precompute = NFFT.FULL # GPU_NFFT only works this way
+
+    tmpVec = adapt(arr, zeros(Complex{T}, Ñ))
+
+    FP = plan_fft!(tmpVec, dims_)
+    BP = plan_bfft!(tmpVec, dims_)
+
+    windowLinInterp, windowPolyInterp, windowHatInvLUT, deconvolveIdx, B = NFFT.precomputation(k, N[dims_], Ñ[dims_], params)
+
+    U = params.storeDeconvolutionIdx ? N : ntuple(d->0,D)
+    tmpVecHat = adapt(arr, zeros(Complex{T}, U))
+
+    deconvIdx = Int32.(adapt(arr, (deconvolveIdx)))
+    winHatInvLUT = Complex{T}.(adapt(arr, (windowHatInvLUT[1]))) 
+    B_ = Complex{T}.(adapt(arr, (B))) # Bit hacky
+
+    GPU_NFFTPlan{T,D, typeof(tmpVec), typeof(deconvIdx), typeof(FP), typeof(BP), typeof(winHatInvLUT), typeof(B_)}(N, NOut, J, k, Ñ, dims_, params, FP, BP, tmpVec, tmpVecHat, 
+               deconvIdx, windowLinInterp, winHatInvLUT, B_)
+end
+
+AbstractNFFTs.size_in(p::GPU_NFFTPlan) = p.N
+AbstractNFFTs.size_out(p::GPU_NFFTPlan) = p.NOut
+
+
+function AbstractNFFTs.convolve!(p::GPU_NFFTPlan{T,D, arrTc}, g::arrTc, fHat::arrH) where {D,T,arr<: AbstractGPUArray, arrTc <: arr, arrH <: arr}
+  mul!(fHat, transpose(p.B), vec(g)) 
+  return
+end
+
+function AbstractNFFTs.convolve_transpose!(p::GPU_NFFTPlan{T,D, arrTc}, fHat::arrH, g::arrTc) where {D,T,arr<: AbstractGPUArray, arrTc <: arr, arrH <: arr}
+  mul!(vec(g), p.B, fHat)
+  return
+end
+
+function AbstractNFFTs.deconvolve!(p::GPU_NFFTPlan{T,D, arrTc}, f::arrF, g::arrTc) where {D,T,arr<: AbstractGPUArray, arrTc <: arr, arrF <: arr}
+  p.tmpVecHat[:] .= vec(f) .* p.windowHatInvLUT
+  g[p.deconvolveIdx] = p.tmpVecHat
+  return
+end
+
+function AbstractNFFTs.deconvolve_transpose!(p::GPU_NFFTPlan{T,D, arrTc}, g::arrTc, f::arrF) where {D,T,arr<: AbstractGPUArray, arrTc <: arr, arrF <: arr}
+  p.tmpVecHat[:] .= broadcast(p.deconvolveIdx) do idx
+    g[idx]
+  end
+  f[:] .= vec(p.tmpVecHat) .* p.windowHatInvLUT
+  return
+end
+
+"""  in-place NFFT on the GPU"""
+function LinearAlgebra.mul!(fHat::arrH, p::GPU_NFFTPlan{T,D, arrT}, f::arrF; 
+                          verbose=false, timing::Union{Nothing,TimingStats} = nothing) where {T,D,arr<: AbstractGPUArray, arrT <: arr, arrH <: arr, arrF <: arr} 
+    NFFT.consistencyCheck(p, f, fHat)
+
+    fill!(p.tmpVec, zero(Complex{T}))
+    t1 = @elapsed @inbounds deconvolve!(p, f, p.tmpVec)
+    t2 = @elapsed p.forwardFFT * p.tmpVec
+    t3 = @elapsed @inbounds convolve!(p, p.tmpVec, fHat)
+    if verbose
+        @info "Timing: deconv=$t1 fft=$t2 conv=$t3"
+    end
+    if timing != nothing
+      timing.conv = t3
+      timing.fft = t2
+      timing.deconv = t1
+    end
+
+    return fHat
+end
+
+"""  in-place adjoint NFFT on the GPU"""
+function LinearAlgebra.mul!(f::arrF, pl::Adjoint{Complex{T},<:GPU_NFFTPlan{T,D, arrT}}, fHat::arrH;
+                       verbose=false, timing::Union{Nothing,TimingStats} = nothing) where {T,D,arr<: AbstractGPUArray, arrT <: arr, arrH <: arr, arrF <: arr}
+    p = pl.parent
+    NFFT.consistencyCheck(p, f, fHat)
+
+    t1 = @elapsed @inbounds convolve_transpose!(p, fHat, p.tmpVec)
+    t2 = @elapsed p.backwardFFT * p.tmpVec
+    t3 = @elapsed @inbounds deconvolve_transpose!(p, p.tmpVec, f)
+    if verbose
+        @info "Timing: conv=$t1 fft=$t2 deconv=$t3"
+    end
+    if timing != nothing
+      timing.conv_adjoint = t1
+      timing.fft_adjoint = t2
+      timing.deconv_adjoint = t3
+    end
+
+    return f
+end
+

test/gpu.jl

@@ -0,0 +1,58 @@
+m = 5
+σ = 2.0
+
+@testset "GPU NFFT Plans" begin
+  for arrayType in arrayTypes
+
+    @testset "GPU_NFFT in multiple dimensions" begin
+      for (u, N) in enumerate([(256,), (32, 32), (12, 12, 12)])
+        eps = [1e-7, 1e-3, 1e-6, 1e-4]
+        for (l, window) in enumerate([:kaiser_bessel, :gauss, :kaiser_bessel_rev, :spline])
+          D = length(N)
+          @info "Testing $arrayType in $D dimensions using $window window"
+
+          J = prod(N)
+          k = rand(Float64, D, J) .- 0.5
+          p = plan_nfft(Array, k, N; m, σ, window, precompute=NFFT.FULL,
+            fftflags=FFTW.ESTIMATE)
+          p_d = plan_nfft(arrayType, k, N; m, σ, window, precompute=NFFT.FULL)
+          pNDFT = NDFTPlan(k, N)
+
+          fHat = rand(Float64, J) + rand(Float64, J) * im
+          f = adjoint(pNDFT) * fHat
+          fHat_d = arrayType(fHat)
+          fApprox_d = adjoint(p_d) * fHat_d
+          fApprox = Array(fApprox_d)
+          e = norm(f[:] - fApprox[:]) / norm(f[:])
+          @debug "error adjoint nfft " e
+          @test e < eps[l]
+
+          gHat = pNDFT * f
+          gHatApprox = Array(p_d * arrayType(f))
+          e = norm(gHat[:] - gHatApprox[:]) / norm(gHat[:])
+          @debug "error nfft " e
+          @test e < eps[l]
+        end
+      end
+    end
+
+    @testset "GPU_NFFT Sampling Density" begin
+
+      # create a 10x10 grid of unit spaced sampling points
+      N = 10
+      g = (0:(N-1)) ./ N .- 0.5
+      x = vec(ones(N) * g')
+      y = vec(g * ones(N)')
+      nodes = cat(x', y', dims=1)
+
+      # approximate the density weights
+      p = plan_nfft(arrayType, nodes, (N, N); m=5, σ=2.0)
+      weights = Array(sdc(p, iters=5))
+
+      @info extrema(vec(weights))
+
+      @test all((≈).(vec(weights), 1 / (N * N), rtol=1e-7))
+
+    end
+  end
+end

test/gpu/cuda.jl

@@ -0,0 +1,5 @@
+using CUDA
+
+arrayTypes = [CuArray]
+
+include(joinpath(@__DIR__(), "..", "runtests.jl"))