import the BBM and BM stuff for tests

Author: ChrisRackauckas

Project.toml

@@ -34,6 +34,7 @@ julia = "1.6"
 
 [extras]
 ArrayInterfaceBlockBandedMatrices = "5331f1e9-51c7-46b0-a9b0-df4434785e0a"
+ArrayInterfaceBandedMatrices = "2e50d22c-5be1-4042-81b1-c572ed69783d"
 BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
 BlockBandedMatrices = "ffab5731-97b5-5995-9138-79e8c1846df0"
 IterativeSolvers = "42fd0dbc-a981-5370-80f2-aaf504508153"
@@ -46,4 +47,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["Test", "ArrayInterfaceBlockBandedMatrices", "BandedMatrices", "BlockBandedMatrices", "IterativeSolvers", "Pkg", "Random", "SafeTestsets", "Zygote", "SparsityDetection", "StaticArrays"]
+test = ["Test", "ArrayInterfaceBandedMatrices", "ArrayInterfaceBlockBandedMatrices", "BandedMatrices", "BlockBandedMatrices", "IterativeSolvers", "Pkg", "Random", "SafeTestsets", "Zygote", "SparsityDetection", "StaticArrays"]

test/test_ad.jl

@@ -2,13 +2,13 @@ using SparseDiffTools
 using ForwardDiff: Dual, jacobian
 using SparseArrays, Test
 using LinearAlgebra
-using BlockBandedMatrices
-using BandedMatrices
+using BlockBandedMatrices, ArrayInterfaceBlockBandedMatrices
+using BandedMatrices, ArrayInterfaceBandedMatrices
 using StaticArrays
 using ArrayInterfaceStaticArrays
 
 fcalls = 0
-function f(dx,x)
+function f(dx, x)
     global fcalls += 1
     for i in 2:length(x)-1
         dx[i] = x[i-1] - 2x[i] + x[i+1]
@@ -30,8 +30,8 @@ function oopf(x)
 end
 
 function nsqf(x)#length(dx)<length(x)
-    global fcalls +=1
-    dx = zero(x)[1:div(length(x),2)]
+    global fcalls += 1
+    dx = zero(x)[1:div(length(x), 2)]
     for i in 2:length(dx)
         dx[i] = x[i-1] - 2x[i] + x[i+1]
     end
@@ -40,48 +40,48 @@ function nsqf(x)#length(dx)<length(x)
 end
 
 function nsqf2(x)#length(dx)>length(x)
-    global fcalls +=1
-    dx = zeros(eltype(x),length(x)*2)
+    global fcalls += 1
+    dx = zeros(eltype(x), length(x) * 2)
     for i in 2:length(x)-1
         dx[i] = x[i-1] - 2x[i] + x[i+1]
     end
     dx[1] = -2x[1] + x[2]
     dx
 end
 
-function nsqf!(dx,x)
-    global fcalls +=1
+function nsqf!(dx, x)
+    global fcalls += 1
     for i in 2:length(dx)
         dx[i] = x[i-1] - 2x[i] + x[i+1]
     end
     dx[1] = -2x[1] + x[2]
     nothing
 end
 
-function nsqf2!(dx,x)
-    global fcalls +=1
+function nsqf2!(dx, x)
+    global fcalls += 1
     for i in 2:length(x)-1
         dx[i] = x[i-1] - 2x[i] + x[i+1]
     end
     dx[1] = -2x[1] + x[2]
     nothing
 end
 
-function staticf(x,N=length(x))
+function staticf(x, N=length(x))
     global fcalls += 1
-    SVector{N}([i == 1 ? -2x[1]+x[2] : (i == N ? x[N-1]-2x[N] : x[i-1]-2x[i]+x[i+1]) for i in 1:N])
+    SVector{N}([i == 1 ? -2x[1] + x[2] : (i == N ? x[N-1] - 2x[N] : x[i-1] - 2x[i] + x[i+1]) for i in 1:N])
 end
 
-function staticnsqf(x,N=div(length(x),2))
+function staticnsqf(x, N=div(length(x), 2))
     global fcalls += 1
-    SVector{N}(vcat([-2x[1]+x[2]],[x[i-1]-2x[i]+x[i+1] for i in 2:N]))
+    SVector{N}(vcat([-2x[1] + x[2]], [x[i-1] - 2x[i] + x[i+1] for i in 2:N]))
 end
 
 function second_derivative_stencil(N)
-    A = zeros(N,N)
+    A = zeros(N, N)
     for i in 1:N, j in 1:N
-        (j-i==-1 || j-i==1) && (A[i,j]=1)
-        j-i==0 && (A[i,j]=-2)
+        (j - i == -1 || j - i == 1) && (A[i, j] = 1)
+        j - i == 0 && (A[i, j] = -2)
     end
     A
 end
@@ -98,140 +98,140 @@ _J = sparse(J)
 
 fcalls = 0
 _J1 = similar(_J)
-forwarddiff_color_jacobian!(_J1, f, x, colorvec = repeat(1:3,10))
+forwarddiff_color_jacobian!(_J1, f, x, colorvec=repeat(1:3, 10))
 @test _J1 ≈ J
 @test fcalls == 1
 
 @info "second passed"
 
 fcalls = 0
-_J1 = forwarddiff_color_jacobian(oopf, x, colorvec = repeat(1:3,10), sparsity = _J, jac_prototype = _J)
+_J1 = forwarddiff_color_jacobian(oopf, x, colorvec=repeat(1:3, 10), sparsity=_J, jac_prototype=_J)
 @test _J1 ≈ J
 @test typeof(_J1) == typeof(_J)
 @test fcalls == 1
 
 @info "third passed"
 
 fcalls = 0
-_J1 = forwarddiff_color_jacobian(oopf, x, colorvec = repeat(1:3,10), sparsity = _J)
+_J1 = forwarddiff_color_jacobian(oopf, x, colorvec=repeat(1:3, 10), sparsity=_J)
 @test _J1 ≈ J
 @test fcalls == 1
 
 #oop with in-place Jacobian
 fcalls = 0
 _oop_jacout = sparse(1.01 .* J) # want to be nonzero to check that the pre-allocated matrix is overwritten properly
-forwarddiff_color_jacobian(_oop_jacout, oopf, x; colorvec = repeat(1:3,10), sparsity = _J, jac_prototype = _J)
+forwarddiff_color_jacobian(_oop_jacout, oopf, x; colorvec=repeat(1:3, 10), sparsity=_J, jac_prototype=_J)
 @test _oop_jacout ≈ J
 @test typeof(_oop_jacout) == typeof(_J)
 @test fcalls == 1
 
 # BandedMatrix
 _oop_jacout = BandedMatrix(-1 => diag(J, -1) .* 1.01, 0 => diag(J, 0) .* 1.01,
-                           1 => diag(J, 1) .* 1.01) # check w/BandedMatrix instead of sparse
+    1 => diag(J, 1) .* 1.01) # check w/BandedMatrix instead of sparse
 fcalls = 0
-forwarddiff_color_jacobian(_oop_jacout, oopf, x; colorvec = repeat(1:3,10), sparsity = _J)
+forwarddiff_color_jacobian(_oop_jacout, oopf, x; colorvec=repeat(1:3, 10), sparsity=_J)
 @test _oop_jacout ≈ J
 @test isa(_oop_jacout, BandedMatrix)
 @test fcalls == 1
 
 @info "4th passed"
 
 fcalls = 0
-_J1 = forwarddiff_color_jacobian(staticf, SVector{30}(x), colorvec = repeat(1:3,10), sparsity = _J, jac_prototype = SMatrix{30,30}(_J))
+_J1 = forwarddiff_color_jacobian(staticf, SVector{30}(x), colorvec=repeat(1:3, 10), sparsity=_J, jac_prototype=SMatrix{30,30}(_J))
 @test _J1 ≈ J
 @test fcalls == 1
 
 @info "5"
 
-_J1 = forwarddiff_color_jacobian(staticf, SVector{30}(x), jac_prototype = SMatrix{30,30}(_J))
+_J1 = forwarddiff_color_jacobian(staticf, SVector{30}(x), jac_prototype=SMatrix{30,30}(_J))
 @test _J1 ≈ J
-_J1 = forwarddiff_color_jacobian(oopf, x, jac_prototype = similar(_J))
+_J1 = forwarddiff_color_jacobian(oopf, x, jac_prototype=similar(_J))
 @test _J1 ≈ J
 _J1 = forwarddiff_color_jacobian(oopf, x)
 @test _J1 ≈ J
 
 #Non-square Jacobian
 #length(dx)<length(x)
-nsqJ = jacobian(nsqf,x)
+nsqJ = jacobian(nsqf, x)
 spnsqJ = sparse(nsqJ)
-_nsqJ = forwarddiff_color_jacobian(nsqf, x, dx = nothing)
+_nsqJ = forwarddiff_color_jacobian(nsqf, x, dx=nothing)
 @test _nsqJ ≈ nsqJ
-_nsqJ = forwarddiff_color_jacobian(nsqf, x, colorvec = repeat(1:3,10), sparsity = spnsqJ)
+_nsqJ = forwarddiff_color_jacobian(nsqf, x, colorvec=repeat(1:3, 10), sparsity=spnsqJ)
 @test _nsqJ ≈ nsqJ
-_nsqJ = forwarddiff_color_jacobian(nsqf, x, jac_prototype = similar(nsqJ))
+_nsqJ = forwarddiff_color_jacobian(nsqf, x, jac_prototype=similar(nsqJ))
 @test _nsqJ ≈ nsqJ
-_nsqJ = forwarddiff_color_jacobian(nsqf, x, colorvec = repeat(1:3,10), sparsity = spnsqJ, jac_prototype = similar(nsqJ))
+_nsqJ = forwarddiff_color_jacobian(nsqf, x, colorvec=repeat(1:3, 10), sparsity=spnsqJ, jac_prototype=similar(nsqJ))
 @test _nsqJ ≈ nsqJ
-_nsqJ = forwarddiff_color_jacobian(nsqf, x, jac_prototype = SMatrix{15,30}(nsqJ))
+_nsqJ = forwarddiff_color_jacobian(nsqf, x, jac_prototype=SMatrix{15,30}(nsqJ))
 @test _nsqJ ≈ nsqJ
 @test typeof(_nsqJ) == typeof(SMatrix{15,30}(nsqJ))
-_nsqJ = forwarddiff_color_jacobian(staticnsqf, SVector{30}(x), jac_prototype = SMatrix{15,30}(nsqJ))
+_nsqJ = forwarddiff_color_jacobian(staticnsqf, SVector{30}(x), jac_prototype=SMatrix{15,30}(nsqJ))
 @test _nsqJ ≈ nsqJ
-_nsqJ = forwarddiff_color_jacobian(staticnsqf, SVector{30}(x), jac_prototype = SMatrix{15,30}(nsqJ), colorvec = repeat(1:3,10), sparsity = spnsqJ)
+_nsqJ = forwarddiff_color_jacobian(staticnsqf, SVector{30}(x), jac_prototype=SMatrix{15,30}(nsqJ), colorvec=repeat(1:3, 10), sparsity=spnsqJ)
 @test _nsqJ ≈ nsqJ
 _nsqJ = similar(nsqJ)
 forwarddiff_color_jacobian!(_nsqJ, nsqf!, x)
 @test _nsqJ ≈ nsqJ
 _nsqJ = similar(nsqJ)
-forwarddiff_color_jacobian!(_nsqJ, nsqf!, x, colorvec = repeat(1:3,10), sparsity = spnsqJ )
+forwarddiff_color_jacobian!(_nsqJ, nsqf!, x, colorvec=repeat(1:3, 10), sparsity=spnsqJ)
 @test _nsqJ ≈ nsqJ
 
 #length(dx)>length(x)
-nsqJ = jacobian(nsqf2,x)
+nsqJ = jacobian(nsqf2, x)
 spnsqJ = sparse(nsqJ)
-_nsqJ = forwarddiff_color_jacobian(nsqf2, x, dx = nothing)
+_nsqJ = forwarddiff_color_jacobian(nsqf2, x, dx=nothing)
 @test _nsqJ ≈ nsqJ
-_nsqJ = forwarddiff_color_jacobian(nsqf2, x, colorvec = repeat(1:3,10), sparsity = spnsqJ)
+_nsqJ = forwarddiff_color_jacobian(nsqf2, x, colorvec=repeat(1:3, 10), sparsity=spnsqJ)
 @test _nsqJ ≈ nsqJ
-_nsqJ = forwarddiff_color_jacobian(nsqf2, x, jac_prototype = similar(nsqJ))
+_nsqJ = forwarddiff_color_jacobian(nsqf2, x, jac_prototype=similar(nsqJ))
 @test _nsqJ ≈ nsqJ
-_nsqJ = forwarddiff_color_jacobian(nsqf2, x, colorvec = repeat(1:3,10), sparsity = spnsqJ, jac_prototype = similar(nsqJ))
+_nsqJ = forwarddiff_color_jacobian(nsqf2, x, colorvec=repeat(1:3, 10), sparsity=spnsqJ, jac_prototype=similar(nsqJ))
 @test _nsqJ ≈ nsqJ
-_nsqJ = forwarddiff_color_jacobian(nsqf2, x, jac_prototype = SMatrix{60,30}(nsqJ))
+_nsqJ = forwarddiff_color_jacobian(nsqf2, x, jac_prototype=SMatrix{60,30}(nsqJ))
 @test _nsqJ ≈ nsqJ
 _nsqJ = similar(nsqJ)
 forwarddiff_color_jacobian!(_nsqJ, nsqf2!, x)
 @test _nsqJ ≈ nsqJ
 _nsqJ = similar(nsqJ)
-forwarddiff_color_jacobian!(_nsqJ, nsqf2!, x, colorvec = repeat(1:3,10), sparsity = spnsqJ )
+forwarddiff_color_jacobian!(_nsqJ, nsqf2!, x, colorvec=repeat(1:3, 10), sparsity=spnsqJ)
 @test _nsqJ ≈ nsqJ
 
 fcalls = 0
 _J1 = similar(_J)
-jac_cache = ForwardColorJacCache(f,x,colorvec = repeat(1:3,10), sparsity = _J1)
+jac_cache = ForwardColorJacCache(f, x, colorvec=repeat(1:3, 10), sparsity=_J1)
 forwarddiff_color_jacobian!(_J1, f, x, jac_cache)
 @test _J1 ≈ J
 @test fcalls == 1
 
 fcalls = 0
 _J1 = similar(_J)
 _denseJ1 = collect(_J1)
-forwarddiff_color_jacobian!(_denseJ1, f, x, colorvec = repeat(1:3,10), sparsity = _J1)
+forwarddiff_color_jacobian!(_denseJ1, f, x, colorvec=repeat(1:3, 10), sparsity=_J1)
 @test _denseJ1 ≈ J
 @test fcalls == 1
 
 fcalls = 0
 _J1 = similar(_J)
 _denseJ1 = collect(_J1)
-jac_cache = ForwardColorJacCache(f,x,colorvec = repeat(1:3,10), sparsity = _J1)
+jac_cache = ForwardColorJacCache(f, x, colorvec=repeat(1:3, 10), sparsity=_J1)
 forwarddiff_color_jacobian!(_denseJ1, f, x, jac_cache)
 @test _denseJ1 ≈ J
 @test fcalls == 1
 
 _Jt = similar(Tridiagonal(J))
-forwarddiff_color_jacobian!(_Jt, f, x, colorvec = repeat(1:3,10), sparsity = _Jt)
+forwarddiff_color_jacobian!(_Jt, f, x, colorvec=repeat(1:3, 10), sparsity=_Jt)
 @test _Jt ≈ J
 
 #https://github.com/JuliaDiff/FiniteDiff.jl/issues/67#issuecomment-516871956
 function f(out, x)
-	x = reshape(x, 100, 100)
-	out = reshape(out, 100, 100)
-	for i in 1:100
-		for j in 1:100
-			out[i, j] = x[i, j] + x[max(i -1, 1), j] + x[min(i+1, size(x, 1)), j] +  x[i, max(j-1, 1)]  + x[i, min(j+1, size(x, 2))]
-		end
-	end
-	return vec(out)
+    x = reshape(x, 100, 100)
+    out = reshape(out, 100, 100)
+    for i in 1:100
+        for j in 1:100
+            out[i, j] = x[i, j] + x[max(i - 1, 1), j] + x[min(i + 1, size(x, 1)), j] + x[i, max(j - 1, 1)] + x[i, min(j + 1, size(x, 2))]
+        end
+    end
+    return vec(out)
 end
 x = rand(10000)
 J = BandedBlockBandedMatrix(Ones(10000, 10000), fill(100, 100), fill(100, 100), (1, 1), (1, 1))
@@ -242,18 +242,18 @@ forwarddiff_color_jacobian!(Jsparse, f, x, colorvec=colors)
 @test J ≈ Jsparse
 
 # Non vector input
-x = rand(2,2)
+x = rand(2, 2)
 oopf(x) = x
-iipf(fx,x) = (fx.=x)
-J = forwarddiff_color_jacobian(oopf,x)
-@test J ≈ Matrix(I,4,4)
+iipf(fx, x) = (fx .= x)
+J = forwarddiff_color_jacobian(oopf, x)
+@test J ≈ Matrix(I, 4, 4)
 J = zero(J)
-forwarddiff_color_jacobian!(J,iipf,x,dx=similar(x))
-@test J ≈ Matrix(I,4,4)
+forwarddiff_color_jacobian!(J, iipf, x, dx=similar(x))
+@test J ≈ Matrix(I, 4, 4)
 
 #1x1 SVector test
-x = SVector{1}([1.])
+x = SVector{1}([1.0])
 f(x) = x
-J = forwarddiff_color_jacobian(f,x)
+J = forwarddiff_color_jacobian(f, x)
 @test J isa SArray
-@test J ≈ SMatrix{1,1}([1.])
+@test J ≈ SMatrix{1,1}([1.0])