mul/ewise rules for basic arithmetic semiring

Project.toml

@@ -5,7 +5,10 @@ version = "0.4.0"
 
 [deps]
 CEnum = "fa961155-64e5-5f13-b03f-caf6b980ea82"
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 ContextVariablesX = "6add18c4-b38d-439d-96f6-d6bc489c04c5"
+FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
@@ -15,8 +18,11 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [compat]
-SSGraphBLAS_jll = "5.1.2"
+CEnum = "0.4"
+ContextVariablesX = "0.1"
+MacroTools = "0.5"
+SSGraphBLAS_jll = "5.1"
 julia = "1.6"
-CEnum = "0.4.1"
-ContextVariablesX = "0.1.1"
-MacroTools = "0.5.6"
+ChainRulesCore = "0.10"
+ChainRulesTestUtils = "0.7"
+FiniteDifferences = "0.12"

src/SuiteSparseGraphBLAS.jl

@@ -87,9 +87,14 @@ include("operations/kronecker.jl")
 include("print.jl")
 include("import.jl")
 include("export.jl")
-
-#EXPERIMENTAL
 include("options.jl")
+#EXPERIMENTAL
+include("chainrules/chainruleutils.jl")
+include("chainrules/mulrules.jl")
+include("chainrules/ewiserules.jl")
+include("chainrules/maprules.jl")
+include("chainrules/reducerules.jl")
+include("chainrules/selectrules.jl")
 #include("random.jl")
 include("misc.jl")
 export libgb

src/chainrules/chainruleutils.jl

@@ -0,0 +1,46 @@
+import FiniteDifferences
+import LinearAlgebra
+import ChainRulesCore: frule, rrule
+using ChainRulesCore
+const RealOrComplex = Union{Real, Complex}
+
+#Required for ChainRulesTestUtils
+function FiniteDifferences.to_vec(M::GBMatrix)
+    I, J, X = findnz(M)
+    function backtomat(xvec)
+        return GBMatrix(I, J, xvec; nrows = size(M, 1), ncols = size(M, 2))
+    end
+    return X, backtomat
+end
+
+function FiniteDifferences.to_vec(v::GBVector)
+    i, x = findnz(v)
+    function backtovec(xvec)
+        return GBVector(i, xvec; nrows=size(v, 1))
+    end
+    return x, backtovec
+end
+
+function FiniteDifferences.rand_tangent(
+    rng::AbstractRNG,
+    x::GBMatrix{T}
+) where {T <: Union{AbstractFloat, Complex}}
+    n = nnz(x)
+    v = rand(rng, -9:0.01:9, n)
+    I, J, _ = findnz(x)
+    return GBMatrix(I, J, v; nrows = size(x, 1), ncols = size(x, 2))
+end
+
+function FiniteDifferences.rand_tangent(
+    rng::AbstractRNG,
+    x::GBVector{T}
+) where {T <: Union{AbstractFloat, Complex}}
+    n = nnz(x)
+    v = rand(rng, -9:0.01:9, n)
+    I, _ = findnz(x)
+    return GBVector(I, v; nrows = size(x, 1))
+end
+
+FiniteDifferences.rand_tangent(rng::AbstractRNG, x::AbstractOp) = NoTangent()
+# LinearAlgebra.norm freaks over the nothings.
+LinearAlgebra.norm(A::GBArray, p::Real=2) = norm(nonzeros(A), p)

src/chainrules/ewiserules.jl

@@ -0,0 +1,71 @@
+#emul TIMES
+function frule(
+    (_, ΔA, ΔB, _),
+    ::typeof(emul),
+    A::GBArray,
+    B::GBArray,
+    ::typeof(BinaryOps.TIMES)
+)
+    Ω = emul(A, B, BinaryOps.TIMES)
+    ∂Ω = emul(ΔA, B, BinaryOps.TIMES) + emul(ΔB, A, BinaryOps.TIMES)
+    return Ω, ∂Ω
+end
+function frule((_, ΔA, ΔB), ::typeof(emul), A::GBArray, B::GBArray)
+    return frule((nothing, ΔA, ΔB, nothing), emul, A, B, BinaryOps.TIMES)
+end
+
+function rrule(::typeof(emul), A::GBArray, B::GBArray, ::typeof(BinaryOps.TIMES))
+    function timespullback(ΔΩ)
+        ∂A = emul(ΔΩ, B)
+        ∂B = emul(ΔΩ, A)
+        return NoTangent(), ∂A, ∂B, NoTangent()
+    end
+    return emul(A, B, BinaryOps.TIMES), timespullback
+end
+
+function rrule(::typeof(emul), A::GBArray, B::GBArray)
+    Ω, fullpb = rrule(emul, A, B, BinaryOps.TIMES)
+    emulpb(ΔΩ) = fullpb(ΔΩ)[1:3]
+    return Ω, emulpb
+end
+
+############
+# eadd rules
+############
+
+# PLUS
+######
+
+function frule(
+    (_, ΔA, ΔB, _),
+    ::typeof(eadd),
+    A::GBArray,
+    B::GBArray,
+    ::typeof(BinaryOps.PLUS)
+)
+    Ω = eadd(A, B, BinaryOps.PLUS)
+    ∂Ω = eadd(ΔA, ΔB, BinaryOps.PLUS)
+    return Ω, ∂Ω
+end
+function frule((_, ΔA, ΔB), ::typeof(eadd), A::GBArray, B::GBArray)
+    return frule((nothing, ΔA, ΔB, nothing), eadd, A, B, BinaryOps.PLUS)
+end
+
+function rrule(::typeof(eadd), A::GBArray, B::GBArray, ::typeof(BinaryOps.PLUS))
+    function pluspullback(ΔΩ)
+        return (
+            NoTangent(),
+            mask(ΔΩ, A; structural = true),
+            mask(ΔΩ, B; structural = true),
+            NoTangent()
+        )
+    end
+    return eadd(A, B, BinaryOps.PLUS), pluspullback
+end
+
+# Do I have to duplicate this? I get 4 tangents instead of 3 if I call the previous rule.
+function rrule(::typeof(eadd), A::GBArray, B::GBArray)
+    Ω, fullpb = rrule(eadd, A, B, BinaryOps.PLUS)
+    eaddpb(ΔΩ) = fullpb(ΔΩ)[1:3]
+    return Ω, eaddpb
+end

src/chainrules/maprules.jl

@@ -0,0 +1,17 @@
+# Per Lyndon. Needs adaptation, and/or needs redefinition of map to use functions rather
+# than AbstractOp.
+#function rrule(map, f, xs)
+#    # Rather than 3 maps really want 1 multimap
+#    ys_and_pullbacks = map(x->rrule(f, x), xs) #Take this to ys = map(f, x)
+#    ys = map(first, ys_and_pullbacks)
+#    pullbacks = map(last, ys_and_pullbacks)
+#    function map_pullback(dys)
+#        _call(f, x) = f(x)
+#        dfs_and_dxs = map(_call, pullbacks, dys)
+#        # but in your case you know it will be NoTangent() so can  skip
+#        df = sum(first, dfs_and_dxs)
+#        dxs = map(last, dfs_and_dxs)
+#        return NoTangent(), df, dxs
+#    end
+#    return ys, map_pullback
+#end

src/chainrules/mulrules.jl

@@ -0,0 +1,51 @@
+# Standard arithmetic mul:
+function frule(
+    (_, ΔA, ΔB),
+    ::typeof(mul),
+    A::GBMatOrTranspose,
+    B::GBMatOrTranspose
+)
+    frule((nothing, ΔA, ΔB, nothing), mul, A, B, Semirings.PLUS_TIMES)
+end
+function frule(
+    (_, ΔA, ΔB, _),
+    ::typeof(mul),
+    A::GBMatOrTranspose,
+    B::GBMatOrTranspose,
+    ::typeof(Semirings.PLUS_TIMES)
+)
+    Ω = mul(A, B)
+    ∂Ω = mul(ΔA, B) + mul(A, ΔB)
+    return Ω, ∂Ω
+end
+# Tests will not pass for this. For two reasons.
+# First is #25, the output inference is not type stable.
+# That's it's own issue.
+
+# Second, to_vec currently works by mapping materialized values back and forth, ie. it knows nothing about nothings.
+# This means they give different answers. FiniteDifferences is probably "incorrect", but I have no proof.
+
+function rrule(
+    ::typeof(mul),
+    A::GBMatOrTranspose,
+    B::GBMatOrTranspose,
+    ::typeof(Semirings.PLUS_TIMES)
+)
+    function mulpullback(ΔΩ)
+        ∂A = mul(ΔΩ, B'; mask=A)
+        ∂B = mul(A', ΔΩ; mask=B)
+        return NoTangent(), ∂A, ∂B, NoTangent()
+    end
+    return mul(A, B), mulpullback
+end
+
+
+function rrule(
+    ::typeof(mul),
+    A::GBMatOrTranspose,
+    B::GBMatOrTranspose
+)
+    Ω, mulpullback = rrule(mul, A, B, Semirings.PLUS_TIMES)
+    pullback(ΔΩ) = mulpullback(ΔΩ)[1:3]
+return Ω, pullback
+end

src/lib/LibGraphBLAS.jl

@@ -27,27 +27,27 @@ macro wraperror(code)
         elseif info == GrB_NO_VALUE
             return nothing
         else
-            if info == GrB_UNINITIALIZED_OBJECT 
+            if info == GrB_UNINITIALIZED_OBJECT
                 throw(UninitializedObjectError)
-            elseif info == GrB_INVALID_OBJECT 
+            elseif info == GrB_INVALID_OBJECT
                 throw(InvalidObjectError)
-            elseif info == GrB_NULL_POINTER 
+            elseif info == GrB_NULL_POINTER
                 throw(NullPointerError)
-            elseif info == GrB_INVALID_VALUE 
+            elseif info == GrB_INVALID_VALUE
                 throw(InvalidValueError)
-            elseif info == GrB_INVALID_INDEX 
+            elseif info == GrB_INVALID_INDEX
                 throw(InvalidIndexError)
-            elseif info == GrB_DOMAIN_MISMATCH 
+            elseif info == GrB_DOMAIN_MISMATCH
                 throw(DomainError(nothing, "GraphBLAS Domain Mismatch"))
             elseif info == GrB_DIMENSION_MISMATCH
                 throw(DimensionMismatch())
-            elseif info == GrB_OUTPUT_NOT_EMPTY 
+            elseif info == GrB_OUTPUT_NOT_EMPTY
                 throw(OutputNotEmptyError)
-            elseif info == GrB_OUT_OF_MEMORY 
+            elseif info == GrB_OUT_OF_MEMORY
                 throw(OutOfMemoryError())
-            elseif info == GrB_INSUFFICIENT_SPACE 
+            elseif info == GrB_INSUFFICIENT_SPACE
                 throw(InsufficientSpaceError)
-            elseif info == GrB_INDEX_OUT_OF_BOUNDS 
+            elseif info == GrB_INDEX_OUT_OF_BOUNDS
                 throw(BoundsError())
             elseif info == GrB_PANIC
                 throw(PANIC)
@@ -843,7 +843,7 @@ for T ∈ valid_vec
             nvals = GrB_Vector_nvals(v)
             I = Vector{GrB_Index}(undef, nvals)
             X = Vector{$type}(undef, nvals)
-            nvals = Ref{GrB_Index}()
+            nvals = Ref{GrB_Index}(nvals)
             $func(I, X, nvals, v)
             nvals[] == length(I) == length(X) || throw(DimensionMismatch())
             return I .+ 1, X

src/matrix.jl

@@ -18,7 +18,7 @@ Create an nrows x ncols GBMatrix M such that M[I[k], J[k]] = X[k]. The dup funct
 to `|` for booleans and `+` for nonbooleans.
 """
 function GBMatrix(
-    I::Vector, J::Vector, X::Vector{T};
+    I::AbstractVector, J::AbstractVector, X::AbstractVector{T};
     dup = BinaryOps.PLUS, nrows = maximum(I), ncols = maximum(J)
 ) where {T}
     A = GBMatrix{T}(nrows, ncols)
@@ -33,14 +33,14 @@ Create an nrows x ncols GBMatrix M such that M[I[k], J[k]] = x.
 The resulting matrix is "iso-valued" such that it only stores `x` once rather than once for
 each index.
 """
-function GBMatrix(I::Vector, J::Vector, x::T;
+function GBMatrix(I::AbstractVector, J::AbstractVector, x::T;
     nrows = maximum(I), ncols = maximum(J)) where {T}
     A = GBMatrix{T}(nrows, ncols)
     build(A, I, J, x)
     return A
 end
 
-function build(A::GBMatrix{T}, I::Vector, J::Vector, x::T) where {T}
+function build(A::GBMatrix{T}, I::AbstractVector, J::AbstractVector, x::T) where {T}
 nnz(A) == 0 || throw(libgb.OutputNotEmptyError("Cannot build matrix with existing elements"))
 length(I) == length(J) || DimensionMismatch("I, J and X must have the same length")
 x = GBScalar(x)
@@ -158,7 +158,8 @@ function Base.show(io::IO, ::MIME"text/plain", A::GBMatrix)
     gxbprint(io, A)
 end
 
-SparseArrays.nonzeros(A::GBArray) = findnz(A)[3]
+SparseArrays.nonzeros(A::GBArray) = findnz(A)[end]
+
 
 # Indexing functions
 ####################

src/operations/ewise.jl

@@ -61,7 +61,6 @@ function emul!(
     desc = nothing
 )
     op, mask, accum, desc = _handlectx(op, mask, accum, desc, BinaryOps.TIMES)
-
     size(w) == size(u) == size(v) || throw(DimensionMismatch())
     op = getoperator(op, optype(u, v))
     accum = getoperator(accum, eltype(w))
@@ -275,6 +274,13 @@ function eadd(
     return eadd!(C, A, B, op; mask, accum, desc)
 end
 
+function Base.:+(A::GBArray, B::GBArray)
+    eadd(A, B, nothing)
+end
+
+function Base.:-(A::GBArray, B::GBArray)
+    eadd(A, B, BinaryOps.MINUS)
+end
 #Elementwise Broadcasts
 #######################
 

src/operations/mul.jl

@@ -59,7 +59,6 @@ function LinearAlgebra.mul!(
     return w
 end
 
-
 """
     mul(A::GBArray, B::GBArray; kwargs...)::GBArray
 

src/operations/transpose.jl

@@ -64,13 +64,47 @@ function Base.copy!(
     return gbtranspose!(C, A.parent; mask, accum, desc)
 end
 
+"""
+    mask!(C::GBArray, A::GBArray, mask::GBArray)
+
+Apply a mask to matrix `A`, storing the results in C.
+
+"""
+function mask!(C::GBArray, A::GBArray, mask::GBArray; structural = false, complement = false)
+    desc = Descriptors.T0
+    structural && (desc = desc + Descriptors.S)
+    complement && (desc = desc + Descriptors.C)
+    gbtranspose!(C, A; mask, desc)
+    return C
+end
+
+"""
+    mask(A::GBArray, mask::GBArray)
+
+Apply a mask to matrix `A`.
+"""
+function mask(A::GBArray, mask::GBArray; structural = false, complement = false)
+    return mask!(similar(A), A, mask; structural, complement)
+end
+
 function Base.copy(
     A::LinearAlgebra.Transpose{<:Any, <:GBMatrix};
     mask = C_NULL, accum = C_NULL, desc::Descriptor = Descriptors.NULL
 )
     return gbtranspose(A.parent; mask, accum, desc)
 end
 
+function Base.copy(v::LinearAlgebra.Transpose{<:Any, <:GBVector})
+    A = GBMatrix{eltype(v)}(size(v, 1), size(v, 2))
+    nz = findnz(v.parent)
+    for i ∈ 1:length(nz[1])
+        println(i)
+        println(nz[1][i], ": ", nz[2][i])
+        A[1, nz[1][i]] = nz[2][i]
+    end
+    return A
+end
+
 function _handletranspose(
     A::GBArray,
     desc::Union{Descriptor, Nothing} = nothing,

src/vector.jl

@@ -14,8 +14,8 @@ GBVector{T}(dims::Dims{1}) where {T} = GBVector{T}(dims...)
 
 Create a GBVector from a vector of indices `I` and a vector of values `X`.
 """
-function GBVector(I::Vector, X::Vector{T}; dup = BinaryOps.PLUS) where {T}
-    x = GBVector{T}(maximum(I))
+function GBVector(I::AbstractVector, X::AbstractVector{T}; dup = BinaryOps.PLUS, nrows = maximum(I)) where {T}
+    x = GBVector{T}(nrows)
     build(x, I, X, dup = dup)
     return x
 end
@@ -27,14 +27,14 @@ Create an nrows length GBVector v such that M[I[k]] = x.
 The resulting vector is "iso-valued" such that it only stores `x` once rather than once for
 each index.
 """
-function GBVector(I::Vector, x::T;
+function GBVector(I::AbstractVector, x::T;
     nrows = maximum(I)) where {T}
     A = GBVector{T}(nrows)
     build(A, I, x)
     return A
 end
 
-function build(A::GBVector{T}, I::Vector, x::T) where {T}
+function build(A::GBVector{T}, I::AbstractVector, x::T) where {T}
 nnz(A) == 0 || throw(libgb.OutputNotEmptyError("Cannot build vector with existing elements"))
 x = GBScalar(x)
 

test/chainrules/chainrulesutils.jl

@@ -0,0 +1,17 @@
+using FiniteDifferences
+function test_to_vec(x::T; check_inferred=true) where {T}
+    check_inferred && @inferred FiniteDifferences.to_vec(x)
+    x_vec, back = FiniteDifferences.to_vec(x)
+    @test x_vec isa Vector
+    @test all(s -> s isa Real, x_vec)
+    check_inferred && @inferred back(x_vec)
+    @test x == back(x_vec)
+    return nothing
+end
+
+@testset "chainrulesutils" begin
+    y = GBMatrix(sprand(10, 10, 0.5))
+    test_to_vec(y)
+    v = GBVector(sprand(10, 0.5))
+    test_to_vec(v)
+end

test/chainrules/ewiserules.jl

@@ -0,0 +1,32 @@
+@testset "Elementwise" begin
+    @testset "Dense" begin
+        @testset "Arithmetic Semiring" begin
+            #dense first
+            Y = GBMatrix(rand(-10.0:0.05:10.0, 10))
+            X = GBMatrix(rand(-10.0:0.05:10.0, 10))
+            test_frule(eadd, X, Y; check_inferred=false)
+            test_frule(eadd, X, Y, BinaryOps.PLUS; check_inferred=false)
+            test_rrule(eadd, X, Y; check_inferred=false)
+            test_rrule(eadd, X, Y, BinaryOps.PLUS; check_inferred=false)
+            test_frule(emul, X, Y; check_inferred=false)
+            test_frule(emul, X, Y, BinaryOps.TIMES; check_inferred=false)
+            test_rrule(emul, X, Y; check_inferred=false)
+            test_rrule(emul, X, Y, BinaryOps.TIMES; check_inferred=false)
+        end
+    end
+
+    @testset "Sparse" begin
+        @testset "Arithmetic Semiring" begin
+            Y = GBMatrix(sprand(10, 0.5)) #using matrix for now until I work out transpose(v::GBVector)
+            X = GBMatrix(sprand(10, 0.5))
+            test_frule(eadd, X, Y; check_inferred=false)
+            test_frule(eadd, X, Y, BinaryOps.PLUS; check_inferred=false)
+            test_rrule(eadd, X, Y; check_inferred=false)
+            test_rrule(eadd, X, Y, BinaryOps.PLUS; check_inferred=false)
+            test_frule(emul, X, Y; check_inferred=false)
+            test_frule(emul, X, Y, BinaryOps.TIMES; check_inferred=false)
+            test_rrule(emul, X, Y; check_inferred=false)
+            test_rrule(emul, X, Y, BinaryOps.TIMES; check_inferred=false)
+        end
+    end
+end

test/chainrules/mulrules.jl

@@ -0,0 +1,21 @@
+@testset "mul" begin
+    @testset "Dense" begin
+        @testset "Arithmetic Semiring" begin
+            M = GBMatrix(rand(-10.0:0.05:10.0, 10, 10))
+            Y = GBMatrix(rand(-10.0:0.05:10.0, 10))
+            test_frule(mul, M, Y; check_inferred=false)
+            test_frule(mul, M, Y, Semirings.PLUS_TIMES; check_inferred=false)
+            test_rrule(mul, M, Y; check_inferred=false)
+            test_rrule(mul, M, Y, Semirings.PLUS_TIMES; check_inferred=false)
+        end
+    end
+
+    @testset "Sparse" begin
+        M = GBMatrix(sprand(100, 10, 0.25))
+        Y = GBMatrix(sprand(10, 0.1)) #using matrix for now until I work out transpose(v::GBVector)
+        test_frule(mul, M, Y; check_inferred=false)
+        test_frule(mul, M, Y, Semirings.PLUS_TIMES; check_inferred=false)
+        test_rrule(mul, M, Y; check_inferred=false)
+        test_rrule(mul, M, Y, Semirings.PLUS_TIMES; check_inferred=false)
+    end
+end

test/runtests.jl

@@ -2,7 +2,7 @@ using SuiteSparseGraphBLAS
 using SparseArrays
 using Test
 using Random
-
+using ChainRulesTestUtils
 Random.seed!(1)
 
 function include_test(path)
@@ -12,6 +12,10 @@ end
 
 println("Testing SuiteSparseGraphBLAS.jl")
 @testset "SuiteSparseGraphBLAS" begin
+
     include_test("gbarray.jl")
     include_test("operations.jl")
+    include_test("chainrules/chainrulesutils.jl")
+    include_test("chainrules/mulrules.jl")
+    include_test("chainrules/mulrules.jl")
 end