Merge branch 'master' into eigen

JuliaDiff · Dec 9, 2020 · 6a519a4 · 6a519a4
2 parents 54a8e95 + 80443b1
commit 6a519a4
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diff --git a/src/ChainRules.jl b/src/ChainRules.jl
@@ -58,8 +58,12 @@ function __init__()
         include("rulesets/packages/NaNMath.jl")
     end
 
+    # Note: drop SpecialFunctions dependency in next breaking release
+    # https://github.com/JuliaDiff/ChainRules.jl/issues/319
     @require SpecialFunctions="276daf66-3868-5448-9aa4-cd146d93841b" begin
-        include("rulesets/packages/SpecialFunctions.jl")
+        if !isdefined(SpecialFunctions, :ChainRulesCore)
+            include("rulesets/packages/SpecialFunctions.jl")
+        end
     end
 end
 

diff --git a/src/rulesets/LinearAlgebra/factorization.jl b/src/rulesets/LinearAlgebra/factorization.jl
@@ -202,20 +202,69 @@ end
 ##### `cholesky`
 #####
 
-function rrule(::typeof(cholesky), X::AbstractMatrix{<:Real})
-    F = cholesky(X)
-    function cholesky_pullback(Ȳ::Composite)
-        ∂X = if F.uplo === 'U'
-            chol_blocked_rev(Ȳ.U, F.U, 25, true)
-        else
-            chol_blocked_rev(Ȳ.L, F.L, 25, false)
-        end
-        return (NO_FIELDS, ∂X)
+function rrule(::typeof(cholesky), A::Real, uplo::Symbol=:U)
+    C = cholesky(A, uplo)
+    function cholesky_pullback(ΔC::Composite)
+        return NO_FIELDS, ΔC.factors[1, 1] / (2 * C.U[1, 1]), DoesNotExist()
+    end
+    return C, cholesky_pullback
+end
+
+function rrule(::typeof(cholesky), A::Diagonal{<:Real}, ::Val{false}; check::Bool=true)
+    C = cholesky(A, Val(false); check=check)
+    function cholesky_pullback(ΔC::Composite)
+        Ā = Diagonal(diag(ΔC.factors) .* inv.(2 .* C.factors.diag))
+        return NO_FIELDS, Ā, DoesNotExist()
+    end
+    return C, cholesky_pullback
+end
+
+# The appropriate cotangent is different depending upon whether A is Symmetric / Hermitian,
+# or just a StridedMatrix.
+# Implementation due to Seeger, Matthias, et al. "Auto-differentiating linear algebra."
+function rrule(
+    ::typeof(cholesky),
+    A::LinearAlgebra.HermOrSym{<:LinearAlgebra.BlasReal, <:StridedMatrix},
+    ::Val{false};
+    check::Bool=true,
+)
+    C = cholesky(A, Val(false); check=check)
+    function cholesky_pullback(ΔC::Composite)
+        Ā, U = _cholesky_pullback_shared_code(C, ΔC)
+        Ā = BLAS.trsm!('R', 'U', 'C', 'N', one(eltype(Ā)) / 2, U.data, Ā)
+        return NO_FIELDS, _symhermtype(A)(Ā), DoesNotExist()
     end
-    return F, cholesky_pullback
+    return C, cholesky_pullback
+end
+
+function rrule(
+    ::typeof(cholesky),
+    A::StridedMatrix{<:LinearAlgebra.BlasReal},
+    ::Val{false};
+    check::Bool=true,
+)
+    C = cholesky(A, Val(false); check=check)
+    function cholesky_pullback(ΔC::Composite)
+        Ā, U = _cholesky_pullback_shared_code(C, ΔC)
+        Ā = BLAS.trsm!('R', 'U', 'C', 'N', one(eltype(Ā)), U.data, Ā)
+        idx = diagind(Ā)
+        @views Ā[idx] .= real.(Ā[idx]) ./ 2
+        return (NO_FIELDS, UpperTriangular(Ā), DoesNotExist())
+    end
+    return C, cholesky_pullback
+end
+
+function _cholesky_pullback_shared_code(C, ΔC)
+    U = C.U
+    Ū = ΔC.U
+    Ā = similar(U.data)
+    Ā = mul!(Ā, Ū, U')
+    Ā = LinearAlgebra.copytri!(Ā, 'U', true)
+    Ā = ldiv!(U, Ā)
+    return Ā, U
 end
 
-function rrule(::typeof(getproperty), F::T, x::Symbol) where T <: Cholesky
+function rrule(::typeof(getproperty), F::T, x::Symbol) where {T <: Cholesky}
     function getproperty_cholesky_pullback(Ȳ)
         C = Composite{T}
         ∂F = if x === :U
@@ -235,161 +284,3 @@ function rrule(::typeof(getproperty), F::T, x::Symbol) where T <: Cholesky
     end
     return getproperty(F, x), getproperty_cholesky_pullback
 end
-
-# See "Differentiation of the Cholesky decomposition" (Murray 2016), pages 5-9 in particular,
-# for derivations. Here we're implementing the algorithms and their transposes.
-
-"""
-    level2partition(A::AbstractMatrix, j::Integer, upper::Bool)
-
-Returns views to various bits of the lower triangle of `A` according to the
-`level2partition` procedure defined in [1] if `upper` is `false`. If `upper` is `true` then
-the transposed views are returned from the upper triangle of `A`.
-
-[1]: "Differentiation of the Cholesky decomposition", Murray 2016
-"""
-function level2partition(A::AbstractMatrix, j::Integer, upper::Bool)
-    n = checksquare(A)
-    @boundscheck checkbounds(1:n, j)
-    if upper
-        r = view(A, 1:j-1, j)
-        d = view(A, j, j)
-        B = view(A, 1:j-1, j+1:n)
-        c = view(A, j, j+1:n)
-    else
-        r = view(A, j, 1:j-1)
-        d = view(A, j, j)
-        B = view(A, j+1:n, 1:j-1)
-        c = view(A, j+1:n, j)
-    end
-    return r, d, B, c
-end
-
-"""
-    level3partition(A::AbstractMatrix, j::Integer, k::Integer, upper::Bool)
-
-Returns views to various bits of the lower triangle of `A` according to the
-`level3partition` procedure defined in [1] if `upper` is `false`. If `upper` is `true` then
-the transposed views are returned from the upper triangle of `A`.
-
-[1]: "Differentiation of the Cholesky decomposition", Murray 2016
-"""
-function level3partition(A::AbstractMatrix, j::Integer, k::Integer, upper::Bool)
-    n = checksquare(A)
-    @boundscheck checkbounds(1:n, j)
-    if upper
-        R = view(A, 1:j-1, j:k)
-        D = view(A, j:k, j:k)
-        B = view(A, 1:j-1, k+1:n)
-        C = view(A, j:k, k+1:n)
-    else
-        R = view(A, j:k, 1:j-1)
-        D = view(A, j:k, j:k)
-        B = view(A, k+1:n, 1:j-1)
-        C = view(A, k+1:n, j:k)
-    end
-    return R, D, B, C
-end
-
-"""
-    chol_unblocked_rev!(Ā::AbstractMatrix, L::AbstractMatrix, upper::Bool)
-
-Compute the reverse-mode sensitivities of the Cholesky factorization in an unblocked manner.
-If `upper` is `false`, then the sensitivites are computed from and stored in the lower triangle
-of `Ā` and `L` respectively. If `upper` is `true` then they are computed and stored in the
-upper triangles. If at input `upper` is `false` and `tril(Ā) = L̄`, at output
-`tril(Ā) = tril(Σ̄)`, where `Σ = LLᵀ`. Analogously, if at input `upper` is `true` and
-`triu(Ā) = triu(Ū)`, at output `triu(Ā) = triu(Σ̄)` where `Σ = UᵀU`.
-"""
-function chol_unblocked_rev!(Σ̄::AbstractMatrix{T}, L::AbstractMatrix{T}, upper::Bool) where T<:Real
-    n = checksquare(Σ̄)
-    j = n
-    @inbounds for _ in 1:n
-        r, d, B, c = level2partition(L, j, upper)
-        r̄, d̄, B̄, c̄ = level2partition(Σ̄, j, upper)
-
-        # d̄ <- d̄ - c'c̄ / d.
-        d̄[1] -= dot(c, c̄) / d[1]
-
-        # [d̄ c̄'] <- [d̄ c̄'] / d.
-        d̄ ./= d
-        c̄ ./= d
-
-        # r̄ <- r̄ - [d̄ c̄'] [r' B']'.
-        r̄ = axpy!(-Σ̄[j,j], r, r̄)
-        r̄ = gemv!(upper ? 'n' : 'T', -one(T), B, c̄, one(T), r̄)
-
-        # B̄ <- B̄ - c̄ r.
-        B̄ = upper ? ger!(-one(T), r, c̄, B̄) : ger!(-one(T), c̄, r, B̄)
-        d̄ ./= 2
-        j -= 1
-    end
-    return (upper ? triu! : tril!)(Σ̄)
-end
-
-function chol_unblocked_rev(Σ̄::AbstractMatrix, L::AbstractMatrix, upper::Bool)
-    return chol_unblocked_rev!(copy(Σ̄), L, upper)
-end
-
-"""
-    chol_blocked_rev!(Σ̄::StridedMatrix, L::StridedMatrix, nb::Integer, upper::Bool)
-
-Compute the sensitivities of the Cholesky factorization using a blocked, cache-friendly
-procedure. `Σ̄` are the sensitivities of `L`, and will be transformed into the sensitivities
-of `Σ`, where `Σ = LLᵀ`. `nb` is the block size to use. If the upper triangle has been used
-to represent the factorization, that is `Σ = UᵀU` where `U := Lᵀ`, then this should be
-indicated by passing `upper = true`.
-"""
-function chol_blocked_rev!(Σ̄::StridedMatrix{T}, L::StridedMatrix{T}, nb::Integer, upper::Bool) where T<:Real
-    n = checksquare(Σ̄)
-    tmp = Matrix{T}(undef, nb, nb)
-    k = n
-    if upper
-        @inbounds for _ in 1:nb:n
-            j = max(1, k - nb + 1)
-            R, D, B, C = level3partition(L, j, k, true)
-            R̄, D̄, B̄, C̄ = level3partition(Σ̄, j, k, true)
-
-            C̄ = trsm!('L', 'U', 'N', 'N', one(T), D, C̄)
-            gemm!('N', 'N', -one(T), R, C̄, one(T), B̄)
-            gemm!('N', 'T', -one(T), C, C̄, one(T), D̄)
-            chol_unblocked_rev!(D̄, D, true)
-            gemm!('N', 'T', -one(T), B, C̄, one(T), R̄)
-            if size(D̄, 1) == nb
-                tmp = axpy!(one(T), D̄, transpose!(tmp, D̄))
-                gemm!('N', 'N', -one(T), R, tmp, one(T), R̄)
-            else
-                gemm!('N', 'N', -one(T), R, D̄ + D̄', one(T), R̄)
-            end
-
-            k -= nb
-        end
-        return triu!(Σ̄)
-    else
-        @inbounds for _ in 1:nb:n
-            j = max(1, k - nb + 1)
-            R, D, B, C = level3partition(L, j, k, false)
-            R̄, D̄, B̄, C̄ = level3partition(Σ̄, j, k, false)
-
-            C̄ = trsm!('R', 'L', 'N', 'N', one(T), D, C̄)
-            gemm!('N', 'N', -one(T), C̄, R, one(T), B̄)
-            gemm!('T', 'N', -one(T), C̄, C, one(T), D̄)
-            chol_unblocked_rev!(D̄, D, false)
-            gemm!('T', 'N', -one(T), C̄, B, one(T), R̄)
-            if size(D̄, 1) == nb
-                tmp = axpy!(one(T), D̄, transpose!(tmp, D̄))
-                gemm!('N', 'N', -one(T), tmp, R, one(T), R̄)
-            else
-                gemm!('N', 'N', -one(T), D̄ + D̄', R, one(T), R̄)
-            end
-
-            k -= nb
-        end
-        return tril!(Σ̄)
-    end
-end
-
-function chol_blocked_rev(Σ̄::AbstractMatrix, L::AbstractMatrix, nb::Integer, upper::Bool)
-    # Convert to `Matrix`s because blas functions require StridedMatrix input.
-    return chol_blocked_rev!(Matrix(Σ̄), Matrix(L), nb, upper)
-end
diff --git a/test/rulesets/LinearAlgebra/factorization.jl b/test/rulesets/LinearAlgebra/factorization.jl
@@ -1,4 +1,15 @@
-using ChainRules: level2partition, level3partition, chol_blocked_rev, chol_unblocked_rev
+function FiniteDifferences.to_vec(C::Cholesky)
+    C_vec, factors_from_vec = to_vec(C.factors)
+    function cholesky_from_vec(v)
+        return Cholesky(factors_from_vec(v), C.uplo, C.info)
+    end
+    return C_vec, cholesky_from_vec
+end
+
+function FiniteDifferences.to_vec(x::Val)
+    Val_from_vec(v) = x
+    return Bool[], Val_from_vec
+end
 
 @testset "Factorizations" begin
     @testset "svd" begin
@@ -73,6 +84,7 @@ using ChainRules: level2partition, level3partition, chol_blocked_rev, chol_unblo
             @test ChainRules._eyesubx!(copy(X)) ≈ I - X
         end
     end
+
     @testset "eigendecomposition" begin
         @testset "eigen/eigen!" begin
             # NOTE: eigen!/eigen are not type-stable, so neither are their frule/rrule
@@ -204,69 +216,54 @@ using ChainRules: level2partition, level3partition, chol_blocked_rev, chol_unblo
             end
         end
     end
-    @testset "cholesky" begin
-        @testset "the thing" begin
-            X = generate_well_conditioned_matrix(10)
-            V = generate_well_conditioned_matrix(10)
-            F, dX_pullback = rrule(cholesky, X)
-            for p in [:U, :L]
-                Y, dF_pullback = rrule(getproperty, F, p)
-                Ȳ = (p === :U ? UpperTriangular : LowerTriangular)(randn(size(Y)))
-                (dself, dF, dp) = dF_pullback(Ȳ)
-                @test dself === NO_FIELDS
-                @test dp === DoesNotExist()
 
-                # NOTE: We're doing Nabla-style testing here and avoiding using the `j′vp`
-                # machinery from FiniteDifferences because that isn't set up to respect
-                # necessary special properties of the input. In the case of the Cholesky
-                # factorization, we need the input to be Hermitian.
-                ΔF = unthunk(dF)
-                _, dX = dX_pullback(ΔF)
-                X̄_ad = dot(unthunk(dX), V)
-                X̄_fd = _fdm(0.0) do ε
-                    dot(Ȳ, getproperty(cholesky(X .+ ε .* V), p))
-                end
-                @test X̄_ad ≈ X̄_fd rtol=1e-6 atol=1e-6
-            end
+    # These tests are generally a bit tricky to write because FiniteDifferences doesn't
+    # have fantastic support for this stuff at the minute.
+    @testset "cholesky" begin
+        @testset "Real" begin
+            C = cholesky(rand() + 0.1)
+            ΔC = Composite{typeof(C)}((factors=rand_tangent(C.factors)))
+            rrule_test(cholesky, ΔC, (rand() + 0.1, randn()))
+        end
+        @testset "Diagonal{<:Real}" begin
+            D = Diagonal(rand(5) .+ 0.1)
+            C = cholesky(D)
+            ΔC = Composite{typeof(C)}((factors=Diagonal(randn(5))))
+            rrule_test(cholesky, ΔC, (D, Diagonal(randn(5))), (Val(false), nothing))
         end
-        @testset "helper functions" begin
-            A = randn(5, 5)
-            r, d, B2, c = level2partition(A, 4, false)
-            R, D, B3, C = level3partition(A, 4, 4, false)
-            @test all(r .== R')
-            @test all(d .== D)
-            @test B2[1] == B3[1]
-            @test all(c .== C)
 
-            # Check that level 2 partition with `upper == true` is consistent with `false`
-            rᵀ, dᵀ, B2ᵀ, cᵀ = level2partition(transpose(A), 4, true)
-            @test r == rᵀ
-            @test d == dᵀ
-            @test B2' == B2ᵀ
-            @test c == cᵀ
+        X = generate_well_conditioned_matrix(10)
+        V = generate_well_conditioned_matrix(10)
+        F, dX_pullback = rrule(cholesky, X, Val(false))
+        @testset "uplo=$p" for p in [:U, :L]
+            Y, dF_pullback = rrule(getproperty, F, p)
+            Ȳ = (p === :U ? UpperTriangular : LowerTriangular)(randn(size(Y)))
+            (dself, dF, dp) = dF_pullback(Ȳ)
+            @test dself === NO_FIELDS
+            @test dp === DoesNotExist()
 
-            # Check that level 3 partition with `upper == true` is consistent with `false`
-            R, D, B3, C = level3partition(A, 2, 4, false)
-            Rᵀ, Dᵀ, B3ᵀ, Cᵀ = level3partition(transpose(A), 2, 4, true)
-            @test transpose(R) == Rᵀ
-            @test transpose(D) == Dᵀ
-            @test transpose(B3) == B3ᵀ
-            @test transpose(C) == Cᵀ
+            # NOTE: We're doing Nabla-style testing here and avoiding using the `j′vp`
+            # machinery from FiniteDifferences because that isn't set up to respect
+            # necessary special properties of the input. In the case of the Cholesky
+            # factorization, we need the input to be Hermitian.
+            ΔF = unthunk(dF)
+            _, dX = dX_pullback(ΔF)
+            X̄_ad = dot(unthunk(dX), V)
+            X̄_fd = central_fdm(5, 1)(0.000_001) do ε
+                dot(Ȳ, getproperty(cholesky(X .+ ε .* V), p))
+            end
+            @test X̄_ad ≈ X̄_fd rtol=1e-4
+        end
 
-            A = Matrix(LowerTriangular(randn(10, 10)))
-            Ā = Matrix(LowerTriangular(randn(10, 10)))
-            # NOTE: BLAS gets angry if we don't materialize the Transpose objects first
-            B = Matrix(transpose(A))
-            B̄ = Matrix(transpose(Ā))
-            @test chol_unblocked_rev(Ā, A, false) ≈ chol_blocked_rev(Ā, A, 1, false)
-            @test chol_unblocked_rev(Ā, A, false) ≈ chol_blocked_rev(Ā, A, 3, false)
-            @test chol_unblocked_rev(Ā, A, false) ≈ chol_blocked_rev(Ā, A, 5, false)
-            @test chol_unblocked_rev(Ā, A, false) ≈ chol_blocked_rev(Ā, A, 10, false)
-            @test chol_unblocked_rev(Ā, A, false) ≈ transpose(chol_unblocked_rev(B̄, B, true))
+        # Ensure that cotangents of cholesky(::StridedMatrix) and
+        # (cholesky ∘ Symmetric)(::StridedMatrix) are equal.
+        @testset "Symmetric" begin
+            X_symmetric, sym_back = rrule(Symmetric, X, :U)
+            C, chol_back_sym = rrule(cholesky, X_symmetric, Val(false))
 
-            @test chol_unblocked_rev(B̄, B, true) ≈ chol_blocked_rev(B̄, B, 1, true)
-            @test chol_unblocked_rev(B̄, B, true) ≈ chol_blocked_rev(B̄, B, 5, true)
-            @test chol_unblocked_rev(B̄, B, true) ≈ chol_blocked_rev(B̄, B, 10, true)
+            Δ = Composite{typeof(C)}((U=UpperTriangular(randn(size(X)))))
+            ΔX_symmetric = chol_back_sym(Δ)[2]
+            @test sym_back(ΔX_symmetric)[2] ≈ dX_pullback(Δ)[2]
         end
     end
 end
diff --git a/test/rulesets/packages/SpecialFunctions.jl b/test/rulesets/packages/SpecialFunctions.jl
@@ -1,5 +1,3 @@
-using SpecialFunctions
-
 @testset "SpecialFunctions" for x in (1.0, -1.0, 0.0, 0.5, 10.0, -17.1, 1.5 + 0.7im)
     test_scalar(SpecialFunctions.erf, x)
     test_scalar(SpecialFunctions.erfc, x)