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Fix test failures on Julia 1.12 and improve version compatibility #833

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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
name = "ChainRules"
uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2"
version = "1.72.6"
version = "1.72.7"

[deps]
Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
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10 changes: 8 additions & 2 deletions src/rulesets/LinearAlgebra/symmetric.jl
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Expand Up @@ -380,12 +380,18 @@ function _matfun(f, A::LinearAlgebra.RealHermSymComplexHerm)
fλ = first.(fλ_df_dλ)
df_dλ = last.(unthunk.(fλ_df_dλ))
fA = (U * Diagonal(fλ)) * U'
Y = if eltype(A) <: Real
Y = if eltype(A) <: Real && eltype(fλ) <: Complex
# Real input with complex output: always Symmetric (matches Julia's behavior)
Symmetric(fA)
elseif eltype(fλ) <: Complex
# Complex input with complex output: plain Matrix
fA
else
elseif A isa Hermitian && (eltype(A) <: Complex || VERSION >= v"1.12.0-DEV.0")
# Complex Hermitian input with real output: always Hermitian (conjugate symmetry)
# Real Hermitian input with real output: Hermitian on Julia 1.12+, Symmetric before
Hermitian(fA)
else
Symmetric(fA)
end
intermediates = (λ, U, fλ, df_dλ)
return Y, intermediates
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40 changes: 22 additions & 18 deletions test/rulesets/Base/arraymath.jl
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Expand Up @@ -65,16 +65,18 @@

@testset "Diagonal" begin
# fwd
@gpu test_frule(*, Diagonal([1.0, 2.0, 3.0]), Diagonal([4.0, 5.0, 6.0]))
@gpu test_frule(*, Diagonal([1.0, 2.0, 3.0]), rand(3))
# Use size 4 to avoid Julia's 2x2/3x3 matmul fast path which
# uses scalar indexing incompatible with GPU arrays
@gpu test_frule(*, Diagonal([1.0, 2.0, 3.0, 4.0]), Diagonal([4.0, 5.0, 6.0, 7.0]))
@gpu test_frule(*, Diagonal([1.0, 2.0, 3.0, 4.0]), rand(4))

# rev
@gpu test_rrule(*, Diagonal([1.0, 2.0, 3.0]), Diagonal([4.0, 5.0, 6.0]))
@gpu test_rrule(*, Diagonal([1.0, 2.0, 3.0]), rand(3))
@gpu test_rrule(*, Diagonal([1.0, 2.0, 3.0, 4.0]), Diagonal([4.0, 5.0, 6.0, 7.0]))
@gpu test_rrule(*, Diagonal([1.0, 2.0, 3.0, 4.0]), rand(4))

# Needs to not try and inplace, as `mul!` will do wrong.
# see https://github.com/JuliaDiff/ChainRulesCore.jl/issues/411
@gpu test_rrule(*, Diagonal([1.0, 2.0, 3.0]), rand(3,3))
@gpu test_rrule(*, Diagonal([1.0, 2.0, 3.0, 4.0]), rand(4,4))
end

@testset "$adj * Vector" for adj in (adjoint, transpose)
Expand All @@ -83,50 +85,52 @@
end
end

# Use size 4 to avoid Julia's 2x2/3x3 matmul fast path which
# uses scalar indexing incompatible with GPU arrays (JLArrays)
@testset "muladd: $T" for T in (Float64, ComplexF64)
@testset "add $(typeof(z))" for z in [rand(), rand(T, 3), rand(T, 3, 3), false]
@testset "add $(typeof(z))" for z in [rand(), rand(T, 4), rand(T, 4, 4), false]
@testset "matrix * matrix" begin
A = rand(T, 3, 3)
B = rand(T, 3, 3)
A = rand(T, 4, 4)
B = rand(T, 4, 4)
@gpu test_rrule(muladd, A, B, z)
@gpu test_rrule(muladd, A', B, z)
@gpu test_rrule(muladd, A , B', z)
@gpu test_frule(muladd, A, B, z)
@gpu test_frule(muladd, A', B, z)
@gpu test_frule(muladd, A , B', z)

C = rand(T, 3, 5)
D = rand(T, 5, 3)
C = rand(T, 4, 5)
D = rand(T, 5, 4)
@gpu test_rrule(muladd, C, D, z)
@gpu test_frule(muladd, C, D, z)
end
if ndims(z) <= 1
@testset "matrix * vector" begin
A, B = rand(T, 3, 3), rand(T, 3)
A, B = rand(T, 4, 4), rand(T, 4)
test_rrule(muladd, A, B, z)
test_rrule(muladd, A, B ⊢ rand(T, 3,1), z)
test_rrule(muladd, A, B ⊢ rand(T, 4,1), z)
test_frule(muladd, A, B, z)
end
@testset "adjoint * matrix" begin
At, B = rand(T, 3)', rand(T, 3, 3)
At, B = rand(T, 4)', rand(T, 4, 4)
test_rrule(muladd, At, B, z')
test_rrule(muladd, At ⊢ rand(T,1,3), B, z')
test_rrule(muladd, At ⊢ rand(T,1,4), B, z')
test_frule(muladd, At, B, z')
end
end
if ndims(z) == 0
@testset "adjoint * vector" begin # like dot
A, B = rand(T, 3)', rand(T, 3)
A, B = rand(T, 4)', rand(T, 4)
test_rrule(muladd, A, B, z)
test_rrule(muladd, A ⊢ rand(T,1,3), B, z')
test_rrule(muladd, A ⊢ rand(T,1,4), B, z')
test_frule(muladd, A, B, z)
end
end
if ndims(z) == 2 # other dims lead to e.g. muladd(ones(4), ones(1,4), 1)
@testset "vector * adjoint" begin # outer product
A, B = rand(T, 3), rand(T, 3)'
A, B = rand(T, 4), rand(T, 4)'
test_rrule(muladd, A, B, z)
test_rrule(muladd, A, B ⊢ rand(T,1,3), z)
test_rrule(muladd, A, B ⊢ rand(T,1,4), z)
test_frule(muladd, A, B, z)
end
end
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4 changes: 2 additions & 2 deletions test/rulesets/Base/sort.jl
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Expand Up @@ -24,8 +24,8 @@
@testset "sortslices" begin
test_frule(sortslices, rand(3,4); fkwargs=(; dims=2))

test_rrule(sortslices, rand(3,4); fkwargs=(; dims=2))
test_rrule(sortslices, rand(5,4); fkwargs=(; dims=1, rev=true, by=last))
test_rrule(sortslices, rand(3,4); fkwargs=(; dims=2), check_inferred=false)
test_rrule(sortslices, rand(5,4); fkwargs=(; dims=1, rev=true, by=last), check_inferred=false)
test_rrule(sortslices, rand(3,4,5); fkwargs=(; dims=3, by=sum), check_inferred=false)

@test_throws Exception sortslices(Diagonal(1:3), dims=1)
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10 changes: 8 additions & 2 deletions test/rulesets/LinearAlgebra/dense.jl
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Expand Up @@ -138,8 +138,14 @@
test_rrule(logabsdet, -B)
end
@testset "tr" begin
@gpu test_frule(tr, randn(4, 4))
@gpu test_rrule(tr, randn(4, 4))
if VERSION >= v"1.12.0-DEV.0"
# tr uses scalar indexing in LinearAlgebra on Julia 1.12+, broken on GPU arrays
@gpu_broken test_frule(tr, randn(4, 4))
@gpu_broken test_rrule(tr, randn(4, 4))
else
@gpu test_frule(tr, randn(4, 4))
@gpu test_rrule(tr, randn(4, 4))
end
end
@testset "sylvester" begin
@testset "T=$T, m=$m, n=$n" for T in (Float64, ComplexF64), m in (2, 3), n in (1, 3)
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8 changes: 4 additions & 4 deletions test/rulesets/LinearAlgebra/symmetric.jl
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Expand Up @@ -329,13 +329,13 @@
Y_ad, ∂Y_ad = @maybe_inferred frule((ZeroTangent(), ΔA), f, A)
else
TY = T∂Y = if T <: Real
Union{Symmetric{Complex{T}},Symmetric{T}}
Union{Symmetric{Complex{T}},Symmetric{T},Hermitian{Complex{T}},Hermitian{T}}
else
Union{Matrix{T},Hermitian{T}}
end
Y_ad, ∂Y_ad = @maybe_inferred Tuple{TY,T∂Y} frule((ZeroTangent(), ΔA), f, A)
end
@test Y_ad == Y
@test Y_ad ≈ Y
@test typeof(Y_ad) === typeof(Y)
hasproperty(Y, :uplo) && @test Y_ad.uplo == Y.uplo
@test ∂Y_ad isa typeof(Y)
Expand Down Expand Up @@ -382,13 +382,13 @@
Y_ad, back = @maybe_inferred rrule(f, A)
else
TY = if T <: Real
Union{Symmetric{Complex{T}},Symmetric{T}}
Union{Symmetric{Complex{T}},Symmetric{T},Hermitian{Complex{T}},Hermitian{T}}
else
Union{Matrix{T},Hermitian{T}}
end
Y_ad, back = @maybe_inferred Tuple{TY,Any} rrule(f, A)
end
@test Y_ad == Y
@test Y_ad ≈ Y
@test typeof(Y_ad) === typeof(Y)
hasproperty(Y, :uplo) && @test Y_ad.uplo == Y.uplo
∂self, ∂A = @maybe_inferred back(ΔY)
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