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Fix gradient and Jacobian for functions with Dual output #770

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Sep 12, 2025
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Fix gradient and Jacobian for functions with Dual output #770

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47adb2c
Fix gradient and Jacobian for functions with `Dual` output
devmotion Sep 3, 2025
dcde31e
Bump version from 1.2.0 to 1.2.1
devmotion Sep 9, 2025
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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 = "ForwardDiff"
uuid = "f6369f11-7733-5829-9624-2563aa707210"
version = "1.2.0"
version = "1.2.1"

[deps]
CommonSubexpressions = "bbf7d656-a473-5ed7-a52c-81e309532950"
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6 changes: 3 additions & 3 deletions ext/ForwardDiffStaticArraysExt.jl
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Expand Up @@ -47,7 +47,7 @@ ForwardDiff._lyap_div!!(A::StaticArrays.MMatrix, λ::AbstractVector) = ForwardDi
result = Expr(:tuple, [:(partials(T, y, $i)) for i in 1:length(x)]...)
return quote
$(Expr(:meta, :inline))
V = StaticArrays.similar_type(S, valtype($y))
V = StaticArrays.similar_type(S, valtype(T, $y))
return V($result)
end
end
Expand Down Expand Up @@ -77,7 +77,7 @@ end
result = Expr(:tuple, [:(partials(T, ydual[$i], $j)) for i in 1:M, j in 1:N]...)
return quote
$(Expr(:meta, :inline))
V = StaticArrays.similar_type(S, valtype(eltype($ydual)), Size($M, $N))
V = StaticArrays.similar_type(S, valtype(T, eltype($ydual)), Size($M, $N))
return V($result)
end
end
Expand All @@ -88,7 +88,7 @@ end
end

function extract_jacobian(::Type{T}, ydual::AbstractArray, x::StaticArray) where T
result = similar(ydual, valtype(eltype(ydual)), length(ydual), length(x))
result = similar(ydual, valtype(T, eltype(ydual)), length(ydual), length(x))
return extract_jacobian!(T, result, ydual, length(x))
end

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11 changes: 11 additions & 0 deletions src/dual.jl
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Expand Up @@ -128,6 +128,17 @@ end
@inline valtype(::Dual{T,V,N}) where {T,V,N} = V
@inline valtype(::Type{Dual{T,V,N}}) where {T,V,N} = V

@inline valtype(::Type{T}, ::V) where {T,V} = valtype(T, V)
@inline valtype(::Type, ::Type{V}) where {V} = V
@inline valtype(::Type{T}, ::Type{Dual{T,V,N}}) where {T,V,N} = V
@inline function valtype(::Type{T}, ::Type{Dual{S,V,N}}) where {T,S,V,N}
if S ≺ T
Dual{S,V,N}
else
throw(DualMismatchError(T,S))
end
end

@inline tagtype(::V) where {V} = Nothing
@inline tagtype(::Type{V}) where {V} = Nothing
@inline tagtype(::Dual{T,V,N}) where {T,V,N} = T
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4 changes: 2 additions & 2 deletions src/gradient.jl
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Expand Up @@ -97,7 +97,7 @@ const GRAD_ERROR = DimensionMismatch("gradient(f, x) expects that f(x) is a real
function vector_mode_gradient(f::F, x, cfg::GradientConfig{T}) where {T, F}
ydual = vector_mode_dual_eval!(f, cfg, x)
ydual isa Real || throw(GRAD_ERROR)
result = similar(x, valtype(ydual))
result = similar(x, valtype(T, ydual))
return extract_gradient!(T, result, ydual)
end

Expand Down Expand Up @@ -156,7 +156,7 @@ function chunk_mode_gradient_expr(result_definition::Expr)
end

@eval function chunk_mode_gradient(f::F, x, cfg::GradientConfig{T,V,N}) where {F,T,V,N}
$(chunk_mode_gradient_expr(:(result = similar(x, valtype(ydual)))))
$(chunk_mode_gradient_expr(:(result = similar(x, valtype(T, ydual)))))
end

@eval function chunk_mode_gradient!(result, f::F, x, cfg::GradientConfig{T,V,N}) where {F,T,V,N}
Expand Down
4 changes: 2 additions & 2 deletions src/jacobian.jl
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Expand Up @@ -128,7 +128,7 @@ function vector_mode_jacobian(f::F, x, cfg::JacobianConfig{T}) where {F,T}
N = chunksize(cfg)
ydual = vector_mode_dual_eval!(f, cfg, x)
ydual isa AbstractArray || throw(JACOBIAN_ERROR)
result = similar(ydual, valtype(eltype(ydual)), length(ydual), N)
result = similar(ydual, valtype(T, eltype(ydual)), length(ydual), N)
extract_jacobian!(T, result, ydual, N)
extract_value!(T, result, ydual)
return result
Expand Down Expand Up @@ -217,7 +217,7 @@ end
seed!(xdual, x)
end,
:(ydual = f(xdual)),
:(result = similar(ydual, valtype(eltype(ydual)), length(ydual), xlen)),
:(result = similar(ydual, valtype(T, eltype(ydual)), length(ydual), xlen)),
:()))
end

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15 changes: 15 additions & 0 deletions test/DualTest.jl
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Expand Up @@ -101,6 +101,21 @@ ForwardDiff.:≺(::Type{OuterTestTag}, ::Type{TestTag}) = false
@test ForwardDiff.valtype(NESTED_FDNUM) == Dual{TestTag,V,M}
@test ForwardDiff.valtype(typeof(NESTED_FDNUM)) == Dual{TestTag,V,M}

@test ForwardDiff.valtype(TestTag, FDNUM) == V
@test ForwardDiff.valtype(TestTag, typeof(FDNUM)) == V
@test ForwardDiff.valtype(TestTag, NESTED_FDNUM) == Dual{TestTag,V,M}
@test ForwardDiff.valtype(TestTag, typeof(NESTED_FDNUM)) == Dual{TestTag,V,M}

@test ForwardDiff.valtype(OuterTestTag, FDNUM) == Dual{TestTag,V,N}
@test ForwardDiff.valtype(OuterTestTag, typeof(FDNUM)) == Dual{TestTag,V,N}
@test ForwardDiff.valtype(OuterTestTag, NESTED_FDNUM) == Dual{TestTag,Dual{TestTag,V,M},N}
@test ForwardDiff.valtype(OuterTestTag, typeof(NESTED_FDNUM)) == Dual{TestTag,Dual{TestTag,V,M},N}

@test_throws ForwardDiff.DualMismatchError(TestTag, OuterTestTag) ForwardDiff.valtype(TestTag, Dual{OuterTestTag}(PRIMAL, PARTIALS))
@test_throws ForwardDiff.DualMismatchError(TestTag, OuterTestTag) ForwardDiff.valtype(TestTag, typeof(Dual{OuterTestTag}(PRIMAL, PARTIALS)))
@test_throws ForwardDiff.DualMismatchError(TestTag, OuterTestTag) ForwardDiff.valtype(TestTag, Dual{OuterTestTag}(Dual{TestTag}(PRIMAL, M_PARTIALS), NESTED_PARTIALS))
@test_throws ForwardDiff.DualMismatchError(TestTag, OuterTestTag) ForwardDiff.valtype(TestTag, typeof(Dual{OuterTestTag}(Dual{TestTag}(PRIMAL, M_PARTIALS), NESTED_PARTIALS)))

#####################
# Generic Functions #
#####################
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31 changes: 31 additions & 0 deletions test/GradientTest.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,11 @@ using DiffTests

include(joinpath(dirname(@__FILE__), "utils.jl"))

struct TestTag end
struct OuterTestTag end
ForwardDiff.:≺(::Type{TestTag}, ::Type{OuterTestTag}) = true
ForwardDiff.:≺(::Type{OuterTestTag}, ::Type{<:Tag}) = true

##################
# hardcoded test #
##################
Expand Down Expand Up @@ -255,4 +260,30 @@ end
end
end

# issue #769
@testset "functions with `Dual` output" begin
x = [Dual{OuterTestTag}(Dual{TestTag}(1.3, 2.1), Dual{TestTag}(0.3, -2.4))]
f(x) = sum(ForwardDiff.value, x)
der = ForwardDiff.derivative(ForwardDiff.value, only(x))

# Vector mode
grad = ForwardDiff.gradient(f, x)
@test grad isa Vector{typeof(der)}
@test grad == [der]
grad = ForwardDiff.gradient(f, SVector{1}(x))
@test grad isa SVector{1,typeof(der)}
@test grad == SVector{1}(der)

# Chunk mode
y = repeat(x, 3)
cfg = ForwardDiff.GradientConfig(f, y, ForwardDiff.Chunk{2}())
grad = ForwardDiff.gradient(f, y, cfg)
@test grad isa Vector{typeof(der)}
@test grad == [der, der, der]
cfg = ForwardDiff.GradientConfig(f, SVector{3}(y), ForwardDiff.Chunk{2}())
grad = ForwardDiff.gradient(f, SVector{3}(y), cfg)
@test grad isa SVector{3,typeof(der)}
@test grad == SVector{3}(der, der, der)
end

end # module
31 changes: 31 additions & 0 deletions test/JacobianTest.jl
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Expand Up @@ -11,6 +11,11 @@ using LinearAlgebra

include(joinpath(dirname(@__FILE__), "utils.jl"))

struct TestTag end
struct OuterTestTag end
ForwardDiff.:≺(::Type{TestTag}, ::Type{OuterTestTag}) = true
ForwardDiff.:≺(::Type{OuterTestTag}, ::Type{<:Tag}) = true

##################
# hardcoded test #
##################
Expand Down Expand Up @@ -308,4 +313,30 @@ end
end
end

# issue #769
@testset "functions with `Dual` output" begin
x = [Dual{OuterTestTag}(Dual{TestTag}(1.3, 2.1), Dual{TestTag}(0.3, -2.4))]
f(x) = map(ForwardDiff.value, x)
der = ForwardDiff.derivative(ForwardDiff.value, only(x))

# Vector mode
jac = ForwardDiff.jacobian(f, x)
@test jac isa Matrix{typeof(der)}
@test jac == [der;;]
jac = ForwardDiff.jacobian(f, SVector{1}(x))
@test jac isa SMatrix{1,1,typeof(der)}
@test jac == SMatrix{1,1}(der)

# Chunk mode
y = repeat(x, 3)
cfg = ForwardDiff.JacobianConfig(f, y, ForwardDiff.Chunk{2}())
jac = ForwardDiff.jacobian(f, y, cfg)
@test jac isa Matrix{typeof(der)}
@test jac == Diagonal([der, der, der])
cfg = ForwardDiff.JacobianConfig(f, SVector{3}(y), ForwardDiff.Chunk{2}())
jac = ForwardDiff.jacobian(f, SVector{3}(y), cfg)
@test jac isa SMatrix{3,3,typeof(der)}
@test jac == Diagonal([der, der, der])
end

end # module
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