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improve performance of hessians with static arrays #751

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9e759cf
improve performance of hessians with static arrays
longemen3000 May 14, 2025
c715da3
ForwardDiffStaticArraysExt.jl: import Partials
longemen3000 May 14, 2025
dd44ec4
Update HessianTest.jl
longemen3000 May 14, 2025
ab86fe8
Update HessianTest.jl
longemen3000 May 14, 2025
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12 changes: 11 additions & 1 deletion ext/ForwardDiffStaticArraysExt.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ module ForwardDiffStaticArraysExt
using ForwardDiff, StaticArrays
using ForwardDiff.LinearAlgebra
using ForwardDiff.DiffResults
using ForwardDiff: Dual, partials, GradientConfig, JacobianConfig, HessianConfig, Tag, Chunk,
using ForwardDiff: Dual, partials, Partials, GradientConfig, JacobianConfig, HessianConfig, Tag, Chunk,
gradient, hessian, jacobian, gradient!, hessian!, jacobian!,
extract_gradient!, extract_jacobian!, extract_value!,
vector_mode_gradient, vector_mode_gradient!,
Expand Down Expand Up @@ -81,6 +81,16 @@ end
end
end

@generated function extract_jacobian(::Type{T}, ydual::Partials{M}, x::S) where {M, T, S<:StaticArray}
N = length(x)
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, $N))
return V($result)
end
end

@inline function ForwardDiff.vector_mode_jacobian(f::F, x::StaticArray) where {F}
T = typeof(Tag(f, eltype(x)))
return extract_jacobian(T, static_dual_eval(T, f, x), x)
Expand Down
9 changes: 9 additions & 0 deletions test/HessianTest.jl
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Expand Up @@ -163,4 +163,13 @@ end
@test ForwardDiff.hessian(x->dot(x,H,x), zeros(3)) ≈ [2 6 10; 6 10 14; 10 14 18]
end

@testset "allocation-free hessian with StaticArrays" begin
#https://github.com/JuliaDiff/ForwardDiff.jl/issues/720
g = r -> (r[1]^2 - 3) * (r[2]^2 - 2)
x = SA_F32[0.5, 2.7]
hres = DiffResults.HessianResult(x)
ForwardDiff.hessian!(hres, g, x)
@test @allocated(ForwardDiff.hessian!(hres, g, x)) == 0
end

end # module
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