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Attempt the implementation of missing reinit! for dual cache #836

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38 changes: 36 additions & 2 deletions ext/LinearSolveForwardDiffExt.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@ module LinearSolveForwardDiffExt

using LinearSolve
using LinearSolve: SciMLLinearSolveAlgorithm, __init, LinearVerbosity, DefaultLinearSolver,
DefaultAlgorithmChoice, defaultalg
DefaultAlgorithmChoice, defaultalg, reinit!
using LinearAlgebra
using ForwardDiff
using ForwardDiff: Dual, Partials
Expand Down Expand Up @@ -342,6 +342,38 @@ function setu!(dc::DualLinearCache, u)
partial_vals!(getfield(dc, :partials_u), u) # Update in-place
end

function SciMLBase.reinit!(cache::DualLinearCache;
A = nothing,
b = nothing,
u = nothing,
p = nothing,
reuse_precs = false)
if !isnothing(A)
setA!(cache, A)
end

if !isnothing(b)
setb!(cache, b)
end

if !isnothing(u)
setu!(cache, u)
end

if !isnothing(p)
cache.linear_cache.p=p
end

isfresh = !isnothing(A)
precsisfresh = !reuse_precs && (isfresh || !isnothing(p))
isfresh |= cache.isfresh
precsisfresh |= cache.linear_cache.precsisfresh
cache.linear_cache.isfresh = true
cache.linear_cache.precsisfresh = precsisfresh

nothing
end

function Base.setproperty!(dc::DualLinearCache, sym::Symbol, val)
# If the property is A or b, also update it in the LinearCache
if sym === :A
Expand Down Expand Up @@ -390,7 +422,9 @@ partial_vals!(out, x) = map!(partial_vals, out, x) # Update in-place
nodual_value(x) = x
nodual_value(x::Dual{T, V, P}) where {T, V <: AbstractFloat, P} = ForwardDiff.value(x)
nodual_value(x::Dual{T, V, P}) where {T, V <: Dual, P} = x.value # Keep the inner dual intact
nodual_value(x::AbstractArray{<:Dual}) = nodual_value!(similar(x, typeof(nodual_value(first(x)))), x)
function nodual_value(x::AbstractArray{<:Dual})
nodual_value!(similar(x, typeof(nodual_value(first(x)))), x)
end
nodual_value!(out, x) = map!(nodual_value, out, x) # Update in-place

function update_partials_list!(partial_matrix::AbstractVector{T}, list_cache) where {T}
Expand Down
8 changes: 7 additions & 1 deletion test/forwarddiff_overloads.jl
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Expand Up @@ -188,6 +188,12 @@ backslash_x_p = A \ b

@test ≈(overload_x_p, backslash_x_p, rtol = 1e-9)

A[1, 1]+=2
cache = overload_x_p.cache
reinit!(cache; A = sparse(A))
overload_x_p = solve!(cache, UMFPACKFactorization())
backslash_x_p = A \ b
@test ≈ (overload_x_p, backslash_x_p, rtol = 1e-9)

# Test that GenericLU doesn't create a DualLinearCache
A, b = h([ForwardDiff.Dual(5.0, 1.0, 0.0), ForwardDiff.Dual(5.0, 0.0, 1.0)])
Expand Down Expand Up @@ -234,4 +240,4 @@ grad = ForwardDiff.gradient(component_linsolve, p_test)
@test grad isa Vector
@test length(grad) == 2
@test !any(isnan, grad)
@test !any(isinf, grad)
@test !any(isinf, grad)
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