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fix type stability of SimpleNonlinearSolve #536

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Mar 3, 2025
Merged

fix type stability of SimpleNonlinearSolve #536

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330ab9f
fix type stability of SimpleNonlinearSolve
oscardssmith Mar 3, 2025
d0075b2
fix simplehalley
oscardssmith Mar 3, 2025
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16 changes: 4 additions & 12 deletions lib/SimpleNonlinearSolve/src/SimpleNonlinearSolve.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,7 @@ const DualNonlinearLeastSquaresProblem = NonlinearLeastSquaresProblem{
} where {iip, T, V, P}

abstract type AbstractSimpleNonlinearSolveAlgorithm <: AbstractNonlinearSolveAlgorithm end
configure_autodiff(prob, alg::AbstractSimpleNonlinearSolveAlgorithm) = alg

const NLBUtils = NonlinearSolveBase.Utils

Expand All @@ -59,12 +60,6 @@ function CommonSolve.solve(
prob::NonlinearProblem, alg::AbstractSimpleNonlinearSolveAlgorithm, args...;
kwargs...
)
cache = SciMLBase.__init(prob, alg, args...; kwargs...)
prob = cache.prob
if cache.retcode == ReturnCode.InitialFailure
return SciMLBase.build_solution(prob, alg, prob.u0,
NonlinearSolveBase.Utils.evaluate_f(prob, prob.u0); cache.retcode)
end
prob = convert(ImmutableNonlinearProblem, prob)
return solve(prob, alg, args...; kwargs...)
end
Expand All @@ -73,9 +68,7 @@ function CommonSolve.solve(
prob::DualNonlinearProblem, alg::AbstractSimpleNonlinearSolveAlgorithm,
args...; kwargs...
)
if hasfield(typeof(alg), :autodiff) && alg.autodiff === nothing
@set! alg.autodiff = AutoForwardDiff()
end
alg = configure_autodiff(prob, alg)
prob = convert(ImmutableNonlinearProblem, prob)
sol, partials = nonlinearsolve_forwarddiff_solve(prob, alg, args...; kwargs...)
dual_soln = nonlinearsolve_dual_solution(sol.u, partials, prob.p)
Expand All @@ -88,9 +81,7 @@ function CommonSolve.solve(
prob::DualNonlinearLeastSquaresProblem, alg::AbstractSimpleNonlinearSolveAlgorithm,
args...; kwargs...
)
if hasfield(typeof(alg), :autodiff) && alg.autodiff === nothing
@set! alg.autodiff = AutoForwardDiff()
end
alg = configure_autodiff(prob, alg)
sol, partials = nonlinearsolve_forwarddiff_solve(prob, alg, args...; kwargs...)
dual_soln = nonlinearsolve_dual_solution(sol.u, partials, prob.p)
return SciMLBase.build_solution(
Expand All @@ -103,6 +94,7 @@ function CommonSolve.solve(
alg::AbstractSimpleNonlinearSolveAlgorithm,
args...; sensealg = nothing, u0 = nothing, p = nothing, kwargs...
)
alg = configure_autodiff(prob, alg)
cache = SciMLBase.__init(prob, alg, args...; kwargs...)
prob = cache.prob
if cache.retcode == ReturnCode.InitialFailure
Expand Down
35 changes: 20 additions & 15 deletions lib/SimpleNonlinearSolve/src/halley.jl
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Expand Up @@ -20,11 +20,20 @@ A low-overhead implementation of Halley's Method.
autodiff = nothing
end

function configure_autodiff(prob, alg::SimpleHalley)
autodiff = something(alg.autodiff, AutoForwardDiff())
autodiff = SciMLBase.has_jac(prob.f) ? autodiff :
NonlinearSolveBase.select_jacobian_autodiff(prob, autodiff)
@set! alg.autodiff = autodiff
alg
end

function SciMLBase.__solve(
prob::ImmutableNonlinearProblem, alg::SimpleHalley, args...;
abstol = nothing, reltol = nothing, maxiters = 1000,
alias_u0 = false, termination_condition = nothing, kwargs...
)
autodiff = alg.autodiff
x = NLBUtils.maybe_unaliased(prob.u0, alias_u0)
fx = NLBUtils.evaluate_f(prob, x)
T = promote_type(eltype(fx), eltype(x))
Expand All @@ -36,23 +45,21 @@ function SciMLBase.__solve(
prob, abstol, reltol, fx, x, termination_condition, Val(:simple)
)

# The way we write the 2nd order derivatives, we know Enzyme won't work there
autodiff = alg.autodiff === nothing ? AutoForwardDiff() : alg.autodiff
@set! alg.autodiff = autodiff

@bb xo = copy(x)

fx_cache = (SciMLBase.isinplace(prob) && !SciMLBase.has_jac(prob.f)) ?
NLBUtils.safe_similar(fx) : fx
jac_cache = Utils.prepare_jacobian(prob, autodiff, fx_cache, x)

if NLBUtils.can_setindex(x)
A = NLBUtils.safe_similar(x, length(x), length(x))
Aaᵢ = NLBUtils.safe_similar(x, length(x))
cᵢ = NLBUtils.safe_similar(x)
else
A, Aaᵢ, cᵢ = x, x, x
Aaᵢ, cᵢ = x, x, x
end

J = Utils.compute_jacobian!!(nothing, prob, autodiff, fx_cache, x, jac_cache)
for _ in 1:maxiters
fx, J, H = Utils.compute_jacobian_and_hessian(autodiff, prob, fx, x)

NLBUtils.can_setindex(x) || (A = J)

# Factorize Once and Reuse
Expand All @@ -67,13 +74,8 @@ function SciMLBase.__solve(
end

aᵢ = J_fact \ NLBUtils.safe_vec(fx)
A_ = NLBUtils.safe_vec(A)
@bb A_ = H × aᵢ
A = NLBUtils.restructure(A, A_)

@bb Aaᵢ = A × aᵢ
@bb A .*= -1
bᵢ = J_fact \ NLBUtils.safe_vec(Aaᵢ)
hvvp = Utils.compute_hvvp(prob, autodiff, fx_cache, x, aᵢ)
bᵢ = J_fact \ NLBUtils.safe_vec(hvvp)

cᵢ_ = NLBUtils.safe_vec(cᵢ)
@bb @. cᵢ_ = (aᵢ * aᵢ) / (-aᵢ + (T(0.5) * bᵢ))
Expand All @@ -84,6 +86,9 @@ function SciMLBase.__solve(

@bb @. x += cᵢ
@bb copyto!(xo, x)

fx = NLBUtils.evaluate_f!!(prob, fx, x)
J = Utils.compute_jacobian!!(J, prob, autodiff, fx_cache, x, jac_cache)
end

return SciMLBase.build_solution(prob, alg, x, fx; retcode = ReturnCode.MaxIters)
Expand Down
13 changes: 9 additions & 4 deletions lib/SimpleNonlinearSolve/src/raphson.jl
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Expand Up @@ -23,12 +23,21 @@ end

const SimpleGaussNewton = SimpleNewtonRaphson

function configure_autodiff(prob, alg::SimpleNewtonRaphson)
autodiff = something(alg.autodiff, AutoForwardDiff())
autodiff = SciMLBase.has_jac(prob.f) ? autodiff :
NonlinearSolveBase.select_jacobian_autodiff(prob, autodiff)
@set! alg.autodiff = autodiff
alg
end

function SciMLBase.__solve(
prob::Union{ImmutableNonlinearProblem, NonlinearLeastSquaresProblem},
alg::SimpleNewtonRaphson, args...;
abstol = nothing, reltol = nothing, maxiters = 1000,
alias_u0 = false, termination_condition = nothing, kwargs...
)
autodiff = alg.autodiff
x = NLBUtils.maybe_unaliased(prob.u0, alias_u0)
fx = NLBUtils.evaluate_f(prob, x)

Expand All @@ -39,10 +48,6 @@ function SciMLBase.__solve(
prob, abstol, reltol, fx, x, termination_condition, Val(:simple)
)

autodiff = SciMLBase.has_jac(prob.f) ? alg.autodiff :
NonlinearSolveBase.select_jacobian_autodiff(prob, alg.autodiff)
@set! alg.autodiff = autodiff

@bb xo = similar(x)
fx_cache = (SciMLBase.isinplace(prob) && !SciMLBase.has_jac(prob.f)) ?
NLBUtils.safe_similar(fx) : fx
Expand Down
22 changes: 8 additions & 14 deletions lib/SimpleNonlinearSolve/src/utils.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -158,26 +158,20 @@ function compute_jacobian!!(J, prob, autodiff, fx, x, ::DINoPreparation)
return J
end

function compute_jacobian_and_hessian(autodiff, prob, _, x::Number)
function compute_hvvp(prob, autodiff, _, x::Number, dir::Number)
H = DI.second_derivative(prob.f, autodiff, x, Constant(prob.p))
fx, J = DI.value_and_derivative(prob.f, autodiff, x, Constant(prob.p))
return fx, J, H
return H*dir
end
function compute_jacobian_and_hessian(autodiff, prob, fx, x)
if SciMLBase.isinplace(prob)
jac_fn = @closure (u, p) -> begin
function compute_hvvp(prob, autodiff, fx, x, dir)
jvp_fn = if SciMLBase.isinplace(prob)
@closure (u, p) -> begin
du = NLBUtils.safe_similar(fx, promote_type(eltype(fx), eltype(u)))
return DI.jacobian(prob.f, du, autodiff, u, Constant(p))
return only(DI.pushforward(prob.f, du, autodiff, u, (dir,), Constant(p)))
end
J, H = DI.value_and_jacobian(jac_fn, autodiff, x, Constant(prob.p))
fx = NLBUtils.evaluate_f!!(prob, fx, x)
return fx, J, H
else
jac_fn = @closure (u, p) -> DI.jacobian(prob.f, autodiff, u, Constant(p))
J, H = DI.value_and_jacobian(jac_fn, autodiff, x, Constant(prob.p))
fx = NLBUtils.evaluate_f!!(prob, fx, x)
return fx, J, H
@closure (u, p) -> only(DI.pushforward(prob.f, autodiff, u, (dir,), Constant(p)))
end
only(DI.pushforward(jvp_fn, autodiff, x, (dir,), Constant(prob.p)))
end

end
4 changes: 2 additions & 2 deletions lib/SimpleNonlinearSolve/test/core/rootfind_tests.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -28,10 +28,10 @@
]

function run_nlsolve_oop(f::F, u0, p = 2.0; solver) where {F}
return solve(NonlinearProblem{false}(f, u0, p), solver; abstol = 1e-9)
return @inferred solve(NonlinearProblem{false}(f, u0, p), solver; abstol = 1e-9)
end
function run_nlsolve_iip(f!::F, u0, p = 2.0; solver) where {F}
return solve(NonlinearProblem{true}(f!, u0, p), solver; abstol = 1e-9)
return @inferred solve(NonlinearProblem{true}(f!, u0, p), solver; abstol = 1e-9)
end
end

Expand Down
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