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refactor: Move NonlinearSolvePolyAlgorithm to Base (#494)
* refactor: Move NonlinearSolvePolyAlgorithm to Base * test: Make NonlinearSolve use 1.3 Base * refactor: Remove unnecessary snippet * refactor: Don't use duplicate solve * refactor: Test Base export NonlinearSolvePolyAlgorithm
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name = "NonlinearSolveBase" | ||
uuid = "be0214bd-f91f-a760-ac4e-3421ce2b2da0" | ||
authors = ["Avik Pal <[email protected]> and contributors"] | ||
version = "1.2.0" | ||
version = "1.3.0" | ||
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[deps] | ||
ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b" | ||
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""" | ||
NonlinearSolvePolyAlgorithm(algs; start_index::Int = 1) | ||
A general way to define PolyAlgorithms for `NonlinearProblem` and | ||
`NonlinearLeastSquaresProblem`. This is a container for a tuple of algorithms that will be | ||
tried in order until one succeeds. If none succeed, then the algorithm with the lowest | ||
residual is returned. | ||
### Arguments | ||
- `algs`: a tuple of algorithms to try in-order! (If this is not a Tuple, then the | ||
returned algorithm is not type-stable). | ||
### Keyword Arguments | ||
- `start_index`: the index to start at. Defaults to `1`. | ||
### Example | ||
```julia | ||
using NonlinearSolve | ||
alg = NonlinearSolvePolyAlgorithm((NewtonRaphson(), Broyden())) | ||
``` | ||
""" | ||
@concrete struct NonlinearSolvePolyAlgorithm <: AbstractNonlinearSolveAlgorithm | ||
static_length <: Val | ||
algs <: Tuple | ||
start_index::Int | ||
end | ||
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function NonlinearSolvePolyAlgorithm(algs; start_index::Int = 1) | ||
@assert 0 < start_index ≤ length(algs) | ||
algs = Tuple(algs) | ||
return NonlinearSolvePolyAlgorithm(Val(length(algs)), algs, start_index) | ||
end | ||
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@concrete mutable struct NonlinearSolvePolyAlgorithmCache <: AbstractNonlinearSolveCache | ||
static_length <: Val | ||
prob <: AbstractNonlinearProblem | ||
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caches <: Tuple | ||
alg <: NonlinearSolvePolyAlgorithm | ||
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best::Int | ||
current::Int | ||
nsteps::Int | ||
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stats::NLStats | ||
total_time::Float64 | ||
maxtime | ||
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retcode::ReturnCode.T | ||
force_stop::Bool | ||
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maxiters::Int | ||
internalnorm | ||
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u0 | ||
u0_aliased | ||
alias_u0::Bool | ||
end | ||
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function SII.symbolic_container(cache::NonlinearSolvePolyAlgorithmCache) | ||
return cache.caches[cache.current] | ||
end | ||
SII.state_values(cache::NonlinearSolvePolyAlgorithmCache) = cache.u0 | ||
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function Base.show(io::IO, ::MIME"text/plain", cache::NonlinearSolvePolyAlgorithmCache) | ||
println(io, "NonlinearSolvePolyAlgorithmCache with \ | ||
$(Utils.unwrap_val(cache.static_length)) algorithms:") | ||
best_alg = ifelse(cache.best == -1, "nothing", cache.best) | ||
println(io, " Best Algorithm: $(best_alg)") | ||
println( | ||
io, " Current Algorithm: [$(cache.current) / $(Utils.unwrap_val(cache.static_length))]" | ||
) | ||
println(io, " nsteps: $(cache.nsteps)") | ||
println(io, " retcode: $(cache.retcode)") | ||
print(io, " Current Cache: ") | ||
NonlinearSolveBase.show_nonlinearsolve_cache(io, cache.caches[cache.current], 4) | ||
end | ||
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function InternalAPI.reinit!( | ||
cache::NonlinearSolvePolyAlgorithmCache, args...; p = cache.p, u0 = cache.u0 | ||
) | ||
foreach(cache.caches) do cache | ||
InternalAPI.reinit!(cache, args...; p, u0) | ||
end | ||
cache.current = cache.alg.start_index | ||
InternalAPI.reinit!(cache.stats) | ||
cache.nsteps = 0 | ||
cache.total_time = 0.0 | ||
end | ||
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function SciMLBase.__init( | ||
prob::AbstractNonlinearProblem, alg::NonlinearSolvePolyAlgorithm, args...; | ||
stats = NLStats(0, 0, 0, 0, 0), maxtime = nothing, maxiters = 1000, | ||
internalnorm = L2_NORM, alias_u0 = false, verbose = true, kwargs... | ||
) | ||
if alias_u0 && !ArrayInterface.ismutable(prob.u0) | ||
verbose && @warn "`alias_u0` has been set to `true`, but `u0` is \ | ||
immutable (checked using `ArrayInterface.ismutable`)." | ||
alias_u0 = false # If immutable don't care about aliasing | ||
end | ||
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u0 = prob.u0 | ||
u0_aliased = alias_u0 ? copy(u0) : u0 | ||
alias_u0 && (prob = SciMLBase.remake(prob; u0 = u0_aliased)) | ||
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return NonlinearSolvePolyAlgorithmCache( | ||
alg.static_length, prob, | ||
map(alg.algs) do solver | ||
SciMLBase.__init( | ||
prob, solver, args...; | ||
stats, maxtime, internalnorm, alias_u0, verbose, kwargs... | ||
) | ||
end, | ||
alg, -1, alg.start_index, 0, stats, 0.0, maxtime, | ||
ReturnCode.Default, false, maxiters, internalnorm, | ||
u0, u0_aliased, alias_u0 | ||
) | ||
end | ||
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@generated function InternalAPI.step!( | ||
cache::NonlinearSolvePolyAlgorithmCache{Val{N}}, args...; kwargs... | ||
) where {N} | ||
calls = [] | ||
cache_syms = [gensym("cache") for i in 1:N] | ||
for i in 1:N | ||
push!(calls, | ||
quote | ||
$(cache_syms[i]) = cache.caches[$(i)] | ||
if $(i) == cache.current | ||
InternalAPI.step!($(cache_syms[i]), args...; kwargs...) | ||
$(cache_syms[i]).nsteps += 1 | ||
if !NonlinearSolveBase.not_terminated($(cache_syms[i])) | ||
if SciMLBase.successful_retcode($(cache_syms[i]).retcode) | ||
cache.best = $(i) | ||
cache.force_stop = true | ||
cache.retcode = $(cache_syms[i]).retcode | ||
else | ||
cache.current = $(i + 1) | ||
end | ||
end | ||
return | ||
end | ||
end) | ||
end | ||
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push!(calls, quote | ||
if !(1 ≤ cache.current ≤ length(cache.caches)) | ||
minfu, idx = findmin_caches(cache.prob, cache.caches) | ||
cache.best = idx | ||
cache.retcode = cache.caches[idx].retcode | ||
cache.force_stop = true | ||
return | ||
end | ||
end) | ||
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return Expr(:block, calls...) | ||
end | ||
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# Original is often determined on runtime information especially for PolyAlgorithms so it | ||
# is best to never specialize on that | ||
function build_solution_less_specialize( | ||
prob::AbstractNonlinearProblem, alg, u, resid; | ||
retcode = ReturnCode.Default, original = nothing, left = nothing, | ||
right = nothing, stats = nothing, trace = nothing, kwargs... | ||
) | ||
return SciMLBase.NonlinearSolution{ | ||
eltype(eltype(u)), ndims(u), typeof(u), typeof(resid), typeof(prob), | ||
typeof(alg), Any, typeof(left), typeof(stats), typeof(trace) | ||
}( | ||
u, resid, prob, alg, retcode, original, left, right, stats, trace | ||
) | ||
end | ||
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function findmin_caches(prob::AbstractNonlinearProblem, caches) | ||
resids = map(caches) do cache | ||
cache === nothing && return nothing | ||
return NonlinearSolveBase.get_fu(cache) | ||
end | ||
return findmin_resids(prob, resids) | ||
end | ||
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@views function findmin_resids(prob::AbstractNonlinearProblem, caches) | ||
norm_fn = prob isa NonlinearLeastSquaresProblem ? Base.Fix2(norm, 2) : | ||
Base.Fix2(norm, Inf) | ||
idx = findfirst(Base.Fix2(!==, nothing), caches) | ||
# This is an internal function so we assume that inputs are consistent and there is | ||
# atleast one non-`nothing` value | ||
fx_idx = norm_fn(caches[idx]) | ||
idx == length(caches) && return fx_idx, idx | ||
fmin = @closure xᵢ -> begin | ||
xᵢ === nothing && return oftype(fx_idx, Inf) | ||
fx = norm_fn(xᵢ) | ||
return ifelse(isnan(fx), oftype(fx, Inf), fx) | ||
end | ||
x_min, x_min_idx = findmin(fmin, caches[(idx + 1):length(caches)]) | ||
x_min < fx_idx && return x_min, x_min_idx + idx | ||
return fx_idx, idx | ||
end |
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@JuliaRegistrator register subdir=lib/NonlinearSolveBase
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Registration pull request created: JuliaRegistries/General/118813
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