Merge pull request #99 from SciML/general_fixes

more general fixes

Author: ChrisRackauckas

Project.toml

@@ -13,9 +13,9 @@ LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
-Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
 SimpleNonlinearSolve = "727e6d20-b764-4bd8-a329-72de5adea6c7"
-StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+SnoopPrecompile = "66db9d55-30c0-4569-8b51-7e840670fc0c"
+StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
 UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
 
 [compat]
@@ -27,9 +27,9 @@ LinearSolve = "1"
 RecursiveArrayTools = "2"
 Reexport = "0.2, 1"
 SciMLBase = "1.73"
-Setfield = "0.7, 0.8, 1"
 SimpleNonlinearSolve = "0.1"
-StaticArrays = "0.12,1.0"
+SnoopPrecompile = "1"
+StaticArraysCore = "1.4"
 UnPack = "1.0"
 julia = "1.6"
 
@@ -38,7 +38,8 @@ BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff"]
+test = ["BenchmarkTools", "SafeTestsets", "Pkg", "Test", "ForwardDiff", "StaticArrays"]

src/NonlinearSolve.jl

@@ -4,17 +4,18 @@ using Reexport
 using UnPack: @unpack
 using FiniteDiff, ForwardDiff
 using ForwardDiff: Dual
-using Setfield
-using StaticArrays
-using RecursiveArrayTools
 using LinearAlgebra
+using StaticArraysCore
+using RecursiveArrayTools
 import ArrayInterfaceCore
 import LinearSolve
 using DiffEqBase
 
 @reexport using SciMLBase
 @reexport using SimpleNonlinearSolve
 
+import SciMLBase: _unwrap_val
+
 abstract type AbstractNonlinearSolveAlgorithm <: SciMLBase.AbstractNonlinearAlgorithm end
 abstract type AbstractNewtonAlgorithm{CS, AD, FDT, ST, CJ} <:
               AbstractNonlinearSolveAlgorithm end
@@ -31,6 +32,20 @@ include("jacobian.jl")
 include("raphson.jl")
 include("ad.jl")
 
+import SnoopPrecompile
+
+SnoopPrecompile.@precompile_all_calls begin for T in (Float32, Float64)
+    prob = NonlinearProblem{false}((u, p) -> u .* u .- p, T(0.1), T(2))
+    for alg in (NewtonRaphson,)
+        solve(prob, alg(), abstol = T(1e-2))
+    end
+
+    prob = NonlinearProblem{true}((du, u, p) -> du[1] = u[1] * u[1] - p[1], T[0.1], T[2])
+    for alg in (NewtonRaphson,)
+        solve(prob, alg(), abstol = T(1e-2))
+    end
+end end
+
 export NewtonRaphson
 
 end # module

src/ad.jl

@@ -23,14 +23,16 @@ function scalar_nlsolve_ad(prob, alg, args...; kwargs...)
     return sol, partials
 end
 
-function SciMLBase.solve(prob::NonlinearProblem{<:Union{Number, SVector}, iip,
+function SciMLBase.solve(prob::NonlinearProblem{<:Union{Number, StaticArraysCore.SVector},
+                                                iip,
                                                 <:Dual{T, V, P}}, alg::NewtonRaphson,
                          args...; kwargs...) where {iip, T, V, P}
     sol, partials = scalar_nlsolve_ad(prob, alg, args...; kwargs...)
     return SciMLBase.build_solution(prob, alg, Dual{T, V, P}(sol.u, partials), sol.resid;
                                     retcode = sol.retcode)
 end
-function SciMLBase.solve(prob::NonlinearProblem{<:Union{Number, SVector}, iip,
+function SciMLBase.solve(prob::NonlinearProblem{<:Union{Number, StaticArraysCore.SVector},
+                                                iip,
                                                 <:AbstractArray{<:Dual{T, V, P}}},
                          alg::NewtonRaphson, args...; kwargs...) where {iip, T, V, P}
     sol, partials = scalar_nlsolve_ad(prob, alg, args...; kwargs...)

src/raphson.jl

@@ -114,13 +114,14 @@ function SciMLBase.__init(prob::NonlinearProblem{uType, iip}, alg::NewtonRaphson
 end
 
 function perform_step!(cache::NewtonRaphsonCache{true})
-    @unpack u, fu, f, p, cache = cache
+    @unpack u, fu, f, p, alg = cache
     @unpack J, linsolve, du1 = cache
     calc_J!(J, cache, cache)
+
     # u = u - J \ fu
-    linsolve = dolinsolve(alg.precs, linsolve, A = J, b = fu, u = du1,
-                          p = p, reltol = cache.abstol)
-    cache.linsolve = linsolve
+    linres = dolinsolve(alg.precs, linsolve, A = J, b = fu, linu = du1,
+                        p = p, reltol = cache.abstol)
+    cache.linsolve = linres.cache
     @. u = u - du1
     f(fu, u, p)
 
@@ -150,6 +151,8 @@ function SciMLBase.solve!(cache::NewtonRaphsonCache)
 
     if cache.iter == cache.maxiters
         cache.retcode = ReturnCode.MaxIters
+    else
+        cache.retcode = ReturnCode.Success
     end
 
     SciMLBase.build_solution(cache.prob, cache.alg, cache.u, cache.fu;

src/utils.jl

@@ -1,111 +1,13 @@
-"""
-    @add_kwonly function_definition
-
-Define keyword-only version of the `function_definition`.
-
-    @add_kwonly function f(x; y=1)
-        ...
-    end
-
-expands to:
-
-    function f(x; y=1)
-        ...
-    end
-    function f(; x = error("No argument x"), y=1)
-        ...
-    end
-"""
-macro add_kwonly(ex)
-    esc(add_kwonly(ex))
-end
-
-add_kwonly(ex::Expr) = add_kwonly(Val{ex.head}, ex)
-
-function add_kwonly(::Type{<:Val}, ex)
-    error("add_only does not work with expression $(ex.head)")
-end
-
-function add_kwonly(::Union{Type{Val{:function}},
-                            Type{Val{:(=)}}}, ex::Expr)
-    body = ex.args[2:end]  # function body
-    default_call = ex.args[1]  # e.g., :(f(a, b=2; c=3))
-    kwonly_call = add_kwonly(default_call)
-    if kwonly_call === nothing
-        return ex
-    end
-
-    return quote
-        begin
-            $ex
-            $(Expr(ex.head, kwonly_call, body...))
-        end
-    end
-end
-
-function add_kwonly(::Type{Val{:where}}, ex::Expr)
-    default_call = ex.args[1]
-    rest = ex.args[2:end]
-    kwonly_call = add_kwonly(default_call)
-    if kwonly_call === nothing
-        return nothing
-    end
-    return Expr(:where, kwonly_call, rest...)
-end
-
-function add_kwonly(::Type{Val{:call}}, default_call::Expr)
-    # default_call is, e.g., :(f(a, b=2; c=3))
-    funcname = default_call.args[1]  # e.g., :f
-    required = []  # required positional arguments; e.g., [:a]
-    optional = []  # optional positional arguments; e.g., [:(b=2)]
-    default_kwargs = []
-    for arg in default_call.args[2:end]
-        if isa(arg, Symbol)
-            push!(required, arg)
-        elseif arg.head == :(::)
-            push!(required, arg)
-        elseif arg.head == :kw
-            push!(optional, arg)
-        elseif arg.head == :parameters
-            @assert default_kwargs == []  # can I have :parameters twice?
-            default_kwargs = arg.args
-        else
-            error("Not expecting to see: $arg")
-        end
-    end
-    if isempty(required) && isempty(optional)
-        # If the function is already keyword-only, do nothing:
-        return nothing
-    end
-    if isempty(required)
-        # It's not clear what should be done.  Let's not support it at
-        # the moment:
-        error("At least one positional mandatory argument is required.")
-    end
-
-    kwonly_kwargs = Expr(:parameters,
-                         [Expr(:kw, pa, :(error($("No argument $pa"))))
-                          for pa in required]..., optional..., default_kwargs...)
-    kwonly_call = Expr(:call, funcname, kwonly_kwargs)
-    # e.g., :(f(; a=error(...), b=error(...), c=1, d=2))
-
-    return kwonly_call
-end
-
-function num_types_in_tuple(sig)
-    length(sig.parameters)
-end
-
-function num_types_in_tuple(sig::UnionAll)
-    length(Base.unwrap_unionall(sig).parameters)
-end
 
 @inline UNITLESS_ABS2(x) = real(abs2(x))
 @inline DEFAULT_NORM(u::Union{AbstractFloat, Complex}) = @fastmath abs(u)
 @inline function DEFAULT_NORM(u::Array{T}) where {T <: Union{AbstractFloat, Complex}}
     sqrt(real(sum(abs2, u)) / length(u))
 end
-@inline function DEFAULT_NORM(u::StaticArray{T}) where {T <: Union{AbstractFloat, Complex}}
+@inline function DEFAULT_NORM(u::StaticArraysCore.StaticArray{T}) where {
+                                                                         T <: Union{
+                                                                               AbstractFloat,
+                                                                               Complex}}
     sqrt(real(sum(abs2, u)) / length(u))
 end
 @inline function DEFAULT_NORM(u::RecursiveArrayTools.AbstractVectorOfArray)
@@ -114,23 +16,6 @@ end
 @inline DEFAULT_NORM(u::AbstractArray) = sqrt(real(sum(UNITLESS_ABS2, u)) / length(u))
 @inline DEFAULT_NORM(u) = norm(u)
 
-"""
-  prevfloat_tdir(x, x0, x1)
-
-Move `x` one floating point towards x0.
-"""
-function prevfloat_tdir(x, x0, x1)
-    x1 > x0 ? prevfloat(x) : nextfloat(x)
-end
-
-function nextfloat_tdir(x, x0, x1)
-    x1 > x0 ? nextfloat(x) : prevfloat(x)
-end
-
-function max_tdir(a, b, x0, x1)
-    x1 > x0 ? max(a, b) : min(a, b)
-end
-
 alg_autodiff(alg::AbstractNewtonAlgorithm{CS, AD}) where {CS, AD} = AD
 alg_autodiff(alg) = false
 
@@ -146,15 +31,14 @@ function value_derivative(f::F, x::R) where {F, R}
 end
 
 # Todo: improve this dispatch
-value_derivative(f::F, x::SVector) where {F} = f(x), ForwardDiff.jacobian(f, x)
+function value_derivative(f::F, x::StaticArraysCore.SVector) where {F}
+    f(x), ForwardDiff.jacobian(f, x)
+end
 
 value(x) = x
 value(x::Dual) = ForwardDiff.value(x)
 value(x::AbstractArray{<:Dual}) = map(ForwardDiff.value, x)
 
-_unwrap_val(::Val{B}) where {B} = B
-_unwrap_val(B) = B
-
 _vec(v) = vec(v)
 _vec(v::Number) = v
 _vec(v::AbstractVector) = v

test/basictests.jl

@@ -30,19 +30,34 @@ end
 const csu0 = 1.0
 
 sol = benchmark_immutable(ff, cu0)
-@test sol.retcode === ReturnCode.Default
+@test sol.retcode === ReturnCode.Success
 @test all(sol.u .* sol.u .- 2 .< 1e-9)
 sol = benchmark_mutable(ff, cu0)
-@test sol.retcode === ReturnCode.Default
+@test sol.retcode === ReturnCode.Success
 @test all(sol.u .* sol.u .- 2 .< 1e-9)
 sol = benchmark_scalar(sf, csu0)
-@test sol.retcode === ReturnCode.Default
+@test sol.retcode === ReturnCode.Success
 @test sol.u * sol.u - 2 < 1e-9
 
 @test (@ballocated benchmark_immutable(ff, cu0)) < 200
 @test (@ballocated benchmark_mutable(ff, cu0)) < 200
 @test (@ballocated benchmark_scalar(sf, csu0)) < 400
 
+function benchmark_inplace(f, u0)
+    probN = NonlinearProblem{true}(f, u0)
+    solver = init(probN, NewtonRaphson(), abstol = 1e-9)
+    sol = solve!(solver)
+end
+
+function ffiip(du, u, p)
+    du .= u .* u .- 2
+end
+u0 = [1.0, 1.0]
+
+sol = benchmark_inplace(ffiip, u0)
+@test sol.retcode === ReturnCode.Success
+@test all(sol.u .* sol.u .- 2 .< 1e-9)
+
 # AD Tests
 using ForwardDiff