chore: format

SciML · Oct 27, 2023 · ffe02c3 · ffe02c3
1 parent 0ae3915
commit ffe02c3
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Showing 5 changed files with 96 additions and 72 deletions.
diff --git a/ext/DiffEqBaseEnzymeExt.jl b/ext/DiffEqBaseEnzymeExt.jl
@@ -7,7 +7,17 @@ isdefined(Base, :get_extension) ? (import Enzyme) : (import ..Enzyme)
 using ChainRulesCore
 using EnzymeCore
 
-function EnzymeCore.EnzymeRules.augmented_primal(config::EnzymeCore.EnzymeRules.ConfigWidth{1}, func::Const{typeof(DiffEqBase.solve_up)}, ::Type{Duplicated{RT}}, prob, sensealg::Union{Const{Nothing}, Const{<:DiffEqBase.AbstractSensitivityAlgorithm}}, u0, p, args...; kwargs...) where RT
+function EnzymeCore.EnzymeRules.augmented_primal(config::EnzymeCore.EnzymeRules.ConfigWidth{
+        1,
+    },
+    func::Const{typeof(DiffEqBase.solve_up)},
+    ::Type{Duplicated{RT}},
+    prob,
+    sensealg::Union{Const{Nothing}, Const{<:DiffEqBase.AbstractSensitivityAlgorithm}},
+    u0,
+    p,
+    args...;
+    kwargs...) where {RT}
     @inline function copy_or_reuse(val, idx)
         if EnzymeCore.EnzymeRules.overwritten(config)[idx] && ismutable(val)
             return deepcopy(val)
@@ -17,24 +27,35 @@ function EnzymeCore.EnzymeRules.augmented_primal(config::EnzymeCore.EnzymeRules.
     end
 
     @inline function arg_copy(i)
-        copy_or_reuse(args[i].val, i+5)
+        copy_or_reuse(args[i].val, i + 5)
     end
-
-    res = DiffEqBase._solve_adjoint(copy_or_reuse(prob.val, 2), copy_or_reuse(sensealg.val, 3), copy_or_reuse(u0.val, 4), copy_or_reuse(p.val, 5), SciMLBase.ChainRulesOriginator(), ntuple(arg_copy, Val(length(args)))...;
+
+    res = DiffEqBase._solve_adjoint(copy_or_reuse(prob.val, 2),
+        copy_or_reuse(sensealg.val, 3), copy_or_reuse(u0.val, 4), copy_or_reuse(p.val, 5),
+        SciMLBase.ChainRulesOriginator(), ntuple(arg_copy, Val(length(args)))...;
         kwargs...)
 
     dres = deepcopy(res[1])::RT
     for v in dres.u
-        v.= 0
+        v .= 0
     end
     tup = (dres, res[2])
     return EnzymeCore.EnzymeRules.AugmentedReturn{RT, RT, Any}(res[1], dres, tup::Any)
 end
 
-function EnzymeCore.EnzymeRules.reverse(config::EnzymeCore.EnzymeRules.ConfigWidth{1}, func::Const{typeof(DiffEqBase.solve_up)}, ::Type{<:Duplicated{RT}}, tape, prob, sensealg::Union{Const{Nothing}, Const{<:DiffEqBase.AbstractSensitivityAlgorithm}}, u0, p, args...; kwargs...) where RT
-	dres, clos = tape
+function EnzymeCore.EnzymeRules.reverse(config::EnzymeCore.EnzymeRules.ConfigWidth{1},
+    func::Const{typeof(DiffEqBase.solve_up)},
+    ::Type{<:Duplicated{RT}},
+    tape,
+    prob,
+    sensealg::Union{Const{Nothing}, Const{<:DiffEqBase.AbstractSensitivityAlgorithm}},
+    u0,
+    p,
+    args...;
+    kwargs...) where {RT}
+    dres, clos = tape
     dres = dres::RT
-	dargs = clos(dres)
+    dargs = clos(dres)
     for (darg, ptr) in zip(dargs, (func, prob, sensealg, u0, p, args...))
         if ptr isa EnzymeCore.Const
             continue
@@ -45,9 +66,9 @@ function EnzymeCore.EnzymeRules.reverse(config::EnzymeCore.EnzymeRules.ConfigWid
         ptr.dval .+= darg
     end
     for v in dres.u
-        v.= 0
+        v .= 0
     end
-    return ntuple(_ -> nothing, Val(length(args)+4))
+    return ntuple(_ -> nothing, Val(length(args) + 4))
 end
 
 end
diff --git a/src/init.jl b/src/init.jl
@@ -47,7 +47,7 @@ end
         @require MPI="da04e1cc-30fd-572f-bb4f-1f8673147195" begin
             include("../ext/DiffEqBaseMPIExt.jl")
         end
-        
+
         @require Enzyme="7da242da-08ed-463a-9acd-ee780be4f1d9" begin
             include("../ext/DiffEqBaseEnzymeExt.jl")
         end

diff --git a/src/stats.jl b/src/stats.jl
@@ -1,62 +1,62 @@
 @static if isdefined(SciMLBase, :DEStats)
-  const Stats = SciMLBase.DEStats
+    const Stats = SciMLBase.DEStats
 else
-  """
-  $(TYPEDEF)
-  
-  Statistics from the differential equation solver about the solution process.
-  
-  ## Fields
-  
-  - nf: Number of function evaluations. If the differential equation is a split function,
-    such as a `SplitFunction` for implicit-explicit (IMEX) integration, then `nf` is the
-    number of function evaluations for the first function (the implicit function)
-  - nf2: If the differential equation is a split function, such as a `SplitFunction`
-    for implicit-explicit (IMEX) integration, then `nf2` is the number of function
-    evaluations for the second function, i.e. the function treated explicitly. Otherwise
-    it is zero.
-  - nw: The number of W=I-gamma*J (or W=I/gamma-J) matrices constructed during the solving
-    process.
-  - nsolve: The number of linear solves `W\b` required for the integration.
-  - njacs: Number of Jacobians calculated during the integration.
-  - nnonliniter: Total number of iterations for the nonlinear solvers.
-  - nnonlinconvfail: Number of nonlinear solver convergence failures.
-  - ncondition: Number of calls to the condition function for callbacks.
-  - naccept: Number of accepted steps.
-  - nreject: Number of rejected steps.
-  - maxeig: Maximum eigenvalue over the solution. This is only computed if the
-    method is an auto-switching algorithm.
-  """
-  mutable struct Stats
-      nf::Int
-      nf2::Int
-      nw::Int
-      nsolve::Int
-      njacs::Int
-      nnonliniter::Int
-      nnonlinconvfail::Int
-      ncondition::Int
-      naccept::Int
-      nreject::Int
-      maxeig::Float64
-  end
-  
-  Base.@deprecate_binding DEStats Stats false
-  
-  Stats(x::Int = -1) = Stats(x, x, x, x, x, x, x, x, x, x, 0.0)
-  
-  function Base.show(io::IO, s::Stats)
-      println(io, summary(s))
-      @printf io "%-50s %-d\n" "Number of function 1 evaluations:" s.nf
-      @printf io "%-50s %-d\n" "Number of function 2 evaluations:" s.nf2
-      @printf io "%-50s %-d\n" "Number of W matrix evaluations:" s.nw
-      @printf io "%-50s %-d\n" "Number of linear solves:" s.nsolve
-      @printf io "%-50s %-d\n" "Number of Jacobians created:" s.njacs
-      @printf io "%-50s %-d\n" "Number of nonlinear solver iterations:" s.nnonliniter
-      @printf io "%-50s %-d\n" "Number of nonlinear solver convergence failures:" s.nnonlinconvfail
-      @printf io "%-50s %-d\n" "Number of rootfind condition calls:" s.ncondition
-      @printf io "%-50s %-d\n" "Number of accepted steps:" s.naccept
-      @printf io "%-50s %-d" "Number of rejected steps:" s.nreject
-      iszero(s.maxeig) || @printf io "\n%-50s %-d" "Maximum eigenvalue recorded:" s.maxeig
-  end
+    """
+    $(TYPEDEF)
+
+    Statistics from the differential equation solver about the solution process.
+
+    ## Fields
+
+    - nf: Number of function evaluations. If the differential equation is a split function,
+      such as a `SplitFunction` for implicit-explicit (IMEX) integration, then `nf` is the
+      number of function evaluations for the first function (the implicit function)
+    - nf2: If the differential equation is a split function, such as a `SplitFunction`
+      for implicit-explicit (IMEX) integration, then `nf2` is the number of function
+      evaluations for the second function, i.e. the function treated explicitly. Otherwise
+      it is zero.
+    - nw: The number of W=I-gamma*J (or W=I/gamma-J) matrices constructed during the solving
+      process.
+    - nsolve: The number of linear solves `W\b` required for the integration.
+    - njacs: Number of Jacobians calculated during the integration.
+    - nnonliniter: Total number of iterations for the nonlinear solvers.
+    - nnonlinconvfail: Number of nonlinear solver convergence failures.
+    - ncondition: Number of calls to the condition function for callbacks.
+    - naccept: Number of accepted steps.
+    - nreject: Number of rejected steps.
+    - maxeig: Maximum eigenvalue over the solution. This is only computed if the
+      method is an auto-switching algorithm.
+    """
+    mutable struct Stats
+        nf::Int
+        nf2::Int
+        nw::Int
+        nsolve::Int
+        njacs::Int
+        nnonliniter::Int
+        nnonlinconvfail::Int
+        ncondition::Int
+        naccept::Int
+        nreject::Int
+        maxeig::Float64
+    end
+
+    Base.@deprecate_binding DEStats Stats false
+
+    Stats(x::Int = -1) = Stats(x, x, x, x, x, x, x, x, x, x, 0.0)
+
+    function Base.show(io::IO, s::Stats)
+        println(io, summary(s))
+        @printf io "%-50s %-d\n" "Number of function 1 evaluations:" s.nf
+        @printf io "%-50s %-d\n" "Number of function 2 evaluations:" s.nf2
+        @printf io "%-50s %-d\n" "Number of W matrix evaluations:" s.nw
+        @printf io "%-50s %-d\n" "Number of linear solves:" s.nsolve
+        @printf io "%-50s %-d\n" "Number of Jacobians created:" s.njacs
+        @printf io "%-50s %-d\n" "Number of nonlinear solver iterations:" s.nnonliniter
+        @printf io "%-50s %-d\n" "Number of nonlinear solver convergence failures:" s.nnonlinconvfail
+        @printf io "%-50s %-d\n" "Number of rootfind condition calls:" s.ncondition
+        @printf io "%-50s %-d\n" "Number of accepted steps:" s.naccept
+        @printf io "%-50s %-d" "Number of rejected steps:" s.nreject
+        iszero(s.maxeig) || @printf io "\n%-50s %-d" "Maximum eigenvalue recorded:" s.maxeig
+    end
 end
diff --git a/test/downstream/kwarg_warn.jl b/test/downstream/kwarg_warn.jl
@@ -9,7 +9,10 @@ tspan = (0.0, 100.0)
 prob = ODEProblem(lorenz, u0, tspan)
 @test_nowarn sol = solve(prob, Tsit5(), reltol = 1e-6)
 sol = solve(prob, Tsit5(), rel_tol = 1e-6, kwargshandle = DiffEqBase.KeywordArgWarn)
-@test_logs (:warn, DiffEqBase.KWARGWARN_MESSAGE) sol=solve(prob, Tsit5(), rel_tol = 1e-6, kwargshandle = DiffEqBase.KeywordArgWarn)
+@test_logs (:warn, DiffEqBase.KWARGWARN_MESSAGE) sol=solve(prob,
+    Tsit5(),
+    rel_tol = 1e-6,
+    kwargshandle = DiffEqBase.KeywordArgWarn)
 @test_throws DiffEqBase.CommonKwargError sol=solve(prob, Tsit5(), rel_tol = 1e-6)
 
 prob = ODEProblem(lorenz, u0, tspan, test = 2.0, kwargshandle = DiffEqBase.KeywordArgWarn)

diff --git a/test/remake_tests.jl b/test/remake_tests.jl
@@ -79,7 +79,7 @@ noise2 = remake(noise1; tspan = tspan2);
 
 # Test remake with TwoPointBVPFunction (manually defined):
 f1 = SciMLBase.TwoPointBVPFunction((u, p, t) -> 1, ((u_a, p) -> 2, (u_b, p) -> 2))
-@test_broken f2 = remake(f1; bc =  ((u_a, p) -> 3, (u_b, p) -> 4))
+@test_broken f2 = remake(f1; bc = ((u_a, p) -> 3, (u_b, p) -> 4))
 @test_broken f1.bc() == 1
 @test_broken f2.bc() == 2