Add test

Author: tansongchen

bench/adaptive_order.jl

@@ -16,25 +16,49 @@ prob = ODEProblem{true, SciMLBase.FullSpecialize}(
     lotka, [1.0, 1.0], (0.0, 10.0), [1.5, 1.0, 3.0, 1.0])
 sol = solve(prob, Vern7(), abstol = 1 / 10^14, reltol = 1 / 10^14)
 
+function f(du, u, p, t)
+    A = 20
+    σ = 0.02
+    t0 = 5.0
+    q = 1 + A * exp(-((t - t0)^2) / (2 * σ^2))
+    du[1] = - q * u[1]
+    du[2] = - q * u[2]
+end
+
+prob2 = ODEProblem{true, SciMLBase.FullSpecialize}(f, [100.0, 100.0], (0.0, 10.0), nothing)
+sol2 = solve(prob2, Vern7(), abstol = 1 / 10^14, reltol = 1 / 10^14);
+
+@btime sol = solve(prob2, ExplicitTaylorAdaptiveOrder(min_order = Val(6), max_order = Val(12)), abstol = 1e-12, reltol = 1e-12);
+cache = init(prob2, ExplicitTaylorAdaptiveOrder(min_order = Val(6), max_order = Val(12)), abstol = 1e-12, reltol = 1e-12);
+sol = solve!(cache)
+
+@btime sol = solve(prob2, ExplicitTaylor(order = Val(12)), abstol = 1e-12, reltol = 1e-12);
+@btime sol = solve(prob2, ExplicitTaylor(order = Val(11)), abstol = 1e-12, reltol = 1e-12);
+
+solve!(cache)
 # Profile
-cache1 = init(prob, ExplicitTaylorAdaptiveOrder(min_order = Val(6), max_order = Val(12)))
-OrdinaryDiffEqTaylorSeries.perform_step!(cache1, cache1.cache, false)
-@profview for _ in 1:10000000
+function profile1()
+    cache1 = init(prob, ExplicitTaylor(order = Val(8)))
     OrdinaryDiffEqTaylorSeries.perform_step!(cache1, cache1.cache, false)
+    @profview for _ in 1:10000000
+        OrdinaryDiffEqTaylorSeries.perform_step!(cache1, cache1.cache, false)
+    end
+    @btime OrdinaryDiffEqTaylorSeries.perform_step!($cache1, $cache1.cache, false)
 end
-@btime OrdinaryDiffEqTaylorSeries.perform_step!(cache1, cache1.cache, false)
-
-# Make work-precision plot
-setups = [Dict(:alg => DP5())
-          Dict(:alg => Tsit5())
-          Dict(:alg => Vern6())
-          Dict(:alg => Vern8())
-          Dict(:alg => ExplicitTaylorAdaptiveOrder(min_order = Val(8), max_order = Val(12)))];
-names = ["DP5", "Tsit5", "Vern6", "Vern8", "Taylor"]
+
+profile1()
+
+# Make work-precision plot"DP5", "Tsit5", "Vern6", "Vern8",
+# setups = [Dict(:alg => DP5())
+#           Dict(:alg => Tsit5())
+#           Dict(:alg => Vern6())
+#           Dict(:alg => Vern8())
+setups = Any[Dict(:alg => ExplicitTaylorAdaptiveOrder(min_order = Val(6), max_order = Val(12)))];
+names = ["Taylor"]
 for order in 6:2:12
     push!(names, "Taylor $(order)")
     push!(setups, Dict(:alg => ExplicitTaylor(order = Val(order + 1))))
 end
-wp = WorkPrecisionSet([prob], abstols, reltols, setups; names = names, appxsol = [sol],
+wp = WorkPrecisionSet([prob2], abstols, reltols, setups; names = names, appxsol = [sol2],
     save_everystep = false, numruns = 100, maxiters = 10000)
 plot(wp)

lib/OrdinaryDiffEqTaylorSeries/src/TaylorSeries_caches.jl

@@ -96,6 +96,7 @@ end
     min_order::Val{P}
     max_order::Val{Q}
     current_order::Base.RefValue{Int}
+    order_history::Vector{Int}
     jets::Vector{FunctionWrapper{Nothing, Tuple{taylorType, uType, tType}}}
     u::uType
     uprev::uType
@@ -124,8 +125,9 @@ function alg_cache(
     atmp = similar(u, uEltypeNoUnits)
     recursivefill!(atmp, false)
     tmp = zero(u)
-    current_order = Ref(min_order_value)
-    ExplicitTaylorAdaptiveOrderCache(alg.min_order, alg.max_order, current_order,
+    current_order = Ref(max_order_value - 1)
+    order_history = Vector{Int}()
+    ExplicitTaylorAdaptiveOrderCache(alg.min_order, alg.max_order, current_order, order_history,
         jets, u, uprev, utaylor, utilde, tmp, atmp,
         alg.stage_limiter!, alg.step_limiter!, alg.thread)
 end

lib/OrdinaryDiffEqTaylorSeries/src/TaylorSeries_perform_step.jl

@@ -110,7 +110,7 @@ end
 
     min_order_value = get_value(min_order)
     max_order_value = get_value(max_order)
-    # println("current order: ", current_order[])
+    push!(cache.order_history, current_order[])
     jet_index = current_order[] - min_order_value + 1
     # compute one additional order for adaptive order
     jet = jets[jet_index + 1]