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Relax type of alg in ensemble solve for optimization #534

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Nov 8, 2023
Merged

Relax type of alg in ensemble solve for optimization #534

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8c17212
Relax type of alg in ensemble solve for optimization
Vaibhavdixit02 Oct 30, 2023
23754a8
Add more methods for EnsembleProblem and add more tests
Vaibhavdixit02 Nov 1, 2023
9431771
revert new dispatches to check if tests pass with the relaxed type si…
Vaibhavdixit02 Nov 3, 2023
275cfe1
add diffeq to project.toml
Vaibhavdixit02 Nov 3, 2023
7e7367b
comment out optimization tests for now try ci
Vaibhavdixit02 Nov 3, 2023
dea66d1
Run optimization tests and add constructors for it
Vaibhavdixit02 Nov 7, 2023
1d87d19
Add compat
Vaibhavdixit02 Nov 7, 2023
e85bdb3
Merge branch 'master' into optensemble
Vaibhavdixit02 Nov 7, 2023
145f495
Add solve
Vaibhavdixit02 Nov 7, 2023
ebcbae3
add nonlinearsolve
Vaibhavdixit02 Nov 7, 2023
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4 changes: 2 additions & 2 deletions src/ensemble/basic_ensemble_solve.jl
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Expand Up @@ -57,10 +57,10 @@ function __solve(prob::EnsembleProblem{<:AbstractVector{<:AbstractSciMLProblem}}
end

function __solve(prob::AbstractEnsembleProblem,
alg::Union{AbstractDEAlgorithm, Nothing},
alg::A,
ensemblealg::BasicEnsembleAlgorithm;
trajectories, batch_size = trajectories,
pmap_batch_size = batch_size ÷ 100 > 0 ? batch_size ÷ 100 : 1, kwargs...)
pmap_batch_size = batch_size ÷ 100 > 0 ? batch_size ÷ 100 : 1, kwargs...) where {A}
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Suggested change
pmap_batch_size = batch_size ÷ 100 > 0 ? batch_size ÷ 100 : 1, kwargs...) where {A}
pmap_batch_size = batch_size ÷ 100 > 0 ? batch_size ÷ 100 : 1, kwargs...) where {A <: SciMLAlgorithm}

I think for this to work it needs a dispatch that's not EnsembleAlgorithm?

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Because of the automatic alg selection in DifferentialEquations.jl?

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I think I could work around this by making a stuct subtype of SciMLAlgorithm and wrap the optimizer's alg in that and handle it in __solve so we might not need to do this then

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Can you double check some cases with this and defaults? Tests seem to pass

num_batches = trajectories ÷ batch_size
num_batches < 1 &&
error("trajectories ÷ batch_size cannot be less than 1, got $num_batches")
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2 changes: 2 additions & 0 deletions test/downstream/Project.toml
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@@ -1,5 +1,7 @@
[deps]
BoundaryValueDiffEq = "764a87c0-6b3e-53db-9096-fe964310641d"
DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
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9 changes: 9 additions & 0 deletions test/downstream/ensemble_diffeq.jl
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@@ -0,0 +1,9 @@
using DifferentialEquations

prob = ODEProblem((u, p, t) -> 1.01u, 0.5, (0.0, 1.0))
function prob_func(prob, i, repeat)
remake(prob, u0 = rand() * prob.u0)
end
ensemble_prob = EnsembleProblem(prob, prob_func = prob_func)
sim = solve(ensemble_prob, Tsit5(), EnsembleThreads(), trajectories = 10)
@test sim isa EnsembleSolution
26 changes: 26 additions & 0 deletions test/downstream/ensemble_nondes.jl
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@@ -0,0 +1,26 @@
using Optimization, OptimizationOptimJL, ForwardDiff, Test

x0 = zeros(2)
rosenbrock(x, p = nothing) = (1 - x[1])^2 + 100 * (x[2] - x[1]^2)^2
l1 = rosenbrock(x0)

optf = OptimizationFunction(rosenbrock, Optimization.AutoForwardDiff())
prob = OptimizationProblem(optf, x0)
sol1 = Optimization.solve(prob, OptimizationOptimJL.BFGS(), maxiters = 5)

ensembleprob = Optimization.EnsembleProblem(prob, [x0, x0 .+ rand(2), x0 .+ rand(2), x0 .+ rand(2)])

sol = Optimization.solve(ensembleprob, OptimizationOptimJL.BFGS(), EnsembleThreads(), trajectories = 4, maxiters = 5)
@test findmin(i -> sol[i].objective, 1:4)[1] < sol1.objective

sol = Optimization.solve(ensembleprob, OptimizationOptimJL.BFGS(), EnsembleDistributed(), trajectories = 4, maxiters = 5)
@test findmin(i -> sol[i].objective, 1:4)[1] < sol1.objective

prob = OptimizationProblem(optf, x0, lb = [-0.5, -0.5], ub = [0.5, 0.5])
ensembleprob = Optimization.EnsembleProblem(prob, 5, prob_func = (prob, i, repeat) -> remake(prob, u0 = rand(-0.5:0.001:0.5, 2)))

sol = Optimization.solve(ensembleprob, OptimizationOptimJL.BFGS(), EnsembleThreads(), trajectories = 5, maxiters = 5)
@test findmin(i -> sol[i].objective, 1:4)[1] < sol1.objective

sol = Optimization.solve(ensembleprob, OptimizationOptimJL.BFGS(), EnsembleDistributed(), trajectories = 5, maxiters = 5)
@test findmin(i -> sol[i].objective, 1:4)[1] < sol1.objective
6 changes: 6 additions & 0 deletions test/runtests.jl
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Expand Up @@ -78,6 +78,12 @@ end
@time @safetestset "Ensemble solution statistics" begin
include("downstream/ensemble_stats.jl")
end
# @time @safetestset "Ensemble Optimization and Nonlinear problems" begin
# include("downstream/ensemble_nondes.jl")
# end
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Is this intended?

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Yeah it was to show that other tests all pass, to get this one working it needs the dispatches we have been discussing

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I'm lost. What's the version that works with all tests?

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The one without type assertion in the __solve method

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Can you put it this in the form you believe works for everything and we can check the downstream tests and these tests?

@time @safetestset "Ensemble with DifferentialEquations automatic algorithm selection" begin
include("downstream/ensemble_diffeq.jl")
end
@time @safetestset "Symbol and integer based indexing of interpolated solutions" begin
include("downstream/symbol_indexing.jl")
end
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