Open
Description
Let's add this example as a new integration test for Mooncake.
using OrdinaryDiffEq, SciMLSensitivity, Mooncake
mooncake_gradient(f, x) = Mooncake.value_and_gradient!!(Mooncake.build_rrule(f, x), f, x)[2][2]
odef(du, u, p, t) = du .= u .* p
const prob = ODEProblem(odef, [2.0], (0.0, 1.0), [3.0])
struct senseloss0{T}
sense::T
end
function (f::senseloss0)(u0p)
prob = ODEProblem{true}(odef, u0p[1:1], (0.0, 1.0), u0p[2:2])
sum(solve(prob, Tsit5(), abstol = 1e-12, reltol = 1e-12, saveat = 0.1))
end
u0p = [2.0, 3.0]
du0p = zeros(2)
dup_mc = mooncake_gradient(senseloss0(InterpolatingAdjoint()), u0p)