Test latest Enzyme #1224
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Test latest Enzyme #1224
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Reproducer 1: using SciMLSensitivity, OrdinaryDiffEq, Enzyme
u0 = [0.0]
p = [2.0, 1.0]
prob = NonlinearProblem((du, u, p) -> du[1] = u[1] - p[1] + p[2], u0, p)
function test_loss(p, prob, alg)
_prob = remake(prob, p = p)
sol = sum(solve(_prob, alg,
sensealg = SteadyStateAdjoint(autojacvec = ReverseDiffVJP())))
return sol
end
function enzyme_gradient(p, prob, alg)
dp = Enzyme.make_zero(p)
dprob = Enzyme.make_zero(prob)
Enzyme.autodiff(Reverse, test_loss, Active, Duplicated(p, dp),
Duplicated(prob, dprob), Const(alg))
return dp
end
dp1_enzyme = enzyme_gradient(p, prob, NewtonRaphson())Reproducer 2, I think, it's a bit hard to read the GC corruption because it messes with the line numbers, but it seems like it's this: using OrdinaryDiffEq, SciMLSensitivity, Enzyme
Enzyme.API.typeWarning!(false)
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)
Enzyme.autodiff(Reverse, senseloss0(InterpolatingAdjoint()), Active, Duplicated(u0p, du0p)) |
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Both are related to Enzyme on the outside, not the EnzymeVJP. So it's GC corruption related to https://github.com/SciML/DiffEqBase.jl/blob/master/ext/DiffEqBaseEnzymeExt.jl. But I'm not sure what weird stuff is in that rule... |
appears to be in the deepcopy |
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This should now be fixed upstream, @ChrisRackauckas can you rerun? |
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