SciML / OrdinaryDiffEq.jl

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
https://diffeq.sciml.ai/latest/
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Floating point error using adjoint_sensitivities when tspan includes 16f0 in v6.89.0 #2492

Closed jesseylin closed 1 month ago

jesseylin commented 1 month ago

Describe the example

Adjoint solve immediately fails with a floating point precision error exactly at 16f0, regardless of the ODEFunction, solver, etc. Seems to not occur in v6.59.3.

(Originally from https://github.com/SciML/SciMLSensitivity.jl/issues/1133)

Minimal Reproducible Example πŸ‘‡

using Random
using OrdinaryDiffEq
using SciMLSensitivity
using LinearAlgebra

using Test

# MWE with OrdinaryDiffEq v6.59.3
flow = function (u, p, t)
    -u + p
end

rng = Xoshiro(1234)
ps = randn(rng, 11)
s0 = randn(rng, 11)

tspan = (0.0f0, prevfloat(16.0f0))
prob = ODEProblem(flow, s0, tspan, ps)
sol = solve(prob, Tsit5())
sensealg = GaussAdjoint(autojacvec=ReverseDiffVJP())

# at prevfloat(16f0)
_, gs = adjoint_sensitivities(sol, Tsit5(); sensealg, g=(u, p, t) -> norm(u))
gs
@test !iszero(gs)

# at 16f0
tspan = (0.0f0, 16.0f0)
prob = ODEProblem(flow, s0, tspan, ps)
sol = solve(prob, Tsit5())
_, gs = adjoint_sensitivities(sol, Tsit5(); sensealg, g=(u, p, t) -> norm(u))
@test !iszero(gs)

Working deps

Status `~/src/mwe/Project.toml`
βŒƒ [1dea7af3] OrdinaryDiffEq v6.59.3
βŒƒ [1ed8b502] SciMLSensitivity v7.51.0
  [37e2e46d] LinearAlgebra
  [9a3f8284] Random

Broken deps

Status `~/src/mwe_broken/Project.toml`
  [1dea7af3] OrdinaryDiffEq v6.89.0
  [1ed8b502] SciMLSensitivity v7.68.0
  [37e2e46d] LinearAlgebra
  [9a3f8284] Random

Working manifest

Status `~/src/mwe/Manifest.toml`
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Broken manifest

Status `~/src/mwe_broken/Manifest.toml`
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oscardssmith commented 1 month ago

This reproducer is great! I have a fix for real coming this time :) the problem was that we did a dumb job picking our initial dt