GaussAdjoint fails on a problem with discrete callbacks

[m-bossart]

Describe the bug 🐞

When using GaussAdjoint() as the sensitivity algorithm, and ODEProblem with a discrete callback fails with a method error. Based on the stacktrace it seems to be a problem related to handling of the callbacks?

Expected behavior

I expect GaussAdjoint to be compatible with this problem.

Minimal Reproducible Example 👇
The minimal example is taken almost directly from the SciMLSensitivity tests (hybrid_de.jl). The only change is that the sensitivity algorithm is changed from ReverseDiffAdjoint() to GaussAdjoint(autojacvec=EnzymeVJP())

using Lux, ComponentArrays, SciMLSensitivity, DiffEqCallbacks, OrdinaryDiffEq, Test # , Plots
using Optimization, OptimizationOptimisers, Zygote, Random

u0 = Float32[2.0; 0.0]
datasize = 100
tspan = (0.0f0, 10.5f0)
dosetimes = [1.0, 2.0, 4.0, 8.0]

function affect!(integrator)
    integrator.u = integrator.u .+ 1
end
cb_ = PresetTimeCallback(dosetimes, affect!, save_positions = (false, false))
function trueODEfunc(du, u, p, t)
    du .= -u
end
t = range(tspan[1], tspan[2], length = datasize)

prob = ODEProblem(trueODEfunc, u0, tspan)
ode_data = Array(solve(prob, Tsit5(), callback = cb_, saveat = t))
dudt2 = Chain(Dense(2, 50, tanh), Dense(50, 2))
ps, st = Lux.setup(Random.default_rng(), dudt2)
ps = ComponentArray(ps)

function dudt(du, u, p, t)
    du[1:2] .= -u[1:2]
    du[3:end] .= first(dudt2(u[1:2], p, st))
end
z0 = Float32[u0; u0]
prob = ODEProblem(dudt, z0, tspan)

affect!(integrator) = integrator.u[1:2] .= integrator.u[3:end]
cb = PresetTimeCallback(dosetimes, affect!, save_positions = (false, false))

function predict_n_ode(ps)
    Array(solve(prob, Tsit5(), u0 = z0, p = ps, callback = cb, saveat = t,
        sensealg = GaussAdjoint(autojacvec=EnzymeVJP())))[1:2, :]
end

function loss_n_ode(ps, _)
    pred = predict_n_ode(ps)
    loss = sum(abs2, ode_data .- pred)
    loss
end
loss_n_ode(ps, nothing)
Zygote.gradient(p -> loss_n_ode(ps, nothing), ps)

Error & Stacktrace ⚠️

ERROR: MethodError: no method matching ndims(::Type{Nothing})

Closest candidates are:
  ndims(::Type{Union{}}, Any...)
   @ Base abstractarray.jl:276
  ndims(::Type{<:MatrixOperator{T, AType}}) where {T, AType}
   @ SciMLOperators C:\Users\Matt Bossart\.julia\packages\SciMLOperators\Kto0y\src\matrix.jl:192
  ndims(::Type{<:Ref})
   @ Base refpointer.jl:96
  ...

Stacktrace:
  [1] Base.Broadcast.BroadcastStyle(::Type{Nothing})
    @ Base.Broadcast .\broadcast.jl:103
  [2] combine_styles(c::Nothing)
    @ Base.Broadcast .\broadcast.jl:460
  [3] combine_styles(c1::Nothing, c2::Base.Broadcast.Broadcasted{…})
    @ Base.Broadcast .\broadcast.jl:461
  [4] materialize!
    @ .\broadcast.jl:911 [inlined]
  [5] (::SciMLSensitivity.var"#affect!#272"{…})(integrator::OrdinaryDiffEq.ODEIntegrator{…})
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\4YtYh\src\callback_tracking.jl:362
  [6] (::DiffEqCallbacks.var"#111#115"{…})(integrator::OrdinaryDiffEq.ODEIntegrator{…})
    @ DiffEqCallbacks C:\Users\Matt Bossart\.julia\packages\DiffEqCallbacks\9fKPq\src\preset_time.jl:58
  [7] apply_discrete_callback!
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\callbacks.jl:613 [inlined]
  [8] apply_discrete_callback!
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\callbacks.jl:644 [inlined]
  [9] apply_discrete_callback!
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\callbacks.jl:635 [inlined]
 [10] apply_discrete_callback!
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\callbacks.jl:628 [inlined]
 [11] handle_callbacks!(integrator::OrdinaryDiffEq.ODEIntegrator{…})
    @ OrdinaryDiffEq C:\Users\Matt Bossart\.julia\packages\OrdinaryDiffEq\HQ92J\src\integrators\integrator_utils.jl:349
 [12] _loopfooter!(integrator::OrdinaryDiffEq.ODEIntegrator{…})
    @ OrdinaryDiffEq C:\Users\Matt Bossart\.julia\packages\OrdinaryDiffEq\HQ92J\src\integrators\integrator_utils.jl:254
 [13] loopfooter!
    @ C:\Users\Matt Bossart\.julia\packages\OrdinaryDiffEq\HQ92J\src\integrators\integrator_utils.jl:207 [inlined]
 [14] solve!(integrator::OrdinaryDiffEq.ODEIntegrator{…})
    @ OrdinaryDiffEq C:\Users\Matt Bossart\.julia\packages\OrdinaryDiffEq\HQ92J\src\solve.jl:558
 [15] #__solve#560
    @ C:\Users\Matt Bossart\.julia\packages\OrdinaryDiffEq\HQ92J\src\solve.jl:7 [inlined]
 [16] __solve
    @ C:\Users\Matt Bossart\.julia\packages\OrdinaryDiffEq\HQ92J\src\solve.jl:1 [inlined]
 [17] solve_call(_prob::ODEProblem{…}, args::Tsit5{…}; merge_callbacks::Bool, kwargshandle::Nothing, kwargs::@Kwargs{…})
    @ DiffEqBase C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:612
 [18] solve_call
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:569 [inlined]
 [19] #solve_up#53
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:1080 [inlined]
 [20] solve_up
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:1066 [inlined]
 [21] #solve#51
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:1003 [inlined]
 [22] _adjoint_sensitivities(sol::ODESolution{…}, sensealg::GaussAdjoint{…}, alg::Tsit5{…}; t::StepRangeLen{…}, dgdu_discrete::Function, dgdp_discrete::Nothing, dgdu_continuous::Nothing, dgdp_continuous::Nothing, g::Nothing, abstol::Float64, reltol::Float64, checkpoints::Vector{…}, corfunc_analytical::Bool, callback::CallbackSet{…}, kwargs::@Kwargs{…})
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\4YtYh\src\gauss_adjoint.jl:580
 [23] _adjoint_sensitivities
    @ C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\4YtYh\src\gauss_adjoint.jl:533 [inlined]
 [24] #adjoint_sensitivities#63
    @ C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\4YtYh\src\sensitivity_interface.jl:401 [inlined]
 [25] (::SciMLSensitivity.var"#adjoint_sensitivity_backpass#308"{…})(Δ::ODESolution{…})
    @ SciMLSensitivity C:\Users\Matt Bossart\.julia\packages\SciMLSensitivity\4YtYh\src\concrete_solve.jl:625
 [26] ZBack
    @ C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\chainrules.jl:211 [inlined]
 [27] (::Zygote.var"#kw_zpullback#53"{…})(dy::ODESolution{…})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\chainrules.jl:237
 [28] #291
    @ C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\lib\lib.jl:206 [inlined]
 [29] (::Zygote.var"#2169#back#293"{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\ZygoteRules\M4xmc\src\adjoint.jl:72
 [30] #solve#51
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:1003 [inlined]
 [31] (::Zygote.Pullback{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [32] #291
    @ C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\lib\lib.jl:206 [inlined]
 [33] (::Zygote.var"#2169#back#293"{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\ZygoteRules\M4xmc\src\adjoint.jl:72
 [34] solve
    @ C:\Users\Matt Bossart\.julia\packages\DiffEqBase\c8MAQ\src\solve.jl:993 [inlined]
 [35] (::Zygote.Pullback{…})(Δ::ODESolution{…})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [36] predict_n_ode
    @ c:\Users\Matt Bossart\OneDrive - UCB-O365\Desktop\Transient Stability\Repositories\SciMLSensitivity.jl\test\hybrid_de.jl:35 [inlined]
 [37] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::Matrix{Float32})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [38] loss_n_ode
    @ c:\Users\Matt Bossart\OneDrive - UCB-O365\Desktop\Transient Stability\Repositories\SciMLSensitivity.jl\test\hybrid_de.jl:40 [inlined]
 [39] #35
    @ c:\Users\Matt Bossart\OneDrive - UCB-O365\Desktop\Transient Stability\Repositories\SciMLSensitivity.jl\test\hybrid_de.jl:45 [inlined]
 [40] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::Float32)
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface2.jl:0
 [41] (::Zygote.var"#75#76"{Zygote.Pullback{Tuple{…}, Tuple{…}}})(Δ::Float32)
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface.jl:91
 [42] gradient(f::Function, args::ComponentVector{Float32, Vector{Float32}, Tuple{Axis{…}}})
    @ Zygote C:\Users\Matt Bossart\.julia\packages\Zygote\nsBv0\src\compiler\interface.jl:148
 [43] top-level scope
    @ c:\Users\Matt Bossart\OneDrive - UCB-O365\Desktop\Transient Stability\Repositories\SciMLSensitivity.jl\test\hybrid_de.jl:45

Environment (please complete the following information):

• Output of using Pkg; Pkg.status()

Status `C:\Users\Matt Bossart\OneDrive - UCB-O365\Desktop\Transient Stability\Repositories\SciMLSensitivity.jl\test\Project.toml`
  [b0b7db55] ComponentArrays v0.15.14
  [459566f4] DiffEqCallbacks v3.6.2
  [b2108857] Lux v0.5.58
  [7f7a1694] Optimization v3.26.3
  [42dfb2eb] OptimizationOptimisers v0.2.1
  [1dea7af3] OrdinaryDiffEq v6.85.0
  [1ed8b502] SciMLSensitivity v7.62.0
  [e88e6eb3] Zygote v0.6.70
  [9a3f8284] Random
  [8dfed614] Test

• Output of using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)

  [47edcb42] ADTypes v1.5.3
  [621f4979] AbstractFFTs v1.5.0
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  [c1ae055f] RealDot v0.1.0
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  [731186ca] RecursiveArrayTools v3.23.1
  [f2c3362d] RecursiveFactorization v0.2.23
  [189a3867] Reexport v1.2.2
  [ae029012] Requires v1.3.0
  [ae5879a3] ResettableStacks v1.1.1
  [37e2e3b7] ReverseDiff v1.15.3
  [79098fc4] Rmath v0.7.1
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  [c0aeaf25] SciMLOperators v0.3.8
  [1ed8b502] SciMLSensitivity v7.62.0
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  [de0858da] Printf
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  [9e88b42a] Serialization
  [1a1011a3] SharedArrays
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  [29816b5a] LibSSH2_jll v1.11.0+1
  [c8ffd9c3] MbedTLS_jll v2.28.2+1
  [14a3606d] MozillaCACerts_jll v2023.1.10
  [4536629a] OpenBLAS_jll v0.3.23+4
  [05823500] OpenLibm_jll v0.8.1+2
  [bea87d4a] SuiteSparse_jll v7.2.1+1
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.8.0+1
  [8e850ede] nghttp2_jll v1.52.0+1
  [3f19e933] p7zip_jll v17.4.0+2
Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

• Output of versioninfo()

Julia Version 1.10.4
Commit 48d4fd4843 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 16 × 11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, tigerlake)
Threads: 1 default, 0 interactive, 1 GC (on 16 virtual cores)
Environment:
  JULIA_EDITOR = code
  JULIA_NUM_THREADS =

[m-bossart]

Seeing #865 now, I will start a PR that adds tests for GaussAdjoint + Callbacks with the hope of getting this covered.

[m-bossart]

Looks like this is actively being finalized here: #1060