Adding a discrete ODE callback makes the differentiation fail #1171
Description
Describe the bug 🐞
When trying to optimize the parameters of an ODE, adding a callback causes the automatic differentiation of the ODE problem to fail.
Expected behavior
Adding an empty callback shouldn't impact the execution of the script below.
Minimal Reproducible Example 👇
using Lux
using StableRNGs
using ComponentArrays
using OrdinaryDiffEq
using Optimization
using OptimizationOptimisers
using SciMLSensitivity
using Enzyme
using Zygote
using Printf
using Statistics
using LinearAlgebra
rbf(x) = exp.(-(x .^ 2))
const model = Lux.Chain(
Dense(1, 5, rbf),
Dense(5, 5, rbf),
Dense(5, 1)
)
rng = StableRNG(1111)
θ, _st = Lux.setup(rng, model)
const st = _st
θ = ComponentVector{Float64}(θ)
size_H₀ = (6,7)
tspan = (0.0,5.0)
tstops=collect(1.:5)
function SIA2D_UDE_closure_proto!(dH, H, θloc, t)
contrib_nn = reshape(model(ones((1,1)), θloc, st)[1], (1,))
dH[:,:] .= contrib_nn .* H[:,:]
end
stop_condition(u,t,integrator) = (t in tstops)
function action!(integrator)
println("action!")
end
cb_MB = DiscreteCallback(stop_condition, action!)
function predict(θloc)
function closure!(dH, H, θloc2, t)
SIA2D_UDE_closure_proto!(dH, H, θloc2, t)
end
H = 4*ones(size_H₀)
iceflow_prob = ODEProblem(
closure!,
H,
tspan,
θloc;
tstops=tstops
)
iceflow_sol = solve(
iceflow_prob, Vern7(),
callback=cb_MB,
# sensealg = QuadratureAdjoint(autojacvec = ReverseDiffVJP(true))
sensealg = QuadratureAdjoint(autojacvec = EnzymeVJP())
)
println("iceflow_sol.t=",iceflow_sol.t)
return Array(iceflow_sol)
end
Ht = 10.0*ones(size_H₀)
function loss_function(θloc)
u = predict(θloc)
println("u:",size(u)," ", typeof(u))
ul = u[:,:,end]
return mean(abs2, Ht .- ul)
end
optf = Optimization.OptimizationFunction((x, p) -> loss_function(x), Optimization.AutoZygote())
optprob = Optimization.OptimizationProblem(optf, θ)
optimizer = OptimizationOptimisers.Adam()
iceflow_trained = Optimization.solve(
optprob,
optimizer,
maxiters=4,
)Error & Stacktrace
julia> include("scripts/mwe_callback.jl")
Precompiling SciMLSensitivity finished.
1 dependency successfully precompiled in 15 seconds. 248 already precompiled.
action!
action!
action!
action!
action!
iceflow_sol.t=[0.0, 0.25551662028928784, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 4.0, 5.0, 5.0]
u:(6, 7, 12) Array{Float64, 3}
action!
action!
action!
action!
action!
action!
ERROR: LoadError: DimensionMismatch: array could not be broadcast to match destination
Stacktrace:
[1] check_broadcast_shape
@ ./broadcast.jl:579 [inlined]
[2] check_broadcast_axes
@ ./broadcast.jl:582 [inlined]
[3] instantiate
@ ./broadcast.jl:309 [inlined]
[4] materialize!
@ ./broadcast.jl:914 [inlined]
[5] materialize!
@ ./broadcast.jl:911 [inlined]
[6] _vecjacobian!(dλ::Adjoint{…}, y::ComponentVector{…}, λ::Adjoint{…}, p::Matrix{…}, t::Float64, S::SciMLSensitivity.CallbackSensitivityFunction{…}, isautojacvec::EnzymeVJP, dgrad::Nothing, dy::Nothing, W::Nothing)
@ SciMLSensitivity ~/.julia/dev/SciMLSensitivity/src/derivative_wrappers.jl:684
[7] vecjacobian!(dλ::Adjoint{…}, y::ComponentVector{…}, λ::Adjoint{…}, p::Matrix{…}, t::Float64, S::SciMLSensitivity.CallbackSensitivityFunction{…}; dgrad::Nothing, dy::Nothing, W::Nothing)
@ SciMLSensitivity ~/.julia/dev/SciMLSensitivity/src/derivative_wrappers.jl:232
[8] _update_integrand_and_dgrad(res::Adjoint{…}, sensealg::QuadratureAdjoint{…}, cb::DiscreteCallback{…}, integrand::AdjointSensitivityIntegrand{…}, adj_prob::ODEProblem{…}, sol::ODESolution{…}, dgdu::SciMLSensitivity.var"#df_iip#324"{…}, dgdp::Nothing, dλ::Vector{…}, dgrad::Adjoint{…}, t::Float64, cur_time::Int64)
@ SciMLSensitivity ~/.julia/dev/SciMLSensitivity/src/quadrature_adjoint.jl:493
[9] update_integrand_and_dgrad(res::Adjoint{…}, sensealg::QuadratureAdjoint{…}, callbacks::CallbackSet{…}, integrand::AdjointSensitivityIntegrand{…}, adj_prob::ODEProblem{…}, sol::ODESolution{…}, dgdu_discrete::Function, dgdp_discrete::Nothing, dλ::Vector{…}, dgrad::Adjoint{…}, ti::Float64, cur_time::Int64)
@ SciMLSensitivity ~/.julia/dev/SciMLSensitivity/src/quadrature_adjoint.jl:452
[10] _adjoint_sensitivities(sol::ODESolution{…}, sensealg::QuadratureAdjoint{…}, alg::Vern7{…}; t::Vector{…}, dgdu_discrete::Function, dgdp_discrete::Nothing, dgdu_continuous::Nothing, dgdp_continuous::Nothing, g::Nothing, abstol::Float64, reltol::Float64, callback::CallbackSet{…}, kwargs::@Kwargs{…})
@ SciMLSensitivity ~/.julia/dev/SciMLSensitivity/src/quadrature_adjoint.jl:403
[11] _adjoint_sensitivities
@ ~/.julia/dev/SciMLSensitivity/src/quadrature_adjoint.jl:338 [inlined]
[12] #adjoint_sensitivities#63
@ ~/.julia/dev/SciMLSensitivity/src/sensitivity_interface.jl:401 [inlined]
[13] (::SciMLSensitivity.var"#adjoint_sensitivity_backpass#323"{…})(Δ::ODESolution{…})
@ SciMLSensitivity ~/.julia/dev/SciMLSensitivity/src/concrete_solve.jl:627
[14] ZBack
@ ~/.julia/packages/Zygote/TWpme/src/compiler/chainrules.jl:212 [inlined]
[15] (::Zygote.var"#kw_zpullback#56"{…})(dy::ODESolution{…})
@ Zygote ~/.julia/packages/Zygote/TWpme/src/compiler/chainrules.jl:238
[16] #295
@ ~/.julia/packages/Zygote/TWpme/src/lib/lib.jl:205 [inlined]
[17] (::Zygote.var"#2169#back#297"{…})(Δ::ODESolution{…})
@ Zygote ~/.julia/packages/ZygoteRules/CkVIK/src/adjoint.jl:72
[18] #solve#42
@ ~/.julia/packages/DiffEqBase/HGITF/src/solve.jl:1043 [inlined]
[19] (::Zygote.Pullback{…})(Δ::ODESolution{…})
@ Zygote ~/.julia/packages/Zygote/TWpme/src/compiler/interface2.jl:0
[20] #295
@ ~/.julia/packages/Zygote/TWpme/src/lib/lib.jl:205 [inlined]
[21] (::Zygote.var"#2169#back#297"{…})(Δ::ODESolution{…})
@ Zygote ~/.julia/packages/ZygoteRules/CkVIK/src/adjoint.jl:72
[22] solve
@ ~/.julia/packages/DiffEqBase/HGITF/src/solve.jl:1033 [inlined]
[23] (::Zygote.Pullback{…})(Δ::ODESolution{…})
@ Zygote ~/.julia/packages/Zygote/TWpme/src/compiler/interface2.jl:0
[24] predict
@ ~/.julia/dev/ODINN/scripts/mwe_callback.jl:58 [inlined]
[25] (::Zygote.Pullback{Tuple{…}, Tuple{…}})(Δ::Array{Float64, 3})
@ Zygote ~/.julia/packages/Zygote/TWpme/src/compiler/interface2.jl:0
[26] loss_function
@ ~/.julia/dev/ODINN/scripts/mwe_callback.jl:70 [inlined]
[27] #2
@ ~/.julia/dev/ODINN/scripts/mwe_callback.jl:76 [inlined]
[28] (::Zygote.var"#78#79"{Zygote.Pullback{Tuple{var"#2#3", ComponentVector{…}, SciMLBase.NullParameters}, Tuple{Zygote.Pullback{…}}}})(Δ::Float64)
@ Zygote ~/.julia/packages/Zygote/TWpme/src/compiler/interface.jl:91
[29] withgradient(::Function, ::ComponentVector{Float64, Vector{Float64}, Tuple{Axis{…}}}, ::Vararg{Any})
@ Zygote ~/.julia/packages/Zygote/TWpme/src/compiler/interface.jl:213
[30] value_and_gradient
@ ~/.julia/packages/DifferentiationInterface/F5K7v/ext/DifferentiationInterfaceZygoteExt/DifferentiationInterfaceZygoteExt.jl:91 [inlined]
[31] value_and_gradient!(f::Function, grad::ComponentVector{…}, prep::DifferentiationInterface.NoGradientPrep, backend::AutoZygote, x::ComponentVector{…}, contexts::DifferentiationInterface.Constant{…})
@ DifferentiationInterfaceZygoteExt ~/.julia/packages/DifferentiationInterface/F5K7v/ext/DifferentiationInterfaceZygoteExt/DifferentiationInterfaceZygoteExt.jl:105
[32] (::OptimizationZygoteExt.var"#fg!#16"{…})(res::ComponentVector{…}, θ::ComponentVector{…})
@ OptimizationZygoteExt ~/.julia/packages/OptimizationBase/gvXsf/ext/OptimizationZygoteExt.jl:53
[33] macro expansion
@ ~/.julia/packages/OptimizationOptimisers/xC7Ic/src/OptimizationOptimisers.jl:101 [inlined]
[34] macro expansion
@ ~/.julia/packages/Optimization/qX4vR/src/utils.jl:32 [inlined]
[35] __solve(cache::OptimizationCache{…})
@ OptimizationOptimisers ~/.julia/packages/OptimizationOptimisers/xC7Ic/src/OptimizationOptimisers.jl:83
[36] solve!(cache::OptimizationCache{…})
@ SciMLBase ~/.julia/packages/SciMLBase/sYmAV/src/solve.jl:187
[37] solve(::OptimizationProblem{…}, ::Adam{…}; kwargs::@Kwargs{…})
@ SciMLBase ~/.julia/packages/SciMLBase/sYmAV/src/solve.jl:95
[38] top-level scope
@ ~/.julia/dev/ODINN/scripts/mwe_callback.jl:81
[39] include(fname::String)
@ Base.MainInclude ./client.jl:494
[40] top-level scope
@ REPL[1]:1
in expression starting at /home/gossarda/.julia/dev/ODINN/scripts/mwe_callback.jl:81
Some type information was truncated. Use `show(err)` to see complete types.
Environment (please complete the following information):
- Output of
using Pkg; Pkg.status()
Details
⌅ [13f3f980] CairoMakie v0.12.18
[b0b7db55] ComponentArrays v0.15.25
[b46f11dc] CoordRefSystems v0.16.9
[f68482b8] Cthulhu v2.16.4
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⌃ [7da242da] Enzyme v0.13.30
[f151be2c] EnzymeCore v0.8.8
[0ef565a4] Geodesy v1.1.0
[9cb796e9] Huginn v0.9.0
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⌃ [b2108857] Lux v1.8.0
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[4b816528] Muninn v0.6.0 `~/.julia/dev/Muninn`
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[36348300] OptimizationOptimJL v0.4.1
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[f5e6c550] Sleipnir v0.9.0 `~/.julia/dev/Sleipnir`
[860ef19b] StableRNGs v1.0.2
[1986cc42] Unitful v1.22.0
⌃ [e88e6eb3] Zygote v0.6.75- Output of
using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)
Details
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versioninfo()
Details
Julia Version 1.10.8Additional context
When I run the script below without callback=cb_MB, everything run smoothly. Using this empty callback yields an error.
The variables ytmp and y involved in the error (line 684 of derivative_wrappers.jl), are respectively a Matrix{Float64} with shape (6, 7) and a ComponentVector with 46 elements.
Note that using this callback also duplicates the solution at the different tstops in the returned array, which I don't get why.