SciML / SciMLSensitivity.jl

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
https://docs.sciml.ai/SciMLSensitivity/stable/
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missing method matching recursive_copyto!(::AbstractVector, ::Tuple) #1082

Closed JakobAsslaender closed 2 months ago

JakobAsslaender commented 3 months ago

Describe the bug 🐞 Hi, in the below MWE, the function _vecjacobian! seems to create of type Vector and tmp1 of type NTuple and there is no method recursive_copyto!(::AbstractVector, ::Tuple).

Adding the method

recursive_copyto!(y::AbstractArray, x::Tuple) = copyto!(y, x)

resolves the error, but I am unsure if this is the proper bug fix.

Expected behavior No error ;)

Minimal Reproducible Example 👇

using Flux, Optimization, OptimizationOptimisers, OrdinaryDiffEq, SciMLSensitivity, Random, Plots, OptimizationOptimJL

##
rng = Xoshiro(1234)
u0 = Float32[0.0; 0.0; 1.0; 1.0]
datasize = 30
tspan = (0.0f0, 1.0f0)
tsteps = range(tspan[1], tspan[2]; length = datasize)

function trueODEfunc(du, u, p, t)
    R1 = 1
    R2 = 10
    ωz = 10
    H = [-R2 -ωz  ωy  0;
          ωz -R2   0  0;
         -ωy   0 -R1 R1;
           0   0   0  0]
    du .= H * u
end

ωy = 10
prob_trueode = ODEProblem(trueODEfunc, u0, tspan)
ode_data = Array(solve(prob_trueode, Tsit5(); saveat = tsteps))

##
ann = Chain(Dense(5, 50, tanh), Dense(50, 4))
p, st = Flux.destructure(ann)

function dudt_(u, p, t)
    st(p)([u..., 1f1])
end

prob_neuralode1 = ODEProblem{false}(dudt_, u0, tspan, p)

function predict_neuralode(p)
    Array(solve(prob_neuralode1, Tsit5(), p = p, saveat = tsteps))
end

function loss_neuralode(p)
    pred = predict_neuralode(p)
    loss = sum(abs2, ode_data .- pred)
    return loss, pred
end

# Do not plot by default for the documentation
# Users should change doplot=true to see the plots callbacks
callback = function (p, l, pred; doplot = false, dim=1)
    println(l)
    # plot current prediction against data
    if doplot
        plt = scatter(tsteps, ode_data[dim, :]; label = "data")
        scatter!(plt, tsteps, pred[dim, :]; label = "prediction")
        display(plot(plt))
    end
    return false
end
callback(p, loss_neuralode(p)...; doplot = true)

## use Optimization.jl to solve the problem
optf = Optimization.OptimizationFunction((x, p) -> loss_neuralode(x), Optimization.AutoZygote())
optprob = Optimization.OptimizationProblem(optf, p)
result_neuralode = Optimization.solve(optprob, OptimizationOptimisers.Adam(0.005); callback = callback, maxiters = 1000)

Error & Stacktrace ⚠️

ERROR: MethodError: no method matching recursive_copyto!(::Vector{Float32}, ::NTuple{4, Float32})

Closest candidates are:
  recursive_copyto!(!Matched::Tuple, ::Tuple)
   @ SciMLSensitivity ~/.julia/packages/SciMLSensitivity/4hOeN/src/parameters_handling.jl:11
  recursive_copyto!(::AbstractArray, !Matched::AbstractArray)
   @ SciMLSensitivity ~/.julia/packages/SciMLSensitivity/4hOeN/src/parameters_handling.jl:9
  recursive_copyto!(::Any, !Matched::Nothing)
   @ SciMLSensitivity ~/.julia/packages/SciMLSensitivity/4hOeN/src/parameters_handling.jl:16
  ...

Stacktrace:

_vecjacobian!(dλ::Vector{…}, y::Vector{…}, λ::Vector{…}, p::Vector{…}, t::Float32, S::SciMLSensitivity.ODEGaussAdjointSensitivityFunction{…}, isautojacvec::ZygoteVJP, dgrad::Nothing, dy::Nothing, W::Nothing) at derivative_wrappers.jl

Environment (please complete the following information):

  [b0b7db55] ComponentArrays v0.15.14
  [aae7a2af] DiffEqFlux v3.5.1
  [587475ba] Flux v0.14.16
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  [ae029012] Requires v1.3.0
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⌅ [214eeab7] fzf_jll v0.43.0+0
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Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`
Julia Version 1.10.4
Commit 48d4fd48430 (2024-06-04 10:41 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: macOS (arm64-apple-darwin22.4.0)
  CPU: 8 × Apple M1
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, apple-m1)
Threads: 4 default, 0 interactive, 2 GC (on 4 virtual cores)
Environment:
  JULIA_EDITOR = code
  JULIA_NUM_THREADS = 4

Additional context It could easily be that the bug is in my script – apologies if this is the case. If my bugfix is actually the right way to go, I would be happy to create a PR.

Thanks for looking into it!

ChrisRackauckas commented 2 months ago

First of all, you don't want to splat for performance. If you just use concatenation instead, this goes away.

function dudt_(u, p, t)
    st(p)([u; 1f1])
end

works fine, and is much better for performance. So just use that.

The underlying issue with your original code is:

using Flux
ann = Chain(Dense(5, 50, tanh), Dense(50, 4))
p, st = Flux.destructure(ann)

using Zygote
function dudt_(u, p, t)
    st(p)([u..., 1f1])
end
out, back = Zygote.pullback(dudt_, u0, p, 0f0)
d_u, d_p, d_t = back(rand(4))
typeof(d_u) # NTuple

Which I'll open as an upstream issue, but for now, this is closed because if you just change the function you're good.