Optimization.LBFGS() can not compute the gradient

[enigne]

Hi, I'm using Optimization.jl in my package: DJUICE.jl to optimize a cost function. The example is here After optimization, the solution is the same as my initial guess. I computed the gradient directly using Enzyme here

But, when comparing with the evaluation from Optimization.jl sol.cache.f.grad(∂J_∂α, prob.u0, prob.p) I got error messages

[ChrisRackauckas]

https://github.com/SciML/OptimizationBase.jl/pull/43 is the solution. Maybe try that branch and see?

[enigne]

Thanks, Chris. I got an error:

ERROR: LoadError: Function to differentiate `MethodInstance for OptimizationEnzymeExt.firstapply(::typeof(DJUICE.costfunction), ::Vector{Float64}, ::DJUICE.FemModel, ::DJUICE.FemModel)` is guaranteed to return an error and doesn't make sense to autodiff. Giving up
Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:35
 [2] macro expansion
   @ ~/.julia/packages/Enzyme/srACB/src/compiler.jl:5845 [inlined]
 [3] macro expansion
   @ ./none:0 [inlined]
 [4] thunk(::Val{…}, ::Type{…}, ::Type{…}, tt::Type{…}, ::Val{…}, ::Val{…}, ::Val{…}, ::Val{…}, ::Val{…}, ::Type{…})
   @ Enzyme.Compiler ./none:0
 [5] autodiff
   @ ~/.julia/packages/Enzyme/srACB/src/Enzyme.jl:234 [inlined]
 [6] (::OptimizationEnzymeExt.var"#grad#50"{OptimizationFunction{…}, DJUICE.FemModel})(res::Vector{Float64}, θ::Vector{Float64}, args::DJUICE.FemModel)
   @ OptimizationEnzymeExt ~/.julia/packages/OptimizationBase/kgHps/ext/OptimizationEnzymeExt.jl:165
 [7] top-level scope
   @ ~/Dartmouth/DJUICE/test/testoptimization.jl:46
 [8] include(fname::String)
   @ Base.MainInclude ./client.jl:489
 [9] top-level scope
   @ REPL[4]:1
in expression starting at /Users/gongcheng/Dartmouth/DJUICE/test/testoptimization.jl:46

The followings are the packages I'm uisng:

  [7da242da] Enzyme v0.12.6
  [7f7a1694] Optimization v3.25.0
  [bca83a33] OptimizationBase v0.0.7 `https://github.com/SciML/OptimizationBase.jl#ChrisRackauckas-patch-1`
  [36348300] OptimizationOptimJL v0.2.3

[ChrisRackauckas]

Well that's progress. Why DJUICE.FemModel is doubled is a good question.

[enigne]

Interesting. Enzyme.autodiff(Enzyme.Reverse, DJUICE.costfunction, Duplicated(α, ∂J_∂α), Duplicated(femmodel,dfemmodel)) works

sol.cache.f.f(prob.u0, prob.p) also works for me.

This error only occurs when I call sol.cache.f.grad(∂J_∂α, prob.u0, prob.p)

And, the solution from optimization did not change.

[ChrisRackauckas]

I think the next step here in improving Enzyme support is finishing up the DifferentiationInterface integration. We're working with @gdalle on this, I'm thinking it may not take more than 2 more weeks. When that's the case, DI will be used as the AD system within Optimization.jl. That means isolating this bug is simpler, as it isolates it to how DI handles Enzyme, which should be improved. If there is still an error, this becomes an Enzyme+DI issue which is something we can solve there.