SciML / DataInterpolations.jl

A library of data interpolation and smoothing functions
MIT License
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ForwardDiff Vectorization with AkimaInterpolations doesn't work #278

Closed astro-kevin closed 1 week ago

astro-kevin commented 1 week ago

Describe the bug 🐞

When trying to vectorize derivatives in ForwardDiff (i.e. compute derivatives for a vector), a MethodError is thrown instead of the expected behavior.

Expected behavior

That the derivative be computed for the entire vector.

Quick edit: I expected the result to be identical to the code here: map(x->ForwardDiff.derivative(test,x), [0.5,1.5])

Minimal Reproducible Example πŸ‘‡

test = AkimaInterpolation([1,2,3], [1,2,3]; extrapolate = true)
ForwardDiff.derivative.(test,[0.5,1.5])

Error & Stacktrace ⚠️

MethodError: no method matching length(::AkimaInterpolation{Vector{Int64}, Vector{Int64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64})

Closest candidates are:
  length(!Matched::Base.MethodSpecializations)
   @ Base reflection.jl:1166
  length(!Matched::ExponentialBackOff)
   @ Base error.jl:267
  length(!Matched::Core.Compiler.InstructionStream)
   @ Base show.jl:2777
  ...

Stacktrace:
 [1] _similar_shape(itr::AkimaInterpolation{Vector{Int64}, Vector{Int64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64}, ::Base.HasLength)
   @ Base ./array.jl:710
 [2] _collect(cont::UnitRange{Int64}, itr::AkimaInterpolation{Vector{Int64}, Vector{Int64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64}, ::Base.HasEltype, isz::Base.HasLength)
   @ Base ./array.jl:765
 [3] collect(itr::AkimaInterpolation{Vector{Int64}, Vector{Int64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64})
   @ Base ./array.jl:759
 [4] broadcastable(x::AkimaInterpolation{Vector{Int64}, Vector{Int64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64})
   @ Base.Broadcast ./broadcast.jl:743
 [5] broadcasted(::typeof(ForwardDiff.derivative), ::AkimaInterpolation{Vector{Int64}, Vector{Int64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Int64}, ::Vector{Float64})
   @ Base.Broadcast ./broadcast.jl:1344
 [6] top-level scope
   @ ~/equil_model/test.ipynb:4

Environment (please complete the following information):

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βŒ… [dad2f222] LLVMExtra_jll v0.0.29+0
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Info Packages marked with βŒƒ and βŒ… have new versions available. Those with βŒƒ may be upgradable, but those with βŒ… are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`
Julia Version 1.10.0
Commit 3120989f39b (2023-12-25 18:01 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 32 Γ— Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, haswell)
  Threads: 47 on 32 virtual cores
sathvikbhagavan commented 1 week ago

It should be

ForwardDiff.derivative.((test,), [0.5, 1.5])

Need to broadcast test as well.

julia> ForwardDiff.derivative.((test,), [0.5, 1.5])
2-element Vector{Float64}:
 1.0
 1.0

julia> map(x->ForwardDiff.derivative(test,x), [0.5,1.5])
2-element Vector{Float64}:
 1.0
 1.0
astro-kevin commented 1 week ago

Thanks.