eigvals! bug

[baggepinnen]

The following example tries to differentiate through eigvals! with both real and complex input arguments. Both fail, but with different errors. ChainRules has rules that should cover both cases

using LinearAlgebra, ForwardDiff, ForwardDiffChainRules, ChainRules
function test_ev(x)
    X = copy(reshape(x, 4, 4))
    X2 = LinearAlgebra.eigvals!(X)
    sum(X2)
end

@ForwardDiff_frule LinearAlgebra.eigvals!(x1::AbstractMatrix{<:ForwardDiff.Dual}; kwargs...)
xr = randn(16)
ForwardDiff.gradient(test_ev, xr)


function test_ev_complex(x)
    X = copy(reshape(x, 4, 4))
    X = X + im*X
    X2 = LinearAlgebra.eigvals!(X)
    sum(X2)
end

@ForwardDiff_frule LinearAlgebra.eigvals!(x1::AbstractMatrix{<:Complex{<:ForwardDiff.Dual}}; kwargs...)
ForwardDiff.gradient(test_ev_complex, xr)

julia> ForwardDiff.gradient(test_ev, xr)
ERROR: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8})

Closest candidates are:
  (::Type{T})(::Real, ::RoundingMode) where T<:AbstractFloat
   @ Base rounding.jl:207
  (::Type{T})(::T) where T<:Number
   @ Core boot.jl:792
  (::Type{T})(::AbstractChar) where T<:Union{AbstractChar, Number}
   @ Base char.jl:50
  ...

Stacktrace:
  [1] convert(#unused#::Type{Float64}, x::ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8})
    @ Base ./number.jl:7
  [2] ComplexF64(re::ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}, im::ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8})
    @ Base ./complex.jl:14
  [3] structfromnt(#unused#::Type{ComplexF64}, x::NamedTuple{(:re, :im), Tuple{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}, ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}}})
    @ NamedTupleTools ~/.julia/packages/NamedTupleTools/7MQH4/src/NamedTupleTools.jl:118
  [4] (::DifferentiableFlatten.var"#22#24"{ComplexF64})(y::Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}})
    @ DifferentiableFlatten ~/.julia/packages/DifferentiableFlatten/ro7xH/src/DifferentiableFlatten.jl:114
  [5] Unflatten
    @ ~/.julia/packages/DifferentiableFlatten/ro7xH/src/DifferentiableFlatten.jl:222 [inlined]
  [6] (::DifferentiableFlatten.var"#3#6"{Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}}, Vector{Int64}, Vector{DifferentiableFlatten.Unflatten{ComplexF64, DifferentiableFlatten.var"#22#24"{ComplexF64}}}, Vector{Vector{Float64}}})(n::Int64)
    @ DifferentiableFlatten ./none:0
  [7] iterate
    @ ./generator.jl:47 [inlined]
  [8] collect(itr::Base.Generator{Base.OneTo{Int64}, DifferentiableFlatten.var"#3#6"{Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}}, Vector{Int64}, Vector{DifferentiableFlatten.Unflatten{ComplexF64, DifferentiableFlatten.var"#22#24"{ComplexF64}}}, Vector{Vector{Float64}}}})
    @ Base ./array.jl:782
  [9] Vector_from_vec
    @ ~/.julia/packages/DifferentiableFlatten/ro7xH/src/DifferentiableFlatten.jl:57 [inlined]
 [10] eigvals!(x1::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Main ~/.julia/packages/ForwardDiffChainRules/2Xt9G/src/ForwardDiffChainRules.jl:96
 [11] eigvals!
    @ ~/.julia/packages/ForwardDiffChainRules/2Xt9G/src/ForwardDiffChainRules.jl:70 [inlined]
 [12] test_ev(x::Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}})
    @ Main ~/.julia/dev/ControlSystems/docs/src/examples/optimization_based_tuning.jl:275
 [13] chunk_mode_gradient(f::typeof(test_ev), x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}}})
    @ ForwardDiff ~/.julia/packages/ForwardDiff/vXysl/src/gradient.jl:123
 [14] gradient(f::Function, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}}}, ::Val{true})
    @ ForwardDiff ~/.julia/packages/ForwardDiff/vXysl/src/gradient.jl:21
 [15] gradient(f::Function, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev), Float64}, Float64, 8}}})
    @ ForwardDiff ~/.julia/packages/ForwardDiff/vXysl/src/gradient.jl:17
 [16] gradient(f::Function, x::Vector{Float64})
    @ ForwardDiff ~/.julia/packages/ForwardDiff/vXysl/src/gradient.jl:17
 [17] top-level scope
    @ REPL[16]:1

julia> ForwardDiff.gradient(test_ev_complex, xr)
ERROR: MethodError: no method matching iterate(::Nothing)

Closest candidates are:
  iterate(::Union{LinRange, StepRangeLen})
   @ Base range.jl:880
  iterate(::Union{LinRange, StepRangeLen}, ::Integer)
   @ Base range.jl:880
  iterate(::T) where T<:Union{Base.KeySet{<:Any, <:Dict}, Base.ValueIterator{<:Dict}}
   @ Base dict.jl:698
  ...

Stacktrace:
 [1] indexed_iterate(I::Nothing, i::Int64)
   @ Base ./tuple.jl:91
 [2] eigvals!(x1::Matrix{Complex{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev_complex), Float64}, Float64, 8}}}; kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
   @ Main ~/.julia/packages/ForwardDiffChainRules/2Xt9G/src/ForwardDiffChainRules.jl:81
 [3] eigvals!
   @ ~/.julia/packages/ForwardDiffChainRules/2Xt9G/src/ForwardDiffChainRules.jl:70 [inlined]
 [4] test_ev_complex(x::Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev_complex), Float64}, Float64, 8}})
   @ Main ~/.julia/dev/ControlSystems/docs/src/examples/optimization_based_tuning.jl:287
 [5] chunk_mode_gradient(f::typeof(test_ev_complex), x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{typeof(test_ev_complex), Float64}, Float64, 8, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev_complex), Float64}, Float64, 8}}})
   @ ForwardDiff ~/.julia/packages/ForwardDiff/vXysl/src/gradient.jl:123
 [6] gradient(f::Function, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{typeof(test_ev_complex), Float64}, Float64, 8, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev_complex), Float64}, Float64, 8}}}, ::Val{true})
   @ ForwardDiff ~/.julia/packages/ForwardDiff/vXysl/src/gradient.jl:21
 [7] gradient(f::Function, x::Vector{Float64}, cfg::ForwardDiff.GradientConfig{ForwardDiff.Tag{typeof(test_ev_complex), Float64}, Float64, 8, Vector{ForwardDiff.Dual{ForwardDiff.Tag{typeof(test_ev_complex), Float64}, Float64, 8}}})
   @ ForwardDiff ~/.julia/packages/ForwardDiff/vXysl/src/gradient.jl:17
 [8] gradient(f::Function, x::Vector{Float64})
   @ ForwardDiff ~/.julia/packages/ForwardDiff/vXysl/src/gradient.jl:17
 [9] top-level scope
   @ REPL[17]:1