# ForwardDiff.jl : ERROR: LoadError: MethodError: convert(...) is ambiguous

**URL:** <https://discourse.julialang.org/t/forwarddiff-jl-error-loaderror-methoderror-convert-is-ambiguous/86132>\
**Category:** General Usage\
**Tags:** zygote, forwarddiff, gradient, hessian\
**Created:** [August 22, 2022, 11:39am UTC](https://discourse.julialang.org/t/forwarddiff-jl-error-loaderror-methoderror-convert-is-ambiguous/86132 "2022-08-22T11:39:41Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![F-YF](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/f-yf/32/17363_2.png) [@F-YF](https://discourse.julialang.org/u/F-YF)\
**Post date:** [August 22, 2022, 11:39am UTC](https://discourse.julialang.org/t/forwarddiff-jl-error-loaderror-methoderror-convert-is-ambiguous/86132/1 "2022-08-22T11:39:41Z")

</div>

My original intention was to use Zygote to achieve the gradient of gradients, but there were a lot of problems with twice automatic differentiation in reverse mode. So I used [Zygote.hessian](https://fluxml.ai/Zygote.jl/latest/utils/#Zygote.hessian) to achieve the mixing forward + reverse mode, and then some problems occurred. The following is my example

```julia
using GenericLinearAlgebra
using Zygote

function f(x)
    A = reshape(sin.(collect(1:25)*x),5,5)
    u, s,_= GenericLinearAlgebra.svd(A)
    A = u *A *u
    A = A / maximum(abs.(A))
    return sum(A)
end
function test()
    x0=0.5
    @show gradient(f,x0)
    @show hessian(f,x0)

end

test()

```

It give

```julia
gradient(f, x0) = (64.0,)
ERROR: LoadError: MethodError: convert(::Type{ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}}, ::ChainRulesCore.ZeroTangent) is ambiguous. Candidates:
  convert(::Type{T}, x::ChainRulesCore.AbstractZero) where T<:Number in ChainRulesCore at ~\.julia\packages\ChainRulesCore\ctmSK\src\tangent_types\abstract_zero.jl:31
  convert(::Type{ForwardDiff.Dual{T, V, N}}, x) where {T, V, N} in ForwardDiff at ~\.julia\packages\ForwardDiff\pDtsf\src\dual.jl:432
Possible fix, define
  convert(::Type{ForwardDiff.Dual{T, V, N}}, ::ChainRulesCore.AbstractZero) where {T, V, N}
Stacktrace:
  [1] fill!(dest::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}}, x::ChainRulesCore.ZeroTangent)
    @ Base .\array.jl:351
  [2] _setindex_zero(::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}}, ::ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}, ::Int64, ::Vararg{Int64})
    @ ChainRules ~\.julia\packages\ChainRules\DUopG\src\rulesets\Base\indexing.jl:104
  [3] ∇getindex(x::Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}}, dy::ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}, inds::CartesianIndex{2})
    @ ChainRules ~\.julia\packages\ChainRules\DUopG\src\rulesets\Base\indexing.jl:89
  [4] (::ChainRules.var"#1564#1566"{Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}}, ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}, Tuple{CartesianIndex{2}}})()
    @ ChainRules ~\.julia\packages\ChainRules\DUopG\src\rulesets\Base\indexing.jl:74
  [5] unthunk
    @ ~\.julia\packages\ChainRulesCore\ctmSK\src\tangent_types\thunks.jl:199 [inlined]
  [6] unthunk(x::ChainRulesCore.InplaceableThunk{ChainRulesCore.Thunk{ChainRules.var"#1564#1566"{Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}}, ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}, Tuple{CartesianIndex{2}}}}, ChainRules.var"#1563#1565"{Matrix{ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}}, ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1}, Tuple{CartesianIndex{2}}}})
    @ ChainRulesCore ~\.julia\packages\ChainRulesCore\ctmSK\src\tangent_types\thunks.jl:232
  [7] wrap_chainrules_output
    @ ~\.julia\packages\Zygote\xGkZ5\src\compiler\chainrules.jl:105 [inlined]
  [8] map
    @ .\tuple.jl:222 [inlined]
  [9] wrap_chainrules_output
    @ ~\.julia\packages\Zygote\xGkZ5\src\compiler\chainrules.jl:106 [inlined]
 [10] ZBack
    @ ~\.julia\packages\Zygote\xGkZ5\src\compiler\chainrules.jl:206 [inlined]
 [11] Pullback
    @ ~\Desktop\Ising_test\test.jl:8 [inlined]
 [12] (::typeof(∂(f)))(Δ::ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1})
    @ Zygote ~\.julia\packages\Zygote\xGkZ5\src\compiler\interface2.jl:0
 [13] (::Zygote.var"#60#61"{typeof(∂(f))})(Δ::ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1})
    @ Zygote ~\.julia\packages\Zygote\xGkZ5\src\compiler\interface.jl:45
 [14] gradient(f::Function, args::ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1})
    @ Zygote ~\.julia\packages\Zygote\xGkZ5\src\compiler\interface.jl:97
 [15] (::Zygote.var"#104#105"{typeof(f)})(x::ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{typeof(f)}, Float64}, Float64, 1})
    @ Zygote ~\.julia\packages\Zygote\xGkZ5\src\lib\grad.jl:66
 [16] derivative(f::Zygote.var"#104#105"{typeof(f)}, x::Float64)
    @ ForwardDiff ~\.julia\packages\ForwardDiff\pDtsf\src\derivative.jl:14
 [17] hessian_dual(f::Function, x::Float64)
    @ Zygote ~\.julia\packages\Zygote\xGkZ5\src\lib\grad.jl:66
 [18] hessian(f::Function, x::Float64)
    @ Zygote ~\.julia\packages\Zygote\xGkZ5\src\lib\grad.jl:62
 [19] macro expansion
    @ .\show.jl:1047 [inlined]
 [20] test()
    @ Main ~\Desktop\Ising_test\test.jl:14
 [21] top-level scope
    @ ~\Desktop\Ising_test\test.jl:20
in expression starting at ~\Desktop\Ising_test\test.jl:20

```

It seems that there is a problem when calling ForwardDiff, but I don’t know how to fix it

---

<div class="post-metadata">

**Author:** ![contradict](https://avatars.discourse-cdn.com/v4/letter/c/ac91a4/32.png) [@contradict](https://discourse.julialang.org/u/contradict)\
**Post date:** [August 22, 2022, 6:53pm UTC](https://discourse.julialang.org/t/forwarddiff-jl-error-loaderror-methoderror-convert-is-ambiguous/86132/2 "2022-08-22T18:53:36Z")

</div>

Playing with this by deleting things from your function until I get something similar that triggers the same error, I found this:

```julia
function f3(x)
    A = ones(5,5)*x
    maximum(A)
end

```

Which gives the same error. I think the most important part of the error is

```julia
Possible fix, define
  convert(::Type{ForwardDiff.Dual{T, V, N}}, ::ChainRulesCore.AbstractZero) where {T, V, N}

```

This definition seems to work, but I am not confident it is correct:

```julia
using ForwardDiff, ChainRulesCore

Base.convert(::Type{ForwardDiff.Dual{T, V, N}}, ::ChainRulesCore.AbstractZero) where {T, V, N} = ForwardDiff.Dual{T, V, N}(zero(V))

```

---

<div class="post-metadata">

**Author:** ![mcabbott](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mcabbott/32/6603_2.png) [@mcabbott](https://discourse.julialang.org/u/mcabbott)\
**Post date:** [August 22, 2022, 7:23pm UTC](https://discourse.julialang.org/t/forwarddiff-jl-error-loaderror-methoderror-convert-is-ambiguous/86132/3 "2022-08-22T19:23:04Z")

</div>

I think that’s a new bug, from these lines:

> <https://github.com/JuliaDiff/ChainRules.jl/blob/77ef0eb15fdd207028c059927f2456819d62df8c/src/rulesets/Base/indexing.jl#L104-L105>

Can you make an issue on ChainRules?

> [@contradict](#):
>
> This definition seems to work

This looks fine to me, for now. But CR and ForwardDiff don’t depend on each other, so some other solution ought to be invented longer-term.

---

<div class="post-metadata">

**Author:** ![F-YF](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/f-yf/32/17363_2.png) [@F-YF](https://discourse.julialang.org/u/F-YF)\
**Post date:** [August 23, 2022, 6:49am UTC](https://discourse.julialang.org/t/forwarddiff-jl-error-loaderror-methoderror-convert-is-ambiguous/86132/4 "2022-08-23T06:49:16Z")

</div>

> [@contradict](#):
>
> This definition seems to work, but I am not confident it is correct:
> 
> ```julia
> using ForwardDiff, ChainRulesCore
> 
> Base.convert(::Type{ForwardDiff.Dual{T, V, N}}, ::ChainRulesCore.AbstractZero) where {T, V, N} = ForwardDiff.Dual{T, V, N}(zero(V))
> 
> ```

This seems to be correct in most cases, but something very strange happened in my project. I’m sorry I can’t reproduce the same error with an example. It has nan type, which is really weird. I’ve avoided it by writing it in an equivalent way, but I don’t know what the problem is. But that’s okay. I don’t want to dwell on it right now. Because there is a new error, I will start a new topic to describe it.

---

<div class="post-metadata">

**Author:** ![F-YF](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/f-yf/32/17363_2.png) [@F-YF](https://discourse.julialang.org/u/F-YF)\
**Post date:** [August 23, 2022, 7:10am UTC](https://discourse.julialang.org/t/forwarddiff-jl-error-loaderror-methoderror-convert-is-ambiguous/86132/5 "2022-08-23T07:10:50Z")

</div>

> [@mcabbott](#):
>
> This looks fine to me, for now. But CR and ForwardDiff don’t depend on each other, so some other solution ought to be invented longer-term.

> [@contradict](#):
>
> This definition seems to work, but I am not confident it is correct:

> [@ForwardDiff.jl: ERROR: LoadError: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{var"#f#1"}, Float64}, Float64, 1})](https://discourse.julialang.org/t/forwarddiff-jl-error-loaderror-methoderror-no-method-matching-float64-forwarddiff-dual-forwarddiff-tag-zygote-var-104-105-var-f-1-float64-float64-1/86175):
>
> I had a lot of problems with ForwardDiff. I used Zygote to do automatic differential calculations before, so I didn’t know much about ForwardDiff. My original intention was to use Zygote to achieve the gradient of gradients, but there were a lot of problems with twice automatic differentiation in reverse mode. So I used [Zygote.hessian](https://fluxml.ai/Zygote.jl/latest/utils/#Zygote.hessian) to achieve the mixing forward + reverse mode, and then many problems occurred. I have solved some problems by myself, and some problems [have been solved by useful…](https://discourse.julialang.org/t/forwarddiff-jl-error-loaderror-methoderror-convert-is-ambiguous/86132)

This is a new problem I encountered with forwarddiff. I wonder if you are interested in it