MethodError from using sum(x, 2)

[domluna]

p = randn(2,3)
ReverseDiff.@forward f(p) = exp.(p) ./ sum(exp.(p), 2) # softmax
f! = ReverseDiff.compile_gradient(x -> sum(f(x)), similar(p))
f!(similar(p), p)

Gives the following error

ERROR: MethodError: no method matching broadcast_deriv_increment!(::Array{ReverseDiff.TrackedReal{Float64,Float64,Void},2}, ::ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}}, ::Void)
Closest candidates are:
  broadcast_deriv_increment!(::AbstractArray{T,N}, ::Any) at /home/dom/.julia/v0.5/ReverseDiff/src/derivatives/elementwise.jl:632
  broadcast_deriv_increment!(::Any, ::Any, ::Ref{T}) at /home/dom/.julia/v0.5/ReverseDiff/src/derivatives/elementwise.jl:569
  broadcast_deriv_increment!(::AbstractArray{T,N}, ::Any, ::AbstractArray{T,N}) at /home/dom/.julia/v0.5/ReverseDiff/src/derivatives/elementwise.jl:673
  ...
 in special_reverse_exec!(::ReverseDiff.SpecialInstruction{Base.#./,Tuple{ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},Array{ReverseDiff.TrackedReal{Float64,Float64,Void},2}},ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},Tuple{Array{Float64,2},Void}}) at /home/dom/.julia/v0.5/ReverseDiff/src/derivatives/elementwise.jl:465
 in reverse_exec!(::ReverseDiff.SpecialInstruction{Base.#./,Tuple{ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},Array{ReverseDiff.TrackedReal{Float64,Float64,Void},2}},ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},Tuple{Array{Float64,2},Void}}) at /home/dom/.julia/v0.5/ReverseDiff/src/tape.jl:74
 in (::##33#34)() at /home/dom/.julia/v0.5/ReverseDiff/src/api/tape.jl:80
 in seeded_reverse_pass!(::Array{Float64,2}, ::ReverseDiff.TrackedReal{Float64,Float64,Void}, ::ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}}, ::ReverseDiff.Compiled{ReverseDiff.GradientTape{##29#30,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void}},##29#30,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void},##31#32,##33#34}) at /home/dom/.julia/v0.5/ReverseDiff/src/api/utils.jl:30
 in seeded_reverse_pass! at /home/dom/.julia/v0.5/ReverseDiff/src/api/tape.jl:41 [inlined]
 in gradient!(::Array{Float64,2}, ::ReverseDiff.Compiled{ReverseDiff.GradientTape{##29#30,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void}},##29#30,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void},##31#32,##33#34}, ::Array{Float64,2}) at /home/dom/.julia/v0.5/ReverseDiff/src/api/gradients.jl:80
 in (::ReverseDiff.##301#302{ReverseDiff.Compiled{ReverseDiff.GradientTape{##29#30,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void}},##29#30,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void},##31#32,##33#34}})(::Array{Float64,2}, ::Array{Float64,2}) at /home/dom/.julia/v0.5/ReverseDiff/src/api/tape.jl:100

If I do ReverseDiff.@forward f(p) = exp.(p) ./ sum(exp.(p)) there's no error. So, I'm guessing it's something to do with adding the dimension to sum over.

[jrevels]

You're attempting to do ReverseDiff.@forward f(p::Array), which is not supported. Only T<:Real arguments are supported - from the ReverseDiff.@forward docs:

  ReverseDiff.@forward(f)(args::Real...)
  ReverseDiff.@forward f(args::Real...) = ...
  ReverseDiff.@forward f = (args::Real...) -> ...

If you remove ReverseDiff.@forward, it should work fine.

There could be a better error message here, and also we should support this in the future.

[domluna]

Should have mentioned this but even without ReverseDiff.@forward it doesn't work.

ERROR: MethodError: no method matching broadcast_deriv_increment!(::Array{ReverseDiff.TrackedReal{Float64,Float64,Void},2}, ::ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}}, ::Void)
Closest candidates are:
  broadcast_deriv_increment!(::AbstractArray{T,N}, ::Any) at /home/dom/.julia/v0.5/ReverseDiff/src/derivatives/elementwise.jl:632
  broadcast_deriv_increment!(::Any, ::Any, ::Ref{T}) at /home/dom/.julia/v0.5/ReverseDiff/src/derivatives/elementwise.jl:569
  broadcast_deriv_increment!(::AbstractArray{T,N}, ::Any, ::AbstractArray{T,N}) at /home/dom/.julia/v0.5/ReverseDiff/src/derivatives/elementwise.jl:673
  ...
 in special_reverse_exec!(::ReverseDiff.SpecialInstruction{Base.#./,Tuple{ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},Array{ReverseDiff.TrackedReal{Float64,Float64,Void},2}},ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},Tuple{Array{Float64,2},Void}}) at /home/dom/.julia/v0.5/ReverseDiff/src/derivatives/elementwise.jl:465
 in reverse_exec!(::ReverseDiff.SpecialInstruction{Base.#./,Tuple{ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},Array{ReverseDiff.TrackedReal{Float64,Float64,Void},2}},ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},Tuple{Array{Float64,2},Void}}) at /home/dom/.julia/v0.5/ReverseDiff/src/tape.jl:74
 in (::##5#6)() at /home/dom/.julia/v0.5/ReverseDiff/src/api/tape.jl:80
 in seeded_reverse_pass!(::Array{Float64,2}, ::ReverseDiff.TrackedReal{Float64,Float64,Void}, ::ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}}, ::ReverseDiff.Compiled{ReverseDiff.GradientTape{##1#2,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void}},##1#2,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void},##3#4,##5#6}) at /home/dom/.julia/v0.5/ReverseDiff/src/api/utils.jl:30
 in seeded_reverse_pass! at /home/dom/.julia/v0.5/ReverseDiff/src/api/tape.jl:41 [inlined]
 in gradient!(::Array{Float64,2}, ::ReverseDiff.Compiled{ReverseDiff.GradientTape{##1#2,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void}},##1#2,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void},##3#4,##5#6}, ::Array{Float64,2}) at /home/dom/.julia/v0.5/ReverseDiff/src/api/gradients.jl:80
 in (::ReverseDiff.##301#302{ReverseDiff.Compiled{ReverseDiff.GradientTape{##1#2,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void}},##1#2,ReverseDiff.TrackedArray{Float64,Float64,2,Array{Float64,2},Array{Float64,2}},ReverseDiff.TrackedReal{Float64,Float64,Void},##3#4,##5#6}})(::Array{Float64,2}, ::Array{Float64,2}) at /home/dom/.julia/v0.5/ReverseDiff/src/api/tape.jl:100

Looks like the same error.

[jrevels]

That looks like what I fixed in #33 - have you updated to the latest release (v0.0.2)? If I copy and paste your code, but remove the @forward, I get:

julia> using ReverseDiff

julia> begin
           p = randn(2,3)
           f(p) = exp.(p) ./ sum(exp.(p), 2) # softmax
           f! = ReverseDiff.compile_gradient(x -> sum(f(x)), similar(p))
           f!(similar(p), p)
       end
2×3 Array{Float64,2}:
 2.77556e-17  5.55112e-17  5.55112e-17
 0.0          0.0          0.0

[domluna]

I was on master which is 2 commits ahead it seems. Went to v0.0.2 and it works.

[jrevels]

Ah, that's not good. Thanks for letting me know. Looks like I should add softmax to the test suite! I'll reopen this.