MethodError from show on custom layer, using @functor with no fields

[kuanchiun]

Package Version

0.13.14

Julia Version

1.8.2

OS / Environment

Windows 10

Describe the bug

I constructed the layer called MinMaxScale, and when I tried to use Flux.Chain() to chain the Dense layer and MinMaxScale layer, I got the method error about the empty collection.

Steps to Reproduce

using Flux
struct MinMaxScale end

Flux.@functor MinMaxScale

function (l::MinMaxScale)(x::AbstractArray)
    minx, maxx = extrema(x)
    return (x .- minx) ./ (maxx - minx)
end

function Base.show(io::IO, l::MinMaxScale)
    print(io, "MinMaxScale")
end

encoder = Chain(
    Dense(100 => 10, gelu),
    Dense(10 => 1, gelu),
    MinMaxScale()
)

Expected Results

The model should be worked

Observed Results

Got Method error

MethodError: reducing over an empty collection is not allowed; consider supplying init to the reducer

Relevant log output

MethodError: reducing over an empty collection is not allowed; consider supplying init to the reducer
Stacktrace:
[1] reduce_empty(op::Base.MappingRF{Flux.var"#350#351"{Flux.var"#348#349"}, Base.BottomRF{typeof(Base.add_sum)}}, #unused#::Type{Union{}})
@ Base .\reduce.jl:356
[2] reduce_empty_iter(op::Base.MappingRF{Flux.var"#350#351"{Flux.var"#348#349"}, Base.BottomRF{typeof(Base.add_sum)}}, itr::NamedTuple{(), Tuple{}}, #unused#::Base.HasEltype)
@ Base .\reduce.jl:379
[3] reduce_empty_iter(op::Base.MappingRF{Flux.var"#350#351"{Flux.var"#348#349"}, Base.BottomRF{typeof(Base.add_sum)}}, itr::NamedTuple{(), Tuple{}})
@ Base .\reduce.jl:378
[4] foldl_impl(op::Base.MappingRF{Flux.var"#350#351"{Flux.var"#348#349"}, Base.BottomRF{typeof(Base.add_sum)}}, nt::Base._InitialValue, itr::NamedTuple{(), Tuple{}})
@ Base .\reduce.jl:49
[5] mapfoldl_impl(f::Flux.var"#350#351"{Flux.var"#348#349"}, op::typeof(Base.add_sum), nt::Base._InitialValue, itr::NamedTuple{(), Tuple{}})
@ Base .\reduce.jl:44
[6] mapfoldl(f::Function, op::Function, itr::NamedTuple{(), Tuple{}}; init::Base._InitialValue)
@ Base .\reduce.jl:170
[7] mapfoldl
@ .\reduce.jl:170 [inlined]
[8] #mapreduce#263
@ .\reduce.jl:302 [inlined]
[9] mapreduce
@ .\reduce.jl:302 [inlined]
[10] #sum#266
@ .\reduce.jl:528 [inlined]
[11] sum
@ .\reduce.jl:528 [inlined]
[12] _childarray_sum(f::Function, x::MinMaxScale)
@ Flux C:\Users\Tung.julia\packages\Flux\Nzh8J\src\layers\show.jl:110
[13] (::Flux.var"#350#351"{Flux.var"#348#349"})(y::MinMaxScale)
@ Flux C:\Users\Tung.julia\packages\Flux\Nzh8J\src\layers\show.jl:110
[14] MappingRF
@ .\reduce.jl:95 [inlined]
[15] afoldl(::Base.MappingRF{Flux.var"#350#351"{Flux.var"#348#349"}, Base.BottomRF{typeof(Base.add_sum)}}, ::Base._InitialValue, ::Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, ::Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, ::MinMaxScale)
@ Base .\operators.jl:550
[16] _foldl_impl(op::Base.MappingRF{Flux.var"#350#351"{Flux.var"#348#349"}, Base.BottomRF{typeof(Base.add_sum)}}, init::Base._InitialValue, itr::Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale})
@ Base .\tuple.jl:277
[17] foldl_impl(op::Base.MappingRF{Flux.var"#350#351"{Flux.var"#348#349"}, Base.BottomRF{typeof(Base.add_sum)}}, nt::Base._InitialValue, itr::Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale})
@ Base .\reduce.jl:48
[18] mapfoldl_impl(f::Flux.var"#350#351"{Flux.var"#348#349"}, op::typeof(Base.add_sum), nt::Base._InitialValue, itr::Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale})
@ Base .\reduce.jl:44
[19] mapfoldl(f::Function, op::Function, itr::Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}; init::Base._InitialValue)
@ Base .\reduce.jl:170
[20] mapfoldl
@ .\reduce.jl:170 [inlined]
[21] #mapreduce#263
@ .\reduce.jl:302 [inlined]
[22] mapreduce
@ .\reduce.jl:302 [inlined]
[23] #sum#266
@ .\reduce.jl:528 [inlined]
[24] sum
@ .\reduce.jl:528 [inlined]
[25] _childarray_sum(f::Function, x::Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale})
@ Flux C:\Users\Tung.julia\packages\Flux\Nzh8J\src\layers\show.jl:110
[26] (::Flux.var"#350#351"{Flux.var"#348#349"})(y::Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale})
@ Flux C:\Users\Tung.julia\packages\Flux\Nzh8J\src\layers\show.jl:110
[27] MappingRF
@ .\reduce.jl:95 [inlined]
[28] _foldl_impl(op::Base.MappingRF{Flux.var"#350#351"{Flux.var"#348#349"}, Base.BottomRF{typeof(Base.add_sum)}}, init::Base._InitialValue, itr::NamedTuple{(:layers,), Tuple{Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}}})
@ Base .\reduce.jl:58
[29] foldl_impl(op::Base.MappingRF{Flux.var"#350#351"{Flux.var"#348#349"}, Base.BottomRF{typeof(Base.add_sum)}}, nt::Base._InitialValue, itr::NamedTuple{(:layers,), Tuple{Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}}})
@ Base .\reduce.jl:48
[30] mapfoldl_impl(f::Flux.var"#350#351"{Flux.var"#348#349"}, op::typeof(Base.add_sum), nt::Base._InitialValue, itr::NamedTuple{(:layers,), Tuple{Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}}})
@ Base .\reduce.jl:44
[31] mapfoldl(f::Function, op::Function, itr::NamedTuple{(:layers,), Tuple{Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}}}; init::Base._InitialValue)
@ Base .\reduce.jl:170
[32] mapfoldl
@ .\reduce.jl:170 [inlined]
[33] #mapreduce#263
@ .\reduce.jl:302 [inlined]
[34] mapreduce
@ .\reduce.jl:302 [inlined]
[35] #sum#266
@ .\reduce.jl:528 [inlined]
[36] sum(f::Function, a::NamedTuple{(:layers,), Tuple{Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}}})
@ Base .\reduce.jl:528
[37] _childarray_sum
@ C:\Users\Tung.julia\packages\Flux\Nzh8J\src\layers\show.jl:110 [inlined]
[38] _big_finale(io::IOContext{IOBuffer}, m::Chain{Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}})
@ Flux C:\Users\Tung.julia\packages\Flux\Nzh8J\src\layers\show.jl:95
[39] _big_show(io::IOContext{IOBuffer}, obj::Chain{Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}}, indent::Int64, name::Nothing)
@ Flux C:\Users\Tung.julia\packages\Flux\Nzh8J\src\layers\show.jl:40
[40] _big_show
@ C:\Users\Tung.julia\packages\Flux\Nzh8J\src\layers\show.jl:17 [inlined]
[41] show(io::IOContext{IOBuffer}, m::MIME{Symbol("text/plain")}, x::Chain{Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}})
@ Flux C:\Users\Tung.julia\packages\Flux\Nzh8J\src\layers\show.jl:7
[42] limitstringmime(mime::MIME{Symbol("text/plain")}, x::Chain{Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}})
@ IJulia C:\Users\Tung.julia\packages\IJulia\6TIq1\src\inline.jl:43
[43] display_mimestring
@ C:\Users\Tung.julia\packages\IJulia\6TIq1\src\display.jl:71 [inlined]
[44] display_dict(x::Chain{Tuple{Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, Dense{typeof(gelu), Matrix{Float32}, Vector{Float32}}, MinMaxScale}})
@ IJulia C:\Users\Tung.julia\packages\IJulia\6TIq1\src\display.jl:102
[45] #invokelatest#2
@ .\essentials.jl:729 [inlined]
[46] invokelatest
@ .\essentials.jl:726 [inlined]
[47] execute_request(socket::ZMQ.Socket, msg::IJulia.Msg)
@ IJulia C:\Users\Tung.julia\packages\IJulia\6TIq1\src\execute_request.jl:112
[48] #invokelatest#2
@ .\essentials.jl:729 [inlined]
[49] invokelatest
@ .\essentials.jl:726 [inlined]
[50] eventloop(socket::ZMQ.Socket)
@ IJulia C:\Users\Tung.julia\packages\IJulia\6TIq1\src\eventloop.jl:8

[mcabbott]

This is a bug. Somewhere in show.jl there's a sum which should have init=0 or something?

But the minimal work-around is not to call @functor MinMaxScale. Since it does not wrap any parameters, there should be no need for Functors to walk inside it.

[ToucheSir]

The custom show method is probably not required either, as the default will print MinMaxScale() as expected.

[kuanchiun]

Removed Flux.@functor and return normal, Thank you @mcabbott !
I will remove the show method, Thank you @ToucheSir