GATConv reducing over empty collection

[lenianiva]

I'm using GATConv inside a heterogeneous convolution layer:

function create_layer_hetero_conv(
    dims::Pair;
    activation = act,
)::HeteroGraphConv
    HeteroGraphConv(
        [
            (src, edge, dst) => GATConv(dims.first => dims.second, activation) for
            (src, edge, dst) in keys(EXAMPLE.edata)
        ];
        aggr = (x, y) -> max.(x, y),
    )
end

In my use case, some node types can have 0 nodes, and this led to

ERROR: LoadError: ArgumentError: reducing over an empty collection is not allowed; consider supplying `init` to the reducer

Stacktrace:
  [1] reduce_empty(op::Base.MappingRF{typeof(identity), typeof(max)}, ::Type{Int64})
    @ Base ./reduce.jl:350
  [2] _mapreduce
    @ ./reduce.jl:421 [inlined]
  [3] _mapreduce_dim
    @ ./reducedim.jl:334 [inlined]
  [4] mapreduce
    @ ./reducedim.jl:326 [inlined]
  [5] _maximum
    @ ./reducedim.jl:984 [inlined]
  [6] _maximum
    @ ./reducedim.jl:983 [inlined]
  [7] maximum
    @ ./reducedim.jl:979 [inlined]
  [8] maximum_dims
    @ ~/.julia/packages/NNlib/1TYHL/src/utils.jl:72 [inlined]
  [9] scatter(op::typeof(max), src::Array{Float32, 3}, idx::Vector{Int64}; init::Nothing, dstsize::Nothing)
    @ NNlib ~/.julia/packages/NNlib/1TYHL/src/scatter.jl:179
 [10] scatter
    @ ~/.julia/packages/NNlib/1TYHL/src/scatter.jl:174 [inlined]
 [11] softmax_edge_neighbors(g::GNNHeteroGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}, e::Array{Float32, 3})
    @ GNNlib ~/.julia/packages/GNNlib/StdFP/src/utils.jl:93
 [12] gat_conv(l::GATConv{Dense{typeof(identity), Matrix{Float32}, Bool}, Nothing, Float64, Float32, Matrix{Float32}, typeof(act), Vector{Float32}}, g::GNNHeteroGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}, x::Tuple{Matrix{Float32}, Matrix{Float32}}, e::Nothing)
    @ GNNlib ~/.julia/packages/GNNlib/StdFP/src/layers/conv.jl:138
 [13] GATConv
    @ ~/.julia/packages/GraphNeuralNetworks/nauyk/src/layers/conv.jl:346 [inlined]
 [14] (::GATConv{Dense{typeof(identity), Matrix{Float32}, Bool}, Nothing, Float64, Float32, Matrix{Float32}, typeof(act), Vector{Float32}})(g::GNNHeteroGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}, x::Tuple{Matrix{Float32}, Matrix{Float32}})
    @ GraphNeuralNetworks ~/.julia/packages/GraphNeuralNetworks/nauyk/src/layers/conv.jl:346
 [15] (::GraphNeuralNetworks.var"#forw#forw##0"{GNNHeteroGraph{Tuple{T, T, Union{Nothing, AbstractVector}} where T<:(AbstractVector{<:Integer})}, @NamedTuple{locus::Matrix{Float32}, constant::Matrix{Float32}, sort::Matrix{Float32}, literal::Matrix{Float32}, hole::Matrix{Float32}}})(l::GATConv{Dense{typeof(identity), Matrix{Float32}, Bool}, Nothing, Float64, Float32, Matrix{Float32}, typeof(act), Vector{Float32}}, et::Tuple{Symbol, Symbol, Symbol})
    @ GraphNeuralNetworks ~/.julia/packages/GraphNeuralNetworks/nauyk/src/layers/heteroconv.jl:61
 [16] #60
    @ ./none:-1 [inlined]
 [17] iterate
    @ ./generator.jl:48 [inlined]
 [18] collect(itr::Base.Generator{Base.Iterators.Zip{Tuple{Vector{GATConv{Dense{typeof(identity), Matrix{Float32}, Bool}, Nothing, Float64, Float32, Matrix{Float32}, typeof(act), Vector{Float32}}}, Vector{Tuple{Symbol, Symbol, Symbol}}}}, GraphNeuralNetworks.var"#60#61"{GraphNeuralNetworks.var"#forw#forw##0"{GNNHeteroGraph{Tuple{T, T, Union{Nothing, AbstractVector}} where T<:(AbstractVector{<:Integer})}, @NamedTuple{locus::Matrix{Float32}, constant::Matrix{Float32}, sort::Matrix{Float32}, literal::Matrix{Float32}, hole::Matrix{Float32}}}}})
    @ Base ./array.jl:790
 [19] (::HeteroGraphConv)(g::GNNHeteroGraph{Tuple{T, T, Union{Nothing, AbstractVector}} where T<:(AbstractVector{<:Integer})}, x::@NamedTuple{locus::Matrix{Float32}, constant::Matrix{Float32}, sort::Matrix{Float32}, literal::Matrix{Float32}, hole::Matrix{Float32}})
    @ GraphNeuralNetworks ~/.julia/packages/GraphNeuralNetworks/nauyk/src/layers/heteroconv.jl:63

How can I workaround this? The same problem doesn't occur if I just use GraphConv.

[lenianiva]

I have a fix here, but I don't know if it is correct: https://github.com/lenianiva/GraphNeuralNetworks.jl/tree/gnn/conv

Edit: Printing out the numbers:

    @info "$e, $t"
[ Info: Array{Float32, 3}(undef, 1, 1, 0), Int64[]

so it seems like the function should return an empty array when the input is empty.