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Fix for quadratic batching in #99 #100

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Fix for quadratic batching in #99 #100

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3e2d3cf
Fix for quadratic batching in #99
tclements Jan 12, 2022
d307c26
Update src/GNNGraphs/utils.jl
tclements Jan 13, 2022
a304a23
Update src/GNNGraphs/transform.jl
tclements Jan 13, 2022
d4e0688
Update src/GNNGraphs/transform.jl
tclements Jan 13, 2022
3dbf70a
Update src/GNNGraphs/utils.jl
tclements Jan 13, 2022
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29 changes: 27 additions & 2 deletions src/GNNGraphs/transform.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -253,8 +253,33 @@ julia> g12.ndata.x
1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
```
"""
Flux.batch(gs::Vector{<:GNNGraph}) = blockdiag(gs...)

function Flux.batch(gs::Vector{<:GNNGraph})
nodes = [g.num_nodes for g in gs]

if all(y -> isa(y, COO_T), [g.graph for g in gs] )
edge_indices = [edge_index(g) for g in gs]
nodesum = cumsum([0, nodes...])[1:end-1]
s = cat_features([ei[1] .+ nodesum[ii] for (ii, ei) in enumerate(edge_indices)])
t = cat_features([ei[2] .+ nodesum[ii] for (ii, ei) in enumerate(edge_indices)])
w = reduce(vcat, [get_edge_weight(g) for g in gs])
w = w isa Vector{Nothing} ? nothing : w
graph = (s, t, w)
graph_indicator = vcat([ones_like(ei[1],Int,nodes[ii]) .+ (ii - 1) for (ii,ei) in enumerate(edge_indices)]...)
elseif all(y -> isa(y, ADJMAT_T), [g.graph for g in gs] )
graph = blockdiag([g.graph for g in gs]...)
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@CarloLucibello CarloLucibello Jan 13, 2022 •
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this blockdiag method does not exist for dense matrices.
Maybe we can use dispatch as follows:

Flux.batch(gs::Vector{<:GNNGraph{<:COO_T}) = ...  # specialized method
Flux.batch(gs::Vector{<:GNNGraph{<:SPARSE_T}) = ...  # specialized method
Flux.batch(gs::Vector{<:GNNGraph{<:SPARSE_T}) = blockdiag(gs...)  # old slow fallback

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Thanks for the comments! I'll work on the dispatching

graph_indicator = vcat([ones_like(graph,Int,nodes[ii]) .+ (ii - 1) for ii in 1:length(nodes)]...)
end

GNNGraph(graph,
sum(nodes),
sum([g.num_edges for g in gs]),
sum([g.num_graphs for g in gs]),
graph_indicator,
cat_features([g.ndata for g in gs]),
cat_features([g.edata for g in gs]),
cat_features([g.gdata for g in gs]),
)
end

"""
unbatch(g::GNNGraph)
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12 changes: 12 additions & 0 deletions src/GNNGraphs/utils.jl
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Expand Up @@ -24,6 +24,18 @@ function cat_features(x1::NamedTuple, x2::NamedTuple)
NamedTuple(k => cat_features(getfield(x1,k), getfield(x2,k)) for k in keys(x1))
end

function cat_features(xs::Vector{<:NamedTuple})
symbols = [sort(collect(keys(x))) for x in xs]
all(y->y==symbols[1], symbols) || @error "cannot concatenate feature data with different keys"
length(xs) == 1 && return xs[1]

# concatenate
syms = symbols[1]
NamedTuple(
k => cat_features([x[k] for x in xs]) for (ii,k) in enumerate(syms)
)
end

# Turns generic type into named tuple
normalize_graphdata(data::Nothing; kws...) = NamedTuple()

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1 change: 1 addition & 0 deletions test/GNNGraphs/transform.jl
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Expand Up @@ -25,6 +25,7 @@

g12 = Flux.batch([g1, g2])
g12b = blockdiag(g1, g2)
@test g12 == g12b

g123 = Flux.batch([g1, g2, g3])
@test g123.graph_indicator == [fill(1, 10); fill(2, 4); fill(3, 7)]
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