create GNNlib.jl

GNNlib/Project.toml

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+name = "GNNlib"
+uuid = "a6a84749-d869-43f8-aacc-be26a1996e48"
+authors = ["Carlo Lucibello and contributors"]
+version = "0.1.0"
+
+[deps]
+Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
+Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
+MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
+NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
+NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+
+[weakdeps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+SimpleWeightedGraphs = "47aef6b3-ad0c-573a-a1e2-d07658019622"
+
+[extensions]
+GNNlibCUDAExt = "CUDA"
+GNNlibSimpleWeightedGraphsExt = "SimpleWeightedGraphs"
+
+[compat]
+Adapt = "3, 4"
+CUDA = "4, 5"
+ChainRulesCore = "1"
+DataStructures = "0.18"
+Functors = "0.4.1"
+Graphs = "1.4"
+KrylovKit = "0.6, 0.7"
+LinearAlgebra = "1"
+MLDatasets = "0.7"
+MLUtils = "0.4"
+MacroTools = "0.5"
+NNlib = "0.9"
+NearestNeighbors = "0.4"
+Random = "1"
+Reexport = "1"
+SimpleWeightedGraphs = "1.4.0"
+SparseArrays = "1"
+Statistics = "1"
+StatsBase = "0.34"
+cuDNN = "1"
+julia = "1.9"
+
+[extras]
+Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
+InlineStrings = "842dd82b-1e85-43dc-bf29-5d0ee9dffc48"
+MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+SimpleWeightedGraphs = "47aef6b3-ad0c-573a-a1e2-d07658019622"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
+
+[targets]
+test = ["Test", "Adapt", "DataFrames", "InlineStrings", "SimpleWeightedGraphs", "Zygote", "FiniteDifferences", "ChainRulesTestUtils", "MLDatasets", "CUDA", "cuDNN"]

GNNlib/README.md

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+# GNNlib.jl
+
+This package contains a collection deep-learning framework agnostic 
+building blocks for graph neural networks such as graph convolutional layers and the implementation
+of GraphGNN. 
+
+In the future it will serve as the foundation of GraphNeuralNetworks.jl (based on Flux,jl).
+GNNlib.jl will be to GraphNeuralNetworks.jl what NNlib.jl is to Flux.jl and Lux.jl. 
+
+This package is currently under development and may break frequentely. 
+It is not meant for final users but for GNN libraries developers.
+Final user should use GraphNeuralNetworks.jl instead.
+
+

GNNlib/ext/GNNlibCUDAExt/GNNGraphs/query.jl

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+
+GNNGraphs._rand_dense_vector(A::CUMAT_T) = CUDA.randn(size(A, 1))

GNNlib/ext/GNNlibCUDAExt/GNNGraphs/transform.jl

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+
+GNNGraphs.dense_zeros_like(a::CUMAT_T, T::Type, sz = size(a)) = CUDA.zeros(T, sz)

GNNlib/ext/GNNlibCUDAExt/GNNGraphs/utils.jl

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+
+GNNGraphs.iscuarray(x::AnyCuArray) = true
+
+
+function sort_edge_index(u::AnyCuArray, v::AnyCuArray)
+    #TODO proper cuda friendly implementation
+    sort_edge_index(u |> Flux.cpu, v |> Flux.cpu) |> Flux.gpu
+end

GNNlib/ext/GNNlibCUDAExt/GNNlibCUDAExt.jl

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+module GNNlibCUDAExt
+
+using CUDA
+using Random, Statistics, LinearAlgebra
+using GNNlib
+using GNNlib.GNNGraphs
+using GNNlib.GNNGraphs: COO_T, ADJMAT_T, SPARSE_T 
+import GNNlib: propagate
+
+const CUMAT_T = Union{CUDA.AnyCuMatrix, CUDA.CUSPARSE.CuSparseMatrix}
+
+include("GNNGraphs/query.jl")
+include("GNNGraphs/transform.jl")
+include("GNNGraphs/utils.jl")
+include("msgpass.jl")
+
+end #module

GNNlib/ext/GNNlibCUDAExt/msgpass.jl

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+
+###### PROPAGATE SPECIALIZATIONS ####################
+
+## COPY_XJ 
+
+## avoid the fast path on gpu until we have better cuda support
+function propagate(::typeof(copy_xj), g::GNNGraph{<:Union{COO_T, SPARSE_T}}, ::typeof(+),
+                   xi, xj::AnyCuMatrix, e)
+    propagate((xi, xj, e) -> copy_xj(xi, xj, e), g, +, xi, xj, e)
+end
+
+## E_MUL_XJ 
+
+## avoid the fast path on gpu until we have better cuda support
+function propagate(::typeof(e_mul_xj), g::GNNGraph{<:Union{COO_T, SPARSE_T}}, ::typeof(+),
+                   xi, xj::AnyCuMatrix, e::AbstractVector)
+    propagate((xi, xj, e) -> e_mul_xj(xi, xj, e), g, +, xi, xj, e)
+end
+
+## W_MUL_XJ 
+
+## avoid the fast path on gpu until we have better cuda support
+function propagate(::typeof(w_mul_xj), g::GNNGraph{<:Union{COO_T, SPARSE_T}}, ::typeof(+),
+                   xi, xj::AnyCuMatrix, e::Nothing)
+    propagate((xi, xj, e) -> w_mul_xj(xi, xj, e), g, +, xi, xj, e)
+end
+
+# function propagate(::typeof(copy_xj), g::GNNGraph, ::typeof(mean), xi, xj::AbstractMatrix, e)
+#     A = adjacency_matrix(g, weighted=false)
+#     D = compute_degree(A)
+#     return xj * A * D
+# end
+
+# # Zygote bug. Error with sparse matrix without nograd
+# compute_degree(A) = Diagonal(1f0 ./ vec(sum(A; dims=2)))
+
+# Flux.Zygote.@nograd compute_degree

GNNlib/ext/GNNlibSimpleWeightedGraphsExt/GNNlibSimpleWeightedGraphsExt.jl

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+module GNNlibSimpleWeightedGraphsExt
+
+using GNNlib
+using Graphs
+using SimpleWeightedGraphs
+
+function GNNlib.GNNGraph(g::T; kws...) where 
+                 {T <: Union{SimpleWeightedGraph, SimpleWeightedDiGraph}}
+   return GNNGraph(g.weights, kws...)
+end
+
+end #module

GNNlib/src/GNNGraphs/GNNGraphs.jl

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+module GNNGraphs
+
+using SparseArrays
+using Functors: @functor
+import Graphs
+using Graphs: AbstractGraph, outneighbors, inneighbors, adjacency_matrix, degree,
+              has_self_loops, is_directed
+import MLUtils
+using MLUtils: getobs, numobs, ones_like, zeros_like, batch
+import NearestNeighbors
+import NNlib
+import StatsBase
+import KrylovKit
+using ChainRulesCore
+using LinearAlgebra, Random, Statistics
+import MLUtils
+import Functors
+
+include("chainrules.jl") # hacks for differentiability
+
+include("datastore.jl")
+export DataStore
+
+include("abstracttypes.jl")
+export AbstractGNNGraph
+
+include("gnngraph.jl")
+export GNNGraph,
+       node_features,
+       edge_features,
+       graph_features
+
+include("gnnheterograph.jl")
+export GNNHeteroGraph,
+       num_edge_types,
+       num_node_types,
+       edge_type_subgraph
+
+include("temporalsnapshotsgnngraph.jl")
+export TemporalSnapshotsGNNGraph,
+       add_snapshot,
+       # add_snapshot!,
+       remove_snapshot
+       # remove_snapshot!
+
+include("query.jl")
+export adjacency_list,
+       edge_index,
+       get_edge_weight,
+       graph_indicator,
+       has_multi_edges,
+       is_directed,
+       is_bidirected,
+       normalized_laplacian,
+       scaled_laplacian,
+       laplacian_lambda_max,
+# from Graphs
+       adjacency_matrix,
+       degree,
+       has_self_loops,
+       has_isolated_nodes,
+       inneighbors,
+       outneighbors,
+       khop_adj
+
+include("transform.jl")
+export add_nodes,
+       add_edges,
+       add_self_loops,
+       getgraph,
+       negative_sample,
+       rand_edge_split,
+       remove_self_loops,
+       remove_edges, 
+       remove_multi_edges,
+       set_edge_weight,
+       to_bidirected,
+       to_unidirected,
+       random_walk_pe,
+       remove_nodes,
+# from Flux
+       batch,
+       unbatch,
+# from SparseArrays
+       blockdiag
+
+include("generate.jl")
+export rand_graph,
+       rand_heterograph,
+       rand_bipartite_heterograph,
+       knn_graph,
+       radius_graph,
+       rand_temporal_radius_graph,
+       rand_temporal_hyperbolic_graph
+
+include("sampling.jl")
+export sample_neighbors
+
+include("operators.jl")
+# Base.intersect
+
+include("convert.jl")
+include("utils.jl")
+
+include("gatherscatter.jl")
+# _gather, _scatter
+
+end #module

GNNlib/src/GNNGraphs/abstracttypes.jl

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+
+const COO_T = Tuple{T, T, V} where {T <: AbstractVector{<:Integer}, V}
+const ADJLIST_T = AbstractVector{T} where {T <: AbstractVector{<:Integer}}
+const ADJMAT_T = AbstractMatrix
+const SPARSE_T = AbstractSparseMatrix # subset of ADJMAT_T
+
+const AVecI = AbstractVector{<:Integer}
+
+# All concrete graph types should be subtypes of AbstractGNNGraph{T}.
+# GNNGraph and GNNHeteroGraph are the two concrete types.
+abstract type AbstractGNNGraph{T} <: AbstractGraph{Int} end

GNNlib/src/GNNGraphs/chainrules.jl

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+# Taken from https://github.com/JuliaDiff/ChainRules.jl/pull/648
+# Remove when merged
+
+function ChainRulesCore.rrule(::Type{T}, ps::Pair...) where {T<:Dict}
+    ks = map(first, ps)
+    project_ks, project_vs = map(ProjectTo, ks), map(ProjectTo∘last, ps)
+    function Dict_pullback(ȳ)
+        dps = map(ks, project_ks, project_vs) do k, proj_k, proj_v
+            dk, dv = proj_k(getkey(ȳ, k, NoTangent())), proj_v(get(ȳ, k, NoTangent()))
+            Tangent{Pair{typeof(dk), typeof(dv)}}(first = dk, second = dv)
+        end
+       return (NoTangent(), dps...)
+    end
+    return T(ps...), Dict_pullback
+end