buildkite for GNNlib + get_graph_type

.buildkite/pipeline.yml

@@ -49,3 +49,54 @@ steps:
       GNN_TEST_AMDGPU: "true"
       GNN_TEST_CPU: "false"
     timeout_in_minutes: 60
+
+  - label: "GNNlib CUDA"
+    plugins:
+      - JuliaCI/julia#v1:
+          version: "1"
+      - JuliaCI/julia-coverage#v1:
+          dirs:
+            - GNNlib/src
+    command: |
+      julia --color=yes --depwarn=yes --project=GNNlib/test -e '
+        import Pkg
+        dev_pkgs = Pkg.PackageSpec[]
+        for pkg in ("GNNGraphs", "GNNlib")
+          push!(dev_pkgs, Pkg.PackageSpec(path=pkg));
+        end
+        Pkg.develop(dev_pkgs)
+        Pkg.add(["CUDA", "cuDNN"])
+        Pkg.test("GNNlib")'
+    agents:
+      queue: "juliagpu"
+      cuda: "*"
+    env:
+      GNN_TEST_CUDA: "true"
+      GNN_TEST_CPU: "false"
+    timeout_in_minutes: 60
+
+  - label: "GNNlib AMDGPU"
+    plugins:
+      - JuliaCI/julia#v1:
+          version: "1"
+      - JuliaCI/julia-coverage#v1:
+          dirs:
+            - GNNlib/src
+    command: |
+      julia --color=yes --depwarn=yes --project=GNNlib/test -e '
+        import Pkg
+        dev_pkgs = Pkg.PackageSpec[]
+        for pkg in ("GNNGraphs", "GNNlib")
+          push!(dev_pkgs, Pkg.PackageSpec(path=pkg));
+        end
+        Pkg.develop(dev_pkgs)
+        Pkg.add(["AMDGPU"])
+        Pkg.test("GNNlib")'
+    agents:
+      queue: "juliagpu"
+      rocm: "*"
+      rocmgpu: "*"
+    env:
+      GNN_TEST_AMDGPU: "true"
+      GNN_TEST_CPU: "false"
+    timeout_in_minutes: 60

GNNGraphs/src/GNNGraphs.jl

@@ -47,6 +47,7 @@ include("query.jl")
 export adjacency_list,
        edge_index,
        get_edge_weight,
+       get_graph_type,
        graph_indicator,
        has_multi_edges,
        is_directed,

GNNGraphs/src/operators.jl

@@ -6,8 +6,8 @@ Intersect two graphs by keeping only the common edges.
 """
 function Base.intersect(g1::GNNGraph, g2::GNNGraph)
     @assert g1.num_nodes == g2.num_nodes
-    @assert graph_type_symbol(g1) == graph_type_symbol(g2)
-    graph_type = graph_type_symbol(g1)
+    @assert get_graph_type(g1) == get_graph_type(g2)
+    graph_type = get_graph_type(g1)
     num_nodes = g1.num_nodes
 
     idx1, _ = edge_encoding(edge_index(g1)..., num_nodes)

GNNGraphs/src/query.jl

@@ -80,9 +80,61 @@ function Graphs.has_edge(g::GNNHeteroGraph, edge_t::EType, i::Integer, j::Intege
     return any((s .== i) .& (t .== j))
 end
 
-graph_type_symbol(::GNNGraph{<:COO_T}) = :coo
-graph_type_symbol(::GNNGraph{<:SPARSE_T}) = :sparse
-graph_type_symbol(::GNNGraph{<:ADJMAT_T}) = :dense
+"""
+    get_graph_type(g::GNNGraph)
+
+Return the underlying representation for the graph `g` as a symbol.
+
+Possible values are:
+- `:coo`: Coordinate list representation. The graph is stored as a tuple of vectors `(s, t, w)`,
+          where `s` and `t` are the source and target nodes of the edges, and `w` is the edge weights.
+- `:sparse`: Sparse matrix representation. The graph is stored as a sparse matrix representing the weighted adjacency matrix.
+- `:dense`: Dense matrix representation. The graph is stored as a dense matrix representing the weighted adjacency matrix.
+
+The default representation for graph constructors GNNGraphs.jl is `:coo`.
+The underlying representation can be accessed through the `g.graph` field.
+
+See also [`GNNGraph`](@ref).
+
+# Examples
+
+The default representation for graph constructors GNNGraphs.jl is `:coo`.
+```jldoctest
+julia> g = rand_graph(5, 10)
+GNNGraph:
+  num_nodes: 5
+  num_edges: 10
+
+julia> get_graph_type(g)
+:coo
+```
+The `GNNGraph` constructor can also be used to create graphs with different representations.
+```jldoctest
+julia> g = GNNGraph([2,3,5], [1,2,4], graph_type=:sparse)
+GNNGraph:
+  num_nodes: 5
+  num_edges: 3
+
+julia> g.graph
+5×5 SparseArrays.SparseMatrixCSC{Int64, Int64} with 3 stored entries:
+ ⋅  ⋅  ⋅  ⋅  ⋅
+ 1  ⋅  ⋅  ⋅  ⋅
+ ⋅  1  ⋅  ⋅  ⋅
+ ⋅  ⋅  ⋅  ⋅  ⋅
+ ⋅  ⋅  ⋅  1  ⋅
+
+julia> get_graph_type(g)
+:sparse
+
+julia> gcoo = GNNGraph(g, graph_type=:coo);
+
+julia> gcoo.graph
+([2, 3, 5], [1, 2, 4], [1, 1, 1])
+``` 
+"""
+get_graph_type(::GNNGraph{<:COO_T}) = :coo
+get_graph_type(::GNNGraph{<:SPARSE_T}) = :sparse
+get_graph_type(::GNNGraph{<:ADJMAT_T}) = :dense
 
 Graphs.nv(g::GNNGraph) = g.num_nodes
 Graphs.ne(g::GNNGraph) = g.num_edges

GNNGraphs/src/transform.jl

@@ -731,6 +731,7 @@ end
 Set `w` as edge weights in the returned graph. 
 """
 function set_edge_weight(g::GNNGraph, w::AbstractVector)
+    # TODO preserve the representation instead of converting to COO
     s, t = edge_index(g)
     @assert length(w) == length(s)
 

GNNGraphs/test/gnngraph.jl

@@ -1,3 +1,6 @@
+# TODO test that the graph type is preserved
+# when constructing a GNNGraph from another
+
 @testset "Constructor: adjacency matrix" begin
     A = sprand(10, 10, 0.5)
     sA, tA, vA = findnz(A)

GNNGraphs/test/query.jl

@@ -257,3 +257,20 @@ if GRAPH_T == :coo
     end
 end       
 
+@testset "get_graph_type" begin
+    g = rand_graph(10, 20, graph_type = GRAPH_T)
+    @test get_graph_type(g) == GRAPH_T
+
+    gsparse = GNNGraph(g, graph_type=:sparse)
+    @test get_graph_type(gsparse) == :sparse
+    @test gsparse.graph isa SparseMatrixCSC
+
+    gcoo = GNNGraph(g, graph_type=:coo)
+    @test get_graph_type(gcoo) == :coo
+    @test gcoo.graph[1:2] isa Tuple{Vector{Int}, Vector{Int}}
+
+
+    gdense = GNNGraph(g, graph_type=:dense)
+    @test get_graph_type(gdense) == :dense
+    @test gdense.graph isa Matrix{Int}
+end

GNNlib/Project.toml

@@ -7,35 +7,35 @@ version = "0.2.2"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
+GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [weakdeps]
+AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 
 [extensions]
+GNNlibAMDGPUExt = "AMDGPU"
 GNNlibCUDAExt = "CUDA"
 
+# GPUArraysCore is not needed as a direct dependency
+# but pinning it to 0.1 avoids problems when we do Pkg.add("CUDA") in testing
+# See https://github.com/JuliaGPU/CUDA.jl/issues/2564
+
 [compat]
+AMDGPU = "1"
 CUDA = "4, 5"
 ChainRulesCore = "1.24"
 DataStructures = "0.18"
 GNNGraphs = "1.0"
+GPUArraysCore = "0.1"
 LinearAlgebra = "1"
 MLUtils = "0.4"
 NNlib = "0.9"
 Random = "1"
 Statistics = "1"
 julia = "1.10"
-
-[extras]
-ReTestItems = "817f1d60-ba6b-4fd5-9520-3cf149f6a823"
-Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
-SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-
-[targets]
-test = ["Test", "ReTestItems", "Reexport", "SparseArrays"]

GNNlib/ext/GNNlibAMDGPUExt.jl

@@ -0,0 +1,34 @@
+module GNNlibAMDGPUExt
+
+using AMDGPU: AnyROCMatrix
+using Random, Statistics, LinearAlgebra
+using GNNlib: GNNlib, propagate, copy_xj, e_mul_xj, w_mul_xj
+using GNNGraphs: GNNGraph, COO_T, SPARSE_T
+
+###### PROPAGATE SPECIALIZATIONS ####################
+
+## COPY_XJ 
+
+## avoid the fast path on gpu until we have better cuda support
+function GNNlib.propagate(::typeof(copy_xj), g::GNNGraph{<:Union{COO_T, SPARSE_T}}, ::typeof(+),
+        xi, xj::AnyROCMatrix, 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 GNNlib.propagate(::typeof(e_mul_xj), g::GNNGraph{<:Union{COO_T, SPARSE_T}}, ::typeof(+),
+        xi, xj::AnyROCMatrix, 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 support
+function GNNlib.propagate(::typeof(w_mul_xj), g::GNNGraph{<:Union{COO_T, SPARSE_T}}, ::typeof(+),
+        xi, xj::AnyROCMatrix, e::Nothing)
+    propagate((xi, xj, e) -> w_mul_xj(xi, xj, e), g, +, xi, xj, e)
+end
+
+end #module

GNNlib/src/msgpass.jl

@@ -184,7 +184,7 @@ xj_sub_xi(xi, xj, e) = xj .- xi
 """
     e_mul_xj(xi, xj, e) = reshape(e, (...)) .* xj
 
-Reshape `e` into broadcast compatible shape with `xj`
+Reshape `e` into a broadcast compatible shape with `xj`
 (by prepending singleton dimensions) then perform
 broadcasted multiplication.
 """

GNNlib/test/Project.toml

@@ -0,0 +1,22 @@
+[deps]
+FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
+GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
+GNNlib = "a6a84749-d869-43f8-aacc-be26a1996e48"
+GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
+MLDataDevices = "7e8f7934-dd98-4c1a-8fe8-92b47a384d40"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
+NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
+Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+
+[compat]
+GPUArraysCore = "0.1"