fix

Author: CarloLucibello

GNNGraphs/test/Project.toml

@@ -1,4 +1,5 @@
 [deps]
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
@@ -13,11 +14,13 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 SimpleWeightedGraphs = "47aef6b3-ad0c-573a-a1e2-d07658019622"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
 TestItems = "1c621080-faea-4a02-84b6-bbd5e436b8fe"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
 [compat]
 GPUArraysCore = "0.1"

GNNGraphs/test/gnngraph.jl

@@ -94,7 +94,7 @@ end
             @test adjacency_matrix(g; dir = :in) == adj_mat
             @test adjacency_matrix(g; dir = :out) == adj_mat
 
-            if TEST_GPU && !(dev isa MetalDevice)
+            if TEST_GPU && !(dev isa MetalDevice) && GRAPH_T != :sparse
                 # See https://github.com/JuliaGPU/CUDA.jl/pull/1093
                 mat_gpu = adjacency_matrix(g_gpu)
                 @test mat_gpu isa AbstractMatrix{Int}
@@ -105,7 +105,7 @@ end
 
         @testset "normalized_laplacian" begin
             mat = normalized_laplacian(g)
-            if TEST_GPU && !(dev isa MetalDevice)
+            if TEST_GPU && !(dev isa MetalDevice) && GRAPH_T != :sparse
                 mat_gpu = normalized_laplacian(g_gpu)
                 @test mat_gpu isa AbstractMatrix{Float32}
                 @test get_device(mat_gpu)isa AbstractGPUDevice
@@ -114,7 +114,7 @@ end
         end
 
         @testset "scaled_laplacian" begin 
-            if TEST_GPU && !(dev isa MetalDevice)
+            if TEST_GPU && !(dev isa MetalDevice) && GRAPH_T != :sparse
                 mat = scaled_laplacian(g)
                 mat_gpu = scaled_laplacian(g_gpu)
                 @test mat_gpu isa AbstractMatrix{Float32}
@@ -132,10 +132,10 @@ end
             @testset "functor" begin
                 s_cpu, t_cpu = edge_index(g)
                 s_gpu, t_gpu = edge_index(g_gpu)
-                @test s_gpu isa AbstractVector{Int}
+                @test s_gpu isa AbstractVector{<:Integer}
                 @test get_device(s_gpu) isa AbstractGPUDevice
                 @test Array(s_gpu) == s_cpu
-                @test t_gpu isa AbstractVector{Int}
+                @test t_gpu isa AbstractVector{<:Integer}
                 @test get_device(t_gpu) isa AbstractGPUDevice
                 @test Array(t_gpu) == t_cpu
             end

GNNGraphs/test/sampling.jl

@@ -108,7 +108,6 @@ end
         @test !isempty(mini_batch_gnn.graph)
 
         num_sampled_nodes = mini_batch_gnn.num_nodes
-        println("Number of nodes in mini-batch: ", num_sampled_nodes)
 
         @test num_sampled_nodes == 2
 

GNNGraphs/test/utils.jl

@@ -94,16 +94,16 @@
 end
 
 @testitem "color_refinement" setup=[GraphsTestModule] begin
-    # TODO: this passes only on julia 1.10. Use a deterministic graph generator
     using .GraphsTestModule
+    using StableRNGs
     for GRAPH_T in GRAPH_TYPES
-        rng = MersenneTwister(17)
+        rng = StableRNG(17)
         g = rand_graph(rng, 10, 20, graph_type = GRAPH_T)
         x0 = ones(Int, 10)
         x, ncolors, niters = color_refinement(g, x0)
-        @test ncolors == 7
+        @test ncolors == 9
         @test niters == 2
-        @test x == [1, 1, 6, 6, 6, 7, 8, 9, 10, 11] 
+        @test x == [6, 7, 8, 9, 10, 11, 12, 6, 13, 14]
 
         x2, _, _ = color_refinement(g)
         @test x2 == x