more updates for the tests

.github/workflows/test_GraphNeuralNetworks.yml

@@ -15,7 +15,7 @@ jobs:
       matrix:
         version:
           - '1.10' # Replace this with the minimum Julia version that your package supports.
-          # - '1' #  '1' will automatically expand to the latest stable 1.x release of Julia.
+          - '1' #  '1' will automatically expand to the latest stable 1.x release of Julia.
           # - 'pre'
         os:
           - ubuntu-latest

GNNGraphs/test/test_utils.jl

@@ -5,224 +5,3 @@ function ngradient(f, x...)
     fdm = central_fdm(5, 1)
     return FiniteDifferences.grad(fdm, f, x...)
 end
-
-const rule_config = Zygote.ZygoteRuleConfig()
-
-# Using this until https://github.com/JuliaDiff/FiniteDifferences.jl/issues/188 is fixed
-function FiniteDifferences.to_vec(x::Integer)
-    Integer_from_vec(v) = x
-    return Int[x], Integer_from_vec
-end
-
-# Test that forward pass on cpu and gpu are the same. 
-# Tests also gradient on cpu and gpu comparing with
-# finite difference methods.
-# Test gradients with respects to layer weights and to input. 
-# If `g` has edge features, it is assumed that the layer can 
-# use them in the forward pass as `l(g, x, e)`.
-# Test also gradient with respect to `e`. 
-function test_layer(l, g::GNNGraph; atol = 1e-5, rtol = 1e-5,
-                    exclude_grad_fields = [],
-                    verbose = false,
-                    test_gpu = TEST_GPU,
-                    outsize = nothing,
-                    outtype = :node)
-
-    # TODO these give errors, probably some bugs in ChainRulesTestUtils
-    # test_rrule(rule_config, x -> l(g, x), x; rrule_f=rrule_via_ad, check_inferred=false)
-    # test_rrule(rule_config, l -> l(g, x), l; rrule_f=rrule_via_ad, check_inferred=false)
-
-    isnothing(node_features(g)) && error("Plese add node data to the input graph")
-    fdm = central_fdm(5, 1)
-
-    x = node_features(g)
-    e = edge_features(g)
-    use_edge_feat = !isnothing(e)
-
-    x64, e64, l64, g64 = to64.([x, e, l, g]) # needed for accurate FiniteDifferences' grad
-    xgpu, egpu, lgpu, ggpu = gpu.([x, e, l, g])
-
-    f(l, g::GNNGraph) = l(g)
-    f(l, g::GNNGraph, x, e) = use_edge_feat ? l(g, x, e) : l(g, x)
-
-    loss(l, g::GNNGraph) =
-        if outtype == :node
-            sum(node_features(f(l, g)))
-        elseif outtype == :edge
-            sum(edge_features(f(l, g)))
-        elseif outtype == :graph
-            sum(graph_features(f(l, g)))
-        elseif outtype == :node_edge
-            gnew = f(l, g)
-            sum(node_features(gnew)) + sum(edge_features(gnew))
-        end
-
-    function loss(l, g::GNNGraph, x, e)
-        y = f(l, g, x, e)
-        if outtype == :node_edge
-            return sum(y[1]) + sum(y[2])
-        else
-            return sum(y)
-        end
-    end
-
-    # TEST OUTPUT
-    y = f(l, g, x, e)
-    if outtype == :node_edge
-        @assert y isa Tuple
-        @test eltype(y[1]) == eltype(x)
-        @test eltype(y[2]) == eltype(e)
-        @test all(isfinite, y[1])
-        @test all(isfinite, y[2])
-        if !isnothing(outsize)
-            @test size(y[1]) == outsize[1]
-            @test size(y[2]) == outsize[2]
-        end
-    else
-        @test eltype(y) == eltype(x)
-        @test all(isfinite, y)
-        if !isnothing(outsize)
-            @test size(y) == outsize
-        end
-    end
-
-    # test same output on different graph formats
-    gcoo = GNNGraph(g, graph_type = :coo)
-    ycoo = f(l, gcoo, x, e)
-    if outtype == :node_edge
-        @test ycoo[1] ≈ y[1]
-        @test ycoo[2] ≈ y[2]
-    else
-        @test ycoo ≈ y
-    end
-
-    g′ = f(l, g)
-    if outtype == :node
-        @test g′.ndata.x ≈ y
-    elseif outtype == :edge
-        @test g′.edata.e ≈ y
-    elseif outtype == :graph
-        @test g′.gdata.u ≈ y
-    elseif outtype == :node_edge
-        @test g′.ndata.x ≈ y[1]
-        @test g′.edata.e ≈ y[2]
-    else
-        @error "wrong outtype $outtype"
-    end
-    if test_gpu
-        ygpu = f(lgpu, ggpu, xgpu, egpu)
-        if outtype == :node_edge
-            @test ygpu[1] isa CuArray
-            @test eltype(ygpu[1]) == eltype(xgpu)
-            @test Array(ygpu[1]) ≈ y[1]
-            @test ygpu[2] isa CuArray
-            @test eltype(ygpu[2]) == eltype(xgpu)
-            @test Array(ygpu[2]) ≈ y[2]
-        else
-            @test ygpu isa CuArray
-            @test eltype(ygpu) == eltype(xgpu)
-            @test Array(ygpu) ≈ y
-        end
-    end
-
-    # TEST x INPUT GRADIENT
-    x̄ = gradient(x -> loss(l, g, x, e), x)[1]
-    x̄_fd = FiniteDifferences.grad(fdm, x64 -> loss(l64, g64, x64, e64), x64)[1]
-    @test eltype(x̄) == eltype(x)
-    @test x̄≈x̄_fd atol=atol rtol=rtol
-
-    if test_gpu
-        x̄gpu = gradient(xgpu -> loss(lgpu, ggpu, xgpu, egpu), xgpu)[1]
-        @test x̄gpu isa CuArray
-        @test eltype(x̄gpu) == eltype(x)
-        @test Array(x̄gpu)≈x̄ atol=atol rtol=rtol
-    end
-
-    # TEST e INPUT GRADIENT
-    if e !== nothing
-        verbose && println("Test e gradient cpu")
-        ē = gradient(e -> loss(l, g, x, e), e)[1]
-        ē_fd = FiniteDifferences.grad(fdm, e64 -> loss(l64, g64, x64, e64), e64)[1]
-        @test eltype(ē) == eltype(e)
-        @test ē≈ē_fd atol=atol rtol=rtol
-
-        if test_gpu
-            verbose && println("Test e gradient gpu")
-            ēgpu = gradient(egpu -> loss(lgpu, ggpu, xgpu, egpu), egpu)[1]
-            @test ēgpu isa CuArray
-            @test eltype(ēgpu) == eltype(ē)
-            @test Array(ēgpu)≈ē atol=atol rtol=rtol
-        end
-    end
-
-    # TEST LAYER GRADIENT - l(g, x, e) 
-    l̄ = gradient(l -> loss(l, g, x, e), l)[1]
-    l̄_fd = FiniteDifferences.grad(fdm, l64 -> loss(l64, g64, x64, e64), l64)[1]
-    test_approx_structs(l, l̄, l̄_fd; atol, rtol, exclude_grad_fields, verbose)
-
-    if test_gpu
-        l̄gpu = gradient(lgpu -> loss(lgpu, ggpu, xgpu, egpu), lgpu)[1]
-        test_approx_structs(lgpu, l̄gpu, l̄; atol, rtol, exclude_grad_fields, verbose)
-    end
-
-    # TEST LAYER GRADIENT - l(g)
-    l̄ = gradient(l -> loss(l, g), l)[1]
-    test_approx_structs(l, l̄, l̄_fd; atol, rtol, exclude_grad_fields, verbose)
-
-    return true
-end
-
-function test_approx_structs(l, l̄, l̄fd; atol = 1e-5, rtol = 1e-5,
-                             exclude_grad_fields = [],
-                             verbose = false)
-    l̄ = l̄ isa Base.RefValue ? l̄[] : l̄           # Zygote wraps gradient of mutables in RefValue 
-    l̄fd = l̄fd isa Base.RefValue ? l̄fd[] : l̄fd           # Zygote wraps gradient of mutables in RefValue 
-
-    for f in fieldnames(typeof(l))
-        f ∈ exclude_grad_fields && continue
-        verbose && println("Test gradient of field $f...")
-        x, g, gfd = getfield(l, f), getfield(l̄, f), getfield(l̄fd, f)
-        test_approx_structs(x, g, gfd; atol, rtol, exclude_grad_fields, verbose)
-        verbose && println("... field $f done!")
-    end
-    return true
-end
-
-function test_approx_structs(x, g::Nothing, gfd; atol, rtol, kws...)
-    # finite diff gradients has to be zero if present
-    @test !(gfd isa AbstractArray) || isapprox(gfd, fill!(similar(gfd), 0); atol, rtol)
-end
-
-function test_approx_structs(x::Union{AbstractArray, Number},
-                             g::Union{AbstractArray, Number}, gfd; atol, rtol, kws...)
-    @test eltype(g) == eltype(x)
-    if x isa CuArray
-        @test g isa CuArray
-        g = Array(g)
-    end
-    @test g≈gfd atol=atol rtol=rtol
-end
-
-"""
-    to32(m)
-
-Convert the `eltype` of model's float parameters to `Float32`.
-Preserves integer arrays.
-"""
-to32(m) = _paramtype(Float32, m)
-
-"""
-    to64(m)
-
-Convert the `eltype` of model's float parameters to `Float64`.
-Preserves integer arrays.
-"""
-to64(m) = _paramtype(Float64, m)
-
-struct GNNEltypeAdaptor{T} end
-
-Adapt.adapt_storage(::GNNEltypeAdaptor{T}, x::AbstractArray{<:AbstractFloat}) where T = convert(AbstractArray{T}, x)
-Adapt.adapt_storage(::GNNEltypeAdaptor{T}, x::AbstractArray{<:Integer}) where T = x
-Adapt.adapt_storage(::GNNEltypeAdaptor{T}, x::AbstractArray{<:Number}) where T = convert(AbstractArray{T}, x)
-
-_paramtype(::Type{T}, m) where T = fmap(adapt(GNNEltypeAdaptor{T}()), m)

GNNlib/src/layers/conv.jl

@@ -253,7 +253,6 @@ function gin_conv(l, g::AbstractGNNGraph, x)
     xj, xi = expand_srcdst(g, x) 
 
     m = propagate(copy_xj, g, l.aggr, xj = xj)
-
     return l.nn((1 .+ ofeltype(xi, l.ϵ)) .* xi .+ m)
 end
 

GraphNeuralNetworks/Project.toml

@@ -33,28 +33,26 @@ LinearAlgebra = "1"
 MLUtils = "0.4"
 MacroTools = "0.5"
 NNlib = "0.9"
+Pkg = "1"
 Random = "1"
 Reexport = "1"
 Statistics = "1"
 TestItemRunner = "1.0.5"
-cuDNN = "1"
 julia = "1.10"
 
 [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"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 InlineStrings = "842dd82b-1e85-43dc-bf29-5d0ee9dffc48"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
-cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
 [targets]
-test = ["Test", "TestItemRunner", "MLDatasets", "Adapt", "DataFrames", "InlineStrings", 
-      "SparseArrays", "Graphs", "Zygote", "FiniteDifferences", "ChainRulesTestUtils", "CUDA", "cuDNN"]
+test = ["Test", "TestItemRunner", "Pkg", "MLDatasets", "Adapt", "DataFrames", "InlineStrings", "SparseArrays", "Graphs", "Zygote", "FiniteDifferences", "ChainRulesTestUtils"]

GraphNeuralNetworks/test/examples/node_classification_cora.jl

@@ -1,14 +1,13 @@
-@testitem "Training Example" setup=[TestModule] begin
-    using .TestModule
+@testmodule TrainingExampleModule begin
     using Flux
     using Flux: onecold, onehotbatch
     using Flux.Losses: logitcrossentropy
     using GraphNeuralNetworks
     using MLDatasets: Cora
     using Statistics, Random
-    using CUDA
-    CUDA.allowscalar(false)
-
+    using Test
+    ENV["DATADEPS_ALWAYS_ACCEPT"] = "true"
+    
     function eval_loss_accuracy(X, y, ids, model, g)
         ŷ = model(g, X)
         l = logitcrossentropy(ŷ[:, ids], y[:, ids])
@@ -21,27 +20,27 @@
         η = 5.0f-3            # learning rate
         epochs = 10         # number of epochs
         seed = 17           # set seed > 0 for reproducibility
-        usecuda = false     # if true use cuda (if available)
+        use_gpu = false     # if true use gpu (if available)
         nhidden = 64        # dimension of hidden features
     end
 
     function train(Layer; verbose = false, kws...)
         args = Args(; kws...)
         args.seed > 0 && Random.seed!(args.seed)
 
-        if args.usecuda && CUDA.functional()
-            device = Flux.gpu
-            args.seed > 0 && CUDA.seed!(args.seed)
+        if args.use_gpu
+            device = gpu_device(force=true)
+            Random.seed!(default_device_rng(device))
         else
-            device = Flux.cpu
+            device = cpu_device()
         end
 
         # LOAD DATA
         dataset = Cora()
         classes = dataset.metadata["classes"]
         g = mldataset2gnngraph(dataset) |> device
         X = g.ndata.features
-        y = onehotbatch(g.ndata.targets |> cpu, classes) |> device # remove when https://github.com/FluxML/Flux.jl/pull/1959 tagged
+        y = onehotbatch(g.ndata.targets, classes)
         train_mask = g.ndata.train_mask
         test_mask = g.ndata.test_mask
         ytrain = y[:, train_mask]
@@ -78,7 +77,7 @@
         return train_res, test_res
     end
 
-    function train_many(; usecuda = false)
+    function train_many(; use_gpu = false)
         for (layer, Layer) in [
             ("GCNConv", (nin, nout) -> GCNConv(nin => nout, relu)),
             ("ResGatedGraphConv", (nin, nout) -> ResGatedGraphConv(nin => nout, relu)),
@@ -96,16 +95,21 @@
             ## ("EdgeConv",(nin, nout) -> EdgeConv(Dense(2nin, nout, relu))), # Fits the training set but does not generalize well
         ]
             @show layer
-            @time train_res, test_res = train(Layer; usecuda, verbose = false)
+            @time train_res, test_res = train(Layer; use_gpu, verbose = false)
             # @show train_res, test_res
             @test train_res.acc > 94
             @test test_res.acc > 69
         end
     end
+end # module
+
+@testitem "training example" setup=[TrainingExampleModule] begin
+    using .TrainingExampleModule
+    TrainingExampleModule.train_many()
+end
 
-    train_many(usecuda = false)
-    # #TODO
-    # if TEST_GPU
-    #     train_many(usecuda = true)
-    # end
+@testitem "training example GPU" setup=[TrainingExampleModule] tags=[:gpu] begin
+    using .TrainingExampleModule
+    TrainingExampleModule.train_many(use_gpu = true)
 end
+

GraphNeuralNetworks/test/layers/basic.jl

@@ -18,7 +18,7 @@
 
         Flux.testmode!(gnn)
 
-        test_layer(gnn, g, rtol = 1e-5, exclude_grad_fields = [:μ, :σ²])
+        test_gradients(gnn, g, x, rtol = 1e-5)
 
         @testset "constructor with names" begin
             m = GNNChain(GCNConv(din => d),
@@ -53,7 +53,7 @@
 
             Flux.trainmode!(gnn)
 
-            test_layer(gnn, g, rtol = 1e-4, atol=1e-4, exclude_grad_fields = [:μ, :σ²])
+            test_gradients(gnn, g, x, rtol = 1e-4, atol=1e-4)
         end
     end