fix zygote error

GNNGraphs/docs/src/guides/datasets.md

@@ -1,7 +1,44 @@
 # Datasets
 
-GNNGraphs.jl doesn't come with its own datasets, but leverages those available in the Julia (and non-Julia) ecosystem. In particular, the [examples in the GraphNeuralNetworks.jl repository](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) make use of the [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl) package. There you will find common graph datasets such as Cora, PubMed, Citeseer, TUDataset and [many others](https://juliaml.github.io/MLDatasets.jl/dev/datasets/graphs/).
+GNNGraphs.jl doesn't come with its own datasets, but leverages those available in the Julia (and non-Julia) ecosystem. 
+
+## MLDatasets.jl
+
+Some of the [examples in the GraphNeuralNetworks.jl repository](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) make use of the [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl) package. There you will find common graph datasets such as Cora, PubMed, Citeseer, TUDataset and [many others](https://juliaml.github.io/MLDatasets.jl/dev/datasets/graphs/).
 For graphs with static structures and temporal features, datasets such as METRLA, PEMSBAY, ChickenPox, and WindMillEnergy are available. For graphs featuring both temporal structures and temporal features, the TemporalBrains dataset is suitable.
 
 GraphNeuralNetworks.jl provides the [`mldataset2gnngraph`](@ref) method for interfacing with MLDatasets.jl.
 
+## PyGDatasets.jl
+
+The package [PyGDatasets.jl](https://github.com/CarloLucibello/PyGDatasets.jl) makes available to Julia users the datasets from the [pytorch geometric](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html) library. 
+
+PyGDatasets' datasets are compatible with GNNGraphs, so no additional conversion is needed. 
+```julia
+julia> using PyGDatasets
+
+julia> dataset = load_dataset("TUDataset", name="MUTAG")
+TUDataset(MUTAG) - InMemoryGNNDataset
+  num_graphs: 188
+  node_features: [:x]
+  edge_features: [:edge_attr]
+  graph_features: [:y]
+  root: /Users/carlo/.julia/scratchspaces/44f67abd-f36e-4be4-bfe5-65f468a62b3d/datasets/TUDataset
+
+julia> g = dataset[1]
+GNNGraph:
+  num_nodes: 17
+  num_edges: 38
+  ndata:
+    x = 7×17 Matrix{Float32}
+  edata:
+    edge_attr = 4×38 Matrix{Float32}
+  gdata:
+    y = 1-element Vector{Int64}
+
+julia> using MLUtils: DataLoader
+
+julia> data_loader = DataLoader(dataset, batch_size=32);
+```
+
+PyGDatasets is based on [PythonCall.jl](https://github.com/JuliaPy/PythonCall.jl). It carries over some heavy dependencies such as python, pytorch and pytorch geometric.

GNNGraphs/src/GNNGraphs.jl

@@ -8,7 +8,7 @@ import NearestNeighbors
 import NNlib
 import StatsBase
 import KrylovKit
-using ChainRulesCore
+import ChainRulesCore as CRC
 using LinearAlgebra, Random, Statistics
 import MLUtils
 using MLUtils: getobs, numobs, ones_like, zeros_like, chunk, batch, rand_like

GNNGraphs/src/chainrules.jl

@@ -1,15 +1,16 @@
 # Taken from https://github.com/JuliaDiff/ChainRules.jl/pull/648
 # Remove when merged
 
-function ChainRulesCore.rrule(::Type{T}, ps::Pair...) where {T<:Dict}
+function CRC.rrule(::Type{T}, ps::Pair...) where {T<:Dict}
     ks = map(first, ps)
-    project_ks, project_vs = map(ProjectTo, ks), map(ProjectTo∘last, ps)
+    project_ks, project_vs = map(CRC.ProjectTo, ks), map(CRC.ProjectTo ∘ last, ps)
     function Dict_pullback(ȳ)
+        dy = CRC.unthunk(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)
+            dk, dv = proj_k(getkey(dy, k, CRC.NoTangent())), proj_v(get(dy, k, CRC.NoTangent()))
+            CRC.Tangent{Pair{typeof(dk), typeof(dv)}}(first = dk, second = dv)
         end
-       return (NoTangent(), dps...)
+       return (CRC.NoTangent(), dps...)
     end
     return T(ps...), Dict_pullback
 end

GNNGraphs/src/convert.jl

@@ -78,7 +78,7 @@ function _findnz_idx(A)
     return s, t, nz
 end
 
-@non_differentiable _findnz_idx(A)
+CRC.@non_differentiable _findnz_idx(A)
 
 function to_coo(A::ADJMAT_T; dir = :out, num_nodes = nothing, weighted = true)
     s, t, nz = _findnz_idx(A)

GNNGraphs/src/gnnheterograph/generate.jl

@@ -95,7 +95,7 @@ See [`rand_heterograph`](@ref) for a more general version.
 
 # Examples
 
-```julia-repl
+```julia
 julia> g = rand_bipartite_heterograph((10, 15), 20)
 GNNHeteroGraph:
   num_nodes: (:A => 10, :B => 15)

GNNGraphs/src/gnnheterograph/gnnheterograph.jl

@@ -273,7 +273,7 @@ end
 
 # TODO this is not correct but Zygote cannot differentiate
 # through dictionary generation
-# @non_differentiable edge_type_subgraph(::Any...)
+# CRC.@non_differentiable edge_type_subgraph(::Any...)
 
 function _ntypes_from_edges(edge_ts::AbstractVector{<:EType})
     ntypes = Symbol[]
@@ -285,7 +285,7 @@ function _ntypes_from_edges(edge_ts::AbstractVector{<:EType})
     return ntypes
 end 
 
-@non_differentiable _ntypes_from_edges(::Any...)
+CRC.@non_differentiable _ntypes_from_edges(::Any...)
 
 function Base.getindex(g::GNNHeteroGraph, node_t::NType)
     return g.ndata[node_t]

GNNGraphs/src/query.jl

@@ -241,37 +241,46 @@ function Graphs.adjacency_matrix(g::GNNGraph{<:ADJMAT_T}, T::DataType = eltype(g
     return dir == :out ? A : A'
 end
 
-function ChainRulesCore.rrule(::typeof(adjacency_matrix), g::G, T::DataType; 
+function CRC.rrule(::typeof(adjacency_matrix), g::G, T::DataType; 
             dir = :out, weighted = true) where {G <: GNNGraph{<:ADJMAT_T}}
     A = adjacency_matrix(g, T; dir, weighted)
     if !weighted
         function adjacency_matrix_pullback_noweight(Δ)
-            return (NoTangent(), ZeroTangent(), NoTangent())  
+            return (CRC.NoTangent(), CRC.ZeroTangent(), CRC.NoTangent())  
         end
         return A, adjacency_matrix_pullback_noweight
     else
         function adjacency_matrix_pullback_weighted(Δ)
-            dg = Tangent{G}(; graph = Δ .* binarize(A))
-            return (NoTangent(), dg, NoTangent())  
+            dy = CRC.unthunk(Δ)
+            dg = CRC.Tangent{G}(; graph = dy .* binarize(dy))
+            return (CRC.NoTangent(), dg, CRC.NoTangent())  
         end
         return A, adjacency_matrix_pullback_weighted
     end
 end
 
-function ChainRulesCore.rrule(::typeof(adjacency_matrix), g::G, T::DataType; 
+function CRC.rrule(::typeof(adjacency_matrix), g::G, T::DataType; 
             dir = :out, weighted = true) where {G <: GNNGraph{<:COO_T}}
     A = adjacency_matrix(g, T; dir, weighted)
     w = get_edge_weight(g)
     if !weighted || w === nothing
         function adjacency_matrix_pullback_noweight(Δ)
-            return (NoTangent(), ZeroTangent(), NoTangent())  
+            return (CRC.NoTangent(), CRC.ZeroTangent(), CRC.NoTangent())  
         end
         return A, adjacency_matrix_pullback_noweight
     else
         function adjacency_matrix_pullback_weighted(Δ)
+            dy = CRC.unthunk(Δ)
             s, t = edge_index(g)
-            dg = Tangent{G}(; graph = (NoTangent(), NoTangent(), NNlib.gather(Δ, s, t)))
-            return (NoTangent(), dg, NoTangent())  
+            @show dy s t
+            #TODO using CRC.@thunk gives an error
+            #TODO use gather when https://github.com/FluxML/NNlib.jl/issues/625 is fixed
+            dw = zeros_like(w)
+            idx = CartesianIndex.(s, t) #TODO remove when https://github.com/FluxML/NNlib.jl/issues/626 is fixed
+            NNlib.gather!(dw, dy, idx)
+            @show dw
+            dg = CRC.Tangent{G}(; graph = (CRC.NoTangent(), CRC.NoTangent(), dw))
+            return (CRC.NoTangent(), dg, CRC.NoTangent())  
         end
         return A, adjacency_matrix_pullback_weighted
     end
@@ -378,34 +387,35 @@ function _degree(A::AbstractMatrix, T::Type, dir::Symbol, edge_weight::Bool, num
            vec(sum(A, dims = 1)) .+ vec(sum(A, dims = 2))
 end
 
-function ChainRulesCore.rrule(::typeof(_degree), graph, T, dir, edge_weight::Nothing, num_nodes)
+function CRC.rrule(::typeof(_degree), graph, T, dir, edge_weight::Nothing, num_nodes)
     degs = _degree(graph, T, dir, edge_weight, num_nodes)
     function _degree_pullback(Δ)
-        return (NoTangent(), NoTangent(), NoTangent(), NoTangent(), NoTangent(), NoTangent())
+        return ntuple(i -> (CRC.NoTangent(),), 6)
     end
     return degs, _degree_pullback
 end
 
-function ChainRulesCore.rrule(::typeof(_degree), A::ADJMAT_T, T, dir, edge_weight::Bool, num_nodes)
+function CRC.rrule(::typeof(_degree), A::ADJMAT_T, T, dir, edge_weight::Bool, num_nodes)
     degs = _degree(A, T, dir, edge_weight, num_nodes)
     if edge_weight === false
         function _degree_pullback_noweights(Δ)
-            return (NoTangent(), NoTangent(), NoTangent(), NoTangent(), NoTangent(), NoTangent())
+            return ntuple(i -> (CRC.NoTangent(),), 6)
         end
         return degs, _degree_pullback_noweights
     else
         function _degree_pullback_weights(Δ)
+            dy = CRC.unthunk(Δ)
             # We propagate the gradient only to the non-zero elements
             # of the adjacency matrix.
             bA = binarize(A)
             if dir == :in
-                dA = bA .* Δ'
+                dA = bA .* dy'
             elseif dir == :out
-                dA = Δ .* bA
+                dA = dy .* bA
             else # dir == :both
-                dA = Δ .* bA + Δ' .* bA
+                dA = dy .* bA + dy' .* bA
             end
-            return (NoTangent(), dA, NoTangent(), NoTangent(), NoTangent(), NoTangent())
+            return (CRC.NoTangent(), dA, CRC.NoTangent(), CRC.NoTangent(), CRC.NoTangent(), CRC.NoTangent())
         end
         return degs, _degree_pullback_weights
     end
@@ -452,7 +462,7 @@ function normalized_adjacency(g::GNNGraph, T::DataType = Float32;
         A = A + I
     end
     degs = vec(sum(A; dims = 2))
-    ChainRulesCore.ignore_derivatives() do
+    CRC.ignore_derivatives() do
         @assert all(!iszero, degs) "Graph contains isolated nodes, cannot compute `normalized_adjacency`."
     end
     inv_sqrtD = Diagonal(inv.(sqrt.(degs)))
@@ -609,12 +619,12 @@ function laplacian_lambda_max(g::GNNGraph, T::DataType = Float32;
     end
 end
 
-@non_differentiable edge_index(x...)
-@non_differentiable adjacency_list(x...)
-@non_differentiable graph_indicator(x...)
-@non_differentiable has_multi_edges(x...)
-@non_differentiable Graphs.has_self_loops(x...)
-@non_differentiable is_bidirected(x...)
-@non_differentiable normalized_adjacency(x...) # TODO remove this in the future
-@non_differentiable normalized_laplacian(x...) # TODO remove this in the future
-@non_differentiable scaled_laplacian(x...) # TODO remove this in the future
+CRC.@non_differentiable edge_index(x...)
+CRC.@non_differentiable adjacency_list(x...)
+CRC.@non_differentiable graph_indicator(x...)
+CRC.@non_differentiable has_multi_edges(x...)
+CRC.@non_differentiable Graphs.has_self_loops(x...)
+CRC.@non_differentiable is_bidirected(x...)
+CRC.@non_differentiable normalized_adjacency(x...) # TODO remove this in the future
+CRC.@non_differentiable normalized_laplacian(x...) # TODO remove this in the future
+CRC.@non_differentiable scaled_laplacian(x...) # TODO remove this in the future

GNNGraphs/src/transform.jl

@@ -808,8 +808,8 @@ function _unbatch_edgemasks(s, t, num_graphs, cumnum_nodes)
     return edgemasks
 end
 
-@non_differentiable _unbatch_nodemasks(::Any...)
-@non_differentiable _unbatch_edgemasks(::Any...)
+CRC.@non_differentiable _unbatch_nodemasks(::Any...)
+CRC.@non_differentiable _unbatch_edgemasks(::Any...)
 
 """
     getgraph(g::GNNGraph, i; nmap=false)
@@ -998,10 +998,10 @@ dense_zeros_like(x, sz = size(x)) = dense_zeros_like(x, eltype(x), sz)
 # """
 ci2t(ci::AbstractVector{<:CartesianIndex}, dims) = ntuple(i -> map(x -> x[i], ci), dims)
 
-@non_differentiable negative_sample(x...)
-@non_differentiable add_self_loops(x...)     # TODO this is wrong, since g carries feature arrays, needs rrule
-@non_differentiable remove_self_loops(x...)  # TODO this is wrong, since g carries feature arrays, needs rrule
-@non_differentiable dense_zeros_like(x...)
+CRC.@non_differentiable negative_sample(x...)
+CRC.@non_differentiable add_self_loops(x...)     # TODO this is wrong, since g carries feature arrays, needs rrule
+CRC.@non_differentiable remove_self_loops(x...)  # TODO this is wrong, since g carries feature arrays, needs rrule
+CRC.@non_differentiable dense_zeros_like(x...)
 
 """
     ppr_diffusion(g::GNNGraph{<:COO_T}, alpha =0.85f0) -> GNNGraph

GNNGraphs/src/utils.jl

@@ -292,9 +292,9 @@ end
 
 binarize(x) = map(>(0), x)
 
-@non_differentiable binarize(x...)
-@non_differentiable edge_encoding(x...)
-@non_differentiable edge_decoding(x...)
+CRC.@non_differentiable binarize(x...)
+CRC.@non_differentiable edge_encoding(x...)
+CRC.@non_differentiable edge_decoding(x...)
 
 ### PRINTING #####
 
@@ -330,11 +330,11 @@ function dims2string(d)
     join(map(string, d), '×')
 end
 
-@non_differentiable normalize_graphdata(::NamedTuple{(), Tuple{}})
-@non_differentiable normalize_graphdata(::Nothing)
+CRC.@non_differentiable normalize_graphdata(::NamedTuple{(), Tuple{}})
+CRC.@non_differentiable normalize_graphdata(::Nothing)
 
 iscuarray(x::AbstractArray) = false 
-@non_differentiable iscuarray(::Any)
+CRC.@non_differentiable iscuarray(::Any)
 
 
 @doc raw"""

GNNGraphs/test/Project.toml

@@ -1,5 +1,4 @@
 [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"
@@ -9,6 +8,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLDataDevices = "7e8f7934-dd98-4c1a-8fe8-92b47a384d40"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
 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"
@@ -20,7 +20,6 @@ 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"

GNNlib/Project.toml

@@ -7,7 +7,6 @@ version = "1.0.0"
 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"
@@ -22,17 +21,12 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 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 = "5"
 ChainRulesCore = "1.24"
 DataStructures = "0.18"
 GNNGraphs = "1.4"
-GPUArraysCore = "0.1"
 LinearAlgebra = "1"
 MLUtils = "0.4"
 NNlib = "0.9"

GNNlib/src/layers/pool.jl

@@ -29,8 +29,8 @@ topk_index(y::Adjoint, k::Int) = topk_index(y', k)
 function set2set_pool(l, g::GNNGraph, x::AbstractMatrix)
     n_in = size(x, 1)    
     qstar = zeros_like(x, (2*n_in, g.num_graphs))
-    h = zeros_like(l.Wh, size(l.Wh, 2))
-    c = zeros_like(l.Wh, size(l.Wh, 2))
+    h = zeros_like(l.lstm.Wh, size(l.lstm.Wh, 2))
+    c = zeros_like(l.lstm.Wh, size(l.lstm.Wh, 2))
     state = (h, c)
     for t in 1:l.num_iters
         q, state = l.lstm(qstar, state)                     # [n_in, n_graphs]

GraphNeuralNetworks/examples/Project.toml

@@ -1,19 +1,16 @@
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-DiffEqFlux = "aae7a2af-3d4f-5e19-a356-7da93b79d9d0"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-NNlibCUDA = "a00861dc-f156-4864-bf3c-e6376f28a68d"
 
 [compat]
-DiffEqFlux = "2"
-Flux = "0.13"
-GraphNeuralNetworks = "0.6"
+Flux = "0.16"
+GraphNeuralNetworks = "1"
 Graphs = "1"
 MLDatasets = "0.7"
-julia = "1.9"
+julia = "1.10"

GraphNeuralNetworks/src/layers/pool.jl

@@ -155,9 +155,6 @@ function Set2Set(n_in::Int, n_iters::Int, n_layers::Int = 1)
     return Set2Set(lstm, n_iters)
 end
 
-function (l::Set2Set)(g, x)
-    m = (; l.lstm, l.num_iters, Wh = l.lstm.Wh)
-    return GNNlib.set2set_pool(m, g, x)
-end
+(l::Set2Set)(g, x) = GNNlib.set2set_pool(l, g, x)
 
 (l::Set2Set)(g::GNNGraph) = GNNGraph(g, gdata = l(g, node_features(g)))