rewrite recurrent temporal layers for Flux v0.16

GNNLux/src/layers/conv.jl

@@ -1261,7 +1261,11 @@ LuxCore.parameterlength(l::GatedGraphConv) = parameterlength(l.gru) + l.dims^2*l
 
 function (l::GatedGraphConv)(g, x, ps, st)
     gru = StatefulLuxLayer{true}(l.gru, ps.gru, _getstate(st, :gru))
-    fgru = (x, h) -> gru((x, (h,)))[1]  # make the forward compatible with Flux.GRUCell style
+    # make the forward compatible with Flux.GRUCell style
+    function fgru(x, h)
+        y, (h, ) = gru((x, (h,)))
+        return y, h
+    end
     m = (; gru=fgru, ps.weight, l.num_layers, l.aggr, l.dims)
     return GNNlib.gated_graph_conv(m, g, x), st
 end

GNNlib/src/layers/conv.jl

@@ -227,8 +227,7 @@ function gated_graph_conv(l, g::GNNGraph, x::AbstractMatrix)
     for i in 1:(l.num_layers)
         m = view(l.weight, :, :, i) * h
         m = propagate(copy_xj, g, l.aggr; xj = m)
-        # in gru forward, hidden state is first argument, input is second
-        h = l.gru(m, h)
+        _, h = l.gru(m, h)
     end
     return h
 end

GNNlib/src/layers/pool.jl

@@ -31,9 +31,9 @@ function set2set_pool(l, g::GNNGraph, x::AbstractMatrix)
     qstar = zeros_like(x, (2*n_in, g.num_graphs))
     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
-        h, c = l.lstm(qstar, (h, c))                     # [n_in, n_graphs]
-        q = h
+        q, state = l.lstm(qstar, state)                     # [n_in, n_graphs]
         qn = broadcast_nodes(g, q)                    # [n_in, n_nodes]
         α = softmax_nodes(g, sum(qn .* x, dims = 1))  # [1, n_nodes]
         r = reduce_nodes(+, g, x .* α)               # [n_in, n_graphs]

GraphNeuralNetworks/Project.toml

@@ -5,6 +5,7 @@ version = "1.0.0-DEV"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
 GNNlib = "a6a84749-d869-43f8-aacc-be26a1996e48"
@@ -18,7 +19,8 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 ChainRulesCore = "1"
-Flux = "0.15"
+ConcreteStructs = "0.2.3"
+Flux = "0.16.0"
 GNNGraphs = "1.4"
 GNNlib = "1"
 LinearAlgebra = "1"

GraphNeuralNetworks/src/GraphNeuralNetworks.jl

@@ -3,12 +3,13 @@ module GraphNeuralNetworks
 using Statistics: mean
 using LinearAlgebra, Random
 using Flux
-using Flux: glorot_uniform, leakyrelu, GRUCell, batch
+using Flux: glorot_uniform, leakyrelu, GRUCell, batch, initialstates
 using MacroTools: @forward
 using NNlib
 using ChainRulesCore
 using Reexport: @reexport
 using MLUtils: zeros_like
+using ConcreteStructs: @concrete
 
 using GNNGraphs:  COO_T, ADJMAT_T, SPARSE_T,
                   check_num_nodes, check_num_edges,
@@ -49,12 +50,12 @@ include("layers/heteroconv.jl")
 export HeteroGraphConv
 
 include("layers/temporalconv.jl")
-export TGCN,
-       A3TGCN,
-       GConvLSTM,
-       GConvGRU,
-       DCGRU,
-       EvolveGCNO
+export GNNRecurrence,
+       GConvGRU, GConvGRUCell,
+       GConvLSTM, GConvLSTMCell,
+       DCGRU, DCGRUCell,
+       EvolveGCNO, EvolveGCNOCell,
+       TGCN, TGCNCell
 
 include("layers/pool.jl")
 export GlobalPool,

GraphNeuralNetworks/src/layers/conv.jl

@@ -1,3 +1,9 @@
+# The implementations of the forward pass of the graph convolutional layers are in the `GNNlib` module,
+# in the src/layers/conv.jl file. The `GNNlib` module is re-exported in the GraphNeuralNetworks module.
+# This annoying for the readability of the code, as the user has to look at two different files to understand
+# the implementation of a single layer, 
+# but it is done for GraphNeuralNetworks.jl and GNNLux.jl to be able to share the same code.
+
 @doc raw"""
     GCNConv(in => out, σ=identity; [bias, init, add_self_loops, use_edge_weight])
 

GraphNeuralNetworks/src/layers/pool.jl

@@ -155,12 +155,6 @@ function Set2Set(n_in::Int, n_iters::Int, n_layers::Int = 1)
     return Set2Set(lstm, n_iters)
 end
 
-function initialstates(cell::LSTMCell)
-    h = zeros_like(cell.Wh, size(cell.Wh, 2))
-    c = zeros_like(cell.Wh, size(cell.Wh, 2))
-    return h, c
-end
-
 function (l::Set2Set)(g, x)
     return GNNlib.set2set_pool(l, g, x)
 end