@functor ->@layer (#484)

* `@functor` ->`@layer`

* reinstate tests

GNNlib/src/msgpass.jl

@@ -45,7 +45,7 @@ struct GNNConv <: GNNLayer
     σ
 end
 
-Flux.@functor GNNConv
+Flux.@layer GNNConv
 
 function GNNConv(ch::Pair{Int,Int}, σ=identity)
     in, out = ch

docs/src/messagepassing.md

@@ -109,7 +109,7 @@ struct GCN{A<:AbstractMatrix, B, F} <: GNNLayer
     σ::F
 end
 
-Flux.@functor GCN # allow gpu movement, select trainable params etc...
+Flux.@layer GCN # allow gpu movement, select trainable params etc...
 
 function GCN(ch::Pair{Int,Int}, σ=identity)
     in, out = ch

docs/src/models.md

@@ -13,7 +13,7 @@ and the *implicit modeling* style based on [`GNNChain`](@ref), more concise but
 In the explicit modeling style, the model is created according to the following steps:
 
 1. Define a new type for your model (`GNN` in the example below). Layers and submodels are fields.
-2. Apply `Flux.@functor` to the new type to make it Flux's compatible (parameters' collection, gpu movement, etc...)
+2. Apply `Flux.@layer` to the new type to make it Flux's compatible (parameters' collection, gpu movement, etc...)
 3. Optionally define a convenience constructor for your model.
 4. Define the forward pass by implementing the call method for your type.
 5. Instantiate the model. 
@@ -30,7 +30,7 @@ struct GNN                                # step 1
     dense
 end
 
-Flux.@functor GNN                         # step 2
+Flux.@layer GNN                         # step 2
 
 function GNN(din::Int, d::Int, dout::Int) # step 3    
     GNN(GCNConv(din => d),

docs/tutorials/introductory_tutorials/gnn_intro_pluto.jl

@@ -182,7 +182,7 @@ begin
         layers::NamedTuple
     end
 
-    Flux.@functor GCN # provides parameter collection, gpu movement and more
+    Flux.@layer GCN # provides parameter collection, gpu movement and more
 
     function GCN(num_features, num_classes)
         layers = (conv1 = GCNConv(num_features => 4),

docs/tutorials/introductory_tutorials/node_classification_pluto.jl

@@ -138,7 +138,7 @@ begin
         layers::NamedTuple
     end
 
-    Flux.@functor MLP
+    Flux.@layer :expand MLP
 
     function MLP(num_features, num_classes, hidden_channels; drop_rate = 0.5)
         layers = (hidden = Dense(num_features => hidden_channels),
@@ -235,7 +235,7 @@ begin
         layers::NamedTuple
     end
 
-    Flux.@functor GCN # provides parameter collection, gpu movement and more
+    Flux.@layer GCN # provides parameter collection, gpu movement and more
 
     function GCN(num_features, num_classes, hidden_channels; drop_rate = 0.5)
         layers = (conv1 = GCNConv(num_features => hidden_channels),

docs/tutorials_broken/temporal_graph_classification_pluto.jl

@@ -117,7 +117,7 @@ begin
 	    dense::Dense
 	end
 
-	Flux.@functor GenderPredictionModel
+	Flux.@layer GenderPredictionModel
 
 	function GenderPredictionModel(; nfeatures = 103, nhidden = 128, activation = relu)
 	    mlp = Chain(Dense(nfeatures, nhidden, activation), Dense(nhidden, nhidden, activation))

examples/graph_classification_temporalbrains.jl

@@ -62,7 +62,7 @@ struct GenderPredictionModel
     dense::Dense
 end
 
-Flux.@functor GenderPredictionModel
+Flux.@layer GenderPredictionModel
 
 function GenderPredictionModel(; nfeatures = 103, nhidden = 128, activation = relu)
     mlp = Chain(Dense(nfeatures, nhidden, activation), Dense(nhidden, nhidden, activation))

notebooks/gnn_intro.ipynb

@@ -354,7 +354,7 @@
     "    layers::NamedTuple\n",
     "end\n",
     "\n",
-    "Flux.@functor GCN # provides parameter collection, gpu movement and more\n",
+    "Flux.@layer :expand GCN # provides parameter collection, gpu movement and more\n",
     "\n",
     "function GCN(num_features, num_classes)\n",
     "    layers = (conv1 = GCNConv(num_features => 4),\n",

notebooks/graph_classification_solved.ipynb

@@ -857,7 +857,7 @@
     "\tact::F\n",
     "end\n",
     "\n",
-    "Flux.@functor MyConv\n",
+    "Flux.@layer MyConv\n",
     "\n",
     "function MyConv((nin, nout)::Pair, act=identity)\n",
     "\tW1 = Flux.glorot_uniform(nout, nin)\n",

src/GraphNeuralNetworks.jl

@@ -3,7 +3,7 @@ module GraphNeuralNetworks
 using Statistics: mean
 using LinearAlgebra, Random
 using Flux
-using Flux: glorot_uniform, leakyrelu, GRUCell, @functor, batch
+using Flux: glorot_uniform, leakyrelu, GRUCell, batch
 using MacroTools: @forward
 using NNlib
 using NNlib: scatter, gather

src/layers/basic.jl

@@ -45,7 +45,7 @@ end
 
 WithGraph(model, g::GNNGraph; traingraph = false) = WithGraph(model, g, traingraph)
 
-@functor WithGraph
+Flux.@layer :expand WithGraph
 Flux.trainable(l::WithGraph) = l.traingraph ? (; l.model, l.g) : (; l.model)
 
 (l::WithGraph)(g::GNNGraph, x...; kws...) = l.model(g, x...; kws...)
@@ -107,7 +107,7 @@ struct GNNChain{T <: Union{Tuple, NamedTuple, AbstractVector}} <: GNNLayer
     layers::T
 end
 
-@functor GNNChain
+Flux.@layer :expand GNNChain
 
 GNNChain(xs...) = GNNChain(xs)
 

src/layers/conv.jl

@@ -76,7 +76,7 @@ struct GCNConv{W <: AbstractMatrix, B, F} <: GNNLayer
     use_edge_weight::Bool
 end
 
-@functor GCNConv
+Flux.@layer GCNConv
 
 function GCNConv(ch::Pair{Int, Int}, σ = identity;
                  init = glorot_uniform,
@@ -167,7 +167,7 @@ function ChebConv(ch::Pair{Int, Int}, k::Int;
     ChebConv(W, b, k)
 end
 
-@functor ChebConv
+Flux.@layer ChebConv
 
 (l::ChebConv)(g, x) = GNNlib.cheb_conv(l, g, x)
 
@@ -225,7 +225,7 @@ struct GraphConv{W <: AbstractMatrix, B, F, A} <: GNNLayer
     aggr::A
 end
 
-@functor GraphConv
+Flux.@layer GraphConv
 
 function GraphConv(ch::Pair{Int, Int}, σ = identity; aggr = +,
                    init = glorot_uniform, bias::Bool = true)
@@ -300,8 +300,7 @@ l = GATConv(in_channel => out_channel, add_self_loops = false, bias = false; hea
 y = l(g, x)       
 ```
 """
-struct GATConv{DX <: Dense, DE <: Union{Dense, Nothing}, DV, T, A <: AbstractMatrix, F, B} <:
-       GNNLayer
+struct GATConv{DX<:Dense,DE<:Union{Dense, Nothing},DV,T,A<:AbstractMatrix,F,B} <: GNNLayer
     dense_x::DX
     dense_e::DE
     bias::B
@@ -315,8 +314,8 @@ struct GATConv{DX <: Dense, DE <: Union{Dense, Nothing}, DV, T, A <: AbstractMat
     dropout::DV
 end
 
-@functor GATConv
-Flux.trainable(l::GATConv) = (dense_x = l.dense_x, dense_e = l.dense_e, bias = l.bias, a = l.a)
+Flux.@layer GATConv
+Flux.trainable(l::GATConv) = (; l.dense_x, l.dense_e, l.bias, l.a)
 
 GATConv(ch::Pair{Int, Int}, args...; kws...) = GATConv((ch[1], 0) => ch[2], args...; kws...)
 
@@ -420,7 +419,7 @@ struct GATv2Conv{T, A1, A2, A3, DV, B, C <: AbstractMatrix, F} <: GNNLayer
     dropout::DV
 end
 
-@functor GATv2Conv
+Flux.@layer GATv2Conv
 Flux.trainable(l::GATv2Conv) = (dense_i = l.dense_i, dense_j = l.dense_j, dense_e = l.dense_e, bias = l.bias, a = l.a)
 
 function GATv2Conv(ch::Pair{Int, Int}, args...; kws...)
@@ -515,7 +514,7 @@ struct GatedGraphConv{W <: AbstractArray{<:Number, 3}, R, A} <: GNNLayer
     aggr::A
 end
 
-@functor GatedGraphConv
+Flux.@layer GatedGraphConv
 
 function GatedGraphConv(dims::Int, num_layers::Int;
                         aggr = +, init = glorot_uniform)
@@ -572,7 +571,7 @@ struct EdgeConv{NN, A} <: GNNLayer
     aggr::A
 end
 
-@functor EdgeConv
+Flux.@layer :expand EdgeConv
 
 EdgeConv(nn; aggr = max) = EdgeConv(nn, aggr)
 
@@ -626,7 +625,7 @@ struct GINConv{R <: Real, NN, A} <: GNNLayer
     aggr::A
 end
 
-@functor GINConv
+Flux.@layer :expand GINConv
 Flux.trainable(l::GINConv) = (nn = l.nn,)
 
 GINConv(nn, ϵ; aggr = +) = GINConv(nn, ϵ, aggr)
@@ -680,7 +679,7 @@ edim = 10
 g = GNNGraph(s, t)
 
 # create dense layer
-nn = Dense(edim, out_channel * in_channel)
+nn = Dense(edim => out_channel * in_channel)
 
 # create layer
 l = NNConv(in_channel => out_channel, nn, tanh, bias = true, aggr = +)
@@ -697,7 +696,7 @@ struct NNConv{W, B, NN, F, A} <: GNNLayer
     aggr::A
 end
 
-@functor NNConv
+Flux.@layer :expand NNConv
 
 function NNConv(ch::Pair{Int, Int}, nn, σ = identity; aggr = +, bias = true,
                 init = glorot_uniform)
@@ -763,7 +762,7 @@ struct SAGEConv{W <: AbstractMatrix, B, F, A} <: GNNLayer
     aggr::A
 end
 
-@functor SAGEConv
+Flux.@layer SAGEConv
 
 function SAGEConv(ch::Pair{Int, Int}, σ = identity; aggr = mean,
                   init = glorot_uniform, bias::Bool = true)
@@ -833,7 +832,7 @@ struct ResGatedGraphConv{W, B, F} <: GNNLayer
     σ::F
 end
 
-@functor ResGatedGraphConv
+Flux.@layer ResGatedGraphConv
 
 function ResGatedGraphConv(ch::Pair{Int, Int}, σ = identity;
                            init = glorot_uniform, bias::Bool = true)
@@ -907,7 +906,7 @@ struct CGConv{D1, D2} <: GNNLayer
     residual::Bool
 end
 
-@functor CGConv
+Flux.@layer CGConv
 
 CGConv(ch::Pair{Int, Int}, args...; kws...) = CGConv((ch[1], 0) => ch[2], args...; kws...)
 
@@ -980,7 +979,7 @@ struct AGNNConv{A <: AbstractVector} <: GNNLayer
     trainable::Bool
 end
 
-@functor AGNNConv
+Flux.@layer AGNNConv
 
 Flux.trainable(l::AGNNConv) = l.trainable ? (; l.β) : (;)
 
@@ -1027,7 +1026,7 @@ struct MEGNetConv{TE, TV, A} <: GNNLayer
     aggr::A
 end
 
-@functor MEGNetConv
+Flux.@layer :expand MEGNetConv
 
 MEGNetConv(ϕe, ϕv; aggr = mean) = MEGNetConv(ϕe, ϕv, aggr)
 
@@ -1108,7 +1107,7 @@ struct GMMConv{A <: AbstractMatrix, B, F} <: GNNLayer
     residual::Bool
 end
 
-@functor GMMConv
+Flux.@layer GMMConv
 
 function GMMConv(ch::Pair{NTuple{2, Int}, Int},
                  σ = identity;
@@ -1191,7 +1190,7 @@ struct SGConv{A <: AbstractMatrix, B} <: GNNLayer
     use_edge_weight::Bool
 end
 
-@functor SGConv
+Flux.@layer SGConv
 
 function SGConv(ch::Pair{Int, Int}, k = 1;
                 init = glorot_uniform,
@@ -1259,7 +1258,7 @@ struct TAGConv{A <: AbstractMatrix, B} <: GNNLayer
     use_edge_weight::Bool
 end
 
-@functor TAGConv
+Flux.@layer TAGConv
 
 function TAGConv(ch::Pair{Int, Int}, k = 3;
                   init = glorot_uniform,
@@ -1269,7 +1268,7 @@ function TAGConv(ch::Pair{Int, Int}, k = 3;
     in, out = ch
     W = init(out, in)
     b = bias ? Flux.create_bias(W, true, out) : false
-    TAGConv(W, b, k, add_self_loops, use_edge_weight)
+    return TAGConv(W, b, k, add_self_loops, use_edge_weight)
 end
 
 (l::TAGConv)(g, x, edge_weight = nothing) = GNNlib.tag_conv(l, g, x, edge_weight)
@@ -1343,10 +1342,10 @@ struct EGNNConv{TE, TX, TH, NF} <: GNNLayer
     residual::Bool
 end
 
-@functor EGNNConv
+Flux.@layer EGNNConv
 
 function EGNNConv(ch::Pair{Int, Int}, hidden_size = 2 * ch[1]; residual = false)
-    EGNNConv((ch[1], 0) => ch[2]; hidden_size, residual)
+    return EGNNConv((ch[1], 0) => ch[2]; hidden_size, residual)
 end
 
 #Follows reference implementation at https://github.com/vgsatorras/egnn/blob/main/models/egnn_clean/egnn_clean.py
@@ -1477,7 +1476,7 @@ struct TransformerConv{TW1, TW2, TW3, TW4, TW5, TW6, TFF, TBN1, TBN2} <: GNNLaye
     sqrt_out::Float32
 end
 
-@functor TransformerConv
+Flux.@layer TransformerConv
 
 function Flux.trainable(l::TransformerConv)
     (; l.W1, l.W2, l.W3, l.W4, l.W5, l.W6, l.FF, l.BN1, l.BN2)
@@ -1568,7 +1567,7 @@ struct DConv <: GNNLayer
     k::Int
 end
 
-@functor DConv
+Flux.@layer DConv
 
 function DConv(ch::Pair{Int, Int}, k::Int; init = glorot_uniform, bias = true)
     in, out = ch

src/layers/heteroconv.jl

@@ -43,7 +43,7 @@ struct HeteroGraphConv
     aggr::Function
 end
 
-Flux.@functor HeteroGraphConv
+Flux.@layer HeteroGraphConv
 
 HeteroGraphConv(itr::Dict; aggr = +) = HeteroGraphConv(pairs(itr); aggr)
 HeteroGraphConv(itr::Pair...; aggr = +) = HeteroGraphConv(itr; aggr)

src/layers/pool.jl

@@ -90,7 +90,7 @@ struct GlobalAttentionPool{G, F}
     ffeat::F
 end
 
-@functor GlobalAttentionPool
+Flux.@layer GlobalAttentionPool
 
 GlobalAttentionPool(fgate) = GlobalAttentionPool(fgate, identity)
 
@@ -146,7 +146,7 @@ struct Set2Set{L} <: GNNLayer
     num_iters::Int
 end
 
-@functor Set2Set
+Flux.@layer Set2Set
 
 function Set2Set(n_in::Int, n_iters::Int, n_layers::Int = 1)
     @assert n_layers >= 1