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create GNNLux
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‎.github/workflows/test_GNNLux.yml

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name: GNNLux
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on:
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pull_request:
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branches:
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- master
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push:
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branches:
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- master
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jobs:
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test:
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name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }}
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: false
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matrix:
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version:
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- '1.10' # Replace this with the minimum Julia version that your package supports.
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# - '1' # '1' will automatically expand to the latest stable 1.x release of Julia.
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# - 'pre'
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os:
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- ubuntu-latest
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arch:
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- x64
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steps:
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- uses: actions/checkout@v4
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- uses: julia-actions/setup-julia@v2
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with:
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version: ${{ matrix.version }}
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arch: ${{ matrix.arch }}
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- uses: julia-actions/cache@v2
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- uses: julia-actions/julia-buildpkg@v1
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- name: Install Julia dependencies and run tests
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shell: julia --project=monorepo {0}
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run: |
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using Pkg
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# dev mono repo versions
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pkg"registry up"
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Pkg.update()
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pkg"dev ./GNNGraphs /GNNlib ./GNNLux"
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Pkg.test("GNNLux"; coverage=true)
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- uses: julia-actions/julia-processcoverage@v1
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with:
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# directories: ./GNNLux/src, ./GNNLux/ext
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directories: ./GNNLux/src
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- uses: codecov/codecov-action@v4
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with:
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files: lcov.info

‎GNNLux/LICENSE

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MIT License
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Copyright (c) 2024 Carlo Lucibello <[email protected]> and contributors
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎GNNLux/Project.toml

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name = "GNNLux"
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uuid = "e8545f4d-a905-48ac-a8c4-ca114b98986d"
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authors = ["Carlo Lucibello and contributors"]
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version = "0.1.0"
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[deps]
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ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471"
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GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
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GNNlib = "a6a84749-d869-43f8-aacc-be26a1996e48"
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Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
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LuxCore = "bb33d45b-7691-41d6-9220-0943567d0623"
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NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
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[extensions]
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[compat]
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julia = "1.10"
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[extras]
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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[targets]
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test = ["Test"]

‎GNNLux/README.md

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# GNNLux.jl
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‎GNNLux/src/GNNLux.jl

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module GNNLux
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using ConcreteStructs: @concrete
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using NNlib: NNlib
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using LuxCore: LuxCore, AbstractExplicitLayer
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using Lux: glorot_uniform, zeros32
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using Reexport: @reexport
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using Random: AbstractRNG
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@reexport using GNNGraphs
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include("layers/conv.jl")
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export GraphConv
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end #module
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‎GNNLux/src/layers/conv.jl

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@doc raw"""
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GraphConv(in => out, σ=identity; aggr=+, bias=true, init=glorot_uniform)
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Graph convolution layer from Reference: [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://arxiv.org/abs/1810.02244).
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Performs:
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```math
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\mathbf{x}_i' = W_1 \mathbf{x}_i + \square_{j \in \mathcal{N}(i)} W_2 \mathbf{x}_j
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```
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where the aggregation type is selected by `aggr`.
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# Arguments
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- `in`: The dimension of input features.
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- `out`: The dimension of output features.
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- `σ`: Activation function.
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- `aggr`: Aggregation operator for the incoming messages (e.g. `+`, `*`, `max`, `min`, and `mean`).
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- `bias`: Add learnable bias.
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- `init`: Weights' initializer.
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# Examples
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```julia
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# create data
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s = [1,1,2,3]
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t = [2,3,1,1]
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in_channel = 3
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out_channel = 5
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g = GNNGraph(s, t)
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x = randn(Float32, 3, g.num_nodes)
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# create layer
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l = GraphConv(in_channel => out_channel, relu, bias = false, aggr = mean)
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# forward pass
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y = l(g, x)
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```
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"""
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@concrete struct GraphConv <: AbstractExplicitLayer
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in_dims::Int
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out_dims::Int
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use_bias::Bool
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init_weight::Function
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init_bias::Function
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σ
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aggr
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end
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function GraphConv(ch::Pair{Int, Int}, σ = identity;
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aggr = +,
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init_weight = glorot_uniform,
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init_bias = zeros32,
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use_bias::Bool = true,
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allow_fast_activation::Bool = true)
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in_dims, out_dims = ch
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σ = allow_fast_activation ? NNlib.fast_act(σ) : σ
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return GraphConv(in_dims, out_dims, use_bias, init_weight, init_bias, σ, aggr)
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end
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function LuxCore.initialparameters(rng::AbstractRNG, l::GraphConv)
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weight1 = l.init_weight(rng, l.out_dims, l.in_dims)
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weight2 = l.init_weight(rng, l.out_dims, l.in_dims)
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if l.use_bias
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bias = l.init_bias(rng, l.out_dims)
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else
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bias = false
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end
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return (; weight1, weight2, bias)
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end
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function LuxCore.parameterlength(l::GraphConv)
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if l.use_bias
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return 2 * l.in_dims * l.out_dims + l.out_dims
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else
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return 2 * l.in_dims * l.out_dims
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end
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end
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statelength(d::GraphConv) = 0
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outputsize(d::GraphConv) = (d.out_dims,)
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function Base.show(io::IO, l::GraphConv)
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print(io, "GraphConv(", l.in_dims, " => ", l.out_dims)
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(l.σ == identity) || print(io, ", ", l.σ)
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(l.aggr == +) || print(io, ", aggr=", l.aggr)
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l.use_bias || print(io, ", use_bias=false")
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print(io, ")")
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end
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(l::GraphConv)(g::GNNGraph, x, ps, st) = GNNlib.graph_conv(l, g, x, ps), st

‎GNNLux/test/layers/conv.jl

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@testset "GraphConv" begin
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rng = MersenneTwister(1234)
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g = rand_graph(10, 20, ndata= rand(Float32, 3, 10))
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l = GraphConv(3 => 5, relu)
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ps = LuxCore.initialparameters(rng, l)
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st = LuxCore.initialstates(rng, l)
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end

‎GNNLux/test/runtests.jl

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using Test
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using Lux
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using GNNLux
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using Random, Statistics
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tests = [
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# "utils",
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# "msgpass",
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# "layers/basic",
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"layers/conv",
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# "layers/heteroconv",
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# "layers/temporalconv",
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# "layers/pool",
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# "examples/node_classification_cora",
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]
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@testset "$t" for t in tests
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include("$t.jl")
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end

‎GNNlib/src/layers/conv.jl

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return Y .+ c.bias
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end
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function graph_conv(l, g::AbstractGNNGraph, x)
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function graph_conv(l, g::AbstractGNNGraph, x, ps)
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check_num_nodes(g, x)
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xj, xi = expand_srcdst(g, x)
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m = propagate(copy_xj, g, l.aggr, xj = xj)
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x = l.σ.(l.weight1 * xi .+ l.weight2 * m .+ l.bias)
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x = l.σ.(ps.weight1 * xi .+ ps.weight2 * m .+ ps.bias)
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return x
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end
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