make GraphNeuralNetworks.jl depend on GNNGraphs.jl (#453)

* make GraphNeuralNetworks.jl depend on GNNGraphs.jl

workflows

drop_nodes(g, p) -> remove_nodes(g, p)

compats

* workflow

Author: CarloLucibello

.github/workflows/tests_GNNGraphs.yml renamed to .github/workflows/test_GNNGraphs.yml

@@ -21,27 +21,28 @@ jobs:
           - ubuntu-latest
         arch:
           - x64
-    env:              # Don't use system Python (needed by PyCall)
-      PYTHON: ""
+
     steps:
       - uses: actions/checkout@v4
       - uses: julia-actions/setup-julia@v2
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
-      - uses: actions/cache@v4
-        env:
-          cache-name: cache-artifacts
-        with:
-          path: ~/.julia/artifacts
-          key: ${{ runner.os }}-test-${{ env.cache-name }}-${{ hashFiles('**/Project.toml') }}
-          restore-keys: |
-            ${{ runner.os }}-test-${{ env.cache-name }}-
-            ${{ runner.os }}-test-
-            ${{ runner.os }}-
+      - uses: julia-actions/cache@v2
       - uses: julia-actions/julia-buildpkg@v1
-      - uses: julia-actions/julia-runtest@v1
+      - name: Install Julia dependencies and run tests
+        shell: julia --project=monorepo {0}
+        run: |
+          using Pkg
+          # dev mono repo versions
+          pkg"registry up"
+          Pkg.update()
+          pkg"dev ./GNNGraphs ."
+          Pkg.test("GraphNeuralNetworks"; coverage=true)
       - uses: julia-actions/julia-processcoverage@v1
+        with:
+          # directories: ./src, ./ext
+          directories: ./src
       - uses: codecov/codecov-action@v4
         with:
-          file: lcov.info
+          files: lcov.info

.github/workflows/tests_GraphNeuralNetworks.yml renamed to .github/workflows/test_GraphNeuralNetworks.yml

@@ -47,7 +47,7 @@ SparseArrays = "1"
 Statistics = "1"
 StatsBase = "0.34"
 cuDNN = "1"
-julia = "1.9"
+julia = "1.10"
 
 [extras]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

GNNlib/Project.toml

@@ -9,6 +9,7 @@ ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
+GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -24,11 +25,9 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [weakdeps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-SimpleWeightedGraphs = "47aef6b3-ad0c-573a-a1e2-d07658019622"
 
 [extensions]
 GraphNeuralNetworksCUDAExt = "CUDA"
-GraphNeuralNetworksSimpleWeightedGraphsExt = "SimpleWeightedGraphs"
 
 [compat]
 Adapt = "3, 4"
@@ -38,6 +37,7 @@ DataStructures = "0.18"
 Flux = "0.14"
 Functors = "0.4.1"
 Graphs = "1.4"
+GNNGraphs = "1.0"
 KrylovKit = "0.6, 0.7, 0.8"
 LinearAlgebra = "1"
 MLDatasets = "0.7"
@@ -47,12 +47,11 @@ NNlib = "0.9"
 NearestNeighbors = "0.4"
 Random = "1"
 Reexport = "1"
-SimpleWeightedGraphs = "1.4.0"
 SparseArrays = "1"
 Statistics = "1"
 StatsBase = "0.34"
 cuDNN = "1"
-julia = "1.9"
+julia = "1.10"
 
 [extras]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
@@ -62,10 +61,9 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
 InlineStrings = "842dd82b-1e85-43dc-bf29-5d0ee9dffc48"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
-SimpleWeightedGraphs = "47aef6b3-ad0c-573a-a1e2-d07658019622"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
 
 [targets]
-test = ["Test", "Adapt", "DataFrames", "InlineStrings", "SimpleWeightedGraphs", "Zygote", "FiniteDifferences", "ChainRulesTestUtils", "MLDatasets", "CUDA", "cuDNN"]
+test = ["Test", "Adapt", "DataFrames", "InlineStrings", "Zygote", "FiniteDifferences", "ChainRulesTestUtils", "MLDatasets", "CUDA", "cuDNN"]

Project.toml

@@ -1,5 +1,15 @@
 # Developer Notes
 
+## Develop Monorepo
+
+GraphNeuralNetworks.jl is package hosted in a monorepo that contains multiple packages. 
+The GraphNeuralNetworks.jl package depends on GNNGraphs.jl, also hosted in the same monorepo.
+
+```julia
+pkg> activate .
+
+pkg> dev ./GNNGraphs
+```
 ## Benchmarking
 
 You can benchmark the effect on performance of your commits using the script `perf/perf.jl`.

docs/src/dev.md

@@ -1,27 +1,37 @@
+module GraphNeuralNetworksCUDAExt
+
+using CUDA
+using Random, Statistics, LinearAlgebra
+using GraphNeuralNetworks
+using GNNGraphs
+using GNNGraphs: COO_T, ADJMAT_T, SPARSE_T 
+import GraphNeuralNetworks: propagate
+
+const CUMAT_T = Union{CUDA.AnyCuMatrix, CUDA.CUSPARSE.CuSparseMatrix}
 
 ###### PROPAGATE SPECIALIZATIONS ####################
 
 ## COPY_XJ 
 
 ## avoid the fast path on gpu until we have better cuda support
 function propagate(::typeof(copy_xj), g::GNNGraph{<:Union{COO_T, SPARSE_T}}, ::typeof(+),
-                   xi, xj::AnyCuMatrix, e)
+            xi, xj::AnyCuMatrix, e)
     propagate((xi, xj, e) -> copy_xj(xi, xj, e), g, +, xi, xj, e)
 end
 
 ## E_MUL_XJ 
 
 ## avoid the fast path on gpu until we have better cuda support
 function propagate(::typeof(e_mul_xj), g::GNNGraph{<:Union{COO_T, SPARSE_T}}, ::typeof(+),
-                   xi, xj::AnyCuMatrix, e::AbstractVector)
+            xi, xj::AnyCuMatrix, e::AbstractVector)
     propagate((xi, xj, e) -> e_mul_xj(xi, xj, e), g, +, xi, xj, e)
 end
 
 ## W_MUL_XJ 
 
 ## avoid the fast path on gpu until we have better cuda support
 function propagate(::typeof(w_mul_xj), g::GNNGraph{<:Union{COO_T, SPARSE_T}}, ::typeof(+),
-                   xi, xj::AnyCuMatrix, e::Nothing)
+                xi, xj::AnyCuMatrix, e::Nothing)
     propagate((xi, xj, e) -> w_mul_xj(xi, xj, e), g, +, xi, xj, e)
 end
 
@@ -35,3 +45,5 @@ end
 # compute_degree(A) = Diagonal(1f0 ./ vec(sum(A; dims=2)))
 
 # Flux.Zygote.@nograd compute_degree
+
+end #module