Merge branch 'branch-24.04' into wheel-python-pure

.github/workflows/pr.yaml

@@ -162,7 +162,7 @@ jobs:
     with:
       build_type: pull-request
       script: ci/test_wheel_cugraph-pyg.sh
-      matrix_filter: map(select(.ARCH == "amd64" and .CUDA_VER == "11.8.0"))
+      matrix_filter: map(select(.ARCH == "amd64"))
   wheel-build-cugraph-equivariant:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/[email protected]

.github/workflows/test.yaml

@@ -76,7 +76,7 @@ jobs:
       date: ${{ inputs.date }}
       sha: ${{ inputs.sha }}
       script: ci/test_wheel_cugraph-pyg.sh
-      matrix_filter: map(select(.ARCH == "amd64" and .CUDA_VER == "11.8.0"))
+      matrix_filter: map(select(.ARCH == "amd64"))
   wheel-tests-cugraph-equivariant:
     secrets: inherit
     uses: rapidsai/shared-workflows/.github/workflows/[email protected]

ci/test_python.sh

@@ -166,7 +166,7 @@ if [[ "${RAPIDS_CUDA_VERSION}" == "11.8.0" ]]; then
       pylibcugraphops \
       cugraph \
       cugraph-dgl \
-      'dgl>=1.1.0.cu*' \
+      'dgl>=1.1.0.cu*,<=2.0.0.cu*' \
       'pytorch>=2.0' \
       'pytorch-cuda>=11.8'
 

ci/test_wheel_cugraph-dgl.sh

@@ -34,6 +34,6 @@ DGL_URL="https://data.dgl.ai/wheels/cu${PYTORCH_CUDA_VER}/repo.html"
 
 rapids-logger "Installing PyTorch and DGL"
 rapids-retry python -m pip install torch --index-url ${PYTORCH_URL}
-rapids-retry python -m pip install dgl --find-links ${DGL_URL}
+rapids-retry python -m pip install dgl==2.0.0 --find-links ${DGL_URL}
 
 python -m pytest python/cugraph-dgl/tests

docs/cugraph/source/api_docs/cugraph/dask-cugraph.rst

@@ -60,12 +60,15 @@ Example
     )
 
     # create graph from input data
-    G = cugraph.DiGraph()
+    G = cugraph.Graph(directed=True)
     G.from_dask_cudf_edgelist(e_list, source='src', destination='dst')
 
     # run PageRank
     pr_df = dask_cugraph.pagerank(G, tol=1e-4)
 
+    # need to call compute to generate results
+    pr_df.compute()
+
     # cluster clean up
     Comms.destroy()
     client.close()

python/cugraph/cugraph/structure/hypergraph.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -581,7 +581,7 @@ def _create_direct_edges(
 def _str_scalar_to_category(size, val):
     return cudf.core.column.build_categorical_column(
         categories=cudf.core.column.as_column([val], dtype="str"),
-        codes=cudf.core.column.column.full(size, 0, dtype=np.int32),
+        codes=cudf.core.column.as_column(0, length=size, dtype=np.int32),
         mask=None,
         size=size,
         offset=0,

python/cugraph/cugraph/tests/data_store/test_property_graph.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+# Copyright (c) 2021-2024, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -1424,6 +1424,10 @@ def test_extract_subgraph_graph_without_vert_props(as_pg_first):
         actual_edgelist = G.edgelist.edgelist_df
 
     assert G.is_directed()
+    expected_edgelist = expected_edgelist.sort_values(
+        by=["src", "dst"], ignore_index=True
+    )
+    actual_edgelist = actual_edgelist.sort_values(by=["src", "dst"], ignore_index=True)
     assert_frame_equal(expected_edgelist, actual_edgelist, check_like=True)
 
 

python/nx-cugraph/_nx_cugraph/__init__.py

@@ -164,6 +164,7 @@
         },
         "louvain_communities": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+            "max_level : int, optional": "Upper limit of the number of macro-iterations (max: 500).",
         },
         "pagerank": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",

python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py

@@ -37,18 +37,23 @@
     _max_level_param = {}
 
 
-@not_implemented_for("directed")
-@networkx_algorithm(
-    extra_params={
-        **_max_level_param,
-        **_dtype_param,
-    },
-    is_incomplete=True,  # seed not supported; self-loops not supported
-    is_different=True,  # RNG different
-    version_added="23.10",
-    _plc="louvain",
-)
-def louvain_communities(
+def _louvain_communities_nx32(
+    G,
+    weight="weight",
+    resolution=1,
+    threshold=0.0000001,
+    seed=None,
+    *,
+    max_level=None,
+    dtype=None,
+):
+    """`seed` parameter is currently ignored, and self-loops are not yet supported."""
+    return _louvain_communities(
+        G, weight, resolution, threshold, max_level, seed, dtype=dtype
+    )
+
+
+def _louvain_communities(
     G,
     weight="weight",
     resolution=1,
@@ -85,16 +90,54 @@ def louvain_communities(
     return [set(G._nodearray_to_list(ids)) for ids in groups.values()]
 
 
-@louvain_communities._can_run
-def _(
-    G,
-    weight="weight",
-    resolution=1,
-    threshold=0.0000001,
-    max_level=None,
-    seed=None,
-    *,
-    dtype=None,
-):
-    # NetworkX allows both directed and undirected, but cugraph only allows undirected.
-    return not G.is_directed()
+_louvain_decorator = networkx_algorithm(
+    extra_params={
+        **_max_level_param,
+        **_dtype_param,
+    },
+    is_incomplete=True,  # seed not supported; self-loops not supported
+    is_different=True,  # RNG different
+    version_added="23.10",
+    _plc="louvain",
+    name="louvain_communities",
+)
+
+if _max_level_param:  # networkx <= 3.2
+    _louvain_communities_nx32.__name__ = "louvain_communities"
+    louvain_communities = not_implemented_for("directed")(
+        _louvain_decorator(_louvain_communities_nx32)
+    )
+
+    @louvain_communities._can_run
+    def _(
+        G,
+        weight="weight",
+        resolution=1,
+        threshold=0.0000001,
+        seed=None,
+        *,
+        max_level=None,
+        dtype=None,
+    ):
+        # NetworkX allows both directed and undirected, but cugraph only undirected.
+        return not G.is_directed()
+
+else:  # networkx >= 3.3
+    _louvain_communities.__name__ = "louvain_communities"
+    louvain_communities = not_implemented_for("directed")(
+        _louvain_decorator(_louvain_communities)
+    )
+
+    @louvain_communities._can_run
+    def _(
+        G,
+        weight="weight",
+        resolution=1,
+        threshold=0.0000001,
+        max_level=None,
+        seed=None,
+        *,
+        dtype=None,
+    ):
+        # NetworkX allows both directed and undirected, but cugraph only undirected.
+        return not G.is_directed()

python/nx-cugraph/nx_cugraph/tests/test_match_api.py

@@ -44,11 +44,6 @@ def test_match_signature_and_names():
         else:
             orig_func = dispatchable_func.orig_func
 
-        if nxver.major == 3 and nxver.minor <= 2 and name == "louvain_communities":
-            # The signature of louvain_communities changed in NetworkX 3.3, and
-            # we updated to match, so we skip this check in older versions.
-            continue
-
         # Matching signatures?
         orig_sig = inspect.signature(orig_func)
         func_sig = inspect.signature(func)