Fix louvain_communities, PropertyGraph, cudf column.full, dgl 2…

….1.0 CI failures (#4215)

* Updates cudf usage in hypergraph to use `cudf.core.column.as_column` instead of the deprecated `cudf.core.column.full` API
* Adds code to call louvain using a signature that's compatible with both pre and post NX 3.3
* Adds an upper bound to the DGL version pin to ensure a compatible version is used
* Updates a PropertyGraph test to handle minor ordering changes in the result DataFrame
* Test matrices starting 24.04 have changed, resulting in an "empty matrix" error when attempting to use the supported combination used for PyG (PyG wheel tests need pytorch with CUDA 12.2 on arm64), so the matrix was simplified to remove the CUDA 11.8 requirement. cc @tingyu66

Authors:
  - Erik Welch (https://github.com/eriknw)
  - Naim (https://github.com/naimnv)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)
  - Ray Douglass (https://github.com/raydouglass)

URL: #4215

.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

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)