Merge branch 'branch-23.12' into docs_c_api

cpp/src/community/detail/refine_impl.cuh

@@ -64,7 +64,7 @@ struct leiden_key_aggregated_edge_op_t {
   weight_t total_edge_weight{};
   weight_t resolution{};  // resolution parameter
   weight_t theta{};       // scaling factor
-  raft::random::DeviceState<raft::random::PCGenerator> device_state{};
+  raft::random::DeviceState<raft::random::PCGenerator>& device_state;
   __device__ auto operator()(
     vertex_t src,
     vertex_t neighboring_leiden_cluster,

cpp/src/prims/detail/nbr_intersection.cuh

@@ -138,7 +138,7 @@ struct update_rx_major_local_degree_t {
   size_t local_edge_partition_idx{};
 
   raft::device_span<size_t const> rx_reordered_group_lasts{};
-  raft::device_span<size_t const> rx_group_firsts{nullptr};
+  raft::device_span<size_t const> rx_group_firsts{};
   raft::device_span<vertex_t const> rx_majors{};
 
   raft::device_span<edge_t> local_degrees_for_rx_majors{};
@@ -200,7 +200,7 @@ struct update_rx_major_local_nbrs_t {
   size_t local_edge_partition_idx{};
 
   raft::device_span<size_t const> rx_reordered_group_lasts{};
-  raft::device_span<size_t const> rx_group_firsts{nullptr};
+  raft::device_span<size_t const> rx_group_firsts{};
   raft::device_span<vertex_t const> rx_majors{};
   raft::device_span<size_t const> local_nbr_offsets_for_rx_majors{};
   raft::device_span<vertex_t> local_nbrs_for_rx_majors{};
@@ -311,10 +311,10 @@ template <typename FirstElementToIdxMap,
           bool multi_gpu>
 struct pick_min_degree_t {
   FirstElementToIdxMap first_element_to_idx_map{};
-  raft::device_span<edge_t const> first_element_offsets{nullptr};
+  raft::device_span<edge_t const> first_element_offsets{};
 
   SecondElementToIdxMap second_element_to_idx_map{};
-  raft::device_span<edge_t const> second_element_offsets{nullptr};
+  raft::device_span<edge_t const> second_element_offsets{};
 
   edge_partition_device_view_t<vertex_t, edge_t, multi_gpu> edge_partition{};
   thrust::optional<edge_partition_edge_property_device_view_t<edge_t, uint32_t const*, bool>>
@@ -473,12 +473,12 @@ template <typename FirstElementToIdxMap,
 struct copy_intersecting_nbrs_and_update_intersection_size_t {
   FirstElementToIdxMap first_element_to_idx_map{};
   raft::device_span<edge_t const> first_element_offsets{};
-  raft::device_span<vertex_t const> first_element_indices{nullptr};
+  raft::device_span<vertex_t const> first_element_indices{};
   optional_property_buffer_view_t first_element_edge_property_values{};
 
   SecondElementToIdxMap second_element_to_idx_map{};
   raft::device_span<edge_t const> second_element_offsets{};
-  raft::device_span<vertex_t const> second_element_indices{nullptr};
+  raft::device_span<vertex_t const> second_element_indices{};
   optional_property_buffer_view_t second_element_edge_property_values{};
 
   edge_partition_device_view_t<vertex_t, edge_t, multi_gpu> edge_partition{};
@@ -487,8 +487,8 @@ struct copy_intersecting_nbrs_and_update_intersection_size_t {
     edge_partition_e_mask{};
 
   VertexPairIterator vertex_pair_first;
-  raft::device_span<size_t const> nbr_intersection_offsets{nullptr};
-  raft::device_span<vertex_t> nbr_intersection_indices{nullptr};
+  raft::device_span<size_t const> nbr_intersection_offsets{};
+  raft::device_span<vertex_t> nbr_intersection_indices{};
 
   optional_property_buffer_mutable_view_t nbr_intersection_e_property_values0{};
   optional_property_buffer_mutable_view_t nbr_intersection_e_property_values1{};
@@ -499,12 +499,11 @@ struct copy_intersecting_nbrs_and_update_intersection_size_t {
     using edge_property_value_t = typename edge_partition_e_input_device_view_t::value_type;
 
     auto pair = *(vertex_pair_first + i);
-
-    vertex_t const* indices0{nullptr};
+    vertex_t const* indices0{};
     std::conditional_t<!std::is_same_v<edge_property_value_t, thrust::nullopt_t>,
                        edge_property_value_t const*,
                        void*>
-      edge_property_values0{nullptr};
+      edge_property_values0{};
 
     edge_t local_edge_offset0{0};
     edge_t local_degree0{0};
@@ -548,11 +547,11 @@ struct copy_intersecting_nbrs_and_update_intersection_size_t {
       local_degree0      = static_cast<edge_t>(first_element_offsets[idx + 1] - local_edge_offset0);
     }
 
-    vertex_t const* indices1{nullptr};
+    vertex_t const* indices1{};
     std::conditional_t<!std::is_same_v<edge_property_value_t, thrust::nullopt_t>,
                        edge_property_value_t const*,
                        void*>
-      edge_property_values1{nullptr};
+      edge_property_values1{};
 
     edge_t local_edge_offset1{0};
     edge_t local_degree1{0};

python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py

@@ -29,6 +29,7 @@
 
 torch = import_optional("torch")
 torch_geometric = import_optional("torch_geometric")
+torch_sparse = import_optional("torch_sparse")
 trim_to_layer = import_optional("torch_geometric.utils.trim_to_layer")
 
 
@@ -200,6 +201,9 @@ def test_cugraph_loader_from_disk_subset():
 
 
 @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available")
+@pytest.mark.skipif(
+    isinstance(torch_sparse, MissingModule), reason="torch-sparse not available"
+)
 def test_cugraph_loader_from_disk_subset_csr():
     m = [2, 9, 99, 82, 11, 13]
     n = torch.arange(1, 1 + len(m), dtype=torch.int32)
@@ -332,6 +336,9 @@ def test_cugraph_loader_e2e_coo():
 
 
 @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available")
+@pytest.mark.skipif(
+    isinstance(torch_sparse, MissingModule), reason="torch-sparse not available"
+)
 @pytest.mark.parametrize("framework", ["pyg", "cugraph-ops"])
 def test_cugraph_loader_e2e_csc(framework):
     m = [2, 9, 99, 82, 9, 3, 18, 1, 12]

python/nx-cugraph/_nx_cugraph/__init__.py

@@ -29,23 +29,76 @@
     # "description": "TODO",
     "functions": {
         # BEGIN: functions
+        "barbell_graph",
         "betweenness_centrality",
+        "bull_graph",
+        "caveman_graph",
+        "chvatal_graph",
+        "circular_ladder_graph",
+        "complete_bipartite_graph",
+        "complete_graph",
+        "complete_multipartite_graph",
+        "connected_components",
+        "cubical_graph",
+        "cycle_graph",
+        "davis_southern_women_graph",
         "degree_centrality",
+        "desargues_graph",
+        "diamond_graph",
+        "dodecahedral_graph",
         "edge_betweenness_centrality",
+        "empty_graph",
+        "florentine_families_graph",
+        "from_pandas_edgelist",
+        "from_scipy_sparse_array",
+        "frucht_graph",
+        "heawood_graph",
+        "house_graph",
+        "house_x_graph",
+        "icosahedral_graph",
         "in_degree_centrality",
+        "is_connected",
         "is_isolate",
         "isolates",
         "k_truss",
+        "karate_club_graph",
+        "krackhardt_kite_graph",
+        "ladder_graph",
+        "les_miserables_graph",
+        "lollipop_graph",
         "louvain_communities",
+        "moebius_kantor_graph",
+        "node_connected_component",
+        "null_graph",
+        "number_connected_components",
         "number_of_isolates",
         "number_of_selfloops",
+        "octahedral_graph",
         "out_degree_centrality",
+        "pappus_graph",
+        "path_graph",
+        "petersen_graph",
+        "sedgewick_maze_graph",
+        "star_graph",
+        "tadpole_graph",
+        "tetrahedral_graph",
+        "trivial_graph",
+        "truncated_cube_graph",
+        "truncated_tetrahedron_graph",
+        "turan_graph",
+        "tutte_graph",
+        "wheel_graph",
         # END: functions
     },
     "extra_docstrings": {
         # BEGIN: extra_docstrings
         "betweenness_centrality": "`weight` parameter is not yet supported.",
         "edge_betweenness_centrality": "`weight` parameter is not yet supported.",
+        "from_pandas_edgelist": "cudf.DataFrame inputs also supported.",
+        "k_truss": (
+            "Currently raises `NotImplementedError` for graphs with more than one connected\n"
+            "component when k >= 3. We expect to fix this soon."
+        ),
         "louvain_communities": "`seed` parameter is currently ignored.",
         # END: extra_docstrings
     },

python/nx-cugraph/lint.yaml

@@ -45,20 +45,20 @@ repos:
       - id: pyupgrade
         args: [--py39-plus]
   - repo: https://github.com/psf/black
-    rev: 23.10.0
+    rev: 23.10.1
     hooks:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.1
+    rev: v0.1.3
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]
   - repo: https://github.com/PyCQA/flake8
     rev: 6.1.0
     hooks:
       - id: flake8
-        args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501']  # Why is this necessary?
+        args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501', '--extend-ignore=SIM105']  # Why is this necessary?
         additional_dependencies: &flake8_dependencies
           # These versions need updated manually
           - flake8==6.1.0
@@ -77,7 +77,7 @@ repos:
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.1
+    rev: v0.1.3
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks

python/nx-cugraph/nx_cugraph/__init__.py

@@ -20,11 +20,11 @@
 from . import convert
 from .convert import *
 
-# from . import convert_matrix
-# from .convert_matrix import *
+from . import convert_matrix
+from .convert_matrix import *
 
-# from . import generators
-# from .generators import *
+from . import generators
+from .generators import *
 
 from . import algorithms
 from .algorithms import *

python/nx-cugraph/nx_cugraph/algorithms/__init__.py

@@ -10,7 +10,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from . import centrality, community
+from . import bipartite, centrality, community, components
+from .bipartite import complete_bipartite_graph
 from .centrality import *
+from .components import *
 from .core import *
 from .isolate import *

python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2023, 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
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from .generators import *

python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py

@@ -0,0 +1,62 @@
+# Copyright (c) 2023, 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
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from numbers import Integral
+
+import cupy as cp
+import networkx as nx
+import numpy as np
+
+from nx_cugraph.generators._utils import _create_using_class, _number_and_nodes
+from nx_cugraph.utils import index_dtype, networkx_algorithm, nodes_or_number
+
+__all__ = [
+    "complete_bipartite_graph",
+]
+
+
+@nodes_or_number([0, 1])
+@networkx_algorithm
+def complete_bipartite_graph(n1, n2, create_using=None):
+    graph_class, inplace = _create_using_class(create_using)
+    if graph_class.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+    orig_n1, unused_nodes1 = n1
+    orig_n2, unused_nodes2 = n2
+    n1, nodes1 = _number_and_nodes(n1)
+    n2, nodes2 = _number_and_nodes(n2)
+    all_indices = cp.indices((n1, n2), dtype=index_dtype)
+    indices0 = all_indices[0].ravel()
+    indices1 = all_indices[1].ravel() + n1
+    del all_indices
+    src_indices = cp.hstack((indices0, indices1))
+    dst_indices = cp.hstack((indices1, indices0))
+    bipartite = cp.zeros(n1 + n2, np.int8)
+    bipartite[n1:] = 1
+    if isinstance(orig_n1, Integral) and isinstance(orig_n2, Integral):
+        nodes = None
+    else:
+        nodes = list(range(n1)) if nodes1 is None else nodes1
+        nodes.extend(range(n2) if nodes2 is None else nodes2)
+        if len(set(nodes)) != len(nodes):
+            raise nx.NetworkXError("Inputs n1 and n2 must contain distinct nodes")
+    G = graph_class.from_coo(
+        n1 + n2,
+        src_indices,
+        dst_indices,
+        node_values={"bipartite": bipartite},
+        id_to_key=nodes,
+        name=f"complete_bipartite_graph({orig_n1}, {orig_n2})",
+    )
+    if inplace:
+        return create_using._become(G)
+    return G

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

@@ -62,7 +62,7 @@ def louvain_communities(
         resolution=resolution,
         do_expensive_check=False,
     )
-    groups = _groupby(clusters, vertices)
+    groups = _groupby(clusters, vertices, groups_are_canonical=True)
     rv = [set(G._nodearray_to_list(node_ids)) for node_ids in groups.values()]
     # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix
     isolates = _isolates(G)

python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2023, 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
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from .connected import *