Merge pull request #76 from bdice/branch-25.04-merge-25.02

Forward-merge branch-25.02 to branch-25.04

.devcontainer/cuda12.5-conda/devcontainer.json → .devcontainer/cuda12.8-conda/devcontainer.json

@@ -3,15 +3,15 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.5",
+      "CUDA": "12.8",
       "PYTHON_PACKAGE_MANAGER": "conda",
       "BASE": "rapidsai/devcontainers:25.04-cpp-mambaforge-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda12.5-conda"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda12.8-conda"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
@@ -20,7 +20,7 @@
   "overrideFeatureInstallOrder": [
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.5-envs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.8-envs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/nx-cugraph,type=bind,consistency=consistent",
@@ -29,7 +29,7 @@
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.5-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.8-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {

.devcontainer/cuda12.5-pip/devcontainer.json → .devcontainer/cuda12.8-pip/devcontainer.json

@@ -3,20 +3,20 @@
     "context": "${localWorkspaceFolder}/.devcontainer",
     "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile",
     "args": {
-      "CUDA": "12.5",
+      "CUDA": "12.8",
       "PYTHON_PACKAGE_MANAGER": "pip",
-      "BASE": "rapidsai/devcontainers:25.04-cpp-cuda12.5-ucx1.18.0-openmpi-ubuntu22.04"
+      "BASE": "rapidsai/devcontainers:25.04-cpp-cuda12.8-ucx1.18.0-openmpi-ubuntu22.04"
     }
   },
   "runArgs": [
     "--rm",
     "--name",
-    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda12.5-pip"
+    "${localEnv:USER:anon}-rapids-${localWorkspaceFolderBasename}-25.04-cuda12.8-pip"
   ],
   "hostRequirements": {"gpu": "optional"},
   "features": {
     "ghcr.io/rapidsai/devcontainers/features/cuda:25.4": {
-      "version": "12.5",
+      "version": "12.8",
       "installcuBLAS": true,
       "installcuSOLVER": true,
       "installcuRAND": true,
@@ -28,15 +28,15 @@
     "ghcr.io/rapidsai/devcontainers/features/cuda",
     "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils"
   ],
-  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.5-venvs}"],
+  "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.8-venvs}"],
   "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"],
   "workspaceFolder": "/home/coder",
   "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/nx-cugraph,type=bind,consistency=consistent",
   "mounts": [
     "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent",
     "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent",
-    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.5-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
+    "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.8-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent"
   ],
   "customizations": {
     "vscode": {

.github/workflows/pr.yaml

@@ -45,7 +45,7 @@ jobs:
     uses: rapidsai/shared-workflows/.github/workflows/[email protected]
     with:
       arch: '["amd64"]'
-      cuda: '["12.5"]'
+      cuda: '["12.8"]'
       build_command: |
         sccache -z;
         build-all --verbose -j$(nproc --ignore=1);

README.md

@@ -154,6 +154,8 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
  │   └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.link_analysis.hits_alg.hits.html#networkx.algorithms.link_analysis.hits_alg.hits">hits</a>
  └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/link_analysis.html#module-networkx.algorithms.link_analysis.pagerank_alg">pagerank_alg</a>
      └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.link_analysis.pagerank_alg.pagerank.html#networkx.algorithms.link_analysis.pagerank_alg.pagerank">pagerank</a>
+<a href="https://networkx.org/documentation/stable/reference/algorithms/link_prediction.html#module-networkx.algorithms.link_prediction">link_prediction</a>
+ └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.link_prediction.jaccard_coefficient.html#networkx.algorithms.link_prediction.jaccard_coefficient">jaccard_coefficient</a>
 <a href="https://networkx.org/documentation/stable/reference/algorithms/operators.html">operators</a>
  └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/operators.html#module-networkx.algorithms.operators.unary">unary</a>
      ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.operators.unary.complement.html#networkx.algorithms.operators.unary.complement">complement</a>

_nx_cugraph/__init__.py

@@ -110,6 +110,7 @@
         "is_tree",
         "is_weakly_connected",
         "isolates",
+        "jaccard_coefficient",
         "k_truss",
         "karate_club_graph",
         "katz_centrality",

benchmarks/pytest-based/bench_algos.py

@@ -197,7 +197,11 @@ def get_graph_obj_for_benchmark(graph_obj, backend_wrapper):
     """
     G = graph_obj
     if backend_wrapper.backend_name == "cugraph-preconverted":
-        G = nxcg.from_networkx(G, preserve_all_attrs=True)
+        G = nxcg.from_networkx(
+            G,
+            preserve_all_attrs=True,
+            use_compat_graph=True,
+        )
     return G
 
 
@@ -319,6 +323,27 @@ def bench_louvain_communities(benchmark, graph_obj, backend_wrapper):
     assert type(result) is list
 
 
+@pytest.mark.skipif("not hasattr(nx.community, 'leiden_communities')")
+def bench_leiden_communities(benchmark, graph_obj, backend_wrapper):
+    G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper)
+    # DiGraphs are not supported
+    if G.is_directed():
+        G = G.to_undirected()
+    if G.__networkx_backend__ not in nx.community.leiden_communities.backends:
+        pytest.skip(
+            reason=f"leiden_communities not implemented by {G.__networkx_backend__!r}"
+        )
+        return
+    result = benchmark.pedantic(
+        target=backend_wrapper(nx.community.leiden_communities),
+        args=(G,),
+        rounds=rounds,
+        iterations=iterations,
+        warmup_rounds=warmup_rounds,
+    )
+    assert type(result) is list
+
+
 def bench_degree_centrality(benchmark, graph_obj, backend_wrapper):
     G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper)
     result = benchmark.pedantic(
@@ -898,6 +923,35 @@ def bench_bipartite_BC_n1000_m3000_k100000(benchmark, backend_wrapper):
     assert type(result) is dict
 
 
+def bench_jaccard(benchmark, graph_obj, backend_wrapper):
+    G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper)
+
+    # ebunch is a list of node pairs to limit the jaccard run.
+    nodes = list(G.nodes)
+    start = nodes[0]
+    ebunch = [(start, n) for n in nodes[1:]]
+    start = nodes[1]
+    ebunch += [(start, n) for n in nodes[2:]]
+    start = nodes[2]
+    ebunch += [(start, n) for n in nodes[3:]]
+
+    # DiGraphs are not supported
+    if G.is_directed():
+        G = G.to_undirected()
+
+    result = benchmark.pedantic(
+        target=backend_wrapper(nx.jaccard_coefficient, force_unlazy_eval=True),
+        args=(G,),
+        kwargs=dict(
+            ebunch=ebunch,
+        ),
+        rounds=rounds,
+        iterations=iterations,
+        warmup_rounds=warmup_rounds,
+    )
+    assert type(result) is list
+
+
 @pytest.mark.skip(reason="benchmark not implemented")
 def bench_complete_bipartite_graph(benchmark, graph_obj, backend_wrapper):
     pass

benchmarks/pytest-based/run-main-benchmarks.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, 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
@@ -58,8 +58,8 @@ fi
 
 for algo in $algos; do
     for dataset in $datasets; do
-	# this script can be used to download benchmarking datasets by name via cugraph.datasets
-    	python get_graph_bench_dataset.py $dataset
+        # this script can be used to download benchmarking datasets by name via cugraph.datasets
+        python get_graph_bench_dataset.py $dataset
         for backend in $backends; do
             name="${backend}__${algo}__${dataset}"
             echo "Running: $backend, $dataset, bench_$algo"

conda/environments/all_cuda-125_arch-x86_64.yaml → conda/environments/all_cuda-128_arch-x86_64.yaml

@@ -6,7 +6,7 @@ channels:
 - conda-forge
 - nvidia
 dependencies:
-- cuda-version=12.5
+- cuda-version=12.8
 - cudf==25.4.*,>=0.0.0a0
 - cupy>=12.0.0
 - graphviz
@@ -33,4 +33,4 @@ dependencies:
 - sphinx-markdown-tables
 - sphinxcontrib-websupport
 - wheel
-name: all_cuda-125_arch-x86_64
+name: all_cuda-128_arch-x86_64

dependencies.yaml

@@ -3,7 +3,7 @@ files:
   all:
     output: [conda]
     matrix:
-      cuda: ["11.8", "12.5"]
+      cuda: ["11.8", "12.8"]
       arch: [x86_64]
     includes:
       - checks
@@ -113,6 +113,10 @@ dependencies:
               cuda: "12.5"
             packages:
               - cuda-version=12.5
+          - matrix:
+              cuda: "12.8"
+            packages:
+              - cuda-version=12.8
   docs:
     common:
       - output_types: [conda]

nx_cugraph/algorithms/__init__.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, 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
@@ -17,6 +17,7 @@
     community,
     components,
     link_analysis,
+    link_prediction,
     operators,
     shortest_paths,
     traversal,
@@ -30,6 +31,7 @@
 from .dag import *
 from .isolate import *
 from .link_analysis import *
+from .link_prediction import *
 from .operators import *
 from .reciprocity import *
 from .shortest_paths import *

nx_cugraph/algorithms/link_prediction.py

@@ -0,0 +1,68 @@
+# Copyright (c) 2025, 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.
+import cupy as cp
+import networkx as nx
+import pylibcugraph as plc
+
+from nx_cugraph.convert import _to_undirected_graph
+from nx_cugraph.utils import index_dtype, networkx_algorithm, not_implemented_for
+
+__all__ = [
+    "jaccard_coefficient",
+]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@networkx_algorithm(version_added="25.02", _plc="jaccard_coefficients")
+def jaccard_coefficient(G, ebunch=None):
+    G = _to_undirected_graph(G)
+
+    # If ebunch is not specified, create pairs representing all non-edges.
+    # This can be an extremely large set and is not realistic for large graphs,
+    # but this is required for NX compatibility.
+    if ebunch is None:
+        A = cp.tri(G._N, G._N, dtype=bool)
+        A[G.src_indices, G.dst_indices] = True
+        u_indices, v_indices = cp.nonzero(~A)
+        if u_indices.size == 0:
+            return iter([])
+        u_indices = u_indices.astype(index_dtype)
+        v_indices = v_indices.astype(index_dtype)
+
+    else:
+        (u, v) = zip(*ebunch)
+        try:
+            # Convert the ebunch lists to cupy arrays for passing to PLC, possibly
+            # mapping to integers if the Graph was renumbered.
+            # Allow the Graph renumber lookup (if renumbering was done) to check
+            # for invalid node IDs in ebunch.
+            u_indices = G._list_to_nodearray(u)
+            v_indices = G._list_to_nodearray(v)
+        except (KeyError, ValueError) as n:
+            raise nx.NodeNotFound(f"Node {n} not in G.")
+
+    (u, v, p) = plc.jaccard_coefficients(
+        resource_handle=plc.ResourceHandle(),
+        graph=G._get_plc_graph(),
+        first=u_indices,
+        second=v_indices,
+        use_weight=False,
+        do_expensive_check=False,
+    )
+
+    u = G._nodearray_to_list(u)
+    v = G._nodearray_to_list(v)
+    p = p.tolist()
+
+    return zip(u, v, p)

nx_cugraph/classes/graph.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, 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
@@ -1009,6 +1009,11 @@ def _get_plc_graph(
         dst_indices = self.dst_indices
         if switch_indices:
             src_indices, dst_indices = dst_indices, src_indices
+
+        # FIXME: the SGGraph constructor arg "symmetrize" will perform all
+        # symmetrization steps required by libcugraph. The edge_array check
+        # should be kept, but all other code in this `if` block should be
+        # removed if possible.
         if symmetrize is not None:
             if edge_array is not None:
                 raise NotImplementedError(
@@ -1147,6 +1152,15 @@ def _nodearray_to_list(self, node_ids: cp.ndarray[IndexValue]) -> list[NodeKey]:
     def _list_to_nodearray(self, nodes: list[NodeKey]) -> cp.ndarray[IndexValue]:
         if (key_to_id := self.key_to_id) is not None:
             nodes = [key_to_id[node] for node in nodes]
+        else:
+            N = self._N
+            for node in nodes:
+                try:
+                    n = int(node)
+                except (TypeError, ValueError):
+                    raise KeyError(node) from None
+                if n != node or n < 0 or n >= N:
+                    raise KeyError(node)
         return cp.array(nodes, dtype=index_dtype)
 
     def _nodearray_to_set(self, node_ids: cp.ndarray[IndexValue]) -> set[NodeKey]: