nx-cugraph: Updates nxcg.Graph classes for API-compatibility with NetworkX Graph classes, needed for zero code change graph generators

ci/run_nx_cugraph_pytests.sh

@@ -6,4 +6,5 @@ set -euo pipefail
 # Support invoking run_nx_cugraph_pytests.sh outside the script directory
 cd "$(dirname "$(realpath "${BASH_SOURCE[0]}")")"/../python/nx-cugraph/nx_cugraph
 
-pytest --capture=no --cache-clear --benchmark-disable "$@" tests
+NX_CUGRAPH_USE_COMPAT_GRAPHS=False pytest --capture=no --cache-clear --benchmark-disable "$@" tests
+NX_CUGRAPH_USE_COMPAT_GRAPHS=True pytest --capture=no --cache-clear --benchmark-disable "$@" tests

ci/test_python.sh

@@ -108,7 +108,7 @@ echo "nx-cugraph coverage from networkx tests: $_coverage"
 echo $_coverage | awk '{ if ($NF == "0.0%") exit 1 }'
 # Ensure all algorithms were called by comparing covered lines to function lines.
 # Run our tests again (they're fast enough) to add their coverage, then create coverage.json
-pytest \
+NX_CUGRAPH_USE_COMPAT_GRAPHS=False pytest \
   --pyargs nx_cugraph \
   --config-file=../pyproject.toml \
   --cov-config=../pyproject.toml \

ci/test_wheel.sh

@@ -37,6 +37,7 @@ else
     DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \
     DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \
     DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT="1000s" \
+    NX_CUGRAPH_USE_COMPAT_GRAPHS=False \
     python -m pytest \
        -v \
        --import-mode=append \

conda/environments/all_cuda-118_arch-x86_64.yaml

@@ -44,7 +44,6 @@ dependencies:
 - nvcc_linux-64=11.8
 - ogb
 - openmpi
-- packaging>=21
 - pandas
 - pre-commit
 - pydantic

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

@@ -49,7 +49,6 @@ dependencies:
 - numpydoc
 - ogb
 - openmpi
-- packaging>=21
 - pandas
 - pre-commit
 - pydantic

dependencies.yaml

@@ -688,7 +688,6 @@ dependencies:
     common:
       - output_types: [conda, pyproject]
         packages:
-          - packaging>=21
             # not needed by nx-cugraph tests, but is required for running networkx tests
           - pytest-mpl
   cugraph_dgl_dev:

python/nx-cugraph/_nx_cugraph/__init__.py

@@ -22,6 +22,7 @@
 
 $ python _nx_cugraph/__init__.py
 """
+import os
 
 from _nx_cugraph._version import __version__
 
@@ -293,12 +294,20 @@ def get_info():
 
     for key in info_keys:
         del d[key]
+
+    d["default_config"] = {
+        "use_compat_graphs": os.environ.get("NX_CUGRAPH_USE_COMPAT_GRAPHS", "true")
+        .strip()
+        .lower()
+        == "true",
+    }
     return d
 
 
-def _check_networkx_version():
-    import warnings
+def _check_networkx_version() -> tuple[int, int]:
+    """Check the version of networkx and return ``(major, minor)`` version tuple."""
     import re
+    import warnings
 
     import networkx as nx
 
@@ -321,6 +330,10 @@ def _check_networkx_version():
             f"{nx.__version__}. Please upgrade (or fix) your Python environment."
         )
 
+    nxver_major = int(version_major)
+    nxver_minor = int(re.match(r"^\d+", version_minor).group())
+    return (nxver_major, nxver_minor)
+
 
 if __name__ == "__main__":
     from pathlib import Path

python/nx-cugraph/lint.yaml

@@ -26,7 +26,7 @@ repos:
       - id: mixed-line-ending
       - id: trailing-whitespace
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.18
+    rev: v0.19
     hooks:
       - id: validate-pyproject
         name: Validate pyproject.toml
@@ -40,29 +40,29 @@ repos:
     hooks:
       - id: isort
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.16.0
+    rev: v3.17.0
     hooks:
       - id: pyupgrade
         args: [--py310-plus]
   - repo: https://github.com/psf/black
-    rev: 24.4.2
+    rev: 24.8.0
     hooks:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.4
+    rev: v0.6.7
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]  # --unsafe-fixes]
   - repo: https://github.com/PyCQA/flake8
-    rev: 7.1.0
+    rev: 7.1.1
     hooks:
       - id: flake8
         args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501', '--extend-ignore=B020,SIM105']  # Why is this necessary?
         additional_dependencies: &flake8_dependencies
           # These versions need updated manually
-          - flake8==7.1.0
-          - flake8-bugbear==24.4.26
+          - flake8==7.1.1
+          - flake8-bugbear==24.8.19
           - flake8-simplify==0.21.0
   - repo: https://github.com/asottile/yesqa
     rev: v1.5.0
@@ -77,7 +77,7 @@ repos:
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.4
+    rev: v0.6.7
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks

python/nx-cugraph/nx_cugraph/__init__.py

@@ -12,6 +12,11 @@
 # limitations under the License.
 from networkx.exception import *
 
+from _nx_cugraph._version import __git_commit__, __version__
+from _nx_cugraph import _check_networkx_version
+
+_nxver: tuple[int, int] = _check_networkx_version()
+
 from . import utils
 
 from . import classes
@@ -32,7 +37,10 @@
 from . import algorithms
 from .algorithms import *
 
-from _nx_cugraph._version import __git_commit__, __version__
-from _nx_cugraph import _check_networkx_version
+from .interface import BackendInterface
 
-_check_networkx_version()
+BackendInterface.Graph = classes.Graph
+BackendInterface.DiGraph = classes.DiGraph
+BackendInterface.MultiGraph = classes.MultiGraph
+BackendInterface.MultiDiGraph = classes.MultiDiGraph
+del BackendInterface

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

@@ -16,6 +16,7 @@
 import networkx as nx
 import numpy as np
 
+from nx_cugraph import _nxver
 from nx_cugraph.generators._utils import _create_using_class, _number_and_nodes
 from nx_cugraph.utils import index_dtype, networkx_algorithm
 
@@ -48,7 +49,7 @@ def complete_bipartite_graph(n1, n2, create_using=None):
         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")
-    if nx.__version__[:3] <= "3.3":
+    if _nxver <= (3, 3):
         name = f"complete_bipartite_graph({orig_n1}, {orig_n2})"
     else:
         name = f"complete_bipartite_graph({n1}, {n2})"

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

@@ -12,9 +12,9 @@
 # limitations under the License.
 import warnings
 
-import networkx as nx
 import pylibcugraph as plc
 
+from nx_cugraph import _nxver
 from nx_cugraph.convert import _to_undirected_graph
 from nx_cugraph.utils import (
     _dtype_param,
@@ -27,7 +27,7 @@
 __all__ = ["louvain_communities"]
 
 # max_level argument was added to NetworkX 3.3
-if nx.__version__[:3] <= "3.2":
+if _nxver <= (3, 2):
     _max_level_param = {
         "max_level : int, optional": (
             "Upper limit of the number of macro-iterations (max: 500)."
@@ -81,7 +81,7 @@ def _louvain_communities(
     node_ids, clusters, modularity = plc.louvain(
         resource_handle=plc.ResourceHandle(),
         graph=G._get_plc_graph(weight, 1, dtype),
-        max_level=max_level,  # TODO: add this parameter to NetworkX
+        max_level=max_level,
         threshold=threshold,
         resolution=resolution,
         do_expensive_check=False,

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

@@ -15,6 +15,7 @@
 import pylibcugraph as plc
 
 import nx_cugraph as nxcg
+from nx_cugraph import _nxver
 from nx_cugraph.convert import _to_undirected_graph
 from nx_cugraph.utils import (
     _get_int_dtype,
@@ -58,9 +59,12 @@ def _(G):
 @networkx_algorithm(is_incomplete=True, version_added="23.12", _plc="k_truss_subgraph")
 def k_truss(G, k):
     if is_nx := isinstance(G, nx.Graph):
+        is_compat_graph = isinstance(G, nxcg.Graph)
         G = nxcg.from_networkx(G, preserve_all_attrs=True)
+    else:
+        is_compat_graph = False
     if nxcg.number_of_selfloops(G) > 0:
-        if nx.__version__[:3] <= "3.2":
+        if _nxver <= (3, 2):
             exc_class = nx.NetworkXError
         else:
             exc_class = nx.NetworkXNotImplemented
@@ -128,6 +132,7 @@ def k_truss(G, k):
         node_values,
         node_masks,
         key_to_id=key_to_id,
+        use_compat_graph=is_compat_graph,
     )
     new_graph.graph.update(G.graph)
     return new_graph

python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py

@@ -15,6 +15,7 @@
 import numpy as np
 import pylibcugraph as plc
 
+from nx_cugraph import _nxver
 from nx_cugraph.convert import _to_graph
 from nx_cugraph.utils import (
     _dtype_param,
@@ -53,7 +54,7 @@ def hits(
     if nstart is not None:
         nstart = G._dict_to_nodearray(nstart, 0, dtype)
     if max_iter <= 0:
-        if nx.__version__[:3] <= "3.2":
+        if _nxver <= (3, 2):
             raise ValueError("`maxiter` must be a positive integer.")
         raise nx.PowerIterationFailedConvergence(max_iter)
     try:

python/nx-cugraph/nx_cugraph/algorithms/operators/unary.py

@@ -23,6 +23,7 @@
 
 @networkx_algorithm(version_added="24.02")
 def complement(G):
+    is_compat_graph = isinstance(G, nxcg.Graph)
     G = _to_graph(G)
     N = G._N
     # Upcast to int64 so indices don't overflow.
@@ -43,6 +44,7 @@ def complement(G):
         src_indices.astype(index_dtype),
         dst_indices.astype(index_dtype),
         key_to_id=G.key_to_id,
+        use_compat_graph=is_compat_graph,
     )
 
 
@@ -51,10 +53,16 @@ def reverse(G, copy=True):
     if not G.is_directed():
         raise nx.NetworkXError("Cannot reverse an undirected graph.")
     if isinstance(G, nx.Graph):
-        if not copy:
+        is_compat_graph = isinstance(G, nxcg.Graph)
+        if not copy and not is_compat_graph:
             raise RuntimeError(
                 "Using `copy=False` is invalid when using a NetworkX graph "
                 "as input to `nx_cugraph.reverse`"
             )
         G = nxcg.from_networkx(G, preserve_all_attrs=True)
-    return G.reverse(copy=copy)
+    else:
+        is_compat_graph = False
+    rv = G.reverse(copy=copy)
+    if is_compat_graph:
+        return rv._to_compat_graph()
+    return rv

python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py

@@ -14,6 +14,7 @@
 import numpy as np
 
 import nx_cugraph as nxcg
+from nx_cugraph import _nxver
 from nx_cugraph.convert import _to_graph
 from nx_cugraph.utils import _dtype_param, _get_float_dtype, networkx_algorithm
 
@@ -57,7 +58,7 @@ def shortest_path(
                 paths = nxcg.all_pairs_dijkstra_path(G, weight=weight, dtype=dtype)
             else:  # method == 'bellman-ford':
                 paths = nxcg.all_pairs_bellman_ford_path(G, weight=weight, dtype=dtype)
-            if nx.__version__[:3] <= "3.4":
+            if _nxver <= (3, 4):
                 paths = dict(paths)
         # To target
         elif method == "unweighted":
@@ -129,7 +130,7 @@ def shortest_path_length(
         # To target
         elif method == "unweighted":
             lengths = nxcg.single_target_shortest_path_length(G, target)
-            if nx.__version__[:3] <= "3.4":
+            if _nxver <= (3, 4):
                 lengths = dict(lengths)
         elif method == "dijkstra":
             lengths = nxcg.single_source_dijkstra_path_length(

python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py

@@ -17,6 +17,7 @@
 import numpy as np
 import pylibcugraph as plc
 
+from nx_cugraph import _nxver
 from nx_cugraph.convert import _to_graph
 from nx_cugraph.utils import _groupby, index_dtype, networkx_algorithm
 
@@ -43,7 +44,7 @@ def single_source_shortest_path_length(G, source, cutoff=None):
 def single_target_shortest_path_length(G, target, cutoff=None):
     G = _to_graph(G)
     rv = _bfs(G, target, cutoff, "Target", return_type="length")
-    if nx.__version__[:3] <= "3.4":
+    if _nxver <= (3, 4):
         return iter(rv.items())
     return rv
 
@@ -61,7 +62,7 @@ def bidirectional_shortest_path(G, source, target):
     # TODO PERF: do bidirectional traversal in core
     G = _to_graph(G)
     if source not in G or target not in G:
-        if nx.__version__[:3] <= "3.3":
+        if _nxver <= (3, 3):
             raise nx.NodeNotFound(
                 f"Either source {source} or target {target} is not in G"
             )