Better handle cudf.pandas in from_pandas_edgelist (#4525)

Optimistically use cupy, but fall back to numpy if necessary.

Also, bump lint versions.

CC @rlratzel

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

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

URL: #4525

Author: eriknw

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: v0.17
+    rev: v0.18
     hooks:
       - id: validate-pyproject
         name: Validate pyproject.toml
@@ -40,7 +40,7 @@ repos:
     hooks:
       - id: isort
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.2
+    rev: v3.16.0
     hooks:
       - id: pyupgrade
         args: [--py39-plus]
@@ -50,18 +50,18 @@ repos:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.4
+    rev: v0.5.1
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]  # --unsafe-fixes]
   - repo: https://github.com/PyCQA/flake8
-    rev: 7.0.0
+    rev: 7.1.0
     hooks:
       - id: flake8
         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==7.0.0
+          - flake8==7.1.0
           - flake8-bugbear==24.4.26
           - flake8-simplify==0.21.0
   - repo: https://github.com/asottile/yesqa
@@ -70,14 +70,14 @@ repos:
       - id: yesqa
         additional_dependencies: *flake8_dependencies
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.2.6
+    rev: v2.3.0
     hooks:
       - id: codespell
         types_or: [python, rst, markdown]
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.4
+    rev: v0.5.1
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks

python/nx-cugraph/nx_cugraph/classes/graph.py

@@ -124,13 +124,13 @@ def from_coo(
         # Easy and fast sanity checks
         if size != new_graph.dst_indices.size:
             raise ValueError
-        for attr in ["edge_values", "edge_masks"]:
-            if datadict := getattr(new_graph, attr):
+        for edge_attr in ["edge_values", "edge_masks"]:
+            if datadict := getattr(new_graph, edge_attr):
                 for key, val in datadict.items():
                     if val.shape[0] != size:
                         raise ValueError(key)
-        for attr in ["node_values", "node_masks"]:
-            if datadict := getattr(new_graph, attr):
+        for node_attr in ["node_values", "node_masks"]:
+            if datadict := getattr(new_graph, node_attr):
                 for key, val in datadict.items():
                     if val.shape[0] != N:
                         raise ValueError(key)

python/nx-cugraph/nx_cugraph/convert_matrix.py

@@ -35,25 +35,35 @@ def from_pandas_edgelist(
 ):
     """cudf.DataFrame inputs also supported; value columns with str is unsuppported."""
     graph_class, inplace = _create_using_class(create_using)
+    # Try to be optimal whether using pandas, cudf, or cudf.pandas
     src_array = df[source].to_numpy()
     dst_array = df[target].to_numpy()
+    try:
+        # Optimistically try to use cupy, but fall back to numpy if necessary
+        src_array = cp.asarray(src_array)
+        dst_array = cp.asarray(dst_array)
+        np_or_cp = cp
+    except ValueError:
+        src_array = np.asarray(src_array)
+        dst_array = np.asarray(dst_array)
+        np_or_cp = np
     # TODO: create renumbering helper function(s)
     # Renumber step 0: node keys
-    nodes = np.unique(np.concatenate([src_array, dst_array]))
+    nodes = np_or_cp.unique(np_or_cp.concatenate([src_array, dst_array]))
     N = nodes.size
     kwargs = {}
     if N > 0 and (
         nodes[0] != 0
         or nodes[N - 1] != N - 1
         or (
             nodes.dtype.kind not in {"i", "u"}
-            and not (nodes == np.arange(N, dtype=np.int64)).all()
+            and not (nodes == np_or_cp.arange(N, dtype=np.int64)).all()
         )
     ):
-        # We need to renumber indices--np.searchsorted to the rescue!
+        # We need to renumber indices--np_or_cp.searchsorted to the rescue!
         kwargs["id_to_key"] = nodes.tolist()
-        src_indices = cp.array(np.searchsorted(nodes, src_array), index_dtype)
-        dst_indices = cp.array(np.searchsorted(nodes, dst_array), index_dtype)
+        src_indices = cp.asarray(np_or_cp.searchsorted(nodes, src_array), index_dtype)
+        dst_indices = cp.asarray(np_or_cp.searchsorted(nodes, dst_array), index_dtype)
     else:
         src_indices = cp.array(src_array)
         dst_indices = cp.array(dst_array)