make cugraph-ops optional for cugraph-gnn packages

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

@@ -33,13 +33,11 @@ dependencies:
 - pandas
 - pre-commit
 - pydantic
-- pylibcugraphops==25.2.*,>=0.0.0a0
 - pytest
 - pytest-benchmark
 - pytest-cov
 - pytest-forked
 - pytest-xdist
-- pytorch-cuda=11.8
 - pytorch>=2.3
 - pytorch_geometric>=2.5,<2.6
 - rapids-build-backend>=0.3.0,<0.4.0.dev0

conda/environments/all_cuda-121_arch-x86_64.yaml

@@ -39,13 +39,11 @@ dependencies:
 - pandas
 - pre-commit
 - pydantic
-- pylibcugraphops==25.2.*,>=0.0.0a0
 - pytest
 - pytest-benchmark
 - pytest-cov
 - pytest-forked
 - pytest-xdist
-- pytorch-cuda=12.1
 - pytorch>=2.3
 - pytorch_geometric>=2.5,<2.6
 - rapids-build-backend>=0.3.0,<0.4.0.dev0

conda/environments/all_cuda-124_arch-x86_64.yaml

@@ -39,13 +39,11 @@ dependencies:
 - pandas
 - pre-commit
 - pydantic
-- pylibcugraphops==25.2.*,>=0.0.0a0
 - pytest
 - pytest-benchmark
 - pytest-cov
 - pytest-forked
 - pytest-xdist
-- pytorch-cuda=12.4
 - pytorch>=2.3
 - pytorch_geometric>=2.5,<2.6
 - rapids-build-backend>=0.3.0,<0.4.0.dev0

conda/recipes/cugraph-dgl/meta.yaml

@@ -1,4 +1,4 @@
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023-2025, NVIDIA CORPORATION.
 
 {% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
 {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
@@ -29,7 +29,6 @@ requirements:
     - numba >=0.57
     - numpy >=1.23,<3.0a0
     - pandas
-    - pylibcugraphops ={{ minor_version }}
     - tensordict >=0.1.2
     - python
     - pytorch >=2.3

conda/recipes/cugraph-pyg/meta.yaml

@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
 
 {% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
 {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %}
@@ -35,7 +35,6 @@ requirements:
     - pytorch >=2.3
     - cupy >=12.0.0
     - cugraph ={{ minor_version }}
-    - pylibcugraphops ={{ minor_version }}
     - tensordict >=0.1.2
     - pytorch_geometric >=2.5,<2.6
 

dependencies.yaml

@@ -18,7 +18,6 @@ files:
       - depends_on_cugraph
       - depends_on_cudf
       - depends_on_dask_cudf
-      - depends_on_pylibcugraphops
       - depends_on_cupy
       - depends_on_pytorch
       - depends_on_dgl
@@ -43,7 +42,6 @@ files:
       - cuda_version
       - docs
       - py_version
-      - depends_on_pylibcugraphops
   test_cpp:
     output: none
     includes:
@@ -114,7 +112,6 @@ files:
       table: project
     includes:
       - depends_on_cugraph
-      - depends_on_pylibcugraphops
       - python_run_cugraph_dgl
   py_test_cugraph_dgl:
     output: pyproject
@@ -140,7 +137,6 @@ files:
       table: project
     includes:
       - depends_on_cugraph
-      - depends_on_pylibcugraphops
       - depends_on_pyg
       - python_run_cugraph_pyg
   py_test_cugraph_pyg:
@@ -164,7 +160,6 @@ files:
     includes:
       - checks
       - depends_on_cugraph
-      - depends_on_pylibcugraphops
       - depends_on_dgl
       - depends_on_pytorch
       - cugraph_dgl_dev
@@ -178,7 +173,6 @@ files:
       - checks
       - depends_on_cugraph
       - depends_on_pyg
-      - depends_on_pylibcugraphops
       - depends_on_pytorch
       - cugraph_pyg_dev
       - test_python_common
@@ -428,21 +422,6 @@ dependencies:
               - *pytorch_pip
               - *tensordict
           - {matrix: null, packages: [*pytorch_pip, *tensordict]}
-      - output_types: [conda]
-        matrices:
-          - matrix: {cuda: "12.1"}
-            packages:
-              - pytorch-cuda=12.1
-          - matrix: {cuda: "12.4"}
-            packages:
-              - pytorch-cuda=12.4
-          - matrix: {cuda: "11.8"}
-            packages:
-              - pytorch-cuda=11.8
-          # pytorch only supports certain CUDA versions... skip
-          # adding pytorch-cuda pinning if any other CUDA version is requested
-          - matrix:
-            packages:
 
   depends_on_dgl:
     specific:
@@ -615,31 +594,6 @@ dependencies:
               - pylibcugraph-cu11==25.2.*,>=0.0.0a0
           - {matrix: null, packages: [*pylibcugraph_unsuffixed]}
 
-  depends_on_pylibcugraphops:
-    common:
-      - output_types: conda
-        packages:
-          - &pylibcugraphops_unsuffixed pylibcugraphops==25.2.*,>=0.0.0a0
-      - output_types: requirements
-        packages:
-          # pip recognizes the index as a global option for the requirements.txt file
-          - --extra-index-url=https://pypi.nvidia.com
-          - --extra-index-url=https://pypi.anaconda.org/rapidsai-wheels-nightly/simple
-    specific:
-      - output_types: [requirements, pyproject]
-        matrices:
-          - matrix:
-              cuda: "12.*"
-              cuda_suffixed: "true"
-            packages:
-              - pylibcugraphops-cu12==25.2.*,>=0.0.0a0
-          - matrix:
-              cuda: "11.*"
-              cuda_suffixed: "true"
-            packages:
-              - pylibcugraphops-cu11==25.2.*,>=0.0.0a0
-          - {matrix: null, packages: [*pylibcugraphops_unsuffixed]}
-
   depends_on_cupy:
     common:
       - output_types: conda

python/cugraph-dgl/conda/cugraph_dgl_dev_cuda-118.yaml

@@ -12,12 +12,10 @@ dependencies:
 - dglteam/label/th23_cu118::dgl>=2.4.0.th23.cu*
 - pre-commit
 - pydantic
-- pylibcugraphops==25.2.*,>=0.0.0a0
 - pytest
 - pytest-benchmark
 - pytest-cov
 - pytest-xdist
-- pytorch-cuda=11.8
 - pytorch>=2.3
 - tensordict>=0.1.2
 - torchdata

python/cugraph-dgl/cugraph_dgl/nn/conv/__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
@@ -12,17 +12,7 @@
 # limitations under the License.
 
 from .base import SparseGraph
-from .gatconv import GATConv
-from .gatv2conv import GATv2Conv
-from .relgraphconv import RelGraphConv
-from .sageconv import SAGEConv
-from .transformerconv import TransformerConv
 
 __all__ = [
     "SparseGraph",
-    "GATConv",
-    "GATv2Conv",
-    "RelGraphConv",
-    "SAGEConv",
-    "TransformerConv",
 ]

python/cugraph-dgl/cugraph_dgl/nn/conv/base.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
@@ -279,6 +279,18 @@ def to(self, device: Union[torch.device, str, int]) -> "cugraph_dgl.nn.SparseGra
         return sg
 
 
+def conditional_class(import_name):
+    def decorator(cls):
+        try:
+            __import__(import_name)
+            return cls
+        except ImportError:
+            return None
+
+    return decorator
+
+
+@conditional_class("pylibcugraphops")
 class BaseConv(torch.nn.Module):
     r"""An abstract base class for cugraph-ops nn module."""
 
@@ -298,7 +310,7 @@ def get_cugraph_ops_CSC(
         g: Union[SparseGraph, dgl.DGLHeteroGraph],
         is_bipartite: bool = False,
         max_in_degree: Optional[int] = None,
-    ) -> ops_torch.CSC:
+    ) -> "ops_torch.CSC":
         """Create CSC structure needed by cugraph-ops."""
 
         if not isinstance(g, (SparseGraph, dgl.DGLHeteroGraph)):
@@ -333,7 +345,7 @@ def get_cugraph_ops_HeteroCSC(
         etypes: Optional[torch.Tensor] = None,
         is_bipartite: bool = False,
         max_in_degree: Optional[int] = None,
-    ) -> ops_torch.HeteroCSC:
+    ) -> "ops_torch.HeteroCSC":
         """Create HeteroCSC structure needed by cugraph-ops."""
 
         if not isinstance(g, (SparseGraph, dgl.DGLHeteroGraph)):

python/cugraph-dgl/cugraph_dgl/tests/conftest.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2024, NVIDIA CORPORATION.
+# Copyright (c) 2021-2025, 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
@@ -22,6 +22,12 @@
 )
 
 
+def pytest_ignore_collect(collection_path, config):
+    """Return True to prevent considering this path for collection."""
+    if "nn" in collection_path.name:
+        return True
+
+
 @pytest.fixture(scope="module")
 def dask_client():
     # start_dask_client will check for the SCHEDULER_FILE and

python/cugraph-dgl/cugraph_dgl/tests/test_utils.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, 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
@@ -11,6 +11,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import pytest
 import cudf
 import cupy as cp
 import numpy as np
@@ -179,6 +180,7 @@ def test_get_source_destination_range():
     assert output_d == expected_output
 
 
+@pytest.mark.skip(reason="Skipping due to missing cugraph-ops backend.")
 def test__create_homogeneous_cugraph_dgl_nn_sparse_graph():
     tensor_d = {
         "sources_range": 1,
@@ -196,6 +198,7 @@ def test__create_homogeneous_cugraph_dgl_nn_sparse_graph():
     assert isinstance(sparse_graph, cugraph_dgl.nn.SparseGraph)
 
 
+@pytest.mark.skip(reason="Skipping due to missing cugraph-ops backend.")
 def test_create_homogeneous_sampled_graphs_from_dataframe_csc():
     df = get_dummy_sampled_df_csc()
     batches = create_homogeneous_sampled_graphs_from_dataframe_csc(df)

python/cugraph-dgl/pyproject.toml

@@ -27,7 +27,6 @@ dependencies = [
     "cugraph==25.2.*,>=0.0.0a0",
     "numba>=0.57",
     "numpy>=1.23,<3.0a0",
-    "pylibcugraphops==25.2.*,>=0.0.0a0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 
 [project.optional-dependencies]

python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml

@@ -10,12 +10,10 @@ dependencies:
 - cugraph==25.2.*,>=0.0.0a0
 - pre-commit
 - pydantic
-- pylibcugraphops==25.2.*,>=0.0.0a0
 - pytest
 - pytest-benchmark
 - pytest-cov
 - pytest-xdist
-- pytorch-cuda=11.8
 - pytorch>=2.3
 - pytorch_geometric>=2.5,<2.6
 - tensordict>=0.1.2

python/cugraph-pyg/cugraph_pyg/examples/graph_sage_mg.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
@@ -29,7 +29,7 @@
 import torch
 import numpy as np
 
-from cugraph_pyg.nn import SAGEConv as CuGraphSAGEConv
+from torch_geometric.nn import SAGEConv
 
 import torch.nn as nn
 import torch.nn.functional as F
@@ -41,22 +41,21 @@
 from typing import List
 
 
-class CuGraphSAGE(nn.Module):
+class GraphSAGE(nn.Module):
     def __init__(self, in_channels, hidden_channels, out_channels, num_layers):
         super().__init__()
 
         self.convs = torch.nn.ModuleList()
-        self.convs.append(CuGraphSAGEConv(in_channels, hidden_channels))
+        self.convs.append(SAGEConv(in_channels, hidden_channels))
         for _ in range(num_layers - 1):
-            conv = CuGraphSAGEConv(hidden_channels, hidden_channels)
+            conv = SAGEConv(hidden_channels, hidden_channels)
             self.convs.append(conv)
 
         self.lin = nn.Linear(hidden_channels, out_channels)
 
-    def forward(self, x, edge, size):
-        edge_csc = CuGraphSAGEConv.to_csc(edge, (size[0], size[0]))
+    def forward(self, x, edge):
         for conv in self.convs:
-            x = conv(x, edge_csc)[: size[1]]
+            x = conv(x, edge)
             x = F.relu(x)
             x = F.dropout(x, p=0.5)
 
@@ -262,7 +261,7 @@ def train(
     )
     td.barrier()
     model = (
-        CuGraphSAGE(in_channels=128, hidden_channels=64, out_channels=349, num_layers=3)
+        GraphSAGE(in_channels=128, hidden_channels=64, out_channels=349, num_layers=3)
         .to(torch.float32)
         .to(device_id)
     )
@@ -316,7 +315,6 @@ def train(
                     hetero_data.edge_index_dict[("paper", "cites", "paper")].to(
                         device_id
                     ),
-                    (len(y_true), len(y_true)),
                 )
                 end_time_forward = time.perf_counter_ns()
                 total_time_forward += (end_time_forward - start_time_forward) / 1e9