Forward-merge branch-23.10 to branch-23.12

ci/release/update-version.sh

@@ -82,6 +82,9 @@ NEXT_SHORT_TAG_PEP440=$(python -c "from setuptools.extern import packaging; prin
 DEPENDENCIES=(
   cudf
   cugraph
+  cugraph-dgl
+  cugraph-pyg
+  cugraph-service-server
   cugraph-service-client
   cuxfilter
   dask-cuda
@@ -93,6 +96,7 @@ DEPENDENCIES=(
   librmm
   pylibcugraph
   pylibcugraphops
+  pylibwholegraph
   pylibraft
   pyraft
   raft-dask

ci/test_python.sh

@@ -65,7 +65,10 @@ popd
 
 rapids-logger "pytest cugraph"
 pushd python/cugraph/cugraph
-export DASK_WORKER_DEVICES="0"
+DASK_WORKER_DEVICES="0" \
+DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \
+DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \
+DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT=20 \
 pytest \
   -v \
   --benchmark-disable \
@@ -200,8 +203,11 @@ if [[ "${RAPIDS_CUDA_VERSION}" == "11.8.0" ]]; then
       --channel pytorch \
       --channel nvidia \
       'pyg=2.3' \
-      'pytorch>=2.0' \
-      'pytorch-cuda>=11.8'
+      'pytorch=2.0.0' \
+      'pytorch-cuda=11.8'
+
+    # Install pyg dependencies (which requires pip)
+    pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu118.html
 
     rapids-mamba-retry install \
       --channel "${CPP_CHANNEL}" \

ci/test_wheel.sh

@@ -21,5 +21,9 @@ arch=$(uname -m)
 if [[ "${arch}" == "aarch64" && ${RAPIDS_BUILD_TYPE} == "pull-request" ]]; then
     python ./ci/wheel_smoke_test_${package_name}.py
 else
-    RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets python -m pytest ./python/${package_name}/${python_package_name}/tests
+    RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets \
+    DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \
+    DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \
+    DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT=20 \
+    python -m pytest ./python/${package_name}/${python_package_name}/tests
 fi

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

@@ -53,6 +53,7 @@ dependencies:
 - pydata-sphinx-theme
 - pylibcugraphops==23.10.*
 - pylibraft==23.10.*
+- pylibwholegraph==23.10.*
 - pytest
 - pytest-benchmark
 - pytest-cov
@@ -73,5 +74,6 @@ dependencies:
 - sphinxcontrib-websupport
 - ucx-proc=*=gpu
 - ucx-py==0.34.*
+- wget
 - wheel
 name: all_cuda-118_arch-x86_64

conda/environments/all_cuda-120_arch-x86_64.yaml

@@ -52,6 +52,7 @@ dependencies:
 - pydata-sphinx-theme
 - pylibcugraphops==23.10.*
 - pylibraft==23.10.*
+- pylibwholegraph==23.10.*
 - pytest
 - pytest-benchmark
 - pytest-cov
@@ -72,5 +73,6 @@ dependencies:
 - sphinxcontrib-websupport
 - ucx-proc=*=gpu
 - ucx-py==0.34.*
+- wget
 - wheel
 name: all_cuda-120_arch-x86_64

dependencies.yaml

@@ -463,6 +463,9 @@ dependencies:
         packages:
           - ipython
           - notebook>=0.5.0
+      - output_types: [conda]
+        packages:
+          - wget
   test_python_common:
     common:
       - output_types: [conda, pyproject]
@@ -481,6 +484,9 @@ dependencies:
           - *numpy
           - python-louvain
           - scikit-learn>=0.23.1
+      - output_types: [conda]
+        packages:
+          - pylibwholegraph==23.10.*
   test_python_pylibcugraph:
     common:
       - output_types: [conda, pyproject]

python/cugraph-dgl/cugraph_dgl/dataloading/__init__.py

@@ -13,7 +13,7 @@
 
 from cugraph_dgl.dataloading.dataset import (
     HomogenousBulkSamplerDataset,
-    HetrogenousBulkSamplerDataset,
+    HeterogenousBulkSamplerDataset,
 )
 from cugraph_dgl.dataloading.neighbor_sampler import NeighborSampler
 from cugraph_dgl.dataloading.dataloader import DataLoader

python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py

@@ -21,7 +21,7 @@
 from dask.distributed import default_client, Event
 from cugraph_dgl.dataloading import (
     HomogenousBulkSamplerDataset,
-    HetrogenousBulkSamplerDataset,
+    HeterogenousBulkSamplerDataset,
 )
 from cugraph_dgl.dataloading.utils.extract_graph_helpers import (
     create_cugraph_graph_from_edges_dict,
@@ -47,19 +47,20 @@ def __init__(
         graph_sampler: cugraph_dgl.dataloading.NeighborSampler,
         sampling_output_dir: str,
         batches_per_partition: int = 50,
-        seeds_per_call: int = 400_000,
+        seeds_per_call: int = 200_000,
         device: torch.device = None,
         use_ddp: bool = False,
         ddp_seed: int = 0,
         batch_size: int = 1024,
         drop_last: bool = False,
         shuffle: bool = False,
+        sparse_format: str = "coo",
         **kwargs,
     ):
         """
         Constructor for CuGraphStorage:
         -------------------------------
-        graph :  CuGraphStorage
+        graph : CuGraphStorage
             The graph.
         indices : Tensor or dict[ntype, Tensor]
             The set of indices.  It can either be a tensor of
@@ -89,7 +90,12 @@ def __init__(
             The seed for shuffling the dataset in
             :class:`torch.utils.data.distributed.DistributedSampler`.
             Only effective when :attr:`use_ddp` is True.
-        batch_size: int,
+        batch_size: int
+            Batch size.
+        sparse_format: str, default = "coo"
+            The sparse format of the emitted sampled graphs. Choose between "csc"
+            and "coo". When using "csc", the graphs are of type
+            cugraph_dgl.nn.SparseGraph.
         kwargs : dict
             Key-word arguments to be passed to the parent PyTorch
             :py:class:`torch.utils.data.DataLoader` class. Common arguments are:
@@ -123,6 +129,12 @@ def __init__(
         ...     for input_nodes, output_nodes, blocks in dataloader:
         ...
         """
+        if sparse_format not in ["coo", "csc"]:
+            raise ValueError(
+                f"sparse_format must be one of 'coo', 'csc', "
+                f"but got {sparse_format}."
+            )
+        self.sparse_format = sparse_format
 
         self.ddp_seed = ddp_seed
         self.use_ddp = use_ddp
@@ -156,11 +168,12 @@ def __init__(
             self.cugraph_dgl_dataset = HomogenousBulkSamplerDataset(
                 total_number_of_nodes=graph.total_number_of_nodes,
                 edge_dir=self.graph_sampler.edge_dir,
+                sparse_format=sparse_format,
             )
         else:
             etype_id_to_etype_str_dict = {v: k for k, v in graph._etype_id_dict.items()}
 
-            self.cugraph_dgl_dataset = HetrogenousBulkSamplerDataset(
+            self.cugraph_dgl_dataset = HeterogenousBulkSamplerDataset(
                 num_nodes_dict=graph.num_nodes_dict,
                 etype_id_dict=etype_id_to_etype_str_dict,
                 etype_offset_dict=graph._etype_offset_d,
@@ -210,14 +223,23 @@ def __iter__(self):
         output_dir = os.path.join(
             self._sampling_output_dir, "epoch_" + str(self.epoch_number)
         )
+        kwargs = {}
         if isinstance(self.cugraph_dgl_dataset, HomogenousBulkSamplerDataset):
-            deduplicate_sources = True
-            prior_sources_behavior = "carryover"
-            renumber = True
+            kwargs["deduplicate_sources"] = True
+            kwargs["prior_sources_behavior"] = "carryover"
+            kwargs["renumber"] = True
+
+            if self.sparse_format == "csc":
+                kwargs["compression"] = "CSR"
+                kwargs["compress_per_hop"] = True
+                # The following kwargs will be deprecated in uniform sampler.
+                kwargs["use_legacy_names"] = False
+                kwargs["include_hop_column"] = False
+
         else:
-            deduplicate_sources = False
-            prior_sources_behavior = None
-            renumber = False
+            kwargs["deduplicate_sources"] = False
+            kwargs["prior_sources_behavior"] = None
+            kwargs["renumber"] = False
 
         bs = BulkSampler(
             output_path=output_dir,
@@ -227,10 +249,9 @@ def __iter__(self):
             seeds_per_call=self._seeds_per_call,
             fanout_vals=self.graph_sampler._reversed_fanout_vals,
             with_replacement=self.graph_sampler.replace,
-            deduplicate_sources=deduplicate_sources,
-            prior_sources_behavior=prior_sources_behavior,
-            renumber=renumber,
+            **kwargs,
         )
+
         if self.shuffle:
             self.tensorized_indices_ds.shuffle()
 

python/cugraph-dgl/cugraph_dgl/dataloading/dataset.py

@@ -19,6 +19,7 @@
 from cugraph_dgl.dataloading.utils.sampling_helpers import (
     create_homogeneous_sampled_graphs_from_dataframe,
     create_heterogeneous_sampled_graphs_from_dataframe,
+    create_homogeneous_sampled_graphs_from_dataframe_csc,
 )
 
 
@@ -33,17 +34,19 @@ def __init__(
         total_number_of_nodes: int,
         edge_dir: str,
         return_type: str = "dgl.Block",
+        sparse_format: str = "coo",
     ):
         if return_type not in ["dgl.Block", "cugraph_dgl.nn.SparseGraph"]:
             raise ValueError(
-                "return_type must be either 'dgl.Block' or \
-                    'cugraph_dgl.nn.SparseGraph' "
+                "return_type must be either 'dgl.Block' or "
+                "'cugraph_dgl.nn.SparseGraph'."
             )
         # TODO: Deprecate `total_number_of_nodes`
         # as it is no longer needed
         # in the next release
         self.total_number_of_nodes = total_number_of_nodes
         self.edge_dir = edge_dir
+        self.sparse_format = sparse_format
         self._current_batch_fn = None
         self._input_files = None
         self._return_type = return_type
@@ -60,10 +63,20 @@ def __getitem__(self, idx: int):
 
         fn, batch_offset = self._batch_to_fn_d[idx]
         if fn != self._current_batch_fn:
-            df = _load_sampled_file(dataset_obj=self, fn=fn)
-            self._current_batches = create_homogeneous_sampled_graphs_from_dataframe(
-                sampled_df=df, edge_dir=self.edge_dir, return_type=self._return_type
-            )
+            if self.sparse_format == "csc":
+                df = _load_sampled_file(dataset_obj=self, fn=fn, skip_rename=True)
+                self._current_batches = (
+                    create_homogeneous_sampled_graphs_from_dataframe_csc(df)
+                )
+            else:
+                df = _load_sampled_file(dataset_obj=self, fn=fn)
+                self._current_batches = (
+                    create_homogeneous_sampled_graphs_from_dataframe(
+                        sampled_df=df,
+                        edge_dir=self.edge_dir,
+                        return_type=self._return_type,
+                    )
+                )
         current_offset = idx - batch_offset
         return self._current_batches[current_offset]
 
@@ -87,7 +100,7 @@ def set_input_files(
         )
 
 
-class HetrogenousBulkSamplerDataset(torch.utils.data.Dataset):
+class HeterogenousBulkSamplerDataset(torch.utils.data.Dataset):
     def __init__(
         self,
         num_nodes_dict: Dict[str, int],
@@ -141,18 +154,18 @@ def set_input_files(
         ----------
         input_directory: str
             input_directory which contains all the files that will be
-            loaded by HetrogenousBulkSamplerDataset
+            loaded by HeterogenousBulkSamplerDataset
         input_file_paths: List[str]
-            File names that will be loaded by the HetrogenousBulkSamplerDataset
+            File names that will be loaded by the HeterogenousBulkSamplerDataset
         """
         _set_input_files(
             self, input_directory=input_directory, input_file_paths=input_file_paths
         )
 
 
-def _load_sampled_file(dataset_obj, fn):
+def _load_sampled_file(dataset_obj, fn, skip_rename=False):
     df = cudf.read_parquet(os.path.join(fn))
-    if dataset_obj.edge_dir == "in":
+    if dataset_obj.edge_dir == "in" and not skip_rename:
         df.rename(
             columns={"sources": "destinations", "destinations": "sources"},
             inplace=True,
@@ -181,7 +194,7 @@ def get_batch_to_fn_d(files):
 
 
 def _set_input_files(
-    dataset_obj: Union[HomogenousBulkSamplerDataset, HetrogenousBulkSamplerDataset],
+    dataset_obj: Union[HomogenousBulkSamplerDataset, HeterogenousBulkSamplerDataset],
     input_directory: Optional[str] = None,
     input_file_paths: Optional[List[str]] = None,
 ) -> None: