[REVIEW]Fix out of index errors encountered with sampling on out of index samples

python/cugraph/cugraph/gnn/__init__.py

@@ -12,4 +12,4 @@
 # limitations under the License.
 
 from .graph_store import CuGraphStore
-from .graph_store import CuFeatureStorage
+from .dgl_extensions.feature_storage import CuFeatureStorage

python/cugraph/cugraph/gnn/dgl_extensions/feature_storage.py

@@ -0,0 +1,100 @@
+# Copyright (c) 2022, 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 cudf
+import dask_cudf
+import cupy as cp
+from cugraph.experimental import MGPropertyGraph
+
+
+class CuFeatureStorage:
+    """
+    Storage for node/edge feature data.
+    """
+
+    def __init__(
+        self, pg, columns, storage_type, backend_lib="torch", indices_offset=0
+    ):
+        self.pg = pg
+        self.columns = columns
+        if backend_lib == "torch":
+            from torch.utils.dlpack import from_dlpack
+        elif backend_lib == "tf":
+            from tensorflow.experimental.dlpack import from_dlpack
+        elif backend_lib == "cupy":
+            from cupy import from_dlpack
+        else:
+            raise NotImplementedError(
+                f"Only PyTorch ('torch'), TensorFlow ('tf'), and CuPy ('cupy') "
+                f"backends are currently supported, got {backend_lib=}"
+            )
+        if storage_type not in ["edge", "node"]:
+            raise NotImplementedError("Only edge and node storage is supported")
+
+        self.storage_type = storage_type
+
+        self.from_dlpack = from_dlpack
+        self.indices_offset = indices_offset
+
+    def fetch(self, indices, device=None, pin_memory=False, **kwargs):
+        """Fetch the features of the given node/edge IDs to the
+        given device.
+
+        Parameters
+        ----------
+        indices : Tensor
+            Node or edge IDs.
+        device : Device
+            Device context.
+        pin_memory :
+
+        Returns
+        -------
+        Tensor
+            Feature data stored in PyTorch Tensor.
+        """
+        # Default implementation uses synchronous fetch.
+
+        indices = cp.asarray(indices)
+        if isinstance(self.pg, MGPropertyGraph):
+            # dask_cudf loc breaks if we provide cudf series/cupy array
+            # https://github.com/rapidsai/cudf/issues/11877
+            indices = indices.get()
+        else:
+            indices = cudf.Series(indices)
+
+        indices = indices + self.indices_offset
+
+        if self.storage_type == "node":
+            subset_df = self.pg.get_vertex_data(
+                vertex_ids=indices, columns=self.columns
+            )
+        else:
+            subset_df = self.pg.get_edge_data(edge_ids=indices, columns=self.columns)
+
+        subset_df = subset_df[self.columns]
+
+        if isinstance(subset_df, dask_cudf.DataFrame):
+            subset_df = subset_df.compute()
+
+        if len(subset_df) == 0:
+            raise ValueError(f"indices = {indices} not found in FeatureStorage")
+        cap = subset_df.to_dlpack()
+        tensor = self.from_dlpack(cap)
+        del cap
+        if device:
+            if not isinstance(tensor, cp.ndarray):
+                # Cant transfer to different device for cupy
+                tensor = tensor.to(device)
+        return tensor

python/cugraph/cugraph/gnn/dgl_extensions/utils/__init__.py

@@ -0,0 +1,12 @@
+# Copyright (c) 2022, 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.

python/cugraph/cugraph/gnn/dgl_extensions/utils/add_data.py

@@ -0,0 +1,62 @@
+# Copyright (c) 2022, 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.
+
+# Utils for adding data to cugraph graphstore objects
+
+
+def _update_feature_map(
+    pg_feature_map, feat_name_obj, contains_vector_features, columns
+):
+    """
+    Update the existing feature map  `pg_feature_map` based on `feat_name_obj`
+    """
+    if contains_vector_features:
+        if feat_name_obj is None:
+            raise ValueError(
+                "feature name must be provided when wrapping"
+                + " multiple columns under a single feature name"
+                + " or a feature map"
+            )
+
+        if isinstance(feat_name_obj, str):
+            pg_feature_map[feat_name_obj] = columns
+
+        elif isinstance(feat_name_obj, dict):
+            covered_columns = []
+            for col in feat_name_obj.keys():
+                current_cols = feat_name_obj[col]
+                # Handle strings too
+                if isinstance(current_cols, str):
+                    current_cols = [current_cols]
+                covered_columns = covered_columns + current_cols
+
+            if set(covered_columns) != set(columns):
+                raise ValueError(
+                    f"All the columns {columns} not covered in {covered_columns} "
+                    f"Please check the feature_map {feat_name_obj} provided"
+                )
+
+            for key, cols in feat_name_obj.items():
+                if isinstance(cols, str):
+                    cols = [cols]
+                pg_feature_map[key] = cols
+        else:
+            raise ValueError(f"{feat_name_obj} should be str or dict")
+    else:
+        if feat_name_obj:
+            raise ValueError(
+                f"feat_name {feat_name_obj} is only valid when "
+                "wrapping multiple columns under feature names"
+            )
+        for col in columns:
+            pg_feature_map[col] = [col]

python/cugraph/cugraph/gnn/dgl_extensions/utils/sampling.py

@@ -0,0 +1,181 @@
+# Copyright (c) 2022, 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.
+
+
+# Utils for sampling on graphstore like objects
+import cugraph
+import cudf
+import cupy as cp
+import dask_cudf
+from cugraph.experimental import PropertyGraph
+
+src_n = PropertyGraph.src_col_name
+dst_n = PropertyGraph.dst_col_name
+type_n = PropertyGraph.type_col_name
+eid_n = PropertyGraph.edge_id_col_name
+vid_n = PropertyGraph.vertex_col_name
+
+
+def get_subgraph_and_src_range_from_edgelist(edge_list, is_mg, reverse_edges=False):
+    if reverse_edges:
+        edge_list = edge_list.rename(columns={src_n: dst_n, dst_n: src_n})
+
+    subgraph = cugraph.MultiGraph(directed=True)
+    if is_mg:
+        # FIXME: Can not switch to renumber = False
+        # For MNMG Algos
+        # Remove when https://github.com/rapidsai/cugraph/issues/2437
+        # lands
+        create_subgraph_f = subgraph.from_dask_cudf_edgelist
+        renumber = True
+        edge_list = edge_list.persist()
+        src_range = edge_list[src_n].min().compute(), edge_list[src_n].max().compute()
+
+    else:
+        # Note: We have to keep renumber = False
+        # to handle cases when the seed_nodes is not present in subgraph
+        create_subgraph_f = subgraph.from_cudf_edgelist
+        renumber = False
+        src_range = edge_list[src_n].min(), edge_list[src_n].max()
+
+    create_subgraph_f(
+        edge_list,
+        source=src_n,
+        destination=dst_n,
+        edge_attr=eid_n,
+        renumber=renumber,
+        # FIXME: renumber=False is not supported for MNMG algos
+        legacy_renum_only=True,
+    )
+
+    return subgraph, src_range
+
+
+def sample_multiple_sgs(
+    sgs,
+    sample_f,
+    start_list_d,
+    start_list_dtype,
+    edge_dir,
+    fanout,
+    with_replacement,
+):
+    start_list_types = list(start_list_d.keys())
+    output_dfs = []
+    for can_etype, (sg, start_list_range) in sgs.items():
+        can_etype = _convert_can_etype_s_to_tup(can_etype)
+        if _edge_types_contains_canonical_etype(can_etype, start_list_types, edge_dir):
+            if edge_dir == "in":
+                subset_type = can_etype[2]
+            else:
+                subset_type = can_etype[0]
+            output = sample_single_sg(
+                sg,
+                sample_f,
+                start_list_d[subset_type],
+                start_list_dtype,
+                start_list_range,
+                fanout,
+                with_replacement,
+            )
+            output_dfs.append(output)
+    if len(output_dfs) == 0:
+        empty_df = cudf.DataFrame({"sources": [], "destinations": [], "indices": []})
+        return empty_df.astype(cp.int32)
+
+    if isinstance(output_dfs[0], dask_cudf.DataFrame):
+        return dask_cudf.concat(output_dfs, ignore_index=True)
+    else:
+        return cudf.concat(output_dfs, ignore_index=True)
+
+
+def sample_single_sg(
+    sg,
+    sample_f,
+    start_list,
+    start_list_dtype,
+    start_list_range,
+    fanout,
+    with_replacement,
+):
+    if isinstance(start_list, dict):
+        start_list = cudf.concat(list(start_list.values()))
+
+    # Uniform sampling fails when the dtype
+    # of the seed dtype is not same as the node dtype
+    start_list = start_list.astype(start_list_dtype)
+
+    # Filter start list by ranges
+    # to enure the seed is with in index values
+    # see below:
+    # https://github.com/rapidsai/cugraph/blob/branch-22.12/cpp/src/prims/per_v_random_select_transform_outgoing_e.cuh
+    start_list = start_list[
+        (start_list >= start_list_range[0]) & (start_list <= start_list_range[1])
+    ]
+    sampled_df = sample_f(
+        sg,
+        start_list=start_list,
+        fanout_vals=[fanout],
+        with_replacement=with_replacement,
+    )
+    return sampled_df
+
+
+def _edge_types_contains_canonical_etype(can_etype, edge_types, edge_dir):
+    src_type, _, dst_type = can_etype
+    if edge_dir == "in":
+        return dst_type in edge_types
+    else:
+        return src_type in edge_types
+
+
+def _convert_can_etype_s_to_tup(canonical_etype_s):
+    src_type, etype, dst_type = canonical_etype_s.split(",")
+    src_type = src_type[2:-1]
+    dst_type = dst_type[2:-2]
+    etype = etype[2:-1]
+    return (src_type, etype, dst_type)
+
+
+def create_dlpack_d(d):
+    dlpack_d = {}
+    for k, df in d.items():
+        if len(df) == 0:
+            dlpack_d[k] = (None, None, None)
+        else:
+            dlpack_d[k] = (
+                df[src_n].to_dlpack(),
+                df[dst_n].to_dlpack(),
+                df[eid_n].to_dlpack(),
+            )
+
+    return dlpack_d
+
+
+def get_underlying_dtype_from_sg(sg):
+    """
+    Returns the underlying dtype of the subgraph
+    """
+    # FIXME: Remove after we have consistent naming
+    # https://github.com/rapidsai/cugraph/issues/2618
+    sg_columns = sg.edgelist.edgelist_df.columns
+    if "src" in sg_columns:
+        # src for single node graph
+        sg_node_dtype = sg.edgelist.edgelist_df["src"].dtype
+    elif src_n in sg_columns:
+        # _SRC_ for multi-node graphs
+        sg_node_dtype = sg.edgelist.edgelist_df[src_n].dtype
+    else:
+        raise ValueError(f"Source column {src_n} not found in the subgraph")
+
+    return sg_node_dtype