[GraphBolt] Remove coo from `FusedCSCSamplingGraph`. (#6849)

Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-133.us-west-2.compute.internal>

python/dgl/graphbolt/impl/fused_csc_sampling_graph.py

@@ -12,11 +12,7 @@ from ...convert import to_homogeneous
 from ...heterograph import DGLGraph
 from ..base import etype_str_to_tuple, etype_tuple_to_str, ORIGINAL_EDGE_ID
 from ..sampling_graph import SamplingGraph
-from .sampled_subgraph_impl import (
-    CSCFormatBase,
-    FusedSampledSubgraphImpl,
-    SampledSubgraphImpl,
-)
+from .sampled_subgraph_impl import CSCFormatBase, SampledSubgraphImpl
 
 
 __all__ = [
@@ -324,9 +320,7 @@ class FusedCSCSamplingGraph(SamplingGraph):
     def in_subgraph(
         self,
         nodes: Union[torch.Tensor, Dict[str, torch.Tensor]],
-        # TODO: clean up once the migration is done.
-        output_cscformat=True,
-    ) -> Union[FusedSampledSubgraphImpl, SampledSubgraphImpl]:
+    ) -> SampledSubgraphImpl:
         """Return the subgraph induced on the inbound edges of the given nodes.
 
         An in subgraph is equivalent to creating a new graph using the incoming
@@ -344,7 +338,7 @@ class FusedCSCSamplingGraph(SamplingGraph):
 
         Returns
         -------
-        FusedSampledSubgraphImpl
+        SampledSubgraphImpl
             The in subgraph.
 
         Examples
@@ -389,60 +383,8 @@ class FusedCSCSamplingGraph(SamplingGraph):
         ), "Nodes cannot have duplicate values."
 
         _in_subgraph = self._c_csc_graph.in_subgraph(nodes)
-        if not output_cscformat:
-            return self._convert_to_fused_sampled_subgraph(_in_subgraph)
-        else:
         return self._convert_to_sampled_subgraph(_in_subgraph)
 
-    def _convert_to_fused_sampled_subgraph(
-        self,
-        C_sampled_subgraph: torch.ScriptObject,
-    ):
-        """An internal function used to convert a fused homogeneous sampled
-        subgraph to general struct 'FusedSampledSubgraphImpl'."""
-        column_num = (
-            C_sampled_subgraph.indptr[1:] - C_sampled_subgraph.indptr[:-1]
-        )
-        column = C_sampled_subgraph.original_column_node_ids.repeat_interleave(
-            column_num
-        )
-        row = C_sampled_subgraph.indices
-        type_per_edge = C_sampled_subgraph.type_per_edge
-        original_edge_ids = C_sampled_subgraph.original_edge_ids
-        has_original_eids = (
-            self.edge_attributes is not None
-            and ORIGINAL_EDGE_ID in self.edge_attributes
-        )
-        if has_original_eids:
-            original_edge_ids = self.edge_attributes[ORIGINAL_EDGE_ID][
-                original_edge_ids
-            ]
-        if type_per_edge is None:
-            # The sampled graph is already a homogeneous graph.
-            node_pairs = (row, column)
-        else:
-            # The sampled graph is a fused homogenized graph, which need to be
-            # converted to heterogeneous graphs.
-            node_pairs = defaultdict(list)
-            original_hetero_edge_ids = {}
-            for etype, etype_id in self.edge_type_to_id.items():
-                src_ntype, _, dst_ntype = etype_str_to_tuple(etype)
-                src_ntype_id = self.node_type_to_id[src_ntype]
-                dst_ntype_id = self.node_type_to_id[dst_ntype]
-                mask = type_per_edge == etype_id
-                hetero_row = row[mask] - self.node_type_offset[src_ntype_id]
-                hetero_column = (
-                    column[mask] - self.node_type_offset[dst_ntype_id]
-                )
-                node_pairs[etype] = (hetero_row, hetero_column)
-                if has_original_eids:
-                    original_hetero_edge_ids[etype] = original_edge_ids[mask]
-            if has_original_eids:
-                original_edge_ids = original_hetero_edge_ids
-        return FusedSampledSubgraphImpl(
-            sampled_csc=node_pairs, original_edge_ids=original_edge_ids
-        )
-
     def _convert_to_homogeneous_nodes(self, nodes, timestamps=None):
         homogeneous_nodes = []
         homogeneous_timestamps = []
@@ -560,9 +502,7 @@ class FusedCSCSamplingGraph(SamplingGraph):
         fanouts: torch.Tensor,
         replace: bool = False,
         probs_name: Optional[str] = None,
-        # TODO: clean up once the migration is done.
-        output_cscformat=True,
-    ) -> Union[FusedSampledSubgraphImpl, SampledSubgraphImpl]:
+    ) -> SampledSubgraphImpl:
         """Sample neighboring edges of the given nodes and return the induced
         subgraph.
 
@@ -604,7 +544,7 @@ class FusedCSCSamplingGraph(SamplingGraph):
 
         Returns
         -------
-        Union[FusedSampledSubgraphImpl, SampledSubgraphImpl]
+        SampledSubgraphImpl
             The sampled subgraph.
 
         Examples
@@ -638,9 +578,6 @@ class FusedCSCSamplingGraph(SamplingGraph):
         C_sampled_subgraph = self._sample_neighbors(
             nodes, fanouts, replace, probs_name
         )
-        if not output_cscformat:
-            return self._convert_to_fused_sampled_subgraph(C_sampled_subgraph)
-        else:
         return self._convert_to_sampled_subgraph(C_sampled_subgraph)
 
     def _check_sampler_arguments(self, nodes, fanouts, probs_name):
@@ -753,9 +690,7 @@ class FusedCSCSamplingGraph(SamplingGraph):
         fanouts: torch.Tensor,
         replace: bool = False,
         probs_name: Optional[str] = None,
-        # TODO: clean up once the migration is done.
-        output_cscformat=True,
-    ) -> Union[FusedSampledSubgraphImpl, SampledSubgraphImpl]:
+    ) -> SampledSubgraphImpl:
         """Sample neighboring edges of the given nodes and return the induced
         subgraph via layer-neighbor sampling from the NeurIPS 2023 paper
         `Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
@@ -799,7 +734,7 @@ class FusedCSCSamplingGraph(SamplingGraph):
 
         Returns
         -------
-        FusedSampledSubgraphImpl
+        SampledSubgraphImpl
             The sampled subgraph.
 
         Examples
@@ -843,10 +778,6 @@ class FusedCSCSamplingGraph(SamplingGraph):
             has_original_eids,
             probs_name,
         )
-
-        if not output_cscformat:
-            return self._convert_to_fused_sampled_subgraph(C_sampled_subgraph)
-        else:
         return self._convert_to_sampled_subgraph(C_sampled_subgraph)
 
     def temporal_sample_neighbors(
@@ -906,7 +837,7 @@ class FusedCSCSamplingGraph(SamplingGraph):
 
         Returns
         -------
-        FusedSampledSubgraphImpl
+        SampledSubgraphImpl
             The sampled subgraph.
         """
         if isinstance(nodes, dict):

tests/python/pytorch/graphbolt/impl/test_ondisk_dataset.py

@@ -1143,9 +1143,8 @@ def test_OnDiskDataset_preprocess_homogeneous():
         subgraph = fused_csc_sampling_graph.sample_neighbors(
             torch.arange(num_samples),
             torch.tensor([fanout]),
-            output_cscformat=False,
         )
-        assert len(subgraph.sampled_csc[0]) <= num_samples
+        assert len(subgraph.sampled_csc.indices) <= num_samples
 
     with tempfile.TemporaryDirectory() as test_dir:
         # All metadata fields are specified.