[GraphBolt] Modify `from_dglgraph()` (#6470)

python/dgl/graphbolt/impl/csc_sampling_graph.py

@@ -825,14 +825,19 @@ def from_dglgraph(
     include_original_edge_id: bool = False,
 ) -> CSCSamplingGraph:
     """Convert a DGLGraph to CSCSamplingGraph."""
+
     homo_g, ntype_count, _ = to_homogeneous(g, return_count=True)
-    # Initialize metadata.
-    node_type_to_id = {ntype: g.get_ntype_id(ntype) for ntype in g.ntypes}
-    edge_type_to_id = {
-        etype_tuple_to_str(etype): g.get_etype_id(etype)
-        for etype in g.canonical_etypes
-    }
-    metadata = GraphMetadata(node_type_to_id, edge_type_to_id)
+
+    if is_homogeneous:
+        metadata = None
+    else:
+        # Initialize metadata.
+        node_type_to_id = {ntype: g.get_ntype_id(ntype) for ntype in g.ntypes}
+        edge_type_to_id = {
+            etype_tuple_to_str(etype): g.get_etype_id(etype)
+            for etype in g.canonical_etypes
+        }
+        metadata = GraphMetadata(node_type_to_id, edge_type_to_id)
 
     # Obtain CSC matrix.
     indptr, indices, edge_ids = homo_g.adj_tensors("csc")

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

@@ -1321,8 +1321,7 @@ def test_from_dglgraph_homogeneous():
     assert gb_g.total_num_edges == dgl_g.num_edges()
     assert torch.equal(gb_g.node_type_offset, torch.tensor([0, 1000]))
     assert gb_g.type_per_edge is None
-    assert gb_g.metadata.node_type_to_id == {"_N": 0}
-    assert gb_g.metadata.edge_type_to_id == {"_N:_E:_N": 0}
+    assert gb_g.metadata is None
 
 
 @unittest.skipIf(