Merge branch 'master' into master

tests/tools/create_chunked_dataset.py

@@ -65,6 +65,7 @@ def rand_edges(num_src, num_dst, num_edges):
     paper_label = np.random.choice(num_classes, num_papers)
     paper_year = np.random.choice(2022, num_papers)
     paper_orig_ids = np.arange(0, num_papers)
+    writes_orig_ids = np.arange(0, g.num_edges('writes'))
 
     # masks.
     if include_masks:
@@ -93,26 +94,38 @@ def rand_edges(num_src, num_dst, num_edges):
     paper_feat_path = os.path.join(input_dir, 'paper/feat.npy')
     with open(paper_feat_path, 'wb') as f:
         np.save(f, paper_feat)
+    g.nodes['paper'].data['feat'] = torch.from_numpy(paper_feat)
 
     paper_label_path = os.path.join(input_dir, 'paper/label.npy')
     with open(paper_label_path, 'wb') as f:
         np.save(f, paper_label)
+    g.nodes['paper'].data['label'] = torch.from_numpy(paper_label)
 
     paper_year_path = os.path.join(input_dir, 'paper/year.npy')
     with open(paper_year_path, 'wb') as f:
         np.save(f, paper_year)
+    g.nodes['paper'].data['year'] = torch.from_numpy(paper_year)
 
     paper_orig_ids_path = os.path.join(input_dir, 'paper/orig_ids.npy')
     with open(paper_orig_ids_path, 'wb') as f:
         np.save(f, paper_orig_ids)
+    g.nodes['paper'].data['orig_ids'] = torch.from_numpy(paper_orig_ids)
 
     cite_count_path = os.path.join(input_dir, 'cites/count.npy')
     with open(cite_count_path, 'wb') as f:
         np.save(f, cite_count)
+    g.edges['cites'].data['count'] = torch.from_numpy(cite_count)
 
     write_year_path = os.path.join(input_dir, 'writes/year.npy')
     with open(write_year_path, 'wb') as f:
         np.save(f, write_year)
+    g.edges['writes'].data['year'] = torch.from_numpy(write_year)
+    g.edges['rev_writes'].data['year'] = torch.from_numpy(write_year)
+
+    writes_orig_ids_path = os.path.join(input_dir, 'writes/orig_ids.npy')
+    with open(writes_orig_ids_path, 'wb') as f:
+        np.save(f, writes_orig_ids)
+    g.edges['writes'].data['orig_ids'] = torch.from_numpy(writes_orig_ids)
 
     node_data = None
     if include_masks:
@@ -193,7 +206,10 @@ def rand_edges(num_src, num_dst, num_edges):
 
     edge_data = {
         'cites': {'count': cite_count_path},
-        'writes': {'year': write_year_path},
+        'writes': {
+            'year': write_year_path,
+            'orig_ids': writes_orig_ids_path
+        },
         'rev_writes': {'year': write_year_path},
     }
 

tests/tools/test_dist_part.py

@@ -58,7 +58,8 @@ def _verify_graph_feats(
             ndata = node_feats[ntype + "/" + name][local_nids]
             assert torch.equal(ndata, true_feats)
 
-    for etype in g.etypes:
+    for c_etype in g.canonical_etypes:
+        etype = c_etype[1]
         etype_id = g.get_etype_id(etype)
         inner_edge_mask = _get_inner_edge_mask(part, etype_id)
         inner_eids = part.edata[dgl.EID][inner_edge_mask]
@@ -75,7 +76,7 @@ def _verify_graph_feats(
                 continue
             true_feats = g.edges[etype].data[name][orig_id]
             edata = edge_feats[etype + "/" + name][local_eids]
-            assert torch.equal(edata == true_feats)
+            assert torch.equal(edata, true_feats)
 
 
 @pytest.mark.parametrize("num_chunks", [1, 8])
@@ -119,13 +120,17 @@ def test_chunk_graph(num_chunks):
 
         # check node_data
         output_node_data_dir = os.path.join(output_dir, "node_data", "paper")
-        for feat in ["feat", "label", "year"]:
+        for feat in ["feat", "label", "year", "orig_ids"]:
+            feat_data = []
             for i in range(num_chunks):
                 chunk_f_name = "{}-{}.npy".format(feat, i)
                 chunk_f_name = os.path.join(output_node_data_dir, chunk_f_name)
                 assert os.path.isfile(chunk_f_name)
                 feat_array = np.load(chunk_f_name)
                 assert feat_array.shape[0] == num_papers // num_chunks
+                feat_data.append(feat_array)
+            feat_data = np.concatenate(feat_data, 0)
+            assert torch.equal(torch.from_numpy(feat_data), g.nodes['paper'].data[feat])
 
         # check edge_data
         num_edges = {
@@ -137,15 +142,21 @@ def test_chunk_graph(num_chunks):
         for etype, feat in [
             ["paper:cites:paper", "count"],
             ["author:writes:paper", "year"],
+            ["author:writes:paper", "orig_ids"],
             ["paper:rev_writes:author", "year"],
         ]:
+            feat_data = []
             output_edge_sub_dir = os.path.join(output_edge_data_dir, etype)
             for i in range(num_chunks):
                 chunk_f_name = "{}-{}.npy".format(feat, i)
                 chunk_f_name = os.path.join(output_edge_sub_dir, chunk_f_name)
                 assert os.path.isfile(chunk_f_name)
                 feat_array = np.load(chunk_f_name)
-            assert feat_array.shape[0] == num_edges[etype] // num_chunks
+                assert feat_array.shape[0] == num_edges[etype] // num_chunks
+                feat_data.append(feat_array)
+            feat_data = np.concatenate(feat_data, 0)
+            assert torch.equal(torch.from_numpy(feat_data),
+                g.edges[etype.split(':')[1]].data[feat])
 
 
 @pytest.mark.parametrize("num_chunks", [1, 3, 8])

tools/distpartitioning/utils.py

@@ -377,6 +377,15 @@ def write_edge_features(edge_features, edge_file):
     edge_file     : string 
         File in which the edge information is serialized
     """
+    # TODO[Rui]: Below is a temporary fix for etype and will be
+    # further refined in the near future as we'll shift to canonical
+    # etypes entirely.
+    def format_etype(etype):
+        etype, name = etype.split('/')
+        etype = etype.split(':')[1]
+        return etype + '/' + name
+    edge_features = {format_etype(etype):
+        data for etype, data in edge_features.items()}
     dgl.data.utils.save_tensors(edge_file, edge_features)
 
 def write_graph_dgl(graph_file, graph_obj, formats, sort_etypes):