[Bug Fix] Add boundary check for heterograph build with card as input (#1192)

* Add boundary check for heterograph build with card as input

* Fix when u or v is empty

* fix test_kernel.py error print

* Revert "fix test_kernel.py error print"

This reverts commit a71c20292549c0ac62a5326c30669ca4bde8febc.

* Turn op validation check on graph and bipartite by default

* upd

* udp

* upd

* update test

python/dgl/convert.py

@@ -21,7 +21,7 @@ __all__ = [
     'to_networkx',
 ]
 
-def graph(data, ntype='_N', etype='_E', card=None, validate=False, **kwargs):
+def graph(data, ntype='_N', etype='_E', card=None, validate=True, **kwargs):
     """Create a graph with one type of nodes and edges.
 
     In the sparse matrix perspective, :func:`dgl.graph` creates a graph
@@ -47,8 +47,8 @@ def graph(data, ntype='_N', etype='_E', card=None, validate=False, **kwargs):
         the largest node ID plus 1. (Default: None)
     validate : bool, optional
         If True, check if node ids are within cardinality, the check process may take
-        some time.
-        If False and card is not None, user would receive a warning. (Default: False)
+        some time. (Default: True)
+        If False and card is not None, user would receive a warning.
     kwargs : key-word arguments, optional
         Other key word arguments. Only comes into effect when we are using a NetworkX
         graph. It can consist of:
@@ -132,7 +132,7 @@ def graph(data, ntype='_N', etype='_E', card=None, validate=False, **kwargs):
     else:
         raise DGLError('Unsupported graph data type:', type(data))
 
-def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, validate=False, **kwargs):
+def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, validate=True, **kwargs):
     """Create a bipartite graph.
 
     The result graph is directed and edges must be from ``utype`` nodes
@@ -163,8 +163,8 @@ def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, validate=Fals
         infer from input data, i.e. the largest node ID plus 1 for each type. (Default: None)
     validate : bool, optional
         If True, check if node ids are within cardinality, the check process may take
-        some time.
-        If False and card is not None, user would receive a warning. (Default: False)
+        some time. (Default: True)
+        If False and card is not None, user would receive a warning.
     kwargs : key-word arguments, optional
         Other key word arguments. Only comes into effect when we are using a NetworkX
         graph. It can consist of:
@@ -433,11 +433,13 @@ def heterograph(data_dict, num_nodes_dict=None):
         if isinstance(data, DGLHeteroGraph):
             rel_graphs.append(data)
         elif srctype == dsttype:
-            rel_graphs.append(graph(data, srctype, etype, card=num_nodes_dict[srctype]))
+            rel_graphs.append(graph(
+                data, srctype, etype,
+                card=num_nodes_dict[srctype], validate=False))
         else:
             rel_graphs.append(bipartite(
                 data, srctype, etype, dsttype,
-                card=(num_nodes_dict[srctype], num_nodes_dict[dsttype])))
+                card=(num_nodes_dict[srctype], num_nodes_dict[dsttype]), validate=False))
 
     return hetero_from_relations(rel_graphs)
 
@@ -590,11 +592,11 @@ def to_hetero(G, ntypes, etypes, ntype_field=NTYPE, etype_field=ETYPE, metagraph
         if stid == dtid:
             rel_graph = graph(
                 (src_of_etype, dst_of_etype), ntypes[stid], etypes[etid],
-                card=ntype_count[stid])
+                card=ntype_count[stid], validate=False)
         else:
             rel_graph = bipartite(
                 (src_of_etype, dst_of_etype), ntypes[stid], etypes[etid], ntypes[dtid],
-                card=(ntype_count[stid], ntype_count[dtid]))
+                card=(ntype_count[stid], ntype_count[dtid]), validate=False)
         rel_graphs.append(rel_graph)
 
     hg = hetero_from_relations(rel_graphs)
@@ -681,7 +683,7 @@ def to_homo(G):
         etype_ids.append(F.full_1d(num_edges, etype_id, F.int64, F.cpu()))
         eids.append(F.arange(0, num_edges))
 
-    retg = graph((F.cat(srcs, 0), F.cat(dsts, 0)), card=total_num_nodes)
+    retg = graph((F.cat(srcs, 0), F.cat(dsts, 0)), card=total_num_nodes, validate=False)
     retg.ndata[NTYPE] = F.cat(ntype_ids, 0)
     retg.ndata[NID] = F.cat(nids, 0)
     retg.edata[ETYPE] = F.cat(etype_ids, 0)
@@ -701,7 +703,7 @@ def to_homo(G):
 # Internal APIs
 ############################################################
 
-def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None, validate=False):
+def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None, validate=True):
     """Internal function to create a graph from incident nodes with types.
 
     utype could be equal to vtype
@@ -735,10 +737,12 @@ def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None, valid
     v = utils.toindex(v)
 
     if validate:
-        if urange is not None and urange <= int(F.asnumpy(F.max(u.tousertensor(), dim=0))):
+        if urange is not None and len(u) > 0 and \
+            urange <= int(F.asnumpy(F.max(u.tousertensor(), dim=0))):
             raise DGLError('Invalid node id {} (should be less than cardinality {}).'.format(
                 urange, int(F.asnumpy(F.max(u.tousertensor(), dim=0)))))
-        if vrange is not None and vrange <= int(F.asnumpy(F.max(v.tousertensor(), dim=0))):
+        if vrange is not None and len(v) > 0 and \
+            vrange <= int(F.asnumpy(F.max(v.tousertensor(), dim=0))):
             raise DGLError('Invalid node id {} (should be less than cardinality {}).'.format(
                 vrange, int(F.asnumpy(F.max(v.tousertensor(), dim=0)))))
     urange = urange or (
@@ -757,7 +761,7 @@ def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None, valid
     else:
         return DGLHeteroGraph(hgidx, [utype, vtype], [etype])
 
-def create_from_edge_list(elist, utype, etype, vtype, urange=None, vrange=None, validate=False):
+def create_from_edge_list(elist, utype, etype, vtype, urange=None, vrange=None, validate=True):
     """Internal function to create a heterograph from a list of edge tuples with types.
 
     utype could be equal to vtype
@@ -895,7 +899,7 @@ def create_from_networkx(nx_graph,
     src = utils.toindex(src)
     dst = utils.toindex(dst)
     num_nodes = nx_graph.number_of_nodes()
-    g = create_from_edges(src, dst, ntype, etype, ntype, num_nodes, num_nodes)
+    g = create_from_edges(src, dst, ntype, etype, ntype, num_nodes, num_nodes, validate=False)
 
     # handle features
     # copy attributes
@@ -1007,7 +1011,9 @@ def create_from_networkx_bipartite(nx_graph,
                 dst.append(bottom_map[e[1]])
     src = utils.toindex(src)
     dst = utils.toindex(dst)
-    g = create_from_edges(src, dst, utype, etype, vtype, len(top_nodes), len(bottom_nodes))
+    g = create_from_edges(
+        src, dst, utype, etype, vtype,
+        len(top_nodes), len(bottom_nodes), validate=False)
 
     # TODO attributes
     assert node_attrs is None, 'Retrieval of node attributes are not supported yet.'

tests/compute/test_heterograph.py

@@ -630,6 +630,27 @@ def test_to_device():
         hg = hg.to(F.cuda())
         assert hg is not None
 
+def test_convert_bound():
+    def _test_bipartite_bound(data, card):
+        try:
+            dgl.bipartite(data, card=card)
+        except dgl.DGLError:
+            return
+        assert False, 'bipartite bound test with wrong uid failed'
+
+    def _test_graph_bound(data, card):
+        try:
+            dgl.graph(data, card=card)
+        except dgl.DGLError:
+            return
+        assert False, 'graph bound test with wrong uid failed'
+
+    _test_bipartite_bound(([1,2],[1,2]),(2,3))
+    _test_bipartite_bound(([0,1],[1,4]),(2,3))
+    _test_graph_bound(([1,3],[1,2]), 3)
+    _test_graph_bound(([0,1],[1,3]),3)
+
+
 def test_convert():
     hg = create_test_heterograph()
     hs = []
@@ -1265,6 +1286,7 @@ if __name__ == '__main__':
     test_view()
     test_view1()
     test_flatten()
+    test_convert_bound()
     test_convert()
     test_to_device()
     test_transform()