[hotfix] node id validity check (#1073)

* fix

* improve

* fix lint

* upd

* fix

* upd

python/dgl/convert.py

@@ -21,7 +21,7 @@ __all__ = [
     'to_networkx',
 ]
 
-def graph(data, ntype='_N', etype='_E', card=None, **kwargs):
+def graph(data, ntype='_N', etype='_E', card=None, validate=False, **kwargs):
     """Create a graph with one type of nodes and edges.
 
     In the sparse matrix perspective, :func:`dgl.graph` creates a graph
@@ -45,6 +45,10 @@ def graph(data, ntype='_N', etype='_E', card=None, **kwargs):
     card : int, optional
         Cardinality (number of nodes in the graph). If None, infer from input data, i.e.
         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)
     kwargs : key-word arguments, optional
         Other key word arguments. Only comes into effect when we are using a NetworkX
         graph. It can consist of:
@@ -101,6 +105,16 @@ def graph(data, ntype='_N', etype='_E', card=None, **kwargs):
     ['follows']
     >>> g.canonical_etypes
     [('user', 'follows', 'user')]
+
+    Check if node ids are within cardinality
+
+    >>> g = dgl.graph(([0, 1, 2], [1, 2, 0]), card=2, validate=True)
+    ...
+    dgl._ffi.base.DGLError: Invalid node id 2 (should be less than cardinality 2).
+    >>> g = dgl.graph(([0, 1, 2], [1, 2, 0]), card=3, validate=True)
+    Graph(num_nodes=3, num_edges=3,
+          ndata_schemes={}
+          edata_schemes={})
     """
     if card is not None:
         urange, vrange = card, card
@@ -108,9 +122,9 @@ def graph(data, ntype='_N', etype='_E', card=None, **kwargs):
         urange, vrange = None, None
     if isinstance(data, tuple):
         u, v = data
-        return create_from_edges(u, v, ntype, etype, ntype, urange, vrange)
+        return create_from_edges(u, v, ntype, etype, ntype, urange, vrange, validate)
     elif isinstance(data, list):
-        return create_from_edge_list(data, ntype, etype, ntype, urange, vrange)
+        return create_from_edge_list(data, ntype, etype, ntype, urange, vrange, validate)
     elif isinstance(data, sp.sparse.spmatrix):
         return create_from_scipy(data, ntype, etype, ntype)
     elif isinstance(data, nx.Graph):
@@ -118,7 +132,7 @@ def graph(data, ntype='_N', etype='_E', card=None, **kwargs):
     else:
         raise DGLError('Unsupported graph data type:', type(data))
 
-def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, **kwargs):
+def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, validate=False, **kwargs):
     """Create a bipartite graph.
 
     The result graph is directed and edges must be from ``utype`` nodes
@@ -147,6 +161,10 @@ def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, **kwargs):
     card : pair of int, optional
         Cardinality (number of nodes in the source and destination group). If None,
         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)
     kwargs : key-word arguments, optional
         Other key word arguments. Only comes into effect when we are using a NetworkX
         graph. It can consist of:
@@ -215,6 +233,16 @@ def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, **kwargs):
     4
     >>> g.edges()
     (tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2]), tensor([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]))
+
+    Check if node ids are within cardinality
+    >>> g = dgl.bipartite(([0, 1, 2], [1, 2, 3]), card=(2, 4), validate=True)
+    ...
+    dgl._ffi.base.DGLError: Invalid node id 2 (should be less than cardinality 2).
+    >>> g = dgl.bipartite(([0, 1, 2], [1, 2, 3]), card=(3, 4), validate=True)
+    >>> g
+    Graph(num_nodes={'_U': 3, '_V': 4},
+          num_edges={('_U', '_E', '_V'): 3},
+          metagraph=[('_U', '_V')])
     """
     if utype == vtype:
         raise DGLError('utype should not be equal to vtype. Use ``dgl.graph`` instead.')
@@ -224,9 +252,9 @@ def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, **kwargs):
         urange, vrange = None, None
     if isinstance(data, tuple):
         u, v = data
-        return create_from_edges(u, v, utype, etype, vtype, urange, vrange)
+        return create_from_edges(u, v, utype, etype, vtype, urange, vrange, validate)
     elif isinstance(data, list):
-        return create_from_edge_list(data, utype, etype, vtype, urange, vrange)
+        return create_from_edge_list(data, utype, etype, vtype, urange, vrange, validate)
     elif isinstance(data, sp.sparse.spmatrix):
         return create_from_scipy(data, utype, etype, vtype)
     elif isinstance(data, nx.Graph):
@@ -667,7 +695,7 @@ def to_homo(G):
 # Internal APIs
 ############################################################
 
-def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None):
+def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None, validate=False):
     """Internal function to create a graph from incident nodes with types.
 
     utype could be equal to vtype
@@ -690,6 +718,8 @@ def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None):
     vrange : int, optional
         The destination node ID range. If None, the value is the
         maximum of the destination node IDs in the edge list plus 1. (Default: None)
+    validate : bool, optional
+        If True, checks if node IDs are within range.
 
     Returns
     -------
@@ -697,6 +727,13 @@ def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None):
     """
     u = utils.toindex(u)
     v = utils.toindex(v)
+    if validate:
+        if urange is not None 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))):
+            raise DGLError('Invalid node id {} (should be less than cardinality {}).'.format(
+                vrange, int(F.asnumpy(F.max(v.tousertensor(), dim=0)))))
     urange = urange or (int(F.asnumpy(F.max(u.tousertensor(), dim=0))) + 1)
     vrange = vrange or (int(F.asnumpy(F.max(v.tousertensor(), dim=0))) + 1)
     if utype == vtype:
@@ -710,7 +747,7 @@ def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None):
     else:
         return DGLHeteroGraph(hgidx, [utype, vtype], [etype])
 
-def create_from_edge_list(elist, utype, etype, vtype, urange=None, vrange=None):
+def create_from_edge_list(elist, utype, etype, vtype, urange=None, vrange=None, validate=False):
     """Internal function to create a heterograph from a list of edge tuples with types.
 
     utype could be equal to vtype
@@ -731,6 +768,9 @@ def create_from_edge_list(elist, utype, etype, vtype, urange=None, vrange=None):
     vrange : int, optional
         The destination node ID range. If None, the value is the
         maximum of the destination node IDs in the edge list plus 1. (Default: None)
+    validate : bool, optional
+        If True, checks if node IDs are within range.
+
 
     Returns
     -------
@@ -742,7 +782,7 @@ def create_from_edge_list(elist, utype, etype, vtype, urange=None, vrange=None):
         u, v = zip(*elist)
         u = list(u)
         v = list(v)
-    return create_from_edges(u, v, utype, etype, vtype, urange, vrange)
+    return create_from_edges(u, v, utype, etype, vtype, urange, vrange, validate)
 
 def create_from_scipy(spmat, utype, etype, vtype, with_edge_id=False):
     """Internal function to create a heterograph from a scipy sparse matrix with types.
@@ -762,6 +802,9 @@ def create_from_scipy(spmat, utype, etype, vtype, with_edge_id=False):
         If True, the entries in the sparse matrix are treated as edge IDs.
         Otherwise, the entries are ignored and edges will be added in
         (source, destination) order.
+    validate : bool, optional
+        If True, checks if node IDs are within range.
+
 
     Returns
     -------

tests/compute/test_heterograph.py

@@ -6,6 +6,8 @@ import scipy.sparse as ssp
 import itertools
 import backend as F
 import networkx as nx
+from dgl import DGLError
+
 
 def create_test_heterograph():
     # test heterograph from the docstring, plus a user -- wishes -- game relation
@@ -93,6 +95,36 @@ def test_create():
     assert g.number_of_nodes('l1') == 3
     assert g.number_of_nodes('l2') == 4
 
+    # test if validate flag works
+    # homo graph
+    fail = False
+    try:
+        g = dgl.graph(
+            ([0, 0, 0, 1, 1, 2], [0, 1, 2, 0, 1, 2]),
+            card=2,
+            validate=True
+        )
+    except DGLError:
+        fail = True
+    finally:
+        assert fail, "should catch a DGLError because node ID is out of bound."
+    # bipartite graph
+    def _test_validate_bipartite(card):
+        fail = False
+        try:
+            g = dgl.bipartite(
+                ([0, 0, 1, 1, 2], [1, 1, 2, 2, 3]),
+                card=card,
+                validate=True
+            )
+        except DGLError:
+            fail = True
+        finally:
+            assert fail, "should catch a DGLError because node ID is out of bound."
+
+    _test_validate_bipartite((3, 3))
+    _test_validate_bipartite((2, 4))
+
 def test_query():
     g = create_test_heterograph()