Fix 0deg update_to and Tree-LSTM model (#51)

* WIP

* WIP

* treelstm dataloader

* Main training loop.

* trainable treelstm script

* fix dependency

* cuda training

* Add tensorized topological traversal

* allowing update_to() with no incoming messages

* fixing partial cases

python/dgl/data/citation_graph.py

@@ -19,15 +19,17 @@ _urls = {
     'pubmed' : 'https://www.dropbox.com/s/fj5q6pi66xhymcm/pubmed.zip?dl=1',
 }
 
-class GCNDataset(object):
+class CitationGraphDataset(object):
     def __init__(self, name):
         self.name = name
+        self.mode = mode
         self.dir = get_download_dir()
         self.zip_file_path='{}/{}.zip'.format(self.dir, name)
         download(_urls[name], path=self.zip_file_path)
         extract_archive(self.zip_file_path, '{}/{}'.format(self.dir, name))
+        self._load()
 
-    def load(self):
+    def _load(self):
         """Loads input data from gcn/data directory
 
         ind.name.x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object;
@@ -87,13 +89,6 @@ class GCNDataset(object):
         val_mask = _sample_mask(idx_val, labels.shape[0])
         test_mask = _sample_mask(idx_test, labels.shape[0])
 
-        #y_train = np.zeros(labels.shape)
-        #y_val = np.zeros(labels.shape)
-        #y_test = np.zeros(labels.shape)
-        #y_train[train_mask, :] = labels[train_mask, :]
-        #y_val[val_mask, :] = labels[val_mask, :]
-        #y_test[test_mask, :] = labels[test_mask, :]
-
         self.graph = graph
         self.features = _preprocess_features(features)
         self.labels = labels
@@ -112,6 +107,12 @@ class GCNDataset(object):
         print('  NumValidationSamples: {}'.format(len(np.nonzero(self.val_mask)[0])))
         print('  NumTestSamples: {}'.format(len(np.nonzero(self.test_mask)[0])))
 
+    def __getitem__(self, idx):
+        return self
+
+    def __len__(self):
+        return 1
+
 def _preprocess_features(features):
     """Row-normalize feature matrix and convert to tuple representation"""
     rowsum = np.array(features.sum(1))
@@ -135,18 +136,15 @@ def _sample_mask(idx, l):
     return mask
 
 def load_cora():
-    data = GCNDataset('cora')
-    data.load()
+    data = CitationGraphDataset('cora')
     return data
 
 def load_citeseer():
-    data = GCNDataset('citeseer')
-    data.load()
+    data = CitationGraphDataset('citeseer')
     return data
 
 def load_pubmed():
-    data = GCNDataset('pubmed')
-    data.load()
+    data = CitationGraphDataset('pubmed')
     return data
 
 class GCNSyntheticDataset(object):
@@ -196,6 +194,12 @@ class GCNSyntheticDataset(object):
         print('  NumValidationSamples: {}'.format(len(np.nonzero(self.val_mask)[0])))
         print('  NumTestSamples: {}'.format(len(np.nonzero(self.test_mask)[0])))
 
+    def __getitem__(self, idx):
+        return self
+
+    def __len__(self):
+        return 1
+
 def get_gnp_generator(args):
     n = args.syn_gnp_n
     p = (2 * np.log(n) / n) if args.syn_gnp_p == 0. else args.syn_gnp_p

python/dgl/data/tree.py

+"""Tree-structured data.
+
+Including:
+    - Stanford Sentiment Treebank
+"""
+from __future__ import absolute_import
+
+from collections import namedtuple
+from nltk.tree import Tree
+from nltk.corpus.reader import BracketParseCorpusReader
+import networkx as nx
+
+import dgl
+import dgl.backend as F
+from dgl.data.utils import download, extract_archive, get_download_dir
+
+_urls = {
+    'sst' : 'https://www.dropbox.com/s/dw8kr2vuq7k4dqi/sst.zip?dl=1',
+}
+
+SSTBatch = namedtuple('SSTBatch', ['graph', 'nid_with_word', 'wordid', 'label'])
+
+class SST(object):
+    """SST"""
+    PAD_WORD=-1
+    def __init__(self, mode='train', vocab_file=None):
+        self.mode = mode
+        self.dir = get_download_dir()
+        self.zip_file_path='{}/sst.zip'.format(self.dir)
+        self.vocab_file = '{}/sst/vocab.txt'.format(self.dir) if vocab_file is None else vocab_file
+        download(_urls['sst'], path=self.zip_file_path)
+        extract_archive(self.zip_file_path, '{}/sst'.format(self.dir))
+        self.trees = []
+        self.num_classes = 5
+        print('Preprocessing...')
+        self._load()
+        print('Dataset creation finished. #Trees:', len(self.trees))
+
+    def _load(self):
+        files = ['{}.txt'.format(self.mode)]
+        corpus = BracketParseCorpusReader('{}/sst'.format(self.dir), files)
+        sents = corpus.parsed_sents(files[0])
+        # load vocab file
+        self.vocab = {}
+        with open(self.vocab_file) as vf:
+            for line in vf.readlines():
+                line = line.strip()
+                self.vocab[line] = len(self.vocab)
+        # build trees
+        for sent in sents:
+            self.trees.append(self._build_tree(sent))
+
+    def _build_tree(self, root):
+        g = nx.DiGraph()
+        def _rec_build(nid, node):
+            for child in node:
+                cid = g.number_of_nodes()
+                if isinstance(child[0], str):
+                    # leaf node
+                    word = self.vocab[child[0].lower()]
+                    g.add_node(cid, x=word, y=int(child.label()))
+                else:
+                    g.add_node(cid, x=SST.PAD_WORD, y=int(child.label()))
+                    _rec_build(cid, child)
+                g.add_edge(cid, nid)
+        # add root
+        g.add_node(0, x=SST.PAD_WORD, y=int(root.label()))
+        _rec_build(0, root)
+        return dgl.DGLGraph(g)
+
+    def __getitem__(self, idx):
+        return self.trees[idx]
+
+    def __len__(self):
+        return len(self.trees)
+
+    @property 
+    def num_vocabs(self):
+        return len(self.vocab)
+
+    @staticmethod
+    def batcher(batch):
+        nid_with_word = []
+        wordid = []
+        label = []
+        gnid = 0
+        for tree in batch:
+            for nid in range(tree.number_of_nodes()):
+                if tree.nodes[nid]['x'] != SST.PAD_WORD:
+                    nid_with_word.append(gnid)
+                    wordid.append(tree.nodes[nid]['x'])
+                label.append(tree.nodes[nid]['y'])
+                gnid += 1
+        batch_trees = dgl.batch(batch)
+        return SSTBatch(graph=batch_trees,
+                        nid_with_word=F.tensor(nid_with_word, dtype=F.int64),
+                        wordid=F.tensor(wordid, dtype=F.int64),
+                        label=F.tensor(label, dtype=F.int64))

python/dgl/graph.py

@@ -93,20 +93,20 @@ class DGLGraph(DiGraph):
         else:
             u = utils.toindex(u)
             num_nodes = len(u)
-        if isinstance(hu, dict):
+        if utils.is_dict_like(hu):
             for key, val in hu.items():
                 assert F.shape(val)[0] == num_nodes
         else:
             assert F.shape(hu)[0] == num_nodes
         # set
         if is_all(u):
-            if isinstance(hu, dict):
+            if utils.is_dict_like(hu):
                 for key, val in hu.items():
                     self._node_frame[key] = val
             else:
                 self._node_frame[__REPR__] = hu
         else:
-            if isinstance(hu, dict):
+            if utils.is_dict_like(hu):
                 self._node_frame[u] = hu
             else:
                 self._node_frame[u] = {__REPR__ : hu}
@@ -171,21 +171,21 @@ class DGLGraph(DiGraph):
             u = utils.toindex(u)
             v = utils.toindex(v)
             num_edges = max(len(u), len(v))
-        if isinstance(h_uv, dict):
+        if utils.is_dict_like(h_uv):
             for key, val in h_uv.items():
                 assert F.shape(val)[0] == num_edges
         else:
             assert F.shape(h_uv)[0] == num_edges
         # set
         if u_is_all:
-            if isinstance(h_uv, dict):
+            if utils.is_dict_like(h_uv):
                 for key, val in h_uv.items():
                     self._edge_frame[key] = val
             else:
                 self._edge_frame[__REPR__] = h_uv
         else:
             eid = self.cached_graph.get_edge_id(u, v)
-            if isinstance(h_uv, dict):
+            if utils.is_dict_like(h_uv):
                 self._edge_frame[eid] = h_uv
             else:
                 self._edge_frame[eid] = {__REPR__ : h_uv}
@@ -206,20 +206,20 @@ class DGLGraph(DiGraph):
         else:
             eid = utils.toindex(eid)
             num_edges = len(eid)
-        if isinstance(h_uv, dict):
+        if utils.is_dict_like(h_uv):
             for key, val in h_uv.items():
                 assert F.shape(val)[0] == num_edges
         else:
             assert F.shape(h_uv)[0] == num_edges
         # set
         if is_all(eid):
-            if isinstance(h_uv, dict):
+            if utils.is_dict_like(h_uv):
                 for key, val in h_uv.items():
                     self._edge_frame[key] = val
             else:
                 self._edge_frame[__REPR__] = h_uv
         else:
-            if isinstance(h_uv, dict):
+            if utils.is_dict_like(h_uv):
                 self._edge_frame[eid] = h_uv
             else:
                 self._edge_frame[eid] = {__REPR__ : h_uv}
@@ -400,7 +400,7 @@ class DGLGraph(DiGraph):
             src_reprs = self.get_n_repr(u)
             edge_reprs = self.get_e_repr_by_id(eid)
             msgs = message_func(src_reprs, edge_reprs)
-        if isinstance(msgs, dict):
+        if utils.is_dict_like(msgs):
             self._msg_frame.append(msgs)
         else:
             self._msg_frame.append({__MSG__ : msgs})
@@ -522,11 +522,11 @@ class DGLGraph(DiGraph):
 
     def _nonbatch_recv(self, u, reduce_func, update_func):
         f_reduce = _get_reduce_func(reduce_func)
-        f_update = update_func
         if is_all(u):
             u = list(range(0, self.number_of_nodes()))
         else:
             u = utils.toindex(u)
+
         for i, uu in enumerate(utils.node_iter(u)):
             # reduce phase
             msgs_batch = [self.edges[vv, uu].pop(__MSG__)
@@ -536,31 +536,31 @@ class DGLGraph(DiGraph):
             else:
                 msgs_reduced = f_reduce(_get_repr(self.nodes[uu]), msgs_batch)
             # update phase
-            ret = f_update(_get_repr(self.nodes[uu]), msgs_reduced)
+            ret = update_func(_get_repr(self.nodes[uu]), msgs_reduced)
             _set_repr(self.nodes[uu], ret)
 
     def _batch_recv(self, v, reduce_func, update_func):
-        f_update = update_func
+        if len(v) == 0:
+            # no vertex to be triggered.
+            return
         null_v, reordered_v, all_reduced_msgs = self._batch_reduce(v, reduce_func)
         if all_reduced_msgs is None:
             # no message; only do recv.
             if is_all(v):
-                self.set_n_repr(f_update(self.get_n_repr(), None))
+                self.set_n_repr(update_func(self.get_n_repr(), None))
             else:
-                self.set_n_repr(f_update(self.get_n_repr(v), None), v)
+                self.set_n_repr(update_func(self.get_n_repr(v), None), v)
         else:
-            # Read the node states in the degree-bucketing order.
+            # Compute new node repr for nodes with no in-coming messages.
             if len(null_v) == 0:
-                null_ns = new_null_ns = None
+                new_null_ns = None
             else:
-                null_ns = self.get_n_repr(null_v)
-                new_null_ns = f_update(null_ns, None)
+                new_null_ns = update_func(self.get_n_repr(null_v), None)
+            # Read the node states in the degree-bucketing order.
             if len(reordered_v) == 0:
-                reordered_ns = new_reordered_ns = None
+                new_reordered_ns = None
             else:
-                reordered_ns = self.get_n_repr(reordered_v)
-                new_reordered_ns = f_update(reordered_ns, all_reduced_msgs)
-
+                new_reordered_ns = update_func(self.get_n_repr(reordered_v), all_reduced_msgs)
             v_tensor = utils.pack2(null_v.totensor(), reordered_v.totensor())
             new_ns = utils.pack2(new_null_ns, new_reordered_ns)
 
@@ -569,7 +569,7 @@ class DGLGraph(DiGraph):
                 _, indices = F.sort(v_tensor)
                 indices = utils.toindex(indices)
                 # TODO(minjie): following code should be included in Frame somehow.
-                if isinstance(new_ns, dict):
+                if utils.is_dict_like(new_ns):
                     for key, val in new_ns.items():
                         idx = indices.totensor(F.get_context(val))
                         self._node_frame[key] = F.gather_row(val, idx)
@@ -581,16 +581,13 @@ class DGLGraph(DiGraph):
                 self.set_n_repr(new_ns, v_tensor)
 
     def _batch_reduce(self, v, reduce_func):
-        if is_all(v) and len(self._msg_frame) == 0:
-            # no message has been sent
+        if self._msg_frame.num_rows == 0:
+            # no message has ever been sent
             return None, None, None
 
         if is_all(v):
             v = list(range(self.number_of_nodes()))
 
-        # freeze message graph
-        self.msg_graph.freeze()
-
         # sanity checks
         v = utils.toindex(v)
         f_reduce = _get_reduce_func(reduce_func)
@@ -609,7 +606,7 @@ class DGLGraph(DiGraph):
                 null_v_bucket = v_bkt
                 continue
                 
-            uu, vv = self.msg_graph.in_edges(v_bkt)
+            uu, vv, _ = self.msg_graph.in_edges(v_bkt)
             in_msg_ids = self.msg_graph.get_edge_id(uu, vv)
             in_msgs = self._msg_frame.select_rows(in_msg_ids)
             # Reshape the column tensor to (B, Deg, ...).
@@ -625,7 +622,6 @@ class DGLGraph(DiGraph):
             non_null_v_buckets.append(v_bkt)
             reduced_msgs.append(f_reduce(dst_reprs, reshaped_in_msgs))
 
-        # FIXME: this will only trigger if reduced_msgs is empty.  Remove?
         if len(reduced_msgs) == 0:
             # no message has been sent to the specified node
             return None, None, None
@@ -641,7 +637,7 @@ class DGLGraph(DiGraph):
                 )
 
         # Pack all reduced msgs together
-        if isinstance(reduced_msgs[0], dict):
+        if utils.is_dict_like(reduced_msgs[0]):
             keys = reduced_msgs[0].keys()
             all_reduced_msgs = {
                     key : F.pack([msg[key] for msg in reduced_msgs])
@@ -713,7 +709,10 @@ class DGLGraph(DiGraph):
             message_func,
             reduce_func,
             update_func):
-        if is_all(u) and is_all(v):
+        if len(u) == 0:
+            # no message
+            assert len(v) == 0
+        elif is_all(u) and is_all(v):
             self.update_all(message_func, reduce_func, update_func, True)
         elif message_func == 'from_src' and reduce_func == 'sum':
             # TODO(minjie): check the validity of edges u->v
@@ -732,7 +731,6 @@ class DGLGraph(DiGraph):
             dat = F.ones((len(u),))
             n = self.number_of_nodes()
             m = len(new2old)
-            # TODO(minjie): context
             adjmat = F.sparse_tensor(idx, dat, [m, n])
             ctx_adjmat = utils.CtxCachedObject(lambda ctx: F.to_context(adjmat, ctx))
             # TODO(minjie): use lazy dict for reduced_msgs
@@ -784,14 +782,17 @@ class DGLGraph(DiGraph):
         assert update_func is not None
         if batchable:
             v = utils.toindex(v)
-            uu, vv = self.cached_graph.in_edges(v)
+            uu, vv, orphan = self.cached_graph.in_edges(v)
             self._batch_update_by_edge(uu, vv, message_func,
                     reduce_func, update_func)
+            # trigger update function for nodes that have no incoming messages.
+            self._batch_recv(orphan, reduce_func, update_func)
         else:
             v = utils.toindex(v)
             for vv in utils.node_iter(v):
                 assert vv in self.nodes
                 uu = list(self.pred[vv])
+                if len(uu) > 0:
                     self._nonbatch_sendto(uu, vv, message_func)
                 self._nonbatch_recv(vv, reduce_func, update_func)
 
@@ -827,7 +828,7 @@ class DGLGraph(DiGraph):
         assert update_func is not None
         if batchable:
             u = utils.toindex(u)
-            uu, vv = self.cached_graph.out_edges(u)
+            uu, vv, _ = self.cached_graph.out_edges(u)
             self._batch_update_by_edge(uu, vv, message_func,
                     reduce_func, update_func)
         else:
@@ -888,11 +889,11 @@ class DGLGraph(DiGraph):
             self._nonbatch_recv(list(self.nodes()), reduce_func, update_func)
 
     def propagate(self,
+                  iterator='bfs',
                   message_func=None,
                   reduce_func=None,
                   update_func=None,
                   batchable=False,
-                  iterator='bfs',
                   **kwargs):
         """Propagate messages and update nodes using iterator.
 
@@ -1072,7 +1073,7 @@ def _get_repr(attr_dict):
         return attr_dict
 
 def _set_repr(attr_dict, attr):
-    if isinstance(attr, dict):
+    if utils.is_dict_like(attr):
         attr_dict.update(attr)
     else:
         attr_dict[__REPR__] = attr

python/dgl/nx_adapt.py

@@ -64,6 +64,13 @@ class AdjInnerDict(MutableMapping):
     def __iter__(self):
         return iter(self._dict)
 
+class AdjInnerDictFactory(object):
+    def __init__(self, cb1, cb2):
+        self._cb1 = cb1
+        self._cb2 = cb2
+    def __call__(self):
+        return AdjInnerDict(self._cb1, self._cb2)
+
 def nx_init(obj,
             add_node_cb,
             add_edge_cb,
@@ -88,7 +95,7 @@ def nx_init(obj,
     """
     # The following codes work for networkx 2.1.
     obj.adjlist_outer_dict_factory = None
-    obj.adjlist_inner_dict_factory = lambda : AdjInnerDict(add_edge_cb, del_edge_cb)
+    obj.adjlist_inner_dict_factory = AdjInnerDictFactory(add_edge_cb, del_edge_cb)
     obj.edge_attr_dict_factory = dict
 
     obj.root_graph = obj

python/dgl/scheduler.py

@@ -31,4 +31,5 @@ def degree_bucketing(cached_graph, v):
     for deg in unique_degrees:
         idx = np.where(degrees == deg)
         v_bkt.append(utils.Index(v_np[idx]))
+    #print('degree-bucketing:', unique_degrees, [len(b) for b in v_bkt])
     return unique_degrees, v_bkt

python/dgl/utils.py

@@ -290,6 +290,9 @@ def cached_member(func):
             return func(self)
     return wrapper
 
+def is_dict_like(obj):
+    return isinstance(obj, Mapping)
+
 def pack2(a, b):
     if a is None:
         return b

tests/pytorch/test_batching.py

@@ -250,6 +250,35 @@ def test_reduce_0deg():
     assert th.allclose(new_repr[1:], old_repr[1:])
     assert th.allclose(new_repr[0], old_repr.sum(0))
 
+def test_update_to_0deg():
+    g = DGLGraph()
+    g.add_nodes_from([0, 1])
+    g.add_edge(0, 1)
+    def _message(src, edge):
+        return src
+    def _reduce(node, msgs):
+        assert msgs is not None
+        return msgs.sum(1)
+    def _update(node, accum):
+        return node * 2 if accum is None else accum
+
+    old_repr = th.randn(2, 5)
+    g.set_n_repr(old_repr)
+    g.update_to(0, _message, _reduce, _update, True)
+    new_repr = g.get_n_repr()
+    assert th.allclose(new_repr[0], old_repr[0] * 2)
+    assert th.allclose(new_repr[1], old_repr[1])
+    g.update_to(1, _message, _reduce, _update, True)
+    new_repr = g.get_n_repr()
+    assert th.allclose(new_repr[1], old_repr[0] * 2)
+
+    old_repr = th.randn(2, 5)
+    g.set_n_repr(old_repr)
+    g.update_to([0, 1], _message, _reduce, _update, True)
+    new_repr = g.get_n_repr()
+    assert th.allclose(new_repr[0], old_repr[0] * 2)
+    assert th.allclose(new_repr[1], old_repr[0])
+
 def _test_delete():
     g = generate_graph()
     ecol = Variable(th.randn(17, D), requires_grad=grad)
@@ -268,4 +297,5 @@ if __name__ == '__main__':
     test_batch_recv2()
     test_update_routines()
     test_reduce_0deg()
+    test_update_to_0deg()
     #test_delete()

tests/pytorch/test_cached_graph.py

@@ -21,13 +21,15 @@ def test_basics():
     u = Index(th.tensor([0, 0, 1, 1, 2, 2]))
     v = Index(th.tensor([1, 2, 2, 3, 4, 5]))
     check_eq(cg.get_edge_id(u, v).totensor(), th.tensor([0, 5, 1, 2, 3, 4]))
-    query = Index(th.tensor([1, 2]))
-    s, d = cg.in_edges(query)
-    check_eq(s.totensor(), th.tensor([0, 0, 1]))
-    check_eq(d.totensor(), th.tensor([1, 2, 2]))
-    s, d = cg.out_edges(query)
-    check_eq(s.totensor(), th.tensor([1, 1, 2, 2]))
-    check_eq(d.totensor(), th.tensor([2, 3, 4, 5]))
+    query = Index(th.tensor([0, 1, 2, 5]))
+    s, d, orphan = cg.in_edges(query)
+    check_eq(s.totensor(), th.tensor([0, 0, 1, 2]))
+    check_eq(d.totensor(), th.tensor([1, 2, 2, 5]))
+    assert orphan.tolist() == [0]
+    s, d, orphan = cg.out_edges(query)
+    check_eq(s.totensor(), th.tensor([0, 0, 1, 1, 2, 2]))
+    check_eq(d.totensor(), th.tensor([1, 2, 2, 3, 4, 5]))
+    assert orphan.tolist() == [5]
 
 if __name__ == '__main__':
     test_basics()

tests/test_basics.py

@@ -75,6 +75,22 @@ def _test_update_routines(g):
     g.update_all()
     check(g, [56, 5, 5, 6, 7, 8, 9, 10, 11, 108])
 
+def _test_update_to_0deg():
+    g = DGLGraph()
+    g.add_node(0, h=2)
+    g.add_node(1, h=1)
+    g.add_edge(0, 1)
+    def _message(src, edge):
+        return src
+    def _reduce(node, msgs):
+        assert msgs is not None
+        return msgs.sum(1)
+    def _update(node, accum):
+        assert accum is None
+        return {'h': node['h'] * 2}
+    g.update_to(0, _message, _reduce, _update)
+    assert g.nodes[0]['h'] == 4
+
 def test_sendrecv():
     g = generate_graph()
     register1(g)
@@ -99,6 +115,8 @@ def test_update_routines():
     register2(g)
     _test_update_routines(g)
 
+    _test_update_to_0deg()
+
 if __name__ == '__main__':
     test_sendrecv()
     test_multi_sendrecv()