Merge pull request #132 from Liwb5/dev

add mmap on hetergraph

examples/GATNE/model.py

@@ -114,29 +114,29 @@ class GATNE(object):
         node_type_embed = fl.gather(node_type_embed, self.train_inputs)
 
         # M_r
+        tn_initializer = fluid.initializer.TruncatedNormalInitializer(
+            loc=0.0, scale=1.0 / math.sqrt(self.embedding_size))
+
         trans_weights = fl.create_parameter(
             shape=[
                 self.edge_type_count, self.embedding_u_size,
                 self.embedding_size // self.att_head
             ],
-            attr=fluid.initializer.TruncatedNormalInitializer(
-                loc=0.0, scale=1.0 / math.sqrt(self.embedding_size)),
+            default_initializer=tn_initializer,
             dtype='float32',
             name='trans_w')
 
         # W_r
         trans_weights_s1 = fl.create_parameter(
             shape=[self.edge_type_count, self.embedding_u_size, self.dim_a],
-            attr=fluid.initializer.TruncatedNormalInitializer(
-                loc=0.0, scale=1.0 / math.sqrt(self.embedding_size)),
+            default_initializer=tn_initializer,
             dtype='float32',
             name='trans_w_s1')
 
         # w_r
         trans_weights_s2 = fl.create_parameter(
             shape=[self.edge_type_count, self.dim_a, self.att_head],
-            attr=fluid.initializer.TruncatedNormalInitializer(
-                loc=0.0, scale=1.0 / math.sqrt(self.embedding_size)),
+            default_initializer=tn_initializer,
             dtype='float32',
             name='trans_w_s2')
 

pgl/heter_graph.py

@@ -14,12 +14,13 @@
 """
     This package implement Heterogeneous Graph structure for handling Heterogeneous graph data.
 """
+import os
 import time
 import numpy as np
 import pickle as pkl
 import time
 import pgl.graph_kernel as graph_kernel
-from pgl.graph import Graph
+from pgl.graph import Graph, MemmapGraph
 
 __all__ = ['HeterGraph', 'SubHeterGraph']
 
@@ -113,6 +114,30 @@ class HeterGraph(object):
 
         self._edge_types = self.edge_types_info()
 
+    def dump(self, path, indegree=False, outdegree=False):
+
+        if indegree:
+            for e_type, g in self._multi_graph.items():
+                g.indegree()
+
+        if outdegree:
+            for e_type, g in self._multi_graph.items():
+                g.outdegree()
+
+        if not os.path.exists(path):
+            os.makedirs(path)
+
+        np.save(os.path.join(path, "num_nodes.npy"), self._num_nodes)
+        np.save(os.path.join(path, "node_types.npy"), self._node_types)
+        with open(os.path.join(path, "edge_types.pkl"), 'wb') as f:
+            pkl.dump(self._edge_types, f)
+        with open(os.path.join(path, "nodes_type_dict.pkl"), 'wb') as f:
+            pkl.dump(self._nodes_type_dict, f)
+
+        for e_type, g in self._multi_graph.items():
+            sub_path = os.path.join(path, e_type)
+            g.dump(sub_path)
+
     @property
     def edge_types(self):
         """Return a list of edge types.
@@ -399,7 +424,7 @@ class HeterGraph(object):
         
         """
         edge_types_info = []
-        for key, _ in self._edges_dict.items():
+        for key, _ in self._multi_graph.items():
             edge_types_info.append(key)
 
         return edge_types_info
@@ -460,3 +485,21 @@ class SubHeterGraph(HeterGraph):
             A list of node ids in parent graph.
         """
         return graph_kernel.map_nodes(nodes, self._to_reindex)
+
+
+class MemmapHeterGraph(HeterGraph):
+    def __init__(self, path):
+        self._num_nodes = np.load(os.path.join(path, 'num_nodes.npy'))
+        self._node_types = np.load(
+            os.path.join(path, 'node_types.npy'), allow_pickle=True)
+
+        with open(os.path.join(path, 'edge_types.pkl'), 'rb') as f:
+            self._edge_types = pkl.load(f)
+
+        with open(os.path.join(path, "nodes_type_dict.pkl"), 'rb') as f:
+            self._nodes_type_dict = pkl.load(f)
+
+        self._multi_graph = {}
+        for e_type in self._edge_types:
+            sub_path = os.path.join(path, e_type)
+            self._multi_graph[e_type] = MemmapGraph(sub_path)

pgl/tests/test_MmapHeterGraph.py

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""test_hetergraph"""
+
+import time
+import unittest
+import json
+import os
+
+import numpy as np
+from pgl.sample import metapath_randomwalk
+from pgl.graph import Graph
+from pgl import heter_graph
+from pgl.heter_graph import MemmapHeterGraph
+
+
+def test_dump():
+    np.random.seed(1)
+    edges = {}
+    # for test no successor
+    edges['c2p'] = [(1, 4), (0, 5), (1, 9), (1, 8), (2, 8), (2, 5), (3, 6),
+                    (3, 7), (3, 4), (3, 8)]
+    edges['p2c'] = [(v, u) for u, v in edges['c2p']]
+    edges['p2a'] = [(4, 10), (4, 11), (4, 12), (4, 14), (4, 13), (6, 12),
+                    (6, 11), (6, 14), (7, 12), (7, 11), (8, 14), (9, 10)]
+    edges['a2p'] = [(v, u) for u, v in edges['p2a']]
+
+    node_types = ['c' for _ in range(4)] + ['p' for _ in range(6)
+                                            ] + ['a' for _ in range(5)]
+    node_types = [(i, t) for i, t in enumerate(node_types)]
+
+    graph = heter_graph.HeterGraph(
+        num_nodes=len(node_types), edges=edges, node_types=node_types)
+
+    graph.dump("./hetergraph_mmap", outdegree=True)
+
+
+def test_load():
+    graph = MemmapHeterGraph("./hetergraph_mmap")
+
+
+class MmapHeterGraphTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.graph = MemmapHeterGraph("./hetergraph_mmap")
+
+    def test_num_nodes_by_type(self):
+        print()
+        n_types = {'c': 4, 'p': 6, 'a': 5}
+        for nt in n_types:
+            num_nodes = self.graph.num_nodes_by_type(nt)
+            self.assertEqual(num_nodes, n_types[nt])
+
+    def test_node_batch_iter(self):
+        print()
+        batch_size = 2
+        ground = [[4, 5], [6, 7], [8, 9]]
+        for idx, nodes in enumerate(
+                self.graph.node_batch_iter(
+                    batch_size=batch_size, shuffle=False, n_type='p')):
+            self.assertEqual(len(nodes), batch_size)
+            self.assertListEqual(list(nodes), ground[idx])
+
+    def test_sample_successor(self):
+        print()
+        nodes = [4, 5, 8]
+        md = 2
+        succes = self.graph.sample_successor(
+            edge_type='p2a', nodes=nodes, max_degree=md, return_eids=False)
+        self.assertIsInstance(succes, list)
+        ground = [[10, 11, 12, 14, 13], [], [14]]
+        for succ, g in zip(succes, ground):
+            self.assertIsInstance(succ, np.ndarray)
+            for i in succ:
+                self.assertIn(i, g)
+
+        nodes = [4]
+        succes = self.graph.sample_successor(
+            edge_type='p2a', nodes=nodes, max_degree=md, return_eids=False)
+        self.assertIsInstance(succes, list)
+        ground = [[10, 11, 12, 14, 13]]
+        for succ, g in zip(succes, ground):
+            self.assertIsInstance(succ, np.ndarray)
+            for i in succ:
+                self.assertIn(i, g)
+
+    def test_successor(self):
+        print()
+        nodes = [4, 5, 8]
+        e_type = 'p2a'
+        succes = self.graph.successor(
+            edge_type=e_type,
+            nodes=nodes, )
+
+        self.assertIsInstance(succes, np.ndarray)
+        ground = [[10, 11, 12, 14, 13], [], [14]]
+        for succ, g in zip(succes, ground):
+            self.assertIsInstance(succ, np.ndarray)
+            self.assertCountEqual(succ, g)
+
+        nodes = [4]
+        e_type = 'p2a'
+        succes = self.graph.successor(
+            edge_type=e_type,
+            nodes=nodes, )
+
+        self.assertIsInstance(succes, np.ndarray)
+        ground = [[10, 11, 12, 14, 13]]
+        for succ, g in zip(succes, ground):
+            self.assertIsInstance(succ, np.ndarray)
+            self.assertCountEqual(succ, g)
+
+    def test_predecessor(self):
+        print()
+        nodes = [11, 12, 13]
+        e_type = 'p2a'
+        pre = self.graph.predecessor(
+            edge_type=e_type,
+            nodes=nodes, )
+
+        self.assertIsInstance(pre, np.ndarray)
+
+        print(pre)
+        ground = [[4, 6, 7], [4, 6, 7], [4]]
+        for succ, g in zip(pre, ground):
+            self.assertIsInstance(succ, np.ndarray)
+            self.assertCountEqual(succ, g)
+
+        nodes = [11]
+        e_type = 'p2a'
+        pre = self.graph.predecessor(
+            edge_type=e_type,
+            nodes=nodes, )
+        print(pre)
+
+        self.assertIsInstance(pre, np.ndarray)
+        ground = [[4, 6, 7]]
+        for p, g in zip(pre, ground):
+            self.assertIsInstance(p, np.ndarray)
+            self.assertCountEqual(p, g)
+
+    def test_sample_nodes(self):
+        print()
+        p_ground = [4, 5, 6, 7, 8, 9]
+        sample_num = 10
+        nodes = self.graph.sample_nodes(sample_num=sample_num, n_type='p')
+
+        self.assertEqual(len(nodes), sample_num)
+        for n in nodes:
+            self.assertIn(n, p_ground)
+
+        # test n_type == None
+        ground = [i for i in range(15)]
+        nodes = self.graph.sample_nodes(sample_num=sample_num, n_type=None)
+        self.assertEqual(len(nodes), sample_num)
+        for n in nodes:
+            self.assertIn(n, ground)
+
+
+if __name__ == "__main__":
+    unittest.main()
+    #  test_dump()