• Home
  • Line#
  • Scopes#
  • Navigate#
  • Raw
  • Download
1# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""Tests for the swig wrapper tf_optimizer."""
16
17from __future__ import absolute_import
18from __future__ import division
19from __future__ import print_function
20from tensorflow.core.framework import attr_value_pb2
21from tensorflow.core.protobuf import config_pb2
22from tensorflow.python.framework import constant_op
23from tensorflow.python.framework import dtypes
24from tensorflow.python.framework import meta_graph
25from tensorflow.python.framework import ops
26from tensorflow.python.framework import tensor_shape
27from tensorflow.python.framework import test_util
28from tensorflow.python.grappler import item as gitem
29from tensorflow.python.grappler import tf_optimizer
30from tensorflow.python.ops import array_ops
31from tensorflow.python.ops import control_flow_ops
32from tensorflow.python.ops import math_ops
33from tensorflow.python.ops import variables
34from tensorflow.python.platform import test
35
36
37class PyWrapOptimizeGraphTest(test.TestCase):
38
39  @test_util.run_deprecated_v1
40  def testBasic(self):
41    """Make sure arguments can be passed correctly."""
42    a = constant_op.constant(10, name='a')
43    b = constant_op.constant(20, name='b')
44    c = math_ops.add_n([a, b], name='c')
45    d = math_ops.add_n([b, c], name='d')
46    train_op = ops.get_collection_ref(ops.GraphKeys.TRAIN_OP)
47    # Being a train_op will make 'd' to be added as a fetch node.
48    train_op.append(d)
49    mg = meta_graph.create_meta_graph_def(graph=ops.get_default_graph())
50
51    config = config_pb2.ConfigProto()
52    rewriter_config = config.graph_options.rewrite_options
53    rewriter_config.optimizers.append('constfold')
54    rewriter_config.min_graph_nodes = -1
55
56    graph = tf_optimizer.OptimizeGraph(config, mg)
57
58    self.assertEqual(len(graph.node), 1)
59    self.assertItemsEqual([node.name for node in graph.node], ['d'])
60
61  @test_util.run_v1_only('b/120545219')
62  def testKeepNodes(self):
63    g = ops.Graph()
64    with g.as_default():
65      a1 = variables.VariableV1(
66          1.0)  # Must be preserved since it's in the collection 'variables'.
67      a2 = constant_op.constant(0, shape=[50, 50], name='keep')
68      ops.add_to_collection('a2', a2)  # Explicitly add to collection.
69      with g._attr_scope(
70          {'_grappler_do_not_remove': attr_value_pb2.AttrValue(b=True)}):
71        a3 = constant_op.constant(0, name='keep2')
72      b = constant_op.constant(1, shape=[100, 10])
73      c = constant_op.constant(0, shape=[10, 30])
74      d = math_ops.matmul(b, c)
75      ops.add_to_collection('train_op', d)  # d is the fetch node.
76
77    # Optimize the graph.
78    mg = meta_graph.create_meta_graph_def(graph=g)
79    config = config_pb2.ConfigProto()
80    rewriter_config = config.graph_options.rewrite_options
81    rewriter_config.min_graph_nodes = -1
82    optimized_graph = tf_optimizer.OptimizeGraph(config, mg)
83
84    # Check that the nodes referenced in various collections have been preserved
85    optimized_graph_nodes = [node.name for node in optimized_graph.node]
86    expected_nodes = [
87        d.op.name, a1.op.name, a2.op.name, a3.op.name, 'Variable/initial_value',
88        'Variable/Assign'
89    ]
90    self.assertEqual(len(optimized_graph_nodes), len(expected_nodes))
91    self.assertAllInSet(optimized_graph_nodes, expected_nodes)
92
93  @test_util.run_v1_only('b/120545219')
94  def testLoops(self):
95    g = ops.Graph()
96    with g.as_default():
97
98      def _Cond(_, counter):
99        return counter < end
100
101      def _Body(buf, counter):
102        buf = array_ops.concat([buf, [counter]], 0)
103        counter += 1
104        return [buf, counter]
105
106      start = array_ops.placeholder(shape=[], dtype=dtypes.int32)
107      end = array_ops.placeholder(shape=[], dtype=dtypes.int32)
108      init_buf = array_ops.zeros(shape=[0], dtype=dtypes.int32)
109      loop_vars = [init_buf, start]
110      shape_inv = [
111          tensor_shape.TensorShape([None]),
112          tensor_shape.TensorShape([])
113      ]
114      buf, _ = control_flow_ops.while_loop(_Cond, _Body, loop_vars, shape_inv)
115
116      f = -array_ops.ones_like(buf, optimize=False)
117      buf_shape = array_ops.shape(buf)
118      f_shape = array_ops.shape(f)
119      ops.add_to_collection('train_op', buf_shape)
120      ops.add_to_collection('train_op', f_shape)
121
122    # Optimize the graph.
123    mg = meta_graph.create_meta_graph_def(graph=g)
124    config = config_pb2.ConfigProto()
125    rewriter_config = config.graph_options.rewrite_options
126    rewriter_config.min_graph_nodes = -1
127    optimized_graph = tf_optimizer.OptimizeGraph(config, mg)
128    mg.graph_def.CopyFrom(optimized_graph)
129
130    # Check that the nodes referenced in various collections have been preserved
131    item = gitem.Item(mg)
132    props = item.GetOpProperties()
133    buf_prop = props[buf.op.name]
134    f_prop = props[f.op.name]
135    self.assertEqual(buf_prop, f_prop)
136
137
138if __name__ == '__main__':
139  test.main()
140