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
16 #include "tensorflow/core/grappler/optimizers/auto_parallel.h"
17 #include "tensorflow/cc/ops/standard_ops.h"
18 #include "tensorflow/core/framework/node_def.pb.h"
19 #include "tensorflow/core/grappler/grappler_item.h"
20 #include "tensorflow/core/grappler/utils.h"
21 #include "tensorflow/core/lib/core/status_test_util.h"
22 #include "tensorflow/core/platform/test.h"
23
24 namespace tensorflow {
25 namespace grappler {
26 namespace {
27
28 class AutoParallelTest : public ::testing::Test {};
29
TEST_F(AutoParallelTest,SimpleParallel)30 TEST_F(AutoParallelTest, SimpleParallel) {
31 tensorflow::Scope s = tensorflow::Scope::DisabledShapeInferenceScope();
32 Output constant_a = ops::Const(s.WithOpName("constant_a"), 1.0f, {1});
33 Output constant_b = ops::Const(s.WithOpName("constant_b"), 1, {1});
34 Output var = ops::Variable(s.WithOpName("var"), {1}, DT_FLOAT);
35 Output assign = ops::Assign(s.WithOpName("assign"), {var}, {constant_a});
36 Output identity = ops::Identity(s.WithOpName("identity"), {var});
37 Output fifo_queue = ops::FIFOQueue(s.WithOpName("fifo_queue"), {DT_FLOAT});
38 auto dequeue = ops::QueueDequeueMany(s.WithOpName("dequeue"), {fifo_queue},
39 {constant_b}, {DT_FLOAT});
40 Output add = ops::AddN(s.WithOpName("add"), {constant_a, dequeue[0]});
41 Output learning_rate = ops::Const(s.WithOpName("learning_rate"), 0.01f, {1});
42 Output apply_gradient = ops::ApplyGradientDescent(
43 s.WithOpName("apply_gradient"), {var}, {learning_rate}, {add});
44
45 GrapplerItem item;
46 item.init_ops.push_back("assign");
47 item.fetch.push_back("apply_gradient");
48 item.init_ops.push_back("assign");
49 TF_CHECK_OK(s.ToGraphDef(&item.graph));
50
51 AutoParallel parallel(2);
52 GraphDef output;
53 Status status = parallel.Optimize(nullptr, item, &output);
54 TF_EXPECT_OK(status);
55 EXPECT_EQ(21, output.node_size());
56
57 const NodeDef& node_assign = output.node(0);
58 EXPECT_EQ("assign", node_assign.name());
59 EXPECT_EQ("AutoParallel-Replica-0/constant_a", node_assign.input(1));
60
61 const NodeDef& node_constant_b = output.node(1);
62 EXPECT_EQ("constant_b", node_constant_b.name());
63
64 const NodeDef& node_fifo_queue = output.node(2);
65 EXPECT_EQ("fifo_queue", node_fifo_queue.name());
66
67 const NodeDef& node_identity = output.node(3);
68 EXPECT_EQ("identity", node_identity.name());
69 EXPECT_EQ("var", node_identity.input(0));
70
71 const NodeDef& node_var = output.node(4);
72 EXPECT_EQ("var", node_var.name());
73
74 const NodeDef& node_div_const0 = output.node(5);
75 EXPECT_EQ("AutoParallel-Replica-0/AutoParallel-Div-Const",
76 node_div_const0.name());
77
78 const NodeDef& node_div0 = output.node(6);
79 EXPECT_EQ("AutoParallel-Replica-0/AutoParallel-Div-apply_gradient",
80 node_div0.name());
81 const NodeDef& node_add0 = output.node(7);
82 EXPECT_EQ("AutoParallel-Replica-0/add", node_add0.name());
83
84 const NodeDef& node_gradient0 = output.node(8);
85 EXPECT_EQ("AutoParallel-Replica-0/apply_gradient", node_gradient0.name());
86
87 const NodeDef& node_constant_a0 = output.node(9);
88 EXPECT_EQ("AutoParallel-Replica-0/constant_a", node_constant_a0.name());
89
90 const NodeDef& node_dequeue0 = output.node(10);
91 EXPECT_EQ("AutoParallel-Replica-0/dequeue", node_dequeue0.name());
92
93 const NodeDef& node_learning_rate0 = output.node(11);
94 EXPECT_EQ("AutoParallel-Replica-0/learning_rate", node_learning_rate0.name());
95
96 const NodeDef& node_div_const1 = output.node(12);
97 EXPECT_EQ("AutoParallel-Replica-1/AutoParallel-Div-Const",
98 node_div_const1.name());
99
100 const NodeDef& node_div1 = output.node(13);
101 EXPECT_EQ("AutoParallel-Replica-1/AutoParallel-Div-apply_gradient",
102 node_div1.name());
103
104 const NodeDef& node_add1 = output.node(14);
105 EXPECT_EQ("AutoParallel-Replica-1/add", node_add1.name());
106
107 const NodeDef& node_gradient1 = output.node(15);
108 EXPECT_EQ("AutoParallel-Replica-1/apply_gradient", node_gradient1.name());
109
110 const NodeDef& node_constant_a1 = output.node(16);
111 EXPECT_EQ("AutoParallel-Replica-1/constant_a", node_constant_a1.name());
112
113 const NodeDef& node_dequeue1 = output.node(17);
114 EXPECT_EQ("AutoParallel-Replica-1/dequeue", node_dequeue1.name());
115
116 const NodeDef& node_learning_rate1 = output.node(18);
117 EXPECT_EQ("AutoParallel-Replica-1/learning_rate", node_learning_rate1.name());
118
119 const NodeDef& node_fetch = output.node(19);
120 EXPECT_EQ("AutoParallel-Control-Fetch", node_fetch.name());
121 EXPECT_EQ("^AutoParallel-Replica-0/apply_gradient", node_fetch.input(0));
122 EXPECT_EQ("^AutoParallel-Replica-1/apply_gradient", node_fetch.input(1));
123
124 const NodeDef& node_gradient = output.node(20);
125 EXPECT_EQ("apply_gradient", node_gradient.name());
126 EXPECT_EQ("^AutoParallel-Control-Fetch", node_gradient.input(0));
127 }
128
129 } // namespace
130 } // namespace grappler
131 } // namespace tensorflow
132