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/* Copyright 2017 The TensorFlow 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.
==============================================================================*/
#include "tensorflow/core/grappler/costs/graph_properties.h"
#include "tensorflow/core/grappler/clusters/single_machine.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/test.h"
namespace tensorflow {
namespace grappler {
namespace {
class GraphPropertiesTest : public ::testing::Test {
public:
void SetUp() override {
// Provision a single machine with 3 cpu cores
cluster_.reset(new SingleMachine(5 * 60, 3, 0));
TF_CHECK_OK(cluster_->Provision());
}
void TearDown() override { cluster_.reset(); }
protected:
std::unique_ptr<SingleMachine> cluster_;
};
TEST_F(GraphPropertiesTest, StaticProperties) {
TrivialTestGraphInputYielder fake_input(4, 1, 10, false,
cluster_->GetDeviceNames());
GrapplerItem item;
CHECK(fake_input.NextItem(&item));
GraphProperties properties(item);
Status s = properties.InferStatically();
TF_CHECK_OK(s);
for (const auto& node : item.graph.node()) {
if (node.op() == "RandomStandardNormal") {
// The node has one input (the shape of the tensor to generate).
EXPECT_EQ(1, properties.GetInputProperties(node.name()).size());
// The const node has one output.
const auto props = properties.GetOutputProperties(node.name());
EXPECT_EQ(1, props.size());
const OpInfo::TensorProperties& prop = props[0];
EXPECT_EQ(DT_FLOAT, prop.dtype());
EXPECT_FALSE(prop.shape().unknown_rank());
EXPECT_EQ(2, prop.shape().dim_size());
EXPECT_EQ(10, prop.shape().dim(0).size());
EXPECT_EQ(1, prop.shape().dim(1).size());
} else if (node.op() == "AddN") {
const auto in_props = properties.GetInputProperties(node.name());
EXPECT_EQ(1, in_props.size());
const OpInfo::TensorProperties& in_prop = in_props[0];
EXPECT_EQ(DT_FLOAT, in_prop.dtype());
EXPECT_FALSE(in_prop.shape().unknown_rank());
EXPECT_EQ(2, in_prop.shape().dim_size());
EXPECT_EQ(10, in_prop.shape().dim(0).size());
EXPECT_EQ(1, in_prop.shape().dim(1).size());
const auto out_props = properties.GetOutputProperties(node.name());
EXPECT_EQ(1, out_props.size());
string in_prop_str;
::tensorflow::protobuf::TextFormat::PrintToString(in_prop, &in_prop_str);
string out_prop_str;
::tensorflow::protobuf::TextFormat::PrintToString(out_props[0],
&out_prop_str);
EXPECT_EQ(in_prop_str, out_prop_str);
}
}
}
TEST_F(GraphPropertiesTest, DynamicProperties) {
TrivialTestGraphInputYielder fake_input(4, 1, 10, false,
cluster_->GetDeviceNames());
GrapplerItem item;
CHECK(fake_input.NextItem(&item));
GraphProperties properties(item);
TF_CHECK_OK(cluster_->Initialize(item));
Status s = properties.InferDynamically(cluster_.get());
TF_CHECK_OK(s);
for (const auto& node : item.graph.node()) {
if (node.op() == "RandomStandardNormal") {
// The random node is missing from the cost graph (why ?)
EXPECT_EQ(0, properties.GetInputProperties(node.name()).size());
} else if (node.op() == "AddN") {
// Since the random node is missing, we can't infer the input properties
// of the first AddN node. The other AddN nodes have the expected
// properties.
if (node.name() == "AddN") {
const auto props = properties.GetInputProperties(node.name());
EXPECT_EQ(1, props.size());
const OpInfo::TensorProperties& prop = props[0];
EXPECT_EQ(DT_INVALID, prop.dtype());
EXPECT_TRUE(prop.shape().unknown_rank());
} else {
const auto props = properties.GetInputProperties(node.name());
EXPECT_EQ(1, props.size());
const OpInfo::TensorProperties& prop = props[0];
EXPECT_EQ(DT_FLOAT, prop.dtype());
EXPECT_FALSE(prop.shape().unknown_rank());
EXPECT_EQ(2, prop.shape().dim_size());
EXPECT_EQ(10, prop.shape().dim(0).size());
EXPECT_EQ(1, prop.shape().dim(1).size());
const auto out_props = properties.GetOutputProperties(node.name());
EXPECT_EQ(1, out_props.size());
string prop_str;
::tensorflow::protobuf::TextFormat::PrintToString(prop, &prop_str);
string out_prop_str;
::tensorflow::protobuf::TextFormat::PrintToString(out_props[0],
&out_prop_str);
EXPECT_EQ(prop_str, out_prop_str);
}
}
}
}
} // namespace
} // namespace grappler
} // namespace tensorflow
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