/* Copyright 2015 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/graph/algorithm.h"

#include <string>

#include <vector>

#include "tensorflow/core/framework/graph.pb.h"

#include "tensorflow/core/graph/graph.h"

#include "tensorflow/core/graph/graph_def_builder.h"

#include "tensorflow/core/graph/subgraph.h"

#include "tensorflow/core/kernels/ops_util.h"

#include "tensorflow/core/lib/core/status.h"

#include "tensorflow/core/lib/core/status_test_util.h"

#include "tensorflow/core/platform/test.h"

// TODO(josh11b): Test setting the "device" field of a NodeDef.

// TODO(josh11b): Test that feeding won't prune targets.

namespace tensorflow {

namespace {

REGISTER_OP("TestParams").Output("o: float");

REGISTER_OP("TestInput").Output("a: float").Output("b: float");

REGISTER_OP("TestMul").Input("a: float").Input("b: float").Output("o: float");

// Compares that the order of nodes in 'inputs' respects the

// pair orders described in 'ordered_pairs'.

bool ExpectBefore(const std::vector<std::pair<string, string>>& ordered_pairs,

const std::vector<Node*>& inputs, string* error) {

for (const std::pair<string, string>& pair : ordered_pairs) {

const string& before_node = pair.first;

const string& after_node = pair.second;

bool seen_before = false;

bool seen_both = false;

for (const Node* node : inputs) {

if (!seen_before && after_node == node->name()) {

*error = strings::StrCat("Saw ", after_node, " before ", before_node);

return false;

}

if (before_node == node->name()) {

seen_before = true;

} else if (after_node == node->name()) {

seen_both = seen_before;

break;

}

}

if (!seen_both) {

*error = strings::StrCat("didn't see either ", before_node, " or ",

after_node);

return false;

}

}

return true;

}

TEST(AlgorithmTest, ReversePostOrder) {

GraphDefBuilder b(GraphDefBuilder::kFailImmediately);

using namespace ::tensorflow::ops; // NOLINT(build/namespaces)

Node* w1 = SourceOp("TestParams", b.opts().WithName("W1"));

Node* w2 = SourceOp("TestParams", b.opts().WithName("W2"));

Node* input =

SourceOp("TestInput", b.opts().WithName("input").WithControlInput(w1));

Node* t1 = BinaryOp("TestMul", w1, {input, 1}, b.opts().WithName("t1"));

BinaryOp("TestMul", w1, {input, 1},

b.opts().WithName("t2").WithControlInput(t1));

BinaryOp("TestMul", w2, {input, 1}, b.opts().WithName("t3"));

Graph g(OpRegistry::Global());

TF_ASSERT_OK(b.ToGraph(&g));

std::vector<Node*> order;

// Test reverse post order:

GetReversePostOrder(g, &order);

// Check that the order respects the dependencies correctly.

std::vector<std::pair<string, string>> reverse_orders = {

{"W1", "input"}, {"W1", "t1"}, {"W1", "t2"}, {"W1", "t3"},

{"input", "t1"}, {"input", "t3"}, {"t1", "t2"}, {"W2", "t3"}};

string error;

EXPECT_TRUE(ExpectBefore(reverse_orders, order, &error)) << error;

// A false ordering should fail the check.

reverse_orders = {{"input", "W1"}};

EXPECT_FALSE(ExpectBefore(reverse_orders, order, &error));

// Test post order:

GetPostOrder(g, &order);

// Check that the order respects the dependencies correctly.

std::vector<std::pair<string, string>> orders = {

{"input", "W1"}, {"t1", "W1"}, {"t2", "W1"}, {"t3", "W1"},

{"t1", "input"}, {"t3", "input"}, {"t2", "t1"}, {"t3", "W2"}};

EXPECT_TRUE(ExpectBefore(orders, order, &error)) << error;

// A false ordering should fail the check.

orders = {{"W1", "t3"}};

EXPECT_FALSE(ExpectBefore(orders, order, &error));

}

} // namespace

} // namespace tensorflow