Identify frame ids for all nodes in a graph.

PiperOrigin-RevId: 166397615
This commit is contained in:
Yao Zhang 2017-08-24 14:46:13 -07:00 committed by TensorFlower Gardener
parent 989713f265
commit c4a58e3fdd
6 changed files with 218 additions and 0 deletions

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@ -45,6 +45,11 @@ bool IsEnter(const NodeDef& node) {
return op == "Enter" || op == "RefEnter"; return op == "Enter" || op == "RefEnter";
} }
bool IsExit(const NodeDef& node) {
const auto& op = node.op();
return op == "Exit" || op == "RefExit";
}
bool IsIdentity(const NodeDef& node) { bool IsIdentity(const NodeDef& node) {
const auto& op = node.op(); const auto& op = node.op();
return op == "Identity" || op == "RefIdentity"; return op == "Identity" || op == "RefIdentity";

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@ -26,6 +26,7 @@ bool IsConcat(const NodeDef& node);
bool IsConstant(const NodeDef& node); bool IsConstant(const NodeDef& node);
bool IsDequeueOp(const NodeDef& node); bool IsDequeueOp(const NodeDef& node);
bool IsEnter(const NodeDef& node); bool IsEnter(const NodeDef& node);
bool IsExit(const NodeDef& node);
bool IsIdentity(const NodeDef& node); bool IsIdentity(const NodeDef& node);
bool IsMerge(const NodeDef& node); bool IsMerge(const NodeDef& node);
bool IsNextIteration(const NodeDef& node); bool IsNextIteration(const NodeDef& node);

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@ -71,3 +71,29 @@ cc_test(
"//tensorflow/core:test_main", "//tensorflow/core:test_main",
], ],
) )
cc_library(
name = "frame",
srcs = ["frame.cc"],
hdrs = ["frame.h"],
visibility = ["//visibility:public"],
deps = [
"//tensorflow/core:lib_internal",
"//tensorflow/core:protos_all_cc",
"//tensorflow/core/grappler:op_types",
"//tensorflow/core/grappler:utils",
],
)
cc_test(
name = "frame_test",
size = "small",
srcs = ["frame_test.cc"],
deps = [
":frame",
"//tensorflow/core:lib_proto_parsing",
"//tensorflow/core:protos_all_cc",
"//tensorflow/core:test",
"//tensorflow/core:test_main",
],
)

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@ -0,0 +1,62 @@
/* 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/utils/frame.h"
#include <deque>
#include <stack>
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/grappler/op_types.h"
#include "tensorflow/core/grappler/utils.h"
namespace tensorflow {
namespace grappler {
int IdentifyFrames(
const GraphDef& graph,
std::unordered_map<const NodeDef*, std::vector<int>>* frames) {
NodeMap node_map(const_cast<GraphDef*>(&graph));
std::deque<std::pair<const NodeDef*, std::vector<int>>> ready_nodes;
for (const NodeDef& node : graph.node()) {
if (node.input_size() == 0) {
std::vector<int> empty;
ready_nodes.emplace_back(&node, empty);
}
}
int frame_id = 0;
while (!ready_nodes.empty()) {
auto ready_node = ready_nodes.front();
for (const auto& fanout : node_map.GetOutputs(ready_node.first->name())) {
if (frames->count(fanout) < 1) {
std::vector<int> frame_ids = ready_node.second;
if (IsExit(*ready_node.first)) {
frame_ids.pop_back();
}
if (IsEnter(*fanout)) {
frame_ids.push_back(frame_id);
frame_id++;
}
ready_nodes.emplace_back(fanout, frame_ids);
} else {
CHECK(ready_node.second == (*frames)[fanout]);
}
}
(*frames)[ready_node.first] = ready_node.second;
ready_nodes.pop_front();
}
return frame_id;
}
} // namespace grappler
} // namespace tensorflow

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@ -0,0 +1,35 @@
/* 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.
==============================================================================*/
#ifndef THIRD_PARTY_TENSORFLOW_CORE_GRAPPLER_UTILS_FRAME_H_
#define THIRD_PARTY_TENSORFLOW_CORE_GRAPPLER_UTILS_FRAME_H_
#include <unordered_map>
#include "tensorflow/core/framework/graph.pb.h"
namespace tensorflow {
namespace grappler {
// Returns the number of frames present in the graph, and populates
// the 'frames' argument with the collection of frames (denoted by their
// frame ids) in the outermost-to-innermost order. Frame ids are arbitrary.
int IdentifyFrames(
const GraphDef& graph,
std::unordered_map<const NodeDef*, std::vector<int>>* frames);
} // namespace grappler
} // namespace tensorflow
#endif // THIRD_PARTY_TENSORFLOW_CORE_GRAPPLER_UTILS_FRAME_H_

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@ -0,0 +1,89 @@
/* 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/utils/frame.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/test.h"
namespace tensorflow {
namespace grappler {
namespace {
class IdentifyFramesTest : public ::testing::Test {
protected:
static NodeDef CreateNode(const string& name,
const std::vector<string>& inputs) {
return CreateNode(name, "", inputs);
}
static NodeDef CreateNode(const string& name, const string& op,
const std::vector<string>& inputs) {
NodeDef node;
node.set_name(name);
if (!op.empty()) {
node.set_op(op);
}
for (const string& input : inputs) {
node.add_input(input);
}
return node;
}
};
TEST_F(IdentifyFramesTest, WithLoop) {
GraphDef graph;
// Create a two-level nested loop
*graph.add_node() = CreateNode("0", {});
*graph.add_node() = CreateNode("1", "Enter", {"0"});
*graph.add_node() = CreateNode("2", {"1"});
*graph.add_node() = CreateNode("3", "Merge", {"2", "14"});
*graph.add_node() = CreateNode("4", {"3"});
*graph.add_node() = CreateNode("5", "Switch", {"4"});
*graph.add_node() = CreateNode("6", {"5"});
*graph.add_node() = CreateNode("7", "Enter", {"6"});
*graph.add_node() = CreateNode("8", {"7"});
*graph.add_node() = CreateNode("9", "Merge", {"8", "12"});
*graph.add_node() = CreateNode("10", {"9"});
*graph.add_node() = CreateNode("11", "Switch", {"10"});
*graph.add_node() = CreateNode("12", "NextIteration", {"11"});
*graph.add_node() = CreateNode("13", "Exit", {"11"});
*graph.add_node() = CreateNode("14", "NextIteration", {"13"});
*graph.add_node() = CreateNode("15", {"5"});
*graph.add_node() = CreateNode("16", "Exit", {"15"});
*graph.add_node() = CreateNode("17", {"16"});
std::unordered_map<const NodeDef*, std::vector<int>> frames;
int num_frames = IdentifyFrames(graph, &frames);
std::unordered_map<string, std::vector<int>> expected = {
{"0", {}}, {"1", {0}}, {"2", {0}}, {"3", {0}},
{"4", {0}}, {"5", {0}}, {"6", {0}}, {"7", {0, 1}},
{"8", {0, 1}}, {"9", {0, 1}}, {"10", {0, 1}}, {"11", {0, 1}},
{"12", {0, 1}}, {"13", {0, 1}}, {"14", {0}}, {"15", {0}},
{"16", {0}}, {"17", {}}};
EXPECT_EQ(num_frames, 2);
std::cout << "Number of frame: " << num_frames << std::endl;
for (const auto& node : frames) {
std::cout << node.first->name() << ": ";
for (int i = 0; i < node.second.size(); i++) {
EXPECT_EQ(expected[node.first->name()][i], node.second[i]);
std::cout << node.second[i] << " ";
}
std::cout << std::endl;
}
}
} // namespace
} // namespace grappler
} // namespace tensorflow