1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
|
/* 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/contrib/lite/toco/graph_transformations/graph_transformations.h"
#include <algorithm>
#include <memory>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "tensorflow/contrib/lite/toco/toco_port.h"
#include "tensorflow/contrib/lite/toco/tooling_util.h"
#include "tensorflow/core/platform/logging.h"
namespace toco {
namespace {
void PrintModelStats(const string& label, const Model& model) {
int quantized_arrays = 0;
for (const auto& array : model.GetArrayMap()) {
if (array.second->quantization_params) {
quantized_arrays++;
}
}
LOG(INFO) << label << ": " << model.operators.size() << " operators, "
<< model.GetArrayMap().size() << " arrays (" << quantized_arrays
<< " quantized)";
}
// Some graphs have RNN back-edges that are discardable, having been
// created typically by TensorFlow import rather than specified by the user.
// Such graphs might have cycles (closed by RNN back-edges) that may be pruned.
// Local graph transformations can't identify such global features,
// so this function performs this global transformation.
//
// The other (and related) thing that is peculiar about RNN back-edges
// is that they do not prevent the arrays that they touch, from being
// pruned. Thus, they may refer to array names which no longer exist.
// The intent is for that to result in the eventual pruning of such
// 'dangling' RNN back-edges. We perform this pruning at the end of this
// function, as the pruning of connected components done here may leave
// more RNN back-edges dangling.
void DiscardUselessConnectedComponentsAndRNNBackEdges(Model* model) {
// Identify the set of arrays that are in 'useful' connected components
// of the graph, which means connected to output arrays.
std::unordered_set<string> useful_arrays;
for (const string& output_array : model->flags.output_arrays()) {
useful_arrays.insert(output_array);
}
bool found_new_useful_arrays;
do {
found_new_useful_arrays = false;
for (const auto& op : model->operators) {
bool op_touches_useful_arrays = false;
for (const string& output : op->outputs) {
op_touches_useful_arrays |= useful_arrays.count(output);
}
if (op_touches_useful_arrays) {
for (const string& input : op->inputs) {
found_new_useful_arrays |= !useful_arrays.count(input);
useful_arrays.insert(input);
}
for (const string& output : op->outputs) {
found_new_useful_arrays |= !useful_arrays.count(output);
useful_arrays.insert(output);
}
}
}
for (const auto& rnn_state : model->flags.rnn_states()) {
bool rnn_back_edge_touches_useful_arrays =
useful_arrays.count(rnn_state.state_array());
if (rnn_back_edge_touches_useful_arrays) {
found_new_useful_arrays |=
!useful_arrays.count(rnn_state.back_edge_source_array());
useful_arrays.insert(rnn_state.back_edge_source_array());
}
}
} while (found_new_useful_arrays);
// Erase arrays that aren't useful, and that are discardable.
model->EraseArrays([&](const string& name) {
return (!useful_arrays.count(name) && IsDiscardableArray(*model, name));
});
// Erase operators that do not produce a useful output array.
for (auto it = model->operators.begin(); it != model->operators.end();) {
// Only need to test the first output, as we simultaneously added all of
// an operator's outputs to the list of output arrays.
if (useful_arrays.count((*it)->outputs[0])) {
++it;
} else {
for (const string& output : (*it)->outputs) {
CHECK(!useful_arrays.count(output));
}
it = model->operators.erase(it);
}
}
// Erase RNN back-edges that are 'dangling' i.e. that touch an array
// that no longer exists. This should only happen for discardable RNN
// back-edges.
std::vector<RnnState> rnn_states_to_keep;
for (const auto& rnn_state : model->flags.rnn_states()) {
const bool dangling =
!model->HasArray(rnn_state.back_edge_source_array()) ||
!model->HasArray(rnn_state.state_array());
if (dangling) {
CHECK(rnn_state.discardable());
} else {
rnn_states_to_keep.push_back(rnn_state);
}
}
model->flags.clear_rnn_states();
for (const auto& rnn_state : rnn_states_to_keep) {
*model->flags.add_rnn_states() = rnn_state;
}
}
bool GraphTransformationsPass(int increment, Model* model,
const GraphTransformationsSet& transformations) {
CHECK(increment == 1 || increment == -1);
bool changed = false;
if (model->operators.empty()) {
LOG(INFO) << "Model is empty!!!";
return false;
}
int op_index = increment == 1 ? 0 : model->operators.size() - 1;
while (true) {
bool changed_now = false;
// Loop over all transformations at the current position in the graph.
for (const auto& transformation : transformations) {
CHECK(!changed_now);
CHECK(transformation->Messages().empty());
changed_now = transformation->Run(model, op_index);
const char* made_a_change_msg =
changed_now ? "made a change" : "did NOT make a change";
const int log_level =
changed_now ? kLogLevelModelChanged : kLogLevelModelUnchanged;
if (transformation->Messages().empty()) {
VLOG(log_level) << transformation->Name() << " " << made_a_change_msg
<< " at op_index=" << op_index << "/"
<< model->operators.size() - 1;
}
for (const string& message : transformation->Messages()) {
VLOG(log_level) << transformation->Name() << " " << made_a_change_msg
<< " at op_index=" << op_index << "/"
<< model->operators.size() - 1 << ": " << message;
}
transformation->ClearMessages();
if (changed_now) {
DumpGraphvizVideoFrame(*model);
if (model->operators.empty()) return true;
op_index = std::min<int>(op_index, model->operators.size() - 1);
// Uncomment for debugging
// CheckInvariants(*model);
}
if (changed_now) {
break;
}
}
if (changed_now) {
changed = true;
} else {
const int op_index_last =
increment == 1 ? model->operators.size() - 1 : 0;
if (op_index == op_index_last) {
break;
}
op_index += increment;
}
}
DiscardUselessConnectedComponentsAndRNNBackEdges(model);
return changed;
}
} // namespace
void RunGraphTransformations(Model* model, const string& msg,
const GraphTransformationsSet& transformations) {
PrintModelStats(toco::port::StringF("Before %s", msg), *model);
int pass_index = 0;
while (GraphTransformationsPass((pass_index % 2) ? -1 : 1, model,
transformations)) {
pass_index++;
const auto& label =
toco::port::StringF("After %s pass %d", msg, pass_index);
PrintModelStats(label, *model);
CheckInvariants(*model);
}
}
} // namespace toco
|
