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
|
/* Copyright 2018 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 <algorithm>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h"
#include "tensorflow/contrib/lite/toco/graph_transformations/remove_trivial_passthrough.h"
#include "tensorflow/contrib/lite/toco/model.h"
#include "tensorflow/contrib/lite/toco/runtime/types.h"
#include "tensorflow/contrib/lite/toco/tooling_util.h"
#include "tensorflow/core/platform/logging.h"
namespace toco {
namespace {
bool OperatorReady(const Model& model, const Operator* op) {
if (!model.HasArray(op->inputs[0]) || !model.HasArray(op->inputs[1]) ||
!model.HasArray(op->outputs[0])) {
// Arrays are missing.
return false;
}
if (!model.GetArray(op->inputs[0]).has_shape() ||
!model.GetArray(op->outputs[0]).has_shape()) {
// Input and output needs the shape.
return false;
}
if (!model.GetArray(op->inputs[1]).buffer) {
// Buffer needs to be a constant.
return false;
}
return true;
}
// Returns whether the reshape could be a transpose.
std::vector<int32> ReshapeToTranspose(const Model& model,
const TensorFlowReshapeOperator* op) {
CHECK(!op->shape.empty());
CHECK(model.HasArray(op->inputs[0]));
CHECK(model.HasArray(op->outputs[0]));
const auto& input_array = model.GetArray(op->inputs[0]);
const auto& output_array = model.GetArray(op->outputs[0]);
CHECK(input_array.has_shape());
CHECK(output_array.has_shape());
std::vector<int> in_shape = input_array.shape().dims();
std::vector<int> out_shape = output_array.shape().dims();
std::vector<int> one_indices;
std::vector<int> not_one_indices;
// Separate into one indices and not one indices.
for (int i = 0; i < in_shape.size(); i++) {
if (in_shape[i] == 1) {
one_indices.push_back(i);
} else {
not_one_indices.push_back(i);
}
}
// Reorder the vertices.
std::vector<int> perm;
perm.reserve(in_shape.size());
int one_index = 0;
int not_one_index = 0;
for (const auto val : out_shape) {
if (val == 1) {
perm.push_back(one_indices[one_index]);
one_index++;
} else {
perm.push_back(not_one_indices[not_one_index]);
not_one_index++;
}
}
return perm;
}
} // namespace
// When a transpose is fed into a reshape, it is possible for the two operators
// to be merged if the reshape does not affect memory ordering and does not
// affects the number of dimensions. This only occurs when only unary dimensions
// are shifting position.
bool MergeReshapeIntoPrecedingTranspose::Run(Model* model,
std::size_t op_index) {
auto it = model->operators.begin() + op_index;
auto* reshape_op = ConvertOperator<TensorFlowReshapeOperator*>(
it->get(), OperatorType::kReshape);
if (reshape_op == nullptr) {
return false;
}
if (!OperatorReady(*model, reshape_op) || reshape_op->shape.empty()) {
return false;
}
const string intermediate_name = reshape_op->inputs[0];
const string output_name = reshape_op->outputs[0];
// Guarantee the input is only consume by the reshape.
if (CountOpsWithInput(*model, intermediate_name) != 1) {
return false;
}
// Check for the parent operator.
const auto& transpose_it = FindOpWithOutput(*model, intermediate_name);
if (transpose_it == model->operators.end()) {
return false;
}
// Find the parent operator and guarantee it is a transpose.
TransposeOperator* transpose_op = ConvertOperator<TransposeOperator*>(
transpose_it->get(), OperatorType::kTranspose);
if (transpose_op == nullptr) {
return false;
}
if (!OperatorReady(*model, transpose_op) || transpose_op->perm.empty()) {
return false;
}
if (!ReshapeIsEquivalentToTranspose(*model, reshape_op,
false /*allow_extra_unary_dimensions*/)) {
return false;
}
// Check that the intermediate is not an output array.
if (!IsDiscardableArray(*model, intermediate_name)) {
AddMessageF(
"Cannot fuse %s and %s as it would invalidate the transpose "
"output array.",
LogName(*transpose_op), LogName(*reshape_op));
return false;
}
AddMessageF("Merging operations %s and %s", LogName(*transpose_op),
LogName(*reshape_op));
// const auto& intermediate_array = model->GetArray(intermediate_name);
// const auto& output_array = model->GetArray(output_name);
auto merged_perm = ReshapeToTranspose(*model, reshape_op);
// Combine the permutations.
const auto& transpose_perm = transpose_op->perm;
for (int i = 0; i < merged_perm.size(); i++) {
merged_perm[i] = transpose_perm[merged_perm[i]];
}
// Remove the reshape as passthrough operation.
if (!RemoveTrivialPassthroughOp(this, model, op_index)) {
return false;
}
// Update transpose_op's constant buffer to contain the new permutation.
model->GetArray(transpose_op->inputs[1])
.GetMutableBuffer<ArrayDataType::kInt32>()
.data = merged_perm;
transpose_op->perm = merged_perm;
// transpose_ops's shape will likely has changed.
model->GetArray(transpose_op->outputs[0]).clear_shape();
return true;
}
} // namespace toco
|
