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
|
/* 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 <iostream>
#include <string>
#include <vector>
#include "absl/memory/memory.h"
#include "absl/strings/string_view.h"
#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h"
#include "tensorflow/contrib/lite/toco/graph_transformations/lstm_utils.h"
#include "tensorflow/contrib/lite/toco/model.h"
#include "tensorflow/contrib/lite/toco/tooling_util.h"
namespace toco {
::tensorflow::Status SplitLstmCellInputs::Run(Model* model,
std::size_t op_index,
bool* modified) {
*modified = false;
// Find lstm cell.
auto op_it = model->operators.begin() + op_index;
auto curr_op = op_it->get();
if (curr_op->type != OperatorType::kLstmCell) {
return ::tensorflow::Status::OK();
}
const auto* curr_lstm_op = static_cast<LstmCellOperator*>(curr_op);
// Already an extended LstmCell. Do not need to split cell inputs.
if (curr_lstm_op->kernel_type != LstmCellOperator::KERNEL_BASIC ||
curr_lstm_op->inputs.size() != LstmCellOperator::NUM_INPUTS) {
return ::tensorflow::Status::OK();
}
// Make sure the WEIGHTS_INPUT and BIASES_INPUT are constant arrays,
// that are able to be split into smaller weight and bias tensors.
if (!IsConstantParameterArray(
*model, curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]) ||
!IsConstantParameterArray(
*model, curr_op->inputs[LstmCellOperator::BIASES_INPUT])) {
return ::tensorflow::Status::OK();
}
// Make sure propagate_fixed_sizes has defined the size of the output.
if (!model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT])
.has_shape()) {
return ::tensorflow::Status::OK();
}
// Emplace a new LstmCell operator with extended inputs (kernel/lstm.cc).
auto lstm_cell_op = absl::make_unique<LstmCellOperator>();
lstm_cell_op->kernel_type = LstmCellOperator::KERNEL_FULL;
lstm_cell_op->inputs.resize(kExtendedLstmInputCount);
int num_input = model->GetArray(curr_op->inputs[LstmCellOperator::DATA_INPUT])
.shape()
.dims(1);
// n_cell and n_output have the same size when there is no projection.
int num_cell =
model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT])
.shape()
.dims(1);
int num_output = num_cell;
// Data input.
lstm_cell_op->inputs[kInputTensor] =
curr_op->inputs[LstmCellOperator::ACTIV_OUTPUT];
// Previous states.
lstm_cell_op->inputs[kInputActivationStateTensor] =
curr_op->inputs[LstmCellOperator::PREV_ACTIV_INPUT];
lstm_cell_op->inputs[kInputCellStateTensor] =
curr_op->inputs[LstmCellOperator::PREV_STATE_INPUT];
// Get original weight tensor and decompose 1 tensor to 8 sub tensors.
Array& kernel =
model->GetArray(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]);
const string base_name(FindLongestCommonPrefix(
curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT],
curr_op->outputs[LstmCellOperator::STATE_OUTPUT]));
// Input weight tensors of size {n_cell, n_input}.
CopySubArrayToArray(
model, &(lstm_cell_op->inputs[kInputToInputWeightsTensor]),
base_name + "weight_i_i", num_cell, num_input, kernel, 0, 0);
CopySubArrayToArray(model, &(lstm_cell_op->inputs[kInputToCellWeightsTensor]),
base_name + "weight_c_i", num_cell, num_input, kernel,
num_cell, 0);
CopySubArrayToArray(
model, &(lstm_cell_op->inputs[kInputToForgetWeightsTensor]),
base_name + "weight_f_i", num_cell, num_input, kernel, num_cell * 2, 0);
CopySubArrayToArray(
model, &(lstm_cell_op->inputs[kInputToOutputWeightsTensor]),
base_name + "weight_o_i", num_cell, num_input, kernel, num_cell * 3, 0);
// Recurrent weight tensors of size {n_cell, n_output}.
CopySubArrayToArray(
model, &(lstm_cell_op->inputs[kRecurrentToInputWeightsTensor]),
base_name + "weight_i_r", num_cell, num_output, kernel, 0, num_input);
CopySubArrayToArray(model,
&(lstm_cell_op->inputs[kRecurrentToCellWeightsTensor]),
base_name + "weight_c_r", num_cell, num_output, kernel,
num_cell, num_input);
CopySubArrayToArray(model,
&(lstm_cell_op->inputs[kRecurrentToForgetWeightsTensor]),
base_name + "weight_f_r", num_cell, num_output, kernel,
num_cell * 2, num_input);
CopySubArrayToArray(model,
&(lstm_cell_op->inputs[kRecurrentToOutputWeightsTensor]),
base_name + "weight_o_r", num_cell, num_output, kernel,
num_cell * 3, num_input);
// Peephole (optional).
CreateOptionalArray(model, &(lstm_cell_op->inputs[kCellToInputWeightsTensor]),
base_name + "peephole_c_i");
CreateOptionalArray(model,
&(lstm_cell_op->inputs[kCellToForgetWeightsTensor]),
base_name + "peephole_c_f");
CreateOptionalArray(model,
&(lstm_cell_op->inputs[kCellToOutputWeightsTensor]),
base_name + "peephole_c_o");
// Get original bias tensor and decompose 1 tensor to 4 sub tensors
Array& bias =
model->GetArray(curr_op->inputs[LstmCellOperator::BIASES_INPUT]);
CopySubArrayToArray(model, &(lstm_cell_op->inputs[kInputGateBiasTensor]),
base_name + "bias_i", num_cell, 1, bias, 0, 0);
CopySubArrayToArray(model, &(lstm_cell_op->inputs[kCellGateBiasTensor]),
base_name + "bias_c", num_cell, 1, bias, num_cell, 0);
CopySubArrayToArray(model, &(lstm_cell_op->inputs[kForgetGateBiasTensor]),
base_name + "bias_f", num_cell, 1, bias, num_cell * 2, 0);
CopySubArrayToArray(model, &(lstm_cell_op->inputs[kOutputGateBiasTensor]),
base_name + "bias_o", num_cell, 1, bias, num_cell * 3, 0);
// Projection (optional).
CreateOptionalArray(model, &(lstm_cell_op->inputs[kProjectionWeightsTensor]),
base_name + "proj_weight");
CreateOptionalArray(model, &(lstm_cell_op->inputs[kProjectionBiasTensor]),
base_name + "proj_bias");
// Reorder and resize LstmCell's outputs.
lstm_cell_op->outputs.resize(
ExtendedLstmCellOutputs::kExtendedLstmOutputCount);
lstm_cell_op->outputs[kOutputStateTensor] =
curr_op->outputs[LstmCellOperator::ACTIV_TEMP];
lstm_cell_op->outputs[kCellStateTensor] =
curr_op->outputs[LstmCellOperator::STATE_OUTPUT];
lstm_cell_op->outputs[kOutputTensor] =
curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT];
// Add the op into model.
model->operators.emplace(op_it, std::move(lstm_cell_op));
AddMessageF("Creating extended LstmCell replacing previous lstm cell");
// Delete arrays and operators replaced by the LSTM cell operator. Order is
// important - DeleteArrayIfUnused() only succeeds if dependent operators
// have been removed first. Start at the output and work towards the input.
// Erase curr lstm op being replaced.
DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT], model);
DeleteArrayIfUnused(curr_op->inputs[LstmCellOperator::BIASES_INPUT], model);
model->operators.erase(FindOp(*model, curr_op));
*modified = true;
return ::tensorflow::Status::OK();
}
} // namespace toco
|
