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
|
/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. 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 "ElementwiseActivationLayer.h"
#include "OperationUtils.h"
#include <cker/operation/ELU.h>
#include <cker/operation/LeakyReLU.h>
#include <cker/operation/Logistic.h>
#include <cker/operation/ReLU.h>
#include <cker/operation/ReLU6.h>
#include <cker/operation/Tanh.h>
namespace onert
{
namespace backend
{
namespace cpu
{
namespace ops
{
ElementwiseActivationLayer::ElementwiseActivationLayer()
: _input(nullptr), _output(nullptr), _kernel()
{
// DO NOTHING
}
void ElementwiseActivationLayer::PopulateLookupTable(const ElementwiseActivationType op_type)
{
const auto input_scale = static_cast<double>(_input->data_scale());
const auto input_zero_point = static_cast<int32_t>(_input->data_zero_point());
const auto output_scale = static_cast<double>(_output->data_scale());
const auto output_zero_point = static_cast<int32_t>(_output->data_zero_point());
const float inverse_scale = 1 / output_scale;
int32_t maxval = std::numeric_limits<uint8_t>::max();
int32_t minval = std::numeric_limits<uint8_t>::min();
for (int32_t val = minval; val <= maxval; ++val)
{
const float dequantized = input_scale * (val - input_zero_point);
float transformed = 0.f;
if (op_type == ElementwiseActivationType::kTanh)
{
transformed = std::tanh(dequantized);
}
else if (op_type == ElementwiseActivationType::kLogistic)
{
transformed = 1.0f / (1.0f + std::exp(-dequantized));
}
else
{
throw std::runtime_error("ElementwiseActivationLayer : unsupported activation type");
}
const float rescaled = std::round(transformed * inverse_scale);
const int32_t quantized = static_cast<int32_t>(rescaled + output_zero_point);
_table[val] = static_cast<uint8_t>(std::max(std::min(maxval, quantized), minval));
}
}
void ElementwiseActivationLayer::EvalUsingLookupTable(const IPortableTensor *input,
IPortableTensor *output)
{
const int size = MatchingFlatSize(getShape(input), getShape(output));
const uint8_t *input_data = getBuffer<uint8_t>(input);
uint8_t *output_data = getBuffer<uint8_t>(output);
for (int i = 0; i < size; ++i)
{
output_data[i] = _table[input_data[i]];
}
}
void ElementwiseActivationLayer::configure(const IPortableTensor *input, IPortableTensor *output,
float alpha, float beta,
ElementwiseActivationType op_type)
{
_input = input;
_output = output;
switch (op_type)
{
case ElementwiseActivationType::kElu:
if (input->data_type() == OperandType::FLOAT32)
{
_kernel = [](const IPortableTensor *input, IPortableTensor *output) {
nnfw::cker::ELU(getShape(input), getBuffer<float>(input), getShape(output),
getBuffer<float>(output));
};
}
else
{
throw std::runtime_error{"ElementwiseActivationLayer(Elu): unsupported data type"};
}
break;
case ElementwiseActivationType::kLogistic:
if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM)
{
PopulateLookupTable(op_type);
_kernel = std::bind(&ElementwiseActivationLayer::EvalUsingLookupTable, this,
std::placeholders::_1, std::placeholders::_2);
}
else if (_input->data_type() == OperandType::FLOAT32)
{
_kernel = [](const IPortableTensor *input, IPortableTensor *output) {
nnfw::cker::Logistic(getShape(input), getBuffer<float>(input), getShape(output),
getBuffer<float>(output));
};
}
else
{
throw std::runtime_error{"ElementwiseActivationLayer(Logistic): unsupported data type"};
}
break;
case ElementwiseActivationType::kReLU:
if (_input->data_type() == OperandType::FLOAT32)
{
if (alpha == std::numeric_limits<float>::infinity() && beta == 0.f)
{
_kernel = [](const IPortableTensor *input, IPortableTensor *output) {
nnfw::cker::ReLU(getShape(input), getBuffer<float>(input), getShape(output),
getBuffer<float>(output));
};
}
else if (alpha == 6.f && beta == 0.f)
{
_kernel = [](const IPortableTensor *input, IPortableTensor *output) {
nnfw::cker::ReLU6(getShape(input), getBuffer<float>(input), getBuffer<float>(output));
};
}
else
{
throw std::runtime_error(
"ElementwiseActivationLayer : This layer suppports only ReLU(0-inf) and ReLU6(0-6)");
}
}
else
{
throw std::runtime_error{"ElementwiseActivationLayer(ReLU): unsupported data type"};
}
break;
case ElementwiseActivationType::kTanh:
if (_input->data_type() == OperandType::QUANT_UINT8_ASYMM)
{
PopulateLookupTable(op_type);
_kernel = std::bind(&ElementwiseActivationLayer::EvalUsingLookupTable, this,
std::placeholders::_1, std::placeholders::_2);
}
else if (_input->data_type() == OperandType::FLOAT32)
{
_kernel = [](const IPortableTensor *input, IPortableTensor *output) {
nnfw::cker::Tanh(getShape(input), getBuffer<float>(input), getShape(output),
getBuffer<float>(output));
};
}
else
{
throw std::runtime_error{"ElementwiseActivationLayer(Logistic): unsupported data type"};
}
break;
case ElementwiseActivationType::kLeakyReLU:
if (_input->data_type() == OperandType::FLOAT32)
{
_kernel = [alpha](const IPortableTensor *input, IPortableTensor *output) {
nnfw::cker::LeakyReLU(nnfw::cker::LeakyReluParams{alpha}, getShape(input),
getBuffer<float>(input), getShape(output),
getBuffer<float>(output));
};
}
else
{
throw std::runtime_error{"ElementwiseActivationLayer(LeakyReLU): unsupported data type"};
}
break;
default:
throw std::runtime_error("ElementwiseActivationLayer: unsupported op type");
}
}
void ElementwiseActivationLayer::run() { _kernel(_input, _output); }
} // namespace ops
} // namespace cpu
} // namespace backend
} // namespace onert
|
