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
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
|
/* 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 <stdlib.h>
#include <string.h>
#include "tensorflow/contrib/lite/builtin_op_data.h"
#include "tensorflow/contrib/lite/kernels/activation_functor.h"
#include "tensorflow/contrib/lite/kernels/internal/round.h"
#include "tensorflow/contrib/lite/kernels/op_macros.h"
namespace tflite {
namespace tensor_utils {
float PortableClip(float f, float abs_limit) {
float result = (abs_limit < f) ? abs_limit : f;
result = (-abs_limit > result) ? -abs_limit : result;
return result;
}
bool PortableIsZeroVector(const float* vector, int v_size) {
for (int i = 0; i < v_size; ++i) {
if (*vector++ != 0.0f) return false;
}
return true;
}
void PortableSymmetricQuantizeFloats(const float* values, const int size,
int8_t* quantized_values,
float* __restrict__ min,
float* __restrict__ max,
float* __restrict__ scaling_factor) {
auto minmax = std::minmax_element(values, values + size);
*min = *minmax.first;
*max = *minmax.second;
const int kScale = 127;
const float range = std::max(std::abs(*min), std::abs(*max));
if (range == 0) {
memset(quantized_values, 0, size * sizeof(int8_t));
*scaling_factor = 1;
return;
}
*scaling_factor = range / kScale;
const float scaling_factor_inv = 1.0f / *scaling_factor;
for (int i = 0; i < size; ++i) {
const int32_t quantized_value =
static_cast<int32_t>(TfLiteRound(values[i] * scaling_factor_inv));
// Clamp: just in case some odd numeric offset.
quantized_values[i] = std::min(kScale, std::max(-kScale, quantized_value));
}
}
void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix,
int m_rows, int m_cols,
const float* vector,
int n_batch, float* result,
int result_stride) {
float* result_in_batch = result;
for (int b = 0; b < n_batch; b++) {
const float* matrix_ptr = matrix;
for (int r = 0; r < m_rows; r++) {
const float* vector_in_batch = vector + b * m_cols;
for (int c = 0; c < m_cols; c++) {
*result_in_batch += *matrix_ptr++ * *vector_in_batch++;
}
result_in_batch += result_stride;
}
}
}
void PortableMatrixBatchVectorMultiplyAccumulate(
const int8_t* __restrict__ matrix, const int m_rows, const int m_cols,
const int8_t* __restrict__ vectors,
const float* __restrict__ scaling_factors, int n_batch,
float* __restrict__ result, int result_stride) {
int batch, row, col;
for (batch = 0; batch < n_batch; ++batch, vectors += m_cols) {
const float batch_scaling_factor = scaling_factors[batch];
// Get the address of the first row.
const int8_t* row_ptr = matrix;
for (row = 0; row < m_rows; ++row, result += result_stride) {
// Initialize the dot product sum for the row to 0.
int32_t dotprod = 0;
// Prefetch the row to cache.
__builtin_prefetch(row_ptr, 0 /* prefetch for read */,
3 /* temporal locality */);
// For every block of 16 8-bit elements (128-bit register) from each row.
for (col = 0; col < m_cols; ++col, ++row_ptr) {
dotprod += (*row_ptr) * (vectors[col]);
} // for col
*result += (dotprod * batch_scaling_factor);
} // for row
} // for batch
}
void PortableVectorVectorCwiseProduct(const float* vector1,
const float* vector2, int v_size,
float* result) {
for (int v = 0; v < v_size; v++) {
*result++ = *vector1++ * *vector2++;
}
}
float PortableVectorVectorDotProduct(const float* vector1, const float* vector2,
int v_size) {
float result = 0.0;
for (int v = 0; v < v_size; v++) {
result += *vector1++ * *vector2++;
}
return result;
}
void PortableBatchVectorBatchVectorDotProduct(const float* vector1,
const float* vector2, int v_size,
int n_batch, float* result,
int result_stride) {
float* result_ptr = result;
const float* vector1_ptr = vector1;
const float* vector2_ptr = vector2;
for (int b = 0; b < n_batch; b++) {
*result_ptr =
PortableVectorVectorDotProduct(vector1_ptr, vector2_ptr, v_size);
vector1_ptr += v_size;
vector2_ptr += v_size;
result_ptr += result_stride;
}
}
void PortableVectorVectorCwiseProductAccumulate(const float* vector1,
const float* vector2,
int v_size, float* result) {
for (int v = 0; v < v_size; v++) {
*result++ += *vector1++ * *vector2++;
}
}
void PortableVectorBatchVectorCwiseProductAccumulate(const float* vector,
int v_size,
const float* batch_vector,
int n_batch,
float* result) {
for (int b = 0; b < n_batch; b++) {
for (int v = 0; v < v_size; v++) {
*result++ += vector[v] * *batch_vector++;
}
}
}
void PortableVectorBatchVectorAssign(const float* vector, int v_size,
int n_batch, float* batch_vector) {
for (int b = 0; b < n_batch; b++) {
memcpy(batch_vector + b * v_size, vector, v_size * sizeof(float));
}
}
void PortableApplySigmoidToVector(const float* vector, int v_size,
float* result) {
auto sigmoid_func = ActivationFunctor(kTfLiteActSigmoid);
for (int v = 0; v < v_size; v++) {
*result++ = (sigmoid_func)(*vector++);
}
}
void PortableApplyActivationToVector(const float* vector, int v_size,
TfLiteFusedActivation activation,
float* result) {
auto activation_func = ActivationFunctor(activation);
for (int v = 0; v < v_size; v++) {
*result++ = (activation_func)(*vector++);
}
}
void PortableCopyVector(const float* vector, int v_size, float* result) {
memcpy(result, vector, v_size * sizeof(float));
}
void PortableSub1Vector(const float* vector, int v_size, float* result) {
for (int v = 0; v < v_size; v++) {
*result++ = 1.0f - *vector++;
}
}
void PortableZeroVector(float* vector, int v_size) {
memset(vector, 0, v_size * sizeof(float));
}
void PortableVectorScalarMultiply(const int8_t* vector, const int v_size,
const float scale, float* result) {
for (int v = 0; v < v_size; ++v) {
*result++ = scale * *vector++;
}
}
void PortableClipVector(const float* vector, int v_size, float abs_limit,
float* result) {
for (int v = 0; v < v_size; v++) {
*result++ = PortableClip(*vector++, abs_limit);
}
}
void PortableVectorShiftLeft(float* vector, int v_size, float shift_value) {
TF_LITE_ASSERT(v_size > 0);
for (int i = 0; i < v_size - 1; i++) {
vector[i] = vector[i + 1];
}
vector[v_size - 1] = shift_value;
}
void PortableReductionSumVector(const float* input_vector, float* output_vector,
int output_size, int reduction_size) {
const float* input_vector_ptr = input_vector;
for (int o = 0; o < output_size; o++) {
for (int r = 0; r < reduction_size; r++) {
output_vector[o] += *input_vector_ptr++;
}
}
}
} // namespace tensor_utils
} // namespace tflite
|
