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/*
* Copyright (c) 2023 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright 2020 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.
*/
#ifndef LUCI_INTERPRETER_PAL_LOGISTIC_H
#define LUCI_INTERPRETER_PAL_LOGISTIC_H
#include "Params.h"
#include "PALUtils.h"
namespace luci_interpreter_pal
{
inline void Logistic(const int flat_size, const float *input_data, float *output_data)
{
const float cutoff_upper = 16.619047164916992188f;
const float cutoff_lower = -9.f;
// Rational for using approximation in reference kernel.
// 0. This approximation gives enough precision for float.
// 1. This works around an issue on an embedded chipset where exp() does not
// return correctly as expected - exp(x) should return inf when overflown
// not 1.701417 IEEE 754 defines representation for inf.
// 2. This will speed up calculation and is matching the behavior in the
// optimized kernels. (check the definition of scalar_logistic_op<float>)
for (int i = 0; i < flat_size; i++)
{
float val = input_data[i];
float result;
if (val > cutoff_upper)
{
result = 1.0f;
}
else if (val < cutoff_lower)
{
result = std::exp(val);
}
else
{
result = 1.f / (1.f + std::exp(-val));
}
output_data[i] = result;
}
}
inline void Logistic(const int flat_size, const int8_t *input_data, float input_scale,
int input_zero_point, int8_t *output_data, float output_scale,
int output_zero_point)
{
const float cutoff_upper = 16.619047164916992188f;
const float cutoff_lower = -9.f;
// Rational for using approximation in reference kernel.
// 0. This approximation gives enough precision for float.
// 1. This works around an issue on an embedded chipset where exp() does not
// return correctly as expected - exp(x) should return inf when overflown
// not 1.701417 IEEE 754 defines representation for inf.
// 2. This will speed up calculation and is matching the behavior in the
// optimized kernels. (check the definition of scalar_logistic_op<float>)
for (int i = 0; i < flat_size; i++)
{
// Dequantize.
float val = static_cast<float>((input_data[i] - input_zero_point) * input_scale);
float result;
if (val > cutoff_upper)
{
result = 1.0f;
}
else if (val < cutoff_lower)
{
result = std::exp(val);
}
else
{
result = 1.f / (1.f + std::exp(-val));
}
// Requantize
int8_t output = static_cast<int8_t>(result / output_scale + output_zero_point);
output_data[i] = output;
}
}
inline void Logistic(int32_t input_multiplier, int32_t input_left_shift, int32_t input_size,
const int16_t *ptr_input_data, int16_t *ptr_output_data)
{
// We use the LUT for sigmoid and take into account, that
// tanh(x) = 2*sigmoid(2*x) - 1
// We scale by 3/4 to expand range [-8,8]->[-10.7,10.7].
// In case of general parameter scale, multiplier 3 is taken into account
// in TanhPrepare function and it is included in
// input_multiplier already.
if (input_multiplier == 0)
{ // power of two case
input_multiplier = 3 << input_left_shift;
input_left_shift = 0;
}
int32_t round = (input_left_shift > 0) ? 1 << (input_left_shift - 1) : 0;
for (int i = 0; i < input_size; ++i, ptr_input_data++, ptr_output_data++)
{
int32_t input_data = ((*ptr_input_data) * input_multiplier + round) >> input_left_shift;
// We do interpolation on unsigned values.
uint32_t abs_input_data = abs(input_data);
// We divide by 2 power of 9, because
// we need to divide by 2 in power of 7 for
// the input conversion + 1/4 from the scale above.
// Define uh as uint32_t type not to make this function overflow.
uint32_t uh = abs_input_data >> 9;
uint32_t result;
if (uh >= 255)
{
// Saturate to maximum.
result = 0x7FFF << 10;
}
else
{
uint32_t ua = sigmoid_table_uint16[uh];
uint32_t ub = sigmoid_table_uint16[uh + 1];
uint32_t ut = abs_input_data & 0x1ff;
// Interpolation is done using the fractional bit.
result = (ua << 9) + ut * (ub - ua);
}
result = (input_data >= 0) ? (result + (1 << 9)) : ((1 << (16 + 9)) - result + (1 << 9) - 1);
// Back to 16-bit.
result >>= 10;
*ptr_output_data = result;
}
}
} // namespace luci_interpreter_pal
#endif // LUCI_INTERPRETER_PAL_LOGISTIC_H
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