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/*
* Copyright (C) 2022 Arm Limited or its affiliates.
*
* SPDX-License-Identifier: Apache-2.0
*
* 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
*
* 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.
*/
/* ----------------------------------------------------------------------
* Project: CMSIS NN Library
* Title: arm_softmax_s16.c
* Description: S16 softmax function
*
* $Date: 9 March 2022
* $Revision: V.1.0.0
*
* Target Processor: Cortex-M cores
*
* -------------------------------------------------------------------- */
#include "arm_nnfunctions.h"
#include "arm_nnsupportfunctions.h"
/**
* @addtogroup Softmax
* @{
*/
arm_status arm_softmax_s16(const int16_t *input,
const int32_t num_rows,
const int32_t row_size,
const int32_t mult,
const int32_t shift,
const cmsis_nn_softmax_lut_s16 *softmax_params,
int16_t *output)
{
int32_t col = 0;
int32_t row_idx;
if (softmax_params->exp_lut == NULL || softmax_params->one_by_one_lut == NULL)
{
return ARM_MATH_ARGUMENT_ERROR;
}
for (row_idx = 0; row_idx < num_rows; ++row_idx)
{
// Find the maximum value in order to ensure numerical stability
int16_t max = *input;
for (col = 1; col < row_size; ++col)
{
max = MAX(max, input[col]);
}
int32_t diff = 0;
int32_t sum = 0;
int16_t *cached_exp_results = output;
for (col = 0; col < row_size; ++col)
{
diff = input[col] - max;
const int32_t scaled_diff = arm_nn_requantize(diff, mult, shift);
const int32_t symmetric_scaled_diff = scaled_diff + NN_Q15_MAX;
const int16_t saturated_symmetric_scaled_diff = MIN(MAX(symmetric_scaled_diff, NN_Q15_MIN), NN_Q15_MAX);
// Lookup from exp table and cache result for next step
const int16_t index = (256 + (saturated_symmetric_scaled_diff >> 7));
const int16_t offset = saturated_symmetric_scaled_diff & 0x7f;
const int16_t base = softmax_params->exp_lut[index];
const int16_t slope = softmax_params->exp_lut[index + 1] - softmax_params->exp_lut[index];
const int16_t delta = (slope * offset + 64) >> 7;
const int16_t result = (base + delta);
cached_exp_results[col] = result;
sum += cached_exp_results[col];
}
const int32_t headroom = __CLZ(sum);
// Compute the reciprocal 1/sum
const int32_t shifted_sum = (((sum) << (headroom - 1)) + (1 << 13)) >> 14;
// Since LUT computes 1/(1 + x), compute x = (sum - 1) => -65536
// Since LUT expects a symmetrical input, recenter from [UINT16_MIN, UINT16_MAX] to [INT16_MIN, INT16_MAX] =>
// -32768 ==> So in total -65536 -32768 => -98304
const int16_t symmetric_shifted_sum = shifted_sum - 98304;
// Lookup from one by one table
const int16_t index = (256 + (symmetric_shifted_sum >> 7));
const int16_t offset = symmetric_shifted_sum & 0x7f;
const int16_t base = softmax_params->one_by_one_lut[index];
const int16_t slope = softmax_params->one_by_one_lut[index + 1] - softmax_params->one_by_one_lut[index];
const int16_t delta = (slope * offset + 64) >> 7;
const int16_t one_by_one_result = (base + delta);
for (col = 0; col < row_size; ++col)
{
const int16_t right_shift = 30 - headroom;
int32_t result = (cached_exp_results[col] * one_by_one_result) >> right_shift;
result = (result + 1) >> 1; // Last shift position and insert round
output[col] = (int16_t)result;
}
output += row_size;
input += row_size;
}
return ARM_MATH_SUCCESS;
}
/**
* @} end of Softmax group
*/
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