/* * SPDX-FileCopyrightText: Copyright 2022-2023 Arm Limited and/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: 5 January 2023 * $Revision: V.2.1.0 * * Target : Arm(R) M-Profile Architecture * * -------------------------------------------------------------------- */ #include "arm_nnfunctions.h" #include "arm_nnsupportfunctions.h" /** * @addtogroup Softmax * @{ */ arm_cmsis_nn_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_CMSIS_NN_ARG_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_CMSIS_NN_SUCCESS; } /** * @} end of Softmax group */