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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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