Line data    Source code

       1              : // SPDX-License-Identifier: Apache-2.0
       2              : /**
       3              :  * @file        util_simd.cpp
       4              :  * @date        09 Jan 2024
       5              :  * @brief       This is a collection of simd util functions. Aim of this file is to
       6              :  * directly call SIMD implemented functions without Tensor.
       7              :  * @see         https://github.com/nnstreamer/nntrainer
       8              :  * @author      Sungsik Kong <ss.kong@samsung.com>
       9              :  * @bug         No known bugs except for NYI items
      10              :  */
      11              : 
      12              : #include <algorithm>
      13              : #include <cmath>
      14              : #include <cpu_backend.h>
      15              : #include <util_simd.h>
      16              : 
      17              : namespace nntrainer {
      18              : 
      19            0 : void calc_trigonometric_vals_dup_util(unsigned int N_half, float *angle,
      20              :                                       float *cos_, float *sin_,
      21              :                                       unsigned int from,
      22              :                                       float attention_scaling) {
      23            0 :   calc_trigonometric_vals_dup(N_half, angle, cos_, sin_, from,
      24              :                               attention_scaling);
      25            0 : }
      26              : 
      27            0 : void swiglu_util(const unsigned int N, float *X, float *Y, float *Z) {
      28            0 :   swiglu(N, X, Y, Z);
      29            0 : }
      30              : 
      31            0 : float max_util(const unsigned int N, float *X) { return max_val(N, X); }
      32              : 
      33            0 : void softmax_util(const unsigned int N, float *X, float *Y) {
      34            0 :   softmax(N, X, Y);
      35            0 : }
      36              : 
      37              : } // namespace nntrainer