xref: /aosp_15_r20/external/libopus/src/mlp.c (revision a58d3d2adb790c104798cd88c8a3aff4fa8b82cc)

/* Copyright (c) 2008-2011 Octasic Inc.
                 2012-2017 Jean-Marc Valin */
/*
   Redistribution and use in source and binary forms, with or without
   modification, are permitted provided that the following conditions
   are met:

   - Redistributions of source code must retain the above copyright
   notice, this list of conditions and the following disclaimer.

   - Redistributions in binary form must reproduce the above copyright
   notice, this list of conditions and the following disclaimer in the
   documentation and/or other materials provided with the distribution.

   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE FOUNDATION OR
   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/

#ifdef HAVE_CONFIG_H
#include "config.h"
#endif

#include <math.h>
#include "opus_types.h"
#include "opus_defines.h"
#include "arch.h"
#include "mlp.h"

#define fmadd(a, b, c) ((a)*(b)+(c))
static OPUS_INLINE float tansig_approx(float x)
{
    const float N0 = 952.52801514f;
    const float N1 = 96.39235687f;
    const float N2 = 0.60863042f;
    const float D0 = 952.72399902f;
    const float D1 = 413.36801147f;
    const float D2 = 11.88600922f;
    float X2, num, den;
    X2 = x*x;
    num = fmadd(fmadd(N2, X2, N1), X2, N0);
    den = fmadd(fmadd(D2, X2, D1), X2, D0);
    num = num*x/den;
    return MAX32(-1.f, MIN32(1.f, num));
}

static OPUS_INLINE float sigmoid_approx(float x)
{
   return .5f + .5f*tansig_approx(.5f*x);
}

static void gemm_accum(float *out, const opus_int8 *weights, int rows, int cols, int col_stride, const float *x)
{
   int i, j;
   for (i=0;i<rows;i++)
   {
      for (j=0;j<cols;j++)
         out[i] += weights[j*col_stride + i]*x[j];
   }
}

void analysis_compute_dense(const AnalysisDenseLayer *layer, float *output, const float *input)
{
   int i;
   int N, M;
   int stride;
   M = layer->nb_inputs;
   N = layer->nb_neurons;
   stride = N;
   for (i=0;i<N;i++)
      output[i] = layer->bias[i];
   gemm_accum(output, layer->input_weights, N, M, stride, input);
   for (i=0;i<N;i++)
      output[i] *= WEIGHTS_SCALE;
   if (layer->sigmoid) {
      for (i=0;i<N;i++)
         output[i] = sigmoid_approx(output[i]);
   } else {
      for (i=0;i<N;i++)
         output[i] = tansig_approx(output[i]);
   }
}

void analysis_compute_gru(const AnalysisGRULayer *gru, float *state, const float *input)
{
   int i;
   int N, M;
   int stride;
   float tmp[MAX_NEURONS];
   float z[MAX_NEURONS];
   float r[MAX_NEURONS];
   float h[MAX_NEURONS];
   M = gru->nb_inputs;
   N = gru->nb_neurons;
   stride = 3*N;
   /* Compute update gate. */
   for (i=0;i<N;i++)
      z[i] = gru->bias[i];
   gemm_accum(z, gru->input_weights, N, M, stride, input);
   gemm_accum(z, gru->recurrent_weights, N, N, stride, state);
   for (i=0;i<N;i++)
      z[i] = sigmoid_approx(WEIGHTS_SCALE*z[i]);

   /* Compute reset gate. */
   for (i=0;i<N;i++)
      r[i] = gru->bias[N + i];
   gemm_accum(r, &gru->input_weights[N], N, M, stride, input);
   gemm_accum(r, &gru->recurrent_weights[N], N, N, stride, state);
   for (i=0;i<N;i++)
      r[i] = sigmoid_approx(WEIGHTS_SCALE*r[i]);

   /* Compute output. */
   for (i=0;i<N;i++)
      h[i] = gru->bias[2*N + i];
   for (i=0;i<N;i++)
      tmp[i] = state[i] * r[i];
   gemm_accum(h, &gru->input_weights[2*N], N, M, stride, input);
   gemm_accum(h, &gru->recurrent_weights[2*N], N, N, stride, tmp);
   for (i=0;i<N;i++)
      h[i] = z[i]*state[i] + (1-z[i])*tansig_approx(WEIGHTS_SCALE*h[i]);
   for (i=0;i<N;i++)
      state[i] = h[i];
}