opus/dnn/nnet.c

/* Copyright (c) 2018 Mozilla
                 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 <stdlib.h>
#include <math.h>
#include "opus_types.h"
#include "arch.h"
#include "common.h"
#include "tansig_table.h"
#include "nnet.h"
#include "nnet_data.h"

#ifdef NO_OPTIMIZATIONS
#warning Compiling without any vectorization. This code will be very slow
#endif


#define SOFTMAX_HACK


static OPUS_INLINE float relu(float x)
{
   return x < 0 ? 0 : x;
}


static void sgemv_accum(float *out, const float *weights, int rows, int cols, int col_stride, const float *x)
{
   int i, j;
   if (rows % 16 == 0)
   {
      sgemv_accum16(out, weights, rows, cols, col_stride, x);
   } else {
      for (i=0;i<rows;i++)
      {
         for (j=0;j<cols;j++)
            out[i] += weights[j*col_stride + i]*x[j];
      }
   }
}

void compute_activation(float *output, float *input, int N, int activation)
{
   int i;
   if (activation == ACTIVATION_SIGMOID) {
      vec_sigmoid(output, input, N);
   } else if (activation == ACTIVATION_TANH) {
      vec_tanh(output, input, N);
   } else if (activation == ACTIVATION_RELU) {
      for (i=0;i<N;i++)
         output[i] = relu(input[i]);
   } else if (activation == ACTIVATION_SOFTMAX) {
#ifdef SOFTMAX_HACK
      RNN_COPY(output, input, N);
      /*for (i=0;i<N;i++)
         output[i] = input[i];*/
#else
      float sum = 0;
      softmax(output, input, N);
      for (i=0;i<N;i++) {
         sum += output[i];
      }
      sum = 1.f/(sum+1e-30);
      for (i=0;i<N;i++)
         output[i] = sum*output[i];
#endif
   } else {
      celt_assert(activation == ACTIVATION_LINEAR);
      for (i=0;i<N;i++)
         output[i] = input[i];
   }
}

void compute_dense(const DenseLayer *layer, float *output, const float *input)
{
   int i;
   int N, M;
   int stride;
   M = layer->nb_inputs;
   N = layer->nb_neurons;
   stride = N;
   celt_assert(input != output);
   for (i=0;i<N;i++)
      output[i] = layer->bias[i];
   sgemv_accum(output, layer->input_weights, N, M, stride, input);
   compute_activation(output, output, N, layer->activation);
}

void compute_mdense(const MDenseLayer *layer, float *output, const float *input)
{
   int i, c;
   int N, M, C;
   int stride;
   float tmp[MAX_MDENSE_TMP];
   celt_assert(input != output);
   M = layer->nb_inputs;
   N = layer->nb_neurons;
   C = layer->nb_channels;
   celt_assert(N*C <= MAX_MDENSE_TMP);
   stride = N*C;
   for (i=0;i<N*C;i++)
      tmp[i] = layer->bias[i];
   sgemv_accum(tmp, layer->input_weights, N*C, M, stride, input);
   compute_activation(tmp, tmp, N*C, ACTIVATION_TANH);
   for (i=0;i<N;i++)
      output[i] = 0;
   for (c=0;c<C;c++)
   {
      for (i=0;i<N;i++)
         output[i] += tmp[c*N + i]*layer->factor[c*N + i];
   }
   compute_activation(output, output, N, layer->activation);
}

#if 0
void compute_gru(const GRULayer *gru, float *state, const float *input)
{
   int i;
   int N, M;
   int stride;
   float tmp[MAX_RNN_NEURONS];
   float z[MAX_RNN_NEURONS];
   float r[MAX_RNN_NEURONS];
   float h[MAX_RNN_NEURONS];
   celt_assert(gru->nb_neurons <= MAX_RNN_NEURONS);
   celt_assert(input != state);
   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];
   if (gru->reset_after)
   {
      for (i=0;i<N;i++)
         z[i] += gru->bias[3*N + i];
   }
   sgemv_accum(z, gru->input_weights, N, M, stride, input);
   sgemv_accum(z, gru->recurrent_weights, N, N, stride, state);
   compute_activation(z, z, N, ACTIVATION_SIGMOID);

   /* Compute reset gate. */
   for (i=0;i<N;i++)
      r[i] = gru->bias[N + i];
   if (gru->reset_after)
   {
      for (i=0;i<N;i++)
         r[i] += gru->bias[4*N + i];
   }
   sgemv_accum(r, &gru->input_weights[N], N, M, stride, input);
   sgemv_accum(r, &gru->recurrent_weights[N], N, N, stride, state);
   compute_activation(r, r, N, ACTIVATION_SIGMOID);

   /* Compute output. */
   for (i=0;i<N;i++)
      h[i] = gru->bias[2*N + i];
   if (gru->reset_after)
   {
      for (i=0;i<N;i++)
         tmp[i] = gru->bias[5*N + i];
      sgemv_accum(tmp, &gru->recurrent_weights[2*N], N, N, stride, state);
      for (i=0;i<N;i++)
         h[i] += tmp[i] * r[i];
      sgemv_accum(h, &gru->input_weights[2*N], N, M, stride, input);
   } else {
      for (i=0;i<N;i++)
         tmp[i] = state[i] * r[i];
      sgemv_accum(h, &gru->input_weights[2*N], N, M, stride, input);
      sgemv_accum(h, &gru->recurrent_weights[2*N], N, N, stride, tmp);
   }
   compute_activation(h, h, N, gru->activation);
   for (i=0;i<N;i++)
      h[i] = z[i]*state[i] + (1-z[i])*h[i];
   for (i=0;i<N;i++)
      state[i] = h[i];
}
#endif

void compute_gru2(const GRULayer *gru, float *state, const float *input)
{
   int i;
   int N, M;
   int stride;
   float zrh[3*MAX_RNN_NEURONS];
   float recur[3*MAX_RNN_NEURONS];
   float *z;
   float *r;
   float *h;
   M = gru->nb_inputs;
   N = gru->nb_neurons;
   z = zrh;
   r = &zrh[N];
   h = &zrh[2*N];
   celt_assert(gru->nb_neurons <= MAX_RNN_NEURONS);
   celt_assert(input != state);
   celt_assert(gru->reset_after);
   stride = 3*N;
   /* Compute update gate. */
#ifdef USE_SU_BIAS
   for (i=0;i<3*N;i++)
      zrh[i] = gru->subias[i];
#else
   for (i=0;i<3*N;i++)
      zrh[i] = gru->bias[i];
#endif
   sgemv_accum8x4(zrh, gru->input_weights, 3*N, M, stride, input);
   for (i=0;i<3*N;i++)
      recur[i] = gru->bias[3*N + i];
   sgemv_accum(recur, gru->recurrent_weights, 3*N, N, stride, state);
   for (i=0;i<2*N;i++)
      zrh[i] += recur[i];
   compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID);
   for (i=0;i<N;i++)
      h[i] += recur[2*N+i]*r[i];
   compute_activation(h, h, N, gru->activation);
   for (i=0;i<N;i++)
      h[i] = z[i]*state[i] + (1-z[i])*h[i];
   for (i=0;i<N;i++)
      state[i] = h[i];
}

void compute_gru3(const GRULayer *gru, float *state, const float *input)
{
   int i;
   int N;
   int stride;
   float zrh[3*MAX_RNN_NEURONS];
   float recur[3*MAX_RNN_NEURONS];
   float *z;
   float *r;
   float *h;
   N = gru->nb_neurons;
   z = zrh;
   r = &zrh[N];
   h = &zrh[2*N];
   celt_assert(gru->nb_neurons <= MAX_RNN_NEURONS);
   celt_assert(input != state);
   celt_assert(gru->reset_after);
   stride = 3*N;
   RNN_COPY(zrh, input, 3*N);
   for (i=0;i<3*N;i++)
      recur[i] = gru->bias[3*N + i];
   sgemv_accum(recur, gru->recurrent_weights, 3*N, N, stride, state);
   for (i=0;i<2*N;i++)
      zrh[i] += recur[i];
   compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID);
   for (i=0;i<N;i++)
      h[i] += recur[2*N+i]*r[i];
   compute_activation(h, h, N, gru->activation);
   for (i=0;i<N;i++)
      h[i] = z[i]*state[i] + (1-z[i])*h[i];
   for (i=0;i<N;i++)
      state[i] = h[i];
}

/* WARNING: for efficiency reasons, this function overwrites the input vector. */
void compute_sparse_gru(const SparseGRULayer *gru, float *state, float *input)
{
   int i, k;
   int N;
   float recur[3*MAX_RNN_NEURONS];
   float *z;
   float *r;
   float *h;
   const float *bias;
   N = gru->nb_neurons;
   z = input;
   r = &input[N];
   h = &input[2*N];
   celt_assert(gru->nb_neurons <= MAX_RNN_NEURONS);
   celt_assert(input != state);
   celt_assert(gru->reset_after);
#ifdef USE_SU_BIAS
   bias = &gru->subias[3*N];
#else
   bias = &gru->bias[3*N];   
#endif
   for (k=0;k<3;k++)
   {
      for (i=0;i<N;i++)
         recur[k*N + i] = bias[k*N + i] + gru->diag_weights[k*N + i]*state[i];
   }
   sparse_sgemv_accum8x4(recur, gru->recurrent_weights, 3*N, N, gru->idx, state);
   for (i=0;i<2*N;i++)
      input[i] += recur[i];
   compute_activation(input, input, 2*N, ACTIVATION_SIGMOID);
   for (i=0;i<N;i++)
      h[i] += recur[2*N+i]*r[i];
   compute_activation(h, h, N, gru->activation);
   for (i=0;i<N;i++)
      state[i] = z[i]*state[i] + (1-z[i])*h[i];
}

void compute_conv1d(const Conv1DLayer *layer, float *output, float *mem, const float *input)
{
   int i;
   int N, M;
   int stride;
   float tmp[MAX_CONV_INPUTS];
   celt_assert(input != output);
   celt_assert(layer->nb_inputs*layer->kernel_size <= MAX_CONV_INPUTS);
   RNN_COPY(tmp, mem, layer->nb_inputs*(layer->kernel_size-1));
   RNN_COPY(&tmp[layer->nb_inputs*(layer->kernel_size-1)], input, layer->nb_inputs);
   M = layer->nb_inputs*layer->kernel_size;
   N = layer->nb_neurons;
   stride = N;
   for (i=0;i<N;i++)
      output[i] = layer->bias[i];
   sgemv_accum(output, layer->input_weights, N, M, stride, tmp);
   compute_activation(output, output, N, layer->activation);
   RNN_COPY(mem, &tmp[layer->nb_inputs], layer->nb_inputs*(layer->kernel_size-1));
}

void compute_embedding(const EmbeddingLayer *layer, float *output, int input)
{
   int i;
   celt_assert(input >= 0);
   celt_assert(input < layer->nb_inputs);
   /*if (layer->dim == 64) printf("%d\n", input);*/
   for (i=0;i<layer->dim;i++)
   {
      output[i] = layer->embedding_weights[input*layer->dim + i];
   }    
}

void accum_embedding(const EmbeddingLayer *layer, float *output, int input)
{
   int i;
   celt_assert(input >= 0);
   celt_assert(input < layer->nb_inputs);
   /*if (layer->dim == 64) printf("%d\n", input);*/
   for (i=0;i<layer->dim;i++)
   {
      output[i] += layer->embedding_weights[input*layer->dim + i];
   }    
}

int sample_from_pdf(const float *pdf, int N, float exp_boost, float pdf_floor)
{
    int i;
    float sum, norm;
    float r;
    float tmp[DUAL_FC_OUT_SIZE];
    celt_assert(N <= DUAL_FC_OUT_SIZE);
    sum = 0;
#ifdef SOFTMAX_HACK
    for (i=0;i<N;i++)
    {
        tmp[i] = pdf[i] * (1.f+exp_boost);
    }
    softmax(tmp, tmp, N);
    for (i=0;i<N;i++)
    {
        sum += tmp[i];
    }
#else
    /* Decrease the temperature of the sampling. */
    for (i=0;i<N;i++)
    {
        tmp[i] = pow(pdf[i], 1.f+exp_boost);
        sum += tmp[i];
    }
#endif
    norm = 1.f/sum;
    /* Convert tmp to a CDF while subtracting the floor */
    tmp[0] = MAX16(0, norm*tmp[0] - pdf_floor);
    for (i=1;i<N;i++)
    {
        tmp[i] = tmp[i-1] + MAX16(0, norm*tmp[i] - pdf_floor);
    }
    /* Do the sampling (from the cdf). */
    r = tmp[N-1] * ((rand()+.5f)/(RAND_MAX+1.f));
    for (i=0;i<N-1;i++)
    {
        if (r <= tmp[i]) return i;
    }
    return N-1;
}