dnn/lossgen.c

/* Copyright (c) 2023 Amazon */
/*
   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
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   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.
*/

/* This packet loss simulator can be used independently of the Opus codebase.
   To do that, you need to compile the following files:
     dnn/lossgen.c
     dnn/lossgen_data.c

   with the following files needed as #include
     dnn/lossgen_data.h
     dnn/lossgen.h
     dnn/nnet_arch.h
     dnn/nnet.h
     dnn/parse_lpcnet_weights.c   (included despite being a C file)
     dnn/vec_avx.h
     dnn/vec.h
     celt/os_support.h
     celt/arch.h
     celt/x86/x86_arch_macros.h
     include/opus_defines.h
     include/opus_types.h

   Additionally, the code in dnn/lossgen_demo.c can be used to generate losses from
   the command line.
*/

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

#include "arch.h"

#include <math.h>
#include "lossgen.h"
#include "os_support.h"
#include "nnet.h"
#include "assert.h"

/* Disable RTCD for this. */
#define RTCD_ARCH c

/* Override assert to avoid undefined/redefined symbols. */
#undef celt_assert
#define celt_assert assert

/* Directly include the C files we need since the symbols won't be exposed if we link in a shared object. */
#include "parse_lpcnet_weights.c"
#include "nnet_arch.h"

#undef compute_linear
#undef compute_activation

/* Force the C version since the SIMD versions may be hidden. */
#define compute_linear(linear, out, in, arch) ((void)(arch),compute_linear_c(linear, out, in))
#define compute_activation(output, input, N, activation, arch) ((void)(arch),compute_activation_c(output, input, N, activation))

#define MAX_RNN_NEURONS_ALL IMAX(LOSSGEN_GRU1_STATE_SIZE, LOSSGEN_GRU2_STATE_SIZE)

/* These two functions are copied from nnet.c to make sure we don't have linking issues. */
void compute_generic_gru_lossgen(const LinearLayer *input_weights, const LinearLayer *recurrent_weights, float *state, const float *in, int arch)
{
  int i;
  int N;
  float zrh[3*MAX_RNN_NEURONS_ALL];
  float recur[3*MAX_RNN_NEURONS_ALL];
  float *z;
  float *r;
  float *h;
  celt_assert(3*recurrent_weights->nb_inputs == recurrent_weights->nb_outputs);
  celt_assert(input_weights->nb_outputs == recurrent_weights->nb_outputs);
  N = recurrent_weights->nb_inputs;
  z = zrh;
  r = &zrh[N];
  h = &zrh[2*N];
  celt_assert(recurrent_weights->nb_outputs <= 3*MAX_RNN_NEURONS_ALL);
  celt_assert(in != state);
  compute_linear(input_weights, zrh, in, arch);
  compute_linear(recurrent_weights, recur, state, arch);
  for (i=0;i<2*N;i++)
     zrh[i] += recur[i];
  compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID, arch);
  for (i=0;i<N;i++)
     h[i] += recur[2*N+i]*r[i];
  compute_activation(h, h, N, ACTIVATION_TANH, arch);
  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_generic_dense_lossgen(const LinearLayer *layer, float *output, const float *input, int activation, int arch)
{
   compute_linear(layer, output, input, arch);
   compute_activation(output, output, layer->nb_outputs, activation, arch);
}


static int sample_loss_impl(
    LossGenState *st,
    float percent_loss)
{
  float input[2];
  float tmp[LOSSGEN_DENSE_IN_OUT_SIZE];
  float out;
  int loss;
  LossGen *model = &st->model;
  input[0] = st->last_loss;
  input[1] = percent_loss;
  compute_generic_dense_lossgen(&model->lossgen_dense_in, tmp, input, ACTIVATION_TANH, 0);
  compute_generic_gru_lossgen(&model->lossgen_gru1_input, &model->lossgen_gru1_recurrent, st->gru1_state, tmp, 0);
  compute_generic_gru_lossgen(&model->lossgen_gru2_input, &model->lossgen_gru2_recurrent, st->gru2_state, st->gru1_state, 0);
  compute_generic_dense_lossgen(&model->lossgen_dense_out, &out, st->gru2_state, ACTIVATION_SIGMOID, 0);
  loss = (float)rand()/RAND_MAX < out;
  st->last_loss = loss;
  return loss;
}

int sample_loss(
    LossGenState *st,
    float percent_loss)
{
   /* Due to GRU being initialized with zeros, the first packets aren't quite random,
      so we skip them. */
   if (!st->used) {
      int i;
      for (i=0;i<100;i++) sample_loss_impl(st, percent_loss);
      st->used = 1;
   }
   return sample_loss_impl(st, percent_loss);
}

void lossgen_init(LossGenState *st)
{
  int ret;
  OPUS_CLEAR(st, 1);
#ifndef USE_WEIGHTS_FILE
  ret = init_lossgen(&st->model, lossgen_arrays);
#else
  ret = 0;
#endif
  celt_assert(ret == 0);
  (void)ret;
}

int lossgen_load_model(LossGenState *st, const void *data, int len) {
  WeightArray *list;
  int ret;
  parse_weights(&list, data, len);
  ret = init_lossgen(&st->model, list);
  opus_free(list);
  if (ret == 0) return 0;
  else return -1;
}

#if 0
#include <stdio.h>
int main(int argc, char **argv) {
  int i, N;
  float p;
  LossGenState st;
  if (argc!=3) {
    fprintf(stderr, "usage: lossgen <percentage> <length>\n");
    return 1;
  }
  lossgen_init(&st);
  p = atof(argv[1]);
  N = atoi(argv[2]);
  for (i=0;i<N;i++) {
    printf("%d\n", sample_loss(&st, p));
  }
}
#endif