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Make loss simulator standalone

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This commit is contained in:
Jean-Marc Valin 2023-12-21 23:05:40 -05:00
parent bd710e97f3
commit caca188b5a
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GPG key ID: 531A52533318F00A
3 changed files with 57 additions and 6 deletions
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61
dnn/lossgen.c
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@ -6,7 +6,53 @@
#include "lossgen.h"
#include "os_support.h"
#include "nnet.h"
#include "lpcnet_private.h"
/* Disable RTCD for this. */
#define RTCD_ARCH c
#include "nnet_arch.h"
#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);
}
int sample_loss(
LossGenState *st,
@ -21,10 +67,10 @@ int sample_loss(
LossGen *model = &st->model;
input[0] = st->last_loss;
input[1] = percent_loss;
compute_generic_dense(&model->lossgen_dense_in, tmp, input, ACTIVATION_TANH, arch);
compute_generic_gru(&model->lossgen_gru1_input, &model->lossgen_gru1_recurrent, st->gru1_state, tmp, arch);
compute_generic_gru(&model->lossgen_gru2_input, &model->lossgen_gru2_recurrent, st->gru2_state, st->gru1_state, arch);
compute_generic_dense(&model->lossgen_dense_out, &out, st->gru2_state, ACTIVATION_SIGMOID, arch);
compute_generic_dense_lossgen(&model->lossgen_dense_in, tmp, input, ACTIVATION_TANH, arch);
compute_generic_gru_lossgen(&model->lossgen_gru1_input, &model->lossgen_gru1_recurrent, st->gru1_state, tmp, arch);
compute_generic_gru_lossgen(&model->lossgen_gru2_input, &model->lossgen_gru2_recurrent, st->gru2_state, st->gru1_state, arch);
compute_generic_dense_lossgen(&model->lossgen_dense_out, &out, st->gru2_state, ACTIVATION_SIGMOID, arch);
loss = (float)rand()/RAND_MAX < out;
st->last_loss = loss;
return loss;
@ -41,6 +87,7 @@ void lossgen_init(LossGenState *st)
ret = 0;
#endif
celt_assert(ret == 0);
(void)ret;
}
int lossgen_load_model(LossGenState *st, const unsigned char *data, int len) {
@ -59,6 +106,10 @@ 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]);