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Sampling directly from the logit

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Avoids having to compute a sigmoid
This commit is contained in:
Jean-Marc Valin 2021-07-08 03:33:44 -04:00
parent e8f70128d5
commit 7d8b00f11d
4 changed files with 21 additions and 7 deletions
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11
dnn/lpcnet.c
View file

@ -77,7 +77,7 @@ void run_frame_network(LPCNetState *lpcnet, float *condition, float *gru_a_condi
if (lpcnet->frame_count < 1000) lpcnet->frame_count++; if (lpcnet->frame_count < 1000) lpcnet->frame_count++;
} }
int run_sample_network(NNetState *net, const float *condition, const float *gru_a_condition, int last_exc, int last_sig, int pred) int run_sample_network(NNetState *net, const float *condition, const float *gru_a_condition, int last_exc, int last_sig, int pred, const float *sampling_logit_table)
{ {
float gru_a_input[3*GRU_A_STATE_SIZE]; float gru_a_input[3*GRU_A_STATE_SIZE];
float in_b[GRU_A_STATE_SIZE+FEATURE_DENSE2_OUT_SIZE]; float in_b[GRU_A_STATE_SIZE+FEATURE_DENSE2_OUT_SIZE];
@ -94,7 +94,7 @@ int run_sample_network(NNetState *net, const float *condition, const float *gru_
RNN_COPY(in_b, net->gru_a_state, GRU_A_STATE_SIZE); RNN_COPY(in_b, net->gru_a_state, GRU_A_STATE_SIZE);
RNN_COPY(&in_b[GRU_A_STATE_SIZE], condition, FEATURE_DENSE2_OUT_SIZE); RNN_COPY(&in_b[GRU_A_STATE_SIZE], condition, FEATURE_DENSE2_OUT_SIZE);
compute_gru2(&gru_b, net->gru_b_state, in_b); compute_gru2(&gru_b, net->gru_b_state, in_b);
return sample_mdense(&dual_fc, net->gru_b_state); return sample_mdense(&dual_fc, net->gru_b_state, sampling_logit_table);
} }
LPCNET_EXPORT int lpcnet_get_size() LPCNET_EXPORT int lpcnet_get_size()
@ -104,8 +104,13 @@ LPCNET_EXPORT int lpcnet_get_size()
LPCNET_EXPORT int lpcnet_init(LPCNetState *lpcnet) LPCNET_EXPORT int lpcnet_init(LPCNetState *lpcnet)
{ {
int i;
memset(lpcnet, 0, lpcnet_get_size()); memset(lpcnet, 0, lpcnet_get_size());
lpcnet->last_exc = lin2ulaw(0.f); lpcnet->last_exc = lin2ulaw(0.f);
for (i=0;i<256;i++) {
float prob = .025+.95*i/255.;
lpcnet->sampling_logit_table[i] = -log((1-prob)/prob);
}
return 0; return 0;
} }
@ -155,7 +160,7 @@ LPCNET_EXPORT void lpcnet_synthesize(LPCNetState *lpcnet, const float *features,
for (j=0;j<LPC_ORDER;j++) pred -= lpcnet->last_sig[j]*lpc[j]; for (j=0;j<LPC_ORDER;j++) pred -= lpcnet->last_sig[j]*lpc[j];
last_sig_ulaw = lin2ulaw(lpcnet->last_sig[0]); last_sig_ulaw = lin2ulaw(lpcnet->last_sig[0]);
pred_ulaw = lin2ulaw(pred); pred_ulaw = lin2ulaw(pred);
exc = run_sample_network(&lpcnet->nnet, condition, gru_a_condition, lpcnet->last_exc, last_sig_ulaw, pred_ulaw); exc = run_sample_network(&lpcnet->nnet, condition, gru_a_condition, lpcnet->last_exc, last_sig_ulaw, pred_ulaw, lpcnet->sampling_logit_table);
pcm = pred + ulaw2lin(exc); pcm = pred + ulaw2lin(exc);
RNN_MOVE(&lpcnet->last_sig[1], &lpcnet->last_sig[0], LPC_ORDER-1); RNN_MOVE(&lpcnet->last_sig[1], &lpcnet->last_sig[0], LPC_ORDER-1);
lpcnet->last_sig[0] = pcm; lpcnet->last_sig[0] = pcm;

1
dnn/lpcnet_private.h
View file

@ -29,6 +29,7 @@ struct LPCNetState {
float old_input[FEATURES_DELAY][FEATURE_CONV2_OUT_SIZE]; float old_input[FEATURES_DELAY][FEATURE_CONV2_OUT_SIZE];
float old_lpc[FEATURES_DELAY][LPC_ORDER]; float old_lpc[FEATURES_DELAY][LPC_ORDER];
float old_gain[FEATURES_DELAY]; float old_gain[FEATURES_DELAY];
float sampling_logit_table[256];
int frame_count; int frame_count;
float deemph_mem; float deemph_mem;
}; };

14
dnn/nnet.c
View file

@ -141,7 +141,7 @@ void compute_mdense(const MDenseLayer *layer, float *output, const float *input)
compute_activation(output, output, N, layer->activation); compute_activation(output, output, N, layer->activation);
} }
int sample_mdense(const MDenseLayer *layer, const float *input) int sample_mdense(const MDenseLayer *layer, const float *input, const float *sampling_logit_table)
{ {
int b, j, N, M, C, stride; int b, j, N, M, C, stride;
M = layer->nb_inputs; M = layer->nb_inputs;
@ -152,7 +152,12 @@ int sample_mdense(const MDenseLayer *layer, const float *input)
celt_assert(N <= DUAL_FC_OUT_SIZE); celt_assert(N <= DUAL_FC_OUT_SIZE);
int val=0; int val=0;
float thresholds[8];
/* Computing all the random thresholds in advance. These thresholds are directly
based on the logit to avoid computing the sigmoid.*/
for (b=0;b<8;b++) thresholds[b] = sampling_logit_table[rand()&0xFF];
for (b=0;b<8;b++) for (b=0;b<8;b++)
{ {
int bit; int bit;
@ -171,9 +176,12 @@ int sample_mdense(const MDenseLayer *layer, const float *input)
sum2 = layer->factor[N + i]*tanh_approx(sum2); sum2 = layer->factor[N + i]*tanh_approx(sum2);
sum1 += sum2; sum1 += sum2;
//sum1 = 1.f/(1 + exp(-sum1)); //sum1 = 1.f/(1 + exp(-sum1));
#if 1 /* Sample the decision based on the logit. */
bit = thresholds[b] < sum1;
#else
sum1 = sigmoid_approx(sum1); sum1 = sigmoid_approx(sum1);
bit = .025+.95*((rand()+.5f)/(RAND_MAX+1.f)) < sum1; bit = .025+.95*((rand()+.5f)/(RAND_MAX+1.f)) < sum1;
#endif
val = (val << 1) | bit; val = (val << 1) | bit;
} }
return val; return val;

2
dnn/nnet.h
View file

@ -97,7 +97,7 @@ void compute_dense(const DenseLayer *layer, float *output, const float *input);
void compute_mdense(const MDenseLayer *layer, float *output, const float *input); void compute_mdense(const MDenseLayer *layer, float *output, const float *input);
int sample_mdense(const MDenseLayer *layer, const float *input); int sample_mdense(const MDenseLayer *layer, const float *input, const float *sampling_logit_table);
void compute_gru(const GRULayer *gru, float *state, const float *input); void compute_gru(const GRULayer *gru, float *state, const float *input);