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assorted fixes and performance improvements #134

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assorted fixes and performance improvements #134

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a1a1355
free memory still allocated in common struct when finished
sleepybishop Aug 14, 2020
73a482b
fix static analyzer warnings found by clang analyzer
sleepybishop Aug 14, 2020
2a55474
define M_PI if it's not, eg. if compiling with -std=c99
sleepybishop Aug 14, 2020
de03f80
fix cosmetic typo in rnn.h
sleepybishop Aug 14, 2020
4a34847
remove custom asserts and default to ACTIVATION_RELU
sleepybishop Aug 14, 2020
accadd3
improve perfomance by using blas-like ops (eg. faxpy/fma) for gru had…
sleepybishop Aug 16, 2020
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2 changes: 1 addition & 1 deletion src/celt_lpc.c
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Original file line number Diff line number Diff line change
Expand Up @@ -204,7 +204,7 @@ int _celt_autocorr(
int n)
{
opus_val32 d;
int i, k;
int i = 0, k = 0;
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@e00E e00E Aug 19, 2020 •
edited
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I don't doubt that the static analyzer complains but these initializations are not required because either way both variables are set to 0 when they are used. There is a general argument for always obviously initializing everything but this project seems to prefer to initialize only when needed so I feel that this change is inconsistent with the project style.

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i think a compromise between initializing everything and being as terse as possible is initializing when a static analyzer highlights a potential problem due to a series of branches, however unlikely that may maybe.

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@e00E e00E Aug 19, 2020 •
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I do believe that these are always initialized (and cannot possibly be unintialized), the static analyzer is just not good enough to figure it out. On the other hand initializing has no drawback either except the mentioned style differences and it is safer in terms of human error. Probably another thing for a maintainer to decide.

int fastN=n-lag;
int shift;
const opus_val16 *xptr;
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5 changes: 5 additions & 0 deletions src/denoise.c
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Expand Up @@ -29,6 +29,10 @@
#include "config.h"
#endif

#ifndef M_PI
#define M_PI (3.14159265358979323846)
#endif

#include <stdlib.h>
#include <string.h>
#include <stdio.h>
Expand Down Expand Up @@ -277,6 +281,7 @@ DenoiseState *rnnoise_create(RNNModel *model) {
}

void rnnoise_destroy(DenoiseState *st) {
if (common.init) opus_fft_free(common.kfft, 0);
free(st->rnn.vad_gru_state);
free(st->rnn.noise_gru_state);
free(st->rnn.denoise_gru_state);
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2 changes: 1 addition & 1 deletion src/kiss_fft.c
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Expand Up @@ -447,7 +447,7 @@ kiss_fft_state *opus_fft_alloc_twiddles(int nfft,void * mem,size_t * lenmem,
if (st) {
opus_int16 *bitrev;
kiss_twiddle_cpx *twiddles;

memset(st, 0, sizeof(kiss_fft_state));
st->nfft=nfft;
#ifdef FIXED_POINT
st->scale_shift = celt_ilog2(st->nfft);
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119 changes: 67 additions & 52 deletions src/rnn.c
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Expand Up @@ -76,33 +76,39 @@ static OPUS_INLINE float relu(float x)
return x < 0 ? 0 : x;
}

static void faxpy(float *restrict a, const rnn_weight *restrict b, int k, float u)
{
if (u == 0.0) return;
for (int idx = 0; idx < k; idx++)
a[idx] += b[idx] * u;
}

void compute_dense(const DenseLayer *layer, float *output, const float *input)
{
int i, j;
int N, M;
int stride;
M = layer->nb_inputs;
N = layer->nb_neurons;
stride = N;
for (i=0;i<N;i++)
{
/* Compute update gate. */
float sum = layer->bias[i];
for (j=0;j<M;j++)
sum += layer->input_weights[j*stride + i]*input[j];
output[i] = WEIGHTS_SCALE*sum;
}
if (layer->activation == ACTIVATION_SIGMOID) {
const rnn_weight *ip = layer->input_weights;
/* Compute update gate. */
for(i = 0; i < N; i++)
output[i] = layer->bias[i];
for (j=0;j<M;j++,ip+=N)
faxpy(output, ip, N, input[j]);
switch (layer->activation) {
case ACTIVATION_SIGMOID:
for (i=0;i<N;i++)
output[i] = sigmoid_approx(output[i]);
} else if (layer->activation == ACTIVATION_TANH) {
output[i] = sigmoid_approx(WEIGHTS_SCALE * output[i]);
break;
case ACTIVATION_TANH:
for (i=0;i<N;i++)
output[i] = tansig_approx(output[i]);
} else if (layer->activation == ACTIVATION_RELU) {
output[i] = tansig_approx(WEIGHTS_SCALE * output[i]);
break;
default:
case ACTIVATION_RELU:
for (i=0;i<N;i++)
output[i] = relu(output[i]);
} else {
*(int*)0=0;
output[i] = relu(WEIGHTS_SCALE * output[i]);
break;
}
}

Expand All @@ -117,42 +123,49 @@ void compute_gru(const GRULayer *gru, float *state, const float *input)
M = gru->nb_inputs;
N = gru->nb_neurons;
stride = 3*N;
for (i=0;i<N;i++)
{
/* Compute update gate. */
float sum = gru->bias[i];
for (j=0;j<M;j++)
sum += gru->input_weights[j*stride + i]*input[j];
for (j=0;j<N;j++)
sum += gru->recurrent_weights[j*stride + i]*state[j];
z[i] = sigmoid_approx(WEIGHTS_SCALE*sum);
}
for (i=0;i<N;i++)
{
/* Compute reset gate. */
float sum = gru->bias[N + i];
for (j=0;j<M;j++)
sum += gru->input_weights[N + j*stride + i]*input[j];
for (j=0;j<N;j++)
sum += gru->recurrent_weights[N + j*stride + i]*state[j];
r[i] = sigmoid_approx(WEIGHTS_SCALE*sum);
}
for (i=0;i<N;i++)
{
/* Compute output. */
float sum = gru->bias[2*N + i];
for (j=0;j<M;j++)
sum += gru->input_weights[2*N + j*stride + i]*input[j];
for (j=0;j<N;j++)
sum += gru->recurrent_weights[2*N + j*stride + i]*state[j]*r[j];
if (gru->activation == ACTIVATION_SIGMOID) sum = sigmoid_approx(WEIGHTS_SCALE*sum);
else if (gru->activation == ACTIVATION_TANH) sum = tansig_approx(WEIGHTS_SCALE*sum);
else if (gru->activation == ACTIVATION_RELU) sum = relu(WEIGHTS_SCALE*sum);
else *(int*)0=0;
h[i] = z[i]*state[i] + (1-z[i])*sum;
const rnn_weight *ip = gru->input_weights;
const rnn_weight *rp = gru->recurrent_weights;
/* Compute update gate. */
for(i = 0; i < N; i++)
z[i] = gru->bias[i];
for (j=0;j<M;j++,ip+=stride)
faxpy(z, ip, N, input[j]);
for (j=0;j<N;j++,rp+=stride)
faxpy(z, rp, N, state[j]);
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];
ip = gru->input_weights + N;
rp = gru->recurrent_weights + N;
for (j=0;j<M;j++,ip+=stride)
faxpy(r, ip, N, input[j]);
for (j=0;j<N;j++,rp+=stride)
faxpy(r, rp, N, state[j]);
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];
ip = gru->input_weights + 2*N;
rp = gru->recurrent_weights + 2*N;
for (j=0;j<M;j++,ip+=stride)
faxpy(h, ip, N, input[j]);
for (j=0;j<N;j++,rp+=stride)
faxpy(h, rp, N, r[j]*state[j]);
for (i=0;i<N;i++) {
switch (gru->activation) {
case ACTIVATION_SIGMOID: h[i] = sigmoid_approx(WEIGHTS_SCALE*h[i]);break;
case ACTIVATION_TANH: h[i] = tansig_approx(WEIGHTS_SCALE*h[i]); break;
default:
case ACTIVATION_RELU: h[i] = relu(WEIGHTS_SCALE*h[i]); break;
}
h[i] = z[i]*state[i] + (1-z[i])*h[i];
}
for (i=0;i<N;i++)
state[i] = h[i];
state[i] = h[i];
}

#define INPUT_SIZE 42
Expand All @@ -162,6 +175,8 @@ void compute_rnn(RNNState *rnn, float *gains, float *vad, const float *input) {
float dense_out[MAX_NEURONS];
float noise_input[MAX_NEURONS*3];
float denoise_input[MAX_NEURONS*3];

memset(dense_out, 0, sizeof(dense_out));
compute_dense(rnn->model->input_dense, dense_out, input);
compute_gru(rnn->model->vad_gru, rnn->vad_gru_state, dense_out);
compute_dense(rnn->model->vad_output, vad, rnn->vad_gru_state);
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2 changes: 1 addition & 1 deletion src/rnn.h
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Expand Up @@ -66,4 +66,4 @@ void compute_gru(const GRULayer *gru, float *state, const float *input);

void compute_rnn(RNNState *rnn, float *gains, float *vad, const float *input);

#endif /* _MLP_H_ */
#endif /* _RNN_H_ */
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I am not able to evaluate the rest of the changes, but applied this one to get it out of the way.