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Avoiding tmp buffer overflows

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This commit is contained in:
Jean-Marc Valin 2022-02-03 00:26:44 -05:00
parent 3e2198c6e1
commit f3bc6bacd2
3 changed files with 7 additions and 5 deletions
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1
dnn/lpcnet.c
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@ -98,7 +98,6 @@ void run_frame_network(LPCNetState *lpcnet, float *gru_a_condition, float *gru_b
compute_conv1d(&feature_conv1, conv1_out, net->feature_conv1_state, in);
if (lpcnet->frame_count < FEATURE_CONV1_DELAY) RNN_CLEAR(conv1_out, FEATURE_CONV1_OUT_SIZE);
compute_conv1d(&feature_conv2, conv2_out, net->feature_conv2_state, conv1_out);
celt_assert(FRAME_INPUT_SIZE == FEATURE_CONV2_OUT_SIZE);
if (lpcnet->frame_count < FEATURES_DELAY) RNN_CLEAR(conv2_out, FEATURE_CONV2_OUT_SIZE);
_lpcnet_compute_dense(&feature_dense1, dense1_out, conv2_out);
_lpcnet_compute_dense(&feature_dense2, condition, dense1_out);

2
dnn/lpcnet_plc.c
View file

@ -62,7 +62,7 @@ LPCNET_EXPORT void lpcnet_plc_destroy(LPCNetPLCState *st) {
}
static void compute_plc_pred(PLCNetState *net, float *out, const float *in) {
float zeros[1024] = {0};
float zeros[3*PLC_MAX_RNN_NEURONS] = {0};
float dense_out[PLC_DENSE1_OUT_SIZE];
_lpcnet_compute_dense(&plc_dense1, dense_out, in);
compute_gruB(&plc_gru1, zeros, net->plc_gru1_state, dense_out);

9
dnn/nnet.c
View file

@ -38,6 +38,7 @@
#include "tansig_table.h"
#include "nnet.h"
#include "nnet_data.h"
#include "plc_data.h"
#ifdef NO_OPTIMIZATIONS
#warning Compiling without any vectorization. This code will be very slow
@ -315,13 +316,15 @@ void compute_gru2(const GRULayer *gru, float *state, const float *input)
state[i] = h[i];
}
#define MAX_RNN_NEURONS_ALL IMAX(MAX_RNN_NEURONS, PLC_MAX_RNN_NEURONS)
void compute_gruB(const GRULayer *gru, const float* gru_b_condition, 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 zrh[3*MAX_RNN_NEURONS_ALL];
float recur[3*MAX_RNN_NEURONS_ALL];
float *z;
float *r;
float *h;
@ -330,7 +333,7 @@ void compute_gruB(const GRULayer *gru, const float* gru_b_condition, float *stat
z = zrh;
r = &zrh[N];
h = &zrh[2*N];
celt_assert(gru->nb_neurons <= MAX_RNN_NEURONS);
celt_assert(gru->nb_neurons <= MAX_RNN_NEURONS_ALL);
celt_assert(input != state);
celt_assert(gru->reset_after);
stride = 3*N;