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Add validation for weights blob

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This commit is contained in:
Jean-Marc Valin 2023-05-20 14:21:58 -04:00
parent 0098fe70ac
commit c7b6935bf2
4 changed files with 62 additions and 25 deletions
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2
dnn/autogen.sh
View file

@ -6,7 +6,7 @@ srcdir=`dirname $0`
test -n "$srcdir" && cd "$srcdir"
#SHA1 of the first commit compatible with the current model
commit=301a9fb
commit=f1071fa
./download_model.sh $commit
echo "Updating build configuration files for lpcnet, please wait...."

6
dnn/lpcnet.c
View file

@ -174,6 +174,7 @@ LPCNET_EXPORT int lpcnet_get_size()
LPCNET_EXPORT int lpcnet_init(LPCNetState *lpcnet)
{
int i;
int ret;
const char* rng_string="LPCNet";
memset(lpcnet, 0, lpcnet_get_size());
lpcnet->last_exc = lin2ulaw(0.f);
@ -182,8 +183,9 @@ LPCNET_EXPORT int lpcnet_init(LPCNetState *lpcnet)
lpcnet->sampling_logit_table[i] = -log((1-prob)/prob);
}
kiss99_srand(&lpcnet->rng, (const unsigned char *)rng_string, strlen(rng_string));
init_lpcnet_model(&lpcnet->model, lpcnet_arrays);
return 0;
ret = init_lpcnet_model(&lpcnet->model, lpcnet_arrays);
celt_assert(ret == 0);
return ret;
}

75
dnn/parse_lpcnet_weights.c
View file

@ -30,6 +30,8 @@
#include "nnet.h"
#define SPARSE_BLOCK_SIZE 32
extern const WeightArray lpcnet_arrays[];
int parse_record(const unsigned char **data, int *len, WeightArray *array) {
@ -71,9 +73,40 @@ int parse_weights(WeightArray **list, const unsigned char *data, int len)
return nb_arrays;
}
static const void *find_array(const WeightArray *arrays, const char *name) {
static const void *find_array_entry(const WeightArray *arrays, const char *name) {
while (arrays->name && strcmp(arrays->name, name) != 0) arrays++;
return arrays->data;
return arrays;
}
static const void *find_array_check(const WeightArray *arrays, const char *name, int size) {
const WeightArray *a = find_array_entry(arrays, name);
if (a && a->size == size) return a->data;
else return NULL;
}
static const void *find_idx_check(const WeightArray *arrays, const char *name, int nb_in, int nb_out, int *total_blocks) {
int remain;
const int *idx;
const WeightArray *a = find_array_entry(arrays, name);
*total_blocks = 0;
if (a == NULL) return NULL;
idx = a->data;
remain = a->size/sizeof(int);
while (remain > 0) {
int nb_blocks;
int i;
nb_blocks = *idx++;
if (remain < nb_blocks+1) return NULL;
for (i=0;i<nb_blocks;i++) {
int pos = *idx++;
if (pos+3 >= nb_in || (pos&0x3)) return NULL;
}
nb_out -= 8;
remain -= nb_blocks+1;
*total_blocks += nb_blocks;
}
if (nb_out != 0) return NULL;
return a->data;
}
int mdense_init(MDenseLayer *layer, const WeightArray *arrays,
@ -85,9 +118,9 @@ int mdense_init(MDenseLayer *layer, const WeightArray *arrays,
int nb_channels,
int activation)
{
if ((layer->bias = find_array(arrays, bias)) == NULL) return 1;
if ((layer->input_weights = find_array(arrays, input_weights)) == NULL) return 1;
if ((layer->factor = find_array(arrays, factor)) == NULL) return 1;
if ((layer->bias = find_array_check(arrays, bias, nb_neurons*nb_channels*sizeof(layer->bias[0]))) == NULL) return 1;
if ((layer->input_weights = find_array_check(arrays, input_weights, nb_inputs*nb_channels*nb_neurons*sizeof(layer->input_weights[0]))) == NULL) return 1;
if ((layer->factor = find_array_check(arrays, factor, nb_channels*nb_neurons*sizeof(layer->factor[0]))) == NULL) return 1;
layer->nb_inputs = nb_inputs;
layer->nb_neurons = nb_neurons;
layer->nb_channels = nb_channels;
@ -102,8 +135,8 @@ int dense_init(DenseLayer *layer, const WeightArray *arrays,
int nb_neurons,
int activation)
{
if ((layer->bias = find_array(arrays, bias)) == NULL) return 1;
if ((layer->input_weights = find_array(arrays, input_weights)) == NULL) return 1;
if ((layer->bias = find_array_check(arrays, bias, nb_neurons*sizeof(layer->bias[0]))) == NULL) return 1;
if ((layer->input_weights = find_array_check(arrays, input_weights, nb_inputs*nb_neurons*sizeof(layer->input_weights[0]))) == NULL) return 1;
layer->nb_inputs = nb_inputs;
layer->nb_neurons = nb_neurons;
layer->activation = activation;
@ -121,11 +154,12 @@ int gru_init(GRULayer *layer, const WeightArray *arrays,
int activation,
int reset_after)
{
if ((layer->bias = find_array(arrays, bias)) == NULL) return 1;
if ((layer->subias = find_array(arrays, subias)) == NULL) return 1;
if ((layer->input_weights = find_array(arrays, input_weights)) == NULL) return 1;
if ((layer->input_weights_idx = find_array(arrays, input_weights_idx)) == NULL) return 1;
if ((layer->recurrent_weights = find_array(arrays, recurrent_weights)) == NULL) return 1;
int total_blocks;
if ((layer->bias = find_array_check(arrays, bias, 6*nb_neurons*sizeof(layer->bias[0]))) == NULL) return 1;
if ((layer->subias = find_array_check(arrays, subias, 6*nb_neurons*sizeof(layer->subias[0]))) == NULL) return 1;
if ((layer->input_weights_idx = find_idx_check(arrays, input_weights_idx, nb_inputs, 3*nb_neurons, &total_blocks)) == NULL) return 1;
if ((layer->input_weights = find_array_check(arrays, input_weights, SPARSE_BLOCK_SIZE*total_blocks*sizeof(layer->input_weights[0]))) == NULL) return 1;
if ((layer->recurrent_weights = find_array_check(arrays, recurrent_weights, 3*nb_neurons*nb_neurons*sizeof(layer->recurrent_weights[0]))) == NULL) return 1;
layer->nb_inputs = nb_inputs;
layer->nb_neurons = nb_neurons;
layer->activation = activation;
@ -143,11 +177,12 @@ int sparse_gru_init(SparseGRULayer *layer, const WeightArray *arrays,
int activation,
int reset_after)
{
if ((layer->bias = find_array(arrays, bias)) == NULL) return 1;
if ((layer->subias = find_array(arrays, subias)) == NULL) return 1;
if ((layer->diag_weights = find_array(arrays, diag_weights)) == NULL) return 1;
if ((layer->recurrent_weights = find_array(arrays, recurrent_weights)) == NULL) return 1;
if ((layer->idx = find_array(arrays, idx)) == NULL) return 1;
int total_blocks;
if ((layer->bias = find_array_check(arrays, bias, 6*nb_neurons*sizeof(layer->bias[0]))) == NULL) return 1;
if ((layer->subias = find_array_check(arrays, subias, 6*nb_neurons*sizeof(layer->subias[0]))) == NULL) return 1;
if ((layer->diag_weights = find_array_check(arrays, diag_weights, 3*nb_neurons*sizeof(layer->diag_weights[0]))) == NULL) return 1;
if ((layer->idx = find_idx_check(arrays, idx, nb_neurons, 3*nb_neurons, &total_blocks)) == NULL) return 1;
if ((layer->recurrent_weights = find_array_check(arrays, recurrent_weights, SPARSE_BLOCK_SIZE*total_blocks*sizeof(layer->recurrent_weights[0]))) == NULL) return 1;
layer->nb_neurons = nb_neurons;
layer->activation = activation;
layer->reset_after = reset_after;
@ -162,8 +197,8 @@ int conv1d_init(Conv1DLayer *layer, const WeightArray *arrays,
int nb_neurons,
int activation)
{
if ((layer->bias = find_array(arrays, bias)) == NULL) return 1;
if ((layer->input_weights = find_array(arrays, input_weights)) == NULL) return 1;
if ((layer->bias = find_array_check(arrays, bias, nb_neurons*sizeof(layer->bias[0]))) == NULL) return 1;
if ((layer->input_weights = find_array_check(arrays, input_weights, kernel_size*nb_inputs*nb_neurons*sizeof(layer->input_weights[0]))) == NULL) return 1;
layer->nb_inputs = nb_inputs;
layer->kernel_size = kernel_size;
layer->nb_neurons = nb_neurons;
@ -176,7 +211,7 @@ int embedding_init(EmbeddingLayer *layer, const WeightArray *arrays,
int nb_inputs,
int dim)
{
if ((layer->embedding_weights = find_array(arrays, embedding_weights)) == NULL) return 1;
if ((layer->embedding_weights = find_array_check(arrays, embedding_weights, nb_inputs*dim*sizeof(layer->embedding_weights[0]))) == NULL) return 1;
layer->nb_inputs = nb_inputs;
layer->dim = dim;
return 0;

4
dnn/training_tf2/dump_lpcnet.py
View file

@ -340,13 +340,13 @@ if __name__ == "__main__":
W = model.get_layer('gru_a').get_weights()[0][3*embed_size:,:]
#FIXME: dump only half the biases
b = model.get_layer('gru_a').get_weights()[2]
dump_dense_layer_impl('gru_a_dense_feature', W, b, 'LINEAR', f, hf)
dump_dense_layer_impl('gru_a_dense_feature', W, b[:len(b)//2], 'LINEAR', f, hf)
W = model.get_layer('gru_b').get_weights()[0][model.rnn_units1:,:]
b = model.get_layer('gru_b').get_weights()[2]
# Set biases to zero because they'll be included in the GRU input part
# (we need regular and SU biases)
dump_dense_layer_impl('gru_b_dense_feature', W, 0*b, 'LINEAR', f, hf)
dump_dense_layer_impl('gru_b_dense_feature', W, 0*b[:len(b)//2], 'LINEAR', f, hf)
dump_grub(model.get_layer('gru_b'), f, hf, model.rnn_units1)
layer_list = []