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cleanup, add signed-unsigned biases

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This commit is contained in:
Jean-Marc Valin 2020-12-30 01:11:55 -05:00
parent 40b309d92b
commit 1707b960de
3 changed files with 11 additions and 7 deletions
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1
dnn/nnet.h
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@ -56,6 +56,7 @@ typedef struct {
typedef struct { typedef struct {
const float *bias; const float *bias;
const float *subias;
const qweight *input_weights; const qweight *input_weights;
const float *recurrent_weights; const float *recurrent_weights;
int nb_inputs; int nb_inputs;

7
dnn/training_tf2/dump_lpcnet.py
View file

@ -138,6 +138,9 @@ def dump_gru_layer(self, f, hf):
f.write('#endif /*DOT_PROD*/\n') f.write('#endif /*DOT_PROD*/\n')
printVector(f, weights[1], name + '_recurrent_weights') printVector(f, weights[1], name + '_recurrent_weights')
printVector(f, weights[-1], name + '_bias') printVector(f, weights[-1], name + '_bias')
subias = weights[-1].copy()
subias[0,:] = subias[0,:] - np.sum(np.clip(weights[0], -1, 1),axis=0)
printVector(f, subias, name + '_subias')
if hasattr(self, 'activation'): if hasattr(self, 'activation'):
activation = self.activation.__name__.upper() activation = self.activation.__name__.upper()
else: else:
@ -148,8 +151,8 @@ def dump_gru_layer(self, f, hf):
reset_after = 1 reset_after = 1
neurons = weights[0].shape[1]//3 neurons = weights[0].shape[1]//3
max_rnn_neurons = max(max_rnn_neurons, neurons) max_rnn_neurons = max(max_rnn_neurons, neurons)
f.write('const GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n' f.write('const GRULayer {} = {{\n {}_bias,\n {}_subias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n'
.format(name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after)) .format(name, name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3)) hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('extern const GRULayer {};\n\n'.format(name)); hf.write('extern const GRULayer {};\n\n'.format(name));

10
dnn/vec.h
View file

@ -194,6 +194,7 @@ static inline void sparse_sgemv_accum16(float *out, const float *w, int rows, co
} }
#ifdef DOT_PROD #ifdef DOT_PROD
#define SCALE (128.f*127.f) #define SCALE (128.f*127.f)
#define SCALE_1 (1.f/128.f/127.f) #define SCALE_1 (1.f/128.f/127.f)
@ -228,11 +229,6 @@ static inline void sgemv_accum8x4(float *out, const qweight *w, int rows, int co
} }
for (i=0;i<rows;i++) out[i] *= SCALE_1; for (i=0;i<rows;i++) out[i] *= SCALE_1;
} }
#else
#define sgemv_accum sgemv_accum8x4
#endif
#ifdef DOT_PROD
#ifdef USE_SU_BIAS #ifdef USE_SU_BIAS
@ -308,6 +304,10 @@ static inline void sparse_sgemv_accum8x4(float *out, const qweight *w, int rows,
#endif /*USE_SU_BIAS*/ #endif /*USE_SU_BIAS*/
#else /*DOT_PROD*/ #else /*DOT_PROD*/
#define sgemv_accum sgemv_accum8x4
static inline void sparse_sgemv_accum8x4(float *out, const qweight *w, int rows, int ignore, const int *idx, const float *x) static inline void sparse_sgemv_accum8x4(float *out, const qweight *w, int rows, int ignore, const int *idx, const float *x)
{ {
int i, j; int i, j;