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Using 8-bit recurrent weights for GRU B

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This commit is contained in:
Jean-Marc Valin 2021-07-21 16:38:35 -04:00
parent 8783ef0088
commit 51ef273e06
4 changed files with 13 additions and 6 deletions
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6
dnn/nnet.c
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@ -283,7 +283,7 @@ void compute_gru2(const GRULayer *gru, float *state, const float *input)
sgemv_accum8x4(zrh, gru->input_weights, 3*N, M, stride, input); sgemv_accum8x4(zrh, gru->input_weights, 3*N, M, stride, input);
for (i=0;i<3*N;i++) for (i=0;i<3*N;i++)
recur[i] = gru->bias[3*N + i]; recur[i] = gru->bias[3*N + i];
sgemv_accum(recur, gru->recurrent_weights, 3*N, N, stride, state); sgemv_accum8x4(recur, gru->recurrent_weights, 3*N, N, stride, state);
for (i=0;i<2*N;i++) for (i=0;i<2*N;i++)
zrh[i] += recur[i]; zrh[i] += recur[i];
compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID); compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID);
@ -326,7 +326,7 @@ void compute_gruB(const GRULayer *gru, const float* gru_b_condition, float *stat
sparse_sgemv_accum8x4(zrh, gru->input_weights, 3*N, M, gru->input_weights_idx, input); sparse_sgemv_accum8x4(zrh, gru->input_weights, 3*N, M, gru->input_weights_idx, input);
for (i=0;i<3*N;i++) for (i=0;i<3*N;i++)
recur[i] = gru->bias[3*N + i]; recur[i] = gru->bias[3*N + i];
sgemv_accum(recur, gru->recurrent_weights, 3*N, N, stride, state); sgemv_accum8x4(recur, gru->recurrent_weights, 3*N, N, stride, state);
for (i=0;i<2*N;i++) for (i=0;i<2*N;i++)
zrh[i] += recur[i]; zrh[i] += recur[i];
compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID); compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID);
@ -361,7 +361,7 @@ void compute_gru3(const GRULayer *gru, float *state, const float *input)
RNN_COPY(zrh, input, 3*N); RNN_COPY(zrh, input, 3*N);
for (i=0;i<3*N;i++) for (i=0;i<3*N;i++)
recur[i] = gru->bias[3*N + i]; recur[i] = gru->bias[3*N + i];
sgemv_accum(recur, gru->recurrent_weights, 3*N, N, stride, state); sgemv_accum8x4(recur, gru->recurrent_weights, 3*N, N, stride, state);
for (i=0;i<2*N;i++) for (i=0;i<2*N;i++)
zrh[i] += recur[i]; zrh[i] += recur[i];
compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID); compute_activation(zrh, zrh, 2*N, ACTIVATION_SIGMOID);

2
dnn/nnet.h
View file

@ -59,7 +59,7 @@ typedef struct {
const float *subias; const float *subias;
const qweight *input_weights; const qweight *input_weights;
const int *input_weights_idx; const int *input_weights_idx;
const float *recurrent_weights; const qweight *recurrent_weights;
int nb_inputs; int nb_inputs;
int nb_neurons; int nb_neurons;
int activation; int activation;

7
dnn/training_tf2/dump_lpcnet.py
View file

@ -138,7 +138,14 @@ def dump_grub(self, f, hf, gru_a_size):
print("printing layer " + name + " of type " + self.__class__.__name__) print("printing layer " + name + " of type " + self.__class__.__name__)
weights = self.get_weights() weights = self.get_weights()
qweight = printSparseVector(f, weights[0][:gru_a_size, :], name + '_weights', have_diag=False) qweight = printSparseVector(f, weights[0][:gru_a_size, :], name + '_weights', have_diag=False)
f.write('#ifdef DOT_PROD\n')
qweight = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)
printVector(f, qweight, name + '_recurrent_weights', dotp=True, dtype='qweight')
f.write('#else /*DOT_PROD*/\n')
printVector(f, weights[1], name + '_recurrent_weights') printVector(f, weights[1], name + '_recurrent_weights')
f.write('#endif /*DOT_PROD*/\n')
printVector(f, weights[-1], name + '_bias') printVector(f, weights[-1], name + '_bias')
subias = weights[-1].copy() subias = weights[-1].copy()
subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0) subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)

4
dnn/training_tf2/lpcnet.py
View file

@ -259,12 +259,12 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 20, train
rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True, name='gru_a', rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True, name='gru_a',
recurrent_constraint = constraint, recurrent_regularizer=quant) recurrent_constraint = constraint, recurrent_regularizer=quant)
rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True, name='gru_b', rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True, name='gru_b',
kernel_constraint=constraint, kernel_regularizer=quant) kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant)
else: else:
rnn = GRU(rnn_units1, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_a', rnn = GRU(rnn_units1, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_a',
recurrent_constraint = constraint, recurrent_regularizer=quant) recurrent_constraint = constraint, recurrent_regularizer=quant)
rnn2 = GRU(rnn_units2, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_b', rnn2 = GRU(rnn_units2, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_b',
kernel_constraint=constraint, kernel_regularizer=quant) kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant)
rnn_in = Concatenate()([cpcm, rep(cfeat)]) rnn_in = Concatenate()([cpcm, rep(cfeat)])
md = MDense(pcm_levels, activation='sigmoid', name='dual_fc') md = MDense(pcm_levels, activation='sigmoid', name='dual_fc')