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WIP: 8-bit SIMD for GRU B

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This commit is contained in:
Jean-Marc Valin 2020-12-29 02:35:29 -05:00
parent e695355ba5
commit 40b309d92b
5 changed files with 58 additions and 7 deletions
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10
dnn/training_tf2/dump_lpcnet.py
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@ -39,7 +39,10 @@ max_rnn_neurons = 1
max_conv_inputs = 1
max_mdense_tmp = 1
def printVector(f, vector, name, dtype='float'):
def printVector(f, vector, name, dtype='float', dotp=False):
if dotp:
vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8))
vector = vector.transpose((2, 0, 3, 1))
v = np.reshape(vector, (-1));
#print('static const float ', name, '[', len(v), '] = \n', file=f)
f.write('static const {} {}[{}] = {{\n '.format(dtype, name, len(v)))
@ -127,7 +130,12 @@ def dump_gru_layer(self, f, hf):
name = self.name
print("printing layer " + name + " of type " + self.__class__.__name__)
weights = self.get_weights()
f.write('#ifdef DOT_PROD\n')
qweight = np.clip((128*weights[0]).astype('int'), -128, 127)
printVector(f, qweight, name + '_weights', dotp=True, dtype='qweight')
f.write('#else /*DOT_PROD*/\n')
printVector(f, weights[0], name + '_weights')
f.write('#endif /*DOT_PROD*/\n')
printVector(f, weights[1], name + '_recurrent_weights')
printVector(f, weights[-1], name + '_bias')
if hasattr(self, 'activation'):