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Adding some sparse GRU support

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Still need to properly dump as sparse.
This commit is contained in:
Jean-Marc Valin 2018-11-28 18:49:19 -05:00
parent ec671ed90e
commit 4de3e53a73
4 changed files with 127 additions and 3 deletions
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46
dnn/dump_lpcnet.py
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@ -41,10 +41,10 @@ max_rnn_neurons = 1
max_conv_inputs = 1
max_mdense_tmp = 1
def printVector(f, vector, name):
def printVector(f, vector, name, dtype='float'):
v = np.reshape(vector, (-1));
#print('static const float ', name, '[', len(v), '] = \n', file=f)
f.write('static const float {}[{}] = {{\n '.format(name, len(v)))
f.write('static const {} {}[{}] = {{\n '.format(dtype, name, len(v)))
for i in range(0, len(v)):
f.write('{}'.format(v[i]))
if (i!=len(v)-1):
@ -59,11 +59,51 @@ def printVector(f, vector, name):
f.write('\n};\n\n')
return;
def printSparseVector(f, A, name):
N = A.shape[0]
W = np.zeros((0,))
diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])
A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))
A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))
A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
printVector(f, diag, name + '_diag')
for i in range(3*N//16):
for j in range(N):
W = np.concatenate([W, A[j, i*16:(i+1)*16]])
printVector(f, W, name)
idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
printVector(f, idx, name + '_idx', dtype='int')
return;
def dump_layer_ignore(self, f, hf):
print("ignoring layer " + self.name + " of type " + self.__class__.__name__)
return False
Layer.dump_layer = dump_layer_ignore
def dump_sparse_gru(self, f, hf):
global max_rnn_neurons
name = 'sparse_' + self.name
print("printing layer " + name + " of type sparse " + self.__class__.__name__)
weights = self.get_weights()
printSparseVector(f, weights[1], name + '_recurrent_weights')
printVector(f, weights[-1], name + '_bias')
if hasattr(self, 'activation'):
activation = self.activation.__name__.upper()
else:
activation = 'TANH'
if hasattr(self, 'reset_after') and not self.reset_after:
reset_after = 0
else:
reset_after = 1
neurons = weights[0].shape[1]//3
max_rnn_neurons = max(max_rnn_neurons, neurons)
f.write('const SparseGRULayer {} = {{\n {}_bias,\n {}_recurrent_weights_diag,\n {}_recurrent_weights,\n {}_recurrent_weights_idx,\n {}, ACTIVATION_{}, {}\n}};\n\n'
.format(name, name, name, name, name, 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 {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('extern const SparseGRULayer {};\n\n'.format(name));
return True
def dump_gru_layer(self, f, hf):
global max_rnn_neurons
name = self.name
@ -205,6 +245,8 @@ for i, layer in enumerate(model.layers):
if layer.dump_layer(f, hf):
layer_list.append(layer.name)
dump_sparse_gru(model.get_layer('gru_a'), f, hf)
hf.write('#define MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons))
hf.write('#define MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs))
hf.write('#define MAX_MDENSE_TMP {}\n\n'.format(max_mdense_tmp))