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Fix some quantization issues

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This commit is contained in:
Jean-Marc Valin 2020-12-30 15:57:16 -05:00
parent 83657d0e43
commit 40b9fd0a75
1 changed files with 10 additions and 9 deletions
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19
dnn/training_tf2/dump_lpcnet.py
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@ -62,12 +62,13 @@ def printVector(f, vector, name, dtype='float', dotp=False):
def printSparseVector(f, A, name): def printSparseVector(f, A, name):
N = A.shape[0] N = A.shape[0]
W = np.zeros((0,)) W = np.zeros((0,), dtype='int')
W0 = np.zeros((0,)) W0 = np.zeros((0,))
diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])]) 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] = 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[:,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:])) A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')
printVector(f, diag, name + '_diag') printVector(f, diag, name + '_diag')
idx = np.zeros((0,), dtype='int') idx = np.zeros((0,), dtype='int')
for i in range(3*N//8): for i in range(3*N//8):
@ -76,22 +77,22 @@ def printSparseVector(f, A, name):
nb_nonzero = 0 nb_nonzero = 0
for j in range(N//4): for j in range(N//4):
block = A[j*4:(j+1)*4, i*8:(i+1)*8] block = A[j*4:(j+1)*4, i*8:(i+1)*8]
qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8]
if np.sum(np.abs(block)) > 1e-10: if np.sum(np.abs(block)) > 1e-10:
nb_nonzero = nb_nonzero + 1 nb_nonzero = nb_nonzero + 1
idx = np.append(idx, j) idx = np.append(idx, j)
vblock = block.transpose((1,0)).reshape((-1,)) vblock = qblock.transpose((1,0)).reshape((-1,))
W0 = np.concatenate([W0, block.reshape((-1,))]) W0 = np.concatenate([W0, block.reshape((-1,))])
W = np.concatenate([W, vblock]) W = np.concatenate([W, vblock])
idx[pos] = nb_nonzero idx[pos] = nb_nonzero
f.write('#ifdef DOT_PROD\n') f.write('#ifdef DOT_PROD\n')
W = np.minimum(127, np.maximum(-128, np.round(W*128))) printVector(f, W, name, dtype='qweight')
printVector(f, W.astype('int'), name, dtype='qweight')
f.write('#else /*DOT_PROD*/\n') f.write('#else /*DOT_PROD*/\n')
printVector(f, W0, name, dtype='qweight') printVector(f, W0, name, dtype='qweight')
f.write('#endif /*DOT_PROD*/\n') f.write('#endif /*DOT_PROD*/\n')
#idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16) #idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
printVector(f, idx, name + '_idx', dtype='int') printVector(f, idx, name + '_idx', dtype='int')
return; return AQ
def dump_layer_ignore(self, f, hf): def dump_layer_ignore(self, f, hf):
print("ignoring layer " + self.name + " of type " + self.__class__.__name__) print("ignoring layer " + self.name + " of type " + self.__class__.__name__)
@ -103,10 +104,10 @@ def dump_sparse_gru(self, f, hf):
name = 'sparse_' + self.name name = 'sparse_' + self.name
print("printing layer " + name + " of type sparse " + self.__class__.__name__) print("printing layer " + name + " of type sparse " + self.__class__.__name__)
weights = self.get_weights() weights = self.get_weights()
printSparseVector(f, weights[1], name + '_recurrent_weights') qweights = printSparseVector(f, weights[1], name + '_recurrent_weights')
printVector(f, weights[-1], name + '_bias') printVector(f, weights[-1], name + '_bias')
subias = weights[-1].copy() subias = weights[-1].copy()
subias[1,:] = subias[1,:] - np.sum(np.clip(weights[1], -1, 1),axis=0) subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0)
printVector(f, subias, name + '_subias') printVector(f, subias, name + '_subias')
if hasattr(self, 'activation'): if hasattr(self, 'activation'):
activation = self.activation.__name__.upper() activation = self.activation.__name__.upper()
@ -131,7 +132,7 @@ def dump_gru_layer(self, f, hf):
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()
f.write('#ifdef DOT_PROD\n') f.write('#ifdef DOT_PROD\n')
qweight = np.clip((128*weights[0]).astype('int'), -128, 127) qweight = np.clip(np.round(128.*weights[0]).astype('int'), -128, 127)
printVector(f, qweight, name + '_weights', dotp=True, dtype='qweight') printVector(f, qweight, name + '_weights', dotp=True, dtype='qweight')
f.write('#else /*DOT_PROD*/\n') f.write('#else /*DOT_PROD*/\n')
printVector(f, weights[0], name + '_weights') printVector(f, weights[0], name + '_weights')
@ -139,7 +140,7 @@ def dump_gru_layer(self, f, hf):
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 = weights[-1].copy()
subias[0,:] = subias[0,:] - np.sum(np.clip(weights[0], -1, 1),axis=0) subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)
printVector(f, subias, name + '_subias') printVector(f, subias, name + '_subias')
if hasattr(self, 'activation'): if hasattr(self, 'activation'):
activation = self.activation.__name__.upper() activation = self.activation.__name__.upper()