ref: 40b9fd0a758ef7c43b763cdde21e61061917f1ee
parent: 83657d0e43d80e1e64273d8d8094b04ed8088172
author: Jean-Marc Valin <jmvalin@jmvalin.ca>
date: Wed Dec 30 10:57:16 EST 2020
Fix some quantization issues
--- a/dnn/training_tf2/dump_lpcnet.py
+++ b/dnn/training_tf2/dump_lpcnet.py
@@ -62,12 +62,13 @@
def printSparseVector(f, A, name):
N = A.shape[0]
- W = np.zeros((0,))
+ W = np.zeros((0,), dtype='int')
W0 = 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:]))
+ AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')printVector(f, diag, name + '_diag')
idx = np.zeros((0,), dtype='int')
for i in range(3*N//8):
@@ -76,22 +77,22 @@
nb_nonzero = 0
for j in range(N//4):
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:
nb_nonzero = nb_nonzero + 1
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,))])
W = np.concatenate([W, vblock])
idx[pos] = nb_nonzero
f.write('#ifdef DOT_PROD\n')- W = np.minimum(127, np.maximum(-128, np.round(W*128)))
- printVector(f, W.astype('int'), name, dtype='qweight')+ printVector(f, W, name, dtype='qweight')
f.write('#else /*DOT_PROD*/\n')printVector(f, W0, name, dtype='qweight')
f.write('#endif /*DOT_PROD*/\n')#idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
printVector(f, idx, name + '_idx', dtype='int')
- return;
+ return AQ
def dump_layer_ignore(self, f, hf):
print("ignoring layer " + self.name + " of type " + self.__class__.__name__)@@ -103,10 +104,10 @@
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')
+ qweights = printSparseVector(f, weights[1], name + '_recurrent_weights')
printVector(f, weights[-1], name + '_bias')
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')
if hasattr(self, 'activation'):
activation = self.activation.__name__.upper()
@@ -131,7 +132,7 @@
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)+ qweight = np.clip(np.round(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')
@@ -139,7 +140,7 @@
printVector(f, weights[1], name + '_recurrent_weights')
printVector(f, weights[-1], name + '_bias')
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')
if hasattr(self, 'activation'):
activation = self.activation.__name__.upper()
--
⑨