1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
| class LAMBOptimizer(tf.train.Optimizer):
'''
LAMBOptimizer optimizer.
# Important Note
- This is NOT an official implementation.
- LAMB optimizer is changed from arXiv v1 ~ v3.
- We implement v3 version (which is the latest version on June, 2019.).
- Our implementation is based on `AdamWeightDecayOptimizer` in BERT (provided by Google).
# References
- LAMB optimier: https://github.com/ymcui/LAMB_Optimizer_TF
- Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. https://arxiv.org/abs/1904.00962v3
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://arxiv.org/abs/1810.04805
# Parameters
- There is nothing special, just the same as `AdamWeightDecayOptimizer`.
'''
def __init__(self,
learning_rate,
weight_decay_rate=0.01,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=None,
name="LAMBOptimizer"):
"""Constructs a LAMBOptimizer."""
super(LAMBOptimizer, self).__init__(False, name)
self.learning_rate=learning_rate
self.weight_decay_rate=weight_decay_rate
self.beta_1=beta_1
self.beta_2=beta_2
self.epsilon=epsilon
self.exclude_from_weight_decay=exclude_from_weight_decay
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
"""See base class."""
assignments=[]
for (grad, param) in grads_and_vars:
if grad is None or param is None:
continue
param_name=self._get_variable_name(param.name)
m=tf.get_variable(
name=param_name + "/lamb_m",
shape=param.shape.as_list(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
v=tf.get_variable(
name=param_name + "/lamb_v",
shape=param.shape.as_list(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
# Standard Adam update.
next_m=(
tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))
next_v=(
tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,
tf.square(grad)))
update=next_m / (tf.sqrt(next_v) + self.epsilon)
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
# since that will interact with the m and v parameters in strange ways.
#
# Instead we want ot decay the weights in a manner that doesn't interact
# with the m/v parameters. This is equivalent to adding the square
# of the weights to the loss with plain (non-momentum) SGD.
if self._do_use_weight_decay(param_name):
update +=self.weight_decay_rate * param
############## BELOW ARE THE SPECIFIC PARTS FOR LAMB ##############
# Note: Here are two choices for scaling function \phi(z)
# minmax: \phi(z)=min(max(z, \gamma_l), \gamma_u)
# identity: \phi(z)=z
# The authors does not mention what is \gamma_l and \gamma_u
# UPDATE: after asking authors, they provide me the code below.
# ratio=array_ops.where(math_ops.greater(w_norm, 0), array_ops.where(
# math_ops.greater(g_norm, 0), (w_norm / g_norm), 1.0), 1.0)
r1=tf.sqrt(tf.reduce_sum(tf.square(param)))
r2=tf.sqrt(tf.reduce_sum(tf.square(update)))
r=tf.where(tf.greater(r1, 0.0),
tf.where(tf.greater(r2, 0.0),
r1 / r2,
1.0),
1.0)
eta=self.learning_rate * r
update_with_lr=eta * update
next_param=param - update_with_lr
assignments.extend(
[param.assign(next_param),
m.assign(next_m),
v.assign(next_v)])
return tf.group(*assignments, name=name)
def _do_use_weight_decay(self, param_name):
"""Whether to use L2 weight decay for `param_name`."""
if not self.weight_decay_rate:
return False
if self.exclude_from_weight_decay:
for r in self.exclude_from_weight_decay:
if re.search(r, param_name) is not None:
return False
return True
def _get_variable_name(self, param_name):
"""Get the variable name from the tensor name."""
m=re.match("^(.*):\\d+$", param_name)
if m is not None:
param_name=m.group(1)
return param_name |
