def get_bert_optimizer(models, type_optimization, learning_rate):
""" Optimizes the network with AdamWithDecay
"""
if type_optimization not in patterns_optimizer:
print('Error. Type optimizer must be one of %s' %
(str(patterns_optimizer.keys())))
parameters_with_decay = []
parameters_with_decay_names = []
parameters_without_decay = []
parameters_without_decay_names = []
no_decay = ['bias', 'gamma', 'beta']
patterns = patterns_optimizer[type_optimization]
for model in models:
for n, p in model.named_parameters():
if any(t in n for t in patterns):
if any(t in n for t in no_decay):
parameters_without_decay.append(p)
parameters_without_decay_names.append(n)
else:
parameters_with_decay.append(p)
parameters_with_decay_names.append(n)
print('The following parameters will be optimized WITH decay:')
print(_ellipse(parameters_with_decay_names, 5, ' , '))
print('The following parameters will be optimized WITHOUT decay:')
print(_ellipse(parameters_without_decay_names, 5, ' , '))
optimizer_grouped_parameters = [
{'params': parameters_with_decay, 'weight_decay': 0.01},
{'params': parameters_without_decay, 'weight_decay': 0.0}
]
optimizer = AdamWithDecay(optimizer_grouped_parameters,
lr=learning_rate)
return optimizer
def get_bert_optimizer(models, bert_learning_rate, base_learning_rate,
weight_decay):
""" Optimizes the network with AdamWithDecay
"""
parameters_with_decay = []
parameters_with_decay_names = []
parameters_without_decay = []
parameters_without_decay_names = []
base_parameters = []
base_parameters_names = []
no_decay = ['bias', 'gamma', 'beta']
for model in models:
for n, p in model.named_parameters():
if p.requires_grad:
# fine-tune BERT
if any(t in n for t in ["bert_model", "bert"]):
if any(t in n for t in no_decay):
parameters_without_decay.append(p)
parameters_without_decay_names.append(n)
else:
parameters_with_decay.append(p)
parameters_with_decay_names.append(n)
else:
base_parameters.append(p)
base_parameters_names.append(n)
print('The following parameters will be optimized WITH decay:')
print(_ellipse(parameters_with_decay_names, 5, ' , '))
print('The following parameters will be optimized WITHOUT decay:')
print(_ellipse(parameters_without_decay_names, 5, ' , '))
print('The following parameters will be optimized NORMALLY:')
print(_ellipse(base_parameters_names, 5, ' , '))
optimizer_grouped_parameters = [{
'params': parameters_with_decay,
'weight_decay': weight_decay,
'lr': bert_learning_rate
}, {
'params': parameters_without_decay,
'weight_decay': 0.0,
'lr': bert_learning_rate
}, {
'params': base_parameters,
'weight_decay': weight_decay,
'lr': base_learning_rate
}]
optimizer = AdamWithDecay(optimizer_grouped_parameters,
lr=base_learning_rate)
return optimizer
def __init__(self, model, opt):
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
parameters_with_decay = []
parameters_with_decay_names = []
parameters_without_decay = []
parameters_without_decay_names = []
base_parameters = []
base_parameters_names = []
for n, p in model.named_parameters():
if p.requires_grad:
# fine-tune BERT
if any(t in n for t in ["transformer"]):
if any(t in n for t in no_decay):
parameters_without_decay.append(p)
parameters_without_decay_names.append(n)
else:
parameters_with_decay.append(p)
parameters_with_decay_names.append(n)
else:
base_parameters.append(p)
base_parameters_names.append(n)
weight_decay = opt['weight_decay']
bert_learning_rate = opt['gpt_lr']
base_learning_rate = opt['lr']
optimizer_grouped_parameters = [
{'params': parameters_with_decay, 'weight_decay': weight_decay, 'lr': bert_learning_rate},
{'params': parameters_without_decay, 'weight_decay': 0.0, 'lr': bert_learning_rate},
{'params': base_parameters, 'weight_decay': weight_decay, 'lr': base_learning_rate}
]
#
print('The following parameters will be optimized WITH decay:')
print(_ellipse(parameters_with_decay_names, 5, ' , '))
print('The following parameters will be optimized WITHOUT decay:')
print(_ellipse(parameters_without_decay_names, 5, ' , '))
print('The following parameters will be optimized NORMALLY:')
print(_ellipse(base_parameters_names, 5, ' , '))
optimizer = OpenAIAdam(optimizer_grouped_parameters,
lr=opt['gpt_lr'],
warmup=opt['warmup_proportion'],
max_grad_norm=opt['gradient_clip'],
t_total=opt.get('optimizer_step', -1))
self.optimizer = optimizer
