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import attr
import math
import torch
import transformers
from seq2struct.utils import registry
registry.register('optimizer', 'adadelta')(torch.optim.Adadelta)
registry.register('optimizer', 'adam')(torch.optim.Adam)
registry.register('optimizer', 'sgd')(torch.optim.SGD)
@registry.register('lr_scheduler', 'warmup_polynomial')
@attr.s
class WarmupPolynomialLRScheduler:
param_groups = attr.ib()
num_warmup_steps = attr.ib()
start_lr = attr.ib()
end_lr = attr.ib()
decay_steps = attr.ib()
power = attr.ib()
def update_lr(self, current_step):
if current_step < self.num_warmup_steps:
warmup_frac_done = current_step / self.num_warmup_steps
new_lr = self.start_lr * warmup_frac_done
else:
new_lr = (
(self.start_lr - self.end_lr) * (1 - (current_step - self.num_warmup_steps) / self.decay_steps) ** self.power
+ self.end_lr)
for param_group in self.param_groups:
param_group['lr'] = new_lr
@registry.register('lr_scheduler', 'warmup_polynomial_group')
@attr.s
class WarmupPolynomialLRSchedulerGroup(WarmupPolynomialLRScheduler):
start_lrs = attr.ib()
"""
Each param group has it's own start lr
start lr is in the same order as param groups,
"""
def update_lr(self, current_step):
for start_lr, param_group in zip(self.start_lrs, self.param_groups):
if current_step < self.num_warmup_steps:
warmup_frac_done = current_step / self.num_warmup_steps
new_lr = start_lr * warmup_frac_done
else:
new_lr = (
(start_lr - self.end_lr) * (1 - (current_step - self.num_warmup_steps) / self.decay_steps) ** self.power
+ self.end_lr)
param_group['lr'] = new_lr
@registry.register('lr_scheduler', 'warmup_cosine')
@attr.s
class WarmupCosineLRScheduler:
param_groups = attr.ib()
num_warmup_steps = attr.ib()
start_lr = attr.ib()
end_lr = attr.ib()
decay_steps = attr.ib()
def update_lr(self, current_step):
if current_step < self.num_warmup_steps:
warmup_frac_done = current_step / self.num_warmup_steps
new_lr = self.start_lr * warmup_frac_done
else:
new_lr = (
(self.start_lr - self.end_lr) * 0.5 * (1 + math.cos(math.pi * (current_step - self.num_warmup_steps) / self.decay_steps))
+ self.end_lr)
for param_group in self.param_groups:
param_group['lr'] = new_lr
@registry.register('lr_scheduler', 'noop')
class NoOpLRScheduler:
def __init__(self, optimizer):
pass
def update_lr(self, current_step):
pass
@registry.register('optimizer', 'bertAdamw')
class BertAdamW(transformers.AdamW):
"""
Given a model and its bert module, create parameter groups with different lr
"""
def __init__(self, non_bert_params, bert_params, lr=1e-3, bert_lr=2e-5, **kwargs):
self.bert_param_group = {"params" : bert_params , "lr": bert_lr, "weight_decay": 0}
self.non_bert_param_group = {"params" : non_bert_params}
params = [self.non_bert_param_group, self.bert_param_group]
if "name" in kwargs: del kwargs["name"] #TODO: fix this
super(BertAdamW, self).__init__(params, lr=lr, **kwargs)
@registry.register('lr_scheduler', 'bert_warmup_polynomial_group')
@attr.s
class BertWarmupPolynomialLRSchedulerGroup(WarmupPolynomialLRScheduler):
"""
Set the lr of bert to be zero when the other param group is warming-up
"""
start_lrs = attr.ib()
# Bert parameters are in the second group by default
def update_lr(self, current_step):
for i, (start_lr, param_group) in enumerate(zip(self.start_lrs, self.param_groups)):
if current_step < self.num_warmup_steps:
if i == 0:
warmup_frac_done = current_step / self.num_warmup_steps
new_lr = start_lr * warmup_frac_done
else: # fix bert during warm-up
assert i == 1
new_lr = 0
else:
new_lr = (
(start_lr - self.end_lr) * (1 - (current_step - self.num_warmup_steps) / self.decay_steps) ** self.power
+ self.end_lr)
param_group['lr'] = new_lr
@registry.register('optimizer', 'adamw')
class AdamW(torch.optim.Optimizer):
"""Implements Adam algorithm.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
**Modified to implement AdamW, see https://arxiv.org/pdf/1711.05101v3.pdf**
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, amsgrad=False):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay, amsgrad=amsgrad)
super(AdamW, self).__init__(params, defaults)
def __setstate__(self, state):
super(AdamW, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
amsgrad = group['amsgrad']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
# MODIFIED HERE
#if group['weight_decay'] != 0:
# grad = grad.add(group['weight_decay'], p.data)
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
# MODIFIED HERE
# Note that weight_decay is ultimately multiplied with the learning rate.
update = exp_avg / denom
if group['weight_decay'] != 0:
update += group['weight_decay'] * p.data
p.data.add_(-step_size * update)
#p.data.addcdiv_(-step_size, exp_avg, denom)
return loss
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