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'''TAKEN from OpenAI LM Code by HuggingFace'''
import math
import torch
from torch.optim import Optimizer
from torch.nn.utils import clip_grad_norm_
def warmup_cosine(x, warmup=0.002):
s = 1 if x <= warmup else 0
return s*(x/warmup) + (1-s)*(0.5 * (1 + torch.cos(math.pi * x)))
def warmup_constant(x, warmup=0.002):
s = 1 if x <= warmup else 0
return s*(x/warmup) + (1-s)*1
def warmup_linear(x, warmup=0.002):
s = 1 if x <= warmup else 0
# print(s)
return (s*(x/warmup) + (1-s))*(1-x)
SCHEDULES = {
'warmup_cosine': warmup_cosine,
'warmup_constant': warmup_constant,
'warmup_linear': warmup_linear,
}
class OpenAIAdam(Optimizer):
"""Implements Open AI version of Adam algorithm with weight decay fix.
"""
def __init__(self, params, lr, schedule, warmup, t_total,
b1=0.9, b2=0.999, e=1e-8, l2=0,
vector_l2=False, max_grad_norm=-1, **kwargs):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if schedule not in SCHEDULES:
raise ValueError("Invalid schedule parameter: {}".format(schedule))
if not 0 <= warmup:
raise ValueError("Invalid warmup: {}".format(warmup))
if not 0.0 <= b1 < 1.0:
raise ValueError("Invalid b1 parameter: {}".format(b1))
if not 0.0 <= b2 < 1.0:
raise ValueError("Invalid b2 parameter: {}".format(b2))
if not 0.0 <= e:
raise ValueError("Invalid epsilon value: {}".format(e))
defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total,
b1=b1, b2=b2, e=e, l2=l2, vector_l2=vector_l2,
max_grad_norm=max_grad_norm)
super(OpenAIAdam, self).__init__(params, defaults)
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:
# print(group['t_total'])
# print(group['warmup'])
# if self.state[group['params'][0]]:
# print(self.state[group['params'][0]]['step'] / group['t_total'])
# print()
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')
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)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['b1'], group['b2']
state['step'] += 1
# Add grad clipping
if group['max_grad_norm'] > 0:
clip_grad_norm_(p, group['max_grad_norm'])
# 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)
denom = exp_avg_sq.sqrt().add_(group['e'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
schedule_fct = SCHEDULES[group['schedule']]
lr_scheduled = (group['lr'] * schedule_fct(state['step'] /
group['t_total'], group['warmup']))
step_size = (lr_scheduled * math.sqrt(bias_correction2) /
bias_correction1)
p.data.addcdiv_(-step_size, exp_avg, denom)
# Add weight decay at the end (fixed version)
if (len(p.size()) > 1 or group['vector_l2']) and group['l2'] > 0:
p.data.add_(-lr_scheduled * group['l2'], p.data)
return loss
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