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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Learning rate decay and weight decay incr functions."""
import math
from megatron import print_rank_0, get_args
class OptimizerParamScheduler(object):
"""Anneals learning rate and weight decay"""
def __init__(self, optimizer, max_lr, min_lr,
lr_warmup_steps, lr_decay_steps, lr_decay_style,
start_wd, end_wd, wd_incr_steps, wd_incr_style,
use_checkpoint_opt_param_scheduler=True,
override_opt_param_scheduler=False):
args = get_args()
# Class values.
self.optimizer = optimizer
self.max_lr = float(max_lr)
self.min_lr = min_lr
assert self.min_lr >= 0.0
assert self.max_lr >= self.min_lr
self.lr_warmup_steps = lr_warmup_steps
self.num_steps = 0
self.lr_decay_steps = lr_decay_steps
assert self.lr_decay_steps > 0
assert self.lr_warmup_steps < self.lr_decay_steps
self.lr_decay_tokens = args.lr_decay_tokens
self.num_tokens = 0
self.lr_warmup_tokens = args.lr_warmup_tokens
self.lr_decay_style = lr_decay_style
self.start_wd = start_wd
self.end_wd = end_wd
assert self.start_wd >= 0.0
assert self.end_wd >= self.start_wd
self.wd_incr_steps = wd_incr_steps
self.wd_incr_style = wd_incr_style
self.override_opt_param_scheduler = override_opt_param_scheduler
self.use_checkpoint_opt_param_scheduler = use_checkpoint_opt_param_scheduler
if self.override_opt_param_scheduler:
assert not self.use_checkpoint_opt_param_scheduler, 'both override and '\
'use-checkpoint are set.'
# Set the learning rate
self.step(0)
print_rank_0('> learning rate decay style: {}'.format(self.lr_decay_style))
def get_wd(self):
""" Weight decay incr functions"""
if self.num_steps > self.wd_incr_steps:
return self.end_wd
if self.wd_incr_style == 'constant':
assert self.start_wd == self.end_wd
return self.end_wd
incr_ratio = float(self.num_steps) / float(self.wd_incr_steps)
assert incr_ratio >= 0.0
assert incr_ratio <= 1.0
delta_wd = self.end_wd - self.start_wd
if self.wd_incr_style == 'linear':
coeff = incr_ratio
elif self.wd_incr_style == 'cosine':
coeff = 0.5 * (math.cos(math.pi * (1 - incr_ratio)) + 1.0)
else:
raise Exception('{} weight decay increment style is not supported.'.format(
self.wd_incr_style))
return self.start_wd + coeff * delta_wd
def get_lr(self):
"""Learning rate decay functions from:
https://openreview.net/pdf?id=BJYwwY9ll pg. 4"""
# Use linear warmup for the initial part.
if self.lr_warmup_tokens is None:
if self.lr_warmup_steps > 0 and self.num_steps <= self.lr_warmup_steps:
if self.num_steps == self.lr_warmup_steps and \
self.lr_decay_tokens is not None:
# The case of step/sample-wise warmup + token-wise decay
self.lr_warmup_tokens = self.num_tokens
return self.max_lr * float(self.num_steps) / \
float(self.lr_warmup_steps)
else:
if self.lr_warmup_tokens > 0 and self.num_tokens <= self.lr_warmup_tokens:
return self.max_lr * float(self.num_tokens) / \
float(self.lr_warmup_tokens)
# If the learning rate is constant, just return the initial value.
if self.lr_decay_style == 'constant':
return self.max_lr
# For any steps larger than `self.lr_decay_steps`, use `self.min_lr`.
if self.lr_decay_tokens is None:
if self.num_steps > self.lr_decay_steps:
return self.min_lr
else:
if self.num_tokens > self.lr_decay_tokens:
return self.min_lr
# If we are done with the warmup period, use the decay style.
if self.lr_decay_style == 'inverse-square-root':
if self.lr_warmup_tokens is None:
warmup_steps = max(self.lr_warmup_steps, 1)
num_steps = max(self.num_steps, 1)
lr = self.max_lr * warmup_steps ** 0.5 / (num_steps ** 0.5)
else:
warmup_tokens = max(self.lr_warmup_tokens, 1)
num_tokens = max(self.num_tokens, 1)
lr = self.max_lr * warmup_tokens ** 0.5 / (num_tokens ** 0.5)
return max(self.min_lr, lr)
if self.lr_decay_tokens is None:
num_steps_ = self.num_steps - self.lr_warmup_steps
decay_steps_ = self.lr_decay_steps - self.lr_warmup_steps
decay_ratio = float(num_steps_) / float(decay_steps_)
else:
num_tokens_ = self.num_tokens - self.lr_warmup_tokens
decay_tokens_ = self.lr_decay_tokens - self.lr_warmup_tokens
decay_ratio = float(num_tokens_) / float(decay_tokens_)
assert decay_ratio >= 0.0
assert decay_ratio <= 1.0
delta_lr = self.max_lr - self.min_lr
if self.lr_decay_style == 'linear':
coeff = (1.0 - decay_ratio)
elif self.lr_decay_style == 'cosine':
coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
else:
raise Exception('{} decay style is not supported.'.format(
self.lr_decay_style))
return self.min_lr + coeff * delta_lr
def step(self, increment, token_num=None):
"""Set lr for all parameters groups."""
if token_num is None:
args = get_args()
token_num = args.consumed_train_tokens
self.num_tokens = token_num
self.num_steps += increment
new_lr = self.get_lr()
new_wd = self.get_wd()
for group in self.optimizer.param_groups:
group['lr'] = new_lr * group.get('lr_mult', 1.0)
group['weight_decay'] = new_wd * group.get('wd_mult', 1.0)
def state_dict(self):
state_dict = {
'max_lr': self.max_lr,
'lr_warmup_steps': self.lr_warmup_steps,
'lr_warmup_tokens': self.lr_warmup_tokens,
'num_steps': self.num_steps,
'num_tokens': self.num_tokens,
'lr_decay_style': self.lr_decay_style,
'lr_decay_steps': self.lr_decay_steps,
'lr_decay_tokens': self.lr_decay_tokens,
'min_lr': self.min_lr,
'start_wd': self.start_wd,
'end_wd': self.end_wd,
'wd_incr_style': self.wd_incr_style,
'wd_incr_steps': self.wd_incr_steps
}
return state_dict
def _check_and_set(self, cls_value, sd_value, name):
"""Auxiliary function for checking the values in the checkpoint and
setting them."""
if self.override_opt_param_scheduler:
print_rank_0(' > overriding {} value to {}'.format(name, cls_value))
return cls_value
if not self.use_checkpoint_opt_param_scheduler:
assert cls_value == sd_value, \
f'OptimizerParamScheduler: class input value {cls_value} and checkpoint' \
f'value {sd_value} for {name} do not match'
print_rank_0(' > using checkpoint value {} for {}'.format(sd_value,
name))
return sd_value
def load_state_dict(self, sd):
if 'start_lr' in sd:
max_lr_ = sd['start_lr']
else:
max_lr_ = sd['max_lr']
self.max_lr = self._check_and_set(self.max_lr, max_lr_,
'learning rate')
self.min_lr = self._check_and_set(self.min_lr, sd['min_lr'],
'minimum learning rate')
if 'warmup_iter' in sd:
lr_warmup_steps_ = sd['warmup_iter']
elif 'warmup_steps' in sd:
lr_warmup_steps_ = sd['warmup_steps']
else:
lr_warmup_steps_ = sd['lr_warmup_steps']
self.lr_warmup_steps = self._check_and_set(self.lr_warmup_steps,
lr_warmup_steps_,
'warmup iterations')
if 'warmup_tokens' in sd:
lr_warmup_tokens_ = sd['warmup_tokens']
else:
lr_warmup_tokens_ = sd['lr_warmup_tokens']
self.lr_warmup_tokens = self._check_and_set(self.lr_warmup_tokens,
lr_warmup_tokens_,
'warmup tokens')
if 'end_iter' in sd:
lr_decay_steps_ = sd['end_iter']
elif 'decay_steps' in sd:
lr_decay_steps_ = sd['decay_steps']
else:
lr_decay_steps_ = sd['lr_decay_steps']
self.lr_decay_steps = self._check_and_set(self.lr_decay_steps, lr_decay_steps_,
'total number of iterations')
if 'decay_tokens' in sd:
lr_decay_tokens_ = sd['decay_tokens']
else:
lr_decay_tokens_ = sd['lr_decay_tokens']
self.lr_decay_tokens = self._check_and_set(self.lr_decay_tokens,
lr_decay_tokens_,
'decay tokens')
if 'decay_style' in sd:
lr_decay_style_ = sd['decay_style']
else:
lr_decay_style_ = sd['lr_decay_style']
self.lr_decay_style = self._check_and_set(self.lr_decay_style,
lr_decay_style_,
'learning rate decay style')
if 'num_iters' in sd:
num_steps = sd['num_iters']
else:
num_steps = sd['num_steps']
if 'num_tokens' in sd:
self.num_tokens = sd['num_tokens']
self.step(increment=num_steps, token_num=self.num_tokens)
if 'start_wd' in sd:
self.start_wd = self._check_and_set(self.start_wd,
sd['start_wd'],
"start weight decay")
self.end_wd = self._check_and_set(self.end_wd,
sd['end_wd'],
"end weight decay")
self.wd_incr_steps = self._check_and_set(self.wd_incr_steps,
sd['wd_incr_steps'],
"total number of weight decay iterations")
self.wd_incr_style = self._check_and_set(self.wd_incr_style,
sd['wd_incr_style'],
"weight decay incr style")
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