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learning_rates.py
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learning_rates.py
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# Copyright (c) 2024, EleutherAI
# This file is based on code by the authors denoted below and has been modified from its original version.
#
# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http:https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Learning rate decay functions."""
import math
from megatron import print_rank_0
class AnnealingLR(object):
"""Anneals the learning rate."""
def __init__(
self,
optimizer,
start_lr,
warmup_iter,
total_iters,
decay_style,
last_iter,
min_lr=0.0,
constant_lr=0.0,
constant_iters=None,
cooldown_iters=None,
timescale=None,
use_checkpoint_lr_scheduler=True,
override_lr_scheduler=False,
use_mup=False,
):
# Class values.
self.optimizer = optimizer
self.start_lr = start_lr
self.min_lr = min_lr
self.constant_lr = constant_lr
self.warmup_iter = warmup_iter
self.num_iters = last_iter
self.end_iter = total_iters
self.constant_iters = constant_iters
self.cooldown_iters = cooldown_iters
self.timescale = timescale
assert self.end_iter > 0
self.decay_style = decay_style
self.override_lr_scheduler = override_lr_scheduler
self.use_checkpoint_lr_scheduler = use_checkpoint_lr_scheduler
self.use_mup = use_mup
if self.override_lr_scheduler:
assert not self.use_checkpoint_lr_scheduler, (
"both override and " "use-checkpoint are set."
)
# Set the learning rate
self.step(self.num_iters)
print_rank_0("> learning rate decay style: {}".format(self.decay_style))
def get_lr(self):
"""Learning rate decay functions from:
https://openreview.net/pdf?id=BJYwwY9ll pg. 4"""
num_iters_ = self.num_iters
# Warmup.
if self.warmup_iter > 0 and self.num_iters <= self.warmup_iter:
return float(self.start_lr) * num_iters_ / self.warmup_iter
num_iters_ = num_iters_ - self.warmup_iter
if self.decay_style == "linear":
end_iter_ = self.end_iter - self.warmup_iter
lr = self.start_lr * (end_iter_ - num_iters_) / end_iter_
elif self.decay_style == "cosine":
end_iter_ = self.end_iter - self.warmup_iter
lr = self.min_lr + (
(self.start_lr - self.min_lr)
/ 2.0
* (math.cos(math.pi * num_iters_ / end_iter_) + 1)
)
elif self.decay_style == "exponential":
# exp(-0.693) = 1/2
end_iter = self.end_iter - self.warmup_iter
lr = self.start_lr * math.exp(-0.693 * num_iters_ / end_iter)
elif self.decay_style == "infinite_cosine" or self.decay_style == "infinite_inv_sqrt":
if num_iters_ <= self.cooldown_iters:
if self.decay_style == "infinite_cosine":
lr = self.constant_lr + (
(self.start_lr-self.constant_lr)
/ 2.0
* (math.cos(math.pi * num_iters_ / self.cooldown_iters) + 1)
)
else:
def inv_f(t):
return (1/math.sqrt(1+(self.timescale*t))) - 1
lr = self.start_lr + (
(self.constant_lr - self.start_lr)
/ inv_f(1)
* (inv_f(num_iters_ / self.cooldown_iters))
)
return lr
else:
num_iters_ = num_iters_ - self.cooldown_iters
if num_iters_ <= self.constant_iters:
# Stay constant for constant_iters
lr = self.constant_lr
else:
# Go from constant iters to min LR using exponential decay in remaining iters
end_iter_ = self.end_iter - self.warmup_iter - self.cooldown_iters - self.constant_iters
num_iters_ = num_iters_ - self.constant_iters
exp_factor = -math.log(self.min_lr/self.constant_lr) / end_iter_
lr = self.constant_lr * math.exp(-1* exp_factor * num_iters_)
else:
lr = self.start_lr
return max(lr, self.min_lr)
def step(self, step_num=None):
"""Set lr for all parameters groups."""
if step_num is None:
step_num = self.num_iters + 1
self.num_iters = step_num
new_lr = self.get_lr()
for group in self.optimizer.param_groups:
if self.use_mup and "width_mult" in group:
group["lr"] = new_lr / group["width_mult"]
else:
group["lr"] = new_lr
def state_dict(self):
state_dict = {
"start_lr": self.start_lr,
"warmup_iter": self.warmup_iter,
"num_iters": self.num_iters,
"decay_style": self.decay_style,
"end_iter": self.end_iter,
"min_lr": self.min_lr,
}
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_lr_scheduler:
print_rank_0(" > overriding {} value to {}".format(name, cls_value))
return cls_value
if not self.use_checkpoint_lr_scheduler:
assert cls_value == sd_value, (
"AnnealingLR: class input value"
"and checkpoint values for {} do not match".format(name)
)
print_rank_0(" > using checkpoint value {} for {}".format(sd_value, name))
return sd_value
def load_state_dict(self, sd):
self.start_lr = self._check_and_set(
self.start_lr, sd["start_lr"], "learning rate"
)
self.min_lr = self._check_and_set(
self.min_lr, sd["min_lr"], "minimum learning rate"
)
self.warmup_iter = self._check_and_set(
self.warmup_iter, sd["warmup_iter"], "warmup iterations"
)
self.end_iter = self._check_and_set(
self.end_iter, sd["end_iter"], "total number of iterations"
)
self.decay_style = self._check_and_set(
self.decay_style, sd["decay_style"], "decay style"
)
self.num_iters = sd["num_iters"]
self.step(self.num_iters)