# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from . import FairseqLRScheduler, register_lr_scheduler
[docs]@register_lr_scheduler('triangular')
class TriangularSchedule(FairseqLRScheduler):
"""Assign LR based on a triangular cyclical schedule.
See https://arxiv.org/pdf/1506.01186.pdf for details.
"""
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
if len(args.lr) > 1:
raise ValueError(
'Cannot use a fixed learning rate schedule with triangular.'
' Consider --lr-scheduler=fixed instead.'
)
lr = args.lr[0]
assert args.max_lr > lr, 'max_lr must be more than lr'
self.min_lr = lr
self.max_lr = args.max_lr
self.stepsize = args.lr_period_updates // 2
self.lr_shrink = args.lr_shrink
self.shrink_min = args.shrink_min
# initial learning rate
self.lr = self.min_lr
self.optimizer.set_lr(self.lr)
[docs] @staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
# fmt: off
parser.add_argument('--max-lr', required=True, type=float, metavar='LR',
help='max learning rate, must be more than args.lr')
parser.add_argument('--lr-period-updates', default=5000, type=float, metavar='LR',
help='initial number of updates per period (cycle length)')
parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS',
help='shrink factor for annealing')
parser.add_argument('--shrink-min', action='store_true',
help='if set, also shrinks min lr')
# fmt: on
[docs] def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr()
[docs] def step_update(self, num_updates):
"""Update the learning rate after each update."""
cycle = math.floor(num_updates / (2 * self.stepsize))
lr_shrink = self.lr_shrink ** cycle
max_lr = self.max_lr * lr_shrink
if self.shrink_min:
min_lr = self.min_lr * lr_shrink
else:
min_lr = self.min_lr
x = abs(num_updates / self.stepsize - 2 * (cycle + 1) + 1)
self.lr = min_lr + (max_lr - min_lr) * max(0, (1 - x))
self.optimizer.set_lr(self.lr)
return self.lr