Source code for fairseq.optim.lr_scheduler.inverse_square_root_schedule

# 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.

from collections import Collection
from dataclasses import dataclass, field
from typing import List

from fairseq.dataclass import FairseqDataclass
from omegaconf import II, DictConfig

from . import FairseqLRScheduler, register_lr_scheduler


@dataclass
class InverseSquareRootScheduleConfig(FairseqDataclass):
    warmup_updates: int = field(
        default=4000,
        metadata={"help": "warmup the learning rate linearly for the first N updates"},
    )
    warmup_init_lr: float = field(
        default=-1,
        metadata={
            "help": "initial learning rate during warmup phase; default is args.lr"
        },
    )
    # TODO common vars at parent class
    lr: List[float] = II("optimization.lr")


[docs]@register_lr_scheduler("inverse_sqrt", dataclass=InverseSquareRootScheduleConfig) class InverseSquareRootSchedule(FairseqLRScheduler): """Decay the LR based on the inverse square root of the update number. We also support a warmup phase where we linearly increase the learning rate from some initial learning rate (``--warmup-init-lr``) until the configured learning rate (``--lr``). Thereafter we decay proportional to the number of updates, with a decay factor set to align with the configured learning rate. During warmup:: lrs = torch.linspace(args.warmup_init_lr, args.lr, args.warmup_updates) lr = lrs[update_num] After warmup:: decay_factor = args.lr * sqrt(args.warmup_updates) lr = decay_factor / sqrt(update_num) """ def __init__(self, cfg: DictConfig, optimizer): super().__init__(cfg, optimizer) if isinstance(cfg.lr, Collection) and len(cfg.lr) > 1: raise ValueError( "Cannot use a fixed learning rate schedule with inverse_sqrt." " Consider --lr-scheduler=fixed instead." ) warmup_end_lr = ( cfg.lr[0] if isinstance(cfg.lr, Collection) else cfg.lr ) if cfg.warmup_init_lr < 0: cfg.warmup_init_lr = ( 0 if cfg.warmup_updates > 0 else warmup_end_lr ) # linearly warmup for the first args.warmup_updates self.lr_step = ( warmup_end_lr - cfg.warmup_init_lr ) / cfg.warmup_updates # then, decay prop. to the inverse square root of the update number self.decay_factor = warmup_end_lr * cfg.warmup_updates ** 0.5 # initial learning rate self.lr = cfg.warmup_init_lr self.optimizer.set_lr(self.lr)
[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.""" if num_updates < self.cfg.warmup_updates: self.lr = self.cfg.warmup_init_lr + num_updates * self.lr_step else: self.lr = self.decay_factor * num_updates ** -0.5 self.optimizer.set_lr(self.lr) return self.lr