old_contents: # Copyright (c) 2017-present, Facebook, Inc.
  # All rights reserved.
  #
  # This source code is licensed under the license found in the LICENSE file in
  # the root directory of this source tree. An additional grant of patent rights
  # can be found in the PATENTS file in the same directory.
  
  import torch.optim.lr_scheduler
  
  from . import FairseqLRScheduler, register_lr_scheduler
  
  
  @register_lr_scheduler('reduce_lr_on_plateau')
  class ReduceLROnPlateau(FairseqLRScheduler):
      """Decay the LR by a factor every time the validation loss plateaus."""
  
      def __init__(self, args, optimizer):
          super().__init__(args, optimizer)
          if len(args.lr) > 1:
              raise ValueError(
                  'Cannot use a fixed learning rate schedule with reduce_lr_on_plateau.'
                  ' Consider --lr-scheduler=fixed instead.'
              )
          self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
              self.optimizer.optimizer, patience=0, factor=args.lr_shrink)
  
      @staticmethod
      def add_args(parser):
          """Add arguments to the parser for this LR scheduler."""
          # fmt: off
          parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS',
                              help='shrink factor for annealing, lr_new = (lr * lr_shrink)')
          # fmt: on
  
      def state_dict(self):
          """Return the LR scheduler state dict."""
          return {
              'best': self.lr_scheduler.best,
              'last_epoch': self.lr_scheduler.last_epoch,
          }
  
      def load_state_dict(self, state_dict):
          """Load an LR scheduler state dict."""
          self.lr_scheduler.best = state_dict['best']
          if 'last_epoch' in state_dict:
              self.lr_scheduler.last_epoch = state_dict['last_epoch']
  
      def step(self, epoch, val_loss=None):
          """Update the learning rate at the end of the given epoch."""
          if val_loss is not None:
              self.lr_scheduler.step(val_loss, epoch)
          else:
              self.lr_scheduler.last_epoch = epoch
          return self.optimizer.get_lr()
  
new_contents: # Copyright (c) 2017-present, Facebook, Inc.
  # All rights reserved.
  #
  # This source code is licensed under the license found in the LICENSE file in
  # the root directory of this source tree. An additional grant of patent rights
  # can be found in the PATENTS file in the same directory.
  
  import torch.optim.lr_scheduler
  
  from . import FairseqLRScheduler, register_lr_scheduler
  
  
  @register_lr_scheduler('reduce_lr_on_plateau')
  class ReduceLROnPlateau(FairseqLRScheduler):
      """Decay the LR by a factor every time the validation loss plateaus."""
  
      def __init__(self, args, optimizer):
          super().__init__(args, optimizer)
          if len(args.lr) > 1:
              raise ValueError(
                  'Cannot use a fixed learning rate schedule with reduce_lr_on_plateau.'
                  ' Consider --lr-scheduler=fixed instead.'
              )
          self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
              self.optimizer.optimizer, patience=0, factor=args.lr_shrink,
              threshold=args.lr_threshold)
  
      @staticmethod
      def add_args(parser):
          """Add arguments to the parser for this LR scheduler."""
          # fmt: off
          parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS',
                              help='shrink factor for annealing, lr_new = (lr * lr_shrink)')
          parser.add_argument('--lr-threshold', default=1e-4, type=float, metavar='LT',
                              help='Threshold for measuring the new optimum, \
                              to only focus on significant changes')
          # fmt: on
  
      def state_dict(self):
          """Return the LR scheduler state dict."""
          return {
              'best': self.lr_scheduler.best,
              'last_epoch': self.lr_scheduler.last_epoch,
          }
  
      def load_state_dict(self, state_dict):
          """Load an LR scheduler state dict."""
          self.lr_scheduler.best = state_dict['best']
          if 'last_epoch' in state_dict:
              self.lr_scheduler.last_epoch = state_dict['last_epoch']
  
      def step(self, epoch, val_loss=None):
          """Update the learning rate at the end of the given epoch."""
          if val_loss is not None:
              self.lr_scheduler.step(val_loss, epoch)
          else:
              self.lr_scheduler.last_epoch = epoch
          return self.optimizer.get_lr()
  
subject: add option to specify lr-threshold while using lr-on-plateau strategy