Source code for fairseq.optim.adadelta

# 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 torch.optim

from . import LegacyFairseqOptimizer, register_optimizer

[docs]@register_optimizer("adadelta") class Adadelta(LegacyFairseqOptimizer): def __init__(self, args, params): super().__init__(args) self._optimizer = torch.optim.Adadelta(params, **self.optimizer_config)
[docs] @staticmethod def add_args(parser): """Add optimizer-specific arguments to the parser.""" # fmt: off parser.add_argument('--adadelta-rho', type=float, default=0.9, metavar='RHO', help='coefficient used for computing a running average of squared gradients') parser.add_argument('--adadelta-eps', type=float, default=1e-6, metavar='EPS', help='term added to the denominator to improve numerical stability') parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', help='weight decay') parser.add_argument('--anneal-eps', action='store_true', help='flag to anneal eps')
# fmt: on @property def optimizer_config(self): """ Return a kwarg dictionary that will be used to override optimizer args stored in checkpoints. This allows us to load a checkpoint and resume training using a different set of optimizer args, e.g., with a different learning rate. """ return { "lr":[0], "rho": self.args.adadelta_rho, "eps": self.args.adadelta_eps, "weight_decay": self.args.weight_decay, } @property def supports_flat_params(self): return True