Source code for fairseq.optim.sgd

# 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("sgd") class SGD(LegacyFairseqOptimizer): def __init__(self, args, params): super().__init__(args) self._optimizer = torch.optim.SGD(params, **self.optimizer_config)
[docs] @staticmethod def add_args(parser): """Add optimizer-specific arguments to the parser.""" # fmt: off parser.add_argument('--momentum', default=0.0, type=float, metavar='M', help='momentum factor') parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD', help='weight decay')
# 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], "momentum": self.args.momentum, "weight_decay": self.args.weight_decay, } @property def supports_flat_params(self): return True