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# 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 fairseq.optim import LegacyFairseqOptimizer, register_optimizer
@register_optimizer("lamb")
class FairseqLAMB(LegacyFairseqOptimizer):
"""LAMB optimizer."""
def __init__(self, args, params):
super().__init__(args)
try:
from apex.optimizers import FusedLAMB
self._optimizer = FusedLAMB(params, **self.optimizer_config)
except ImportError:
raise ImportError("Please install apex to use LAMB optimizer")
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--lamb-betas', default='(0.9, 0.999)', metavar='B',
help='betas for LAMB optimizer')
parser.add_argument('--lamb-eps', type=float, default=1e-8, metavar='D',
help='epsilon for LAMB optimizer')
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": self.args.lr[0],
"betas": eval(self.args.lamb_betas),
"eps": self.args.lamb_eps,
"weight_decay": self.args.weight_decay,
}
@property
def supports_flat_params(self):
return False
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