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# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
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from . import FairseqOptimizer, register_optimizer
from apex.optimizers.fused_adam import FusedAdam


@register_optimizer('adam')
class FairseqAdam(FairseqOptimizer):
    def __init__(self, args, params):
        super().__init__(args, params)
        self._optimizer = FusedAdam(params, **self.optimizer_config)

    @staticmethod
    def add_args(parser):
        """Add optimizer-specific arguments to the parser."""
        parser.add_argument('--adam-betas', default=(0.9, 0.999), nargs=2, type=float, metavar='B1 B2',
                            help='betas for Adam optimizer')
        parser.add_argument('--adam-eps', type=float, default=1e-8, metavar='D',
                            help='epsilon for Adam optimizer')

    @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': self.args.adam_betas,
            'eps': self.args.adam_eps,
            'weight_decay': self.args.weight_decay,
        }