# 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.
"""
LAMB optimizer from github.com/cybertronai/pytorch-lamb.
"""

import math

import torch
import torch.optim

from . import FairseqOptimizer, register_optimizer


@register_optimizer('lamb')
class FairseqLamb(FairseqOptimizer):

    def __init__(self, args, params):
        super().__init__(args, params)
        self._optimizer = Lamb(params, **self.optimizer_config)

    @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,
        }


class Lamb(torch.optim.Optimizer):
    r"""Implements Lamb algorithm.
    It has been proposed in `Reducing BERT Pre-Training Time from 3 Days to 76 Minutes`_.
    Arguments:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float, optional): learning rate (default: 1e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square (default: (0.9, 0.999))
        eps (float, optional): term added to the denominator to improve
            numerical stability (default: 1e-8)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        adam (bool, optional): always use trust ratio = 1, which turns this into
            Adam. Useful for comparison purposes.
    .. _Reducing BERT Pre-Training Time from 3 Days to 76 Minutes:
        https://arxiv.org/abs/1904.00962
    """

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=0, adam=False):
        if not 0.0 <= lr:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {}".format(eps))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay)
        self.adam = adam
        super(Lamb, self).__init__(params, defaults)

    def step(self, closure=None):
        """Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('Lamb does not support sparse gradients, consider SparseAdam instad.')

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p.data)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']

                state['step'] += 1

                if group['weight_decay'] != 0:
                    grad.add_(group['weight_decay'], p.data)

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                denom = exp_avg_sq.sqrt().add_(group['eps'])

                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
                # Apply bias to lr to avoid broadcast.
                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                adam_step = exp_avg / denom
                # L2 norm uses sum, but here since we're dividing, use mean to avoid overflow.
                r1 = p.data.pow(2).mean().sqrt()
                r2 = adam_step.pow(2).mean().sqrt()
                r = 1 if r1 == 0 or r2 == 0 else  min(r1/r2, 10)
                state['r1'] = r1
                state['r2'] = r2
                state['r'] = r
                if self.adam:
                    r = 1

                p.data.add_(-step_size * r, adam_step)

        return loss
