# Copyright (c) Alibaba, Inc. and its affiliates.
import math

import torch
from mmcv.runner import OPTIMIZERS
from torch.optim import Optimizer

from easycv.framework.errors import RuntimeError


@OPTIMIZERS.register_module()
class Lamb(Optimizer):
    """A pure pytorch variant of FuseLAMB (NvLamb variant) optimizer.
    This class is copied from `timm`_. The LAMB was proposed in `Large Batch
    Optimization for Deep Learning - Training BERT in 76 minutes`_.
    .. _timm:
        https://github.com/rwightman/pytorch-image-models/blob/master/timm/optim/lamb.py
    .. _Large Batch Optimization for Deep Learning - Training BERT in 76 minutes:
        https://arxiv.org/abs/1904.00962
    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 norm. (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)
        grad_averaging (bool, optional): whether apply (1-beta2) to grad when
            calculating running averages of gradient. (default: True)
        max_grad_norm (float, optional): value used to clip global grad norm
            (default: 1.0)
        trust_clip (bool): enable LAMBC trust ratio clipping (default: False)
        always_adapt (boolean, optional): Apply adaptive learning rate to 0.0
            weight decay parameter (default: False)
    """  # noqa: E501

    def __init__(self,
                 params,
                 lr=1e-3,
                 bias_correction=True,
                 betas=(0.9, 0.999),
                 eps=1e-6,
                 weight_decay=0.01,
                 grad_averaging=True,
                 max_grad_norm=1.0,
                 trust_clip=False,
                 always_adapt=False):
        defaults = dict(
            lr=lr,
            bias_correction=bias_correction,
            betas=betas,
            eps=eps,
            weight_decay=weight_decay,
            grad_averaging=grad_averaging,
            max_grad_norm=max_grad_norm,
            trust_clip=trust_clip,
            always_adapt=always_adapt)
        super().__init__(params, defaults)

    @torch.no_grad()
    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:
            with torch.enable_grad():
                loss = closure()

        device = self.param_groups[0]['params'][0].device
        one_tensor = torch.tensor(
            1.0, device=device
        )  # because torch.where doesn't handle scalars correctly
        global_grad_norm = torch.zeros(1, device=device)
        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad
                if grad.is_sparse:
                    raise RuntimeError(
                        'Lamb does not support sparse gradients, consider '
                        'SparseAdam instead.')
                global_grad_norm.add_(grad.pow(2).sum())

        global_grad_norm = torch.sqrt(global_grad_norm)
        # FIXME it'd be nice to remove explicit tensor conversion of scalars
        #  when torch.where promotes
        # scalar types properly https://github.com/pytorch/pytorch/issues/9190
        max_grad_norm = torch.tensor(
            self.defaults['max_grad_norm'], device=device)
        clip_global_grad_norm = torch.where(global_grad_norm > max_grad_norm,
                                            global_grad_norm / max_grad_norm,
                                            one_tensor)

        for group in self.param_groups:
            bias_correction = 1 if group['bias_correction'] else 0
            beta1, beta2 = group['betas']
            grad_averaging = 1 if group['grad_averaging'] else 0
            beta3 = 1 - beta1 if grad_averaging else 1.0

            # assume same step across group now to simplify things
            # per parameter step can be easily support by making it tensor, or
            # pass list into kernel
            if 'step' in group:
                group['step'] += 1
            else:
                group['step'] = 1

            if bias_correction:
                bias_correction1 = 1 - beta1**group['step']
                bias_correction2 = 1 - beta2**group['step']
            else:
                bias_correction1, bias_correction2 = 1.0, 1.0

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.div_(clip_global_grad_norm)
                state = self.state[p]

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

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(grad, alpha=beta3)  # m_t
                exp_avg_sq.mul_(beta2).addcmul_(
                    grad, grad, value=1 - beta2)  # v_t

                denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(
                    group['eps'])
                update = (exp_avg / bias_correction1).div_(denom)

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

                if weight_decay != 0 or group['always_adapt']:
                    # Layer-wise LR adaptation. By default, skip adaptation on
                    # parameters that are
                    # excluded from weight decay, unless always_adapt == True,
                    # then always enabled.
                    w_norm = p.norm(2.0)
                    g_norm = update.norm(2.0)
                    # FIXME nested where required since logical and/or not
                    #  working in PT XLA
                    trust_ratio = torch.where(
                        w_norm > 0,
                        torch.where(g_norm > 0, w_norm / g_norm, one_tensor),
                        one_tensor,
                    )
                    if group['trust_clip']:
                        # LAMBC trust clipping, upper bound fixed at one
                        trust_ratio = torch.minimum(trust_ratio, one_tensor)
                    update.mul_(trust_ratio)

                p.add_(update, alpha=-group['lr'])

        return loss