# 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 math
import types

import torch
import torch.optim
import torch.distributed as dist

from . import FairseqOptimizer, register_optimizer


@register_optimizer('adam')
class FairseqAdam(FairseqOptimizer):
    """Adam optimizer for fairseq.

    Important note: this optimizer corresponds to the "AdamW" variant of
    Adam in its weight decay behavior. As such, it is most closely
    analogous to torch.optim.AdamW from PyTorch.
    """

    def __init__(self, args, params):
        super().__init__(args)
        if torch.cuda.is_available():
            try:
                from apex.optimizers import FusedAdam as _FusedAdam  # noqa
                self._optimizer = FusedAdam(params, **self.optimizer_config)
            except ImportError:
                self._optimizer = Adam(params, **self.optimizer_config)
        else:
            self._optimizer = Adam(params, **self.optimizer_config)

    @staticmethod
    def add_args(parser):
        """Add optimizer-specific arguments to the parser."""
        # fmt: off
        parser.add_argument('--adam-betas', default='(0.9, 0.999)', metavar='B',
                            help='betas for Adam optimizer')
        parser.add_argument('--adam-eps', type=float, default=1e-8, metavar='D',
                            help='epsilon for Adam 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.adam_betas),
            'eps': self.args.adam_eps,
            'weight_decay': self.args.weight_decay,
        }

    def average_params(self):
        """Reduce Params is only used during BMUF distributed training."""
        state_dict = self.optimizer.state_dict()
        total_gpus = float(dist.get_world_size())

        for _, value in state_dict["state"].items():
            value["exp_avg"] /= total_gpus
            value["exp_avg_sq"] /= total_gpus
            dist.all_reduce(value["exp_avg"], op=dist.ReduceOp.SUM)
            dist.all_reduce(value["exp_avg_sq"], op=dist.ReduceOp.SUM)


class Adam(torch.optim.Optimizer):
    """Implements Adam algorithm.

    This implementation is modified from torch.optim.Adam based on:
    `Fixed Weight Decay Regularization in Adam`
    (see https://arxiv.org/abs/1711.05101)

    It has been proposed in `Adam: A Method for Stochastic Optimization`_.

    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)
        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`_

    .. _Adam\: A Method for Stochastic Optimization:
        https://arxiv.org/abs/1412.6980
    .. _On the Convergence of Adam and Beyond:
        https://openreview.net/forum?id=ryQu7f-RZ
    """

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=0, amsgrad=False):
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay, amsgrad=amsgrad)
        super(Adam, self).__init__(params, defaults)

    @property
    def supports_memory_efficient_fp16(self):
        return True

    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.float()
                if grad.is_sparse:
                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
                amsgrad = group['amsgrad']

                p_data_fp32 = p.data.float()

                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_fp32)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state['max_exp_avg_sq'] = torch.zeros_like(p_data_fp32)
                else:
                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
                    if amsgrad:
                        state['max_exp_avg_sq'] = state['max_exp_avg_sq'].type_as(p_data_fp32)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                if amsgrad:
                    max_exp_avg_sq = state['max_exp_avg_sq']
                beta1, beta2 = group['betas']

                state['step'] += 1

                # 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)
                if amsgrad:
                    # Maintains the maximum of all 2nd moment running avg. till now
                    torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
                    # Use the max. for normalizing running avg. of gradient
                    denom = max_exp_avg_sq.sqrt().add_(group['eps'])
                else:
                    denom = exp_avg_sq.sqrt().add_(group['eps'])

                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                if group['weight_decay'] != 0:
                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)

                p_data_fp32.addcdiv_(-step_size, exp_avg, denom)

                p.data.copy_(p_data_fp32)

        return loss


class FusedAdam(torch.optim.Optimizer):
    """
    Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via
    ``python setup.py install --cuda_ext --cpp_ext``.

    It has been proposed in `Adam: A Method for Stochastic Optimization`_.

    Compared to the original version in Apex, the fairseq version casts grads
    and params to FP32 internally to support ``--memory-efficient-fp16``.

    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)
        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
            algorithm from the paper `On the Convergence of Adam and Beyond`_
            (default: False) NOT SUPPORTED in FusedAdam!
        eps_inside_sqrt (boolean, optional): in the 'update parameters' step,
            adds eps to the bias-corrected second moment estimate before
            evaluating square root instead of adding it to the square root of
            second moment estimate as in the original paper. (default: False)
    .. _Adam\: A Method for Stochastic Optimization:
        https://arxiv.org/abs/1412.6980
    .. _On the Convergence of Adam and Beyond:
        https://openreview.net/forum?id=ryQu7f-RZ
    """

    def __init__(self, params,
                 lr=1e-3, bias_correction = True,
                 betas=(0.9, 0.999), eps=1e-8, eps_inside_sqrt=False,
                 weight_decay=0., max_grad_norm=0., amsgrad=False):
        global fused_adam_cuda
        import importlib
        fused_adam_cuda = importlib.import_module("fused_adam_cuda")

        if amsgrad:
            raise RuntimeError('FusedAdam does not support the AMSGrad variant.')
        defaults = dict(lr=lr, bias_correction=bias_correction,
                        betas=betas, eps=eps, weight_decay=weight_decay,
                        max_grad_norm=max_grad_norm)
        super(FusedAdam, self).__init__(params, defaults)
        self.eps_mode = 0 if  eps_inside_sqrt else 1

    @property
    def supports_memory_efficient_fp16(self):
        return True

    def step(self, closure=None, grads=None, scale=1., grad_norms=None):
        """Performs a single optimization step.
        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
            grads (list of tensors, optional): weight gradient to use for the
                optimizer update. If gradients have type torch.half, parameters
                are expected to be in type torch.float. (default: None)
            output params (list of tensors, optional): A reduced precision copy
                of the updated weights written out in addition to the regular
                updated weights. Have to be of same type as gradients. (default: None)
            scale (float, optional): factor to divide gradient tensor values
                by before applying to weights. (default: 1)
        """
        loss = None
        if closure is not None:
            loss = closure()

        if grads is None:
            grads_group = [None]*len(self.param_groups)
        # backward compatibility
        # assuming a list/generator of parameter means single group
        elif isinstance(grads, types.GeneratorType):
            grads_group = [grads]
        elif type(grads[0])!=list:
            grads_group = [grads]
        else:
            grads_group = grads

        if grad_norms is None:
            grad_norms = [None]*len(self.param_groups)

        for group, grads_this_group, grad_norm in zip(self.param_groups, grads_group, grad_norms):
            if grads_this_group is None:
               grads_this_group = [None]*len(group['params'])

            # compute combined scale factor for this group
            combined_scale = scale
            if group['max_grad_norm'] > 0:
                # norm is in fact norm*scale
                clip = ((grad_norm / scale) + 1e-6) / group['max_grad_norm']
                if clip > 1:
                    combined_scale = clip * scale

            bias_correction = 1 if group['bias_correction'] else 0

            for p, grad in zip(group['params'], grads_this_group):
                #note: p.grad should not ever be set for correct operation of mixed precision optimizer that sometimes sends None gradients
                if p.grad is None and grad is None:
                    continue
                if grad is None:
                    grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('FusedAdam does not support sparse gradients, please consider SparseAdam instead')

                p_data_fp32 = p.data.float()

                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_fp32)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
                else:
                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)

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

                state['step'] += 1

                out_p = p.data
                fused_adam_cuda.adam(p_data_fp32,
                                     out_p,
                                     exp_avg,
                                     exp_avg_sq,
                                     grad,
                                     group['lr'],
                                     beta1,
                                     beta2,
                                     group['eps'],
                                     combined_scale,
                                     state['step'],
                                     self.eps_mode,
                                     bias_correction,
                                     group['weight_decay'])

        return loss
