# Modification by Preferred Networks:
# Copyright (c) 2020      Preferred Networks, Inc.
#
# From PyTorch:
#
# Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
# Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
# Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
# Copyright (c) 2011-2013 NYU                      (Clement Farabet)
# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
# Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
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# Copyright (c) 2016-present, Facebook Inc. All rights reserved.
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# Copyright (c) 2016 Facebook Inc.
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# Copyright (c) 2015 Google Inc.
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# Copyright (c) 2015 Yangqing Jia
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import torch
from torch.optim.optimizer import Optimizer


class LARSSGD(Optimizer):
    def __init__(self, params, lr=None, momentum=0, dampening=0,
                 weight_decay=0, nesterov=False, lars_eta=0.0, lars_threshold=1e-2, eps=1e-9):
        if lr is not None and lr < 0.0:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if momentum < 0.0:
            raise ValueError("Invalid momentum value: {}".format(momentum))
        if weight_decay < 0.0:
            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))

        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
                        weight_decay=weight_decay, nesterov=nesterov,
                        lars_eta=lars_eta, lars_threshold=lars_threshold)
        if nesterov and (momentum <= 0 or dampening != 0):
            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
        super(LARSSGD, self).__init__(params, defaults)
        self.eps = eps

    def __setstate__(self, state):
        super(LARSSGD, self).__setstate__(state)
        for group in self.param_groups:
            group.setdefault('nesterov', False)

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            weight_decay = group['weight_decay']
            momentum = group['momentum']
            dampening = group['dampening']
            nesterov = group['nesterov']
            lars_eta = group['lars_eta']
            lars_threshold = group['lars_threshold']
            lr = group['lr']

            for p in group['params']:
                if p.grad is None:
                    continue
                d_p = p.grad.data
                if lars_eta == 0.0:
                    local_lr = lr
                else:
                    p_norm = torch.norm(p.data)
                    g_norm = torch.norm(d_p)
                    if p_norm > lars_threshold:
                        local_lr = lr * lars_eta * p_norm / (self.eps + g_norm + weight_decay * p_norm)
                    else:
                        local_lr = lr
                if weight_decay != 0:
                    d_p.add_(weight_decay, p.data)
                if momentum != 0:
                    param_state = self.state[p]
                    if 'momentum_buffer' not in param_state:
                        buf = param_state['momentum_buffer'] = torch.clone(d_p).detach()
                        buf.mul_(-(1 - dampening) * local_lr)
                    else:
                        buf = param_state['momentum_buffer']
                        buf.mul_(momentum).add_(-(1 - dampening) * local_lr, d_p)
                    if nesterov:
                        d_p = d_p.add(momentum, buf)
                    else:
                        d_p = buf
                p.data.add_(d_p)

        return loss
