"""NovoGrad Optimizer.
Original impl by Masashi Kimura (Convergence Lab): https://github.com/convergence-lab/novograd
Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
    - https://arxiv.org/abs/1905.11286
"""

import torch
from torch.optim.optimizer import Optimizer
import math


class NovoGrad(Optimizer):
    def __init__(self, params, grad_averaging=False, lr=0.1, betas=(0.95, 0.98), eps=1e-8, weight_decay=0):
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        super(NovoGrad, self).__init__(params, defaults)
        self._lr = lr
        self._beta1 = betas[0]
        self._beta2 = betas[1]
        self._eps = eps
        self._wd = weight_decay
        self._grad_averaging = grad_averaging

        self._momentum_initialized = False

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

        if not self._momentum_initialized:
            for group in self.param_groups:
                for p in group['params']:
                    if p.grad is None:
                        continue
                    state = self.state[p]
                    grad = p.grad.data
                    if grad.is_sparse:
                        raise RuntimeError('NovoGrad does not support sparse gradients')

                    v = torch.norm(grad)**2
                    m = grad/(torch.sqrt(v) + self._eps) + self._wd * p.data
                    state['step'] = 0
                    state['v'] = v
                    state['m'] = m
                    state['grad_ema'] = None
            self._momentum_initialized = True

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                state = self.state[p]
                state['step'] += 1

                step, v, m = state['step'], state['v'], state['m']
                grad_ema = state['grad_ema']

                grad = p.grad.data
                g2 = torch.norm(grad)**2
                grad_ema = g2 if grad_ema is None else grad_ema * \
                    self._beta2 + g2 * (1. - self._beta2)
                grad *= 1.0 / (torch.sqrt(grad_ema) + self._eps)

                if self._grad_averaging:
                    grad *= (1. - self._beta1)

                g2 = torch.norm(grad)**2
                v = self._beta2*v + (1. - self._beta2)*g2
                m = self._beta1*m + (grad / (torch.sqrt(v) + self._eps) + self._wd * p.data)
                bias_correction1 = 1 - self._beta1 ** step
                bias_correction2 = 1 - self._beta2 ** step
                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                state['v'], state['m']  = v, m
                state['grad_ema'] = grad_ema
                p.data.add_(-step_size, m)
        return loss