#Ranger deep learning optimizer - RAdam + Lookahead combined.
#https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer

import math
import torch
from torch.optim.optimizer import Optimizer, required
import itertools as it
#from torch.optim import Optimizer
#credit - Lookahead implementation from LonePatient - https://github.com/lonePatient/lookahead_pytorch/blob/master/optimizer.py
#credit2 - RAdam code by https://github.com/LiyuanLucasLiu/RAdam/blob/master/radam.py
#changes 8/31/19 - fix references to *self*.N_sma_threshold; 
                #changed eps to 1e-5 as better default than 1e-8.

class Ranger(Optimizer):
    
    def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(.95,0.999), eps=1e-5, weight_decay=0):
        #parameter checks
        if not 0.0 <= alpha <= 1.0:
            raise ValueError(f'Invalid slow update rate: {alpha}')
        if not 1 <= k:
            raise ValueError(f'Invalid lookahead steps: {k}')
        if not lr > 0:
            raise ValueError(f'Invalid Learning Rate: {lr}')
        if not eps > 0:
            raise ValueError(f'Invalid eps: {eps}')
        
        #parameter comments:
        # beta1 (momentum) of .95 seems to work better than .90...
        #N_sma_threshold of 5 seems better in testing than 4.
        #In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.
        
        #prep defaults and init torch.optim base
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        super().__init__(params,defaults)
        
        #adjustable threshold
        self.N_sma_threshhold = N_sma_threshhold
        
        #now we can get to work...
        for group in self.param_groups:
            group["step_counter"] = 0
            #print("group step counter init")
                      
        #look ahead params
        self.alpha = alpha
        self.k = k 
        
        #radam buffer for state
        self.radam_buffer = [[None,None,None] for ind in range(10)]
        
        #lookahead weights
        self.slow_weights = [[p.clone().detach() for p in group['params']]
                                for group in self.param_groups]
        
        #don't use grad for lookahead weights
        for w in it.chain(*self.slow_weights):
            w.requires_grad = False
        
    def __setstate__(self, state):
        print("set state called")
        super(Ranger, self).__setstate__(state)
       
        
    def step(self, closure=None):
        loss = None
        #note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.  
        #Uncomment if you need to use the actual closure...
        
        #if closure is not None:
            #loss = closure()
            
        #------------ radam
        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('RAdam does not support sparse gradients')
    
                p_data_fp32 = p.data.float()
    
                state = self.state[p]
    
                if len(state) == 0:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
                    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, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']
    
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
    
                state['step'] += 1
                buffered = self.radam_buffer[int(state['step'] % 10)]
                if state['step'] == buffered[0]:
                    N_sma, step_size = buffered[1], buffered[2]
                else:
                    buffered[0] = state['step']
                    beta2_t = beta2 ** state['step']
                    N_sma_max = 2 / (1 - beta2) - 1
                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
                    buffered[1] = N_sma
                    if N_sma > self.N_sma_threshhold:
                        step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])
                    else:
                        step_size = 1.0 / (1 - beta1 ** state['step'])
                    buffered[2] = step_size
    
                if group['weight_decay'] != 0:
                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
    
                if N_sma > self.N_sma_threshhold:
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
                else:
                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)
    
                p.data.copy_(p_data_fp32)
        
        
        #---------------- end radam step
        
        #look ahead tracking and updating if latest batch = k
        for group,slow_weights in zip(self.param_groups,self.slow_weights):
            group['step_counter'] += 1
            if group['step_counter'] % self.k != 0:
                continue
            for p,q in zip(group['params'],slow_weights):
                if p.grad is None:
                    continue
                q.data.add_(self.alpha,p.data - q.data)
                p.data.copy_(q.data)
            
        
            
        return loss