text: # Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.
  
  # https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer 
  # and/or
  # https://github.com/lessw2020/Best-Deep-Learning-Optimizers
  
  # Ranger has now been used to capture 12 records on the FastAI leaderboard.
  
  # This version = 20.4.11   
  
  # Credits:
  # Gradient Centralization --> https://arxiv.org/abs/2004.01461v2 (a new optimization technique for DNNs), github:  https://github.com/Yonghongwei/Gradient-Centralization
  # RAdam -->  https://github.com/LiyuanLucasLiu/RAdam
  # Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.
  # Lookahead paper --> MZhang,G Hinton  https://arxiv.org/abs/1907.08610
  
  # summary of changes: 
  # 4/11/20 - add gradient centralization option.  Set new testing benchmark for accuracy with it, toggle with use_gc flag at init.  
  # full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights), 
  # supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.
  # changes 8/31/19 - fix references to *self*.N_sma_threshold; 
  # changed eps to 1e-5 as better default than 1e-8.
  
  import math
  import torch
  from torch.optim.optimizer import Optimizer, required
  
  
  
  class Ranger(Optimizer):
  
      def __init__(self, params, lr=1e-3,                       # lr
                  alpha=0.5, k=6, N_sma_threshhold=5,           # Ranger options
                  betas=(.95,0.999), eps=1e-5, weight_decay=0,  # Adam options
                  use_gc=True, gc_conv_only=False               # Gradient centralization on or off, applied to conv layers only or conv + fc layers
                  ):   
  
          #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, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold, eps=eps, weight_decay=weight_decay)
          super().__init__(params,defaults)
  
          #adjustable threshold
          self.N_sma_threshhold = N_sma_threshhold
  
          
          #look ahead params
  
          self.alpha = alpha
          self.k = k 
  
          #radam buffer for state
          self.radam_buffer = [[None,None,None] for ind in range(10)]
  
          #gc on or off
          self.use_gc=use_gc
          
          #level of gradient centralization
          self.gc_gradient_threshold = 3 if gc_conv_only else 1
          
          
          print(f"Ranger optimizer loaded. \nGradient Centralization usage = {self.use_gc}")
          if (self.use_gc and self.gc_gradient_threshold==1):
              print(f"GC applied to both conv and fc layers")
          elif (self.use_gc and self.gc_gradient_threshold==3):
              print(f"GC applied to conv layers only")
                                                                              
                                                                               
  
          
  
      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()
  
          #Evaluate averages and grad, update param tensors
          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('Ranger optimizer does not support sparse gradients')
  
                  p_data_fp32 = p.data.float()
  
                  state = self.state[p]  #get state dict for this param
  
                  if len(state) == 0:   #if first time to run...init dictionary with our desired entries
                      #if self.first_run_check==0:
                          #self.first_run_check=1
                          #print("Initializing slow buffer...should not see this at load from saved model!")
                      state['step'] = 0
                      state['exp_avg'] = torch.zeros_like(p_data_fp32)
                      state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
  
                      #look ahead weight storage now in state dict 
                      state['slow_buffer'] = torch.empty_like(p.data)
                      state['slow_buffer'].copy_(p.data)
  
                  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)
  
                  #begin computations 
                  exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                  beta1, beta2 = group['betas']
                
                  
                  #GC operation for Conv layers and FC layers       
                  if grad.dim() > self.gc_gradient_threshold:                    
                     grad.add_(-grad.mean(dim = tuple(range(1,grad.dim())), keepdim = True))
                             
                  
  
                  state['step'] += 1
  
                  #compute variance mov avg
                  exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                  #compute mean moving avg
                  exp_avg.mul_(beta1).add_(1 - beta1, grad)
  
  
                  
  
  
                  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)
  
                  # apply lr
                  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)
  
                  #integrated look ahead...
                  #we do it at the param level instead of group level
                  if state['step'] % group['k'] == 0:
                      slow_p = state['slow_buffer'] #get access to slow param tensor
                      slow_p.add_(self.alpha, p.data - slow_p)  #(fast weights - slow weights) * alpha
                      p.data.copy_(slow_p)  #copy interpolated weights to RAdam param tensor
  
          return loss
  