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- #Ranger deep learning optimizer - RAdam + Lookahead combined.
- #https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
-
- #Ranger has now been used to capture 12 records on the FastAI leaderboard.
-
- #This version = 9.3.19
-
- #Credits:
- #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:
- #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
- import itertools as it
-
-
-
- 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, 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
-
- #now we can get to work...
- #removed as we now use step from RAdam...no need for duplicate step counting
- #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)]
-
- #self.first_run_check=0
-
- #lookahead weights
- #9/2/19 - lookahead param tensors have been moved to state storage.
- #This should resolve issues with load/save where weights were left in GPU memory from first load, slowing down future runs.
-
- #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()
-
- #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']
-
- #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)
-
- 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)
-
- #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
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