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- # 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 been used to capture 12 records on the FastAI leaderboard.
-
- # This version = 2020.9.4
-
-
- # 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:
- # 9/4/20 - updated addcmul_ signature to avoid warning. Integrates latest changes from GC developer (he did the work for this), and verified on performance on private dataloader.
- # 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.
-
- # Apache License 2.0 LICENSE code copy from https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
- # please refer to https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer/blob/master/LICENSE
-
-
- import math
- import torch
- from torch.optim.optimizer import Optimizer, required
-
-
- def centralized_gradient(x, use_gc=True, gc_conv_only=False):
- '''credit - https://github.com/Yonghongwei/Gradient-Centralization '''
- if use_gc:
- if gc_conv_only:
- if len(list(x.size())) > 3:
- x.add_(-x.mean(dim=tuple(range(1, len(list(x.size())))), keepdim=True))
- else:
- if len(list(x.size())) > 1:
- x.add_(-x.mean(dim=tuple(range(1, len(list(x.size())))), keepdim=True))
- return x
-
-
- 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
- # Gradient centralization on or off, applied to conv layers only or conv + fc layers
- use_gc=True, gc_conv_only=False, gc_loc=True
- ):
-
- # 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 dataloader (.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.gc_loc = gc_loc
- self.use_gc = use_gc
- self.gc_conv_only = gc_conv_only
- # 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_conv_only == False):
- print(f"GC applied to both conv and fc layers")
- elif (self.use_gc and self.gc_conv_only == True):
- 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))
- if self.gc_loc:
- grad = centralized_gradient(grad, use_gc=self.use_gc, gc_conv_only=self.gc_conv_only)
-
- state['step'] += 1
-
- # compute variance mov avg
- exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
-
- # compute mean moving avg
- exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
-
- 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'])
- G_grad = exp_avg / denom
- else:
- G_grad = exp_avg
-
- if group['weight_decay'] != 0:
- G_grad.add_(p_data_fp32, alpha=group['weight_decay'])
- # GC operation
- if self.gc_loc == False:
- G_grad = centralized_gradient(G_grad, use_gc=self.use_gc, gc_conv_only=self.gc_conv_only)
-
- p_data_fp32.add_(G_grad, alpha=-step_size * group['lr'])
- 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:
- # get access to slow param tensor
- slow_p = state['slow_buffer']
- # (fast weights - slow weights) * alpha
- slow_p.add_(p.data - slow_p, alpha=self.alpha)
- # copy interpolated weights to RAdam param tensor
- p.data.copy_(slow_p)
-
- return loss
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