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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 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 | |
| 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 | |
| def __setstate__(self, state): | |
| super(Ranger, self).__setstate__(state) | |
| def step(self, closure=None): | |
| loss = None | |
| # 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 |