Spaces:
Build error
Build error
| #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 |