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| """RAdam Optimizer. | |
| Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam | |
| Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265 | |
| """ | |
| import math | |
| import torch | |
| from torch.optim.optimizer import Optimizer | |
| class RAdam(Optimizer): | |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): | |
| defaults = dict( | |
| lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, | |
| buffer=[[None, None, None] for _ in range(10)]) | |
| super(RAdam, self).__init__(params, defaults) | |
| def __setstate__(self, state): | |
| super(RAdam, self).__setstate__(state) | |
| def step(self, closure=None): | |
| loss = None | |
| if closure is not None: | |
| with torch.enable_grad(): | |
| loss = closure() | |
| for group in self.param_groups: | |
| for p in group['params']: | |
| if p.grad is None: | |
| continue | |
| grad = p.grad.float() | |
| if grad.is_sparse: | |
| raise RuntimeError('RAdam does not support sparse gradients') | |
| p_fp32 = p.float() | |
| state = self.state[p] | |
| if len(state) == 0: | |
| state['step'] = 0 | |
| state['exp_avg'] = torch.zeros_like(p_fp32) | |
| state['exp_avg_sq'] = torch.zeros_like(p_fp32) | |
| else: | |
| state['exp_avg'] = state['exp_avg'].type_as(p_fp32) | |
| state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_fp32) | |
| exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] | |
| beta1, beta2 = group['betas'] | |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) | |
| exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) | |
| state['step'] += 1 | |
| buffered = group['buffer'][int(state['step'] % 10)] | |
| if state['step'] == buffered[0]: | |
| num_sma, step_size = buffered[1], buffered[2] | |
| else: | |
| buffered[0] = state['step'] | |
| beta2_t = beta2 ** state['step'] | |
| num_sma_max = 2 / (1 - beta2) - 1 | |
| num_sma = num_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) | |
| buffered[1] = num_sma | |
| # more conservative since it's an approximated value | |
| if num_sma >= 5: | |
| step_size = group['lr'] * math.sqrt( | |
| (1 - beta2_t) * | |
| (num_sma - 4) / (num_sma_max - 4) * | |
| (num_sma - 2) / num_sma * | |
| num_sma_max / (num_sma_max - 2)) / (1 - beta1 ** state['step']) | |
| else: | |
| step_size = group['lr'] / (1 - beta1 ** state['step']) | |
| buffered[2] = step_size | |
| if group['weight_decay'] != 0: | |
| p_fp32.add_(p_fp32, alpha=-group['weight_decay'] * group['lr']) | |
| # more conservative since it's an approximated value | |
| if num_sma >= 5: | |
| denom = exp_avg_sq.sqrt().add_(group['eps']) | |
| p_fp32.addcdiv_(exp_avg, denom, value=-step_size) | |
| else: | |
| p_fp32.add_(exp_avg, alpha=-step_size) | |
| p.copy_(p_fp32) | |
| return loss | |