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|
|
|
| """RAdam optimizer.
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|
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| This code is drived from https://github.com/LiyuanLucasLiu/RAdam.
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| """
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|
|
| import math
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| import torch
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|
|
| from torch.optim.optimizer import Optimizer
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|
|
|
|
| class RAdam(Optimizer):
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| """Rectified Adam optimizer."""
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|
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| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
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| """Initilize RAdam optimizer."""
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| defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
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| self.buffer = [[None, None, None] for ind in range(10)]
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| super(RAdam, self).__init__(params, defaults)
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|
|
| def __setstate__(self, state):
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| """Set state."""
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| super(RAdam, self).__setstate__(state)
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|
|
| def step(self, closure=None):
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| """Run one step."""
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| loss = None
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| if closure is not None:
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| loss = closure()
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|
|
| for group in self.param_groups:
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|
|
| for p in group['params']:
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| if p.grad is None:
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| continue
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| grad = p.grad.data.float()
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| if grad.is_sparse:
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| raise RuntimeError('RAdam does not support sparse gradients')
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|
|
| p_data_fp32 = p.data.float()
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|
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| state = self.state[p]
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|
|
| if len(state) == 0:
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| state['step'] = 0
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| state['exp_avg'] = torch.zeros_like(p_data_fp32)
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| state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
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| else:
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| state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
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| state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
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|
|
| exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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| beta1, beta2 = group['betas']
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|
|
| exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
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| exp_avg.mul_(beta1).add_(1 - beta1, grad)
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|
|
| state['step'] += 1
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| buffered = self.buffer[int(state['step'] % 10)]
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| if state['step'] == buffered[0]:
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| N_sma, step_size = buffered[1], buffered[2]
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| else:
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| buffered[0] = state['step']
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| beta2_t = beta2 ** state['step']
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| N_sma_max = 2 / (1 - beta2) - 1
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| N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
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| buffered[1] = N_sma
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|
|
|
|
| if N_sma >= 5:
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| step_size = math.sqrt(
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| (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'])
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| else:
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| step_size = 1.0 / (1 - beta1 ** state['step'])
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| buffered[2] = step_size
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|
|
| if group['weight_decay'] != 0:
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| p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
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|
|
|
|
| if N_sma >= 5:
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| denom = exp_avg_sq.sqrt().add_(group['eps'])
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| p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
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| else:
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| p_data_fp32.add_(-step_size * group['lr'], exp_avg)
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|
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| p.data.copy_(p_data_fp32)
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|
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| return loss
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|
|
