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90 lines
3.4 KiB
90 lines
3.4 KiB
"""RAdam Optimizer.
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Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam
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Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265
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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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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
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defaults = dict(
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lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
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buffer=[[None, None, None] for _ 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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super(RAdam, self).__setstate__(state)
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@torch.no_grad()
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def step(self, closure=None):
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loss = None
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if closure is not None:
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with torch.enable_grad():
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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.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_fp32 = p.float()
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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_fp32)
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state['exp_avg_sq'] = torch.zeros_like(p_fp32)
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else:
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state['exp_avg'] = state['exp_avg'].type_as(p_fp32)
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state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_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_(grad, grad, value=1 - beta2)
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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state['step'] += 1
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buffered = group['buffer'][int(state['step'] % 10)]
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if state['step'] == buffered[0]:
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num_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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num_sma_max = 2 / (1 - beta2) - 1
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num_sma = num_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
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buffered[1] = num_sma
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# more conservative since it's an approximated value
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if num_sma >= 5:
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step_size = group['lr'] * math.sqrt(
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(1 - beta2_t) *
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(num_sma - 4) / (num_sma_max - 4) *
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(num_sma - 2) / num_sma *
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num_sma_max / (num_sma_max - 2)) / (1 - beta1 ** state['step'])
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else:
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step_size = group['lr'] / (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_fp32.add_(p_fp32, alpha=-group['weight_decay'] * group['lr'])
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# more conservative since it's an approximated value
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if num_sma >= 5:
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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p_fp32.addcdiv_(exp_avg, denom, value=-step_size)
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else:
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p_fp32.add_(exp_avg, alpha=-step_size)
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p.copy_(p_fp32)
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return loss
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