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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, degenerated_to_sgd=True): |
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if not 0.0 <= lr: |
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raise ValueError("Invalid learning rate: {}".format(lr)) |
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if not 0.0 <= eps: |
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raise ValueError("Invalid epsilon value: {}".format(eps)) |
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if not 0.0 <= betas[0] < 1.0: |
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raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) |
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if not 0.0 <= betas[1] < 1.0: |
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raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) |
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self.degenerated_to_sgd = degenerated_to_sgd |
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if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict): |
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for param in params: |
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if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]): |
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param['buffer'] = [[None, None, None] for _ in range(10)] |
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defaults = dict(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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def step(self, closure=None): |
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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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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_(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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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 / ( |
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N_sma_max - 2)) / (1 - beta1 ** state['step']) |
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elif self.degenerated_to_sgd: |
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step_size = 1.0 / (1 - beta1 ** state['step']) |
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else: |
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step_size = -1 |
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buffered[2] = step_size |
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if N_sma >= 5: |
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if group['weight_decay'] != 0: |
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p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr']) |
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denom = exp_avg_sq.sqrt().add_(group['eps']) |
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p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size * group['lr']) |
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p.data.copy_(p_data_fp32) |
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elif step_size > 0: |
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if group['weight_decay'] != 0: |
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p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * group['lr']) |
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p_data_fp32.add_(exp_avg, alpha=-step_size * group['lr']) |
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p.data.copy_(p_data_fp32) |
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return loss |
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