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| """ AdamW Optimizer | |
| Impl copied from PyTorch master | |
| """ | |
| import math | |
| import torch | |
| from torch.optim.optimizer import Optimizer | |
| class AdamW(Optimizer): | |
| r"""Implements AdamW algorithm. | |
| The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_. | |
| The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_. | |
| Arguments: | |
| params (iterable): iterable of parameters to optimize or dicts defining | |
| parameter groups | |
| lr (float, optional): learning rate (default: 1e-3) | |
| betas (Tuple[float, float], optional): coefficients used for computing | |
| running averages of gradient and its square (default: (0.9, 0.999)) | |
| eps (float, optional): term added to the denominator to improve | |
| numerical stability (default: 1e-8) | |
| weight_decay (float, optional): weight decay coefficient (default: 1e-2) | |
| amsgrad (boolean, optional): whether to use the AMSGrad variant of this | |
| algorithm from the paper `On the Convergence of Adam and Beyond`_ | |
| (default: False) | |
| .. _Adam\: A Method for Stochastic Optimization: | |
| https://arxiv.org/abs/1412.6980 | |
| .. _Decoupled Weight Decay Regularization: | |
| https://arxiv.org/abs/1711.05101 | |
| .. _On the Convergence of Adam and Beyond: | |
| https://openreview.net/forum?id=ryQu7f-RZ | |
| """ | |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, | |
| weight_decay=1e-2, amsgrad=False): | |
| if not 0.0 <= lr: | |
| raise ValueError("Invalid learning rate: {}".format(lr)) | |
| if not 0.0 <= eps: | |
| raise ValueError("Invalid epsilon value: {}".format(eps)) | |
| if not 0.0 <= betas[0] < 1.0: | |
| raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) | |
| if not 0.0 <= betas[1] < 1.0: | |
| raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) | |
| defaults = dict(lr=lr, betas=betas, eps=eps, | |
| weight_decay=weight_decay, amsgrad=amsgrad) | |
| super(AdamW, self).__init__(params, defaults) | |
| def __setstate__(self, state): | |
| super(AdamW, self).__setstate__(state) | |
| for group in self.param_groups: | |
| group.setdefault('amsgrad', False) | |
| def step(self, closure=None): | |
| """Performs a single optimization step. | |
| Arguments: | |
| closure (callable, optional): A closure that reevaluates the model | |
| and returns the loss. | |
| """ | |
| loss = None | |
| if closure is not None: | |
| loss = closure() | |
| for group in self.param_groups: | |
| for p in group['params']: | |
| if p.grad is None: | |
| continue | |
| # Perform stepweight decay | |
| p.data.mul_(1 - group['lr'] * group['weight_decay']) | |
| # Perform optimization step | |
| grad = p.grad.data | |
| if grad.is_sparse: | |
| raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead') | |
| amsgrad = group['amsgrad'] | |
| state = self.state[p] | |
| # State initialization | |
| if len(state) == 0: | |
| state['step'] = 0 | |
| # Exponential moving average of gradient values | |
| state['exp_avg'] = torch.zeros_like(p.data) | |
| # Exponential moving average of squared gradient values | |
| state['exp_avg_sq'] = torch.zeros_like(p.data) | |
| if amsgrad: | |
| # Maintains max of all exp. moving avg. of sq. grad. values | |
| state['max_exp_avg_sq'] = torch.zeros_like(p.data) | |
| exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] | |
| if amsgrad: | |
| max_exp_avg_sq = state['max_exp_avg_sq'] | |
| beta1, beta2 = group['betas'] | |
| state['step'] += 1 | |
| bias_correction1 = 1 - beta1 ** state['step'] | |
| bias_correction2 = 1 - beta2 ** state['step'] | |
| # Decay the first and second moment running average coefficient | |
| exp_avg.mul_(beta1).add_(1 - beta1, grad) | |
| exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) | |
| if amsgrad: | |
| # Maintains the maximum of all 2nd moment running avg. till now | |
| torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) | |
| # Use the max. for normalizing running avg. of gradient | |
| denom = (max_exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) | |
| else: | |
| denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) | |
| step_size = group['lr'] / bias_correction1 | |
| p.data.addcdiv_(-step_size, exp_avg, denom) | |
| return loss | |