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- import torch
- import math
- from torch.optim import Optimizer
-
- class Adam(Optimizer):
- r"""Implements Adam algorithm.
-
- It has been proposed in `Adam: A Method for Stochastic Optimization`_.
-
- 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 (L2 penalty) (default: 0)
- 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
- .. _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=0, 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(Adam, self).__init__(params, defaults)
-
- def __setstate__(self, state):
- super(Adam, 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
-
- p_grad_cpu = p.grad.data.cpu()
- p_data_cpu = p.data.cpu()
-
- if p_grad_cpu.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_cpu, memory_format=torch.preserve_format)
- # Exponential moving average of squared gradient values
- state['exp_avg_sq'] = torch.zeros_like(p_data_cpu, memory_format=torch.preserve_format)
- if amsgrad:
- # Maintains max of all exp. moving avg. of sq. grad. values
- state['max_exp_avg_sq'] = torch.zeros_like(p_data_cpu, memory_format=torch.preserve_format)
-
- 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']
-
- if group['weight_decay'] != 0:
- p_grad_cpu.add_(group['weight_decay'], p_data_cpu)
-
- # Decay the first and second moment running average coefficient
- exp_avg.mul_(beta1).add_(1 - beta1, p_grad_cpu)
- exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, p_grad_cpu, p_grad_cpu)
- 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_cpu.addcdiv_(-step_size, exp_avg, denom)
- p.data.copy_(p_data_cpu)
-
- return loss
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