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- import math
-
- import torch
- from torch.optim.optimizer import Optimizer
-
-
- class AdaBound(Optimizer):
- """Implements AdaBound algorithm.
- It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`_.
- Arguments:
- params (iterable): iterable of parameters to optimize or dicts defining
- parameter groups
- lr (float, optional): Adam 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))
- final_lr (float, optional): final (SGD) learning rate (default: 0.1)
- gamma (float, optional): convergence speed of the bound functions (default: 1e-3)
- 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)
- amsbound (boolean, optional): whether to use the AMSBound variant of this algorithm
- .. Adaptive Gradient Methods with Dynamic Bound of Learning Rate:
- https://openreview.net/forum?id=Bkg3g2R9FX
- """
-
- def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, gamma=1e-3,
- eps=1e-8, weight_decay=0, amsbound=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]))
- if not 0.0 <= final_lr:
- raise ValueError("Invalid final learning rate: {}".format(final_lr))
- if not 0.0 <= gamma < 1.0:
- raise ValueError("Invalid gamma parameter: {}".format(gamma))
- defaults = dict(lr=lr, betas=betas, final_lr=final_lr, gamma=gamma, eps=eps,
- weight_decay=weight_decay, amsbound=amsbound)
- super(AdaBound, self).__init__(params, defaults)
-
- self.base_lrs = list(map(lambda group: group['lr'], self.param_groups))
-
- def __setstate__(self, state):
- super(AdaBound, self).__setstate__(state)
- for group in self.param_groups:
- group.setdefault('amsbound', 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, base_lr in zip(self.param_groups, self.base_lrs):
- for p in group['params']:
- if p.grad is None:
- continue
- grad = p.grad.data
- if grad.is_sparse:
- raise RuntimeError(
- 'Adam does not support sparse gradients, please consider SparseAdam instead')
- amsbound = group['amsbound']
-
- 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 amsbound:
- # 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 amsbound:
- max_exp_avg_sq = state['max_exp_avg_sq']
- beta1, beta2 = group['betas']
-
- state['step'] += 1
-
- if group['weight_decay'] != 0:
- grad = grad.add(group['weight_decay'], p.data)
-
- # 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 amsbound:
- # 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().add_(group['eps'])
- else:
- denom = exp_avg_sq.sqrt().add_(group['eps'])
-
- bias_correction1 = 1 - beta1 ** state['step']
- bias_correction2 = 1 - beta2 ** state['step']
- step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
-
- # Applies bounds on actual learning rate
- # lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay
- final_lr = group['final_lr'] * group['lr'] / base_lr
- lower_bound = final_lr * (1 - 1 / (group['gamma'] * state['step'] + 1))
- upper_bound = final_lr * (1 + 1 / (group['gamma'] * state['step']))
- step_size = torch.full_like(denom, step_size)
- step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(exp_avg)
-
- p.data.add_(-step_size)
-
- return loss
-
-
- class AdaBoundW(Optimizer):
- """Implements AdaBound algorithm with Decoupled Weight Decay (arxiv.org/abs/1711.05101)
- It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`_.
- Arguments:
- params (iterable): iterable of parameters to optimize or dicts defining
- parameter groups
- lr (float, optional): Adam 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))
- final_lr (float, optional): final (SGD) learning rate (default: 0.1)
- gamma (float, optional): convergence speed of the bound functions (default: 1e-3)
- 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)
- amsbound (boolean, optional): whether to use the AMSBound variant of this algorithm
- .. Adaptive Gradient Methods with Dynamic Bound of Learning Rate:
- https://openreview.net/forum?id=Bkg3g2R9FX
- """
-
- def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, gamma=1e-3,
- eps=1e-8, weight_decay=0, amsbound=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]))
- if not 0.0 <= final_lr:
- raise ValueError("Invalid final learning rate: {}".format(final_lr))
- if not 0.0 <= gamma < 1.0:
- raise ValueError("Invalid gamma parameter: {}".format(gamma))
- defaults = dict(lr=lr, betas=betas, final_lr=final_lr, gamma=gamma, eps=eps,
- weight_decay=weight_decay, amsbound=amsbound)
- super(AdaBoundW, self).__init__(params, defaults)
-
- self.base_lrs = list(map(lambda group: group['lr'], self.param_groups))
-
- def __setstate__(self, state):
- super(AdaBoundW, self).__setstate__(state)
- for group in self.param_groups:
- group.setdefault('amsbound', 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, base_lr in zip(self.param_groups, self.base_lrs):
- for p in group['params']:
- if p.grad is None:
- continue
- grad = p.grad.data
- if grad.is_sparse:
- raise RuntimeError(
- 'Adam does not support sparse gradients, please consider SparseAdam instead')
- amsbound = group['amsbound']
-
- 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 amsbound:
- # 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 amsbound:
- max_exp_avg_sq = state['max_exp_avg_sq']
- beta1, beta2 = group['betas']
-
- state['step'] += 1
-
- # 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 amsbound:
- # 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().add_(group['eps'])
- else:
- denom = exp_avg_sq.sqrt().add_(group['eps'])
-
- bias_correction1 = 1 - beta1 ** state['step']
- bias_correction2 = 1 - beta2 ** state['step']
- step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
-
- # Applies bounds on actual learning rate
- # lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay
- final_lr = group['final_lr'] * group['lr'] / base_lr
- lower_bound = final_lr * (1 - 1 / (group['gamma'] * state['step'] + 1))
- upper_bound = final_lr * (1 + 1 / (group['gamma'] * state['step']))
- step_size = torch.full_like(denom, step_size)
- step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(exp_avg)
-
- if group['weight_decay'] != 0:
- decayed_weights = torch.mul(p.data, group['weight_decay'])
- p.data.add_(-step_size)
- p.data.sub_(decayed_weights)
- else:
- p.data.add_(-step_size)
-
- return loss
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