Ross Wightman
6 years ago
parent
0a84dd5890
commit
89147a91e6
@ -1,4 +1,3 @@
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from optim.adabound import AdaBound
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from optim.nadam import Nadam
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from optim.rmsprop_tf import RMSpropTF
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from optim.optim_factory import create_optimizer
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@ -1,121 +0,0 @@
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import math
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import torch
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from torch.optim import Optimizer
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class AdaBound(Optimizer):
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"""Implements AdaBound algorithm.
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It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`_.
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Arguments:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): Adam learning rate (default: 1e-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.999))
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final_lr (float, optional): final (SGD) learning rate (default: 0.1)
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gamma (float, optional): convergence speed of the bound functions (default: 1e-3)
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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amsbound (boolean, optional): whether to use the AMSBound variant of this algorithm
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.. Adaptive Gradient Methods with Dynamic Bound of Learning Rate:
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https://openreview.net/forum?id=Bkg3g2R9FX
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Originally taken from https://github.com/Luolc/AdaBound
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NOTE: Has not provided good (or even decent) results on large datasets like ImageNet
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, gamma=1e-3,
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eps=1e-8, weight_decay=0, amsbound=False):
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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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if not 0.0 <= final_lr:
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raise ValueError("Invalid final learning rate: {}".format(final_lr))
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if not 0.0 <= gamma < 1.0:
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raise ValueError("Invalid gamma parameter: {}".format(gamma))
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defaults = dict(lr=lr, betas=betas, final_lr=final_lr, gamma=gamma, eps=eps,
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weight_decay=weight_decay, amsbound=amsbound)
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super(AdaBound, self).__init__(params, defaults)
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self.base_lrs = list(map(lambda group: group['lr'], self.param_groups))
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def __setstate__(self, state):
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super(AdaBound, self).__setstate__(state)
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for group in self.param_groups:
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group.setdefault('amsbound', False)
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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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, base_lr in zip(self.param_groups, self.base_lrs):
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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
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if grad.is_sparse:
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raise RuntimeError(
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'Adam does not support sparse gradients, please consider SparseAdam instead')
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amsbound = group['amsbound']
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p.data)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p.data)
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if amsbound:
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# Maintains max of all exp. moving avg. of sq. grad. values
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state['max_exp_avg_sq'] = torch.zeros_like(p.data)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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if amsbound:
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max_exp_avg_sq = state['max_exp_avg_sq']
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beta1, beta2 = group['betas']
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state['step'] += 1
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if group['weight_decay'] != 0:
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grad = grad.add(group['weight_decay'], p.data)
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# Decay the first and second moment running average coefficient
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exp_avg.mul_(beta1).add_(1 - beta1, grad)
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
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if amsbound:
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# Maintains the maximum of all 2nd moment running avg. till now
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torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
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# Use the max. for normalizing running avg. of gradient
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denom = max_exp_avg_sq.sqrt().add_(group['eps'])
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else:
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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bias_correction1 = 1 - beta1 ** state['step']
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bias_correction2 = 1 - beta2 ** state['step']
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step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
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# Applies bounds on actual learning rate
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# lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay
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final_lr = group['final_lr'] * group['lr'] / base_lr
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lower_bound = final_lr * (1 - 1 / (group['gamma'] * state['step'] + 1))
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upper_bound = final_lr * (1 + 1 / (group['gamma'] * state['step']))
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step_size = torch.full_like(denom, step_size)
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step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(exp_avg)
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p.data.add_(-step_size)
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return loss
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