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89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
import torch
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from .optimizer import Optimizer
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class Adamax(Optimizer):
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"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
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It has been proposed in `Adam: A Method for Stochastic Optimization`__.
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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): learning rate (default: 2e-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
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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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__ https://arxiv.org/abs/1412.6980
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"""
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def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8,
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weight_decay=0):
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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 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
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super(Adamax, self).__init__(params, defaults)
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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 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
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if grad.is_sparse:
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raise RuntimeError('Adamax does not support sparse gradients')
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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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state['exp_avg'] = torch.zeros_like(p.data)
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state['exp_inf'] = torch.zeros_like(p.data)
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exp_avg, exp_inf = state['exp_avg'], state['exp_inf']
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beta1, beta2 = group['betas']
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eps = group['eps']
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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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# Update biased first moment estimate.
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exp_avg.mul_(beta1).add_(1 - beta1, grad)
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# Update the exponentially weighted infinity norm.
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norm_buf = torch.cat([
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exp_inf.mul_(beta2).unsqueeze(0),
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grad.abs().add_(eps).unsqueeze_(0)
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], 0)
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torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, exp_inf.new().long()))
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bias_correction = 1 - beta1 ** state['step']
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clr = group['lr'] / bias_correction
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p.data.addcdiv_(-clr, exp_avg, exp_inf)
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return loss
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