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123 lines
5.0 KiB
123 lines
5.0 KiB
""" AdamW Optimizer
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Impl copied from PyTorch master
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NOTE: Builtin optim.AdamW is used by the factory, this impl only serves as a Python based reference, will be removed
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someday
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"""
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import math
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import torch
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from torch.optim.optimizer import Optimizer
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class AdamW(Optimizer):
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r"""Implements AdamW algorithm.
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The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
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The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
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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: 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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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 coefficient (default: 1e-2)
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amsgrad (boolean, optional): whether to use the AMSGrad variant of this
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algorithm from the paper `On the Convergence of Adam and Beyond`_
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(default: False)
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.. _Adam\: A Method for Stochastic Optimization:
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https://arxiv.org/abs/1412.6980
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.. _Decoupled Weight Decay Regularization:
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https://arxiv.org/abs/1711.05101
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.. _On the Convergence of Adam and Beyond:
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https://openreview.net/forum?id=ryQu7f-RZ
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
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weight_decay=1e-2, amsgrad=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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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay, amsgrad=amsgrad)
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super(AdamW, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(AdamW, self).__setstate__(state)
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for group in self.param_groups:
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group.setdefault('amsgrad', False)
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@torch.no_grad()
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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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with torch.enable_grad():
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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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# Perform stepweight decay
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p.data.mul_(1 - group['lr'] * group['weight_decay'])
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# Perform optimization step
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grad = p.grad
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if grad.is_sparse:
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raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
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amsgrad = group['amsgrad']
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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)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p)
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if amsgrad:
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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)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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if amsgrad:
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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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bias_correction1 = 1 - beta1 ** state['step']
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bias_correction2 = 1 - beta2 ** state['step']
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# Decay the first and second moment running average coefficient
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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if amsgrad:
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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() / math.sqrt(bias_correction2)).add_(group['eps'])
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else:
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denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
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step_size = group['lr'] / bias_correction1
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p.addcdiv_(exp_avg, denom, value=-step_size)
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return loss
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