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125 lines
5.0 KiB
125 lines
5.0 KiB
""" Adan Optimizer
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Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
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https://arxiv.org/abs/2208.06677
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Implementation adapted from https://github.com/sail-sg/Adan
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"""
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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 Adan(Optimizer):
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"""
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Implements a pytorch variant of Adan
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Adan was proposed in
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Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
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https://arxiv.org/abs/2208.06677
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Arguments:
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params (iterable): iterable of parameters to optimize or dicts defining parameter groups.
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lr (float, optional): learning rate. (default: 1e-3)
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betas (Tuple[float, float, flot], optional): coefficients used for computing
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running averages of gradient and its norm. (default: (0.98, 0.92, 0.99))
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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): decoupled weight decay (L2 penalty) (default: 0)
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no_prox (bool): how to perform the decoupled weight decay (default: False)
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"""
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def __init__(
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self,
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params,
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lr=1e-3,
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betas=(0.98, 0.92, 0.99),
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eps=1e-8,
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weight_decay=0.0,
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no_prox=False,
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):
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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 <= betas[2] < 1.0:
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raise ValueError("Invalid beta parameter at index 2: {}".format(betas[2]))
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, no_prox=no_prox)
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super(Adan, self).__init__(params, defaults)
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@torch.no_grad()
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def restart_opt(self):
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for group in self.param_groups:
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group['step'] = 0
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for p in group['params']:
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if p.requires_grad:
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state = self.state[p]
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# State initialization
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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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# Exponential moving average of gradient difference
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state['exp_avg_diff'] = torch.zeros_like(p)
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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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"""
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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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beta1, beta2, beta3 = group['betas']
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# assume same step across group now to simplify things
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# per parameter step can be easily support by making it tensor, or pass list into kernel
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if 'step' in group:
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group['step'] += 1
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else:
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group['step'] = 1
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bias_correction1 = 1.0 - beta1 ** group['step']
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bias_correction2 = 1.0 - beta2 ** group['step']
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bias_correction3 = 1.0 - beta3 ** group['step']
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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
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state = self.state[p]
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if len(state) == 0:
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state['exp_avg'] = torch.zeros_like(p)
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state['exp_avg_diff'] = torch.zeros_like(p)
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state['exp_avg_sq'] = torch.zeros_like(p)
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state['pre_grad'] = grad.clone()
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exp_avg, exp_avg_sq, exp_avg_diff = state['exp_avg'], state['exp_avg_diff'], state['exp_avg_sq']
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grad_diff = grad - state['pre_grad']
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exp_avg.lerp_(grad, 1. - beta1) # m_t
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exp_avg_diff.lerp_(grad_diff, 1. - beta2) # diff_t (v)
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update = grad + beta2 * grad_diff
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exp_avg_sq.mul_(beta3).addcmul_(update, update, value=1. - beta3) # n_t
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denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction3)).add_(group['eps'])
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update = (exp_avg / bias_correction1 + beta2 * exp_avg_diff / bias_correction2).div_(denom)
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if group['no_prox']:
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p.data.mul_(1 - group['lr'] * group['weight_decay'])
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p.add_(update, alpha=-group['lr'])
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else:
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p.add_(update, alpha=-group['lr'])
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p.data.div_(1 + group['lr'] * group['weight_decay'])
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state['pre_grad'].copy_(grad)
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return loss
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