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245 lines
12 KiB
245 lines
12 KiB
import math
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import torch
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from torch.optim.optimizer import Optimizer
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from tabulate import tabulate
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from colorama import Fore, Back, Style
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version_higher = ( torch.__version__ >= "1.5.0" )
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class AdaBelief(Optimizer):
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r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch
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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-16)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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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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weight_decouple (boolean, optional): ( default: True) If set as True, then
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the optimizer uses decoupled weight decay as in AdamW
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fixed_decay (boolean, optional): (default: False) This is used when weight_decouple
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is set as True.
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When fixed_decay == True, the weight decay is performed as
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$W_{new} = W_{old} - W_{old} \times decay$.
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When fixed_decay == False, the weight decay is performed as
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$W_{new} = W_{old} - W_{old} \times decay \times lr$. Note that in this case, the
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weight decay ratio decreases with learning rate (lr).
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rectify (boolean, optional): (default: True) If set as True, then perform the rectified
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update similar to RAdam
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degenerated_to_sgd (boolean, optional) (default:True) If set as True, then perform SGD update
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when variance of gradient is high
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print_change_log (boolean, optional) (default: True) If set as True, print the modifcation to
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default hyper-parameters
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reference: AdaBelief Optimizer, adapting stepsizes by the belief in observed gradients, NeurIPS 2020
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16,
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weight_decay=0, amsgrad=False, weight_decouple=True, fixed_decay=False, rectify=True,
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degenerated_to_sgd=True, print_change_log = True):
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# ------------------------------------------------------------------------------
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# Print modifications to default arguments
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if print_change_log:
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print(Fore.RED + 'Please check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.')
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print(Fore.RED + 'Modifications to default arguments:')
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default_table = tabulate([
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['adabelief-pytorch=0.0.5','1e-8','False','False'],
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['>=0.1.0 (Current 0.2.0)','1e-16','True','True']],
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headers=['eps','weight_decouple','rectify'])
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print(Fore.RED + default_table)
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recommend_table = tabulate([
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['Recommended eps = 1e-8', 'Recommended eps = 1e-16'],
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],
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headers=['SGD better than Adam (e.g. CNN for Image Classification)','Adam better than SGD (e.g. Transformer, GAN)'])
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print(Fore.BLUE + recommend_table)
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print(Fore.BLUE +'For a complete table of recommended hyperparameters, see')
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print(Fore.BLUE + 'https://github.com/juntang-zhuang/Adabelief-Optimizer')
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print(Fore.GREEN + 'You can disable the log message by setting "print_change_log = False", though it is recommended to keep as a reminder.')
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print(Style.RESET_ALL)
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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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self.degenerated_to_sgd = degenerated_to_sgd
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if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
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for param in params:
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if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):
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param['buffer'] = [[None, None, None] for _ in range(10)]
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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay, amsgrad=amsgrad, buffer=[[None, None, None] for _ in range(10)])
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super(AdaBelief, self).__init__(params, defaults)
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self.degenerated_to_sgd = degenerated_to_sgd
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self.weight_decouple = weight_decouple
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self.rectify = rectify
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self.fixed_decay = fixed_decay
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if self.weight_decouple:
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print('Weight decoupling enabled in AdaBelief')
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if self.fixed_decay:
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print('Weight decay fixed')
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if self.rectify:
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print('Rectification enabled in AdaBelief')
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if amsgrad:
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print('AMSGrad enabled in AdaBelief')
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def __setstate__(self, state):
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super(AdaBelief, 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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def reset(self):
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for group in self.param_groups:
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for p in group['params']:
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state = self.state[p]
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amsgrad = group['amsgrad']
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# State initialization
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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,memory_format=torch.preserve_format) \
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if version_higher else torch.zeros_like(p.data)
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# Exponential moving average of squared gradient values
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state['exp_avg_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
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if version_higher else torch.zeros_like(p.data)
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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_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
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if version_higher else torch.zeros_like(p.data)
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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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# cast data type
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half_precision = False
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if p.data.dtype == torch.float16:
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half_precision = True
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p.data = p.data.float()
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p.grad = p.grad.float()
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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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'AdaBelief 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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beta1, beta2 = group['betas']
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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,memory_format=torch.preserve_format) \
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if version_higher else torch.zeros_like(p.data)
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# Exponential moving average of squared gradient values
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state['exp_avg_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
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if version_higher else torch.zeros_like(p.data)
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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_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
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if version_higher else torch.zeros_like(p.data)
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# perform weight decay, check if decoupled weight decay
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if self.weight_decouple:
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if not self.fixed_decay:
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p.data.mul_(1.0 - group['lr'] * group['weight_decay'])
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else:
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p.data.mul_(1.0 - group['weight_decay'])
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else:
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if group['weight_decay'] != 0:
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grad.add_(p.data, alpha=group['weight_decay'])
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# get current state variable
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exp_avg, exp_avg_var = state['exp_avg'], state['exp_avg_var']
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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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# Update first and second moment running average
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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grad_residual = grad - exp_avg
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exp_avg_var.mul_(beta2).addcmul_( grad_residual, grad_residual, value=1 - beta2)
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if amsgrad:
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max_exp_avg_var = state['max_exp_avg_var']
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# Maintains the maximum of all 2nd moment running avg. till now
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torch.max(max_exp_avg_var, exp_avg_var.add_(group['eps']), out=max_exp_avg_var)
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# Use the max. for normalizing running avg. of gradient
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denom = (max_exp_avg_var.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
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else:
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denom = (exp_avg_var.add_(group['eps']).sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
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# update
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if not self.rectify:
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# Default update
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step_size = group['lr'] / bias_correction1
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p.data.addcdiv_( exp_avg, denom, value=-step_size)
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else: # Rectified update, forked from RAdam
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buffered = group['buffer'][int(state['step'] % 10)]
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if state['step'] == buffered[0]:
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N_sma, step_size = buffered[1], buffered[2]
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else:
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buffered[0] = state['step']
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beta2_t = beta2 ** state['step']
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N_sma_max = 2 / (1 - beta2) - 1
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N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
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buffered[1] = N_sma
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# more conservative since it's an approximated value
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if N_sma >= 5:
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step_size = math.sqrt(
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(1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
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N_sma_max - 2)) / (1 - beta1 ** state['step'])
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elif self.degenerated_to_sgd:
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step_size = 1.0 / (1 - beta1 ** state['step'])
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else:
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step_size = -1
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buffered[2] = step_size
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if N_sma >= 5:
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denom = exp_avg_var.sqrt().add_(group['eps'])
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p.data.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
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elif step_size > 0:
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p.data.add_( exp_avg, alpha=-step_size * group['lr'])
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if half_precision:
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p.data = p.data.half()
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p.grad = p.grad.half()
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return loss
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