Source code for libauc.optimizers.adam

import math
import torch

[docs] class Adam(torch.optim.Optimizer): r"""Implements Adam algorithm. This code is adapted from `PyTorch codebase <>`__. It has been proposed in `Adam: A Method for Stochastic Optimization`_. The implementation of the L2 penalty follows changes proposed in `Decoupled Weight Decay Regularization`_. Arguments: params (iterable): iterable of parameters to optimize lr (float): learning rate (default: ``1e-3``) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: ``(0.9, 0.999)``) eps (float, optional): term added to the denominator to improve numerical stability (default: ``1e-8``) weight_decay (float, optional): weight decay (L2 penalty) (default: ``0``) epoch_decay (float, optional): epoch decay (epoch-wise l2 penalty) (default: ``0.0``) amsgrad (boolean, optional): whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond`_ (default: ``False``) device (torch.device, optional): the device used for optimization, e.g., 'cpu' or 'cuda' (default: ``None``) .. _Adam\: A Method for Stochastic Optimization: .. _Decoupled Weight Decay Regularization: .. _On the Convergence of Adam and Beyond: """ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, clip_value=1.0, epoch_decay=0, weight_decay=0, amsgrad=False, verbose=True, device=None, **kwargs): if not device: self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') else: self.device = device if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) self.params = list(params) = lr self.model_ref = self.__init_model_ref__(self.params) if epoch_decay > 0 else None self.model_acc = self.__init_model_acc__(self.params) if epoch_decay > 0 else None self.T = 0 # for epoch_decay self.steps = 0 # total optim steps self.verbose = verbose # print updates for lr/regularizer defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, epoch_decay=epoch_decay, amsgrad=amsgrad, clip_value=clip_value, model_ref=self.model_ref, model_acc=self.model_acc) super(Adam, self).__init__(self.params, defaults) def __setstate__(self, state): super(Adam, self).__setstate__(state) for group in self.param_groups: group.setdefault('amsgrad', False) def __init_model_ref__(self, params): model_ref = [] if not isinstance(params, list): params = list(params) for var in params: if var is not None: model_ref.append(torch.empty(var.shape).normal_(mean=0, std=0.01).to(self.device)) return model_ref def __init_model_acc__(self, params): model_acc = [] if not isinstance(params, list): params = list(params) for var in params: if var is not None: model_acc.append(torch.zeros(var.shape, dtype=torch.float32, device=self.device, requires_grad=False).to(self.device)) return model_acc
[docs] @torch.no_grad() def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: = group['lr'] model_ref = group['model_ref'] model_acc = group['model_acc'] epoch_decay = group['epoch_decay'] clip_value = group['clip_value'] weight_decay = group['weight_decay'] for i, p in enumerate(group['params']): if p.grad is None: continue if epoch_decay > 0: grad = torch.clamp( , -clip_value, clip_value) + epoch_decay*( - model_ref[i].data) + weight_decay* else: grad = torch.clamp( , -clip_value, clip_value) + weight_decay* if grad.is_sparse: raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead') amsgrad = group['amsgrad'] state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) if amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] if amsgrad: max_exp_avg_sq = state['max_exp_avg_sq'] beta1, beta2 = group['betas'] state['step'] += 1 bias_correction1 = 1 - beta1 ** state['step'] bias_correction2 = 1 - beta2 ** state['step'] # Decay the first and second moment running average coefficient exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) if amsgrad: # Maintains the maximum of all 2nd moment running avg. till now torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) # Use the max. for normalizing running avg. of gradient denom = (max_exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) else: denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) step_size = / bias_correction1 p.addcdiv_(exp_avg, denom, value=-step_size) if epoch_decay > 0: model_acc[i].data = model_acc[i].data + self.T += 1 self.steps += 1 return loss
[docs] def update_lr(self, decay_factor=None): r"""Updates learning rate given a decay factor.""" if decay_factor != None: self.param_groups[0]['lr'] = self.param_groups[0]['lr']/decay_factor if self.verbose: print ('Reducing learning rate to %.5f !'%(self.param_groups[0]['lr']))
[docs] def update_regularizer(self, decay_factor=None): r"""Updates learning rate given a decay factor and resets epoch-decay regularizer.""" if decay_factor != None: self.param_groups[0]['lr'] = self.param_groups[0]['lr']/decay_factor if self.verbose: print ('Reducing learning rate to %.5f @ T=%s!'%(self.param_groups[0]['lr'], self.steps)) if self.verbose: print ('Updating regularizer @ T=%s!'%(self.steps)) for i, param in enumerate(self.model_ref): self.model_ref[i].data = self.model_acc[i].data/self.T for i, param in enumerate(self.model_acc): self.model_acc[i].data = torch.zeros(param.shape, dtype=torch.float32, device=self.device, requires_grad=False).to(self.device) self.T = 0