Source code for libauc.optimizers.sopa

import math
import torch

[docs] class SOPA(torch.optim.Optimizer): r""" Stochastic Optimization for One-way pAUC (SOPA) is used for optimizing :obj:`~libauc.losses.pAUC_CVaR_Loss`. The key update steps are summarized as follows: 1. Initialize :math:`\mathbf s^1=0, \mathbf w_0` 2. For :math:`t=1, \ldots, T`: 3. :math:`\hspace{5mm}` Sample two mini-batches :math:`\mathcal B_+\subset\mathcal S_+` and :math:`\mathcal B_-\subset\mathcal S_-`. 4. :math:`\hspace{5mm}` Compute :math:`p_{ij} =\mathbb I (\ell(h(\mathbf w_t, \mathbf x_i) - h(\mathbf w_t, \mathbf x_j)) - s^t_i> 0)` for each positive-negative pair (:math:`\mathbf x_i\in\mathcal B_+, \mathbf x_j\in\mathcal B_-`) 5. :math:`\hspace{5mm}` Update :math:`s^{t+1}_i =s^t_i - \frac{\eta_2}{n_+} (1 - \frac{\sum_j p_{ij}}{\beta |\mathcal B_-|} )` for each positive data. 6. :math:`\hspace{5mm}` Compute a gradient estimator: .. math:: \nabla_t = \frac{1}{\beta |\mathcal B_+||\mathcal B_-|}\sum_{\mathbf x_i\in\mathcal B_+} \sum_{\mathbf x_j\in \mathcal B_-}p_{ij}\nabla_\mathbf w L(\mathbf w_t; \mathbf x_i, \mathbf x_j) 7. :math:`\hspace{5mm}` Update :math:`\mathbf w_{t+1} =\mathbf w_t - \eta_1 \nabla_t` (or Momentum/Adam style) For more details, please refer to `When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee <>`__. Args: params (iterable): iterable of parameters to optimize loss_fn (callable): loss function used for optimization (default: ``None``) lr (float): learning rate (default: ``0.1``) mode (str): optimization mode, 'sgd' or 'adam' (default: ``'sgd'``) weight_decay (float, optional): weight decay (L2 penalty) (default: ``1e-5``) epoch_decay (float, optional): epoch decay (epoch-wise l2 penalty) (default: ``0.0``) momentum (float, optional): momentum factor for 'sgd' mode (default: ``0.9``) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square for 'adam' mode (default: ``(0.9, 0.999)``) eps (float, optional): term added to the denominator to improve numerical stability for 'adam' mode (default: ``1e-8``) amsgrad (bool, optional): whether to use the AMSGrad variant of 'adam' mode from the paper `On the Convergence of Adam and Beyond` (default: ``False``) verbose (bool, optional): whether to print optimization progress (default: ``True``) device (torch.device, optional): the device used for optimization, e.g., 'cpu' or 'cuda' (default: ``None``) Example: >>> optimizer = libauc.optimizers.SOPA(model.parameters(), loss_fn=loss_fn, lr=0.1, momentum=0.9) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() Reference: .. [1] Zhu, Dixian and Li, Gang and Wang, Bokun and Wu, Xiaodong and Yang, Tianbao. "When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee." In International Conference on Machine Learning, pp. 27548-27573. PMLR, 2022. """ def __init__(self, params, mode='adam', eta=1.0, lr=1e-3, clip_value=1.0, weight_decay=0, epoch_decay=0, betas=(0.9, 0.999), eps=1e-8, amsgrad=False, momentum=0.9, nesterov=False, dampening=0, verbose=False, 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)) if not 0.0 <= epoch_decay: raise ValueError("Invalid epoch_decay value: {}".format(epoch_decay)) if not isinstance(mode, str): raise ValueError("Invalid mode type: {}".format(mode)) self.params = list(params) # support optimizing partial parameters of models = lr self.mode = mode.lower() 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 optimization steps self.verbose = verbose # print updates for lr/regularizer self.epoch_decay = epoch_decay assert self.mode in ['adam', 'sgd'], "Keyword is not found in [`adam`, `sgd`]!" defaults = dict(lr=lr, betas=betas, eps=eps, momentum=momentum, nesterov=nesterov, dampening=dampening, epoch_decay=epoch_decay, weight_decay=weight_decay, amsgrad=amsgrad, clip_value=clip_value, model_ref=self.model_ref, model_acc=self.model_acc) super(SOPA, self).__init__(self.params, defaults) def __setstate__(self, state): r""" # Set default options for sgd mode and adam mode """ super(SOPA, self).__setstate__(state) for group in self.param_groups: if self.mode == 'sgd': group.setdefault('nesterov', False) elif self.mode == 'adam': group.setdefault('amsgrad', False) else: NotImplementedError 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'] momentum = group['momentum'] dampening = group['dampening'] nesterov = group['nesterov'] model_ref = group['model_ref'] model_acc = group['model_acc'] clip_value = group['clip_value'] weight_decay = group['weight_decay'] epoch_decay = group['epoch_decay'] if self.mode == 'adam': for i, p in enumerate(group['params']): if p.grad is None: continue #grad = p.grad 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'] #if group['weight_decay'] != 0: # grad = grad.add(p, alpha=group['weight_decay']) 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 = group['lr'] / bias_correction1 p.addcdiv_(exp_avg, denom, value=-step_size) if epoch_decay > 0: model_acc[i].data = model_acc[i].data + elif self.mode == 'sgd': for i, p in enumerate(group['params']): if p.grad is None: continue #d_p = p.grad if epoch_decay > 0: d_p = torch.clamp( , -clip_value, clip_value) + epoch_decay*( - model_ref[i].data) + weight_decay* else: d_p = torch.clamp( , -clip_value, clip_value) + weight_decay* if momentum != 0: param_state = self.state[p] if 'momentum_buffer' not in param_state: buf = param_state['momentum_buffer'] = torch.clone(d_p).detach() else: buf = param_state['momentum_buffer'] buf.mul_(momentum).add_(d_p, alpha=1 - dampening) if nesterov: d_p = d_p.add(buf, alpha=momentum) else: d_p = buf p.add_(d_p, alpha=-group['lr']) if epoch_decay > 0: model_acc[i].data = model_acc[i].data + self.steps += 1 self.T += 1 return loss
[docs] def update_lr(self, decay_factor=None): if decay_factor != None: self.param_groups[0]['lr'] = self.param_groups[0]['lr']/decay_factor # for learning rate print ('Reducing lr to %.5f @ T=%s!'%(self.param_groups[0]['lr'], self.steps))
[docs] def update_regularizer(self, decay_factor=None): r"""Updates learning rate given a decay factor and resets epoch-level 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