import torch from torch.optim import Optimizer class RMSpropTF(Optimizer): """Implements RMSprop algorithm (TensorFlow style epsilon) NOTE: This is a direct cut-and-paste of PyTorch RMSprop with eps applied before sqrt to closer match Tensorflow for matching hyper-params. Proposed by G. Hinton in his `course `_. The centered version first appears in `Generating Sequences With Recurrent Neural Networks `_. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-2) momentum (float, optional): momentum factor (default: 0) alpha (float, optional): smoothing (decay) constant (default: 0.9) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-10) centered (bool, optional) : if ``True``, compute the centered RMSProp, the gradient is normalized by an estimation of its variance weight_decay (float, optional): weight decay (L2 penalty) (default: 0) decoupled_decay (bool, optional): decoupled weight decay as per https://arxiv.org/abs/1711.05101 lr_in_momentum (bool, optional): learning rate scaling is included in the momentum buffer update as per defaults in Tensorflow """ def __init__(self, params, lr=1e-2, alpha=0.9, eps=1e-10, weight_decay=0, momentum=0., centered=False, decoupled_decay=False, lr_in_momentum=True): 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 <= momentum: raise ValueError("Invalid momentum value: {}".format(momentum)) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) if not 0.0 <= alpha: raise ValueError("Invalid alpha value: {}".format(alpha)) defaults = dict(lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay, decoupled_decay=decoupled_decay, lr_in_momentum=lr_in_momentum) super(RMSpropTF, self).__init__(params, defaults) def __setstate__(self, state): super(RMSpropTF, self).__setstate__(state) for group in self.param_groups: group.setdefault('momentum', 0) group.setdefault('centered', False) 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: loss = closure() for group in self.param_groups: for p in group['params']: if p.grad is None: continue grad = p.grad.data if grad.is_sparse: raise RuntimeError('RMSprop does not support sparse gradients') state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 state['square_avg'] = torch.ones_like(p.data) # PyTorch inits to zero if group['momentum'] > 0: state['momentum_buffer'] = torch.zeros_like(p.data) if group['centered']: state['grad_avg'] = torch.zeros_like(p.data) square_avg = state['square_avg'] one_minus_alpha = 1. - group['alpha'] state['step'] += 1 if group['weight_decay'] != 0: if group['decoupled_decay']: p.data.add_(-group['weight_decay'], p.data) else: grad = grad.add(group['weight_decay'], p.data) # Tensorflow order of ops for updating squared avg square_avg.add_(one_minus_alpha, grad.pow(2) - square_avg) # square_avg.mul_(alpha).addcmul_(1 - alpha, grad, grad) # PyTorch original if group['centered']: grad_avg = state['grad_avg'] grad_avg.add_(one_minus_alpha, grad - grad_avg) # grad_avg.mul_(alpha).add_(1 - alpha, grad) # PyTorch original avg = square_avg.addcmul(-1, grad_avg, grad_avg).add(group['eps']).sqrt_() # eps moved in sqrt else: avg = square_avg.add(group['eps']).sqrt_() # eps moved in sqrt if group['momentum'] > 0: buf = state['momentum_buffer'] # Tensorflow accumulates the LR scaling in the momentum buffer if group['lr_in_momentum']: buf.mul_(group['momentum']).addcdiv_(group['lr'], grad, avg) p.data.add_(-buf) else: # PyTorch scales the param update by LR buf.mul_(group['momentum']).addcdiv_(grad, avg) p.data.add_(-group['lr'], buf) else: p.data.addcdiv_(-group['lr'], grad, avg) return loss