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119 lines
4.7 KiB
119 lines
4.7 KiB
""" Nvidia NovoGrad Optimizer.
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Original impl by Nvidia from Jasper example:
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- https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
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Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
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- https://arxiv.org/abs/1905.11286
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"""
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import torch
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from torch.optim.optimizer import Optimizer
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import math
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class NvNovoGrad(Optimizer):
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"""
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Implements Novograd algorithm.
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Args:
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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: (0.95, 0.98))
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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): weight decay (L2 penalty) (default: 0)
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grad_averaging: gradient averaging
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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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"""
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def __init__(self, params, lr=1e-3, betas=(0.95, 0.98), eps=1e-8,
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weight_decay=0, grad_averaging=False, amsgrad=False):
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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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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay,
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grad_averaging=grad_averaging,
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amsgrad=amsgrad)
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super(NvNovoGrad, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(NvNovoGrad, 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 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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grad = p.grad.data
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if grad.is_sparse:
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raise RuntimeError('Sparse gradients are not supported.')
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amsgrad = group['amsgrad']
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state = self.state[p]
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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)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device)
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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_sq'] = torch.zeros([]).to(state['exp_avg'].device)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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if amsgrad:
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max_exp_avg_sq = state['max_exp_avg_sq']
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beta1, beta2 = group['betas']
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state['step'] += 1
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norm = torch.sum(torch.pow(grad, 2))
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if exp_avg_sq == 0:
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exp_avg_sq.copy_(norm)
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else:
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exp_avg_sq.mul_(beta2).add_(norm, alpha=1 - beta2)
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if amsgrad:
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# Maintains the maximum of all 2nd moment running avg. till now
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torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
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# Use the max. for normalizing running avg. of gradient
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denom = max_exp_avg_sq.sqrt().add_(group['eps'])
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else:
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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grad.div_(denom)
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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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if group['grad_averaging']:
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grad.mul_(1 - beta1)
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exp_avg.mul_(beta1).add_(grad)
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p.data.add_(exp_avg, alpha=-group['lr'])
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return loss
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