Source code for lmflow.optim.novograd

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import torch
import torch.optim as optim

[docs] class NovoGrad(optim.Optimizer): def __init__(self, params, lr=0.01, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, grad_averaging=False, amsgrad=False): 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)) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, grad_averaging=grad_averaging, amsgrad=amsgrad) super(NovoGrad, self).__init__(params, defaults)
[docs] def __setstate__(self, state): super(NovoGrad, self).__setstate__(state) for group in self.param_groups: group.setdefault('amsgrad', False)
[docs] def step(self, closure=None): 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('NovoGrad does not support sparse gradients') amsgrad = group['amsgrad'] state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 state['exp_avg'] = torch.zeros_like(p.data) state['exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device) if amsgrad: state['max_exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device) 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 norm = torch.sum(torch.pow(grad, 2)) if exp_avg_sq == 0: exp_avg_sq.copy_(norm) else: exp_avg_sq.mul_(beta2).add_(1 - beta2, norm) 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().add_(group['eps']) else: denom = exp_avg_sq.sqrt().add_(group['eps']) grad.div_(denom) if group['weight_decay'] != 0: grad.add_(group['weight_decay'], p.data) if group['grad_averaging']: grad.mul_(1 - beta1) exp_avg.mul_(beta1).add_(grad) p.data.add_(-group['lr'], exp_avg) return loss