Source code for lmflow.optim.adamax

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

import torch
from torch.optim.optimizer import Optimizer

[docs] class Adamax(Optimizer): def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): 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) super(Adamax, self).__init__(params, defaults)
[docs] def __setstate__(self, state): super(Adamax, self).__setstate__(state)
[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('Adamax does not support sparse gradients') state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 state['exp_avg'] = torch.zeros_like(p.data) state['exp_inf'] = torch.zeros_like(p.data) exp_avg, exp_inf = state['exp_avg'], state['exp_inf'] beta1, beta2 = group['betas'] state['step'] += 1 if group['weight_decay'] != 0: grad = grad.add(group['weight_decay'], p.data) # Update biased first moment estimate exp_avg.mul_(beta1).add_(1 - beta1, grad) # Update the exponentially weighted infinity norm norm_buf = torch.cat([ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().unsqueeze_(0) ], 0) torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, exp_inf.new().long())) bias_correction = 1 - beta1 ** state['step'] clr = group['lr'] / bias_correction p.data.addcdiv_(-clr, exp_avg, exp_inf + group['eps']) return loss