Source code for lmflow.optim.adam

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

import torch
from torch.optim.optimizer import Optimizer

[docs] class Adam(Optimizer): def __init__(self, params, lr=0.001, betas=(0.9, 0.999), eps=1e-8): defaults = dict(lr=lr, betas=betas, eps=eps) super(Adam, self).__init__(params, defaults)
[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 state = self.state[p] if len(state) == 0: state['step'] = 0 state['exp_avg'] = torch.zeros_like(p.data) state['exp_avg_sq'] = torch.zeros_like(p.data) exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] beta1, beta2 = group['betas'] state['step'] += 1 exp_avg.mul_(beta1).add_(1 - beta1, grad) exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) bias_correction1 = 1 - beta1 ** state['step'] bias_correction2 = 1 - beta2 ** state['step'] step_size = group['lr'] * (bias_correction2 ** 0.5) / bias_correction1 denom = exp_avg_sq.sqrt().add_(group['eps']) p.data.addcdiv_(-step_size, exp_avg, denom) return loss