adam_lrd.py

import math
import torch
from torch.optim.optimizer import Optimizer

class Adam_LRD(Optimizer):

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=0, dropout=0.0):
        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]))
        defaults = dict(lr=lr, betas=betas, eps=eps,
                        weight_decay=weight_decay, dropout=dropout)
        super(Adam_LRD, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(Adam_LRD, self).__setstate__(state)
        for group in self.param_groups:
            group.setdefault('amsgrad', False)

    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('Adam does not support sparse gradients, please consider SparseAdam instead')

                state = self.state[p]

                ## mask
                m = torch.ones_like(p.data) * group['dropout']
                mask = torch.bernoulli(m)

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    # Exponential moving average of gradient values
                    state['exp_avg'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    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

                if group['weight_decay'] != 0:
                    grad.add_(group['weight_decay'], p.data)

                # Decay the first and second moment running average coefficient
                exp_avg.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                # 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'])

                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                ##dropout learning rate
                lr_dropout = step_size / denom.clone()
                lr_dropout=lr_dropout * mask

                p.data.addcmul_(-1, exp_avg, lr_dropout)

                # p.data.addcdiv_(-step_size, exp_avg2, denom)

        return loss