Source code for holocron.optim.radam

# -*- coding: utf-8 -*-

'''
Rectified Adam optimizer
'''

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




class RAdam(Optimizer):
    """Implements the RAdam optimizer from https://arxiv.org/pdf/1908.03265.pdf

    Args:
        params (iterable): iterable of parameters to optimize or dicts defining parameter groups
        lr (float, optional): learning rate
        betas (Tuple[float, float], optional): coefficients used for running averages (default: (0.9, 0.999))
        eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
    """
    def __init__(self, params, lr=1e-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]))
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        super(RAdam, self).__init__(params, defaults)

    def step(self, closure=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:

            # Get group-shared variables
            beta1, beta2 = group['betas']
            sma_inf = group.get('sma_inf')
            # Compute max length of SMA on first step
            if not isinstance(sma_inf, float):
                group['sma_inf'] = 2 / (1 - beta2) - 1
                sma_inf = group.get('sma_inf')

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('RAdam does not support sparse gradients')

                state = self.state[p]

                # 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']

                state['step'] += 1

                # 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)

                # Bias corrections
                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']

                # Compute length of SMA
                sma_t = sma_inf - 2 * state['step'] * (1 - bias_correction2) / bias_correction2

                # Weight decay
                if group['weight_decay'] != 0:
                    p.data.add_(-group['lr'] * group['weight_decay'], p.data)

                if sma_t > 4:
                    # Variance rectification term
                    r_t = math.sqrt((sma_t - 4) * (sma_t - 2) * sma_inf / ((sma_inf - 4) * (sma_inf - 2) * sma_t))
                    # Adaptive momentum
                    p.data.addcdiv_(-group['lr'] * r_t, exp_avg / bias_correction1,
                                    (exp_avg_sq / bias_correction2).sqrt().add_(group['eps']))
                else:
                    # Unadapted momentum
                    p.data.add_(-group['lr'], exp_avg / bias_correction1)

        return loss