Source code for holocron.optim.lamb

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

'''
Rectified Adam optimizer
'''

import torch
from torch.optim.optimizer import Optimizer




class Lamb(Optimizer):
    """Implements the Lamb optimizer from `"Large batch optimization for deep learning: training BERT in 76 minutes"
    <https://arxiv.org/pdf/1904.00962v3.pdf>`_.

    Args:
        params (iterable): iterable of parameters to optimize or dicts defining parameter groups
        lr (float, optional): learning rate
        betas (Tuple[float, float], optional): beta 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)
        scale_clip (tuple, optional): the lower and upper bounds for the weight norm in local LR of LARS
    """
    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0,
                 scale_clip=None):
        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(Lamb, self).__init__(params, defaults)
        # LARS arguments
        self.scale_clip = scale_clip
        if self.scale_clip is None:
            self.scale_clip = (0, 10)

    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:
            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']
                beta1, beta2 = group['betas']

                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)

                # Gradient term correction
                update = torch.zeros_like(p.data)
                denom = exp_avg_sq.sqrt().add_(group['eps'])
                update.addcdiv_(1, exp_avg, denom)

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

                # LARS
                p_norm = p.data.pow(2).sum().sqrt()
                update_norm = update.pow(2).sum().sqrt()
                phi_p = p_norm.clamp(*self.scale_clip)
                # Compute the local LR
                if phi_p == 0 or update_norm == 0:
                    local_lr = 1
                else:
                    local_lr = phi_p / update_norm

                state['local_lr'] = local_lr

                p.data.add_(-group['lr'] * local_lr, update)

        return loss