Fixed Weight Decay Regularization in Adam

from fairseq import nccl, utils

from fairseq.multiprocessing_event_loop import MultiprocessingEventLoop, Future

from fairseq.nag import NAG

from fairseq.optim.nag import NAG

from fairseq.optim.adam import Adam

class MultiprocessingTrainer(MultiprocessingEventLoop):

                'betas': eval(self.args.adam_betas),

                'weight_decay': self.args.weight_decay,

            }

            return torch.optim.Adam(self.model.parameters(), **self._override_optim_state)

            return Adam(self.model.parameters(), **self._override_optim_state)

        elif self.args.optimizer == 'nag':

            self._override_optim_state = {

                'lr': self.args.lr[0],

# Copyright (c) 2017-present, Facebook, Inc.

# All rights reserved.

#

# This source code is licensed under the license found in the LICENSE file in

# the root directory of this source tree. An additional grant of patent rights

# can be found in the PATENTS file in the same directory.

#

import math

import torch

from torch.optim.optimizer import Optimizer

class Adam(Optimizer):

    """Implements Adam algorithm.

    It has been proposed in `Adam: A Method for Stochastic Optimization`_.

    Arguments:

        params (iterable): iterable of parameters to optimize or dicts defining

            parameter groups

        lr (float, optional): learning rate (default: 1e-3)

        betas (Tuple[float, float], optional): coefficients used for computing

            running averages of gradient and its square (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)

        amsgrad (boolean, optional): whether to use the AMSGrad variant of this

            algorithm from the paper `On the Convergence of Adam and Beyond`_

    .. _Adam\: A Method for Stochastic Optimization:

        https://arxiv.org/abs/1412.6980

    .. _On the Convergence of Adam and Beyond:

        https://openreview.net/forum?id=ryQu7f-RZ

    """

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,

                 weight_decay=0, amsgrad=False):

        defaults = dict(lr=lr, betas=betas, eps=eps,

                        weight_decay=weight_decay, amsgrad=amsgrad)

        super(Adam, 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:

            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')

                amsgrad = group['amsgrad']

                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)

                    if amsgrad:

                        # Maintains max of all exp. moving avg. of sq. grad. values

                        state['max_exp_avg_sq'] = torch.zeros_like(p.data)

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']

                if amsgrad:

                    max_exp_avg_sq = state['max_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)

                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

                if group['weight_decay'] != 0:

                    p.data.add_(-group['weight_decay'], p.data)

                p.data.addcdiv_(-step_size, exp_avg, denom)

        return loss