lightningdot/uniter_model/optim/adamw.py

"""
AdamW optimizer (weight decay fix)
copied from hugginface
"""
import math

import torch
from torch.optim import Optimizer


class AdamW(Optimizer):
    """ Implements Adam algorithm with weight decay fix.
    Parameters:
        lr (float): learning rate. Default 1e-3.
        betas (tuple of 2 floats): Adams beta parameters (b1, b2).
            Default: (0.9, 0.999)
        eps (float): Adams epsilon. Default: 1e-6
        weight_decay (float): Weight decay. Default: 0.0
        correct_bias (bool): can be set to False to avoid correcting bias
            in Adam (e.g. like in Bert TF repository). Default True.
    """
    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6,
                 weight_decay=0.0, correct_bias=True):
        if lr < 0.0:
            raise ValueError(
                "Invalid learning rate: {} - should be >= 0.0".format(lr))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter: {} - "
                             "should be in [0.0, 1.0[".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter: {} - "
                             "should be in [0.0, 1.0[".format(betas[1]))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {} - "
                             "should be >= 0.0".format(eps))
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
                        correct_bias=correct_bias)
        super(AdamW, 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')

                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
                # In-place operations to update the averages at the same time
                exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1.0 - beta2, grad, grad)
                denom = exp_avg_sq.sqrt().add_(group['eps'])

                step_size = group['lr']
                if group['correct_bias']:  # No bias correction for Bert
                    bias_correction1 = 1.0 - beta1 ** state['step']
                    bias_correction2 = 1.0 - beta2 ** state['step']
                    step_size = (step_size * math.sqrt(bias_correction2)
                                 / bias_correction1)

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

                # Just adding the square of the weights to the loss function is
                # *not* the correct way of using L2 regularization/weight decay
                # with Adam, since that will interact with the m and v
                # parameters in strange ways.
                #
                # Instead we want to decay the weights in a manner that doesn't
                # interact with the m/v parameters. This is equivalent to
                # adding the square of the weights to the loss with plain
                # (non-momentum) SGD.
                # Add weight decay at the end (fixed version)
                if group['weight_decay'] > 0.0:
                    p.data.add_(-group['lr'] * group['weight_decay'], p.data)

        return loss