Dear all,
I tried to implement the " Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile" algorithm, from here 35, built on top of the Adam optimizer.
This is the algorithmic structure, and I am using common learning rate for both steps:

image.png750×384 15.4 KB

Could it please be possible for experts with pytorch to give me some feedback for my code (am newbie on pytorch)? This is the code:

import math
import torch
from torch.optim import Optimizer


class OptMirrorAdam(Optimizer):
    """Implements Optimistic Adam algorithm. Built on official implementation of Adam by pytorch. 
       See "Optimistic Mirror Descent in Saddle-Point Problems: Gointh the Extra (-Gradient) Mile"
       double blind review, paper: https://openreview.net/pdf?id=Bkg8jjC9KQ 

    Standard Adam 
    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):
        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, amsgrad=amsgrad)
        super(OptMirrorAdam, self).__init__(params, defaults)

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

    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
        
        # Do not allow training with out closure 
        if closure is  None:
            raise ValueError("This algorithm requires a closure definition for the evaluation of the intermediate gradient")
            
        
        # Create a copy of the initial parameters 
        param_groups_copy = self.param_groups.copy()
        
        # ############### First update of gradients ############################################
        # ######################################################################################
        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_1'] = torch.zeros_like(p.data)
                    state['exp_avg_2'] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state['exp_avg_sq_1'] = torch.zeros_like(p.data)
                    state['exp_avg_sq_2'] = torch.zeros_like(p.data)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state['max_exp_avg_sq_1'] = torch.zeros_like(p.data)
                        state['max_exp_avg_sq_2'] = torch.zeros_like(p.data)
                # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 
                        
                        
                        
                        
                exp_avg1, exp_avg_sq1 = state['exp_avg_1'], state['exp_avg_sq_1']
                if amsgrad:
                    max_exp_avg_sq1 = state['max_exp_avg_sq_1']
                beta1, beta2 = group['betas']

                
                # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ 
                # Step will be updated once  
                state['step'] += 1
                # @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
                
                
                if group['weight_decay'] != 0:
                    grad = grad.add(group['weight_decay'], p.data)

                # Decay the first and second moment running average coefficient
                exp_avg1.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq1.mul_(beta2).addcmul_(1 - beta2, grad, grad)

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

                # *****************************************************
                # Additional steps, to get bias corrected running means  
                exp_avg1 = torch.div(exp_avg1, bias_correction1)
                exp_avg_sq1 = torch.div(exp_avg_sq1, bias_correction2)
                # *****************************************************
                                
                if amsgrad:
                    # Maintains the maximum of all 2nd moment running avg. till now
                    torch.max(max_exp_avg_sq1, exp_avg_sq1, out=max_exp_avg_sq1)
                    # Use the max. for normalizing running avg. of gradient
                    denom1 = max_exp_avg_sq1.sqrt().add_(group['eps'])
                else:
                    denom1 = exp_avg_sq1.sqrt().add_(group['eps'])

                step_size1 = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                p.data.addcdiv_(-step_size1, exp_avg1, denom1)


        
        # Perform additional backward step to calculate stochastic gradient - WATING STATE 
        loss = closure()
        
        #  
        # ############### Second evaluation of gradient step #######################################
        # ######################################################################################
        for (group, group_copy) in zip(self.param_groups,param_groups_copy ):
            for (p, p_copy) in zip(group['params'],group_copy['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]

                        
                        
                exp_avg2, exp_avg_sq2 = state['exp_avg_2'], state['exp_avg_sq_2']
                if amsgrad:
                    max_exp_avg_sq2 = state['max_exp_avg_sq_2']
                beta1, beta2 = group['betas']
                
                
                if group['weight_decay'] != 0:
                    grad = grad.add(group['weight_decay'], p.data)

                # Decay the first and second moment running average coefficient
                exp_avg2.mul_(beta1).add_(1 - beta1, grad)
                exp_avg_sq2.mul_(beta2).addcmul_(1 - beta2, grad, grad)

                # *****************************************************
                # Additional steps, to get bias corrected running means  
                exp_avg2 = torch.div(exp_avg2, bias_correction1)
                exp_avg_sq2 = torch.div(exp_avg_sq2, bias_correction2)
                # *****************************************************
                                
                if amsgrad:
                    # Maintains the maximum of all 2nd moment running avg. till now
                    torch.max(max_exp_avg_sq2, exp_avg_sq2, out=max_exp_avg_sq2)
                    # Use the max. for normalizing running avg. of gradient
                    denom2 = max_exp_avg_sq2.sqrt().add_(group['eps'])
                else:
                    denom2 = exp_avg_sq2.sqrt().add_(group['eps'])

                step_size2 = group['lr'] * math.sqrt(bias_correction2) / bias_correction1

                p_copy.data.addcdiv_(-step_size2, exp_avg2, denom2)
                p = p_copy # pass parameters to the initial weight variables.
        
        
        
        
        return loss

Thank you very much for your time.