(self, closure=None)
return loss
Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
Performs a single optimization step.
Performs a single optimization step .
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
def step ( self , closure = None ) : 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