RangerVA with GC

[ryancinsight]

Hello,

Thank you for your work on these optimizers btw. I was testing a couple out and was performing quite well with the RangerVA originally. Then, when your gradient centralization was added I got further improvements but it also seemed to be overtraining the train set more easily despite using the same parameters. Therefore, I tried to implement combining the gradient centralization into the RangerVA algorithm and so far it seems to be performing quite well and faster since it seems I can use larger batch sizes. I was wondering if you could quickly check, whenever you have some free time, if I implemented correctly in the code below since you are so used to this optimizer.

Best

``
class RangerVA(Optimizer):

def __init__(self, params, lr=1e-3, 
             alpha=0.5, k=6, n_sma_threshhold=5, betas=(.95,0.999), 
             eps=1e-5, weight_decay=0, amsgrad=True, transformer='softplus', smooth=50,
             grad_transformer='square',use_gc=True, gc_conv_only=False):
    #parameter checks
    if not 0.0 <= alpha <= 1.0:
        raise ValueError(f'Invalid slow update rate: {alpha}')
    if not 1 <= k:
        raise ValueError(f'Invalid lookahead steps: {k}')
    if not lr > 0:
        raise ValueError(f'Invalid Learning Rate: {lr}')
    if not eps > 0:
        raise ValueError(f'Invalid eps: {eps}')

    #prep defaults and init torch.optim base
    defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, 
                    n_sma_threshhold=n_sma_threshhold, eps=eps, weight_decay=weight_decay,
                    smooth=smooth, transformer=transformer, grad_transformer=grad_transformer,
                   amsgrad=amsgrad,use_gc=use_gc, gc_conv_only=gc_conv_only )
    super().__init__(params,defaults)

    #adjustable threshold
    self.n_sma_threshhold = n_sma_threshhold   

    #look ahead params
    self.alpha = alpha
    self.k = k 

    #radam buffer for state
    self.radam_buffer = [[None,None,None] for ind in range(10)]
    
    #gc on or off
    self.use_gc=use_gc
    #level of gradient centralization
    self.gc_gradient_threshold = 3 if gc_conv_only else 1
    print(f"Ranger optimizer loaded. \nGradient Centralization usage = {self.use_gc}")
    if (self.use_gc and self.gc_gradient_threshold==1):
        print(f"GC applied to both conv and fc layers")
    elif (self.use_gc and self.gc_gradient_threshold==3):
        print(f"GC applied to conv layers only")


def __setstate__(self, state):
    print("set state called")
    super(RangerVA, self).__setstate__(state)


def step(self, closure=None):
    loss = None
    #Evaluate averages and grad, update param tensors
    for group in self.param_groups:
        for p in group['params']:
            if p.grad is None:
                continue
            grad = p.grad.data.double()
            if grad.is_sparse:
                raise RuntimeError('Ranger optimizer does not support sparse gradients')
            
            amsgrad = group['amsgrad']
            smooth = group['smooth']
            grad_transformer = group['grad_transformer']

            p_data_fp32 = p.data.double()

            state = self.state[p]  #get state dict for this param

            if len(state) == 0:   
                state['step'] = 0
                state['exp_avg'] = torch.zeros_like(p_data_fp32)
                state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
                if amsgrad:
                    # Maintains max of all exp. moving avg. of sq. grad. values
                    state['max_exp_avg_sq'] = torch.zeros_like(p.data)                    

                #look ahead weight storage now in state dict 
                state['slow_buffer'] = torch.empty_like(p.data)
                state['slow_buffer'].copy_(p.data)

            else:
                state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
                                  

            #begin computations 
            exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
            beta1, beta2 = group['betas']
            if amsgrad:
                max_exp_avg_sq = state['max_exp_avg_sq']  
                # 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
                denomc = max_exp_avg_sq.clone()
            else:
                denomc = exp_avg_sq.clone()
            #GC operation for Conv layers and FC layers       
            if grad.dim() > self.gc_gradient_threshold:                    
                grad.add_(-grad.mean(dim = tuple(range(1,grad.dim())), keepdim = True))

            state['step'] += 1              

            #compute variance mov avg
            exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
            #compute mean moving avg
            exp_avg.mul_(beta1).add_(1 - beta1, grad)
            buffered = self.radam_buffer[int(state['step'] % 10)]
            if state['step'] == buffered[0]:
                N_sma, step_size = buffered[1], buffered[2]
            else:
                buffered[0] = state['step']
                beta2_t = beta2 ** state['step']
                N_sma_max = 2 / (1 - beta2) - 1
                N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
                buffered[1] = N_sma
                if N_sma > self.n_sma_threshhold:
                    step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])
                else:
                    step_size = 1.0 / (1 - beta1 ** state['step'])
                buffered[2] = step_size

            
            ##transformer
            if grad_transformer == 'square':
                grad_tmp = grad**2
                denomc.sqrt_() 
            elif grad_transformer == 'abs':
                grad_tmp = grad.abs()


            exp_avg_sq.mul_(beta2).add_((1 - beta2)*grad_tmp)

            if group['weight_decay'] != 0:
                p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
            bias_correction1 = 1 - beta1 ** state['step']
            bias_correction2 = 1 - beta2 ** state['step']
            step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1                

            
            # ...let's use calibrated alr 
            if N_sma > self.n_sma_threshhold:
                if  group['transformer'] =='softplus':
                    sp = torch.nn.Softplus( smooth)
                    denomf = sp( denomc)
                    p_data_fp32.addcdiv_(-step_size, exp_avg, denomf )
                else:
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
            else:
                p_data_fp32.add_(-step_size * group['lr'], exp_avg)
            p.data.copy_(p_data_fp32)

            #integrated look ahead...
            #we do it at the param level instead of group level
            if state['step'] % group['k'] == 0:
                slow_p = state['slow_buffer'] #get access to slow param tensor
                slow_p.add_(self.alpha, p.data - slow_p)  #(fast weights - slow weights) * alpha
                p.data.copy_(slow_p)  #copy interpolated weights to RAdam param tensor

    return loss