During training, I found some spikes of loss at epoch  as follows

Based on the loss of previous loss, if the loss different between current and previous too big, I will not update the gradient. Do we have any function in pytorch do check implement it? Currently, I used as

current_loss = cross_entropy(input, target)
if current_loss- prev_loss> threshold):
   current_loss.backward()
   prev_loss = current_loss