If the gradients overflow due to loss scaling, the scaling value will be lowered and you will see this message.
If you encounter a NaN output, something in your model went wrong or the input contained invalid values.
If you see the loss scaling information not in every iteration, you can still ignore it, or are you seeing it in every step?