Could you try to fit a very small data sample (e.g. just one or five samples) and see if your model correctly overfits on this small sample?
If that’s not possible, there might be other problems in your code.
However, if that works, I would try to scale up the problem using a bit more samples and see, when the model training breaks. Maybe your model’s architecture is currently not suitable for the problem or your model has just not enough capacity.