Maybe I misunderstand your problem. Teacher Forcing is usually applied to the decoder in case of Sequence-to-Sequence models, where you generate, say, a sentence. For example, the prediction of the 4th word depends on the prediction of the 3rd word (no teacher forcing) or the ground truth of the 3rd word (teacher forcing).
A language model is usually not a Sequence-to-Sequence model but more like a Sequence-to-NextWord model, basically a simply classifier. So you don’t have a decoder where Teacher Forcing is applicable. I don’t see any sense in applying Teacher Forcing to the encoder, i.e., the RNN for the input sequence.