Thanks for your reply. When training a seq2seq text-to-speech model, the alignment usually means a diagonal line that aligns the outputs with the inputs based on the attention mechanism. When a neural model picks up this alignment, it can be can be used to predict. When the neural model fails, it can not work normally(not predicting anything useful).
And i found that the model can be trained normally on 1080ti just now. However, It seems that the 1080ti just need more iterations to pick up this aligment compared to 2080ti. For example, when training the model with same config, the model picks up an meaningful alignment about 30k iterations on the 2080ti while the model on 1080ti needs about 50k iterations to pick up this alignment.