TPU padding in seq2seq codes 
Hi
In the readme of seq2seq models https://github.com/huggingface/transformers/tree/master/examples/seq2seq it is written:

All sequences should be padded to be of equal length otherwise it leads to extremely slow training. (finetune_trainer.py does this automatically when running on TPU.)

could you point me please where in the codes this is done? and what do you mean here, isn't it like normal case that in each batch, we pad the sentence to the equal length? could you tell me the differences from what is done normally? 

thanks

Best
Rabeeh 
I understood this is implemented in  Seq2SeqDataCollator with adding padding=longest. 