[trainer] fix label smoothing for default compute_loss
# What does this PR do?

Keeps 'labels' in the inputs which are passed to model.

Without this change, the model I'm using (PegasusForConditionalGeneration) can't calculate loss and generate outputs. Change assumes that all other models also need 'labels' in inputs.

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## Who can review?

trainer: @sgugger

No, if you leave the labels in the outputs, the model will then compute the loss without label smoothing which is inefficient (since it will re-compute the proper loss afterwards).<|||||>Oh you are right, I guess I was overeager with the PR, sorry.

After looking more into the way arguments are passed to forward, I'll just need to shift tokens in 'labels' and add them to inputs as 'decoder_input_ids' (my confusion was from the fact that, for the model I'm using it is done automatically, but inside forward)

Thanks for the quick answer!<|||||>No worries! And let us know if there is a problem with the generation of `decoder_input_ids` for Pegasus as we would need to fix it. :-)