🚀 Feature request

Currently, the EncoderDecoderModel class in PyTorch automatically creates the decoder_input_ids based on the labels provided by the user (similar to how this is done for T5/BART). This should also be implemented for TFEncoderDecoderModel, because currently users should manually provide decoder_input_ids to the model.

One can take a look at the TF implementation

progress_bar_callback has a separate attribute on the Trainer like this: https://github.com/PyTorchLightning/pytorch-lightning/blob/01cf7a2ac5a6db588d53c70da1db8d0adcc7641c/pytorch_lightning/trainer/connectors/callback_connector.py#L97-L102

Should we define a property like this which is derived from the callbacks property? https://github.com/PyTorchLightning/pytorch-lightning/blob/40945a517

Change tensor.data to tensor.detach() due to
pytorch/pytorch#6990 (comment)
tensor.detach() is more robust than tensor.data.

🙌🙌🙌🙌🙌

New Operator

Describe the operator

Why is this operator necessary? What does it accomplish?

This is a frequently used operator in tensorflow/keras

Can this operator be constructed using existing onnx operators?

If so, why not add it as a function?

I don't know.

Is this operator used by any model currently? Which one?

Are you willing to contribute it?

f-strings offer better readability/performance than str.format and %, so we should use them in all places in our codebase unless there is good reason to keep the older syntax.

NOTE FOR CONTRIBUTORS: To avoid large PRs and possible merge conflicts, do 1-3 modules per PR. Also, feel free to ignore the files located under datasets/*.

Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.