🚀 Feature request

Fast Tokenizer for DeBERTA-V3 and mDeBERTa-V3

Motivation

DeBERTa V3 is an improved version of DeBERTa. With the V3 version, the authors also released a multilingual model "mDeBERTa-base" that outperforms XLM-R-base. However, DeBERTa V3 currently lacks a FastTokenizer implementation which makes it impossible to use with some of the example scripts (They require a Fa

I want to train a detector based on object365 dataset, but object365 is pretty large, and caused out of memory error in my computer.
I want to split the annotation file to 10, such as ann1,ann2,...ann10, then build 10 datasets and concatenate them, but I'm not sure whether it's
gonna work or not.
Any better suggestion?

Proposed refactor

Rename pytorch_lightning/loggers/csv_logs.py to pytorch_lightning/loggers/csv.py

Motivation

None of the other Logger files have "logs" as a suffix in the filename, e.g. comet.py, neptune.py, tensorboard.py

cc @Borda @justusschock @awaelchli @akihironitta @rohitgr7

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

🚀 Feature

Motivation

paper "LEARNING TO REPRESENT PROGRAMS WITH GRAPHS" which encode computer programs as graphs, with rich semantic information, however, most code implementation on this dataset VarMisuse is based on TensorFlow, like [tf-gnn-samples](https://github.com/microsof

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?

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.