T5: Create position related tensors directly on device instead of CPU
# What does this PR do?

The current implementation of the `compute_bias` function of the T5 model creates tensors on the CPU (`memory_position` and `context_position`) and then moves them to the corresponding device with `.to()`. 
While this has minimal impact in single-gpu training, in multi-gpu large batch training, as the number of GPUs increases this reduces GPU utilization.
This short PR addresses the issue. 

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR (@patrickvonplaten).
