T5 rpe fix 01
# What does this PR do ?

Fix the RPE implementation converging slower with loss consistently higher than NeMo APE and Megatron-LM RPE implementation.

**Collection**: NLP

# Changelog 
- Changed how RPE parameters are initialized and passed on.

# Usage
* Train with `model.position_embedding_type='relative'` config.

```python
python /home/workspace/NeMo/examples/nlp/language_modeling/megatron_t5_pretraining.py model.position_embedding_type='relative'
```

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [x] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
- [ ] Documentation


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
