Fix sequence parallel compatibility issues
# What does this PR do ?

When you do not enable mcore or use loss mask, sequence parallel will not work properly. This PR fixes this problem.

**Collection**: NLP

# Changelog 
- Modify post_language_model_processing to make sequence-parallel and loss-mask compatible.
- Mark the parameters of layernorms with sequence-parallel so that sequence-parallel can be calculated correctly on layernorms.
- For the correctness check of sequence-parallel(unit test), the wikitext dataset has been added.

# Usage
* This is a feature compatibility fix, you can now use sequence-parallel without enabling mcore.

# 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**:
- [x] New Feature
- [x] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Please help review, thank you! @MaximumEntropy, @ericharper, @ekmb, @yzhang123, @VahidooX, @vladgets, @okuchaiev

# Additional Information

The new unit test will start the PyTorch TCP Store and may conflict with other unit tests. Please use an independent process for unit testing.
