Add LayerNorm1P, weight decay for LN and unscaled initialization
# What does this PR do ?

Port Megatron features to match GPT3 convergence

**Collection**: NLP/Megatron

# Changelog 
- Add LayerNorm1P class implementing numerically stable LayerNorm with formula: `(self.weight + 1) * x + self.bias` instead of `self.weight * x + self.bias`
- add option to use weight decay also on LayerNorm weights
- add option to use unscaled residuals initialization

All 3 options are exposed with new hydra flags. With default flag values, the **model behaviour is unchanged**.

# Usage
Override these flags to enable respective features described above:
- model.normalization=layernorm1p
- model.do_layer_norm_weight_decay=1
- model.use_scaled_init_method=0

Each of the flags work independently from each other

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**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?
- [x] 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
- [ ] Bugfix
- [ ] Documentation

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# Additional Information

