Consolidate gpt continue training script into pretraining script
Can we add github CI test for this and then also test the regular GPT pretraining, with resuming, is the same before and after this PR? We should also add docs for this, the readme here is a good start. sure i will add some tests. the gpt resume behavior is not changed in my local tests @ericharper  as I work on CI. I did some local tests 
![image](https://github.com/NVIDIA/NeMo/assets/54727607/87434f33-aaa7-4f6a-aac4-086948815a4a)

# What does this PR do ?

## Continual Learning with Pretrained Checkpoints

Continual learning enables large language models (LLMs) to acquire new skills and remain current with the swiftly advancing landscape of human knowledge. This guide details how to engage in continual learning using existing pretrained checkpoints with the `examples/nlp/language_modeling/megatron_gpt_pretraining.py` script. This process is applicable to various models including Llama 1/2/3, Gemma, Mistral, Mixtral, and others.

### Configuration for Continual Learning
To enable continual learning, modify the `model` configuration in your script as follows:

```yaml
model:
  # Use the following settings to specify the source for continual learning:
  restore_from_path: null  # Set this to a .nemo file path to restore only the model weights.
  restore_from_ckpt: null  # Set this to a checkpoint path to restore both model weights and optimizer states.
```

- **restore_from_path**: Use this when you want to restore only the model weights from a `.nemo` file.
- **restore_from_ckpt**: Use this to restore both model weights and optimizer states from a PyTorch checkpoint.

### Adjusting Training Configurations
Upon engaging in continual learning, it is possible and often beneficial to modify various training configurations:
- **Model Parallelism**: Depending on the computational resources available, you may adjust the model's parallelism settings.
- **Data Blend**: You can change the dataset or modify how data is blended during training to better suit the new training objectives.
- **Learning Rate Scheduler**: Adjusting the learning rate schedule can help optimize training for the new conditions.

Refer to the *pretrain* section of the documentation for details on configuring these parameters.

### Important Notes on Continual Learning
- **State Reset**: When engaging in continual learning, all states including model parallelism configuration, dataset, learning rate scheduler, and randomness are reset, except for the states loaded from the checkpoint.
- **Resuming Training**: If training is interrupted (e.g., due to a system failure) and you wish to resume the continual learning process, ensure that the restore flags (`restore_from_path` or `restore_from_ckpt`) are set back to `null`. This prevents the system from resetting the states again, which could lead to incorrect results.

**Collection**: [nlp]

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# Additional Information
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