Megatron model parallel autoresume
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Adds training auto-resume for NLP models that use model parallel megatron-lm. The existing autoresume logic depends on there being only one checkpoint, this is not the case for model parallel models. There is a checkpoint for each model parallel rank.

We were able to add this feature with minimal changes to exp_manager. The only functional change is to log a warning rather than throw an error when exp_manager detects multiple `last` checkpoints. We also use the exp_manager to set some properties in `AppState` that we need in `NLPModel`.

# Usage
```
python examples/nlp/text_classification/text_classification_with_bert.py \
	--config-path=${CONFIG_PATH} \
	--config-name=${CONFIG_NAME} \
        ...
	exp_manager.exp_dir=${EXP_DIR}/nemo_experiments \
	exp_manager.create_checkpoint_callback=true \
	+exp_manager.create_wandb_logger=true \
	+exp_manager.wandb_logger_kwargs.name=${EXP_NAME} \
	+exp_manager.wandb_logger_kwargs.project=${PROJECT_NAME} \
	exp_manager.name=${EXP_NAME} \
	+exp_manager.resume_if_exists=true \
	+exp_manager.resume_ignore_no_checkpoint=true \
	+exp_manager.version=0 \
	+exp_manager.use_datetime_version=false \
```
