Add sequence packing support for SFTPackedDataset

[ashors1]

What does this PR do ?

Adds sequence packing for SFTPackedDataset. Thanks @RadhaGulhane13 for the contribution!

Changelog

• Please update the CHANGELOG.md under next version with high level changes in this PR.

Usage

Original usage example from #259:

The following documentation can be referred to for dataset preparation and adjusting training configurations : https://github.com/NVIDIA/NeMo/blob/main/docs/source/features/throughput_optimizations.rst#how-to-run-sftpeft-with-packed-sequence

# Provide packed sequences dataset path
TRAIN_DATA_PATH="<train_dataset_path>/packed_32768_seed0.npy"
VALID_DATA_PATH="<val_dataset_path>/packed_32768_seed0.npy"

python <path_to_Nemo-Aligner>/examples/nlp/gpt/train_gpt_sft.py \
  trainer.precision=bf16 \
  trainer.num_nodes=1 \
  trainer.devices=8 \
  trainer.sft.max_steps=-1 \
  trainer.sft.limit_val_batches=40 \
  trainer.sft.val_check_interval=1000 \
  model.megatron_amp_O2=True \
  model.restore_from_path=/path/to/your/mcore_gpt.nemo \
  model.optim.lr=5e-6 \
  model.data.chat=True \
  model.data.num_workers=0 \
  model.data.train_ds.micro_batch_size=1 \
  model.data.train_ds.global_batch_size=128 \
  model.data.train_ds.max_seq_length=4096 \
  +model.data.train_ds.packed_sequence=True \
  model.data.train_ds.micro_batch_size=1 \
  model.data.train_ds.global_batch_size=128 \
  model.data.train_ds.file_path=${TRAIN_DATA_PATH} \
  +model.data.validation_ds.packed_sequence=True \
  model.data.validation_ds.micro_batch_size=1 \
  model.data.validation_ds.global_batch_size=128 \
  model.data.validation_ds.file_path=${VALID_DATA_PATH}  \
  exp_manager.create_wandb_logger=True \
  exp_manager.explicit_log_dir=/results \
  exp_manager.wandb_logger_kwargs.project=sft_run \
  exp_manager.wandb_logger_kwargs.name=chat_sft_run \
  exp_manager.checkpoint_callback_params.save_nemo_on_train_end=True \
  exp_manager.resume_if_exists=True \
  exp_manager.resume_ignore_no_checkpoint=True \
  exp_manager.create_checkpoint_callback=True \
  exp_manager.checkpoint_callback_params.monitor=validation_loss

Before your PR is "Ready for review"

Pre checks:

• Make sure you read and followed Contributor guidelines
• Did you write any new necessary tests?
• Did you add or update any necessary documentation? Make sure to also update the NeMo Framework User Guide which contains the tutorials

Checklist when contributing a new algorithm

• Does the trainer resume and restore model state all states?
• Does the trainer support all parallelism techniques(PP, TP, DP)?
• Does the trainer support max_steps=-1 and validation?
• Does the trainer only call APIs defined in alignable_interface.py?
• Does the trainer have proper logging?

Additional Information

• Related to # (issue)