Seq2Seq finetuning

[alextrott16]

This PR covers seq-to-seq finetuning (such as IFT) for LLMs. These changes create a new dataloading codepath. All major changes are within llm/src/data/finetuning/.

At a high level:

• finetuning/collator.py a one-size-fits-all (for single-turn) seq2seq collator that turns properly formatted, tokenized examples into batches suitable for training with any of our LLM models.
• finetuning/tasks.py provides a scaffold for registering functions to format+tokenize examples from different datasources.
• finetuning/dataloader.py exposes build_finetuning_dataloader and interprets the dataloader config.
• finetuning/convert_finetuning_dataset.py simplifies the conversion of HF datasets into an MDS format that the dataloader can build from.
• finetuning/README.md explains how to use these tools.

Other changes:

• Various __init__s to simplify importing
• main.py can call build_finetuning_dataloader
• Example finetuning YAMLs in yamls/mosaic_gpt/finetuning/
• Tests
• Improvements to packing.py

[alextrott16]

Note that this will need to be partially re-written after https://github.com/mosaicml/examples/pull/314 goes through.