remove_non_tensor_columns

[garrett361]

In #765 additional string metadata was added to the dataset which conflicts with the application of the DataCollatorForSeq2Seq collator function, which expects only tensor data. This makes finetune.py fail (when --packing False) with errors like:

Traceback (most recent call last):
  File "/proj/data-eng/swanand/sft_dpo/venv_sft_dpo/venv/venv_sft_dpo/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 767, in convert_to_tensors
    tensor = as_tensor(value)
  File "/proj/data-eng/swanand/sft_dpo/venv_sft_dpo/venv/venv_sft_dpo/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 729, in as_tensor
    return torch.tensor(value)
ValueError: too many dimensions 'str'

This error arises when trying to create a tensor from a list of strings, e.g. torch.tensor(["hello"]).

This PR adds a utility for filtering non-tensor columns out of the dataset before using and uses the filter in both the sft and dpo scripts.

CC @hamishivi @jacob-morrison

The issue specifically is the addition of the DATASET_ORIGIN_KEY metadata here.

I believe this wrapper is only needed for finetune.py and dpo_cache_tune.py, and not needed for reward_modeling.py or reward_modeling_eval.py, but have only tested finetune.py with these fixed e2e.

[hamishivi]

Thanks for noticing and the PR, I believe this is fixed by #825 (which I just merged).

[garrett361]

Yes, hadn't seen that one! Thanks.