Support packing for pretokenized datasets

[kmehant]

At this point, trl returns the dataset as is if the provided dataset has signs of being tokenized already.

trl/trl/trainer/sft_trainer.py

Line 503 in 98ad01d

if column_names and "input_ids" in column_names:

Additionally, I see the ConstantLengthDataset

trl/trl/trainer/utils.py

Line 426 in 98ad01d

class ConstantLengthDataset(IterableDataset):

has been written only in support of data that is not pretokenized and it should be possible to extend to pretokenized case as well.

Is there of any interest to support packing for pretokenized datasets? if so, I will be interested to contribute.

[github-actions]

This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

[kmehant]

@lvwerra requesting your opinion.

[qgallouedec]

@kmehant thanks for sharing this feature request.
Can you briefly describe why you need this feature? Or why you can't do without this feature?
It's undoubtedly an interesting feature to have, but I'm worried about the implementation, which risks adding yet another level of complexity. Have you found a way of implementing it? What elements are affected by the changes?

[kmehant]

@qgallouedec thanks for circling back.

In my opinion supporting is not complex. Here is a version implementing this - https://github.com/kmehant/trl/tree/pack-pretok

changes / comparison with main - https://github.com/huggingface/trl/compare/main...kmehant:trl:pack-pretok?expand=1

Steps to try this version

Install trl from my fork

git clone -b pack-pretok https://github.com/kmehant/trl.git
cd trl 
pip install .

Sample training code

from trl import SFTTrainer
from transformers import AutoTokenizer
tok = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
t = tok.encode("We adopted exactly the same architecture and tokenizer as Llama 2.")
d = {"input_ids": [t]*10}
import datasets
data = datasets.Dataset.from_dict(d)
trainer = SFTTrainer(
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    train_dataset=data,
    max_seq_length=10,
    packing=True,
)
trainer.train()

Sample output looks like

{'train_runtime': 18.4487, 'train_samples_per_second': 2.927, 'train_steps_per_second': 0.163, 'train_loss': 2.972621281941732, 'epoch': 3.0}
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:18<00:00,  6.15s/it]
TrainOutput(global_step=3, training_loss=2.972621281941732, metrics={'train_runtime': 18.4487, 'train_samples_per_second': 2.927, 'train_steps_per_second': 0.163, 'total_flos': 3351820124160.0, 'train_loss': 2.972621281941732, 'epoch': 3.0})

Thank you. I can raise a PR out of this and add tests as needed.

[qgallouedec]

Thanks! It's actually simpler than I expected.
Can you open a PR?
Would it be possible to directly infer if the dataset is tokenized in ConstantLengthDataset?

[kmehant]

@qgallouedec Have raised a PR here - #2011

Would it be possible to directly infer if the dataset is tokenized in ConstantLengthDataset?

Thanks, included that in the PR.

[kmehant]

@qgallouedec any update on this thread? Thanks