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# Data preparation
This folder contains scripts for converting text data from original sources (HF, JSON) to the Mosaic [StreamingDataset](https://github.com/mosaicml/streaming) format for consumption by our training scripts. StreamingDataset is designed to make training on large datasets from cloud storage as fast, cheap, and scalable as possible. In particular, it is custom built for multi-node, distributed training for large models while maximizing correctness guarantees, performance, and ease of use.
They following scripts will run on CPUs (no GPUs needed). Execute them in an environment with `python` and `llm-foundry` dependencies installed. All scripts should run from the `./llm-foundry/scripts/data_prep` directory.
## Converting a pretraining dataset
### HuggingFace data
In this example, we use the `convert_dataset_hf.py` script to convert a HuggingFace `c4` dataset into a `StreamingDataset`, using the `EleutherAI/gpt-neox-20b` tokenizer. The resulting directory is saved at `./llm-foundry/scripts/data_prep/my-copy-c4`.
Currently supports `c4` and `The Pile`.
<!--pytest.mark.skip-->
```bash
# Convert C4 dataset to StreamingDataset format
python convert_dataset_hf.py \
--dataset c4 --data_subset en \
--out_root my-copy-c4 --splits train_small val_small \
--concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>' \
--compression zstd
```
### JSON data
Using the `convert_dataset_json.py` script...
<!--pytest.mark.skip-->
```bash
# Convert json dataset to StreamingDataset format
python convert_dataset_json.py \
--path ./example_data/arxiv.jsonl \
--out_root my-copy-arxiv --split train \
--concat_tokens 2048 --tokenizer EleutherAI/gpt-neox-20b --eos_text '<|endoftext|>' \
--compression zstd
```
Where `--path` can be a single json file, or a folder containing json files. `--split` denotes the intended split (hf defaults to `train`).
## Converting a finetuning dataset
Using the `convert_finetuning_dataset.py` script you can run a command such as:
<!--pytest.mark.skip-->
```bash
python convert_finetuning_dataset.py --dataset "Muennighoff/P3" \
--splits "train" "validation" \
--preprocessor "llmfoundry.data.finetuning.tasks:p3_preprocessing_function"\
--out_root "/path/to/your/output_directory"
```
This example assumes:
- `"Muennighoff/P3"` is the dataset you want to convert. Substitute "Muennighoff/P3" with the name or path of your dataset.
- `train` and `validation` are the splits of the dataset to convert.
- `llmfoundry.data.finetuning.tasks:p3_preprocessing_function` is a string that provides the name or import path of the function used to preprocess the dataset. Substitute it with your actual preprocessor. See [tasks](https://github.com/mosaicml/llm-foundry/blob/main/llmfoundry/data/finetuning/tasks.py) for available functions and examples.
- `s3://<bucket>/muennighoff-p3` is the root path of your output directory where MDS shards will be stored. Replace this with the actual path to your output directory.
Please note that you need to fill in actual values for "your_preprocessing_function" and "/path/to/your/output_directory" in the command above for it to work correctly.
Also, if you want to keep a local copy of the output when `out_root` is remote, you can use the `--local` argument:
<!--pytest.mark.skip-->
```bash
python convert_finetuning_dataset.py --dataset "squad" --splits "train" "validation" --preprocessor "your_preprocessing_function" --out_root "s3://your_bucket/output_directory" --local "/path/to/local/directory"
```
Remember that all these command line arguments should be filled with your actual dataset name/path, preprocessing function, and output directories.
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