data-selection 1.0.2

Data Selection with Importance Resampling

Data Selection for Language Models via Importance Resampling (DSIR)

This repository contains the DSIR data selection tool for selecting relevant language model training data from any raw data source given a target dataset, as well as pre-filtered datasets and some pretrained models.

DSIR is built for:

• fast, large-scale (trillion-token scale) data selection from large raw text datasets (Pile, RefinedWeb, RedPajama, ...). There is almost no overhead to selecting more examples (unlike retrieval), other than the time it takes to write the extra examples to disk.
• selecting data that is distributed like a given target dataset (domain-specific data, Wikipedia, ...). Relevance and diversity are balanced automatically by matching the distribution of the target dataset on a feature space (e.g., n-gram frequencies).

Compute needed:

• 1 CPU node
• a decent amount of RAM (at least 64GB for most large datasets - need to hold a few floats per example in memory)
• a high number of cores. The data selection speed scales linearly with the CPU cores.

Code related to the DSIR paper's experiments are in the experimental/ directory.

Quickstart

Install with pip:

pip install data-selection

Install from source by cloning this repo and installing via pip:

git clone [e-mail]:/p-lambda/dsir
pip install ./dsir

To select data, simply initialize a HashedNgramDSIR object and call the following functions:

from data_selection import HashedNgramDSIR

raw_datasets = [<list of paths>]
target_datasets = [<list of paths>]

dsir = HashedNgramDSIR(raw_datasets, target_datasets, cache_dir='/scr/dsir_cache')
dsir.fit_importance_estimator(num_tokens_to_fit='auto')
dsir.compute_importance_weights()
dsir.resample(out_dir='resampled', num_to_sample=10000000, cache_dir='/scr/resampled_cache')

Running this would write 10M documents in jsonl files inside an output directory named resampled. The files will first be written to cache_dir and moved to out_dir upon completion (set cache_dir to None to skip this step). For best performance, use uncompressed jsonl files stored on local file storage for all data paths and use as many CPU cores as possible, which allows each file to be virtually sharded across multiple cores. Custom functions for reading the data paths and extracting the text field from each example can be provided via the {raw,target}_load_dataset_fn and {raw,target}_parse_example_fn arguments to the constructor. The number of tokens to use for fitting the importance weight estimator can be tuned with the num_tokens_to_fit argument (set to all to fit on full dataset). Top-k retrieval instead of sampling without replacement (the default) can be done by specifying top_k=True to the resample method.

For target datasets with a mixture of code and natural language, consider splitting up the code and natural language into separate target distributions (and resampling once with respect to each target) for best performance.

The dsir intermediate results (after fit_importance_estimator and compute_importance_weights) can be saved and loaded for later use, for example to resample 100M documents instead:

dsir.save('dsir_params.pkl')

# later on
dsir.load('dsir_params.pkl')
dsir.resample(out_dir='resampled', num_to_sample=100000000, cache_dir='/scr/resampled_cache')

The save method can be called at any time to save partial results.

Speed benchmark on The Pile

Using 1 CPU node with 96GB RAM and 96 cores, we can select data from the full (decompressed) Pile dataset in less than 4.5 hours. The Pile dataset was first decompressed and placed onto the node's local file storage. The breakdown of timings for each step are:

• Fit importance estimator (with num_tokens_to_fit="auto"): 59.28 seconds
• Compute importance weights: 4.36 hours
• Resample 10M documents (with cache_dir=None and out_dir is a local storage location): 353.68 seconds
• Total: 4.47 hours

Subsequent resampling with the same target data is very cheap, and the runtime does not scale with the number of documents to select (unlike retrieval). Resampling 100M documents takes the same amount of time (less than 6 minutes) as resampling 10M documents:

• Resample 10M documents: 353.68 seconds
• Resample 100M documents: 352.69 seconds

Citation Information

Paper: https://arxiv.org/abs/2302.03169

@article{xie2023data,
  author = {Sang Michael Xie and Shibani Santurkar and Tengyu Ma and Percy Liang},
  journal = {Advances in Neural Information Processing Systems (NeurIPS)},
  title = {Data Selection for Language Models via Importance Resampling},
  year = {2023},
}