mimir.py

"""
    Data used for experiments with MIMIR. Processed train/test splits for models trained on the Pile (for now).
    Processing data at HF end.
"""

from datasets import (
    GeneratorBasedBuilder,
    SplitGenerator,
    DownloadManager,
    BuilderConfig,
)
import json
import os

import datasets

from typing import List


_HOMEPAGE = "http://github.com/iamgroot42/mimir"

_DESCRIPTION = """\
Member and non-member splits for our MI experiments using MIMIR. Data is available for each source.
We also cache neighbors (generated for the NE attack).
"""

_CITATION = """\
@article{duan2024do,
  title={Do Membership Inference Attacks Work on Large Language Models?},
  author={Duan*, Michael and \textbf{A. Suri*} and Mireshghallah, Niloofar and Min, Sewon and Shi, Weijia and Zettlemoyer, Luke and Tsvetkov, Yulia and Choi, Yejin and Evans, David and Hajishirzi, Hannaneh},
  journal={arXiv preprint arXiv:???},
  year={2024}
}
"""

_DOWNLOAD_URL = "https://huggingface.co/datasets/iamgroot42/mimir/resolve/main/"


class MimirConfig(BuilderConfig):
    """BuilderConfig for Mimir dataset."""

    def __init__(self, *args, subsets: List[str]=[], **kwargs):
        """Constructs a MimirConfig.

        Args:
            **kwargs: keyword arguments forwarded to super.
        """
        super(MimirConfig, self).__init__(**kwargs)
        self.subsets = subsets


class MimirDataset(GeneratorBasedBuilder):
    # Assuming 'VERSION' is defined
    VERSION = datasets.Version("1.0.0")

    # Define the builder configs
    BUILDER_CONFIG_CLASS = MimirConfig
    BUILDER_CONFIGS = [
        MimirConfig(
            name="arxiv",
            subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"],
            description="This split contains data from the Pile's Arxiv subset at various n-gram overlap thresholds"
        ),
        MimirConfig(
            name="dm_mathematics",
            subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"],
            description="This split contains data from the Pile's DM Mathematics subset at various n-gram overlap thresholds"
        ),
        MimirConfig(
            name="github",
            subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"],
            description="This split contains data from the Pile's GitHub subset at various n-gram overlap thresholds"
        ),
        MimirConfig(
            name="hackernews", 
            subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"],
            description="This split contains data from the Pile's HackerNews subset at various n-gram overlap thresholds"
        ),
        MimirConfig(
            name="pile_cc", 
            subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"],
            description="This split contains data from the Pile's Pile CC subset at various n-gram overlap thresholds"
        ),
        MimirConfig(
            name="pubmed_central", 
            subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"],
            description="This split contains data from the Pile's PubMed Central subset at various n-gram overlap thresholds"
        ),
        MimirConfig(
            name="wikipedia_(en)",
            subsets=["ngram_7_0.2", "ngram_13_0.2", "ngram_13_0.8"],
            description="This split contains data from the Pile's Wikipedia subset at various n-gram overlap thresholds"
        ),
        MimirConfig(
            name="full_pile", description="This split contains data from multiple sources in the Pile",
        ),
        MimirConfig(
            name="c4", description="This split contains data the C4 dataset",
        ),
        MimirConfig(
            name="temporal_arxiv", 
            subsets=["2020_08", "2021_01", "2021_06", "2022_01", "2022_06", "2023_01", "2023_06"],
            description="This split contains benchmarks where non-members are selected from various months from 2020-08 and onwards",
        ),
        MimirConfig(
            name="temporal_wiki", description="This split contains benchmarks where non-members are selected from 2023-08 and onwards",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(
                {"text": datasets.Sequence(datasets.Value("string"))}
            ),
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # Citation for the dataset
            # citation=_CITATION,
        )

    def _split_generators(self, dl_manager: DownloadManager):
        """Returns SplitGenerators."""
        # Path to the data files
        NEIGHBOR_SUFFIX = "_neighbors_25_bert_in_place_swap"
        parent_dir = (
            "cache_100_200_10000_512"
            if self.config.name == "full_pile"
            else "cache_100_200_1000_512"
        )

        if len(self.config.subsets) > 0:
            subset_splits = [f"{self.config.name}_{subset}" for subset in self.config.subsets]
        else:
            subset_splits = [self.config.name]

        file_paths = {}
        for subset_split in subset_splits:
            # Add standard member and non-member paths
            file_paths[f"{subset_split}_member"] = os.path.join(parent_dir, "train", subset_split + ".jsonl")
            file_paths[f"{subset_split}_nonmember"] = os.path.join(parent_dir, "test", subset_split + ".jsonl")
            
            # Load associated neighbors
            file_paths[f"{subset_split}_member_neighbors"] = os.path.join(
                parent_dir,
                "train_neighbors",
                subset_split + f"{NEIGHBOR_SUFFIX}.jsonl",
            )
            file_paths[f"{subset_split}_nonmember_neighbors"] = os.path.join(
                parent_dir,
                "test_neighbors",
                subset_split + f"{NEIGHBOR_SUFFIX}.jsonl",
            )

        # Now that we know which files to load, download them
        download_paths = []
        k2i = {}
        for i, (k, v) in enumerate(file_paths.items()):
            download_paths.append(_DOWNLOAD_URL + v)
            k2i[k] = i
        data_dir = dl_manager.download_and_extract(download_paths)

        splits = []
        for k in file_paths.keys():
            splits.append(SplitGenerator(name=k, gen_kwargs={"file_path": data_dir[k2i[k]]}))
        return splits

    def _generate_examples(self, file_path):
        """Yields examples."""
        # Open the specified .jsonl file and read each line
        with open(file_path, "r") as f:
            for id, line in enumerate(f):
                data = json.loads(line)
                if type(data) != list:
                    data = [data]
                yield id, {"text": data}