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# Copyright 2022 MosaicML LLM Foundry authors
# SPDX-License-Identifier: Apache-2.0
import os
import platform
from argparse import ArgumentParser, Namespace
from typing import Dict, Iterable, List, Optional, Union
import datasets as hf_datasets
import psutil
from streaming import MDSWriter
from torch.utils.data import DataLoader, IterableDataset
from tqdm import tqdm
from llmfoundry.data.finetuning.tasks import dataset_constructor
def parse_args() -> Namespace:
"""Parse commandline arguments."""
parser = ArgumentParser(description='Convert dataset into MDS format.')
parser.add_argument(
'--dataset',
type=str,
required=True,
help=
'Name/path of the dataset (e.g., first argument to `datasets.load_dataset`)'
)
parser.add_argument('--data_subset',
type=str,
default=None,
help='(Optional) subset of data to use.')
parser.add_argument('--splits',
nargs='+',
default=['train', 'validation'],
help='Which splits of the dataset to convert.')
parser.add_argument('--preprocessor',
type=str,
default=None,
help='Name or import path of function used to preprocess (reformat) the dataset. ' +\
'See README for additional details.')
parser.add_argument(
'--skip-preprocessing',
action='store_true',
help=
'Whether to skip preprocesing (e.g., if the dataset is already formatted correctly)'
)
parser.add_argument(
'--out_root',
type=str,
required=True,
help=
'Root path of output directory where MDS shards will be stored. Can be a remote URI.'
)
parser.add_argument(
'--local',
type=str,
default=None,
help=
'(Optional) root path of local directory if you want to keep a local copy when out_root is remote.'
)
parser.add_argument('--compression',
type=str,
default=None,
help='(Optional) name of compression algorithm to use.')
parser.add_argument('--num_workers', type=int, required=False, default=None)
parsed = parser.parse_args()
if os.path.isdir(parsed.out_root) and len(
set(os.listdir(parsed.out_root)).intersection(set(
parsed.splits))) > 0:
raise ValueError(
f'--out_root={parsed.out_root} contains {os.listdir(parsed.out_root)} which cannot overlap with the requested splits {parsed.splits}.'
)
return parsed
class SimpleDataset(IterableDataset):
"""An IterableDataset that returns text samples for MDSWriter.
Returns dicts of {'key': bytes} for each 'key' in `columns`
"""
def __init__(self, dataset_name: str, data_subset: Union[str, None],
split: str, columns: List[str]):
self.hf_dataset = hf_datasets.load_dataset(path=dataset_name,
name=data_subset,
split=split,
streaming=True)
self.columns = columns
def __iter__(self) -> Iterable[Dict[str, bytes]]:
for sample in self.hf_dataset:
# convert to bytes to store in MDS binary format
yield {key: sample[key].encode('utf-8') for key in self.columns}
def build_dataloader(dataset: SimpleDataset,
batch_size: int,
num_workers: Optional[int] = None) -> DataLoader:
if num_workers is None:
# Multiple workers is only supported on linux machines
if 'linux' in platform.platform().lower():
num_workers = max(1, psutil.cpu_count())
else:
num_workers = 0
# If using multiple workers, configure each worker to prefetch as many samples as it can, up to
# the aggregate device batch size
# If not using workers, the torch DataLoader expects the default value for prefetch_factor,
# which non-intuitively must be 2.
# If on macOS, PyTorch requires prefetch_factor set to None since num_workers is always zero
if 'macos' in platform.platform().lower() and num_workers == 0:
prefetch_factor = None
else:
prefetch_factor = max(1, 2 * batch_size //
num_workers) if num_workers > 0 else 2
return DataLoader(
dataset=dataset,
sampler=None,
batch_size=batch_size,
num_workers=num_workers,
prefetch_factor=prefetch_factor,
)
def generate_samples(
loader: DataLoader,
truncate_num_samples: Optional[int] = None
) -> Iterable[Dict[str, bytes]]:
"""Generator over samples of a dataloader.
Args:
loader (DataLoader): A dataloader emitting batches like {key: [sample0_bytes, sample1_bytes, sample2_bytes, ...]}
truncate_num_samples (Optional[int]): An optional # of samples to stop at.
Yields:
Sample dicts.
"""
n_samples = 0
for batch in loader:
keys = list(batch.keys())
current_bs = len(batch[keys[0]])
for idx in range(current_bs):
if truncate_num_samples is not None and n_samples == truncate_num_samples:
return
n_samples += 1
yield {k: v[idx] for k, v in batch.items()}
def main(args: Namespace) -> None:
"""Main: create a streaming dataset.
Args:
args (Namespace): Commandline arguments.
"""
if args.skip_preprocessing:
preprocessing_fn = lambda x: x # Just an identity function
else:
preprocessor_str = args.preprocessor
preprocessing_fn = dataset_constructor.get_preprocessing_fn_from_str(
preprocessor=preprocessor_str, dataset_name=args.dataset)
if preprocessing_fn is None:
raise ValueError(
'`args.preprocessor` was not set and no preprocessing function ' +\
'has been registered for `args.dataset`. If this was intentional ' +\
'(e.g., because your dataset is already correctly formatted), ' +\
'include the "--skip-preprocessing" flag to avoid this error.'
)
columns = ['prompt', 'response']
for split_name in args.splits:
dataset = hf_datasets.load_dataset(path=args.dataset,
name=args.data_subset,
split=split_name,
streaming=True)
loader = build_dataloader(dataset=dataset,
batch_size=512,
num_workers=args.num_workers)
samples = generate_samples(loader)
# Write samples
print(f'Converting {split_name} to MDS format...')
out = os.path.join(args.out_root, split_name)
if args.local is not None:
out = (os.path.join(args.local, split_name), out)
keep_local = True
else:
keep_local = False
with MDSWriter(columns={key: 'str' for key in columns},
out=out,
compression=args.compression,
keep_local=keep_local) as out:
for sample in tqdm(samples, desc=split_name):
formatted_sample = preprocessing_fn(sample)
if ('prompt'
not in formatted_sample) or ('response'
not in formatted_sample):
raise KeyError(
'Unable to tokenize example because it has not been properly formatted. ' +\
'"prompt" and "response" are required keys but at least one was missing ' +\
f'from {formatted_sample=}.'
)
encoded_sample = {
key: formatted_sample[key].encode('utf-8')
for key in columns
}
out.write(encoded_sample)
if __name__ == '__main__':
"""Example for converting Muennighoff/P3:
>>> python convert_finetuning_dataset.py \
>>> --dataset "Muennighoff/P3" \
>>> --splits train validation \
>>> --preprocessor llmfoundry.data.finetuning.tasks:p3_preprocessing_function \
>>> --out_root s3://<bucket>/muennighoff-p3
"""
main(parse_args())
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