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# Copyright 2023 Together Computer
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""RedPajama V2: Quality annotated Web Text Documents."""
import json
import datasets
import traceback
import os
import gzip
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
RedPajama V2 is a Data Foundation of Web Text Documents with Quality Annotations.
"""
with open("_CC_SNAPSHOT_IDS", "r") as f:
_CC_SNAPSHOT_IDS = [line.strip() for line in f]
_URL_BASE = 'https://data.together.xyz/redpajama-data-v2/v1.0.0'
_LANGUAGES = ("en", "de", "fr", "es", "it")
_SAMPLE_SNAPSHOT_ID = "2023-06"
_LISTINGS_PATTERN = "listings/{language}-{snapshot}-{partition}.txt"
class RedPajamaDataV2Config(datasets.BuilderConfig):
"""BuilderConfig for RedPajama."""
def __init__(self, *args, language, partition, snapshots, **kwargs):
"""BuilderConfig for RedPajama.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(RedPajamaDataV2Config, self).__init__(**kwargs)
self.partition = partition
self.snapshots = snapshots
self.language = language
_BUILDER_CONFIGS = [
RedPajamaDataV2Config(
name=f'_sample',
partition='sample',
snapshots=None,
language=None,
version=datasets.Version("1.0.0", ""),
description=f"RedPajamaV2 Sample",
),
# this one is just an alias for the sample
RedPajamaDataV2Config(
name=f'sample',
partition='sample',
snapshots=None,
language=None,
version=datasets.Version("1.0.0", ""),
description=f"RedPajamaV2 Sample",
)
]
for lang in _LANGUAGES:
_BUILDER_CONFIGS.extend(
[
# single snapshot
RedPajamaDataV2Config(
name=f'{lang}-head-middle-{snapshot}',
partition='head_middle',
snapshots=[snapshot],
language=lang,
version=datasets.Version("1.0.0", ""),
description=f"RedPajamaV2 head-middle {lang}-{snapshot}",
)
for snapshot in _CC_SNAPSHOT_IDS
] + [
# all snapshots
RedPajamaDataV2Config(
name=f'{lang}-head-middle-all',
partition='head_middle',
snapshots=_CC_SNAPSHOT_IDS,
language=lang,
version=datasets.Version("1.0.0", ""),
description=f"RedPajamaV2 head-middle {lang}"
)
]
)
_BUILDER_CONFIGS.extend(
[
# single snapshot
RedPajamaDataV2Config(
name=f'{lang}-tail-{snapshot}',
partition='tail',
snapshots=[snapshot],
language=lang,
version=datasets.Version("1.0.0", ""),
description=f"RedPajamaV2 tail {lang}-{snapshot}",
)
for snapshot in _CC_SNAPSHOT_IDS
] + [
# all snapshots
RedPajamaDataV2Config(
name=f'{lang}-tail-all',
partition='tail',
snapshots=_CC_SNAPSHOT_IDS,
language=lang,
version=datasets.Version("1.0.0", ""),
description=f"RedPajamaV2 tail {lang}"
)
]
)
class RedPajamaV2(datasets.GeneratorBasedBuilder):
""" RedPajama V2: Quality annotated Web Text Documents. """
BUILDER_CONFIGS = _BUILDER_CONFIGS
def _info(self):
if self.config.partition == "tail":
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"raw_content": datasets.Value("string"),
"doc_id": datasets.Value("string"),
"meta": datasets.Value("string"),
}
),
supervised_keys=None,
)
else:
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"raw_content": datasets.Value("string"),
"doc_id": datasets.Value("string"),
"meta": datasets.Value("string"),
"quality_signals": datasets.Value("string")
}
),
supervised_keys=None,
)
def _split_generators_sample(self, dl_manager):
# fetch documents
with open("sample/sample_listings.txt", "r") as fd:
listings = [line.strip() for line in fd]
# fetch documents
docs_files = dl_manager.download({
_SAMPLE_SNAPSHOT_ID: [
f"sample/documents/{lst}.json.gz" for lst in listings
]
})
# fetch quality signals
signals_files = dl_manager.download({
_SAMPLE_SNAPSHOT_ID: [
f"sample/quality_signals/{lst}.signals.json.gz"
for lst in listings
]
})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"documents_files": {
_SAMPLE_SNAPSHOT_ID: docs_files[_SAMPLE_SNAPSHOT_ID]
},
"quality_signals_files": {
_SAMPLE_SNAPSHOT_ID: signals_files[_SAMPLE_SNAPSHOT_ID]
}
}
)
]
def _split_generators_full(self, dl_manager):
url_lists = dl_manager.download_and_extract({
snapshot_id: _LISTINGS_PATTERN.format(
language=self.config.language,
snapshot=snapshot_id,
partition=self.config.partition,
)
for snapshot_id in self.config.snapshots
})
listings_ids = {}
for snapshot_id, listings_file in url_lists.items():
with open(listings_file, encoding="utf-8") as f:
listings_ids[snapshot_id] = [line.strip() for line in f]
# build urls pointing to documents
document_urls = {
snapshot_id: [
os.path.join(_URL_BASE, f"documents/{lst_id}.json.gz")
for lst_id in listings_ids[snapshot_id]
]
for snapshot_id in self.config.snapshots
}
documents_files = dl_manager.download(document_urls)
# build urls pointing to quality signals
if self.config.partition == "head_middle":
quality_signals_urls = {
snapshot_id: [
os.path.join(
_URL_BASE,
f"quality_signals/{lst_id}.signals.json.gz"
)
for lst_id in listings_ids[snapshot_id]
]
for snapshot_id in self.config.snapshots
}
quality_signals_files = dl_manager.download(
quality_signals_urls
)
else:
quality_signals_files = {}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"documents_files": {
snapshot_id: documents_files[snapshot_id]
for snapshot_id in self.config.snapshots
},
"quality_signals_files": {
snapshot_id: quality_signals_files.get(snapshot_id)
for snapshot_id in self.config.snapshots
}
}
)
]
def _split_generators(self, dl_manager):
if self.config.name.endswith("sample"):
return self._split_generators_sample(dl_manager)
return self._split_generators_full(dl_manager)
def _generate_examples(self, documents_files, quality_signals_files):
""" This function returns examples """
snapshots = list(documents_files.keys())
key = 0
for snapshot in snapshots:
docs_files = documents_files[snapshot]
if self.config.partition in ("head_middle", "sample"):
qs_files = quality_signals_files[snapshot]
else:
qs_files = None
assert len(docs_files) == len(qs_files)
for doc_file, qs_file in zip(docs_files, qs_files):
with gzip.open(doc_file, "rt", encoding="utf-8") as df:
with gzip.open(qs_file, "rt", encoding="utf-8") as qf:
for row, (doc, qs) in enumerate(zip(df, qf)):
try:
doc = json.loads(doc)
qs = json.loads(qs)
doc_id = qs["id"]
meta = {
"url": doc["url"],
"language": doc["language"],
"source_domain": doc["source_domain"],
"date_download": doc["date_download"],
"digest": doc["digest"],
}
if self.config.partition == "tail":
yield key, {
"raw_content": doc["raw_content"],
"doc_id": doc_id,
"meta": json.dumps(meta),
}
else:
yield key, {
"raw_content": doc["raw_content"],
"doc_id": doc_id,
"meta": json.dumps(meta),
"quality_signals": json.dumps(
qs["quality_signals"]
),
}
key += 1
except Exception as e:
print(f'doc_file: {doc_file}')
print(f'qs_file: {qs_file}')
print(f'row: {row}')
traceback.print_exc()
raise e
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