# 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 from typing import List logger = datasets.logging.get_logger(__name__) _DESCRIPTION = """\ RedPajama V2: an Open Dataset for Training Large Language Models """ _URL_BASE = 'https://data.together.xyz/redpajama-data-v2/v1.0.0' _LANGUAGES = ("en", "de", "fr", "es", "it") _LISTINGS_PATTERN = "listings/{language}-{snapshot}-{partition}.txt" _CC_SNAPSHOT_IDS = ( "2014-15", "2014-23", "2014-35", "2014-41", "2014-42", "2014-49", "2014-52", "2015-14", "2015-22", "2015-27", "2015-32", "2015-35", "2015-40", "2015-48", "2016-07", "2016-18", "2016-22", "2016-26", "2016-30", "2016-36", "2016-40", "2016-44", "2016-50", "2017-04", "2017-09", "2017-17", "2017-22", "2017-26", "2017-30", "2017-34", "2017-39", "2017-43", "2017-47", "2017-51", "2018-05", "2018-09", "2018-13", "2018-17", "2018-22", "2018-26", "2018-30", "2018-34", "2018-39", "2018-43", "2018-47", "2018-51", "2019-04", "2019-09", "2019-13", "2019-18", "2019-22", "2019-26", "2019-30", "2019-35", "2019-39", "2019-43", "2019-47", "2019-51", "2020-05", "2020-10", "2020-16", "2020-24", "2020-29", "2020-34", "2020-40", "2020-45", "2020-50", "2021-04", "2021-10", "2021-17", "2021-21", "2021-25", "2021-31", "2021-39", "2021-43", "2021-49", "2022-05", "2022-21", "2022-27", "2022-33", "2022-40", "2022-49", "2023-06", "2023-14" ) class RedPajamaDataV2Config(datasets.BuilderConfig): """BuilderConfig for RedPajama.""" def __init__(self, *args, **kwargs): """BuilderConfig for RedPajama. Args: **kwargs: keyword arguments forwarded to super. """ super(RedPajamaDataV2Config, self).__init__(**kwargs) self.partition: str = kwargs.pop("partition", "all") self.snapshots: List[str] = kwargs.pop("snapshots", _CC_SNAPSHOT_IDS) self.languages: List[str] = kwargs.pop("languages", _LANGUAGES) class RedPajamaV2(datasets.GeneratorBasedBuilder): """ RedPajama V2: Quality annotated Web Text Documents. """ DEFAULT_CONFIG_NAME = "default" BUILDER_CONFIGS = [ RedPajamaDataV2Config( name='default', version=datasets.Version("1.0.0", ""), description=f"RedPajamaV2", ), RedPajamaDataV2Config( name='sample', version=datasets.Version("1.0.0", ""), description=f"RedPajamaV2 Sample", ) ] def _info(self): 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 sample_listings = dl_manager.download_and_extract( "sample/sample_listings.txt" ) with open(sample_listings, "r") as fd: listings = [line.strip() for line in fd] # fetch documents documents_files = dl_manager.download({ "head_middle": [ f"sample/documents/{lst}.json.gz" for lst in listings ] }) # fetch quality signals quality_signals_files = dl_manager.download({ "head_middle": [ f"sample/quality_signals/{lst}.signals.json.gz" for lst in listings ] }) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "listings_ids": {"head_middle": listings}, "documents_files": documents_files, "quality_signals_files": quality_signals_files } ) ] def _split_generators_full(self, dl_manager): snapshots = getattr(self.config, 'snapshots', _CC_SNAPSHOT_IDS) languages = getattr(self.config, 'languages', _LANGUAGES) partition = getattr(self.config, 'partition', 'all') partitions = { "all": ["head_middle", "tail"] }.get(partition, [partition]) # nested structure: partition -> urls listings_files_urls = {} for part in partitions: listings_files_urls[part] = [] for snapshot_id in snapshots: for lang in languages: listings_files_urls[part].append( _LISTINGS_PATTERN.format( language=lang, snapshot=snapshot_id, partition=part, ) ) # fetch listings from hub listings_files = dl_manager.download_and_extract(listings_files_urls) # fetch listings listings_ids = {} for part, part_listings_files in listings_files.items(): listings_ids[part] = [] for listings_file in part_listings_files: with open(listings_file, encoding="utf-8") as f: listings_ids[part].extend([ line.strip() for line in f ]) # build urls pointing to documents and quality signals document_urls = {} quality_signals_urls = {} for part, part_listings_ids in listings_ids.items(): document_urls[part] = [] quality_signals_urls[part] = [] for lst_id in part_listings_ids: document_urls[part].append( os.path.join(_URL_BASE, f"documents/{lst_id}.json.gz") ) if part != "head_middle": continue quality_signals_urls[part].append( os.path.join( _URL_BASE, f"quality_signals/{lst_id}.signals.json.gz" ) ) documents_files = dl_manager.download(document_urls) quality_signals_files = dl_manager.download(quality_signals_urls) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "listings_ids": listings_ids, "documents_files": documents_files, "quality_signals_files": quality_signals_files } ) ] 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, listings_ids, documents_files, quality_signals_files ): key = 0 for part in documents_files.keys(): part_docs_files = documents_files[part] part_qs_files = quality_signals_files[part] part_listings_ids = listings_ids[part] if len(part_qs_files) == 0: for sample in self._handle_tail_partition( part, part_docs_files, part_listings_ids ): yield key, sample key += 1 continue for sample in self._handle_head_middle_partition( part, part_docs_files, part_qs_files, part_listings_ids ): yield key, sample key += 1 def _handle_tail_partition(self, part, docs_files, listings_ids): for doc_file, listing_id in zip(docs_files, listings_ids): with gzip.open(doc_file, "rt", encoding="utf-8") as df: for row, doc in enumerate(df): doc_id = f"{listing_id}.json.gz/{row}" try: yield self.handle_record(part, doc_id, doc, None) except Exception as e: print(f'doc_file: {doc_file}') print(f'row: {row}') traceback.print_exc() raise e def _handle_head_middle_partition( self, part, docs_files, qs_files, listings_ids ): assert len(docs_files) == len(qs_files) listings_ids = listings_ids[:len(docs_files)] for doc_file, qs_file, listings_id in zip( docs_files, qs_files, listings_ids ): try: 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)): doc_id = f"{listings_id}.json.gz/{row}" try: yield self.handle_record(part, doc_id, doc, qs) except Exception as e: print(f'failed handling row {row} in ' f'{doc_file} ({qs_file})') traceback.print_exc() continue except gzip.BadGzipFile as e: # skip broken gzip files print(f'BadGzipFile: {doc_file, qs_file}') traceback.print_exc() continue @staticmethod def handle_record(part, doc_id, doc, qs): doc = json.loads(doc) qs = json.loads(qs) if qs is not None else {} meta = { "url": doc["url"], "partition": part, "language": doc["language"], "source_domain": doc["source_domain"], "date_download": doc["date_download"], "digest": doc["digest"], } quality_signals = json.dumps(qs.get("quality_signals", {})) return { "raw_content": doc["raw_content"], "doc_id": doc_id, "meta": json.dumps(meta), "quality_signals": quality_signals, }