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import os
import re
import multiprocessing
from pathlib import Path
from typing import Dict, List
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer
os.environ["TOKENIZERS_PARALLELISM"] = "false"
DATASET_NAME_PATTERN = re.compile(r"[^a-zA-Z0-9]")
def download_dataset(
ds_name: str,
ds_config: str = None,
ds_split: str = "train",
):
"""
Download a dataset from the HuggingFace Hub. Will only save the
Args:
ds_name (`str`):
The name of the dataset to load.
ds_config (`str`, *optional*, Defaults to `None`):
The configuration of the dataset to load.
ds_split (`str`, *optional*, Defaults to `"train"`):
The split of the dataset to load.
Returns:
len(ds) (`int`):
The number of rows in the dataset.
"""
if ds_name == "wikipedia":
ds = load_wikipedia(ds_name, ds_config)
else:
if ds_config == "":
ds_config = None
ds = load_dataset(ds_name, ds_config, split=ds_split)
chunk_and_save_dataset(
ds, ds_name=ds_name, ds_config=ds_config, suffix=f"_{ds_split}_raw"
)
return len(ds)
def load_wikipedia(ds_name, ds_config):
"""
Stream the wikipedia dataset from the HuggingFace Hub.
Args:
ds_name (`str`):
The name of the dataset to load. Must be `"wikipedia"`.
ds_config (`str`, *optional*, Defaults to `None`):
The configuration of the dataset to load.
Returns:
ds (`datasets.Dataset`):
"""
ds = load_dataset(ds_name, ds_config, streaming=True, split="train")
def gen():
for example in ds:
yield {"text": example["text"]}
return Dataset.from_generator(gen)
def chunk_and_save_dataset(
ds: Dataset,
chunk_size: int = 20_000,
ds_name: str = None,
ds_config: str = None,
suffix: str = "",
):
"""
Chunk a dataset into smaller datasets of size `chunk_size`.
The name of the dataset will be used to create a folder in `/data`.
Args:
ds (`Dataset`):
The dataset to chunk.
chunk_size (`int`, *optional*, Defaults to `20_000`):
The size of each chunk. Defaults to `20_000`.
ds_name (`str`, *optional*, Defaults to `None`):
The name of the dataset to load.
ds_config (`str`, *optional*, Defaults to `None`):
The configuration of the dataset to load.
suffix (`str`, *optional*, Defaults to `""`):
The suffix to add to the dataset name.
Returns:
chunks (`List[Dataset]`):
The list of chunks.
"""
if ds_config is None:
ds_config = ""
folder = Path("/data") / DATASET_NAME_PATTERN.sub("", ds_name + ds_config)
folder.mkdir(exist_ok=True, parents=True)
for chunk_num, start_idx in enumerate(range(0, len(ds), chunk_size)):
end_idx = min(start_idx + chunk_size, len(ds))
temp = ds.select(range(start_idx, end_idx))
temp.to_parquet(str(folder / f"chunk_{chunk_num}{suffix}"))
def tokenize_dataset(
ds_name: str,
ds_config: str = None,
ds_split: str = "train",
model_name: str = None,
opt_level: str = None,
column_name: str = "text",
num2skip: int = 0,
num2embed: int = -1,
):
"""
Tokenize the examples using the tokenizer. Sort by length
Args:
ds_name (`str`):
The name of the dataset to load.
ds_config (`str`, *optional*, Defaults to `None`):
The configuration of the dataset to load.
model_name (`str`, *optional*, Defaults to `None`):
The name of the model to use for tokenization.
opt_level (`str`, *optional*, Defaults to `None`):
The optimization level to use for tokenization.
column_name (`str`, *optional*, defaults to `text`):
column name to use for tokenization. Defaults to `text`
num2skip (`int`, *optional*, defaults to `0`):
number of rows to skip. Defaults to `0`
num2embed (`int`, *optional*, defaults to `-1`):
number of rows to embed. Defaults to `-1`, which means all rows.
Returns:
ds (`Dataset`):
"""
# TODO: option for controlling length for models that can go shorter/longer than 512
folder = Path("/data") / DATASET_NAME_PATTERN.sub("", ds_name + ds_config)
files = list(map(str, folder.glob(f"chunk_*_{ds_split}_raw")))
ds = load_dataset("parquet", data_files=files, split="train")
if num2embed == -1:
num2embed = len(ds)
ds = ds.select(range(num2skip, num2skip + num2embed))
tokenizer = AutoTokenizer.from_pretrained(model_name)
padding = "max_length" if opt_level == "O4" else False
max_length = 512
def tokenize(
examples: Dict[str, List[str]],
):
tokenized = tokenizer(
examples[column_name],
truncation=True,
padding=padding,
max_length=max_length,
)
tokenized["length"] = [len(x) for x in tokenized["input_ids"]]
return tokenized
tds = ds.map(
tokenize,
batched=True,
batch_size=1000,
remove_columns=set(ds.column_names) - {column_name},
num_proc=multiprocessing.cpu_count(),
desc="Tokenizing",
)
# sort to minimize padding
if padding != "max_length":
tds = tds.sort("length")
chunk_and_save_dataset(
tds, ds_name=ds_name, ds_config=ds_config, suffix=f"_{ds_split}_tokenized"
)
def load_tokenized_dataset(
ds_name: str,
ds_config: str = None,
ds_split: str = "train",
):
"""
Load a tokenized dataset from disk.
Args:
ds_name (`str`):
The name of the dataset to load.
ds_config (`str`, *optional*, Defaults to `None`):
The configuration of the dataset to load.
ds_split (`str`, *optional*, Defaults to `"train"`):
The split of the dataset to load.
Returns:
ds (`Dataset`):
"""
folder = Path("/data") / DATASET_NAME_PATTERN.sub("", ds_name + ds_config)
files = list(map(str, folder.glob(f"chunk_*_{ds_split}_tokenized")))
return load_dataset("parquet", data_files=files, split="train")
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