import gzip import multiprocessing import os import shutil import time from argparse import Namespace from collections import Counter import numpy as np from datasets import load_dataset, utils import re from huggingface_hub import Repository from multiprocessing import Pool from tqdm import tqdm # Settings config = { "dataset_name": "./data/github", "num_workers": 96, "line_max": 1000, "out_path": "./data/github-code", "repo_name": "github-code", "org": "lvwerra", "shard_size": 1000 << 20} args = Namespace(**config) PATTERN = re.compile(r'\s+') def get_hash(example): """Get hash of content field.""" return {"hash": hash(re.sub(PATTERN, '', example["content"]))} def line_stats(example): """Calculates mean and max line length of file.""" line_lengths = [len(line) for line in example["content"].splitlines()] return {"line_mean": np.mean(line_lengths), "line_max": max(line_lengths)} def alpha_stats(example): """Calculates mean and max line length of file.""" alpha_frac = np.mean([c.isalnum() for c in example["content"]]) return {"alpha_frac": alpha_frac} def check_uniques(example, uniques): """Check if current hash is still in set of unique hashes and remove if true.""" if example["hash"] in uniques: uniques.remove(example["hash"]) return True else: return False def is_autogenerated(example, scan_width=5): """Check if file is autogenerated by looking for keywords in the first few lines of the file.""" keywords = ["auto-generated", "autogenerated", "automatically generated"] lines = example["content"].splitlines() for _, line in zip(range(scan_width), lines): for keyword in keywords: if keyword in line.lower(): return {"autogenerated": True} else: return {"autogenerated": False} def preprocess(example): """Chain all preprocessing steps into one function to not fill cache.""" results = dict() results.update(get_hash(example)) results.update(line_stats(example)) return results def filter(example, uniques, args): """Filter dataset with heuristics.""" if not check_uniques(example, uniques): return False elif example["line_max"] > args.line_max: return False else: return True def save_shard(shard_tuple): """Save shard""" filename, shard = shard_tuple shard.to_parquet(filename) # Load dataset t_start = time.time() ds = load_dataset(args.dataset_name, split="train", chunksize=40<<20) print(f"Time to load dataset: {time.time()-t_start:.2f}") # Run preprocessing t_start = time.time() ds = ds.map(preprocess, num_proc=args.num_workers) print(f"Time to preprocess dataset: {time.time()-t_start:.2f}") print(ds) # Deduplicate hashes uniques = set(ds.unique("hash")) frac = len(uniques) / len(ds) print(f"Fraction of duplicates: {1-frac:.2%}") # Deduplicate data and apply heuristics t_start = time.time() ds = ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args}) ds = ds.remove_columns(["line_mean", "line_max", "copies", "hash"]) print(f"Time to filter dataset: {time.time()-t_start:.2f}") print(f"Size of filtered dataset: {len(ds)}") # Save dataset in repo repo = Repository( local_dir=args.out_path, clone_from=args.org + "/" + args.repo_name, repo_type="dataset", private=True, use_auth_token=True, git_user="lvwerra", git_email="leandro.vonwerra@gmail.com", ) os.mkdir(args.out_path + "/data") if ds._indices is not None: dataset_nbytes = ds.data.nbytes * len(ds._indices) / len(ds.data) else: dataset_nbytes = ds.data.nbytes num_shards = int(dataset_nbytes / args.shard_size) + 1 print(f"Number of shards: {num_shards}") t_start = time.time() shards = (ds.shard(num_shards=num_shards, index=i, contiguous=True) for i in range(num_shards)) filenames = (f"{args.out_path}/data/train-{index:05d}-of-{num_shards:05d}.parquet" for index in range(num_shards)) with Pool(16) as p: list(tqdm(p.imap_unordered(save_shard, zip(filenames, shards), chunksize=4), total=num_shards)) print(f"Time to save dataset: {time.time()-t_start:.2f}") # To push to hub run `git add` and `git push` inside dataset repo folder