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import gzip |
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import multiprocessing |
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import os |
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import shutil |
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import time |
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from argparse import Namespace |
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from collections import Counter |
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import numpy as np |
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from datasets import load_dataset, utils |
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import re |
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from huggingface_hub import Repository |
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from multiprocessing import Pool |
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from tqdm import tqdm |
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config = { |
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"dataset_name": "./data/github", |
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"num_workers": 96, |
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"line_max": 1000, |
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"out_path": "./data/github-code", |
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"repo_name": "github-code", |
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"org": "lvwerra", |
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"shard_size": 1000 << 20} |
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args = Namespace(**config) |
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PATTERN = re.compile(r'\s+') |
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def hash_func(text): |
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return hashlib.md5(re.sub(PATTERN, '', text).encode("utf-8")).hexdigest() |
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def get_hash(example): |
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"""Get hash of content field.""" |
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return {"hash": hash_func(example["content"])} |
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def line_stats(example): |
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"""Calculates mean and max line length of file.""" |
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line_lengths = [len(line) for line in example["content"].splitlines()] |
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return {"line_mean": np.mean(line_lengths), "line_max": max(line_lengths)} |
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def alpha_stats(example): |
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"""Calculates mean and max line length of file.""" |
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alpha_frac = np.mean([c.isalnum() for c in example["content"]]) |
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return {"alpha_frac": alpha_frac} |
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def check_uniques(example, uniques): |
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"""Check if current hash is still in set of unique hashes and remove if true.""" |
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if example["hash"] in uniques: |
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uniques.remove(example["hash"]) |
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return True |
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else: |
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return False |
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def is_autogenerated(example, scan_width=5): |
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"""Check if file is autogenerated by looking for keywords in the first few lines of the file.""" |
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keywords = ["auto-generated", "autogenerated", "automatically generated"] |
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lines = example["content"].splitlines() |
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for _, line in zip(range(scan_width), lines): |
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for keyword in keywords: |
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if keyword in line.lower(): |
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return {"autogenerated": True} |
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else: |
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return {"autogenerated": False} |
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def preprocess(example): |
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"""Chain all preprocessing steps into one function to not fill cache.""" |
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results = dict() |
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results.update(get_hash(example)) |
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results.update(line_stats(example)) |
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return results |
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def filter(example, uniques, args): |
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"""Filter dataset with heuristics.""" |
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if not check_uniques(example, uniques): |
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return False |
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elif example["line_max"] > args.line_max: |
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return False |
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else: |
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return True |
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def save_shard(shard_tuple): |
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"""Save shard""" |
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filename, shard = shard_tuple |
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shard.to_parquet(filename) |
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t_start = time.time() |
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ds = load_dataset(args.dataset_name, split="train", chunksize=40<<20) |
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print(f"Time to load dataset: {time.time()-t_start:.2f}") |
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t_start = time.time() |
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ds = ds.map(preprocess, num_proc=args.num_workers) |
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print(f"Time to preprocess dataset: {time.time()-t_start:.2f}") |
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print(ds) |
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uniques = set(ds.unique("hash")) |
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frac = len(uniques) / len(ds) |
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print(f"Fraction of duplicates: {1-frac:.2%}") |
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t_start = time.time() |
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ds = ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args}) |
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ds = ds.remove_columns(["line_mean", "line_max", "copies", "hash"]) |
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print(f"Time to filter dataset: {time.time()-t_start:.2f}") |
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print(f"Size of filtered dataset: {len(ds)}") |
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repo = Repository( |
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local_dir=args.out_path, |
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clone_from=args.org + "/" + args.repo_name, |
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repo_type="dataset", |
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private=True, |
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use_auth_token=True, |
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git_user="lvwerra", |
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git_email="[email protected]", |
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) |
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os.mkdir(args.out_path + "/data") |
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if ds._indices is not None: |
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dataset_nbytes = ds.data.nbytes * len(ds._indices) / len(ds.data) |
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else: |
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dataset_nbytes = ds.data.nbytes |
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num_shards = int(dataset_nbytes / args.shard_size) + 1 |
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print(f"Number of shards: {num_shards}") |
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t_start = time.time() |
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shards = (ds.shard(num_shards=num_shards, index=i, contiguous=True) for i in range(num_shards)) |
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filenames = (f"{args.out_path}/data/train-{index:05d}-of-{num_shards:05d}.parquet" for index in range(num_shards)) |
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with Pool(16) as p: |
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list(tqdm(p.imap_unordered(save_shard, zip(filenames, shards), chunksize=4), total=num_shards)) |
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print(f"Time to save dataset: {time.time()-t_start:.2f}") |
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