Create github_preprocessing.py

github_preprocessing.py

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+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