File size: 8,791 Bytes
bf2c02f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | """Scan filtered/filtered_*.jsonl, report:
1. Top template-style basenames (counts + total bytes + duplicate ratio per file).
2. Top high-frequency lines (stripped) across the whole corpus, with sample coverage.
3. Top short-phrase n-grams (3-gram and 5-gram over tokens of stripped lines), sampled.
Output: filtered_repetition_report.json + .md
Pure-stdlib, single-pass over disk, ~10GB.
"""
from __future__ import annotations
import json
import os
import re
import sys
from collections import Counter, defaultdict
from glob import glob
from pathlib import Path
SRC_DIR = Path("/raid/data/weifeng/Datasets/filtered")
OUT_JSON = Path("/raid/data/weifeng/Datasets/filtered_repetition_report.json")
OUT_MD = Path("/raid/data/weifeng/Datasets/filtered_repetition_report.md")
TEMPLATE_BASENAMES = {
"__init__.py", "setup.py", "config.py", "conf.py", "settings.py",
"constants.py", "version.py", "_version.py", "manage.py",
"decorators.py", "exceptions.py", "errors.py", "utils.py",
"types.py", "schemas.py", "logging.py", "logger.py",
"__main__.py", "wsgi.py", "asgi.py", "urls.py", "apps.py",
"models.py", "admin.py", # not always template, but check
}
# heuristic: line frequency we care about
TOP_LINES_KEEP = 2000 # keep top N lines for report
TOP_BASENAMES_KEEP = 500
NGRAM_TOP_KEEP = 500
NGRAM_SAMPLE_EVERY = 20 # only do n-gram counting on every Nth file to save memory
# strip whitespace, ignore very short / very long lines for stats
MIN_LINE_LEN = 8
MAX_LINE_LEN = 200
# token regex for n-grams over line text
TOKEN_RE = re.compile(r"[A-Za-z_][A-Za-z0-9_]*|[^\s\w]")
def main() -> None:
files = sorted(glob(str(SRC_DIR / "filtered_*.jsonl")))
print(f"found {len(files)} jsonl files", flush=True)
basename_counter: Counter[str] = Counter()
basename_bytes: Counter[str] = Counter()
basename_dup_lines: Counter[str] = Counter() # how many duplicate lines (within file) total per basename
line_counter: Counter[str] = Counter()
line_sample_coverage: Counter[str] = Counter() # how many distinct samples contain the line
ngram3_counter: Counter[tuple] = Counter()
ngram5_counter: Counter[tuple] = Counter()
total_samples = 0
total_bytes = 0
py_samples = 0
for fi, fp in enumerate(files):
do_ngram = (fi % NGRAM_SAMPLE_EVERY == 0)
try:
with open(fp, encoding="utf-8") as f:
for line in f:
try:
o = json.loads(line)
except Exception:
continue
path = o.get("path", "") or ""
text = o.get("text", "") or ""
if not text:
continue
total_samples += 1
total_bytes += len(text)
basename = os.path.basename(path) if path else "<unknown>"
basename_counter[basename] += 1
basename_bytes[basename] += len(text)
if path.endswith(".py"):
py_samples += 1
# within-file line duplication: how repetitive is THIS file?
file_lines = [ln.strip() for ln in text.split("\n")]
file_line_cnt = Counter(ln for ln in file_lines if MIN_LINE_LEN <= len(ln) <= MAX_LINE_LEN)
dup_here = sum(c - 1 for c in file_line_cnt.values() if c > 1)
basename_dup_lines[basename] += dup_here
# cross-corpus line stats
seen_in_this_sample: set[str] = set()
for ln, c in file_line_cnt.items():
line_counter[ln] += c
if ln not in seen_in_this_sample:
line_sample_coverage[ln] += 1
seen_in_this_sample.add(ln)
if do_ngram and len(file_lines) > 5:
# take a slice to bound memory
for ln in file_lines[:400]:
toks = TOKEN_RE.findall(ln)
if len(toks) < 3:
continue
for i in range(len(toks) - 2):
ngram3_counter[tuple(toks[i:i + 3])] += 1
if len(toks) >= 5:
for i in range(len(toks) - 4):
ngram5_counter[tuple(toks[i:i + 5])] += 1
# prune to avoid OOM
if total_samples % 100000 == 0:
print(f" [{total_samples}] processed; "
f"line_counter={len(line_counter):,} ngram3={len(ngram3_counter):,}",
flush=True)
if len(line_counter) > 5_000_000:
# keep top half
cutoff = line_counter.most_common(2_000_000)
line_counter = Counter(dict(cutoff))
line_sample_coverage = Counter({k: line_sample_coverage[k] for k, _ in cutoff})
if len(ngram3_counter) > 3_000_000:
ngram3_counter = Counter(dict(ngram3_counter.most_common(1_000_000)))
if len(ngram5_counter) > 3_000_000:
ngram5_counter = Counter(dict(ngram5_counter.most_common(1_000_000)))
except Exception as e:
print(f" ERR {fp}: {e}", file=sys.stderr, flush=True)
print(f"DONE: {total_samples:,} samples, {total_bytes/1e9:.2f} GB", flush=True)
# ----- build report -----
top_basenames = basename_counter.most_common(TOP_BASENAMES_KEEP)
top_lines = line_counter.most_common(TOP_LINES_KEEP)
top_ngram3 = ngram3_counter.most_common(NGRAM_TOP_KEEP)
top_ngram5 = ngram5_counter.most_common(NGRAM_TOP_KEEP)
report = {
"total_samples": total_samples,
"total_bytes": total_bytes,
"py_samples": py_samples,
"n_files_scanned": len(files),
"top_basenames": [
{
"basename": b,
"count": c,
"pct_of_corpus": round(c / total_samples * 100, 3),
"total_bytes": basename_bytes[b],
"dup_lines_within_files": basename_dup_lines[b],
}
for b, c in top_basenames
],
"top_lines_corpus_wide": [
{
"line": ln,
"occurrences": c,
"n_samples_containing": line_sample_coverage[ln],
"pct_samples": round(line_sample_coverage[ln] / total_samples * 100, 3),
}
for ln, c in top_lines[:500]
],
"top_3grams": [{"tokens": list(t), "count": c} for t, c in top_ngram3],
"top_5grams": [{"tokens": list(t), "count": c} for t, c in top_ngram5],
"ngram_sampling_note": f"n-gram counted on every {NGRAM_SAMPLE_EVERY}th file, first 400 lines per sample",
}
OUT_JSON.write_text(json.dumps(report, ensure_ascii=False, indent=2))
print(f"wrote {OUT_JSON} ({OUT_JSON.stat().st_size/1e6:.1f} MB)", flush=True)
# ----- markdown summary -----
md = []
md.append(f"# Filtered pretrain repetition report\n")
md.append(f"- samples: **{total_samples:,}**, bytes: **{total_bytes/1e9:.2f} GB**, .py: {py_samples:,}\n")
md.append(f"- files scanned: {len(files)}\n\n")
md.append("## Top template basenames (by sample count)\n\n")
md.append("| basename | count | % corpus | total bytes | dup lines (within-file) |\n|---|---:|---:|---:|---:|\n")
for b, c in top_basenames[:60]:
md.append(f"| `{b}` | {c} | {c/total_samples*100:.2f}% | {basename_bytes[b]/1e6:.1f} MB | {basename_dup_lines[b]} |\n")
md.append("\n## Top corpus-wide lines (frequency)\n\n")
md.append("| line | occurrences | samples containing | % samples |\n|---|---:|---:|---:|\n")
for ln, c in top_lines[:80]:
disp = ln.replace("|", "\\|")[:120]
md.append(f"| `{disp}` | {c} | {line_sample_coverage[ln]} | {line_sample_coverage[ln]/total_samples*100:.2f}% |\n")
md.append("\n## Top 5-grams (token-level)\n\n")
md.append("| 5-gram | count |\n|---|---:|\n")
for t, c in top_ngram5[:60]:
disp = " ".join(t).replace("|", "\\|")[:120]
md.append(f"| `{disp}` | {c} |\n")
md.append("\n## Top 3-grams (token-level)\n\n")
md.append("| 3-gram | count |\n|---|---:|\n")
for t, c in top_ngram3[:60]:
disp = " ".join(t).replace("|", "\\|")[:120]
md.append(f"| `{disp}` | {c} |\n")
OUT_MD.write_text("".join(md))
print(f"wrote {OUT_MD}", flush=True)
if __name__ == "__main__":
main()
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