File size: 8,753 Bytes
9d614f1 | 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 | """PUBLIC INTERFACE of Data_Module. Other modules may import ONLY this file.
run_clean(cfg) -> dict
1. surgically cleans chat.jsonl -> cleaned_data/sft_clean.jsonl (+ post-write verification)
2. streams every SOURCE_REPOS entry (dataset first, model fallback), blacklist -> garbage filter
-> context-aware dedup (seeded with chat.jsonl for cross-deduplication)
3. mixes PRETRAIN_CONV_RATIO of chat samples into cleaned_data/pretrain_clean.jsonl
"""
from __future__ import annotations
import gc
import json
from pathlib import Path
from typing import Dict, List, Optional
from .internal.blacklist_gatekeeper import BlacklistGatekeeper
from .internal.context_deduplicator import ContextDeduplicator
from .internal.hf_universal_streamer import HFUniversalStreamer, RepoTelemetry
from .internal.pretrain_garbage_filter import PretrainGarbageFilter
from .internal.sft_surgical_cleaner import (
build_pretrain_chat_samples, clean_chat_file, verify_pretrain_jsonl, verify_sft_jsonl, write_starter_chat,
)
def cleaned_paths(cfg: Dict) -> Dict[str, Path]:
out = Path(cfg["root"]) / "cleaned_data"
return {"dir": out, "sft": out / "sft_clean.jsonl", "pretrain": out / "pretrain_clean.jsonl",
"rejected": out / "sft_rejected.jsonl"}
def ensure_chat_file(cfg: Dict) -> Path:
"""Create the 40-line starter chat.jsonl (37 clean + 3 intentionally broken lines) if it does not exist."""
chat_file = Path(cfg["chat_file"])
if write_starter_chat(chat_file):
print(f"[Data] {chat_file.name} not found -> generated the 40-line starter dataset at {chat_file}")
print(" (3 lines are broken/duplicate on purpose: the cleaner below will excise them and re-verify)")
return chat_file
def cleaned_data_ready(cfg: Dict) -> bool:
p = cleaned_paths(cfg)
return p["sft"].exists() and p["pretrain"].exists() and p["sft"].stat().st_size > 0
def _parse_limit(value) -> Optional[int]:
if value is None:
return None
if str(value).strip().lower() in ("full", "all", "none", ""):
return None
try:
return max(1, int(value))
except ValueError:
return None
def _print_table(rows: List[RepoTelemetry]) -> None:
header = ["Repo", "Type", "[Total Discovered]", "[Skipped by Blacklist]",
"[Destroyed Garbage/Dup]", "[Kept Clean Samples]"]
body = [[r.repo, r.repo_type, str(r.total_discovered), str(r.skipped_blacklist),
str(r.destroyed), str(r.kept)] for r in rows]
widths = [max(len(header[i]), *(len(row[i]) for row in body)) if body else len(header[i])
for i in range(len(header))]
line = "+" + "+".join("-" * (w + 2) for w in widths) + "+"
print("\n TELEMETRY [Total Discovered] -> [Skipped by Blacklist] -> [Destroyed Garbage/Dup] -> [Kept]")
print(" " + line)
print(" | " + " | ".join(h.ljust(w) for h, w in zip(header, widths)) + " |")
print(" " + line)
for row in body:
print(" | " + " | ".join(c.ljust(w) for c, w in zip(row, widths)) + " |")
print(" " + line)
for r in rows:
print(f" {r.repo}: garbage-gate={r.garbage_gate} too-large={r.too_large} read-err={r.read_errors} "
f"filter-destroyed={r.filter_destroyed} dup-files={r.duplicate_files} "
f"partial-dedup(sub-sections excised)={r.partial_dedup}"
+ (f" ERROR: {r.error}" if r.error else ""))
def run_clean(cfg: Dict) -> Dict:
paths = cleaned_paths(cfg)
paths["dir"].mkdir(parents=True, exist_ok=True)
token = cfg.get("hf_token") or None
chat_file = Path(cfg["chat_file"])
print("\n[Data] === STEP 1/3: SFT surgical cleaning of chat.jsonl ===")
records, sft_report = clean_chat_file(chat_file, paths["sft"], paths["rejected"], verbose=True)
print(f" chat.jsonl: {sft_report['raw_lines']} lines -> {sft_report['kept']} clean "
f"({sft_report['removed']} removed, {sft_report['repaired_modes']} mode repairs)")
if not records:
raise RuntimeError("No usable SFT records after cleaning chat.jsonl")
print("\n[Data] === STEP 2/3: streaming + filtering source repos ===")
gate = BlacklistGatekeeper(cfg.get("extra_blocked_extensions", []))
garbage_filter = PretrainGarbageFilter(json_quota=cfg.get("json_quota", 0.15))
dedup = ContextDeduplicator()
for rec in records: # cross-dedup: repo vs chat.jsonl
dedup.seed_text("\n".join([rec["user"], rec["thinking"], rec["assistant"]]))
telemetry: List[RepoTelemetry] = []
repo_tmp = paths["dir"] / "_repo_text.tmp.jsonl"
repo_docs = 0
try:
streamer = HFUniversalStreamer(token, gate)
except Exception as exc:
streamer = None
print(f" !! could not start HF streamer: {exc}")
with open(repo_tmp, "w", encoding="utf-8") as sink:
for entry in cfg.get("source_repos", []):
repo = entry.get("repo") if isinstance(entry, dict) else str(entry)
override = cfg.get("limit_override")
limit = _parse_limit(override if override not in (None, "") else
(entry.get("limit", 150) if isinstance(entry, dict) else 150))
tel = RepoTelemetry(repo=repo)
telemetry.append(tel)
if streamer is None:
tel.error = "streamer unavailable"
continue
try:
for item in streamer.stream(repo, limit, tel):
cleaned = garbage_filter.process(item["path"], item["ext"], item["text"])
if cleaned is None:
tel.filter_destroyed += 1
continue
doc, info = dedup.process(item["path"], cleaned)
if doc is None:
tel.duplicate_files += 1
continue
if info["status"] == "partial":
tel.partial_dedup += 1
sink.write(json.dumps({"text": doc, "source": repo, "path": item["path"]},
ensure_ascii=False) + "\n")
tel.kept += 1
repo_docs += 1
except Exception as exc:
tel.error = f"{type(exc).__name__}: {exc}"
print(f" !! stream error on {repo}: {tel.error}")
_print_table(telemetry)
if garbage_filter.stats:
print(" filter stats: " + ", ".join(f"{k}={v}" for k, v in sorted(garbage_filter.stats.items())))
if repo_docs == 0:
print(" !! WARNING: no repo documents survived - pre-training will use chat.jsonl data only.")
print("\n[Data] === STEP 3/3: building pretrain_clean.jsonl (repo text + chat mix) ===")
ratio = float(cfg.get("conv_ratio", 0.25))
chat_samples = build_pretrain_chat_samples(records, repo_docs, ratio)
total = repo_docs + len(chat_samples)
gap = max(1, repo_docs // len(chat_samples)) if chat_samples else 0
written_chat = 0
with open(paths["pretrain"], "w", encoding="utf-8") as out, open(repo_tmp, "r", encoding="utf-8") as src:
for i, line in enumerate(src, 1):
out.write(line)
if chat_samples and written_chat < len(chat_samples) and i % gap == 0:
out.write(json.dumps({"text": chat_samples[written_chat], "source": "chat.jsonl"},
ensure_ascii=False) + "\n")
written_chat += 1
while written_chat < len(chat_samples):
out.write(json.dumps({"text": chat_samples[written_chat], "source": "chat.jsonl"},
ensure_ascii=False) + "\n")
written_chat += 1
try:
repo_tmp.unlink()
except OSError:
pass
check = verify_pretrain_jsonl(paths["pretrain"])
print(f" [PRETRAIN-VERIFY] {'PASSED' if check['ok'] else 'FAILED'}: {check['lines']} lines "
f"({repo_docs} repo docs + {written_chat} chat samples = "
f"{(100.0 * written_chat / max(total, 1)):.0f}% conversation share)")
if not check["ok"]:
raise RuntimeError("pretrain_clean.jsonl failed verification: " + "; ".join(check["errors"][:5]))
sft_check = verify_sft_jsonl(paths["sft"])
if not sft_check["ok"]:
raise RuntimeError("sft_clean.jsonl failed final verification")
summary = {"sft_records": len(records), "repo_docs": repo_docs, "chat_pretrain_samples": written_chat,
"sft_path": str(paths["sft"]), "pretrain_path": str(paths["pretrain"])}
del records, chat_samples, dedup, garbage_filter, telemetry
gc.collect()
print(f"[Data] done -> {paths['sft'].name}, {paths['pretrain'].name} (RAM released)")
return summary |