File size: 10,680 Bytes
0572321 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | """Build every dataset Sanad needs, in one go.
python scripts/make_all_data.py
data/
operator/ operator_train.jsonl operator_val.jsonl operator_eval.jsonl (120 held-out scenarios)
guardian/ guardian_train.jsonl guardian_val.jsonl guardian_eval.jsonl (252 review packets + expected decision)
verifier/ verifier_train.jsonl verifier_val.jsonl verifier_eval.jsonl (re-checks + expected status)
stats.json counts, label balance, diversity and length
Each agent is trained separately (its own LoRA adapter on the same base model).
Every row: {"messages": [...], "tools": [...], "meta": {"role": ..., ...}}
Names of hosts, VMs, datastores, cluster and vCenter change in every episode.
"""
from __future__ import annotations
import json
import random
import sys
from collections import Counter
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from sanad_sim import FAULTS, Simulator # noqa: E402
from sanad_sim.checks import check_grounding # noqa: E402
from sanad_sim.guardian_data import EVAL_MODES, MODE_CYCLE, GuardianBuilder # noqa: E402
from sanad_sim.teacher import ScriptedTeacher # noqa: E402
from sanad_sim.verifier_data import CASE_CYCLE, CASES as V_EVAL_CASES, VerifierBuilder # noqa: E402
FIDS = list(FAULTS)
SEEDS = { # role -> split -> base seed (ranges never overlap)
"operator": {"train": 0, "val": 500_000, "eval": 900_000},
"guardian": {"train": 1_100_000, "val": 1_500_000, "eval": 1_900_000},
"verifier": {"train": 2_100_000, "val": 2_500_000, "eval": 2_900_000},
}
SIZES = {"operator": {"train": 70, "val": 8}, "guardian": {"train": 80, "val": 10},
"verifier": {"train": 40, "val": 5}}
SPECIAL_SIZES = {"train": {"OK00": 150, "UNK00": 100}, "val": {"OK00": 20, "UNK00": 15}}
P_TOOL_ERROR = 0.15
# operator
def operator_row(fid, seed, lang, teacher):
rng = random.Random(seed * 7919 + 13)
probe = Simulator(seed).new_episode(fid, lang=lang)
errs = None
if fid not in ("UNK00",) and rng.random() < P_TOOL_ERROR:
errs = [{"tool": probe.inj.evidence[0][0], "call": 1,
"error": rng.choice(["timeout after 30s", "connection reset by vCenter", "503 Service Unavailable"])}]
ep = Simulator(seed).new_episode(fid, lang=lang, tool_errors=errs)
s = teacher.run(ep, rng)
fa = s["first_action"]
if fid == "UNK00":
if check_grounding(s["final_answer"], ep.trace):
return None, "grounding"
else:
chk = ep.code_checks(s["final_answer"], fa and fa["action"], fa and fa["args"])
if not chk["pass"]:
return None, f"checks {[i['type'] for i in chk['issues']]}"
for action, args in ep.inj.solution:
ep.execute(action, args)
if not ep.resolved():
return None, "fix did not resolve"
msgs = ep.display(s["messages"])
# re-check grounding on what the model will actually see (after renaming)
shown = [{"output": json.loads(m["content"])} for m in msgs if m["role"] == "tool"]
shown += [{"tool": "propose_action", "args": tc["function"]["arguments"], "output": {}}
for m in msgs if m["role"] == "assistant" for tc in m.get("tool_calls", [])
if tc["function"]["name"] == "propose_action"]
if fid != "UNK00" and check_grounding(msgs[-1]["content"], shown):
return None, "grounding after rename"
meta = {"role": "operator", "fault_id": fid, "seed": seed, "lang": lang, "tool_error": bool(errs),
"ground_truth": ep.display(ep.ground_truth())}
return {"messages": msgs, "tools": s["tools"], "meta": meta}, None
def build_operator(split, teacher, drops):
rows, base, n = [], SEEDS["operator"][split], SIZES["operator"][split]
plan = [(fid, n) for fid in FIDS] + list(SPECIAL_SIZES[split].items())
for k, (fid, count) in enumerate(plan):
for i in range(count):
seed = base + k * 1000 + i
row, why = operator_row(fid, seed, "ar" if i % 2 else "en", teacher)
if row:
rows.append(row)
else:
drops[f"operator {fid}: {why}"] += 1
return rows
# guardian / verifier
FALLBACK_MODES = ["wrong", "policy", "ignored_error", "correct"]
def build_guardian(split, gb, drops):
rows, base, n = [], SEEDS["guardian"][split], SIZES["guardian"][split]
for k, fid in enumerate(FIDS):
for i in range(n):
seed = base + k * 1000 + i
lang = "ar" if i % 2 else "en"
mode = MODE_CYCLE[(i * 7 + k) % len(MODE_CYCLE)] # spread modes evenly in every split
row = gb.build(fid, seed, lang, mode)
for alt in FALLBACK_MODES:
if row:
break
drops[f"guardian {fid}: {mode} -> {alt}"] += 1
row = gb.build(fid, seed, lang, alt)
if row:
rows.append(row)
return rows
def build_verifier(split, vb):
rows, base, n = [], SEEDS["verifier"][split], SIZES["verifier"][split]
for k, fid in enumerate(FIDS):
for i in range(n):
seed = base + k * 1000 + i
case = CASE_CYCLE[(i * 4 + k) % len(CASE_CYCLE)]
rows.append(vb.build(fid, seed, "ar" if (i + k) % 2 else "en", case))
return rows
# eval sets
def build_evals(teacher, gb, vb):
ev_op = []
order = FIDS * 2 + random.Random(7).sample(FIDS, 16) + ["OK00"] * 10 + ["UNK00"] * 10
for j, fid in enumerate(order):
seed = SEEDS["operator"]["eval"] + j
lang = "ar" if j % 2 else "en"
ep = Simulator(seed).new_episode(fid, lang=lang)
s = teacher.run(Simulator(seed).new_episode(fid, lang=lang), random.Random(seed))
ev_op.append({"id": f"op-{j:03d}", "fault_id": fid, "seed": seed, "lang": lang,
"messages": ep.display(s["messages"][:2]), "tools": s["tools"],
"ground_truth": ep.display(ep.ground_truth()),
"rebuild": f"Simulator({seed}).new_episode('{fid}', lang='{lang}')"})
ev_g = []
for k, fid in enumerate(FIDS):
for i, mode in enumerate(EVAL_MODES):
seed = SEEDS["guardian"]["eval"] + k * 1000 + i
row = gb.build(fid, seed, "ar" if i % 2 else "en", mode)
for alt in FALLBACK_MODES:
if row:
break
row = gb.build(fid, seed, "ar" if i % 2 else "en", alt)
if row:
ev_g.append({"id": f"g-{len(ev_g):03d}", "fault_id": fid, "seed": seed,
"lang": row["meta"]["lang"], "mode": row["meta"]["mode"],
"messages": row["messages"][:2], "tools": row["tools"],
"expected": json.loads(row["messages"][-1]["content"]), "gold_messages": row["messages"],
"meta": row["meta"]})
ev_v = []
for k, fid in enumerate(FIDS):
for i, case in enumerate(V_EVAL_CASES):
seed = SEEDS["verifier"]["eval"] + k * 1000 + i
lang = "ar" if (i + k) % 2 else "en"
row = vb.build(fid, seed, lang, case)
if row["meta"]["case"] != case:
continue # this case does not exist for this fault
ev_v.append({"id": f"v-{len(ev_v):03d}", "fault_id": fid, "seed": seed, "lang": lang, "case": case,
"messages": row["messages"][:2], "tools": row["tools"],
"expected": json.loads(row["messages"][-1]["content"]), "gold_messages": row["messages"],
"meta": row["meta"]})
return ev_op, ev_g, ev_v
# stats
def tok_est(row) -> int:
return int(len(json.dumps(row["messages"], ensure_ascii=False)) / 3.2
+ len(json.dumps(row["tools"])) / 3.6)
def diversity(rows):
users = [r["messages"][1]["content"] for r in rows]
finals = [r["messages"][-1]["content"] for r in rows]
return {"unique_requests_pct": round(100 * len(set(users)) / len(users), 1),
"unique_final_answers_pct": round(100 * len(set(finals)) / len(finals), 1)}
def write(path: Path, rows):
with open(path, "w", encoding="utf-8") as f:
for r in rows:
f.write(json.dumps(r, ensure_ascii=False) + "\n")
def main():
out = ROOT / "data"
for d in ("operator", "guardian", "verifier"):
(out / d).mkdir(parents=True, exist_ok=True)
teacher, gb, vb = ScriptedTeacher(), GuardianBuilder(), VerifierBuilder()
stats, drops = {}, Counter()
allrows = {}
for split in ("train", "val"):
op = build_operator(split, teacher, drops)
g = build_guardian(split, gb, drops)
v = build_verifier(split, vb)
for name, rows in (("operator", op), ("guardian", g), ("verifier", v)):
write(out / name / f"{name}_{split}.jsonl", rows)
toks = [tok_est(r) for r in rows]
labels = Counter(r["meta"].get("label") or r["meta"]["fault_id"][:1] for r in rows)
stats[f"{name}_{split}"] = {
"rows": len(rows), "arabic_pct": round(100 * sum(r["meta"]["lang"] == "ar" for r in rows) / len(rows), 1),
"labels": dict(labels) if name != "operator" else None,
"tokens_avg": int(sum(toks) / len(toks)), "tokens_max": max(toks), "tokens_total": sum(toks),
**diversity(rows)}
allrows[split] = op + g + v
print(split, {k: v["rows"] for k, v in stats.items() if k.endswith(split)})
ev_op, ev_g, ev_v = build_evals(teacher, gb, vb)
write(out / "operator" / "operator_eval.jsonl", ev_op)
write(out / "guardian" / "guardian_eval.jsonl", ev_g)
write(out / "verifier" / "verifier_eval.jsonl", ev_v)
stats["eval"] = {"operator": len(ev_op), "guardian": len(ev_g), "verifier": len(ev_v),
"guardian_labels": dict(Counter(e["expected"]["decision"] for e in ev_g)),
"verifier_labels": dict(Counter(e["expected"]["status"] for e in ev_v))}
stats["dropped_or_replaced"] = dict(drops.most_common(25))
stats["train_total_rows"] = len(allrows["train"])
stats["train_total_tokens_est"] = sum(tok_est(r) for r in allrows["train"])
leak = sum("__BADVM__" in json.dumps(r, ensure_ascii=False) for r in allrows["train"])
stats["placeholder_leaks"] = leak
(out / "stats.json").write_text(json.dumps(stats, indent=2, ensure_ascii=False))
print(json.dumps({k: stats[k] for k in ("eval", "train_total_rows", "train_total_tokens_est",
"placeholder_leaks")}, ensure_ascii=False))
if __name__ == "__main__":
main()
|