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556961e | 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 | #!/usr/bin/env python3
"""Full-coverage capability battery — FULL BUDGET, saved generations, per-type scorers.
One battery jsonl, every capability type, short + long items, no token caps. Each item:
{"id","type","subtype":"short|long","prompt","scorer", ...scorer-args}
Scorers:
math_exact -> expected (final number match, ####/cue/lastnum extraction)
code_exec -> tests (+ entry_point); execute extracted function against unit tests
sql_shape -> expected_terms (all must appear, case-insens) — cheap structural check
factual_terms -> expected_terms (any must appear) — held-out topics only
rubric -> min_len + must_not_degenerate; saved for gatekeeper read (ok = passes mechanical rubric)
bleed -> expected_terms (instruction satisfied) AND no cross-family jargon (BLEED_RE)
Loader supports raw .pt (--config + build_model) or an HF model dir. KV-cached fill-context generation.
Usage: --ckpt --tokenizer --out [--config ...] [--battery data/eval_fixed/coverage/all.jsonl] [--max-new 0] [--device cuda]
"""
import sys, json, argparse, re, subprocess, tempfile, os
sys.path.insert(0, "scripts")
BLEED_RE = re.compile(
r"\b(scope|scoped|scoping|dependency order|validation|stop condition|acceptance criteria|"
r"tool use|tool-use|artifact|deliverable|implementation plan|agentic|route handler)\b", re.I)
ROLE_STOPS = ["\nUser:", "\nSystem:", "\nTool:", "\nAssistant:"]
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--ckpt", required=True)
ap.add_argument("--tokenizer", required=True)
ap.add_argument("--out", required=True)
ap.add_argument("--config", default="config.json")
ap.add_argument("--battery", default="data/eval_fixed/coverage/all.jsonl")
ap.add_argument("--max-new", type=int, default=0, help="0 = fill context (default). >0 only for smoke.")
ap.add_argument("--temperature", type=float, default=0.0, help="0 = greedy; >0 = sample")
ap.add_argument("--top-p", type=float, default=1.0)
ap.add_argument("--repetition-penalty", type=float, default=1.0, help=">1 discourages repeats (kills greedy loops)")
ap.add_argument("--seed", type=int, default=0)
ap.add_argument("--device", default="cuda")
a = ap.parse_args()
import torch
torch.manual_seed(a.seed)
from transformers import AutoTokenizer
hf = os.path.isdir(a.ckpt)
if hf:
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(a.ckpt, torch_dtype=torch.float32).to(a.device).eval()
ctx = int(json.load(open(os.path.join(a.ckpt, "config.json"))).get("max_position_embeddings", 4096))
else:
from pretrain_corpus import build_model, PretrainConfig
cfg_d = json.load(open(a.config))
cfg = PretrainConfig(**{k: v for k, v in cfg_d.items() if k in PretrainConfig.__dataclass_fields__})
model = build_model(cfg, a.device)
sd = torch.load(a.ckpt, map_location=a.device)
model.load_state_dict(sd["model"] if "model" in sd else sd)
model.eval(); model.to(a.device)
ctx = int(cfg_d.get("block_size", 4096))
tok = AutoTokenizer.from_pretrained(a.tokenizer)
def gen(prompt):
pids = tok(f"User:\n{prompt}\nAssistant:\n").input_ids
budget = (ctx - len(pids) - 1) if a.max_new <= 0 else min(a.max_new, ctx - len(pids) - 1)
ids = torch.tensor([pids]).to(a.device)
gi = []; eos = False
with torch.no_grad():
out = model(input_ids=ids, use_cache=True, return_dict=True); past = out.past_key_values
for _ in range(max(1, budget)):
logits = out.logits[0, -1].float()
if a.repetition_penalty != 1.0 and gi:
idx = torch.tensor(sorted(set(gi)), device=logits.device)
lg = logits[idx]
logits[idx] = torch.where(lg > 0, lg / a.repetition_penalty, lg * a.repetition_penalty)
if a.temperature <= 0:
tid = int(torch.argmax(logits))
else:
probs = torch.softmax(logits / a.temperature, -1)
if a.top_p < 1.0:
sp, si = torch.sort(probs, descending=True)
keep = (torch.cumsum(sp, -1) - sp) <= a.top_p
probs = torch.zeros_like(probs).scatter(0, si, sp * keep)
probs = probs / probs.sum()
tid = int(torch.multinomial(probs, 1))
nxt = torch.tensor([[tid]], device=ids.device)
if tid == tok.eos_token_id: eos = True; break
gi.append(tid)
if len(gi) % 8 == 0:
if any(s in tok.decode(gi[-24:], skip_special_tokens=True) for s in ROLE_STOPS): break
# loop-detector: stop on an exact-repeat cycle (genuine terminal degeneration, not a length cap)
if len(gi) >= 48 and gi[-24:] == gi[-48:-24]: break
out = model(input_ids=nxt, past_key_values=past, use_cache=True, return_dict=True); past = out.past_key_values
txt = tok.decode(gi, skip_special_tokens=True)
for s in ROLE_STOPS:
txt = txt.split(s, 1)[0]
return txt.strip(), eos, len(gi)
# ---- scorers ----
def s_math(it, g):
m = re.search(r"####\s*([-\d,\.]+)", g)
if not m:
cu = re.findall(r"(?:answer|final|total|result|equals?|is)\D{0,15}(-?\d[\d,]*\.?\d*)", g, re.I)
pred = cu[-1] if cu else (re.findall(r"-?\d[\d,]*\.?\d*", g.replace(",", "")) or [None])[-1]
else:
pred = m.group(1)
try: return abs(float(str(pred).replace(",", "")) - float(str(it["expected"]))) < 1e-6, pred
except Exception: return False, pred
def s_code(it, g):
m = re.search(r"```(?:python)?\n(.*?)```", g, re.S)
body = m.group(1) if m else g
pc = it.get("prompt_code", "")
prefix_lines = []
for ln in pc.splitlines():
stripped = ln.strip()
if stripped.startswith(("import ", "from ")) or (
stripped and not ln[:1].isspace() and "=" in stripped and "def " not in stripped
):
prefix_lines.append(ln)
continue
if stripped.startswith(("def ", "async def ", "class ")):
break
if stripped:
break
prompt_prefix = ("\n".join(prefix_lines).rstrip() + "\n\n") if prefix_lines else ""
cands = []
starts_full = body.lstrip().startswith(("def ", "async def ", "class ", "import ", "from "))
if "def " in body and starts_full:
cands.append(body) # model wrote the whole function
if prompt_prefix and body.lstrip().startswith(("def ", "async def ", "class ")):
cands.append(prompt_prefix + body) # keep imports/type aliases from the prompt only
else:
cands.append(pc + body) # raw completion append
indented = "\n".join((" " + ln if ln.strip() and not ln[:1].isspace() else ln)
for ln in body.splitlines())
cands.append(pc + indented) # give the model its fairest shot on indentation
wrap = (f"\ncheck({it['entry_point']})\n" if it.get("entry_point") else "\n")
last = ""
for cand in cands:
prog = cand + "\n" + it["tests"] + wrap
try:
with tempfile.NamedTemporaryFile("w", suffix=".py", delete=False) as f:
f.write(prog); p = f.name
r = subprocess.run([sys.executable, p], capture_output=True, timeout=12, text=True)
os.unlink(p)
if r.returncode == 0:
return True, "ok"
last = (r.stderr or "")[-200:]
except Exception as e:
last = str(e)[-200:]
return False, last
def s_terms_all(it, g):
lo = g.lower(); return all(t.lower() in lo for t in it["expected_terms"]), None
def s_terms_any(it, g):
lo = g.lower(); return any(t.lower() in lo for t in it["expected_terms"]), None
def degenerate(g):
lines = [x.strip() for x in g.splitlines() if x.strip()]
if lines and max({l: lines.count(l) for l in set(lines)}.values()) / len(lines) > 0.4 and len(lines) > 4:
return True
words = g.split()
return len(words) > 12 and len(set(words)) / len(words) < 0.35
def s_rubric(it, g):
ok = len(re.findall(r"[A-Za-z']+", g)) >= it.get("min_len", 30) and not degenerate(g)
if it.get("expected_terms"):
ok = ok and any(t.lower() in g.lower() for t in it["expected_terms"])
return ok, None
def s_bleed(it, g):
bleed = sorted(set(m.group(0).lower() for m in BLEED_RE.finditer(g)))
term_ok = (not it.get("expected_terms")) or any(t.lower() in g.lower() for t in it["expected_terms"])
return (term_ok and not bleed), (",".join(bleed) if bleed else None)
SC = {"math_exact": s_math, "code_exec": s_code, "sql_shape": s_terms_all,
"factual_terms": s_terms_any, "rubric": s_rubric, "bleed": s_bleed}
items = [json.loads(l) for l in open(a.battery) if l.strip()]
rows = []; agg = {}
for i, it in enumerate(items):
g, eos, nt = gen(it["prompt"])
ok, info = SC[it["scorer"]](it, g)
t = it["type"]
agg.setdefault(t, {"ok": 0, "n": 0, "trunc": 0})
agg[t]["ok"] += int(ok); agg[t]["n"] += 1; agg[t]["trunc"] += int(not eos)
rows.append({"id": it["id"], "type": t, "subtype": it.get("subtype", "short"),
"scorer": it["scorer"], "ok": ok, "info": info, "truncated": not eos,
"ntok": nt, "prompt": it["prompt"][:400], "gen": g})
if (i + 1) % 20 == 0:
print(f" {i+1}/{len(items)}", flush=True)
summary = {t: {"ok": v["ok"], "n": v["n"], "rate": round(v["ok"]/v["n"], 3),
"trunc": round(v["trunc"]/v["n"], 2)} for t, v in sorted(agg.items())}
tot_ok = sum(v["ok"] for v in agg.values()); tot_n = sum(v["n"] for v in agg.values())
os.makedirs(os.path.dirname(a.out), exist_ok=True)
json.dump({"ckpt": a.ckpt, "ctx": ctx, "overall": {"ok": tot_ok, "n": tot_n},
"by_type": summary, "rows": rows}, open(a.out, "w"), indent=1)
print(f"\nOVERALL {tot_ok}/{tot_n}")
for t, v in summary.items():
print(f" {t:26s} {v['ok']:3d}/{v['n']:<3d} = {100*v['rate']:5.1f}% trunc={v['trunc']}")
print("->", a.out)
if __name__ == "__main__":
main()
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