File size: 8,981 Bytes
67ebcef | 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 | """Commit-on-consensus (CoC) generation: an inference-time halting mechanism.
The reasoning is generated in chunks (--chunk tokens, KV cache of the slot is reused). After every chunk, while the
model is still thinking, a probe closes the thinking at the last sentence end (the model's own closing pattern:
"...." + </think>) and lets the model write its final answer greedily on a separate slot. When two consecutive probes
give the same extracted answer, the reasoning is committed there: the output becomes reasoning[:cut] + </think> +
that probe's answer. Code tasks are never probed (no cheap answer equivalence).
--no-probe runs the identical chunked generation without probes: the control for measuring CoC's own contribution
(both runs are token-for-token identical until CoC commits).
Output records have the same format as gen.py, plus "coc_cut" (None if not committed).
usage: gen_coc.py --inp data/quick/mini.jsonl --out results/coc/mini.jsonl --max-tokens 6144 [--no-probe]
"""
import argparse
import json
import os
import queue
import re
import sys
import threading
import time
import urllib.request
import zlib
from concurrent.futures import ThreadPoolExecutor
from transformers import AutoTokenizer
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from grade import last_boxed # noqa: E402
ap = argparse.ArgumentParser()
ap.add_argument("--inp", nargs="+", required=True)
ap.add_argument("--out", required=True)
ap.add_argument("--max-tokens", type=int, default=16384)
ap.add_argument("--chunk", type=int, default=1024)
ap.add_argument("--agree", type=int, default=2, help="consecutive identical probe answers needed to commit")
ap.add_argument("--probe-tokens", type=int, default=400)
ap.add_argument("--no-probe", action="store_true")
ap.add_argument("--record-all", action="store_true",
help="shadow mode that never stops probing: stores every probe (cut, answer, text) for offline "
"simulation of other chunk/agreement settings")
ap.add_argument("--shadow", action="store_true",
help="on consensus, record the committed answer but keep generating to the end: one run yields both "
"outcomes on the same trajectory (exact pairing) and also training data")
ap.add_argument("--workers", type=int, default=24, help="main slots 0..W-1, probe slots W..2W-1 (server -np 2W)")
ap.add_argument("--temperature", type=float, default=0.6)
ap.add_argument("--top-p", type=float, default=0.95)
ap.add_argument("--top-k", type=int, default=50)
ap.add_argument("--seed", type=int, default=0)
ap.add_argument("--port", type=int, default=8080)
ap.add_argument("--tokenizer", default="models/LFM3-1.7B")
args = ap.parse_args()
THINK_END, EOS = 124902, 124900
tok = AutoTokenizer.from_pretrained(args.tokenizer)
ENDS = set()
for i_ in range(len(tok)):
t_ = tok.convert_ids_to_tokens(i_)
if t_ is not None and (t_.endswith(".") or t_.endswith("\u010a")):
ENDS.add(i_)
items = []
for path in args.inp:
items += [json.loads(l) for l in open(path, encoding="utf-8")]
done = set()
if os.path.exists(args.out):
done = {json.loads(l)["id"] for l in open(args.out, encoding="utf-8")}
items = [it for it in items if it["id"] not in done]
print(len(items), "items", flush=True)
os.makedirs(os.path.dirname(args.out) or ".", exist_ok=True)
out_f = open(args.out, "a", encoding="utf-8")
lock = threading.Lock()
slots = queue.Queue()
for w in range(args.workers):
slots.put(w)
stats = {"n": 0, "commit": 0, "t0": time.time(), "tok": 0}
def post(body):
for attempt in range(5):
try:
req = urllib.request.Request(f"http://localhost:{args.port}/completion", data=json.dumps(body).encode(),
headers={"Content-Type": "application/json"})
return json.loads(urllib.request.urlopen(req, timeout=3600).read())
except Exception:
if attempt == 4:
raise
time.sleep(2)
def extract(task, text):
"""answer identity used for the consensus test"""
if task in ("mmlupro",):
m = re.findall(r"answer is:?\s*\(?\**\s*([A-J])\b", text) or re.findall(r"\\boxed\{\(?([A-J])\)?\}", text)
return m[-1] if m else None
if task == "bbh":
m = re.findall(r"answer is:?\s*(.+)", text, re.I)
a = m[-1] if m else (last_boxed(text) or "")
a = re.sub(r"\\boxed\{(.*)\}", r"\1", a).strip().strip("*$ .").lower()
return a or None
b = last_boxed(text)
if b is None and task == "gsm8k":
nums = re.findall(r"-?\d[\d,]*\.?\d*", text)
b = nums[-1] if nums else None
return re.sub(r"\s+", "", b) if b else None
def snap(gen, lo):
for j in range(len(gen), lo, -1):
if gen[j - 1] in ENDS:
return j
return None
def run(it):
w = slots.get()
try:
ids = tok.apply_chat_template([{"role": "user", "content": it["prompt"]}], tokenize=True,
add_generation_prompt=True, return_dict=False)
task = it["task"]
probe = not args.no_probe and not task.startswith("code") and task not in ("humaneval", "mbpp", "ifeval")
base_seed = args.seed * 1000003 + zlib.crc32(it["id"].encode()) % 1000003 * 7
gen, stop, last_ans, prev_cut, commit, n_chunk, n_probe = [], None, None, 0, None, 0, 0
plog = []
while True:
n_pred = min(args.chunk, args.max_tokens - len(gen))
if n_pred <= 0:
stop = "limit"
break
res = post({"prompt": ids + gen, "n_predict": n_pred, "temperature": args.temperature,
"top_p": args.top_p, "top_k": args.top_k, "min_p": 0.0, "repeat_penalty": 1.0,
"seed": base_seed + 7919 * n_chunk, "cache_prompt": True, "id_slot": w,
"samplers": ["top_k", "top_p", "temperature"], "return_tokens": True})
n_chunk += 1
gen += res["tokens"]
if res.get("stop_type") == "eos" or (res["tokens"] and res["tokens"][-1] == EOS):
stop = "eos"
break
if not probe or THINK_END in gen:
continue
cut = snap(gen, prev_cut)
if cut is None:
continue
prev_cut = cut
pr = post({"prompt": ids + gen[:cut] + [THINK_END], "n_predict": args.probe_tokens, "temperature": 0.0,
"cache_prompt": False, "id_slot": args.workers + w, "return_tokens": True})
n_probe += 1
ans = extract(task, pr["content"])
if args.record_all:
plog.append({"cut": cut, "ans": ans, "text": pr["content"], "n": len(pr["tokens"])})
continue
if ans is not None and ans == last_ans:
commit = (cut, pr)
if not args.shadow:
break
probe = False
continue
last_ans = ans
shadow = {}
if commit and args.shadow:
cut, pr = commit
shadow = {"coc_completion": "<think>" + tok.decode(gen[:cut]) + "</think>" + pr["content"],
"coc_n_tokens": cut + len(pr["tokens"]), "coc_tokens": gen[:cut] + [THINK_END] + pr["tokens"]}
commit = None
if commit:
cut, pr = commit
text = tok.decode(gen[:cut]) + "</think>" + pr["content"]
n_tok, trunc, stop_type = cut + len(pr["tokens"]), False, "commit"
else:
if gen and gen[-1] == EOS:
gen = gen[:-1]
text = tok.decode(gen)
n_tok, trunc, stop_type = len(gen), stop == "limit", stop
rec = {"id": it["id"], "task": task, "sample": 0, "completion": "<think>" + text, "n_tokens": n_tok,
"truncated": trunc, "stop_type": stop_type, "coc_cut": commit[0] if commit else None,
"probes": n_probe, "gen_tokens": len(gen), **shadow}
if args.record_all:
rec["probe_log"] = plog
if shadow:
rec["coc_cut"] = shadow["coc_n_tokens"]
with lock:
out_f.write(json.dumps(rec, ensure_ascii=False) + "\n")
out_f.flush()
stats["n"] += 1
stats["commit"] += bool(commit) or bool(shadow)
stats["tok"] += len(gen)
if stats["n"] % 20 == 0:
el = time.time() - stats["t0"]
print(f"{stats['n']}/{len(items)} commits {stats['commit']} {stats['tok'] / el:.0f} gen tok/s "
f"{el / 60:.1f} min", flush=True)
finally:
slots.put(w)
with ThreadPoolExecutor(args.workers) as ex:
list(ex.map(run, items))
print("done", stats, flush=True)
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