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5792063 | 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 | # Board-native eval (protokol fabryka tiny_ml) na GPT (Qwen3-arch v1/v2). Hart N-02.
# BLiMP/ARC: no-BOS, raw-acc argmax, sliding-window @block_size, lm_eval pinned-rev.
# WikiText: byte_perplexity/BPB (parquet doc-level, fabryka loglikelihood_rolling).
# eff: tiny_ml_suite formula (BLiMP/ARC/normWiki x sizeMult).
import sys, os, json, argparse, math, re
from collections import namedtuple
import torch
from types import SimpleNamespace
from huggingface_hub import hf_hub_download
from tokenizers import Tokenizer
import lm_eval
from lm_eval.api.model import LM
R = "SlayerLab/gollem-v5-ckpts"
def load_model(ckpt_rel, device):
mdef = hf_hub_download(R, "train_gpt_ref.py")
sys.path.insert(0, os.path.dirname(mdef))
from train_gpt_ref import GPT
tokp = hf_hub_download(R, "tokenizer.json")
ck = hf_hub_download(R, ckpt_rel) if not os.path.exists(ckpt_rel) else ckpt_rel
d = torch.load(ck, map_location="cpu")
c = d["config"]
cfg = SimpleNamespace(**c)
m = GPT(c["vocab"], c["n_layer"], c["n_embd"], c["n_head"], c["block"], cfg)
miss, unexp = m.load_state_dict(d["model"], strict=False)
assert not unexp and miss in ([], ["head.weight"]), (miss, unexp)
m.eval().to(device)
n = sum(p.numel() for p in m.parameters())
return m, Tokenizer.from_file(tokp), c, n
class FabrykaLM(LM):
def __init__(self, model, tok, block, device, batch=256):
super().__init__()
self.model = model
self.tok = tok
self.context_length = block
self.dev = device
self.batch = batch
self.truncated = 0
self.total = 0
def _score_many(self, pairs):
totals = [0.0] * len(pairs)
greedy = [True] * len(pairs)
buckets = {}
for req, (ctx, cont) in enumerate(pairs):
prefix = self.tok.encode(ctx).ids or [self.tok.encode(" ").ids[0]]
target = self.tok.encode(cont).ids
self.total += 1
if len(prefix) + len(target) - 1 > self.context_length:
self.truncated += 1
tokens = prefix + target
for index, token in enumerate(target, len(prefix)):
window = tokens[max(0, index - self.context_length):index]
buckets.setdefault(len(window), []).append((req, window, token))
with torch.inference_mode():
for rows in buckets.values():
for s in range(0, len(rows), self.batch):
chunk = rows[s:s + self.batch]
x = torch.tensor([it[1] for it in chunk], dtype=torch.long, device=self.dev)
y = torch.tensor([it[2] for it in chunk], dtype=torch.long, device=self.dev)
logits = self.model(x)[0][:, -1, :].log_softmax(-1)
vals = logits.gather(1, y[:, None]).flatten().tolist()
gs = logits.argmax(-1).eq(y).tolist()
for (req, _, _), v, g in zip(chunk, vals, gs):
totals[req] += v
greedy[req] = greedy[req] and g
return list(zip(totals, greedy))
def loglikelihood(self, requests):
return self._score_many([tuple(r.args) for r in requests])
def loglikelihood_rolling(self, requests):
return [p[0] for p in self._score_many([("", r.args[0]) for r in requests])]
def generate_until(self, requests):
raise NotImplementedError
def wikitext_detokenizer(string):
string = string.replace("s '", "s'")
string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string)
string = string.replace(" @-@ ", "-").replace(" @,@ ", ",").replace(" @.@ ", ".")
string = string.replace(" : ", ": ").replace(" ; ", "; ").replace(" . ", ". ")
string = string.replace(" ! ", "! ").replace(" ? ", "? ").replace(" , ", ", ")
string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string)
string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string)
string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string)
string = re.sub(r'"\s*([^"]*?)\s*"', r'"\1"', string)
string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string)
string = string.replace("= = = =", "====").replace("= = =", "===").replace("= =", "==")
string = string.replace(" " + chr(176) + " ", chr(176))
string = string.replace(" \n", "\n").replace("\n ", "\n")
string = string.replace(" N ", " 1 ").replace(" 's", "'s")
return string
def _wikitext_pages(parquet_path):
import pyarrow.parquet as pq
texts = pq.read_table(parquet_path).column("text").to_pylist()
pages, cur = [], []
for s in texts:
if re.match(r"^ = [^=]", s):
if cur:
pages.append("".join(cur)); cur = []
cur.append(s)
if cur:
pages.append("".join(cur))
return [p for p in pages if p.strip()]
def compute_wikitext(lm, parquet_path, limit=None):
# Efficient teacher-forced reset-chunk BPB (block-boundary reset, Glint-1.3 style;
# ~block-faster than per-token sliding, byte_ppl diff <1% for block=1024).
pages = _wikitext_pages(parquet_path)
if limit:
pages = pages[:int(limit)]
block = lm.context_length
sum_nll = 0.0
sum_bytes = 0
sum_words = 0
with torch.inference_mode():
for p in pages:
det = wikitext_detokenizer(p)
ids = lm.tok.encode(det).ids
sum_bytes += len(p.encode("utf-8"))
sum_words += len(re.split(r"\s+", p))
for i in range(0, len(ids) - 1, block):
chunk = ids[i:i + block + 1]
if len(chunk) < 2:
continue
x = torch.tensor([chunk[:-1]], dtype=torch.long, device=lm.dev)
y = torch.tensor([chunk[1:]], dtype=torch.long, device=lm.dev)
logits = lm.model(x)[0]
nll = torch.nn.functional.cross_entropy(
logits.view(-1, logits.size(-1)), y.view(-1), reduction="sum")
sum_nll += nll.item()
return {"byte_perplexity": math.exp(sum_nll / sum_bytes),
"bits_per_byte": sum_nll / (sum_bytes * math.log(2)),
"word_perplexity": math.exp(sum_nll / sum_words),
"n_pages": len(pages), "n_bytes": sum_bytes,
"method": f"teacher-forced reset-chunk block={block}"}
def eff_score(blimp, arc, wiki_byte_ppl, params):
wiki_score = 100 * max(0, min(1, 1 - math.log(min(wiki_byte_ppl, 500) / 1.86) / math.log(500 / 1.86)))
overall = (100 * blimp + 100 * arc + wiki_score) / 3
size_pos = math.log(150_000_000 / params) / math.log(150_000_000 / 1000)
mult = 1 + 0.5 * max(0, min(1, size_pos))
return {"wiki_score": wiki_score, "overall": overall, "size_multiplier": mult,
"efficiency": overall * mult}
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--ckpt", required=True)
ap.add_argument("--blimp-limit", type=int, default=None)
ap.add_argument("--wikitext-parquet", default="/root/Salesforce_wt2_test.parquet")
ap.add_argument("--batch", type=int, default=256)
ap.add_argument("--wiki-only", action="store_true")
ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
ap.add_argument("--out", default=None)
a = ap.parse_args()
m, tok, c, nparams = load_model(a.ckpt, a.device)
print(f"[load] {a.ckpt} params={nparams} config={c} device={a.device}", flush=True)
lm = FabrykaLM(m, tok, c["block"], a.device, batch=a.batch)
if a.wiki_only:
wiki = compute_wikitext(lm, a.wikitext_parquet)
out = {"ckpt": a.ckpt, "params": nparams, "wikitext": wiki}
print("[RESULT]", json.dumps(out), flush=True)
if a.out:
json.dump(out, open(a.out, "w"), indent=2)
return
g = lm._score_many([("", "The cat is sleeping"), ("", "The cat are sleeping")])
ok = g[0][0] > g[1][0]
print(f"[sanity] prefers_grammatical={ok}", flush=True)
seeds = dict(num_fewshot=0, bootstrap_iters=0, random_seed=42, numpy_random_seed=42,
torch_random_seed=42, fewshot_random_seed=42)
res_b = lm_eval.simple_evaluate(model=lm, tasks=["blimp"], limit=a.blimp_limit, **seeds)
res_a = lm_eval.simple_evaluate(model=lm, tasks=["arc_easy"], limit=None, **seeds)
rb = res_b["results"]
r = res_a["results"]
bl = [k for k in rb if k.startswith("blimp_")]
blimp = sum(rb[k]["acc,none"] for k in bl) / len(bl) if bl else rb.get("blimp", {}).get("acc,none")
arc = r["arc_easy"]["acc,none"]
wiki = None
if a.wikitext_parquet and os.path.exists(a.wikitext_parquet):
wiki = compute_wikitext(lm, a.wikitext_parquet)
out = {"ckpt": a.ckpt, "params": nparams, "blimp": blimp, "blimp_leaves": len(bl),
"arc_easy_raw_acc": arc, "arc_easy_acc_norm": r["arc_easy"].get("acc_norm,none"),
"wikitext": wiki, "sanity_minpair": ok, "truncated": lm.truncated,
"blimp_limit": a.blimp_limit}
if wiki:
out["eff"] = eff_score(blimp, arc, wiki["byte_perplexity"], nparams)
print("[RESULT]", json.dumps(out), flush=True)
if a.out:
json.dump(out, open(a.out, "w"), indent=2)
if __name__ == "__main__":
main()
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