File size: 3,828 Bytes
5760ae6 | 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 | import argparse
import json
import re
import time
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
DEFAULT_SYSTEM = "You are a helpful assistant."
CLOSERS = ("</think>", "<|end_of_thinking|>", "<|/think|>")
def strip_thinking(text):
best = -1
for marker in CLOSERS:
i = text.rfind(marker)
if i != -1:
best = max(best, i + len(marker))
return text[best:] if best != -1 else text
def load_tasks(path):
tasks = []
with open(path, encoding="utf-8") as fh:
for line in fh:
line = line.strip()
if line:
tasks.append(json.loads(line))
return tasks
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--model", required=True)
ap.add_argument("--adapter", default=None)
ap.add_argument("--tasks", default="data/tasks_public.jsonl")
ap.add_argument("--system", default=DEFAULT_SYSTEM)
ap.add_argument("--effort", default=None)
ap.add_argument("--max-new", type=int, default=384)
ap.add_argument("--batch", type=int, default=8)
ap.add_argument("--limit", type=int, default=0)
ap.add_argument("--device", default="auto", choices=["auto", "cuda", "cpu"])
ap.add_argument("--dtype", default="float16", choices=["float16", "bfloat16", "float32"])
args = ap.parse_args()
device = args.device
if device == "auto":
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16, "float32": torch.float32}[args.dtype]
if device == "cpu":
dtype = torch.float32
tok = AutoTokenizer.from_pretrained(args.model)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
tok.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(args.model, dtype=dtype, device_map=device)
if args.adapter:
from peft import PeftModel
model = PeftModel.from_pretrained(model, args.adapter)
model.eval()
tasks = load_tasks(args.tasks)
if args.limit:
tasks = tasks[: args.limit]
prompts = []
for t in tasks:
user = t["q"].rstrip()
if args.effort:
user += "\n<|effort={}|>".format(args.effort)
messages = [{"role": "system", "content": args.system}, {"role": "user", "content": user}]
prompts.append(tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True))
hits = {"__all__": [0, 0]}
by_domain, by_lang = {}, {}
started = time.time()
for start in range(0, len(prompts), args.batch):
chunk = prompts[start:start + args.batch]
enc = tok(chunk, return_tensors="pt", padding=True, add_special_tokens=False).to(model.device)
with torch.no_grad():
out = model.generate(**enc, max_new_tokens=args.max_new, do_sample=False,
pad_token_id=tok.pad_token_id)
gen = out[:, enc["input_ids"].shape[1]:]
for j in range(len(chunk)):
t = tasks[start + j]
text = strip_thinking(tok.decode(gen[j], skip_special_tokens=True))
ok = bool(re.search(t["re"], text, re.IGNORECASE))
for bucket, key in ((hits, "__all__"), (by_domain, t["domain"]), (by_lang, t["lang"])):
b = bucket.setdefault(key, [0, 0])
b[1] += 1
b[0] += int(ok)
total = hits["__all__"]
print("ABench75 | overall {:.1f}% ({}/{}) | {:.0f}s".format(
100 * total[0] / max(1, total[1]), total[0], total[1], time.time() - started))
for name, bucket in (("domain", by_domain), ("lang", by_lang)):
print(" by {}: ".format(name) + " | ".join(
"{} {:.1f}%".format(k, 100 * v[0] / max(1, v[1])) for k, v in sorted(bucket.items())))
if __name__ == "__main__":
main()
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