File size: 3,445 Bytes
3ca4001 | 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 | # /// script
# requires-python = ">=3.10"
# dependencies = [
# "torch",
# "transformers[torch]",
# "huggingface_hub",
# "bitsandbytes",
# "accelerate",
# ]
# ///
"""Print FULL replies + extracted blocks for a few code prompts.
The eval report only stores reply_first_160, which hides why v2 scored 0% ast_rate
while the base model scored 93.3%. This dumps everything so we can tell whether the
model emits broken code or the extractor mis-parses a well-formed reply.
"""
import json
import os
import re
import sys
import torch
from transformers import (AutoModelForCausalLM, AutoTokenizer,
BitsAndBytesConfig)
REPO = sys.argv[1] if len(sys.argv) > 1 else "Taimwe/securecoder-30b-pro-v2-merged"
MAXNEW = 1024
PROMPTS = [
"Write a Python function `def is_palindrome(s: str) -> bool:` that checks whether a string reads the same forwards and backwards, ignoring case and non-alphanumeric characters.",
"Write a Python function `def two_sum(nums: list[int], target: int) -> list[int]:` returning the indices of the two numbers adding to target, or an empty list.",
"Write a Python function `def flatten(nested: list) -> list:` that flattens an arbitrarily nested list.",
]
token = os.environ.get("HF_TOKEN")
tok = AutoTokenizer.from_pretrained(REPO, token=token)
model = AutoModelForCausalLM.from_pretrained(
REPO, token=token,
quantization_config=BitsAndBytesConfig(
load_in_4bit=True, bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True),
dtype=torch.bfloat16, device_map="auto")
out = []
for i, p in enumerate(PROMPTS, 1):
text = tok.apply_chat_template([{"role": "user", "content": p}],
tokenize=False, add_generation_prompt=True)
ids = tok(text, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
with torch.no_grad():
gen = model.generate(ids, max_new_tokens=MAXNEW, do_sample=False,
eos_token_id=tok.eos_token_id, pad_token_id=tok.eos_token_id)
new_ids = gen[0, ids.shape[-1]:]
reply = tok.decode(new_ids, skip_special_tokens=True)
# exactly the eval harness's extraction
blocks = re.findall(r"```(?:python)?\s*\n(.*?)```", reply, re.S)
block = max(blocks, key=len) if blocks else None
if block is None:
s = reply.find("def ")
block = reply[s:] if s >= 0 else None
if block:
block = re.split(r"\n```", block, maxsplit=1)[0]
err = None
if block:
try:
compile(block, "<t>", "exec")
except Exception as exc:
err = "%s: %s" % (type(exc).__name__, exc)
out.append({"prompt": p[:80], "n_new_tokens": int(len(new_ids)),
"n_fenced_blocks": len(blocks),
"block_len": len(block) if block else 0,
"compile_error": err, "full_reply": reply})
print("\n" + "=" * 74)
print("PROMPT %d new_tokens=%d fenced_blocks=%d block_len=%s"
% (i, len(new_ids), len(blocks), len(block) if block else 0))
print("COMPILE:", err or "OK")
print("-" * 74)
print(reply[:2500])
dest = sys.argv[2] if len(sys.argv) > 2 else "/data/dbg.json"
with open(dest, "w", encoding="utf-8") as fh:
json.dump(out, fh, indent=2, default=str)
print("\nWROTE", dest) |