File size: 4,572 Bytes
6920ebd | 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 | import json
import multiprocessing
import os
import re
import sys
import time
os.environ["CUDA_HOME"] = "/usr/local/cuda-13.0"
os.environ["PATH"] = f"/usr/local/cuda-13.0/bin:{os.environ.get('PATH', '')}"
MODEL_DIR = "/home/user/models/DeepSeek-V4-Flash"
sys.path.insert(0, os.path.join(MODEL_DIR, "encoding"))
from encoding_dsv4 import encode_messages
from datasets import load_dataset
from vllm import LLM, SamplingParams
OUTPUT_FILE = sys.argv[1] if len(sys.argv) > 1 else "/home/user/humaneval_results_deepseek_v4.jsonl"
THINKING_MODE = "thinking"
def check_correctness(full_code: str, timeout: int = 15):
def run_test(result_dict):
try:
exec_globals = {}
exec(full_code, exec_globals)
result_dict["passed"] = True
except Exception:
result_dict["passed"] = False
manager = multiprocessing.Manager()
result_dict = manager.dict()
proc = multiprocessing.Process(target=run_test, args=(result_dict,))
proc.start()
proc.join(timeout)
if proc.is_alive():
proc.kill()
manager.shutdown()
return False
passed = result_dict.get("passed", False)
manager.shutdown()
return passed
def extract_code_block(text: str) -> str | None:
blocks = re.findall(r"```(?:python)?\s*\n(.*?)```", text, re.DOTALL)
if blocks:
for b in blocks:
b = b.strip()
if "def " in b or "import " in b or b.startswith("from "):
return b
return blocks[-1].strip()
return None
def main():
print("Loading HumanEval dataset...")
ds = load_dataset("openai_humaneval", split="test")
print(f"Loaded {len(ds)} problems")
print("Loading model with vLLM...")
llm = LLM(
model=MODEL_DIR,
tensor_parallel_size=1,
dtype="auto",
kv_cache_dtype="fp8",
max_model_len=32768,
trust_remote_code=True,
)
sampling_params = SamplingParams(
temperature=0.0,
top_p=0.95,
max_tokens=4096,
stop=["<｜end▁of▁sentence｜>"],
)
formatted_prompts = []
metadata = []
for example in ds:
prompt = example["prompt"]
messages = [
{
"role": "user",
"content": f"Write a Python function to solve the following problem:\n\n{prompt}",
},
]
formatted = encode_messages(messages, thinking_mode=THINKING_MODE)
formatted_prompts.append(formatted)
metadata.append({
"task_id": example["task_id"],
"entry_point": example["entry_point"],
"test": example["test"],
"prompt": example["prompt"],
})
print(f"Sample prompt: {formatted_prompts[0][:200]}...")
print(f"Generating completions for {len(formatted_prompts)} problems...")
start = time.time()
outputs = llm.generate(formatted_prompts, sampling_params)
elapsed = time.time() - start
print(f"Generation completed in {elapsed:.2f}s ({elapsed / len(outputs):.2f}s per sample)")
results = []
for out, meta in zip(outputs, metadata):
raw = out.outputs[0].text.strip()
if THINKING_MODE == "thinking":
end_idx = raw.find("</think>")
if end_idx != -1:
code_text = raw[end_idx + len("</think>"):].strip()
else:
code_text = raw
else:
code_text = raw
code = extract_code_block(code_text)
if not code:
code = code_text
code = re.sub(r"^```(?:python)?\s*", "", code)
code = re.sub(r"\s*```$", "", code)
code = code.strip()
results.append({
"task_id": meta["task_id"],
"entry_point": meta["entry_point"],
"prompt": meta["prompt"],
"test": meta["test"],
"generation": code,
"raw_output": raw,
})
with open(OUTPUT_FILE, "w") as f:
for r in results:
f.write(json.dumps(r) + "\n")
print(f"Results saved to {OUTPUT_FILE}")
passed = 0
for r in results:
gen = r["generation"]
if f"def {r['entry_point']}" in gen:
full_code = f"{gen}\n{r['test']}\ncheck({r['entry_point']})"
else:
full_code = f"{r['prompt']}\n{gen}\n{r['test']}\ncheck({r['entry_point']})"
if check_correctness(full_code):
passed += 1
total = len(results)
print(f"\nPass@1: {passed}/{total} = {passed / total * 100:.2f}%")
if __name__ == "__main__":
main()
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