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# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "datasets", "transformers", "accelerate", "torch"]
# ///
"""GRPO with g++ compiler reward (online RL). For Hugging Face Jobs (uv)."""
from __future__ import annotations
import os
import re
import shutil
import subprocess
import tempfile
from pathlib import Path
from datasets import load_dataset
from peft import LoraConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import GRPOConfig, GRPOTrainer
DATASET_ID = os.environ.get("DATASET_ID", "gonzalolinares/cpp-compiler-grpo")
SFT_ADAPTER = os.environ.get("BASE_MODEL", "gonzalolinares/qwen25-1.5b-cpp-sft")
DPO_ADAPTER = os.environ.get("DPO_MODEL", "gonzalolinares/qwen25-1.5b-cpp-dpo")
BASE_MODEL = os.environ.get("FALLBACK_MODEL", "Qwen/Qwen2.5-1.5B-Instruct")
HUB_MODEL_ID = os.environ.get("HUB_MODEL_ID", "gonzalolinares/qwen25-1.5b-cpp-grpo")
OUTPUT_DIR = os.environ.get("OUTPUT_DIR", "qwen25-1.5b-cpp-grpo")
CODE_FENCE_RE = re.compile(r"```(?:cpp|c\+\+)?\s*([\s\S]*?)```", re.IGNORECASE)
def ensure_gpp() -> None:
if shutil.which("g++"):
return
print("Installing build-essential for g++...")
subprocess.run(
["bash", "-lc", "apt-get update -qq && apt-get install -y -qq build-essential"],
check=True,
)
if not shutil.which("g++"):
raise RuntimeError("g++ not available after apt install")
def extract_code(text: str) -> str:
m = CODE_FENCE_RE.search(text)
if m:
return m.group(1).strip() + "\n"
lines = text.splitlines()
start = 0
for i, line in enumerate(lines):
if line.lstrip().startswith("#include") or re.match(r"\s*int\s+main\b", line):
start = i
break
return "\n".join(lines[start:]).strip() + "\n"
def judge_code(code: str, expected_stdout: str | None = None) -> float:
code = extract_code(code)
if not code.strip():
return 0.0
with tempfile.TemporaryDirectory(prefix="grpo_judge_") as tmp:
root = Path(tmp)
src = root / "prog.cpp"
bin_path = root / "prog"
src.write_text(code, encoding="utf-8")
try:
cp = subprocess.run(
["g++", "-std=c++20", "-O0", "-Wall", "-o", str(bin_path), str(src)],
capture_output=True,
text=True,
timeout=15.0,
)
except subprocess.TimeoutExpired:
return 0.0
if cp.returncode != 0:
return 0.0
reward = 1.0
if expected_stdout:
try:
rp = subprocess.run(
[str(bin_path)],
capture_output=True,
text=True,
timeout=5.0,
)
if rp.returncode == 0 and (rp.stdout or "") == expected_stdout:
reward += 0.5
else:
reward = max(reward - 0.25, 0.5)
except subprocess.TimeoutExpired:
reward = max(reward - 0.25, 0.5)
return round(reward, 3)
def completion_text(completion) -> str:
if isinstance(completion, list):
if completion and isinstance(completion[-1], dict):
return str(completion[-1].get("content", ""))
return str(completion)
return str(completion)
def compile_reward(
prompts,
completions,
expected_stdout=None,
**kwargs,
) -> list[float]:
rewards: list[float] = []
for i, completion in enumerate(completions):
text = completion_text(completion)
exp = None
if expected_stdout is not None:
exp = expected_stdout[i] if expected_stdout[i] else None
rewards.append(judge_code(text, expected_stdout=exp))
return rewards
def load_policy():
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, torch_dtype="auto")
try:
model = PeftModel.from_pretrained(model, SFT_ADAPTER)
model = model.merge_and_unload()
print(f"Merged SFT adapter from {SFT_ADAPTER}")
except Exception as e:
print(f"SFT merge skipped ({e})")
try:
model = PeftModel.from_pretrained(model, DPO_ADAPTER)
model = model.merge_and_unload()
print(f"Merged DPO adapter from {DPO_ADAPTER}")
except Exception as e:
print(f"DPO merge skipped ({e})")
return model, tokenizer
def main() -> None:
ensure_gpp()
ds = load_dataset(DATASET_ID, split="train")
if "prompt" not in ds.column_names:
raise SystemExit(f"Dataset needs 'prompt' column; got {ds.column_names}")
model, tokenizer = load_policy()
trainer = GRPOTrainer(
model=model,
processing_class=tokenizer,
reward_funcs=[compile_reward],
train_dataset=ds,
peft_config=LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
),
args=GRPOConfig(
output_dir=OUTPUT_DIR,
num_train_epochs=1,
per_device_train_batch_size=1,
gradient_accumulation_steps=4,
num_generations=4,
max_completion_length=512,
learning_rate=5e-6,
logging_steps=5,
save_strategy="steps",
save_steps=50,
save_total_limit=1,
temperature=0.7,
bf16=True,
remove_unused_columns=False,
push_to_hub=False,
hub_model_id=HUB_MODEL_ID,
report_to="none",
),
)
trainer.train()
trainer.model.push_to_hub(HUB_MODEL_ID, private=False)
tokenizer.push_to_hub(HUB_MODEL_ID, private=False)
print(f"Pushed GRPO model to {HUB_MODEL_ID}")
if __name__ == "__main__":
main()
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