"""Self-correction recipe for CODE. Same pattern as math sc_v2 (which gave +5 recovery). Pipeline: 1. MBPP-train problems (374 sanitized + extended). 2. Greedy attempt. If passes → save as right→stays-right positive. 3. If fails → prompt with "Wait, let me reconsider" + sample 4 at temp=0.8. If any pass → mine (problem, wrong, reflection, correct) self-correction trace. 4. Train on mixed dataset. 5. Eval HE + MBPP. Mix teaches model: commit to right answers, fix wrong ones. """ import os, json, time, re, subprocess, tempfile, argparse, gc, random os.environ.setdefault("HF_HOME", "/workspace/hf") os.environ["TRANSFORMERS_VERBOSITY"] = "error" import torch from datasets import load_dataset T0 = time.time() def log(m): print(f"[{time.time()-T0:7.1f}s] {m}", flush=True) RECONSIDER_TAG = "\n\n# Wait — that doesn't look right. Let me reconsider:\n\n" def run_python(code, timeout=8): with tempfile.NamedTemporaryFile("w", suffix=".py", delete=False) as f: f.write(code); path = f.name try: r = subprocess.run(["python3", path], capture_output=True, timeout=timeout, text=True, cwd="/tmp") return r.returncode == 0 except subprocess.TimeoutExpired: return False finally: try: os.unlink(path) except: pass def vllm_gen(llm, prompts, max_new=400, temperature=0.0, n=1, prefill_texts=None): from vllm import SamplingParams sp = SamplingParams(temperature=temperature, top_p=0.95 if temperature > 0 else 1.0, max_tokens=max_new, n=n, stop=["\nclass Test", "\nif __name__", "\n\nprint", "\nassert "]) if prefill_texts is None: out = llm.generate(prompts, sp, use_tqdm=False) else: # Each prompt is concatenated with prefill text full_prompts = [p + pre for p, pre in zip(prompts, prefill_texts)] out = llm.generate(full_prompts, sp, use_tqdm=False) if n == 1: return [o.outputs[0].text for o in out] return [[c.text for c in o.outputs] for o in out] def he_prompt(p): return p["prompt"] def mbpp_prompt(p): return f"# Task: {p['prompt']}\n# Tests:\n# " + "\n# ".join(p["test_list"]) + "\n\n" def main(): ap = argparse.ArgumentParser() ap.add_argument("--model", required=True) ap.add_argument("--n_mining", type=int, default=300) ap.add_argument("--max_self_corrections", type=int, default=80) ap.add_argument("--max_positives", type=int, default=80) ap.add_argument("--tag", required=True) args = ap.parse_args() out_dir = f"/workspace/code_sc/{args.tag}" os.makedirs(out_dir, exist_ok=True) random.seed(42) from vllm import LLM from transformers import AutoTokenizer log(f"loading {args.model} into vLLM") tok = AutoTokenizer.from_pretrained(args.model) if tok.pad_token is None: tok.pad_token = tok.eos_token llm = LLM(model=args.model, dtype="bfloat16", gpu_memory_utilization=0.85, max_model_len=2048) log(f" loaded") he = list(load_dataset("openai_humaneval", split="test")) mbpp_test = list(load_dataset("mbpp", "sanitized", split="test"))[:100] mbpp_full = list(load_dataset("mbpp", split="train")) random.shuffle(mbpp_full) seeds = [] for p in mbpp_full[:args.n_mining]: prompt_text = p.get("prompt") or p.get("text", "") if prompt_text and p.get("test_list"): seeds.append({"prompt": prompt_text, "test_list": p["test_list"]}) log(f" HE: {len(he)}, MBPP-test: {len(mbpp_test)}, mining seeds: {len(seeds)}") # --- BASE eval log("=== BASE eval ===") he_outs = vllm_gen(llm, [he_prompt(p) for p in he], max_new=400) base_he = sum(1 for p, raw in zip(he, he_outs) if run_python(p["prompt"] + raw + "\n\n" + p["test"] + f"\n\ncheck({p['entry_point']})", 10)) log(f" HE base: {base_he}/{len(he)}") mbpp_outs = vllm_gen(llm, [mbpp_prompt(p) for p in mbpp_test], max_new=400) base_mbpp = sum(1 for p, raw in zip(mbpp_test, mbpp_outs) if run_python(raw + "\n\n" + "\n".join(p["test_list"]), 10)) log(f" MBPP base: {base_mbpp}/{len(mbpp_test)}") # --- Mine: greedy on all seeds log(f"=== mining: greedy attempt on {len(seeds)} seeds ===") t0 = time.time() greedy_outs = vllm_gen(llm, [mbpp_prompt(p) for p in seeds], max_new=400) log(f" greedy gen in {time.time()-t0:.1f}s") t1 = time.time() right = [] # greedy correct (positives) wrong = [] # greedy wrong (candidates for self-correction) for p, raw in zip(seeds, greedy_outs): test_code = raw + "\n\n" + "\n".join(p["test_list"]) if run_python(test_code, timeout=8): right.append({"problem": p["prompt"], "tests": p["test_list"], "solution": raw.strip()}) else: wrong.append({"problem": p["prompt"], "tests": p["test_list"], "wrong": raw.strip()}) log(f" verify: {len(right)} greedy-correct, {len(wrong)} hard") # --- For wrong: prefill wrong + reconsider tag, sample 4 attempts log(f"=== self-correction sampling on {len(wrong)} hard problems ===") sc_pairs = [] if wrong: base_prompts = [mbpp_prompt({"prompt": w["problem"], "test_list": w["tests"]}) for w in wrong] prefills = [w["wrong"] + RECONSIDER_TAG for w in wrong] # Generate 4 attempts each via temperature t0 = time.time() sc_outs = vllm_gen(llm, base_prompts, max_new=400, temperature=0.8, n=4, prefill_texts=prefills) log(f" sc gen in {time.time()-t0:.1f}s") t1 = time.time() for w, attempts in zip(wrong, sc_outs): for a in attempts: test_code = a + "\n\n" + "\n".join(w["tests"]) if run_python(test_code, timeout=8): full_trace = w["wrong"] + RECONSIDER_TAG + a.strip() sc_pairs.append({"problem": w["problem"], "tests": w["tests"], "full_trace": full_trace}) break # one per problem log(f" sc verify in {time.time()-t1:.1f}s — {len(sc_pairs)} self-correction traces") # Cap and sample random.shuffle(right); random.shuffle(sc_pairs) right = right[:args.max_positives] sc_pairs = sc_pairs[:args.max_self_corrections] log(f"=== final: {len(sc_pairs)} self-correction + {len(right)} right→stays-right = {len(sc_pairs)+len(right)} examples ===") if len(sc_pairs) + len(right) < 10: log("too few examples — exiting"); return with open(f"{out_dir}/sc_pairs.jsonl", "w") as fh: for r in sc_pairs: fh.write(json.dumps(r) + "\n") with open(f"{out_dir}/positives.jsonl", "w") as fh: for r in right: fh.write(json.dumps(r) + "\n") # --- Train LoRA on MIXED dataset log("=== TRAINING ===") del llm; gc.collect(); torch.cuda.empty_cache() from transformers import AutoModelForCausalLM, TrainingArguments, Trainer from datasets import Dataset as HFDataset from peft import LoraConfig, get_peft_model train_examples = [] for r in sc_pairs: train_examples.append({"problem": r["problem"], "tests": r["tests"], "target": r["full_trace"]}) for r in right: train_examples.append({"problem": r["problem"], "tests": r["tests"], "target": r["solution"]}) random.shuffle(train_examples) def mk_ex(r): user = f"# Task: {r['problem']}\n# Tests:\n# " + "\n# ".join(r['tests']) + "\n\n" target = r["target"] full = user + target full_ids = tok(full, add_special_tokens=False)["input_ids"] user_ids = tok(user, add_special_tokens=False)["input_ids"] MAX = 1280 full_ids = full_ids[:MAX] labels = list(full_ids) n_user = min(len(user_ids), len(labels)) for i in range(n_user): labels[i] = -100 pad = MAX - len(full_ids) return {"input_ids": full_ids + [tok.pad_token_id]*pad, "attention_mask": [1]*len(full_ids) + [0]*pad, "labels": labels + [-100]*pad} model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=torch.bfloat16, device_map="cuda:0") lora_cfg = LoraConfig(r=16, lora_alpha=32, lora_dropout=0.05, bias="none", target_modules=["q_proj", "k_proj", "v_proj", "o_proj"], task_type="CAUSAL_LM") model = get_peft_model(model, lora_cfg) ds_train = HFDataset.from_list([mk_ex(r) for r in train_examples]) targs = TrainingArguments( output_dir=f"{out_dir}/ckpt", num_train_epochs=2, per_device_train_batch_size=1, gradient_accumulation_steps=4, learning_rate=1e-4, bf16=True, logging_steps=20, save_strategy="no", report_to="none", remove_unused_columns=False, warmup_ratio=0.05, ) Trainer(model=model, args=targs, train_dataset=ds_train, tokenizer=tok).train() log("training done") adapter_dir = f"{out_dir}/adapter" model.save_pretrained(adapter_dir) del model; gc.collect(); torch.cuda.empty_cache() # --- TRAINED eval from vllm import LLM from vllm.lora.request import LoRARequest llm = LLM(model=args.model, dtype="bfloat16", gpu_memory_utilization=0.85, max_model_len=2048, enable_lora=True, max_lora_rank=16) lora_req = LoRARequest("tf_adapter", 1, adapter_dir) from vllm import SamplingParams sp = SamplingParams(temperature=0, max_tokens=500, stop=["\nclass Test", "\nif __name__"]) log("=== TRAINED eval ===") he_outs = [o.outputs[0].text for o in llm.generate([he_prompt(p) for p in he], sp, lora_request=lora_req, use_tqdm=False)] tr_he = sum(1 for p, raw in zip(he, he_outs) if run_python(p["prompt"] + raw + "\n\n" + p["test"] + f"\n\ncheck({p['entry_point']})", 10)) mbpp_outs = [o.outputs[0].text for o in llm.generate([mbpp_prompt(p) for p in mbpp_test], sp, lora_request=lora_req, use_tqdm=False)] tr_mbpp = sum(1 for p, raw in zip(mbpp_test, mbpp_outs) if run_python(raw + "\n\n" + "\n".join(p["test_list"]), 10)) result = { "model": args.model, "n_sc": len(sc_pairs), "n_positives": len(right), "n_total": len(train_examples), "humaneval": {"base": base_he, "trained": tr_he, "delta": tr_he-base_he, "n": len(he)}, "mbpp": {"base": base_mbpp, "trained": tr_mbpp, "delta": tr_mbpp-base_mbpp, "n": len(mbpp_test)}, "elapsed_s": time.time()-T0, } with open(f"{out_dir}/result.json", "w") as fh: json.dump(result, fh, indent=2) print() print("=" * 70) print(f" {args.model} — CODE SELF-CORRECTION ({len(sc_pairs)} sc + {len(right)} positives)") print(f" HumanEval: base={base_he}/{len(he)} trained={tr_he}/{len(he)} Δ={tr_he-base_he:+d}") print(f" MBPP: base={base_mbpp}/{len(mbpp_test)} trained={tr_mbpp}/{len(mbpp_test)} Δ={tr_mbpp-base_mbpp:+d}") print(f" Time: {time.time()-T0:.0f}s") print("=" * 70) if __name__ == "__main__": main()