"""Recursive self-bootstrap: iter1->iter2->iter3. Iter k: - Use model from previous iter (or base for iter 1) - Mine pairs on MBPP-train - Train fresh LoRA from BASE on accumulated pairs - Eval on HE """ 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) def run_python(code, timeout=10): 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 mbpp_prompt(p): return f"# Task: {p['prompt']}\n# Tests:\n# " + "\n# ".join(p["test_list"]) + "\n\n" def he_prompt(p): return p["prompt"] def vllm_gen(llm, prompts, max_new=400, temperature=0.0, n=1, lora_req=None, stops=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=stops or ["\nclass Test", "\nif __name__", "\n\nprint", "\nassert "]) if lora_req: out = llm.generate(prompts, sp, lora_request=lora_req, use_tqdm=False) else: out = llm.generate(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 main(): ap = argparse.ArgumentParser() ap.add_argument("--model", required=True) ap.add_argument("--tag", required=True) ap.add_argument("--out_dir", required=True) ap.add_argument("--n_iters", type=int, default=3) ap.add_argument("--n_mining", type=int, default=200) ap.add_argument("--attempts_per", type=int, default=8) args = ap.parse_args() os.makedirs(args.out_dir, exist_ok=True) from vllm import LLM, SamplingParams from vllm.lora.request import LoRARequest from transformers import AutoTokenizer log(f"loading {args.model}") tok = AutoTokenizer.from_pretrained(args.model) if tok.pad_token is None: tok.pad_token = tok.eos_token he = list(load_dataset("openai_humaneval", split="test")) mbpp_full = list(load_dataset("mbpp", split="train")) random.seed(42); random.shuffle(mbpp_full) seeds_pool = [] for p in mbpp_full[:args.n_mining * args.n_iters]: prompt_text = p.get("prompt") or p.get("text", "") if prompt_text and p.get("test_list"): seeds_pool.append({"prompt": prompt_text, "test_list": p["test_list"]}) log(f"seeds pool: {len(seeds_pool)}") iter_results = [] accumulated_pairs = [] current_adapter = None # path for it in range(1, args.n_iters + 1): log(f"\n========== ITER {it} ==========") # Load model (with current adapter if exists) llm = LLM(model=args.model, dtype="bfloat16", gpu_memory_utilization=0.85, max_model_len=2048, enable_lora=(current_adapter is not None), max_lora_rank=16) lora_req = LoRARequest("cur", 1, current_adapter) if current_adapter else None log(f" loaded {'(with adapter)' if current_adapter else '(base)'}") # Mine pairs using current model seeds = seeds_pool[(it-1)*args.n_mining:it*args.n_mining] log(f" mining from {len(seeds)} new seeds") prompts = [mbpp_prompt(p) for p in seeds] greedy_outs = vllm_gen(llm, prompts, max_new=400, lora_req=lora_req) hard_idx = [] for i, (p, raw) in enumerate(zip(seeds, greedy_outs)): test_code = raw + "\n\n" + "\n".join(p["test_list"]) if not run_python(test_code, 8): hard_idx.append(i) log(f" greedy: {len(seeds)-len(hard_idx)} pass, {len(hard_idx)} hard") if hard_idx: hard_prompts = [mbpp_prompt(seeds[i]) for i in hard_idx] sample_outs = vllm_gen(llm, hard_prompts, max_new=400, temperature=0.8, n=args.attempts_per, lora_req=lora_req) new_pairs = [] for j, i in enumerate(hard_idx): attempts = sample_outs[j] passes = [] for a in attempts: if run_python(a + "\n\n" + "\n".join(seeds[i]["test_list"]), 8): passes.append(a); break if passes: new_pairs.append({"problem": seeds[i]["prompt"], "tests": seeds[i]["test_list"], "broken": greedy_outs[i].strip(), "fixed": passes[0].strip(), "iter": it}) accumulated_pairs.extend(new_pairs) log(f" mined {len(new_pairs)} new pairs (cumulative: {len(accumulated_pairs)})") # Eval current model on HE log(f" eval HE...") he_outs = vllm_gen(llm, [he_prompt(p) for p in he], max_new=400, lora_req=lora_req, stops=["\nclass ", "\nif __name__", "\n\nprint"]) he_correct = 0 for p, raw in zip(he, he_outs): full = p["prompt"] + "\n" + raw test_code = full + "\n\n" + p["test"] + f"\n\ncheck({p['entry_point']})" if run_python(test_code, 10): he_correct += 1 log(f" HE iter{it} (pre-train): {he_correct}/{len(he)}") iter_results.append({"iter": it, "he_pretrain": he_correct, "cumulative_pairs": len(accumulated_pairs)}) # Tear down vLLM, train new adapter on accumulated pairs del llm; gc.collect(); torch.cuda.empty_cache() if len(accumulated_pairs) < 5: log(f" too few pairs to train, skipping iter {it} training") continue from transformers import AutoModelForCausalLM, TrainingArguments, Trainer from datasets import Dataset as HFDataset from peft import LoraConfig, get_peft_model def mk_ex(r): user = (f"# Task: {r['problem']}\n# Tests:\n# " + "\n# ".join(r['tests']) + "\n" f"# My broken attempt:\n{r['broken']}\n# Corrected:\n") target = r["fixed"] full = user + target full_ids = tok(full, add_special_tokens=False)["input_ids"] user_ids = tok(user, add_special_tokens=False)["input_ids"] MAX = 1024 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} log(f" training fresh adapter on {len(accumulated_pairs)} pairs...") 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 accumulated_pairs]) targs = TrainingArguments( output_dir=f"{args.out_dir}/iter{it}_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() adapter_dir = f"{args.out_dir}/iter{it}_adapter" model.save_pretrained(adapter_dir) del model; gc.collect(); torch.cuda.empty_cache() current_adapter = adapter_dir # Re-eval with new adapter to get post-train HE log(f" eval post-train HE...") 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(f"iter{it}", it, current_adapter) he_outs = vllm_gen(llm, [he_prompt(p) for p in he], max_new=400, lora_req=lora_req, stops=["\nclass ", "\nif __name__", "\n\nprint"]) he_correct = 0 for p, raw in zip(he, he_outs): full = p["prompt"] + "\n" + raw test_code = full + "\n\n" + p["test"] + f"\n\ncheck({p['entry_point']})" if run_python(test_code, 10): he_correct += 1 log(f" HE iter{it} (post-train): {he_correct}/{len(he)}") iter_results[-1]["he_posttrain"] = he_correct del llm; gc.collect(); torch.cuda.empty_cache() # Save pairs and results with open(f"{args.out_dir}/pairs.jsonl", "w") as fh: for r in accumulated_pairs: fh.write(json.dumps(r) + "\n") result = {"model": args.model, "tag": args.tag, "n_iters": args.n_iters, "iter_results": iter_results, "total_pairs": len(accumulated_pairs), "elapsed_s": time.time() - T0} with open(f"{args.out_dir}/result.json", "w") as fh: json.dump(result, fh, indent=2) print() print("=" * 70) print(f" {args.model} — RECURSIVE BOOTSTRAP") for r in iter_results: pre = r.get("he_pretrain", "-") post = r.get("he_posttrain", "-") print(f" iter {r['iter']}: cum_pairs={r['cumulative_pairs']} HE_pre={pre} HE_post={post}") print(f" Time: {time.time()-T0:.0f}s") print("=" * 70) if __name__ == "__main__": main()