"""Compound recipe + TTS: train recipe, then measure best-of-N on TOP of recipe-trained model. Tests if recipe-trained model has BETTER sample diversity / quality at inference.""" 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 he_score_outputs(he, outs): c = 0 for p, raw in zip(he, outs): code = raw if "```python" in code: code = code.split("```python",1)[1] if "```" in code: code = code.split("```",1)[0] full = p["prompt"] + "\n" + code test_code = full + "\n\n" + p["test"] + f"\n\ncheck({p['entry_point']})" if run_python(test_code, 10): c += 1 return c def main(): ap = argparse.ArgumentParser() ap.add_argument("--model", required=True) ap.add_argument("--tag", required=True) ap.add_argument("--out_dir", required=True) args = ap.parse_args() os.makedirs(args.out_dir, exist_ok=True) random.seed(42) from vllm import LLM, SamplingParams 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 llm = LLM(model=args.model, dtype="bfloat16", gpu_memory_utilization=0.85, max_model_len=2048) log("loaded") he = list(load_dataset("openai_humaneval", split="test")) # 4 metrics: # A) raw greedy # B) raw + best-of-8 # C) recipe greedy # D) recipe + best-of-8 sp_g = SamplingParams(temperature=0, max_tokens=400, stop=["\nclass ", "\nif __name__", "\n\nprint"]) sp_s = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=400, n=8, stop=["\nclass ", "\nif __name__", "\n\nprint"]) log("A) raw greedy") he_outs = [o.outputs[0].text for o in llm.generate([he_prompt(p) for p in he], sp_g, use_tqdm=False)] A_raw_greedy = he_score_outputs(he, he_outs) log(f" raw greedy: {A_raw_greedy}/{len(he)}") log("B) raw best-of-8") he_samples = llm.generate([he_prompt(p) for p in he], sp_s, use_tqdm=False) B_raw_bo8 = 0 for p, outset in zip(he, he_samples): for o in outset.outputs: code = o.text if "```python" in code: code = code.split("```python",1)[1] if "```" in code: code = code.split("```",1)[0] full = p["prompt"] + "\n" + code test_code = full + "\n\n" + p["test"] + f"\n\ncheck({p['entry_point']})" if run_python(test_code, 10): B_raw_bo8 += 1; break log(f" raw best-of-8: {B_raw_bo8}/{len(he)}") # Mine pairs log("mining pairs from MBPP-train...") mbpp_full = list(load_dataset("mbpp", split="train")) random.shuffle(mbpp_full) seeds = [] for p in mbpp_full[:200]: 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"]}) sp_mine = SamplingParams(temperature=0, max_tokens=400, stop=["\nclass Test", "\nif __name__"]) g_outs = [o.outputs[0].text for o in llm.generate([mbpp_prompt(p) for p in seeds], sp_mine, use_tqdm=False)] hard_idx = [i for i, (p, raw) in enumerate(zip(seeds, g_outs)) if not run_python(raw + "\n\n" + "\n".join(p["test_list"]), 8)] log(f" hard: {len(hard_idx)}") pairs = [] if hard_idx: sp_m2 = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=400, n=8, stop=["\nclass Test", "\nif __name__"]) hard_prompts = [mbpp_prompt(seeds[i]) for i in hard_idx] sample_outs = llm.generate(hard_prompts, sp_m2, use_tqdm=False) for j, i in enumerate(hard_idx): for o in sample_outs[j].outputs: if run_python(o.text + "\n\n" + "\n".join(seeds[i]["test_list"]), 8): pairs.append({"problem": seeds[i]["prompt"], "tests": seeds[i]["test_list"], "broken": g_outs[i].strip(), "fixed": o.text.strip()}); break log(f" mined {len(pairs)} pairs") # Train LoRA del llm; gc.collect(); torch.cuda.empty_cache() if len(pairs) < 5: log("too few pairs, exit"); return 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") full = user + r["fixed"] 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("training...") 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 pairs]) targs = TrainingArguments( output_dir=f"{args.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() adapter_dir = f"{args.out_dir}/adapter" model.save_pretrained(adapter_dir) del model; gc.collect(); torch.cuda.empty_cache() # C, D from vllm import LLM as LLM2 from vllm.lora.request import LoRARequest llm = LLM2(model=args.model, dtype="bfloat16", gpu_memory_utilization=0.85, max_model_len=2048, enable_lora=True, max_lora_rank=16) lora_req = LoRARequest("trained", 1, adapter_dir) log("C) recipe greedy") he_outs = [o.outputs[0].text for o in llm.generate([he_prompt(p) for p in he], sp_g, lora_request=lora_req, use_tqdm=False)] C_rec_greedy = he_score_outputs(he, he_outs) log(f" recipe greedy: {C_rec_greedy}/{len(he)}") log("D) recipe best-of-8") he_samples = llm.generate([he_prompt(p) for p in he], sp_s, lora_request=lora_req, use_tqdm=False) D_rec_bo8 = 0 for p, outset in zip(he, he_samples): for o in outset.outputs: code = o.text if "```python" in code: code = code.split("```python",1)[1] if "```" in code: code = code.split("```",1)[0] full = p["prompt"] + "\n" + code test_code = full + "\n\n" + p["test"] + f"\n\ncheck({p['entry_point']})" if run_python(test_code, 10): D_rec_bo8 += 1; break log(f" recipe best-of-8: {D_rec_bo8}/{len(he)}") result = { "model": args.model, "n_pairs": len(pairs), "raw_greedy": A_raw_greedy, "raw_bo8": B_raw_bo8, "recipe_greedy": C_rec_greedy, "recipe_bo8": D_rec_bo8, "n": len(he), "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} — RECIPE × TTS COMPOUND (HumanEval, n={len(he)}, {len(pairs)} pairs)") print(f" A) Raw greedy: {A_raw_greedy:>3}/{len(he)} ({100*A_raw_greedy/len(he):.1f}%)") print(f" B) Raw best-of-8: {B_raw_bo8:>3}/{len(he)} ({100*B_raw_bo8/len(he):.1f}%)") print(f" C) Recipe greedy: {C_rec_greedy:>3}/{len(he)} ({100*C_rec_greedy/len(he):.1f}%)") print(f" D) Recipe best-of-8: {D_rec_bo8:>3}/{len(he)} ({100*D_rec_bo8/len(he):.1f}%)") print(f" Synergy: D - max(B,C) = {D_rec_bo8 - max(B_raw_bo8, C_rec_greedy):+d} (>0 = real synergy)") print(f" Time: {time.time()-T0:.0f}s") print("=" * 70) if __name__ == "__main__": main()