tinyforge-zero/recipe/confirm.py

"""Confirm the peak +5 result on full HumanEval (164 problems) and try the cliff at 39 pairs."""
import os, sys, json, time, re, gc, subprocess, tempfile, argparse
os.environ.setdefault("HF_HOME", "/workspace/hf")
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "0")
os.environ["TRANSFORMERS_VERBOSITY"] = "error"

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from datasets import load_dataset, Dataset as HFDataset
from peft import LoraConfig, get_peft_model

T0 = time.time()
def log(m): print(f"[{time.time()-T0:7.1f}s] {m}", flush=True)


def extract_code(text):
    if "```python" in text: text = text.split("```python", 1)[1]
    elif "```" in text: text = text.split("```", 1)[1]
    if "```" in text: text = text.split("```", 1)[0]
    return text.strip()


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 gen_batch(model, tok, prompts, max_new=400, temperature=0.0, batch=4):
    outs = []
    for i in range(0, len(prompts), batch):
        chunk = prompts[i:i+batch]
        texts = []
        for p in chunk:
            msgs = [{"role": "system", "content": "You are a Python coder."},
                    {"role": "user", "content": p}]
            texts.append(tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True))
        inp = tok(texts, return_tensors="pt", padding=True, truncation=True, max_length=1500).to(model.device)
        with torch.no_grad():
            out = model.generate(**inp, max_new_tokens=max_new, do_sample=temperature > 0,
                                 temperature=temperature if temperature > 0 else 1.0, top_p=0.95,
                                 pad_token_id=tok.eos_token_id)
        for j in range(out.size(0)):
            outs.append(tok.decode(out[j][inp.input_ids.shape[1]:], skip_special_tokens=True))
    return outs


def humaneval_full(model, tok):
    he = list(load_dataset("openai_humaneval", split="test"))
    log(f"  full HumanEval: {len(he)} problems")
    prompts = [p["prompt"] + "\n# Complete the function above." for p in he]
    outs = gen_batch(model, tok, prompts, max_new=400, temperature=0.0, batch=4)
    correct = 0
    for p, raw in zip(he, outs):
        code = extract_code(raw) if "```" in raw else raw
        full = p["prompt"] + "\n" + code if "def " not in code else code
        test_code = full + "\n\n" + p["test"] + f"\n\ncheck({p['entry_point']})"
        if run_python(test_code, timeout=10): correct += 1
    return correct, len(he)


def make_example(r, tok):
    user = f"Implement: {r['signature']}\n\nTests:\n{chr(10).join(r['tests'])}\n\nMy attempt:\n```python\n{r['broken']}\n```\n\nError:\n{r['error']}\n\nFix and output the corrected code only."
    assistant = f"```python\n{r['fixed']}\n```"
    msgs_pre = [{"role": "system", "content": "You are a Python coder."},
                {"role": "user", "content": user}]
    msgs_full = msgs_pre + [{"role": "assistant", "content": assistant}]
    pre = tok.apply_chat_template(msgs_pre, tokenize=False, add_generation_prompt=True)
    full = tok.apply_chat_template(msgs_full, tokenize=False)
    pre_ids = tok(pre, add_special_tokens=False)["input_ids"]
    full_ids = tok(full, add_special_tokens=False)["input_ids"]
    MAX = 1024
    full_ids = full_ids[:MAX]
    labels = list(full_ids)
    n_pre = min(len(pre_ids), len(labels))
    for i in range(n_pre): 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}


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--n_pairs", type=int, default=21, help="how many pairs from the saved set to train on")
    ap.add_argument("--epochs", type=int, default=2)
    ap.add_argument("--seed", type=int, default=42)
    ap.add_argument("--tag", required=True)
    args = ap.parse_args()

    torch.manual_seed(args.seed)

    pairs_path = "/workspace/bootstrap/bs_7b_v3/pairs.jsonl"
    pairs = [json.loads(l) for l in open(pairs_path)]
    log(f"loaded {len(pairs)} pairs from prior bootstrap run")
    pairs_use = pairs[:args.n_pairs]
    log(f"using {len(pairs_use)} for this run")

    out_dir = f"/workspace/confirm/{args.tag}"
    os.makedirs(out_dir, exist_ok=True)

    log("loading Qwen/Qwen2.5-7B")
    tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B")
    if tok.pad_token is None: tok.pad_token = tok.eos_token
    tok.padding_side = "left"
    model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B", dtype=torch.bfloat16, device_map="cuda:0")

    # Eval base
    model.eval()
    log("eval BASE on full HumanEval")
    base_corr, base_total = humaneval_full(model, tok)
    log(f"  BASE: {base_corr}/{base_total}")

    # Apply LoRA + train
    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)
    log("LoRA applied")

    tok.padding_side = "right"
    examples = [make_example(r, tok) for r in pairs_use]
    ds = HFDataset.from_list(examples)
    targs = TrainingArguments(
        output_dir=f"{out_dir}/ckpt", num_train_epochs=args.epochs,
        per_device_train_batch_size=1, gradient_accumulation_steps=4,
        learning_rate=1e-4, bf16=True, logging_steps=10,
        save_strategy="no", report_to="none", remove_unused_columns=False, warmup_ratio=0.05,
        seed=args.seed,
    )
    log(f"training on {len(ds)} pairs, {args.epochs} epochs")
    Trainer(model=model, args=targs, train_dataset=ds, processing_class=tok).train()
    log("training done")
    tok.padding_side = "left"

    # Eval trained
    model.eval()
    log("eval TRAINED on full HumanEval")
    tr_corr, tr_total = humaneval_full(model, tok)
    log(f"  TRAINED: {tr_corr}/{tr_total}")

    result = {
        "n_pairs_used": len(pairs_use), "epochs": args.epochs, "seed": args.seed,
        "base": [base_corr, base_total], "trained": [tr_corr, tr_total],
        "delta": tr_corr - base_corr,
        "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"  N_PAIRS: {len(pairs_use)}   EPOCHS: {args.epochs}   SEED: {args.seed}")
    print(f"  HUMAN-EVAL FULL:  base={base_corr}/{base_total}  trained={tr_corr}/{tr_total}  Δ={tr_corr-base_corr:+d}")
    print(f"  time: {time.time()-T0:.0f}s")
    print("=" * 70)


if __name__ == "__main__":
    main()