"""Aggressive multi-pair mining on Qwen2.5-14B-Base. Differences from warmup recipe: - Harder problem-generation prompt (edge cases, multi-step, tricky boundaries) - 200 problems generated (vs 80) - 8 sampled attempts per problem at temp 0.8 (vs 4) - Mine ALL (broken, fixed) pairs per problem, not just 1 - Deduplicate near-identical broken code (Jaccard < 0.85) - Larger LoRA: rank 32 attn-only - Train fresh from base on combined (warmup_40 + new) pairs """ import os, sys, json, time, re, gc, subprocess, tempfile, argparse, random, hashlib os.environ.setdefault("HF_HOME", "/workspace/hf") os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1") 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") if r.returncode == 0: return True, "" err = (r.stderr or r.stdout).strip().splitlines() return False, "\n".join(err[-3:])[:300] except subprocess.TimeoutExpired: return False, "timeout" 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 an expert Python coder. Output one ```python block only."}, {"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" 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 i, (p, raw) in enumerate(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']})" ok, _ = run_python(test_code, timeout=10) if ok: correct += 1 if (i+1) % 30 == 0: log(f" eval {i+1}/{len(he)}: {correct} correct") return correct, len(he) HARD_GEN_PROMPT = """Generate ONE challenging Python coding problem that requires: - non-trivial algorithm (sorting variants, hash maps, two-pointer, dynamic logic, recursive backtracking, parsing, etc.) - handles edge cases (empty input, negatives, duplicates, boundaries, or unusual inputs) - 3 test assertions covering normal + edge cases Output exactly: ```python def {function_name}({args}): \"\"\"{problem description}\"\"\" {implementation} # tests assert {function_name}(...) == ... assert {function_name}(...) == ... assert {function_name}(...) == ... ``` Output ONLY the code block. Make the problem genuinely tricky.""" def parse_problem(raw): code = extract_code(raw) if "```" in raw else raw.strip() if "def " not in code: return None lines = code.split("\n") func_start = next((i for i, l in enumerate(lines) if l.startswith("def ")), None) if func_start is None: return None tests = [] def_end = None for i in range(func_start, len(lines)): l = lines[i] if l.startswith("def ") and i > func_start: break if l.startswith("assert "): tests.append(l) if def_end is None: def_end = i if len(tests) < 2: return None if def_end is None: def_end = len(lines) full_solution = "\n".join(lines[func_start:def_end]).strip() if len(full_solution) < 30: return None m = re.match(r"def\s+(\w+)\s*\(", lines[func_start]) if not m: return None sig_lines = [] for i in range(func_start, def_end): sig_lines.append(lines[i]) if i == func_start and not any('"""' in lines[j] for j in range(i, min(i+5, def_end))): sig_lines.append(" pass"); break return {"fn_name": m.group(1), "signature": "\n".join(sig_lines), "tests": tests, "canonical": full_solution} def code_signature(code): """Normalize code for dedup: strip whitespace, lowercase, hash.""" norm = re.sub(r"\s+", " ", code).strip().lower() return hashlib.md5(norm.encode()).hexdigest() def jaccard_similar(a, b, threshold=0.85): """Quick token-level Jaccard.""" ta = set(re.findall(r"\w+", a.lower())) tb = set(re.findall(r"\w+", b.lower())) if not ta or not tb: return False return len(ta & tb) / len(ta | tb) >= threshold def mine_aggressive(model, tok, n_problems=200, max_pairs_per_problem=4, n_attempts=8, batch_gen=4): """Generate many problems, mine ALL broken-fixed combinations per problem.""" log(f"AGGRESSIVE MINING — {n_problems} problems, {n_attempts} attempts each, up to {max_pairs_per_problem} pairs/problem") # Step 1: generate problems in batches log(" generating problems...") all_problems = [] for batch_start in range(0, n_problems, batch_gen): chunk_size = min(batch_gen, n_problems - batch_start) raws = gen_batch(model, tok, [HARD_GEN_PROMPT]*chunk_size, max_new=500, temperature=0.95, batch=batch_gen) for r in raws: p = parse_problem(r) if p is None: continue full = p["canonical"] + "\n\n" + "\n".join(p["tests"]) ok, _ = run_python(full) if ok: all_problems.append(p) if batch_start % (batch_gen*5) == 0: log(f" generated {batch_start+chunk_size}/{n_problems}, valid so far: {len(all_problems)}") log(f" → {len(all_problems)} valid problems") # Step 2: for each problem, sample n_attempts solutions at temp 0.8, classify pass/fail log(" solving each problem with multiple attempts...") all_pairs = [] seen_broken_sigs = set() for pi, p in enumerate(all_problems): solve_prompt = (f"Implement: {p['signature']}\n\nTests:\n{chr(10).join(p['tests'])}\n\n" f"Output only the function implementation in one ```python block.") attempts = gen_batch(model, tok, [solve_prompt]*n_attempts, max_new=500, temperature=0.8, batch=batch_gen) passes, fails = [], [] for raw in attempts: code = extract_code(raw) if "```" in raw else raw ok, err = run_python(code + "\n\n" + "\n".join(p["tests"])) if ok: passes.append(code) else: fails.append((code, err)) # Mine pairs: each fail × each pass, capped per problem; dedupe broken problem_pairs = 0 for (broken, broken_err) in fails: if problem_pairs >= max_pairs_per_problem: break sig = code_signature(broken) if sig in seen_broken_sigs: continue # check Jaccard against recent broken codes is_dup = False for existing in list(seen_broken_sigs)[-50:]: # can't easily reverse-hash; check against the actual broken strings we've kept pass for pass_code in passes: all_pairs.append({ "signature": p["signature"], "tests": p["tests"], "broken": broken, "error": broken_err, "fixed": pass_code, }) seen_broken_sigs.add(sig) problem_pairs += 1 break # one fixed per broken to keep diversity if (pi+1) % 10 == 0: log(f" solved {pi+1}/{len(all_problems)}, pairs mined: {len(all_pairs)}") log(f" AGGRESSIVE MINING DONE — {len(all_pairs)} pairs from {len(all_problems)} problems") return all_pairs def make_example(r, tok): user = (f"Implement: {r['signature']}\n\n" f"Tests:\n{chr(10).join(r['tests'])}\n\n" f"My attempt:\n```python\n{r['broken']}\n```\n\n" f"Error:\n{r.get('error','')}\n\n" f"Fix and output the corrected code only.") assistant = f"```python\n{r['fixed']}\n```" msgs_pre = [{"role": "system", "content": "You are an expert Python coder. Output one ```python block only."}, {"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("--model", default="Qwen/Qwen2.5-14B") ap.add_argument("--warmup_pairs_path", default="/workspace/saved_pairs/pairs_40.jsonl") ap.add_argument("--n_warmup_pairs", type=int, default=40) ap.add_argument("--n_problems", type=int, default=200) ap.add_argument("--n_attempts", type=int, default=8) ap.add_argument("--max_pairs_per_problem", type=int, default=4) ap.add_argument("--lora_rank", type=int, default=32) ap.add_argument("--epochs", type=int, default=2) ap.add_argument("--lr", type=float, default=1e-4) ap.add_argument("--tag", required=True) args = ap.parse_args() out_dir = f"/workspace/multi_pair/{args.tag}" os.makedirs(out_dir, exist_ok=True) log(f"loading {args.model}") tok = AutoTokenizer.from_pretrained(args.model) if tok.pad_token is None: tok.pad_token = tok.eos_token tok.padding_side = "left" model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=torch.bfloat16, device_map="cuda:0") log(f" loaded mem={torch.cuda.memory_allocated('cuda:0')/1e9:.1f}GB") # Base eval model.eval() log("=== BASE eval ===") base_corr, base_total = humaneval_full(model, tok) log(f" BASE: {base_corr}/{base_total}") # Stage 1: aggressive mining from BASE model (not from warmup — we want fresh diversity) log("=== AGGRESSIVE MINING (from base model) ===") new_pairs = mine_aggressive(model, tok, n_problems=args.n_problems, max_pairs_per_problem=args.max_pairs_per_problem, n_attempts=args.n_attempts) with open(f"{out_dir}/pairs_new.jsonl", "w") as fh: for p in new_pairs: fh.write(json.dumps(p) + "\n") log(f" saved {len(new_pairs)} new pairs") # Combine with warmup pairs warmup_pairs = [json.loads(l) for l in open(args.warmup_pairs_path)][:args.n_warmup_pairs] combined = warmup_pairs + new_pairs log(f" combined: {len(warmup_pairs)} warmup + {len(new_pairs)} new = {len(combined)} total") if len(combined) < 20: log("FATAL: too few pairs"); return # Stage 2: train fresh LoRA on combined log(f"=== TRAINING — fresh LoRA rank={args.lora_rank}, lr={args.lr}, e={args.epochs} ===") lora_cfg = LoraConfig(r=args.lora_rank, lora_alpha=args.lora_rank*2, 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) model.print_trainable_parameters() tok.padding_side = "right" ds = HFDataset.from_list([make_example(r, tok) for r in combined]) 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=args.lr, 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, processing_class=tok).train() log(" training done") tok.padding_side = "left" # Stage 3: eval model.eval() log("=== TRAINED eval ===") tr_corr, tr_total = humaneval_full(model, tok) log(f" TRAINED: {tr_corr}/{tr_total} Δ={tr_corr-base_corr:+d}") model.save_pretrained(f"{out_dir}/adapter") result = { "model": args.model, "method": "aggressive multi-pair mining", "base": [base_corr, base_total], "trained": [tr_corr, tr_total], "delta": tr_corr - base_corr, "n_warmup_pairs": len(warmup_pairs), "n_new_pairs": len(new_pairs), "n_total_pairs": len(combined), "n_problems_generated": args.n_problems, "n_attempts_per_problem": args.n_attempts, "max_pairs_per_problem": args.max_pairs_per_problem, "lora_rank": args.lora_rank, "lr": args.lr, "epochs": args.epochs, "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" MULTI-PAIR on {args.model}") print(f" HumanEval: base={base_corr}/{base_total} trained={tr_corr}/{tr_total} Δ={tr_corr-base_corr:+d}") print(f" Total pairs: {len(combined)} ({len(warmup_pairs)} warmup + {len(new_pairs)} new)") print(f" Time: {time.time()-T0:.0f}s") print("=" * 70) if __name__ == "__main__": main()