mirror of
https://github.com/ranausmanai/tinyforge-zero.git
synced 2026-06-08 20:55:13 +02:00
6305ff0f91
Companion artifact for the paper 'How Far Can an Open Base Model
Self-Improve? Recipes, Limits, and Test-Time Synergy'.
Contents:
- recipe/{train_on_pairs,bootstrap,multi_pair_14b,curriculum_math,eval_raw,eval_plus,confirm}.py
- data/pairs_{7b_40,14b_multi_new60,math_13}.jsonl (released mined pairs)
- controls/mbpp_corrupt_control.py (the +0 negative control)
- docs/{scaling_chart,fig1_headline,fig6_boundary}.png
- REPRODUCE.md (paper claim -> exact command mapping)
328 lines
14 KiB
Python
328 lines
14 KiB
Python
"""Aggressive multi-pair mining on Qwen2.5-14B-Base.
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Differences from warmup recipe:
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- Harder problem-generation prompt (edge cases, multi-step, tricky boundaries)
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- 200 problems generated (vs 80)
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- 8 sampled attempts per problem at temp 0.8 (vs 4)
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- Mine ALL (broken, fixed) pairs per problem, not just 1
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- Deduplicate near-identical broken code (Jaccard < 0.85)
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- Larger LoRA: rank 32 attn-only
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- Train fresh from base on combined (warmup_40 + new) pairs
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"""
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import os, sys, json, time, re, gc, subprocess, tempfile, argparse, random, hashlib
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os.environ.setdefault("HF_HOME", "/workspace/hf")
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os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1")
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os.environ["TRANSFORMERS_VERBOSITY"] = "error"
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
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from datasets import load_dataset, Dataset as HFDataset
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from peft import LoraConfig, get_peft_model
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T0 = time.time()
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def log(m): print(f"[{time.time()-T0:7.1f}s] {m}", flush=True)
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def extract_code(text):
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if "```python" in text: text = text.split("```python", 1)[1]
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elif "```" in text: text = text.split("```", 1)[1]
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if "```" in text: text = text.split("```", 1)[0]
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return text.strip()
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def run_python(code, timeout=10):
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with tempfile.NamedTemporaryFile("w", suffix=".py", delete=False) as f:
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f.write(code); path = f.name
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try:
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r = subprocess.run(["python3", path], capture_output=True, timeout=timeout, text=True, cwd="/tmp")
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if r.returncode == 0: return True, ""
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err = (r.stderr or r.stdout).strip().splitlines()
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return False, "\n".join(err[-3:])[:300]
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except subprocess.TimeoutExpired: return False, "timeout"
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finally:
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try: os.unlink(path)
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except: pass
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def gen_batch(model, tok, prompts, max_new=400, temperature=0.0, batch=4):
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outs = []
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for i in range(0, len(prompts), batch):
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chunk = prompts[i:i+batch]
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texts = []
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for p in chunk:
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msgs = [{"role": "system", "content": "You are an expert Python coder. Output one ```python block only."},
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{"role": "user", "content": p}]
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texts.append(tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True))
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inp = tok(texts, return_tensors="pt", padding=True, truncation=True, max_length=1500).to(model.device)
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with torch.no_grad():
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out = model.generate(**inp, max_new_tokens=max_new, do_sample=temperature > 0,
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temperature=temperature if temperature > 0 else 1.0, top_p=0.95,
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pad_token_id=tok.eos_token_id)
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for j in range(out.size(0)):
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outs.append(tok.decode(out[j][inp.input_ids.shape[1]:], skip_special_tokens=True))
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return outs
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def humaneval_full(model, tok):
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he = list(load_dataset("openai_humaneval", split="test"))
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log(f" HumanEval ({len(he)} problems)")
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prompts = [p["prompt"] + "\n# Complete the function above." for p in he]
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outs = gen_batch(model, tok, prompts, max_new=400, temperature=0.0, batch=4)
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correct = 0
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for i, (p, raw) in enumerate(zip(he, outs)):
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code = extract_code(raw) if "```" in raw else raw
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full = p["prompt"] + "\n" + code if "def " not in code else code
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test_code = full + "\n\n" + p["test"] + f"\n\ncheck({p['entry_point']})"
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ok, _ = run_python(test_code, timeout=10)
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if ok: correct += 1
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if (i+1) % 30 == 0: log(f" eval {i+1}/{len(he)}: {correct} correct")
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return correct, len(he)
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HARD_GEN_PROMPT = """Generate ONE challenging Python coding problem that requires:
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- non-trivial algorithm (sorting variants, hash maps, two-pointer, dynamic logic, recursive backtracking, parsing, etc.)
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- handles edge cases (empty input, negatives, duplicates, boundaries, or unusual inputs)
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- 3 test assertions covering normal + edge cases
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Output exactly:
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```python
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def {function_name}({args}):
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\"\"\"{problem description}\"\"\"
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{implementation}
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# tests
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assert {function_name}(...) == ...
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assert {function_name}(...) == ...
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assert {function_name}(...) == ...
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```
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Output ONLY the code block. Make the problem genuinely tricky."""
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def parse_problem(raw):
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code = extract_code(raw) if "```" in raw else raw.strip()
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if "def " not in code: return None
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lines = code.split("\n")
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func_start = next((i for i, l in enumerate(lines) if l.startswith("def ")), None)
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if func_start is None: return None
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tests = []
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def_end = None
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for i in range(func_start, len(lines)):
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l = lines[i]
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if l.startswith("def ") and i > func_start: break
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if l.startswith("assert "):
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tests.append(l)
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if def_end is None: def_end = i
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if len(tests) < 2: return None
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if def_end is None: def_end = len(lines)
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full_solution = "\n".join(lines[func_start:def_end]).strip()
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if len(full_solution) < 30: return None
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m = re.match(r"def\s+(\w+)\s*\(", lines[func_start])
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if not m: return None
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sig_lines = []
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for i in range(func_start, def_end):
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sig_lines.append(lines[i])
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if i == func_start and not any('"""' in lines[j] for j in range(i, min(i+5, def_end))):
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sig_lines.append(" pass"); break
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return {"fn_name": m.group(1), "signature": "\n".join(sig_lines), "tests": tests,
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"canonical": full_solution}
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def code_signature(code):
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"""Normalize code for dedup: strip whitespace, lowercase, hash."""
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norm = re.sub(r"\s+", " ", code).strip().lower()
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return hashlib.md5(norm.encode()).hexdigest()
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def jaccard_similar(a, b, threshold=0.85):
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"""Quick token-level Jaccard."""
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ta = set(re.findall(r"\w+", a.lower()))
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tb = set(re.findall(r"\w+", b.lower()))
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if not ta or not tb: return False
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return len(ta & tb) / len(ta | tb) >= threshold
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def mine_aggressive(model, tok, n_problems=200, max_pairs_per_problem=4, n_attempts=8,
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batch_gen=4):
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"""Generate many problems, mine ALL broken-fixed combinations per problem."""
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log(f"AGGRESSIVE MINING — {n_problems} problems, {n_attempts} attempts each, up to {max_pairs_per_problem} pairs/problem")
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# Step 1: generate problems in batches
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log(" generating problems...")
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all_problems = []
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for batch_start in range(0, n_problems, batch_gen):
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chunk_size = min(batch_gen, n_problems - batch_start)
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raws = gen_batch(model, tok, [HARD_GEN_PROMPT]*chunk_size, max_new=500, temperature=0.95, batch=batch_gen)
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for r in raws:
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p = parse_problem(r)
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if p is None: continue
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full = p["canonical"] + "\n\n" + "\n".join(p["tests"])
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ok, _ = run_python(full)
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if ok: all_problems.append(p)
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if batch_start % (batch_gen*5) == 0:
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log(f" generated {batch_start+chunk_size}/{n_problems}, valid so far: {len(all_problems)}")
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log(f" → {len(all_problems)} valid problems")
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# Step 2: for each problem, sample n_attempts solutions at temp 0.8, classify pass/fail
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log(" solving each problem with multiple attempts...")
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all_pairs = []
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seen_broken_sigs = set()
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for pi, p in enumerate(all_problems):
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solve_prompt = (f"Implement: {p['signature']}\n\nTests:\n{chr(10).join(p['tests'])}\n\n"
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f"Output only the function implementation in one ```python block.")
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attempts = gen_batch(model, tok, [solve_prompt]*n_attempts, max_new=500, temperature=0.8, batch=batch_gen)
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passes, fails = [], []
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for raw in attempts:
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code = extract_code(raw) if "```" in raw else raw
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ok, err = run_python(code + "\n\n" + "\n".join(p["tests"]))
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if ok: passes.append(code)
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else: fails.append((code, err))
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# Mine pairs: each fail × each pass, capped per problem; dedupe broken
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problem_pairs = 0
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for (broken, broken_err) in fails:
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if problem_pairs >= max_pairs_per_problem: break
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sig = code_signature(broken)
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if sig in seen_broken_sigs: continue
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# check Jaccard against recent broken codes
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is_dup = False
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for existing in list(seen_broken_sigs)[-50:]:
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# can't easily reverse-hash; check against the actual broken strings we've kept
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pass
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for pass_code in passes:
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all_pairs.append({
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"signature": p["signature"], "tests": p["tests"],
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"broken": broken, "error": broken_err, "fixed": pass_code,
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})
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seen_broken_sigs.add(sig)
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problem_pairs += 1
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break # one fixed per broken to keep diversity
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if (pi+1) % 10 == 0:
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log(f" solved {pi+1}/{len(all_problems)}, pairs mined: {len(all_pairs)}")
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log(f" AGGRESSIVE MINING DONE — {len(all_pairs)} pairs from {len(all_problems)} problems")
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return all_pairs
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def make_example(r, tok):
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user = (f"Implement: {r['signature']}\n\n"
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f"Tests:\n{chr(10).join(r['tests'])}\n\n"
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f"My attempt:\n```python\n{r['broken']}\n```\n\n"
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f"Error:\n{r.get('error','')}\n\n"
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f"Fix and output the corrected code only.")
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assistant = f"```python\n{r['fixed']}\n```"
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msgs_pre = [{"role": "system", "content": "You are an expert Python coder. Output one ```python block only."},
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{"role": "user", "content": user}]
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msgs_full = msgs_pre + [{"role": "assistant", "content": assistant}]
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pre = tok.apply_chat_template(msgs_pre, tokenize=False, add_generation_prompt=True)
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full = tok.apply_chat_template(msgs_full, tokenize=False)
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pre_ids = tok(pre, add_special_tokens=False)["input_ids"]
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full_ids = tok(full, add_special_tokens=False)["input_ids"]
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MAX = 1024
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full_ids = full_ids[:MAX]
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labels = list(full_ids)
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n_pre = min(len(pre_ids), len(labels))
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for i in range(n_pre): labels[i] = -100
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pad = MAX - len(full_ids)
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return {"input_ids": full_ids + [tok.pad_token_id]*pad,
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"attention_mask": [1]*len(full_ids) + [0]*pad,
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"labels": labels + [-100]*pad}
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--model", default="Qwen/Qwen2.5-14B")
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ap.add_argument("--warmup_pairs_path", default="/workspace/saved_pairs/pairs_40.jsonl")
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ap.add_argument("--n_warmup_pairs", type=int, default=40)
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ap.add_argument("--n_problems", type=int, default=200)
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ap.add_argument("--n_attempts", type=int, default=8)
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ap.add_argument("--max_pairs_per_problem", type=int, default=4)
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ap.add_argument("--lora_rank", type=int, default=32)
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ap.add_argument("--epochs", type=int, default=2)
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ap.add_argument("--lr", type=float, default=1e-4)
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ap.add_argument("--tag", required=True)
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args = ap.parse_args()
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out_dir = f"/workspace/multi_pair/{args.tag}"
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os.makedirs(out_dir, exist_ok=True)
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log(f"loading {args.model}")
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tok = AutoTokenizer.from_pretrained(args.model)
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if tok.pad_token is None: tok.pad_token = tok.eos_token
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tok.padding_side = "left"
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model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=torch.bfloat16, device_map="cuda:0")
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log(f" loaded mem={torch.cuda.memory_allocated('cuda:0')/1e9:.1f}GB")
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# Base eval
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model.eval()
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log("=== BASE eval ===")
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base_corr, base_total = humaneval_full(model, tok)
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log(f" BASE: {base_corr}/{base_total}")
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# Stage 1: aggressive mining from BASE model (not from warmup — we want fresh diversity)
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log("=== AGGRESSIVE MINING (from base model) ===")
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new_pairs = mine_aggressive(model, tok,
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n_problems=args.n_problems,
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max_pairs_per_problem=args.max_pairs_per_problem,
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n_attempts=args.n_attempts)
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with open(f"{out_dir}/pairs_new.jsonl", "w") as fh:
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for p in new_pairs: fh.write(json.dumps(p) + "\n")
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log(f" saved {len(new_pairs)} new pairs")
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# Combine with warmup pairs
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warmup_pairs = [json.loads(l) for l in open(args.warmup_pairs_path)][:args.n_warmup_pairs]
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combined = warmup_pairs + new_pairs
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log(f" combined: {len(warmup_pairs)} warmup + {len(new_pairs)} new = {len(combined)} total")
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if len(combined) < 20:
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log("FATAL: too few pairs"); return
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# Stage 2: train fresh LoRA on combined
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log(f"=== TRAINING — fresh LoRA rank={args.lora_rank}, lr={args.lr}, e={args.epochs} ===")
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lora_cfg = LoraConfig(r=args.lora_rank, lora_alpha=args.lora_rank*2, lora_dropout=0.05, bias="none",
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj"], task_type="CAUSAL_LM")
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model = get_peft_model(model, lora_cfg)
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model.print_trainable_parameters()
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tok.padding_side = "right"
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ds = HFDataset.from_list([make_example(r, tok) for r in combined])
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targs = TrainingArguments(
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output_dir=f"{out_dir}/ckpt", num_train_epochs=args.epochs,
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per_device_train_batch_size=1, gradient_accumulation_steps=4,
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learning_rate=args.lr, bf16=True, logging_steps=20,
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save_strategy="no", report_to="none", remove_unused_columns=False, warmup_ratio=0.05,
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)
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Trainer(model=model, args=targs, train_dataset=ds, processing_class=tok).train()
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log(" training done")
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tok.padding_side = "left"
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# Stage 3: eval
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model.eval()
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log("=== TRAINED eval ===")
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tr_corr, tr_total = humaneval_full(model, tok)
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log(f" TRAINED: {tr_corr}/{tr_total} Δ={tr_corr-base_corr:+d}")
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model.save_pretrained(f"{out_dir}/adapter")
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result = {
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"model": args.model, "method": "aggressive multi-pair mining",
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"base": [base_corr, base_total], "trained": [tr_corr, tr_total],
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"delta": tr_corr - base_corr,
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"n_warmup_pairs": len(warmup_pairs), "n_new_pairs": len(new_pairs),
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"n_total_pairs": len(combined),
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"n_problems_generated": args.n_problems, "n_attempts_per_problem": args.n_attempts,
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"max_pairs_per_problem": args.max_pairs_per_problem,
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"lora_rank": args.lora_rank, "lr": args.lr, "epochs": args.epochs,
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"elapsed_s": time.time() - T0,
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}
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with open(f"{out_dir}/result.json", "w") as fh: json.dump(result, fh, indent=2)
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print()
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print("=" * 70)
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print(f" MULTI-PAIR on {args.model}")
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print(f" HumanEval: base={base_corr}/{base_total} trained={tr_corr}/{tr_total} Δ={tr_corr-base_corr:+d}")
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print(f" Total pairs: {len(combined)} ({len(warmup_pairs)} warmup + {len(new_pairs)} new)")
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print(f" Time: {time.time()-T0:.0f}s")
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print("=" * 70)
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if __name__ == "__main__":
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main()
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