mirror of
https://github.com/ranausmanai/tinyforge-zero.git
synced 2026-06-08 20:55:13 +02:00
826f934d2e
Reorganizes the repo so every section of the paper has a corresponding
script. Previously only the core recipe + control + evals were here.
New subdirs:
- tts/ — test-time sampling (§2.2, §3.3): scaling sweep, HE, MATH-500,
AIME, 14B-recipe + TTS, 8B-raw-TTS control.
- experiments/ — every §3 finding as a runnable script:
· self_consistency (§3.4)
· recipe_x_tts_synergy (§3.5, novel)
· mbpp_seeded_cross_arch (§3.9)
· cross_domain_code_to_math (§3.10)
· self_correction_math_{naive,fixed} (§3.10, the
catastrophic-then-recovered case)
· math500_seeded_mining (§3.10 distribution mismatch)
· bcb_hard_eval (§3.10 distribution mismatch)
· recursive_bootstrap (§3.10 plateau)
· diversity_cued_mining (§3.10 low yield)
· aime_scaling (TTS curve)
· star_baseline_gsm8k (related-work baseline)
- evals/ — moved out of recipe/ (eval_raw, eval_plus, confirm)
Also adds: bootstrap_14b_4bit_harvest, curriculum_code, math_bootstrap to
recipe/ for completeness.
REPRODUCE.md now maps each paper section / table / figure to its exact
script and expected output.
322 lines
14 KiB
Python
322 lines
14 KiB
Python
"""TinyForge-Zero on CODE with self-difficulty curriculum.
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Loop:
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1. Generate problem (seeded fresh or amplified/simplified from pool)
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2. Greedy solve. Verify against tests.
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- If correct → easy → amplify
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- If wrong → try 4 sampled attempts
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- If at-edge (some pass, some fail) → MINE pair
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- If all fail → too hard → simplify
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3. Train periodically. Eval on HumanEval.
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"""
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import os, sys, json, time, re, gc, subprocess, tempfile, argparse, random
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os.environ.setdefault("HF_HOME", "/workspace/hf")
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os.environ.setdefault("CUDA_VISIBLE_DEVICES", "0")
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os.environ["TRANSFORMERS_VERBOSITY"] = "error"
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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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=8):
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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 a Python coder."},
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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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SEED_GEN_PROMPT = """Generate ONE simple Python coding problem with a clear function spec and 3 test assertions.
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Output exactly:
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```python
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def {function_name}({args}):
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\"\"\"{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."""
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AMPLIFY_PROMPT = """Take this Python coding problem and make it HARDER (add an edge case, additional constraint, or trickier logic). Keep the format with function + 3 assert tests.
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Original:
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```python
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{original}
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```
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Output the harder version (function + tests) in one ```python block."""
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SIMPLIFY_PROMPT = """Take this Python coding problem and make it EASIER (remove an edge case, simplify the logic). Keep the format with function + 3 assert tests.
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Original:
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```python
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{original}
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```
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Output the easier version (function + tests) in one ```python block."""
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def parse_problem(text):
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code = extract_code(text) if "```" in text else text.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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fn_name = m.group(1)
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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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if i > func_start and '"""' in lines[i] and (i > func_start+1 and '"""' in lines[i-1] or lines[i].count('"""') >= 2):
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break
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return {"fn_name": fn_name, "signature": "\n".join(sig_lines), "tests": tests,
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"canonical": full_solution, "raw": code}
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def humaneval_full(model, tok, n=164):
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he = list(load_dataset("openai_humaneval", split="test"))[:n]
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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 p, raw in 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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return correct, len(he)
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def make_train_example(r, tok):
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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."
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assistant = f"```python\n{r['fixed']}\n```"
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msgs_pre = [{"role": "system", "content": "You are a Python coder."},
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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/Qwen2.5-7B")
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ap.add_argument("--iterations", type=int, default=16)
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ap.add_argument("--problems_per_iter", type=int, default=8)
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ap.add_argument("--train_every", type=int, default=4)
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ap.add_argument("--seed", type=int, default=42)
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ap.add_argument("--tag", required=True)
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args = ap.parse_args()
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random.seed(args.seed); torch.manual_seed(args.seed)
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out_dir = f"/workspace/curriculum_code/{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, 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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model.eval()
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log("INITIAL eval on HumanEval")
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base_correct, base_total = humaneval_full(model, tok)
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log(f" base: {base_correct}/{base_total}")
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lora_cfg = LoraConfig(r=16, lora_alpha=32, 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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accumulated = []
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problem_pool = []
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for it in range(1, args.iterations + 1):
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it_t = time.time()
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if not problem_pool:
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gen_prompts = [SEED_GEN_PROMPT for _ in range(args.problems_per_iter)]
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raw = gen_batch(model, tok, gen_prompts, max_new=400, temperature=0.9)
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seeded = []
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for r in raw:
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parsed = parse_problem(r)
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if not parsed: continue
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full = parsed["canonical"] + "\n\n" + "\n".join(parsed["tests"])
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ok, _ = run_python(full)
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if ok: seeded.append(parsed)
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problem_pool.extend(seeded)
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log(f"iter {it}: seeded {len(seeded)} fresh (pool={len(problem_pool)})")
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random.shuffle(problem_pool)
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attempt_problems = problem_pool[:args.problems_per_iter]
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problem_pool = problem_pool[args.problems_per_iter:]
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if not attempt_problems:
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log(f"iter {it}: empty pool"); continue
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# Greedy solve
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greedy_prompts = [f"Implement: {p['signature']}\n\nTests:\n{chr(10).join(p['tests'])}\n\nOutput only the function in one ```python block." for p in attempt_problems]
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greedy_outs = gen_batch(model, tok, greedy_prompts, max_new=300, temperature=0.0)
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new_pairs = 0
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amp_targets = []; sim_targets = []
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for p, raw in zip(attempt_problems, greedy_outs):
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code = extract_code(raw) if "```" in raw else raw
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ok, _ = run_python(code + "\n\n" + "\n".join(p["tests"]))
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if ok:
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amp_targets.append(p)
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else:
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# at-edge check via sampling
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solve_prompt = f"Implement: {p['signature']}\n\nTests:\n{chr(10).join(p['tests'])}\n\nOutput only the function in one ```python block."
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atts = gen_batch(model, tok, [solve_prompt]*4, max_new=300, temperature=0.7)
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broken = None; broken_err = None; fixed = None
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for ra in atts:
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c = extract_code(ra) if "```" in ra else ra
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ok2, err = run_python(c + "\n\n" + "\n".join(p["tests"]))
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if ok2 and fixed is None: fixed = c
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elif not ok2 and broken is None: broken = c; broken_err = err
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if broken and fixed: break
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if broken and fixed:
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accumulated.append({"signature": p["signature"], "tests": p["tests"],
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"broken": broken, "error": broken_err, "fixed": fixed})
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new_pairs += 1
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else:
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sim_targets.append(p)
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log(f"iter {it}: {len(attempt_problems)} attempted, +{new_pairs} pairs (total: {len(accumulated)}). amp={len(amp_targets)}, sim={len(sim_targets)} [{time.time()-it_t:.0f}s]")
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# Generate amplified / simplified for next iter
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if amp_targets:
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amp_prompts = [AMPLIFY_PROMPT.format(original=p["raw"]) for p in amp_targets[:args.problems_per_iter]]
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amp_outs = gen_batch(model, tok, amp_prompts, max_new=400, temperature=0.7)
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for r in amp_outs:
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parsed = parse_problem(r)
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if not parsed: continue
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full = parsed["canonical"] + "\n\n" + "\n".join(parsed["tests"])
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ok, _ = run_python(full)
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if ok: problem_pool.append(parsed)
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if sim_targets:
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sim_prompts = [SIMPLIFY_PROMPT.format(original=p["raw"]) for p in sim_targets[:args.problems_per_iter//2]]
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sim_outs = gen_batch(model, tok, sim_prompts, max_new=400, temperature=0.7)
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for r in sim_outs:
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parsed = parse_problem(r)
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if not parsed: continue
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full = parsed["canonical"] + "\n\n" + "\n".join(parsed["tests"])
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ok, _ = run_python(full)
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if ok: problem_pool.append(parsed)
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with open(f"{out_dir}/pairs.jsonl", "w") as fh:
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for r in accumulated: fh.write(json.dumps(r) + "\n")
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if it % args.train_every == 0 and len(accumulated) >= 10:
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log(f" TRAINING on {len(accumulated)} pairs")
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tok.padding_side = "right"
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ds = HFDataset.from_list([make_train_example(r, tok) for r in accumulated])
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targs = TrainingArguments(
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output_dir=f"{out_dir}/ckpt", num_train_epochs=2,
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per_device_train_batch_size=1, gradient_accumulation_steps=4,
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learning_rate=1e-4, bf16=True, logging_steps=10,
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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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tok.padding_side = "left"
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model.eval()
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corr, tot = humaneval_full(model, tok)
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log(f" HumanEval @ iter {it}: {corr}/{tot} Δ={corr-base_correct:+d}")
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model.train()
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model.eval()
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final_correct, final_total = humaneval_full(model, tok)
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result = {
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"model": args.model, "iterations": args.iterations,
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"n_pairs": len(accumulated),
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"base": [base_correct, base_total],
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"trained": [final_correct, final_total],
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"delta": final_correct - base_correct,
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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" CURRICULUM TINYFORGE-ZERO-CODE — {args.model}")
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print(f" HumanEval: base={base_correct}/{base_total} trained={final_correct}/{final_total} Δ={final_correct-base_correct:+d}")
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print(f" Self-mined pairs: {len(accumulated)}")
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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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