""" Build SFT dataset from griffith-bigdata/sft_text2sql (deepseek-reasoner prompts) with CoT completions generated by the thanhdath Llama-3B planner. Pipeline: 1. Load griffith prompts (rich natural-language schema + External Knowledge + Question) 2. Reformat as our planning prompt (griffith schema + "Planning:" suffix) 3. Run thanhdath planner to generate CoT completions 4. Execute the predicted SQL and keep only correct predictions 5. Save as SFT dataset: (prompt, CoT_completion) pairs Output: data/hf_planner_sft_griffith Completion format: full CoT text ending with ```sql ... ``` (same as existing planner outputs) """ import json, os, re, sys, random, sqlite3, threading, requests from datasets import Dataset, DatasetDict ROOT = "/weka/s225250685/mats-tist" os.chdir(ROOT) sys.path.insert(0, ROOT) from data_processing.planner import is_execution_correct HF_CACHE = "/weka/s225250685/Huggingface/hub" OUT = "data/hf_planner_sft_griffith" PLANNER_URL = "http://localhost:8100" # thanhdath planner served externally before this script runs PLANNER_MODEL = "planner" MAX_TOKENS = 1024 TEMPERATURE = 0.0 # greedy for SFT data (deterministic, highest quality) SEED = 42 print("Loading BIRD train gold SQL...", flush=True) with open("data/sft_bird_with_evidence_train_text2sql.json") as f: bird_train = json.load(f) print(f"BIRD train: {len(bird_train)} questions", flush=True) def safe_exec(db_path, sql, timeout=5): result = [None]; err = [None] def _run(): try: conn = sqlite3.connect(db_path) conn.text_factory = lambda b: b.decode(errors="ignore") result[0] = conn.execute(sql).fetchmany(10) conn.close() except Exception as e: err[0] = str(e) t = threading.Thread(target=_run, daemon=True) t.start(); t.join(timeout) if t.is_alive(): return None, "TIMEOUT" return result[0], err[0] def extract_sql(cot_text): """Extract SQL from the planner CoT output (```sql...``` or ```...``` block).""" m = re.search(r"```(?:sql)?\s*(.*?)\s*```", cot_text, re.DOTALL) if m: sql = m.group(1).strip() if sql.upper().startswith("SQL"): sql = sql[3:].strip() return sql # Fallback: last non-empty line lines = [l.strip() for l in cot_text.strip().split("\n") if l.strip()] return lines[-1] if lines else "" def llama3_chat(prompt): """Build raw vLLM completion prompt in Llama-3 format.""" return (f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n" f"{prompt}<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n") def call_planner(prompt_text): """Call vLLM completions endpoint, return CoT text or None.""" raw_prompt = llama3_chat(prompt_text) try: r = requests.post(f"{PLANNER_URL}/v1/completions", json={ "model": PLANNER_MODEL, "prompt": raw_prompt, "max_tokens": MAX_TOKENS, "temperature": TEMPERATURE, "n": 1, "seed": SEED, "stop": ["<|eot_id|>"], }, timeout=60) r.raise_for_status() return r.json()["choices"][0]["text"].strip() except Exception as e: return None # Build prompt: griffith schema text + our "Planning:" suffix # The griffith user message already has: # "Database Schema:\n...\nExternal Knowledge:\n...\nQuestion: ...\n" # We append "Planning:" to trigger CoT output. def build_planning_prompt(griffith_user_msg): """Convert griffith user message to our planning prompt format.""" # Strip trailing whitespace, then append Planning: trigger return griffith_user_msg.rstrip() + "\n\nPlanning:" print("Loading griffith-bigdata/sft_text2sql (deepseek-reasoner)...", flush=True) from datasets import load_dataset ds_raw = load_dataset("griffith-bigdata/sft_text2sql", split="train_sft", cache_dir=HF_CACHE) ds_dr = ds_raw.filter(lambda x: x["model_name"] == "deepseek-reasoner") print(f"deepseek-reasoner rows: {len(ds_dr)}", flush=True) rows = [] n_correct = 0 n_wrong = 0 n_skip = 0 n_qmismatch = 0 for i, row in enumerate(ds_dr): sid = int(row["sample_id"]) if sid < 0 or sid >= len(bird_train): n_skip += 1 continue msgs = row["messages"] user_msg = msgs[1]["content"] # griffith schema + evidence + question # Cross-check question matches BIRD q_match = re.search(r"Question:\s*(.+?)(?:\n|$)", user_msg) griffith_q = q_match.group(1).strip() if q_match else "" bird_q = bird_train[sid]["question"].strip() if griffith_q.lower() != bird_q.lower(): n_qmismatch += 1 n_skip += 1 continue gold_sql = bird_train[sid]["sql"] db_id = bird_train[sid].get("db_id", "") db_path = (bird_train[sid].get("db_path") or f"data/train_databases/{db_id}/{db_id}.sqlite") # Build planning prompt planning_prompt = build_planning_prompt(user_msg) # Run thanhdath planner (greedy) cot_text = call_planner(planning_prompt) if not cot_text: n_wrong += 1 continue # Extract and execute predicted SQL pred_sql = extract_sql(cot_text) if not pred_sql: n_wrong += 1 continue gold_res, _ = safe_exec(db_path, gold_sql) pred_res, err = safe_exec(db_path, pred_sql) if err or not is_execution_correct(gold_res, pred_res): n_wrong += 1 continue # Correct prediction — keep this (prompt, CoT) pair rows.append({ "prompt": planning_prompt, # griffith schema + Planning: trigger "completion": cot_text, # full CoT: Goal→Condition→Tables→SQL "sample_id": sid, "db_id": db_id, "question": bird_q, "gold_sql": gold_sql, }) n_correct += 1 if (i + 1) % 500 == 0: print(f" [{i+1}/{len(ds_dr)}] correct={n_correct} wrong={n_wrong} skip={n_skip}", flush=True) print(f"\nFinal: {n_correct} correct / {n_correct+n_wrong} attempted ({n_correct/(n_correct+n_wrong)*100:.1f}% pass rate)", flush=True) print(f"Skipped: {n_skip} (q_mismatch={n_qmismatch})", flush=True) if not rows: print("ERROR: no correct pairs collected — check planner endpoint", flush=True) sys.exit(1) # Sanity check: show 3 examples for ex in rows[:3]: print(f"\n sid={ex['sample_id']} db={ex['db_id']}", flush=True) print(f" Q: {ex['question'][:70]}", flush=True) print(f" CoT: {ex['completion'][:120]}...", flush=True) # 90/10 split random.seed(42) random.shuffle(rows) n_train = int(0.9 * len(rows)) DatasetDict({ "train": Dataset.from_list(rows[:n_train]), "test": Dataset.from_list(rows[n_train:]), }).save_to_disk(OUT) print(f"\nSaved → {OUT} (train={n_train}, test={len(rows)-n_train})", flush=True)