data_expander.py

import os
import sys
import json
import torch
import hashlib
from pathlib import Path
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
import sys

# --- PATCH FOR TRANSFORMERS VERSION MISMATCH ---
try:
    import transformers.activations
    if not hasattr(transformers.activations, "PytorchGELUTanh"):
        # Mapping the old name to the new existing one
        transformers.activations.PytorchGELUTanh = transformers.activations.GELUActivation
except ImportError:
    pass
# ------------------------------------------------------

import os
import json
import torch
# ... baaki ke saare purane imports

# Force script to use only the 2 free GPUs (e.g., 0 and 7)
os.environ["CUDA_VISIBLE_DEVICES"] = "0,7"

PROJECT_ROOT = os.path.abspath(os.path.dirname(__file__))
if PROJECT_ROOT not in sys.path:
    sys.path.insert(0, PROJECT_ROOT)

from data_factory.schemas import SCHEMA_CONTEXT

# AWQ model is 4x smaller and much faster
MODEL_NAME = "Qwen/Qwen2.5-72B-Instruct-AWQ"
INPUT_FILE = "llm_hybrid_templates.json"
OUTPUT_FILE = "nl2sql_50k_elite_dataset.jsonl"
VARIATIONS_PER_SQL = 20
BATCH_SIZE = 64  # AWQ allows much larger batches!

SYSTEM_PROMPT = "You are an expert SQL analyst. Write a single SELECT query that answers the question. Output ONLY the SQL query — no markdown, no explanation, no backticks."

EXPANSION_PROMPT = """
You are an expert linguist and NL2SQL data augmentor. I have a SQLite database schema and a complex SQL query.
Generate exactly {count} completely different natural language questions that this exact SQL query answers.

RULES:
- Personas: Executive (direct), Non-tech (wordy), Analyst (technical), Curious (investigative).
- Structure: Completely change sentence flow.
- No direct column/table names.

DATABASE SCHEMA:
{schema}

SQL QUERY:
{sql}

OUTPUT FORMAT:
Return ONLY a valid JSON array of objects: [{{"persona": "...", "question": "..."}}]
"""

def extract_json_array(raw_text):
    text = raw_text.strip()
    start = text.find("[")
    end = text.rfind("]")
    if start != -1 and end != -1:
        return text[start:end+1]
    return "[]"

def get_hash(text):
    return hashlib.md5(text.lower().strip().encode('utf-8')).hexdigest()

def main():
    if not os.path.exists(INPUT_FILE):
        print(f"Error: {INPUT_FILE} not found.")
        sys.exit(1)
        
    with open(INPUT_FILE, "r") as f:
        base_templates = json.load(f)
        
    print(f"🚀 Loading {MODEL_NAME} on 2 GPUs...")
    
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, padding_side="left")
    tokenizer.pad_token = tokenizer.eos_token

    # Model loading (AWQ version automatically handles quantization)
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME,
        device_map="auto",
        torch_dtype=torch.float16, # AWQ models use float16/bfloat16 for weights
        low_cpu_mem_usage=True
    )
    
    seen_hashes = set()
    total_saved = 0
    if os.path.exists(OUTPUT_FILE):
        with open(OUTPUT_FILE, "r") as f:
            for line in f:
                total_saved += 1 # Quick count
    
    pbar = tqdm(total=len(base_templates) * VARIATIONS_PER_SQL, initial=total_saved)
    
    # Batch processing
    for i in range(0, len(base_templates), BATCH_SIZE):
        batch = base_templates[i:i + BATCH_SIZE]
        prompts = []
        
        for temp in batch:
            msg = [
                {"role": "system", "content": "You output only JSON arrays."},
                {"role": "user", "content": EXPANSION_PROMPT.format(count=VARIATIONS_PER_SQL, schema=SCHEMA_CONTEXT[temp['domain']], sql=temp['sql'])}
            ]
            prompts.append(tokenizer.apply_chat_template(msg, tokenize=False, add_generation_prompt=True))

        inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
        
        try:
            with torch.no_grad():
                # Increased speed: AWQ handles large batches efficiently
                outputs = model.generate(
                    **inputs,
                    max_new_tokens=2048,
                    temperature=0.5,
                    do_sample=True,
                    pad_token_id=tokenizer.eos_token_id
                )
            
            responses = tokenizer.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
            
            with open(OUTPUT_FILE, "a", encoding="utf-8") as out_file:
                for idx, resp in enumerate(responses):
                    questions_data = json.loads(extract_json_array(resp))
                    sql = batch[idx]["sql"]
                    domain = batch[idx]["domain"]
                    
                    for item in questions_data:
                        q = item.get("question", "")
                        if len(q) > 10:
                            q_hash = get_hash(q + sql)
                            if q_hash not in seen_hashes:
                                seen_hashes.add(q_hash)
                                record = {
                                    "prompt": [
                                        {"role": "system", "content": SYSTEM_PROMPT},
                                        {"role": "user", "content": f"SCHEMA: {SCHEMA_CONTEXT[domain]}\nQUESTION: {q}"}
                                    ],
                                    "sql": sql
                                }
                                out_file.write(json.dumps(record, ensure_ascii=False) + "\n")
                                total_saved += 1
                                pbar.update(1)
                out_file.flush()
        except Exception as e:
            print(f"Batch failed: {e}")
            continue

    pbar.close()

if __name__ == "__main__":
    main()