prepare_gd_data.py

#!/usr/bin/env python3
"""

Prepare GD level data for modded-nanogpt training.

Converts tokenized levels to .bin format compatible with the data loader.

Uses multiprocessing for fast tokenization.



Usage (local files):

    python prepare_gd_data.py --input data/gd_raw --output data/gd_levels --tokenizer tokenizer.model



Usage (HuggingFace dataset):

    python prepare_gd_data.py --hf-repo tldne/gd-levels --hf-file levels_deduped.jsonl --output data/gd_levels --tokenizer tokenizer.model

"""

import argparse
import json
import numpy as np
from pathlib import Path
import sentencepiece as spm
from tqdm import tqdm
from huggingface_hub import hf_hub_download
from multiprocessing import Pool, cpu_count
from functools import partial
import os

# Only include these fields in training (must match train_tokenizer.py!)
INCLUDE_FIELDS = [
    'level_id',           # Derived from source_file
    'level_name',
    'level_type',
    'binary_version', 
    'description_decoded',
    'song_id',
    # level_string handled separately at the end
]


def format_training_sample(record: dict) -> str:
    """

    Format a level record as a training string.

    Only includes specific metadata fields + level_string.

    Must match train_tokenizer.py exactly!

    """
    parts = []
    
    # Extract level_id from source_file: "Level_12345.gmd2" -> "12345"
    source_file = record.get('source_file', '')
    if source_file:
        level_id = source_file.lstrip("Level_").rstrip(".gmd2")
        parts.append(f"<level_id>{level_id}")
    
    # Add only the included metadata fields
    for key in INCLUDE_FIELDS:
        if key == 'level_id':
            continue  # Already handled above
        value = record.get(key)
        if value is not None and value != "":
            parts.append(f"<{key}>{value}")
    
    # Add level string at the end (the main content)
    if record.get('level_string'):
        parts.append(f"<level_string>{record['level_string']}")
    
    return "".join(parts)


def write_bin_file(tokens: np.ndarray, output_path: Path):
    """Write tokens to .bin file with modded-nanogpt header format."""
    header = np.zeros(256, dtype=np.int32)
    header[0] = 20240520  # Magic number
    header[1] = 1         # Version
    header[2] = len(tokens)
    
    with output_path.open("wb") as f:
        f.write(header.tobytes())
        f.write(tokens.astype(np.uint16).tobytes())
    
    return len(tokens)


# Worker function for multiprocessing
def tokenize_record(record_json: str, tokenizer_path: str) -> np.ndarray | None:
    """Tokenize a single record. Returns None if should be skipped."""
    try:
        # Load tokenizer in worker (each worker needs its own instance)
        if not hasattr(tokenize_record, '_sp'):
            tokenize_record._sp = spm.SentencePieceProcessor()
            tokenize_record._sp.load(tokenizer_path)
        sp = tokenize_record._sp
        
        record = json.loads(record_json)
        if not record.get('level_string'):
            return None
            
        text = format_training_sample(record)
        if not text or len(text) > 5_000_000:
            return None
            
        tokens = [sp.bos_id()] + sp.encode(text) + [sp.eos_id()]
        if len(tokens) > 10_000_000:
            return None
            
        return np.array(tokens, dtype=np.uint16)
    except:
        return None


def process_levels_parallel(

    input_dir: Path,

    output_dir: Path,

    tokenizer_path: Path,

    shard_size: int = 100_000_000,

    val_ratio: float = 0.01,

    num_workers: int = None,

):
    """Process all levels using multiprocessing and create train/val shards."""
    
    if num_workers is None:
        num_workers = max(1, cpu_count() - 1)
    
    print(f"Using {num_workers} workers for tokenization")
    
    # Load tokenizer to get token IDs
    print(f"Loading tokenizer from {tokenizer_path}")
    sp = spm.SentencePieceProcessor()
    sp.load(str(tokenizer_path))
    print(f"BOS ID: {sp.bos_id()}, EOS ID: {sp.eos_id()}")
    
    # Find all input files
    jsonl_files = list(input_dir.glob("*.jsonl"))
    json_files = list(input_dir.glob("*.json"))
    level_files = jsonl_files + json_files
    
    if not level_files:
        jsonl_files = list(input_dir.glob("**/*.jsonl"))
        json_files = list(input_dir.glob("**/*.json"))
        level_files = jsonl_files + json_files
    
    print(f"Found {len(level_files)} input files")
    
    # Collect all records as JSON strings
    print("Loading records...")
    all_records = []
    for lf in tqdm(level_files, desc="Reading files"):
        try:
            if lf.suffix == ".jsonl":
                with open(lf, "r", encoding="utf-8") as f:
                    for line in f:
                        line = line.strip()
                        if line:
                            all_records.append(line)
            else:
                with open(lf, "r", encoding="utf-8") as f:
                    data = json.load(f)
                records = data if isinstance(data, list) else [data]
                for record in records:
                    all_records.append(json.dumps(record))
        except Exception as e:
            print(f"Error reading {lf}: {e}")
    
    print(f"Loaded {len(all_records):,} records")
    
    # Tokenize in parallel
    print(f"Tokenizing with {num_workers} workers...")
    tokenize_fn = partial(tokenize_record, tokenizer_path=str(tokenizer_path))
    
    all_levels = []
    total_tokens = 0
    
    with Pool(num_workers) as pool:
        results = list(tqdm(
            pool.imap(tokenize_fn, all_records, chunksize=100),
            total=len(all_records),
            desc="Tokenizing"
        ))
    
    for tokens in results:
        if tokens is not None:
            all_levels.append(tokens)
            total_tokens += len(tokens)
    
    print(f"Tokenized {len(all_levels):,} levels with {total_tokens:,} total tokens")
    
    # Shuffle and split
    np.random.seed(42)
    indices = np.random.permutation(len(all_levels))
    
    val_count = max(1, int(len(all_levels) * val_ratio))
    val_indices = indices[:val_count]
    train_indices = indices[val_count:]
    
    print(f"Train: {len(train_indices):,} levels, Val: {len(val_indices):,} levels")
    
    # Create output directory
    output_dir.mkdir(parents=True, exist_ok=True)
    
    # Write train shards
    train_tokens = np.concatenate([all_levels[i] for i in tqdm(train_indices, desc="Concatenating train")])
    num_train_shards = max(1, len(train_tokens) // shard_size)
    
    print(f"Writing {num_train_shards} train shards...")
    shard_size_actual = len(train_tokens) // num_train_shards
    for i in tqdm(range(num_train_shards), desc="Writing train shards"):
        start = i * shard_size_actual
        end = start + shard_size_actual if i < num_train_shards - 1 else len(train_tokens)
        shard = train_tokens[start:end]
        
        output_path = output_dir / f"train_{i:04d}.bin"
        n = write_bin_file(shard, output_path)
    
    # Write val shards
    val_tokens = np.concatenate([all_levels[i] for i in tqdm(val_indices, desc="Concatenating val")])
    num_val_shards = max(1, len(val_tokens) // shard_size)
    
    print(f"Writing {num_val_shards} val shards...")
    shard_size_actual = len(val_tokens) // num_val_shards
    for i in range(num_val_shards):
        start = i * shard_size_actual
        end = start + shard_size_actual if i < num_val_shards - 1 else len(val_tokens)
        shard = val_tokens[start:end]
        
        output_path = output_dir / f"val_{i:04d}.bin"
        n = write_bin_file(shard, output_path)
    
    # Summary
    print("\n=== Summary ===")
    print(f"Train tokens: {len(train_tokens):,}")
    print(f"Val tokens: {len(val_tokens):,}")
    print(f"Total tokens: {len(train_tokens) + len(val_tokens):,}")
    print(f"Output dir: {output_dir}")


def main():
    parser = argparse.ArgumentParser(description="Prepare GD levels for modded-nanogpt")
    
    # Input options (either local or HF)
    input_group = parser.add_mutually_exclusive_group(required=True)
    input_group.add_argument("--input", "-i", type=Path, help="Input directory/file with level JSONs/JSONL")
    input_group.add_argument("--hf-repo", type=str, help="HuggingFace repo ID (e.g., tldne/gd-levels)")
    
    parser.add_argument("--hf-file", type=str, default="levels_deduped.jsonl", help="File to download from HF repo")
    parser.add_argument("--output", "-o", type=Path, required=True, help="Output directory for .bin files")
    parser.add_argument("--tokenizer", "-t", type=Path, required=True, help="Path to tokenizer.model")
    parser.add_argument("--shard-size", type=int, default=100_000_000, help="Tokens per shard")
    parser.add_argument("--val-ratio", type=float, default=0.01, help="Validation split ratio")
    parser.add_argument("--workers", "-w", type=int, default=None, help="Number of workers (default: cpu_count - 1)")
    
    args = parser.parse_args()
    
    # Handle HuggingFace download
    if args.hf_repo:
        print(f"Downloading {args.hf_file} from {args.hf_repo}...")
        downloaded_path = hf_hub_download(
            repo_id=args.hf_repo,
            filename=args.hf_file,
            repo_type="dataset",
        )
        print(f"Downloaded to: {downloaded_path}")
        import tempfile
        import shutil
        temp_dir = Path(tempfile.mkdtemp())
        shutil.copy(downloaded_path, temp_dir / args.hf_file)
        input_dir = temp_dir
    else:
        input_dir = args.input
        temp_dir = None
    
    process_levels_parallel(
        input_dir=input_dir,
        output_dir=args.output,
        tokenizer_path=args.tokenizer,
        shard_size=args.shard_size,
        val_ratio=args.val_ratio,
        num_workers=args.workers,
    )
    
    # Cleanup temp dir if created
    if temp_dir:
        import shutil
        shutil.rmtree(temp_dir)


if __name__ == "__main__":
    main()