#!/usr/bin/env python3 """ Transform the mixed_pretrain JSONL shards into a unified parquet dataset where every record has a source_code column and an llvm_ir column. SLTrans records already contain both — the / tags are parsed out into separate columns. Records from other sources (peS2o, TheStack, OpenWebMath) have their text placed in source_code and llvm_ir set to null, keeping the schema consistent. Output schema ------------- source_code string source code or prose text llvm_ir string LLVM IR (null for non-SLTrans records) language string programming language or content category ir_type string Perf_Optimized / Size_Optimized (null if not SLTrans) source_dataset string sltrans | pes2o | the_stack | openwebmath est_tokens int64 whitespace token estimate Usage ----- python build_ir_dataset.py python build_ir_dataset.py --input mixed_pretrain --output ir_dataset.parquet """ from __future__ import annotations import argparse import json import re import sys from pathlib import Path import pandas as pd import pyarrow as pa import pyarrow.parquet as pq from tqdm import tqdm # ── SLTrans text parsing ─────────────────────────────────────────────────────── # Matches the format written by build_mixed_dataset.py: # \n{code}\n\n\n{ir}\n _SOURCE_PAT = re.compile(r"\n(.*?)\n", re.DOTALL) _IR_PAT = re.compile(r"\n(.*?)\n", re.DOTALL) def parse_sltrans(text: str) -> tuple[str, str]: src = _SOURCE_PAT.search(text) ir = _IR_PAT.search(text) return (src.group(1) if src else ""), (ir.group(1) if ir else "") # ── language normalisation ───────────────────────────────────────────────────── # Maps non-SLTrans sources to a human-readable language/category label. def _language(source: str, meta: dict) -> str: if source == "the_stack": return meta.get("lang") or meta.get("language") or "code" if source == "pes2o": return "scientific" if source == "openwebmath": return "math" return "" # ── record transformer ───────────────────────────────────────────────────────── def transform(rec: dict) -> dict: source = rec["source"] text = rec.get("text", "") meta = rec.get("meta", {}) if source == "sltrans": source_code, llvm_ir = parse_sltrans(text) language = meta.get("language", "") ir_type = meta.get("ir_type", None) elif source == "stack_llvm": # Unpaired IR from TheStack: the text IS the IR, no corresponding source source_code = None llvm_ir = text language = "LLVM" ir_type = None else: source_code = text llvm_ir = None language = _language(source, meta) ir_type = None return { "source_code": source_code, "llvm_ir": llvm_ir, "language": language, "ir_type": ir_type, "source_dataset": source, "est_tokens": rec.get("est_tokens", 0), } # ── parquet schema ───────────────────────────────────────────────────────────── SCHEMA = pa.schema([ pa.field("source_code", pa.large_utf8()), pa.field("llvm_ir", pa.large_utf8()), pa.field("language", pa.large_utf8()), pa.field("ir_type", pa.large_utf8()), pa.field("source_dataset", pa.large_utf8()), pa.field("est_tokens", pa.int64()), ]) def main() -> None: ap = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, ) ap.add_argument("--input", default="mixed_pretrain", help="Directory containing JSONL shards (default: mixed_pretrain)") ap.add_argument("--output", default="ir_dataset.parquet", help="Output parquet path (default: ir_dataset.parquet)") ap.add_argument("--batch-size", type=int, default=10_000, help="Records per parquet row group (default: 10000)") args = ap.parse_args() data_dir = Path(args.input) if not data_dir.is_dir(): print(f"ERROR: input directory not found: {data_dir}", file=sys.stderr) sys.exit(1) shards = sorted(data_dir.glob("*.jsonl")) if not shards: print(f"No JSONL files found in {data_dir}", file=sys.stderr) sys.exit(1) out_path = Path(args.output) print(f"Input : {data_dir.resolve()} ({len(shards)} shards)") print(f"Output : {out_path.resolve()}") print(f"Schema : {[f.name for f in SCHEMA]}") print() writer = pq.ParquetWriter(out_path, SCHEMA) batch: list[dict] = [] total = sltrans_ok = sltrans_partial = 0 def flush(rows: list[dict]) -> None: df = pd.DataFrame(rows) # Ensure all columns present even if batch is from a single source for col in [f.name for f in SCHEMA]: if col not in df.columns: df[col] = None df["est_tokens"] = df["est_tokens"].fillna(0).astype("int64") table = pa.Table.from_pandas(df[[ f.name for f in SCHEMA ]], schema=SCHEMA) writer.write_table(table) for shard in tqdm(shards, desc="shards", unit="file"): with shard.open(encoding="utf-8") as f: for line in f: line = line.strip() if not line: continue rec = json.loads(line) row = transform(rec) if rec["source"] == "sltrans": if row["source_code"] and row["llvm_ir"]: sltrans_ok += 1 else: sltrans_partial += 1 batch.append(row) total += 1 if len(batch) >= args.batch_size: flush(batch) batch.clear() if batch: flush(batch) writer.close() # ── summary ──────────────────────────────────────────────────────────────── pf = pq.ParquetFile(out_path) print() print("=" * 60) print("BUILD COMPLETE") print("=" * 60) print(f"Total records written : {total:,}") print(f"SLTrans (both fields) : {sltrans_ok:,}") if sltrans_partial: print(f"SLTrans (parse miss) : {sltrans_partial:,} <- check text format") print() print("Output schema:") print(pf.schema_arrow) print() # Quick per-source count from the written file df = pf.read(columns=["source_dataset"]).to_pandas() print("Records per source_dataset:") for src, cnt in df["source_dataset"].value_counts().items(): print(f" {src:<14} {cnt:>10,}") print() print(f"Output: {out_path.resolve()}") if __name__ == "__main__": main()