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import argparse
import json
import os
import sys
from collections import Counter
from dataclasses import asdict
from pathlib import Path
from typing import Any
import numpy as np
from datasets import load_dataset
from huggingface_hub import HfApi
from tqdm import tqdm
from transformers import AutoTokenizer
from kawk50m.cli import configure_utf8_stdio
from kawk50m.text import FilterConfig
from scripts.prepare_corpus import processed_documents
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description=(
"Stream a pinned FineWeb2 shard, strictly filter Korean documents, "
"and write resumable uint16 token chunks without keeping raw text."
)
)
parser.add_argument("--dataset", default="HuggingFaceFW/fineweb-2")
parser.add_argument("--subset", default="kor_Hang")
parser.add_argument("--split", default="train")
parser.add_argument("--revision", default=None)
parser.add_argument("--tokenizer-dir", required=True)
parser.add_argument("--output-dir", required=True)
parser.add_argument("--dataset-num-shards", type=int, default=4)
parser.add_argument("--dataset-shard-index", type=int, required=True)
parser.add_argument("--skip-documents", type=int, default=0)
parser.add_argument("--max-output-tokens", type=int, required=True)
parser.add_argument("--tokens-per-chunk", type=int, default=250_000_000)
parser.add_argument("--shuffle-buffer", type=int, default=10_000)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--workers", type=int, default=4)
parser.add_argument("--worker-batch-size", type=int, default=64)
parser.add_argument("--tokenize-batch-size", type=int, default=512)
parser.add_argument("--cache-dir", default=".cache/huggingface/datasets")
parser.add_argument("--resume", action="store_true")
return parser.parse_args()
def atomic_json(path: Path, value: dict[str, Any]) -> None:
temporary = path.with_suffix(path.suffix + ".tmp")
with temporary.open("w", encoding="utf-8") as handle:
json.dump(value, handle, ensure_ascii=False, indent=2)
handle.write("\n")
os.replace(temporary, path)
def output_row(row: dict[str, Any], index: int) -> dict[str, Any] | None:
text = row.get("text")
if not isinstance(text, str) or not text.strip():
return None
return {
"id": f"fineweb2:{row.get('id', index)}",
"text": text,
"source": "fineweb2:kor_Hang",
"url": row.get("url"),
"license": "ODC-By-1.0",
}
def main() -> None:
configure_utf8_stdio()
args = parse_args()
for name in (
"dataset_num_shards",
"max_output_tokens",
"tokens_per_chunk",
"shuffle_buffer",
"workers",
"worker_batch_size",
"tokenize_batch_size",
):
if getattr(args, name) <= 0:
raise ValueError(f"--{name.replace('_', '-')} must be positive")
if not 0 <= args.dataset_shard_index < args.dataset_num_shards:
raise ValueError("--dataset-shard-index is outside the shard range")
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
state_path = output_dir / "state.json"
filter_config = FilterConfig()
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_dir, use_fast=False, legacy=True
)
tokenizer.model_max_length = int(1e30)
if len(tokenizer) > np.iinfo(np.uint16).max:
raise ValueError("uint16 requires a vocabulary no larger than 65,535")
if tokenizer.eos_token_id is None:
raise ValueError("Tokenizer must define eos_token_id")
if args.resume:
if not state_path.is_file():
raise FileNotFoundError(f"Resume state is missing: {state_path}")
state = json.loads(state_path.read_text(encoding="utf-8"))
expected = {
"dataset": args.dataset,
"subset": args.subset,
"split": args.split,
"dataset_num_shards": args.dataset_num_shards,
"dataset_shard_index": args.dataset_shard_index,
"skip_documents": args.skip_documents,
"max_output_tokens": args.max_output_tokens,
"tokens_per_chunk": args.tokens_per_chunk,
"tokenizer_vocab_size": len(tokenizer),
"filter": asdict(filter_config),
}
for key, value in expected.items():
if state.get(key) != value:
raise ValueError(
f"Resume state mismatch for {key}: "
f"saved={state.get(key)!r}, requested={value!r}"
)
revision = state["revision"]
seed = int(state["seed"])
shuffle_buffer = int(state["shuffle_buffer"])
documents_seen = int(state["documents_seen"])
tokens_written = int(state["tokens_written"])
completed_chunks = int(state["completed_chunks"])
current_chunk_tokens = int(state["current_chunk_tokens"])
counters = Counter(
{key: int(value) for key, value in state["counts"].items()}
)
if state.get("complete"):
print(json.dumps(state, ensure_ascii=False, indent=2))
return
else:
if state_path.exists() or list(output_dir.glob("tokens-*.bin*")):
raise FileExistsError(
f"{output_dir} contains prior stream-tokenization artifacts"
)
revision = args.revision or HfApi().dataset_info(args.dataset).sha
seed = args.seed
shuffle_buffer = args.shuffle_buffer
documents_seen = 0
tokens_written = 0
completed_chunks = 0
current_chunk_tokens = 0
counters: Counter[str] = Counter()
def final_path(index: int) -> Path:
return output_dir / f"tokens-{index:05d}.bin"
def temporary_path(index: int) -> Path:
return output_dir / f"tokens-{index:05d}.bin.tmp"
def state_value(*, complete: bool) -> dict[str, Any]:
return {
"dataset": args.dataset,
"subset": args.subset,
"split": args.split,
"revision": revision,
"license": "ODC-By-1.0; subject to CommonCrawl Terms of Use",
"dataset_num_shards": args.dataset_num_shards,
"dataset_shard_index": args.dataset_shard_index,
"skip_documents": args.skip_documents,
"seed": seed,
"shuffle_buffer": shuffle_buffer,
"max_output_tokens": args.max_output_tokens,
"tokens_per_chunk": args.tokens_per_chunk,
"tokenizer": str(Path(args.tokenizer_dir).resolve()),
"tokenizer_vocab_size": len(tokenizer),
"filter": asdict(filter_config),
"upstream_deduplication": "FineWeb2 global MinHash deduplication",
"documents_seen": documents_seen,
"tokens_written": tokens_written,
"completed_chunks": completed_chunks,
"current_chunk_tokens": current_chunk_tokens,
"counts": dict(sorted(counters.items())),
"complete": complete,
}
current_final = final_path(completed_chunks)
current_temporary = temporary_path(completed_chunks)
if current_chunk_tokens:
if current_final.is_file() and not current_temporary.exists():
completed_chunks += 1
current_chunk_tokens = 0
atomic_json(
state_path,
state_value(complete=tokens_written >= args.max_output_tokens),
)
current_final = final_path(completed_chunks)
current_temporary = temporary_path(completed_chunks)
elif current_temporary.is_file():
expected_bytes = current_chunk_tokens * np.dtype(np.uint16).itemsize
with current_temporary.open("r+b") as handle:
handle.truncate(expected_bytes)
else:
raise RuntimeError("Resume state points to a missing current token chunk")
elif current_final.exists() or current_temporary.exists():
raise RuntimeError("Unexpected token chunk exists beyond committed state")
dataset = load_dataset(
args.dataset,
name=args.subset,
split=args.split,
revision=revision,
streaming=True,
cache_dir=args.cache_dir,
)
dataset = dataset.shard(
num_shards=args.dataset_num_shards,
index=args.dataset_shard_index,
contiguous=True,
)
dataset = dataset.shuffle(seed=seed, buffer_size=shuffle_buffer)
total_skip = args.skip_documents + documents_seen
if total_skip:
dataset = dataset.skip(total_skip)
def documents() -> Any:
for index, row in enumerate(dataset, start=total_skip):
document = output_row(row, index)
if document is not None:
yield document
processed = processed_documents(
documents(),
filter_config=filter_config,
workers=args.workers,
batch_size=args.worker_batch_size,
limit=None,
)
result_batch: list[Any] = []
handle = current_temporary.open("ab")
progress = tqdm(initial=documents_seen, unit="docs")
def finalize_chunk(*, reopen: bool) -> None:
nonlocal handle, completed_chunks, current_chunk_tokens
handle.flush()
os.fsync(handle.fileno())
handle.close()
atomic_json(state_path, state_value(complete=False))
os.replace(temporary_path(completed_chunks), final_path(completed_chunks))
completed_chunks += 1
current_chunk_tokens = 0
atomic_json(
state_path,
state_value(complete=tokens_written >= args.max_output_tokens),
)
if reopen:
handle = temporary_path(completed_chunks).open("ab")
def commit_batch() -> bool:
nonlocal documents_seen, tokens_written, current_chunk_tokens
if not result_batch:
return False
accepted_texts: list[str] = []
for _, text, reason, _, _, _ in result_batch:
if reason is None:
accepted_texts.append(text)
counters["accepted"] += 1
else:
counters[f"rejected_{reason}"] += 1
documents_seen += len(result_batch)
counters["seen"] += len(result_batch)
progress.update(len(result_batch))
result_batch.clear()
if accepted_texts:
encoded = tokenizer(
accepted_texts,
add_special_tokens=False,
return_attention_mask=False,
return_token_type_ids=False,
)["input_ids"]
flat: list[int] = []
for token_ids in encoded:
flat.extend(token_ids)
flat.append(tokenizer.eos_token_id)
remaining = args.max_output_tokens - tokens_written
if len(flat) > remaining:
flat = flat[:remaining]
if flat:
array = np.asarray(flat, dtype=np.uint16)
array.tofile(handle)
tokens_written += len(array)
current_chunk_tokens += len(array)
atomic_json(
state_path,
state_value(complete=tokens_written >= args.max_output_tokens),
)
if (
current_chunk_tokens >= args.tokens_per_chunk
or tokens_written >= args.max_output_tokens
):
finalize_chunk(reopen=tokens_written < args.max_output_tokens)
return tokens_written >= args.max_output_tokens
try:
for result in processed:
result_batch.append(result)
if len(result_batch) >= args.tokenize_batch_size and commit_batch():
break
else:
commit_batch()
if current_chunk_tokens and not handle.closed:
handle.flush()
atomic_json(state_path, state_value(complete=False))
finally:
close_processed = getattr(processed, "close", None)
if close_processed is not None:
close_processed()
if not handle.closed:
handle.close()
progress.close()
complete = tokens_written >= args.max_output_tokens
atomic_json(state_path, state_value(complete=complete))
print(json.dumps(state_value(complete=complete), ensure_ascii=False, indent=2))
if not complete:
raise RuntimeError(
"FineWeb2 shard ended before the requested token target was reached"
)
# Some pyarrow/fsspec streaming combinations abort in interpreter finalizers
# after a remote iterator is intentionally stopped early. All output handles,
# worker generators, and the atomic state file are already closed above.
# A direct successful exit avoids turning a fully committed shard into a
# false pipeline failure (and avoids producing a large core dump).
sys.stdout.flush()
sys.stderr.flush()
os._exit(0)
if __name__ == "__main__":
main()
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