File size: 10,567 Bytes
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"""Build deterministic, document-isolated OC-LM Strict-Small training epochs."""
from __future__ import annotations
import argparse
import hashlib
import json
from pathlib import Path
import numpy as np
from tokenizers import Tokenizer
EXPECTED_WORDS = {
"bnc_spoken.train.txt": 762_073,
"childes.train.txt": 2_841_101,
"gutenberg.train.txt": 2_557_721,
"open_subtitles.train.txt": 2_282_877,
"simple_wiki.train.txt": 1_531_437,
"switchboard.train.txt": 24_791,
}
LENGTHS = (128,) * 6 + (256,) * 2 + (512,) * 2
SPECIAL = {"unk": 0, "bos": 1, "eos": 2, "pad": 3, "mask": 4}
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(8 << 20), b""):
digest.update(chunk)
return digest.hexdigest()
def flush_row(handles, tokens, segments, words, objective, length):
if not tokens:
return 0
used = len(tokens)
tokens.extend([SPECIAL["pad"]] * (length - used))
segments.extend([0] * (length - used))
np.asarray(tokens, dtype="<u2").tofile(handles["tokens"])
np.asarray(segments, dtype="<u2").tofile(handles["segments"])
np.asarray([used], dtype="<u2").tofile(handles["lengths"])
np.asarray([words], dtype="<u4").tofile(handles["words"])
np.asarray([objective], dtype="u1").tofile(handles["objectives"])
return 1
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--raw", type=Path, required=True)
parser.add_argument("--tokenizer", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--seed", type=int, default=20260904)
parser.add_argument("--batch-lines", type=int, default=4096)
parser.add_argument("--schedule", choices=["cycle", "fixed512"], required=True)
args = parser.parse_args()
args.output.mkdir(parents=True, exist_ok=False)
schedule = (128,256,512,128,256,512,128,256,512,512) if args.schedule == "cycle" else (512,)*10
tokenizer = Tokenizer.from_file(str(args.tokenizer / "tokenizer.json"))
if sha256(args.tokenizer / "tokenizer.json") != "98dfab9eabdd78aed27c025ec1bdbd881172c6339c0b491f9e1612e9a36fcbf8":
raise RuntimeError("shared DST tokenizer hash mismatch")
observed_special = {
"unk": tokenizer.token_to_id("<unk>"),
"bos": tokenizer.token_to_id("<s>"),
"eos": tokenizer.token_to_id("</s>"),
"pad": tokenizer.token_to_id("<pad>"),
"mask": tokenizer.token_to_id("<mask>"),
}
if observed_special != SPECIAL or tokenizer.get_vocab_size() != 16_384:
raise RuntimeError(f"tokenizer contract mismatch: {observed_special}")
documents: list[list[int]] = []
word_counts: list[int] = []
raw_manifest = {}
for path in sorted(args.raw.glob("*.txt")):
expected = EXPECTED_WORDS.get(path.name)
if expected is None:
raise RuntimeError(f"unexpected raw file: {path.name}")
file_words = 0
batch_text: list[str] = []
batch_words: list[int] = []
def emit_batch() -> None:
if not batch_text:
return
encoded = [item.ids for item in tokenizer.encode_batch(batch_text, add_special_tokens=False)]
for ids, count in zip(encoded, batch_words, strict=True):
documents.append([SPECIAL["bos"], *ids, SPECIAL["eos"]])
word_counts.append(count)
batch_text.clear()
batch_words.clear()
with path.open("r", encoding="utf-8") as handle:
for raw_line in handle:
text = raw_line.rstrip("\r\n")
count = len(text.split())
if count == 0:
continue
file_words += count
batch_text.append(text)
batch_words.append(count)
if len(batch_text) >= args.batch_lines:
emit_batch()
emit_batch()
if file_words != expected:
raise RuntimeError(f"word mismatch for {path.name}: {file_words} != {expected}")
raw_manifest[path.name] = {
"bytes": path.stat().st_size,
"sha256": sha256(path),
"words": file_words,
}
if sum(word_counts) != 10_000_000:
raise RuntimeError("Strict-Small corpus must contain exactly 10,000,000 words")
docs_token_count = sum(map(len, documents))
epochs = []
for epoch, length in enumerate(schedule):
epoch_dir = args.output / f"epoch_{epoch:02d}_len_{length}"
epoch_dir.mkdir(parents=True, exist_ok=True)
paths = {
name: epoch_dir / f"{name}.bin"
for name in ("tokens", "segments", "lengths", "words", "objectives")
}
handles = {name: path.open("wb") for name, path in paths.items()}
rng = np.random.default_rng(args.seed + epoch)
order = rng.permutation(len(documents))
boundary_rng = np.random.default_rng(args.seed + 100000 + epoch)
expected_stream = hashlib.sha256()
row_tokens: list[int] = []
row_segments: list[int] = []
row_words = 0
segment = 0
rows = 0
for doc_index in order:
doc = documents[int(doc_index)]
expected_stream.update(np.asarray(doc, dtype='<u2').tobytes())
# Long documents get a new first-chunk boundary each epoch.
# Retain the prefix and tail; never rotate tokens or join future to past.
first_size = length - int(boundary_rng.integers(0, length)) if len(doc) > length else None
if first_size is not None and row_tokens:
rows += flush_row(handles, row_tokens, row_segments, row_words, (rows + epoch) & 1, length)
row_tokens, row_segments, row_words, segment = [], [], 0, 0
cursor = 0
while cursor < len(doc):
room = length - len(row_tokens)
if cursor == 0 and first_size is not None:
room = min(room, first_size)
take = min(room, len(doc) - cursor)
row_tokens.extend(doc[cursor : cursor + take])
row_segments.extend([segment] * take)
cursor += take
if cursor == len(doc):
row_words += word_counts[int(doc_index)]
segment += 1
if len(row_tokens) == length or (first_size is not None and cursor == first_size):
rows += flush_row(
handles,
row_tokens,
row_segments,
row_words,
(rows + epoch) & 1,
length,
)
row_tokens, row_segments, row_words, segment = [], [], 0, 0
if row_tokens:
rows += flush_row(
handles,
row_tokens,
row_segments,
row_words,
(rows + epoch) & 1,
length,
)
for handle in handles.values():
handle.flush()
handle.close()
words = np.memmap(paths["words"], mode="r", dtype="<u4", shape=(rows,))
lengths = np.memmap(paths["lengths"], mode="r", dtype="<u2", shape=(rows,))
objectives = np.memmap(paths["objectives"], mode="r", dtype="u1", shape=(rows,))
if int(words.sum(dtype=np.uint64)) != 10_000_000:
raise RuntimeError(f"epoch {epoch} word total mismatch")
if int(lengths.sum(dtype=np.uint64)) != docs_token_count:
raise RuntimeError(f"epoch {epoch} token total mismatch")
token_rows = np.memmap(paths['tokens'], mode='r', dtype='<u2', shape=(rows, length))
observed_stream = hashlib.sha256()
for start in range(0, rows, 4096):
chunk = token_rows[start:start+4096]
mask = np.arange(length)[None, :] < np.asarray(lengths[start:start+4096])[:, None]
observed_stream.update(chunk[mask].tobytes())
if observed_stream.hexdigest() != expected_stream.hexdigest():
raise RuntimeError('Packed token order/content changed')
if abs(int((objectives == 0).sum()) - int((objectives == 1).sum())) > 1:
raise RuntimeError(f"epoch {epoch} objective balance mismatch")
epoch_record = {
"epoch": epoch,
"nonpad_stream_sha256": observed_stream.hexdigest(),
"stream_readback": "PASS_EXACT_ORDER_CONTENT_NO_OMISSIONS",
"sequence_length": length,
"rows": rows,
"nonpad_token_positions": int(lengths.sum(dtype=np.uint64)),
"raw_words": int(words.sum(dtype=np.uint64)),
"causal_sequences": int((objectives == 0).sum()),
"mntp_sequences": int((objectives == 1).sum()),
"files": {
name: {"bytes": path.stat().st_size, "sha256": sha256(path)}
for name, path in paths.items()
},
}
(epoch_dir / "manifest.json").write_text(
json.dumps(epoch_record, indent=2, sort_keys=True) + "\n", encoding="utf-8"
)
epochs.append(epoch_record)
print(json.dumps(epoch_record, sort_keys=True), flush=True)
manifest = {
"schema_version": 1,
"builder": "DST_CYCLIC_ENRICHED_100M_20260908/scripts/build_schedule_data.py",
"boundary_policy": "Long-document first chunk length uniform 1..sequence_length each epoch; all prefixes/tails retained; document-isolated causal masks",
"builder_sha256": sha256(Path(__file__)),
"schedule": args.schedule,
"seed": args.seed,
"raw": raw_manifest,
"documents": len(documents),
"words_per_pass": sum(word_counts),
"token_positions_per_pass": docs_token_count,
"tokenizer": {
"path": str(args.tokenizer),
"tokenizer_json_sha256": sha256(args.tokenizer / "tokenizer.json"),
"tokenizer_config_sha256": sha256(args.tokenizer / "tokenizer_config.json"),
"vocab_size": tokenizer.get_vocab_size(),
"special_ids": observed_special,
},
"epochs": epochs,
}
manifest_path = args.output / "dataset_manifest.json"
manifest_path.write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n", encoding="utf-8")
print(f"manifest_sha256={sha256(manifest_path)}")
if __name__ == "__main__":
main()
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