File size: 8,668 Bytes
e69b72a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 | """Packed token corpora for STRATA language-model pretraining.
Documents are tokenised, separated by an end-of-sequence token, and packed into
a single flat token array on disk (``tokens.bin``) with a JSON sidecar
(``meta.json``). Training reads fixed-length, non-overlapping windows via
:class:`PackedLMDataset` and a memory map, so multi-billion-token corpora never
need to fit in RAM.
Tokenisation is passed in as a callable returning token ids, which keeps this
module independent of any specific tokenizer and lets the byte tokenizer use a
fast vectorised path instead of building span lattices per byte.
"""
from __future__ import annotations
import hashlib
import json
from collections import Counter
from dataclasses import dataclass, field
from pathlib import Path
from typing import Callable, Iterable
import numpy as np
TokenizeIds = Callable[[str], np.ndarray]
TOKENS_FILENAME = "tokens.bin"
META_FILENAME = "meta.json"
def choose_token_dtype(vocab_size: int) -> np.dtype:
"""Smallest unsigned integer dtype that can hold every id in the vocab."""
if vocab_size <= 0:
raise ValueError("vocab_size must be positive")
if vocab_size <= np.iinfo(np.uint16).max + 1:
return np.dtype(np.uint16)
if vocab_size <= np.iinfo(np.uint32).max + 1:
return np.dtype(np.uint32)
raise ValueError(f"vocab_size {vocab_size} too large for uint32 token ids")
@dataclass(slots=True)
class PackedCorpusMeta:
"""Sidecar metadata describing a packed token corpus."""
num_tokens: int
dtype: str
vocab_size: int
eos_id: int
tokenizer_name: str
documents: int
per_language_tokens: dict[str, int]
sha256: str
seed: int | None = None
source: dict[str, object] = field(default_factory=dict)
def to_dict(self) -> dict[str, object]:
return {
"num_tokens": self.num_tokens,
"dtype": self.dtype,
"vocab_size": self.vocab_size,
"eos_id": self.eos_id,
"tokenizer_name": self.tokenizer_name,
"documents": self.documents,
"per_language_tokens": self.per_language_tokens,
"sha256": self.sha256,
"seed": self.seed,
"source": self.source,
}
@classmethod
def from_dict(cls, data: dict[str, object]) -> "PackedCorpusMeta":
return cls(
num_tokens=int(data["num_tokens"]),
dtype=str(data["dtype"]),
vocab_size=int(data["vocab_size"]),
eos_id=int(data["eos_id"]),
tokenizer_name=str(data.get("tokenizer_name", "")),
documents=int(data.get("documents", 0)),
per_language_tokens=dict(data.get("per_language_tokens", {}) or {}), # type: ignore[arg-type]
sha256=str(data.get("sha256", "")),
seed=data.get("seed"), # type: ignore[arg-type]
source=dict(data.get("source", {}) or {}), # type: ignore[arg-type]
)
def write(self, out_dir: Path) -> Path:
path = out_dir / META_FILENAME
path.write_text(json.dumps(self.to_dict(), indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
return path
@classmethod
def read(cls, out_dir: Path) -> "PackedCorpusMeta":
return cls.from_dict(json.loads((out_dir / META_FILENAME).read_text(encoding="utf-8")))
def build_packed_corpus(
*,
documents: Iterable[tuple[str, str]],
tokenize_ids: TokenizeIds,
eos_id: int,
vocab_size: int,
tokenizer_name: str,
out_dir: Path,
max_tokens: int | None = None,
per_language_tokens: dict[str, int] | None = None,
seed: int | None = None,
source: dict[str, object] | None = None,
flush_every_tokens: int = 8_000_000,
logger=None,
) -> PackedCorpusMeta:
"""Tokenise ``(language, text)`` documents and pack them into ``tokens.bin``.
``per_language_tokens`` caps how many tokens each language contributes (for
balanced multilingual mixtures); ``max_tokens`` caps the total. Both are
honoured mid-document at token granularity.
"""
out_dir.mkdir(parents=True, exist_ok=True)
dtype = choose_token_dtype(vocab_size)
if not (0 <= eos_id < vocab_size):
raise ValueError(f"eos_id {eos_id} out of range for vocab_size {vocab_size}")
tokens_path = out_dir / TOKENS_FILENAME
caps = dict(per_language_tokens or {})
lang_counts: Counter[str] = Counter()
hasher = hashlib.sha256()
total = 0
docs = 0
buffer: list[np.ndarray] = []
buffered = 0
def flush() -> None:
nonlocal buffered
if not buffer:
return
chunk = np.concatenate(buffer)
chunk.tofile(handle)
hasher.update(chunk.tobytes())
buffer.clear()
buffered = 0
with tokens_path.open("wb") as handle:
for language, text in documents:
if max_tokens is not None and total >= max_tokens:
break
if caps and all(lang_counts[lang] >= cap for lang, cap in caps.items()):
break
if language in caps and lang_counts[language] >= caps[language]:
continue
if not text:
continue
ids = np.asarray(tokenize_ids(text), dtype=dtype)
if ids.size == 0:
continue
ids = np.append(ids, np.asarray([eos_id], dtype=dtype))
# Enforce caps precisely at token granularity.
if language in caps:
remaining = caps[language] - lang_counts[language]
if remaining <= 0:
continue
if ids.size > remaining:
ids = ids[:remaining]
if max_tokens is not None and total + ids.size > max_tokens:
ids = ids[: max_tokens - total]
if ids.size == 0:
continue
if ids.max() >= vocab_size:
raise ValueError(
f"token id {int(ids.max())} >= vocab_size {vocab_size}; "
"tokenizer/model vocab mismatch"
)
buffer.append(ids)
buffered += ids.size
total += ids.size
lang_counts[language] += int(ids.size)
docs += 1
if buffered >= flush_every_tokens:
flush()
if logger is not None:
logger.info("packed %d tokens (%d docs)", total, docs)
flush()
meta = PackedCorpusMeta(
num_tokens=total,
dtype=dtype.name,
vocab_size=vocab_size,
eos_id=eos_id,
tokenizer_name=tokenizer_name,
documents=docs,
per_language_tokens=dict(lang_counts),
sha256=hasher.hexdigest(),
seed=seed,
source=dict(source or {}),
)
meta.write(out_dir)
if logger is not None:
logger.info("packed corpus: %d tokens, %d docs -> %s", total, docs, tokens_path)
return meta
class PackedLMDataset:
"""Fixed-length, non-overlapping windows over a packed token corpus.
Implements the ``torch.utils.data.Dataset`` protocol (``__len__`` /
``__getitem__``) without importing torch at module load; each item is a 1-D
``int64`` tensor of length ``seq_len`` suitable for causal-LM training where
the model shifts inputs and labels internally.
"""
def __init__(self, corpus_dir: str | Path, *, seq_len: int) -> None:
if seq_len <= 1:
raise ValueError("seq_len must be > 1")
self.corpus_dir = Path(corpus_dir)
self.meta = PackedCorpusMeta.read(self.corpus_dir)
self.seq_len = seq_len
self.dtype = np.dtype(self.meta.dtype)
tokens_path = self.corpus_dir / TOKENS_FILENAME
if not tokens_path.exists():
raise FileNotFoundError(f"packed tokens not found: {tokens_path}")
self._tokens = np.memmap(tokens_path, dtype=self.dtype, mode="r")
self.num_windows = (len(self._tokens)) // seq_len
if self.num_windows == 0:
raise ValueError(
f"corpus has {len(self._tokens)} tokens, too few for seq_len {seq_len}"
)
def __len__(self) -> int:
return self.num_windows
def __getitem__(self, index: int):
import torch
if index < 0:
index += self.num_windows
if not 0 <= index < self.num_windows:
raise IndexError(index)
start = index * self.seq_len
window = np.asarray(self._tokens[start : start + self.seq_len], dtype=np.int64)
return torch.from_numpy(window)
def total_tokens_used(self) -> int:
return self.num_windows * self.seq_len
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