File size: 11,764 Bytes
685e018 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 | """Memory-mapped packed-token datasets and deterministic sampling."""
from __future__ import annotations
import json
import math
from bisect import bisect_right
from pathlib import Path
from typing import Any
import numpy as np
import torch
from torch import Tensor
from torch.utils.data import Dataset, Sampler
from diffusion_lm.tokenizer import token_metadata_path
PACKED_TOKEN_FORMAT = "mini-diffusion-lm-packed-tokens-v1"
PACKED_MANIFEST_FORMAT = "mini-diffusion-lm-packed-manifest-v1"
def _read_json(path: Path) -> dict[str, Any]:
try:
with path.open("r", encoding="utf-8") as handle:
value = json.load(handle)
except json.JSONDecodeError as exc:
raise ValueError(f"invalid JSON in {path}: {exc}") from exc
if not isinstance(value, dict):
raise ValueError(f"expected a JSON object in {path}")
return value
def load_token_metadata(path: str | Path) -> dict[str, Any]:
metadata_path = token_metadata_path(path)
if not metadata_path.is_file():
raise FileNotFoundError(
f"token metadata not found: {metadata_path}; encode data with mini-mdlm-tokenizer"
)
metadata = _read_json(metadata_path)
if metadata.get("format") != PACKED_TOKEN_FORMAT:
raise ValueError(f"unsupported token file metadata in {metadata_path}")
return metadata
def load_packed_manifest(path: str | Path) -> dict[str, Any]:
manifest_path = Path(path)
if not manifest_path.is_file():
raise FileNotFoundError(f"packed-token manifest not found: {manifest_path}")
manifest = _read_json(manifest_path)
if manifest.get("format") != PACKED_MANIFEST_FORMAT:
raise ValueError(f"unsupported packed-token manifest in {manifest_path}")
shards = manifest.get("shards")
if not isinstance(shards, list) or not shards:
raise ValueError(f"packed-token manifest has no shards: {manifest_path}")
return manifest
def _validate_dtype(name: object, *, context: Path) -> np.dtype[Any]:
try:
dtype = np.dtype(name)
except TypeError as exc:
raise ValueError(f"invalid token dtype {name!r} in {context}") from exc
if dtype not in (np.dtype("uint16"), np.dtype("uint32")):
raise ValueError(f"unsupported token dtype {dtype.name!r} in {context}")
return dtype
class PackedTokenDataset(Dataset[Tensor]):
"""Expose deterministic non-overlapping blocks from one packed token file."""
def __init__(self, path: str | Path, sequence_length: int) -> None:
if sequence_length <= 0:
raise ValueError("sequence_length must be positive")
self.path = Path(path)
if not self.path.is_file():
raise FileNotFoundError(f"packed token file not found: {self.path}")
self.metadata = load_token_metadata(self.path)
self.sequence_length = sequence_length
self._dtype = _validate_dtype(self.metadata.get("dtype"), context=self.path)
self._tokens = np.memmap(self.path, mode="r", dtype=self._dtype)
expected_count = int(self.metadata["token_count"])
if self._tokens.size != expected_count:
raise ValueError(
f"metadata says {expected_count} tokens but {self.path} contains "
f"{self._tokens.size}"
)
self._blocks = self._tokens.size // sequence_length
if self._blocks == 0:
raise ValueError(
f"dataset has {self._tokens.size} tokens, fewer than one "
f"{sequence_length}-token block"
)
def __len__(self) -> int:
return self._blocks
def __getitem__(self, index: int) -> Tensor:
if index < 0:
index += self._blocks
if not 0 <= index < self._blocks:
raise IndexError(index)
start = index * self.sequence_length
block = np.asarray(self._tokens[start : start + self.sequence_length]).astype(
np.int64, copy=True
)
return torch.from_numpy(block)
class ManifestPackedTokenDataset(Dataset[Tensor]):
"""Expose one logical dataset backed by independently memory-mapped token shards.
Blocks never cross shard boundaries. At most ``sequence_length - 1`` trailing tokens per shard
are ignored, keeping source shards independently replaceable and resumable.
"""
def __init__(self, path: str | Path, sequence_length: int) -> None:
if sequence_length <= 0:
raise ValueError("sequence_length must be positive")
self.path = Path(path)
self.metadata = load_packed_manifest(self.path)
self.sequence_length = sequence_length
self._dtype = _validate_dtype(self.metadata.get("dtype"), context=self.path)
declared_token_count = int(self.metadata.get("token_count", -1))
declared_document_count = int(self.metadata.get("document_count", -1))
shard_token_count = 0
shard_document_count = 0
self._shard_paths: list[Path] = []
self._shard_token_counts: list[int] = []
self._block_ends: list[int] = []
total_blocks = 0
for position, raw_shard in enumerate(self.metadata["shards"]):
if not isinstance(raw_shard, dict):
raise ValueError(f"shard {position} in {self.path} is not an object")
raw_path = raw_shard.get("path")
if not isinstance(raw_path, str) or not raw_path:
raise ValueError(f"shard {position} in {self.path} has no path")
shard_path = Path(raw_path)
if not shard_path.is_absolute():
shard_path = self.path.parent / shard_path
if not shard_path.is_file():
raise FileNotFoundError(f"packed token shard not found: {shard_path}")
token_count = int(raw_shard.get("token_count", -1))
document_count = int(raw_shard.get("document_count", -1))
if token_count < 0 or document_count < 0:
raise ValueError(f"invalid counts for shard {shard_path}")
expected_bytes = token_count * self._dtype.itemsize
if shard_path.stat().st_size != expected_bytes:
raise ValueError(
f"manifest says {token_count} tokens but {shard_path} has "
f"{shard_path.stat().st_size} bytes"
)
shard_token_count += token_count
shard_document_count += document_count
blocks = token_count // sequence_length
if blocks:
self._shard_paths.append(shard_path)
self._shard_token_counts.append(token_count)
total_blocks += blocks
self._block_ends.append(total_blocks)
if shard_token_count != declared_token_count:
raise ValueError(
f"manifest token_count is {declared_token_count}, shard total is "
f"{shard_token_count}"
)
if shard_document_count != declared_document_count:
raise ValueError(
f"manifest document_count is {declared_document_count}, shard total is "
f"{shard_document_count}"
)
if total_blocks == 0:
raise ValueError(
f"dataset has no shard containing a full {sequence_length}-token block"
)
self._blocks = total_blocks
self._maps: list[np.memmap[Any, Any] | None] = [None] * len(self._shard_paths)
def __len__(self) -> int:
return self._blocks
def _map(self, shard_index: int) -> np.memmap[Any, Any]:
tokens = self._maps[shard_index]
if tokens is None:
tokens = np.memmap(self._shard_paths[shard_index], mode="r", dtype=self._dtype)
self._maps[shard_index] = tokens
return tokens
def __getitem__(self, index: int) -> Tensor:
if index < 0:
index += self._blocks
if not 0 <= index < self._blocks:
raise IndexError(index)
shard_index = bisect_right(self._block_ends, index)
previous_end = 0 if shard_index == 0 else self._block_ends[shard_index - 1]
local_block = index - previous_end
start = local_block * self.sequence_length
tokens = self._map(shard_index)
block = np.asarray(tokens[start : start + self.sequence_length]).astype(
np.int64, copy=True
)
return torch.from_numpy(block)
def __getstate__(self) -> dict[str, Any]:
state = self.__dict__.copy()
# Reopen mappings inside each DataLoader worker rather than pickling file descriptors.
state["_maps"] = [None] * len(self._shard_paths)
return state
PackedDataset = PackedTokenDataset | ManifestPackedTokenDataset
def load_packed_dataset(path: str | Path, sequence_length: int) -> PackedDataset:
"""Load a legacy single-file dataset or a manifest-backed sharded dataset."""
candidate = Path(path)
if candidate.suffix == ".json" and candidate.is_file():
value = _read_json(candidate)
if value.get("format") == PACKED_MANIFEST_FORMAT:
return ManifestPackedTokenDataset(candidate, sequence_length)
return PackedTokenDataset(candidate, sequence_length)
_UINT64_MASK = (1 << 64) - 1
def _splitmix64(value: int) -> int:
value = (value + 0x9E3779B97F4A7C15) & _UINT64_MASK
value = ((value ^ (value >> 30)) * 0xBF58476D1CE4E5B9) & _UINT64_MASK
value = ((value ^ (value >> 27)) * 0x94D049BB133111EB) & _UINT64_MASK
return value ^ (value >> 31)
def _affine_permutation_parameters(size: int, seed: int, epoch: int) -> tuple[int, int]:
"""Return ``a, b`` for the bijection ``(a*x+b) mod size``."""
if size == 1:
return 0, 0
mixed = _splitmix64((seed & _UINT64_MASK) ^ _splitmix64(epoch & _UINT64_MASK))
offset = mixed % size
multiplier = _splitmix64(mixed) % size
if multiplier == 0:
multiplier = 1
while math.gcd(multiplier, size) != 1:
multiplier += 1
if multiplier == size:
multiplier = 1
return multiplier, offset
class DeterministicBatchSampler(Sampler[list[int]]):
"""Infinite epoch permutations with exact batch-cursor resume and O(1) memory.
Each epoch uses a seeded affine bijection over dataset indexes. Unlike ``randperm().tolist()``,
memory use is independent of corpus size and a resumed batch can be calculated directly.
"""
def __init__(
self,
dataset_size: int,
batch_size: int,
*,
seed: int,
start_batch: int = 0,
) -> None:
if dataset_size <= 0 or batch_size <= 0:
raise ValueError("dataset_size and batch_size must be positive")
if start_batch < 0:
raise ValueError("start_batch must be non-negative")
self.dataset_size = dataset_size
self.batch_size = batch_size
self.seed = seed
self.start_batch = start_batch
self.batches_per_epoch = (dataset_size + batch_size - 1) // batch_size
def __iter__(self):
epoch = self.start_batch // self.batches_per_epoch
batch_in_epoch = self.start_batch % self.batches_per_epoch
while True:
multiplier, offset = _affine_permutation_parameters(
self.dataset_size, self.seed, epoch
)
for batch_index in range(batch_in_epoch, self.batches_per_epoch):
start = batch_index * self.batch_size
stop = min(start + self.batch_size, self.dataset_size)
yield [
(multiplier * position + offset) % self.dataset_size
for position in range(start, stop)
]
epoch += 1
batch_in_epoch = 0
|