Spaces:
Running
Running
File size: 13,937 Bytes
80ebcb3 5e3ed88 80ebcb3 5e3ed88 80ebcb3 5e3ed88 80ebcb3 5e3ed88 80ebcb3 5e3ed88 80ebcb3 5e3ed88 80ebcb3 5e3ed88 80ebcb3 5e3ed88 80ebcb3 5e3ed88 80ebcb3 5e3ed88 80ebcb3 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 |
import pathlib
from typing import Any, Callable, Dict, Iterable, List, Optional, Union
import torch
from tqdm.auto import tqdm
from .. import utils
from ..logging import get_logger
logger = get_logger()
def initialize_preprocessor(
rank: int,
num_items: int,
processor_fn: Dict[str, Callable[[Dict[str, Any]], Dict[str, Any]]],
save_dir: Optional[str] = None,
enable_precomputation: bool = False,
) -> Union["InMemoryDistributedDataPreprocessor", "PrecomputedDistributedDataPreprocessor"]:
if enable_precomputation:
return PrecomputedDistributedDataPreprocessor(rank, num_items, processor_fn, save_dir)
return InMemoryDistributedDataPreprocessor(rank, num_items, processor_fn)
class DistributedDataProcessorMixin:
def consume(self, *args, **kwargs):
raise NotImplementedError("DistributedDataProcessorMixin::consume must be implemented by the subclass.")
def consume_once(self, *args, **kwargs):
raise NotImplementedError("DistributedDataProcessorMixin::consume_once must be implemented by the subclass.")
@property
def requires_data(self):
raise NotImplementedError("DistributedDataProcessorMixin::requires_data must be implemented by the subclass.")
class InMemoryDistributedDataPreprocessor(DistributedDataProcessorMixin):
def __init__(
self, rank: int, num_items: int, processor_fn: Dict[str, Callable[[Dict[str, Any]], Dict[str, Any]]]
) -> None:
super().__init__()
self._rank = rank
self._num_items = num_items
self._processor_fn = processor_fn
self._cached_samples = []
self._buffer = InMemoryDataBuffer(num_items)
self._preprocessed_iterator: Union["InMemoryDataIterable", "InMemoryOnceDataIterable"] = None
def consume(
self,
data_type: str,
components: Dict[str, Any],
data_iterator,
generator: Optional[torch.Generator] = None,
cache_samples: bool = False,
use_cached_samples: bool = False,
drop_samples: bool = False,
) -> Iterable[Dict[str, Any]]:
if data_type not in self._processor_fn.keys():
raise ValueError(f"Invalid data type: {data_type}. Supported types: {list(self._processor_fn.keys())}")
if cache_samples:
if use_cached_samples:
raise ValueError("Cannot cache and use cached samples at the same time.")
if drop_samples:
raise ValueError("Cannot cache and drop samples at the same time.")
for i in range(self._num_items):
if use_cached_samples:
item = self._cached_samples[i]
else:
item = next(data_iterator)
if cache_samples:
self._cached_samples.append(item)
item = self._processor_fn[data_type](**item, **components, generator=generator)
self._buffer.add(data_type, item)
if drop_samples:
del self._cached_samples
self._cached_samples = []
self._preprocessed_iterator = InMemoryDataIterable(self._rank, data_type, self._buffer)
return iter(self._preprocessed_iterator)
def consume_once(
self,
data_type: str,
components: Dict[str, Any],
data_iterator,
generator: Optional[torch.Generator] = None,
cache_samples: bool = False,
use_cached_samples: bool = False,
drop_samples: bool = False,
) -> Iterable[Dict[str, Any]]:
if data_type not in self._processor_fn.keys():
raise ValueError(f"Invalid data type: {data_type}. Supported types: {list(self._processor_fn.keys())}")
if cache_samples:
if use_cached_samples:
raise ValueError("Cannot cache and use cached samples at the same time.")
if drop_samples:
raise ValueError("Cannot cache and drop samples at the same time.")
for i in range(self._num_items):
if use_cached_samples:
item = self._cached_samples[i]
else:
item = next(data_iterator)
if cache_samples:
self._cached_samples.append(item)
item = self._processor_fn[data_type](**item, **components, generator=generator)
self._buffer.add(data_type, item)
if drop_samples:
del self._cached_samples
self._cached_samples = []
self._preprocessed_iterator = InMemoryOnceDataIterable(self._rank, data_type, self._buffer)
return iter(self._preprocessed_iterator)
@property
def requires_data(self):
if self._preprocessed_iterator is None:
return True
return self._preprocessed_iterator.requires_data
class PrecomputedDistributedDataPreprocessor(DistributedDataProcessorMixin):
def __init__(
self,
rank: int,
num_items: int,
processor_fn: Dict[str, Callable[[Dict[str, Any]], Dict[str, Any]]],
save_dir: str,
) -> None:
super().__init__()
self._rank = rank
self._num_items = num_items
self._processor_fn = processor_fn
self._save_dir = pathlib.Path(save_dir)
self._cached_samples = []
self._preprocessed_iterator: Union["PrecomputedDataIterable", "PrecomputedOnceDataIterable"] = None
self._save_dir.mkdir(parents=True, exist_ok=True)
subdirectories = [f for f in self._save_dir.iterdir() if f.is_dir()]
utils.delete_files(subdirectories)
def consume(
self,
data_type: str,
components: Dict[str, Any],
data_iterator,
generator: Optional[torch.Generator] = None,
cache_samples: bool = False,
use_cached_samples: bool = False,
drop_samples: bool = False,
) -> Iterable[Dict[str, Any]]:
if data_type not in self._processor_fn.keys():
raise ValueError(f"Invalid data type: {data_type}. Supported types: {list(self._processor_fn.keys())}")
if cache_samples:
if use_cached_samples:
raise ValueError("Cannot cache and use cached samples at the same time.")
if drop_samples:
raise ValueError("Cannot cache and drop samples at the same time.")
for i in tqdm(range(self._num_items), desc=f"Rank {self._rank}", total=self._num_items):
if use_cached_samples:
item = self._cached_samples[i]
else:
item = next(data_iterator)
if cache_samples:
self._cached_samples.append(item)
item = self._processor_fn[data_type](**item, **components, generator=generator)
_save_item(self._rank, i, item, self._save_dir, data_type)
if drop_samples:
del self._cached_samples
self._cached_samples = []
self._preprocessed_iterator = PrecomputedDataIterable(self._rank, self._save_dir, data_type)
return iter(self._preprocessed_iterator)
def consume_once(
self,
data_type: str,
components: Dict[str, Any],
data_iterator,
generator: Optional[torch.Generator] = None,
cache_samples: bool = False,
use_cached_samples: bool = False,
drop_samples: bool = False,
) -> Iterable[Dict[str, Any]]:
if data_type not in self._processor_fn.keys():
raise ValueError(f"Invalid data type: {data_type}. Supported types: {list(self._processor_fn.keys())}")
if cache_samples:
if use_cached_samples:
raise ValueError("Cannot cache and use cached samples at the same time.")
if drop_samples:
raise ValueError("Cannot cache and drop samples at the same time.")
for i in tqdm(range(self._num_items), desc=f"Processing data on rank {self._rank}", total=self._num_items):
if use_cached_samples:
item = self._cached_samples[i]
else:
item = next(data_iterator)
if cache_samples:
self._cached_samples.append(item)
item = self._processor_fn[data_type](**item, **components, generator=generator)
_save_item(self._rank, i, item, self._save_dir, data_type)
if drop_samples:
del self._cached_samples
self._cached_samples = []
self._preprocessed_iterator = PrecomputedOnceDataIterable(self._rank, self._save_dir, data_type)
return iter(self._preprocessed_iterator)
@property
def requires_data(self):
if self._preprocessed_iterator is None:
return True
return self._preprocessed_iterator.requires_data
class InMemoryDataIterable:
"""
An iterator that loads data items from an in-memory buffer. Once all the data is consumed,
`requires_data` is set to True, indicating that the more data is required and the preprocessor's
consume method should be called again.
"""
def __init__(self, rank: int, data_type: str, buffer: "InMemoryDataBuffer") -> None:
self._rank = rank
self._data_type = data_type
self._buffer = buffer
self._requires_data = False
def __iter__(self) -> Iterable[Dict[str, Any]]:
while (length := self._buffer.get_length(self._data_type)) > 0:
if length <= 1:
self._requires_data = True
yield self._buffer.get(self._data_type)
def __len__(self) -> int:
return self._buffer.get_length(self._data_type)
@property
def requires_data(self):
return self._requires_data
class InMemoryOnceDataIterable:
"""
An iterator that loads data items from an in-memory buffer. This iterator will never set
`requires_data` to True, as it is assumed that all the data was configured to be preprocessed
by the user. The data will indefinitely be cycled from the buffer.
"""
def __init__(self, rank: int, data_type: str, buffer: "InMemoryDataBuffer") -> None:
self._rank = rank
self._data_type = data_type
self._buffer = buffer
self._requires_data = False
def __iter__(self) -> Iterable[Dict[str, Any]]:
assert len(self) > 0, "No data available in the buffer."
while True:
item = self._buffer.get(self._data_type)
yield item
self._buffer.add(self._data_type, item)
def __len__(self) -> int:
return self._buffer.get_length(self._data_type)
@property
def requires_data(self):
return self._requires_data
class PrecomputedDataIterable:
"""
An iterator that loads preconfigured number of data items from disk. Once all the data is
loaded, `requires_data` is set to True, indicating that the more data is required and
the preprocessor's consume method should be called again.
"""
def __init__(self, rank: int, save_dir: str, data_type: str) -> None:
self._rank = rank
self._save_dir = pathlib.Path(save_dir)
self._num_items = len(list(self._save_dir.glob(f"{data_type}-{rank}-*.pt")))
self._data_type = data_type
self._requires_data = False
def __iter__(self) -> Iterable[Dict[str, Any]]:
for i in range(self._num_items):
if i == self._num_items - 1:
self._requires_data = True
yield _load_item(self._rank, i, self._save_dir, self._data_type)
def __len__(self) -> int:
return self._num_items
@property
def requires_data(self):
return self._requires_data
class PrecomputedOnceDataIterable:
"""
An infinite iterator that loads preprocessed data from disk. Once initialized, this iterator
will never set `requires_data` to True, as it is assumed that all the data was configured to
be preprocessed by the user.
"""
def __init__(self, rank: int, save_dir: str, data_type: str) -> None:
self._rank = rank
self._save_dir = pathlib.Path(save_dir)
self._num_items = len(list(self._save_dir.glob(f"{data_type}-{rank}-*.pt")))
self._data_type = data_type
self._requires_data = False
def __iter__(self) -> Iterable[Dict[str, Any]]:
index = 0
while True:
yield _load_item(self._rank, index, self._save_dir, self._data_type)
index = (index + 1) % self._num_items
def __len__(self) -> int:
return self._num_items
@property
def requires_data(self):
return self._requires_data
class InMemoryDataBuffer:
def __init__(self, max_limit: int = -1) -> None:
self.max_limit = max_limit
self.buffer: Dict[str, List[str]] = {}
def add(self, data_type: str, item: Dict[str, Any]) -> None:
if data_type not in self.buffer:
self.buffer[data_type] = []
if self.max_limit != -1 and len(self.buffer[data_type]) >= self.max_limit:
logger.log_freq(
"WARN",
"IN_MEMORY_DATA_BUFFER_FULL",
"Buffer is full. Dropping the oldest item. This message will be logged every 64th time this happens.",
64,
)
self.buffer[data_type].pop(0)
self.buffer[data_type].append(item)
def get(self, data_type: str) -> Dict[str, Any]:
return self.buffer[data_type].pop(0)
def get_length(self, data_type: str) -> int:
return len(self.buffer[data_type])
def _save_item(rank: int, index: int, item: Dict[str, Any], directory: pathlib.Path, data_type: str) -> None:
filename = directory / f"{data_type}-{rank}-{index}.pt"
torch.save(item, filename.as_posix())
def _load_item(rank: int, index: int, directory: pathlib.Path, data_type: str) -> Dict[str, Any]:
filename = directory / f"{data_type}-{rank}-{index}.pt"
return torch.load(filename.as_posix(), weights_only=True)
|