Spaces:
Running on Zero
Running on Zero
File size: 11,156 Bytes
05182f3 | 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 | """Weighted sample-level mixing for multiple latent WebDataset sources."""
from __future__ import annotations
import logging
import math
from collections.abc import Iterator, Mapping, Sequence
from dataclasses import dataclass
from typing import Any
import torch
from torch.utils.data import IterableDataset
from .tensor import PytorchTensorWebDataset
from .utils import (
DEFAULT_WDS_SAMPLE_SHUFFLE,
DEFAULT_WDS_SAMPLE_SHUFFLE_INITIAL,
derive_webdataset_seed,
)
logger = logging.getLogger(__name__)
_WDS_SOURCE_CHOICE_STREAM_ID = 0x5744_5305
_WDS_SOURCE_NAMESPACE_STRIDE = 0x100
@dataclass(frozen=True)
class WebDatasetSource:
"""Validated configuration for one independently indexed WebDataset."""
source_id: str
data_path: str
annotation_path: str
weight: float
def normalize_webdataset_sources(raw_sources: Any) -> list[WebDatasetSource]:
"""Validate the public ``data_sources`` config and normalize its weights."""
if isinstance(raw_sources, (str, bytes)) or not isinstance(raw_sources, Sequence):
raise TypeError("data_sources must be a non-empty list of mappings.")
if not raw_sources:
raise ValueError("data_sources must contain at least one WebDataset source.")
sources: list[WebDatasetSource] = []
seen_ids: set[str] = set()
for index, raw_source in enumerate(raw_sources):
if not isinstance(raw_source, Mapping):
raise TypeError(f"data_sources[{index}] must be a mapping.")
source_id = raw_source.get("id", f"source_{index}")
if not isinstance(source_id, str) or not source_id.strip():
raise ValueError(f"data_sources[{index}].id must be a non-empty string.")
source_id = source_id.strip()
if source_id in seen_ids:
raise ValueError(f"Duplicate data source id: {source_id!r}.")
seen_ids.add(source_id)
data_path = raw_source.get("data_path")
if not isinstance(data_path, str) or not data_path:
raise ValueError(f"data_sources[{index}].data_path must be a non-empty string.")
if not data_path.rstrip("/\\").lower().endswith("shards"):
raise ValueError(
f"data_sources[{index}].data_path must name a directory ending "
f"in 'shards', got {data_path!r}."
)
annotation_path = raw_source.get("annotation_path")
if not isinstance(annotation_path, str) or not annotation_path:
raise ValueError(f"data_sources[{index}].annotation_path must be a non-empty string.")
weight = raw_source.get("weight")
if isinstance(weight, bool) or not isinstance(weight, (int, float)):
raise TypeError(f"data_sources[{index}].weight must be a positive number.")
weight = float(weight)
if not math.isfinite(weight) or weight <= 0:
raise ValueError(f"data_sources[{index}].weight must be finite and greater than zero.")
sources.append(
WebDatasetSource(
source_id=source_id,
data_path=data_path,
annotation_path=annotation_path,
weight=weight,
)
)
weight_sum = sum(source.weight for source in sources)
return [
WebDatasetSource(
source_id=source.source_id,
data_path=source.data_path,
annotation_path=source.annotation_path,
weight=source.weight / weight_sum,
)
for source in sources
]
def validate_multi_source_selection(
raw_sources: Any,
*,
legacy_fields: Mapping[str, Any],
) -> list[WebDatasetSource]:
"""Validate opt-in selection and reject ambiguous legacy fields."""
conflicting_fields = [name for name, value in legacy_fields.items() if value is not None]
if conflicting_fields:
joined = ", ".join(conflicting_fields)
raise ValueError(
f"data_sources cannot be combined with non-null legacy field(s): {joined}."
)
return normalize_webdataset_sources(raw_sources)
class WeightedMultiSourceDataset(IterableDataset):
"""Mix independent iterable datasets at sample granularity.
Training sources are expected to be infinite resampled streams. For finite
validation sources, an exhausted source is removed from the choice set and
iteration ends after every source has been consumed once.
"""
def __init__(
self,
datasets: Sequence[IterableDataset],
sources: Sequence[WebDatasetSource],
*,
seed: int,
global_rank: int,
resampled: bool,
length: int | None = None,
) -> None:
super().__init__()
if len(datasets) != len(sources) or not datasets:
raise ValueError("datasets and sources must have the same non-zero length.")
if isinstance(seed, bool) or not isinstance(seed, int):
raise TypeError("Multi-source seed must be a non-boolean integer.")
if isinstance(global_rank, bool) or not isinstance(global_rank, int):
raise TypeError("Multi-source global_rank must be a non-boolean integer.")
if global_rank < 0:
raise ValueError("Multi-source global_rank must be non-negative.")
self.datasets = list(datasets)
self.sources = list(sources)
self._weights = [source.weight for source in sources]
self._deterministic = True
self._seed = seed
self._global_rank = global_rank
self._resampled = resampled
if length is None:
self._length = sum(len(dataset) for dataset in self.datasets)
else:
self._length = min(
int(length),
sum(len(dataset) for dataset in self.datasets),
)
logger.info(
"Initialized %s with sources=%s, resampled=%s, deterministic=%s, reported_length=%d",
type(self).__name__,
[
{
"id": source.source_id,
"weight": source.weight,
"samples": len(dataset),
}
for source, dataset in zip(
self.sources,
self.datasets,
strict=True,
)
],
self._resampled,
self._deterministic,
self._length,
)
def _worker_address(self) -> tuple[int, int]:
worker = torch.utils.data.get_worker_info()
if worker is None:
return 0, 1
return worker.id, worker.num_workers
def _choice_seed(self) -> int:
worker_id, num_workers = self._worker_address()
return derive_webdataset_seed(
self._seed,
self._global_rank,
worker_id,
num_workers,
_WDS_SOURCE_CHOICE_STREAM_ID,
)
@property
def dataloader_seed(self) -> int | None:
"""Return an isolated DataLoader seed for deterministic mixing."""
return derive_webdataset_seed(
self._seed,
self._global_rank,
worker_id=0,
num_workers=1,
stream_id=_WDS_SOURCE_CHOICE_STREAM_ID + 1,
)
def _make_generator(self) -> torch.Generator:
generator = torch.Generator(device="cpu")
generator.manual_seed(self._choice_seed())
return generator
def _choose_source(
self,
generator: torch.Generator,
active: Sequence[bool],
) -> int:
total_weight = sum(
weight for weight, is_active in zip(self._weights, active, strict=True) if is_active
)
if total_weight <= 0:
raise RuntimeError("No active WebDataset source remains.")
target = torch.rand((), generator=generator, dtype=torch.float64).item() * total_weight
cumulative = 0.0
last_active = -1
for index, (weight, is_active) in enumerate(zip(self._weights, active, strict=True)):
if not is_active:
continue
last_active = index
cumulative += weight
if target < cumulative:
return index
return last_active
def _tag_sample(self, sample: dict[str, Any], source_index: int) -> dict[str, Any]:
if not isinstance(sample, dict):
raise TypeError(
f"WebDataset source {self.sources[source_index].source_id!r} "
f"yielded {type(sample).__name__}, expected dict."
)
tagged = dict(sample)
metadata = dict(tagged.get("metadata", {}))
metadata.update(
{
"source_id": self.sources[source_index].source_id,
"source_index": source_index,
"source_weight": self.sources[source_index].weight,
}
)
tagged["metadata"] = metadata
return tagged
def __iter__(self) -> Iterator[dict[str, Any]]:
iterators: list[Iterator[dict[str, Any]]] = [iter(dataset) for dataset in self.datasets]
active = [True] * len(iterators)
generator = self._make_generator()
while any(active):
source_index = self._choose_source(generator, active)
try:
sample = next(iterators[source_index])
except StopIteration:
if self._resampled:
source_id = self.sources[source_index].source_id
raise RuntimeError(
f"Resampled WebDataset source {source_id!r} exhausted unexpectedly."
) from None
active[source_index] = False
continue
yield self._tag_sample(sample, source_index)
def __len__(self) -> int:
return self._length
def build_multi_source_tensor_webdataset(
raw_sources: Any,
*,
is_train: bool,
seed: int,
global_rank: int,
length: int | None,
target_frame_num: int | None = None,
sample_shuffle: int = DEFAULT_WDS_SAMPLE_SHUFFLE,
sample_shuffle_initial: int = DEFAULT_WDS_SAMPLE_SHUFFLE_INITIAL,
) -> WeightedMultiSourceDataset:
"""Build one latent WebDataset pipeline per source plus a weighted mixer."""
sources = normalize_webdataset_sources(raw_sources)
datasets: list[PytorchTensorWebDataset] = []
for source_index, source in enumerate(sources):
datasets.append(
PytorchTensorWebDataset(
data_path=source.data_path,
annotation_path=source.annotation_path,
target_frame_num=target_frame_num,
shuffle=sample_shuffle if is_train else 0,
shuffle_initial=sample_shuffle_initial,
shardshuffle=is_train,
resampled=is_train,
seed=seed,
global_rank=global_rank,
seed_namespace=(source_index + 1) * _WDS_SOURCE_NAMESPACE_STRIDE,
)
)
return WeightedMultiSourceDataset(
datasets,
sources,
seed=seed,
global_rank=global_rank,
resampled=is_train,
length=length,
)
|