Coverage for python/lsst/pipe/base/quantum_graph_skeleton.py: 43%

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1# This file is part of pipe_base. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (http://www.lsst.org). 

6# See the COPYRIGHT file at the top-level directory of this distribution 

7# for details of code ownership. 

8# 

9# This software is dual licensed under the GNU General Public License and also 

10# under a 3-clause BSD license. Recipients may choose which of these licenses 

11# to use; please see the files gpl-3.0.txt and/or bsd_license.txt, 

12# respectively. If you choose the GPL option then the following text applies 

13# (but note that there is still no warranty even if you opt for BSD instead): 

14# 

15# This program is free software: you can redistribute it and/or modify 

16# it under the terms of the GNU General Public License as published by 

17# the Free Software Foundation, either version 3 of the License, or 

18# (at your option) any later version. 

19# 

20# This program is distributed in the hope that it will be useful, 

21# but WITHOUT ANY WARRANTY; without even the implied warranty of 

22# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

23# GNU General Public License for more details. 

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26# along with this program. If not, see <http://www.gnu.org/licenses/>. 

27 

28"""An under-construction version of QuantumGraph and various helper 

29classes. 

30""" 

31 

32from __future__ import annotations 

33 

34__all__ = ( 

35 "QuantumGraphSkeleton", 

36 "QuantumKey", 

37 "TaskInitKey", 

38 "DatasetKey", 

39 "PrerequisiteDatasetKey", 

40) 

41 

42from collections.abc import Iterable, Iterator, MutableMapping, Set 

43from typing import TYPE_CHECKING, Any, ClassVar, Literal, NamedTuple 

44 

45import networkx 

46from lsst.daf.butler import DataCoordinate, DataIdValue, DatasetRef 

47from lsst.utils.logging import getLogger 

48 

49if TYPE_CHECKING: 

50 pass 

51 

52_LOG = getLogger(__name__) 

53 

54 

55class QuantumKey(NamedTuple): 

56 """Identifier type for quantum keys in a `QuantumGraphSkeleton`.""" 

57 

58 task_label: str 

59 """Label of the task in the pipeline.""" 

60 

61 data_id_values: tuple[DataIdValue, ...] 

62 """Data ID values of the quantum. 

63 

64 Note that keys are fixed given `task_label`, so using only the values here 

65 speeds up comparisons. 

66 """ 

67 

68 is_task: ClassVar[Literal[True]] = True 

69 """Whether this node represents a quantum or task initialization rather 

70 than a dataset (always `True`). 

71 """ 

72 

73 

74class TaskInitKey(NamedTuple): 

75 """Identifier type for task init keys in a `QuantumGraphSkeleton`.""" 

76 

77 task_label: str 

78 """Label of the task in the pipeline.""" 

79 

80 is_task: ClassVar[Literal[True]] = True 

81 """Whether this node represents a quantum or task initialization rather 

82 than a dataset (always `True`). 

83 """ 

84 

85 

86class DatasetKey(NamedTuple): 

87 """Identifier type for dataset keys in a `QuantumGraphSkeleton`.""" 

88 

89 parent_dataset_type_name: str 

90 """Name of the dataset type (never a component).""" 

91 

92 data_id_values: tuple[DataIdValue, ...] 

93 """Data ID values of the dataset. 

94 

95 Note that keys are fixed given `parent_dataset_type_name`, so using only 

96 the values here speeds up comparisons. 

97 """ 

98 

99 is_task: ClassVar[Literal[False]] = False 

100 """Whether this node represents a quantum or task initialization rather 

101 than a dataset (always `False`). 

102 """ 

103 

104 is_prerequisite: ClassVar[Literal[False]] = False 

105 

106 

107class PrerequisiteDatasetKey(NamedTuple): 

108 """Identifier type for prerequisite dataset keys in a 

109 `QuantumGraphSkeleton`. 

110 

111 Unlike regular datasets, prerequisites are not actually required to come 

112 from a find-first search of `input_collections`, so we don't want to 

113 assume that the same data ID implies the same dataset. Happily we also 

114 don't need to search for them by data ID in the graph, so we can use the 

115 dataset ID (UUID) instead. 

116 """ 

117 

118 parent_dataset_type_name: str 

119 """Name of the dataset type (never a component).""" 

120 

121 dataset_id_bytes: bytes 

122 """Dataset ID (UUID) as raw bytes.""" 

123 

124 is_task: ClassVar[Literal[False]] = False 

125 """Whether this node represents a quantum or task initialization rather 

126 than a dataset (always `False`). 

127 """ 

128 

129 is_prerequisite: ClassVar[Literal[True]] = True 

130 

131 

132class QuantumGraphSkeleton: 

133 """An under-construction quantum graph. 

134 

135 QuantumGraphSkeleton is intended for use inside `QuantumGraphBuilder` and 

136 its subclasses. 

137 

138 Parameters 

139 ---------- 

140 task_labels : `~collections.abc.Iterable` [ `str` ] 

141 The labels of all tasks whose quanta may be included in the graph, in 

142 topological order. 

143 

144 Notes 

145 ----- 

146 QuantumGraphSkeleton models a bipartite version of the quantum graph, in 

147 which both quanta and datasets are represented as nodes and each type of 

148 node only has edges to the other type. 

149 

150 Square-bracket (`getitem`) indexing returns a mutable mapping of a node's 

151 flexible attributes. 

152 

153 The details of the `QuantumGraphSkeleton` API (e.g. which operations 

154 operate on multiple nodes vs. a single node) are set by what's actually 

155 needed by current quantum graph generation algorithms. New variants can be 

156 added as needed, but adding all operations that *might* be useful for some 

157 future algorithm seems premature. 

158 """ 

159 

160 def __init__(self, task_labels: Iterable[str]): 

161 self._tasks: dict[str, tuple[TaskInitKey, set[QuantumKey]]] = {} 

162 self._xgraph: networkx.DiGraph = networkx.DiGraph() 

163 self._global_init_outputs: set[DatasetKey] = set() 

164 for task_label in task_labels: 

165 task_init_key = TaskInitKey(task_label) 

166 self._tasks[task_label] = (task_init_key, set()) 

167 self._xgraph.add_node(task_init_key) 

168 

169 def __contains__(self, key: QuantumKey | TaskInitKey | DatasetKey | PrerequisiteDatasetKey) -> bool: 

170 return key in self._xgraph.nodes 

171 

172 def __getitem__( 

173 self, key: QuantumKey | TaskInitKey | DatasetKey | PrerequisiteDatasetKey 

174 ) -> MutableMapping[str, Any]: 

175 return self._xgraph.nodes[key] 

176 

177 @property 

178 def n_nodes(self) -> int: 

179 """The total number of nodes of all types.""" 

180 return len(self._xgraph.nodes) 

181 

182 @property 

183 def n_edges(self) -> int: 

184 """The total number of edges.""" 

185 return len(self._xgraph.edges) 

186 

187 def has_task(self, task_label: str) -> bool: 

188 """Test whether the given task is in this skeleton. 

189 

190 Tasks are only added to the skeleton at initialization, but may be 

191 removed by `remove_task` if they end up having no quanta. 

192 """ 

193 return task_label in self._tasks 

194 

195 def get_task_init_node(self, task_label: str) -> TaskInitKey: 

196 """Return the graph node that represents a task's initialization.""" 

197 return self._tasks[task_label][0] 

198 

199 def get_quanta(self, task_label: str) -> Set[QuantumKey]: 

200 """Return the quanta for the given task label. 

201 

202 Parameters 

203 ---------- 

204 task_label : `str` 

205 Label for the task. 

206 

207 Returns 

208 ------- 

209 quanta : `~collections.abc.Set` [ `QuantumKey` ] 

210 A set-like object with the identifiers of all quanta for the given 

211 task. *The skeleton object's set of quanta must not be modified 

212 while iterating over this container; make a copy if mutation during 

213 iteration is necessary.* 

214 """ 

215 return self._tasks[task_label][1] 

216 

217 @property 

218 def global_init_outputs(self) -> Set[DatasetKey]: 

219 """The set of dataset nodes that are not associated with any task.""" 

220 return self._global_init_outputs 

221 

222 def iter_all_quanta(self) -> Iterator[QuantumKey]: 

223 """Iterate over all quanta from any task, in topological (but otherwise 

224 unspecified) order. 

225 """ 

226 for _, quanta in self._tasks.values(): 

227 yield from quanta 

228 

229 def iter_outputs_of(self, quantum_key: QuantumKey | TaskInitKey) -> Iterator[DatasetKey]: 

230 """Iterate over the datasets produced by the given quantum.""" 

231 return self._xgraph.successors(quantum_key) 

232 

233 def iter_inputs_of( 

234 self, quantum_key: QuantumKey | TaskInitKey 

235 ) -> Iterator[DatasetKey | PrerequisiteDatasetKey]: 

236 """Iterate over the datasets consumed by the given quantum.""" 

237 return self._xgraph.predecessors(quantum_key) 

238 

239 def update(self, other: QuantumGraphSkeleton) -> None: 

240 """Copy all nodes from ``other`` to ``self``. 

241 

242 The tasks in ``other`` must be a subset of the tasks in ``self`` (this 

243 method is expected to be used to populate a skeleton for a full 

244 from independent-subgraph skeletons). 

245 """ 

246 for task_label, (_, quanta) in other._tasks.items(): 

247 self._tasks[task_label][1].update(quanta) 

248 self._xgraph.update(other._xgraph) 

249 

250 def add_quantum_node(self, task_label: str, data_id: DataCoordinate, **attrs: Any) -> QuantumKey: 

251 """Add a new node representing a quantum.""" 

252 key = QuantumKey(task_label, data_id.values_tuple()) 

253 self._xgraph.add_node(key, data_id=data_id, **attrs) 

254 self._tasks[key.task_label][1].add(key) 

255 return key 

256 

257 def add_dataset_node( 

258 self, 

259 parent_dataset_type_name: str, 

260 data_id: DataCoordinate, 

261 is_global_init_output: bool = False, 

262 **attrs: Any, 

263 ) -> DatasetKey: 

264 """Add a new node representing a dataset.""" 

265 key = DatasetKey(parent_dataset_type_name, data_id.values_tuple()) 

266 self._xgraph.add_node(key, data_id=data_id, **attrs) 

267 if is_global_init_output: 

268 assert isinstance(key, DatasetKey) 

269 self._global_init_outputs.add(key) 

270 return key 

271 

272 def add_prerequisite_node( 

273 self, 

274 parent_dataset_type_name: str, 

275 ref: DatasetRef, 

276 **attrs: Any, 

277 ) -> PrerequisiteDatasetKey: 

278 """Add a new node representing a prerequisite input dataset.""" 

279 key = PrerequisiteDatasetKey(parent_dataset_type_name, ref.id.bytes) 

280 self._xgraph.add_node(key, data_id=ref.dataId, ref=ref, **attrs) 

281 return key 

282 

283 def remove_quantum_node(self, key: QuantumKey, remove_outputs: bool) -> None: 

284 """Remove a node representing a quantum. 

285 

286 Parameters 

287 ---------- 

288 key : `QuantumKey` 

289 Identifier for the node. 

290 remove_outputs : `bool` 

291 If `True`, also remove all dataset nodes produced by this quantum. 

292 If `False`, any such dataset nodes will become overall inputs. 

293 """ 

294 _, quanta = self._tasks[key.task_label] 

295 quanta.remove(key) 

296 if remove_outputs: 

297 to_remove = list(self._xgraph.successors(key)) 

298 to_remove.append(key) 

299 self._xgraph.remove_nodes_from(to_remove) 

300 else: 

301 self._xgraph.remove_node(key) 

302 

303 def remove_dataset_nodes(self, keys: Iterable[DatasetKey | PrerequisiteDatasetKey]) -> None: 

304 """Remove nodes representing datasets.""" 

305 self._xgraph.remove_nodes_from(keys) 

306 

307 def remove_task(self, task_label: str) -> None: 

308 """Fully remove a task from the skeleton. 

309 

310 All init-output datasets and quanta for the task must already have been 

311 removed. 

312 """ 

313 task_init_key, quanta = self._tasks.pop(task_label) 

314 assert not quanta, "Cannot remove task unless all quanta have already been removed." 

315 assert not list(self._xgraph.successors(task_init_key)) 

316 self._xgraph.remove_node(task_init_key) 

317 

318 def add_input_edges( 

319 self, 

320 task_key: QuantumKey | TaskInitKey, 

321 dataset_keys: Iterable[DatasetKey | PrerequisiteDatasetKey], 

322 ) -> None: 

323 """Add edges connecting datasets to a quantum that consumes them. 

324 

325 Notes 

326 ----- 

327 This must only be called if the task node has already been added. 

328 Use `add_input_edge` if this cannot be assumed. 

329 

330 Dataset nodes that are not already present will be created. 

331 """ 

332 assert task_key in self._xgraph 

333 self._xgraph.add_edges_from((dataset_key, task_key) for dataset_key in dataset_keys) 

334 

335 def remove_input_edges( 

336 self, 

337 task_key: QuantumKey | TaskInitKey, 

338 dataset_keys: Iterable[DatasetKey | PrerequisiteDatasetKey], 

339 ) -> None: 

340 """Remove edges connecting datasets to a quantum that consumes them.""" 

341 self._xgraph.remove_edges_from((dataset_key, task_key) for dataset_key in dataset_keys) 

342 

343 def add_input_edge( 

344 self, 

345 task_key: QuantumKey | TaskInitKey, 

346 dataset_key: DatasetKey | PrerequisiteDatasetKey, 

347 ignore_unrecognized_quanta: bool = False, 

348 ) -> bool: 

349 """Add an edge connecting a dataset to a quantum that consumes it. 

350 

351 Parameters 

352 ---------- 

353 task_key : `QuantumKey` or `TaskInitKey` 

354 Identifier for the quantum node. 

355 dataset_key : `DatasetKey` or `PrerequisiteKey` 

356 Identifier for the dataset node. 

357 ignore_unrecognized_quanta : `bool`, optional 

358 If `False`, do nothing if the quantum node is not already present. 

359 If `True`, the quantum node is assumed to be present. 

360 

361 Returns 

362 ------- 

363 added : `bool` 

364 `True` if an edge was actually added, `False` if the quantum was 

365 not recognized and the edge was not added as a result. 

366 

367 Notes 

368 ----- 

369 Dataset nodes that are not already present will be created. 

370 """ 

371 if ignore_unrecognized_quanta and task_key not in self._xgraph: 

372 return False 

373 self._xgraph.add_edge(dataset_key, task_key) 

374 return True 

375 

376 def add_output_edge(self, task_key: QuantumKey | TaskInitKey, dataset_key: DatasetKey) -> None: 

377 """Add an edge connecting a dataset to the quantum that produces it. 

378 

379 Parameters 

380 ---------- 

381 task_key : `QuantumKey` or `TaskInitKey` 

382 Identifier for the quantum node. Must identify a node already 

383 present in the graph. 

384 dataset_key : `DatasetKey` 

385 Identifier for the dataset node. Must identify a node already 

386 present in the graph. 

387 """ 

388 assert task_key in self._xgraph 

389 assert dataset_key in self._xgraph 

390 self._xgraph.add_edge(task_key, dataset_key) 

391 

392 def remove_orphan_datasets(self) -> None: 

393 """Remove any dataset nodes that do not have any edges.""" 

394 for orphan in list(networkx.isolates(self._xgraph)): 

395 if not orphan.is_task and orphan not in self._global_init_outputs: 

396 self._xgraph.remove_node(orphan) 

397 

398 def extract_overall_inputs(self) -> dict[DatasetKey | PrerequisiteDatasetKey, DatasetRef]: 

399 """Find overall input datasets. 

400 

401 Returns 

402 ------- 

403 datasets : `dict` [ `DatasetKey` or `PrerequisiteDatasetKey`, \ 

404 `~lsst.daf.butler.DatasetRef` ] 

405 Overall-input datasets, including prerequisites and init-inputs. 

406 """ 

407 result = {} 

408 for generation in networkx.algorithms.topological_generations(self._xgraph): 

409 for dataset_key in generation: 

410 if dataset_key.is_task: 

411 continue 

412 try: 

413 result[dataset_key] = self[dataset_key]["ref"] 

414 except KeyError: 

415 raise AssertionError( 

416 f"Logic bug in QG generation: dataset {dataset_key} was never resolved." 

417 ) 

418 break 

419 return result