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

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

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

12# (at your option) any later version. 

13# 

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

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

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

17# GNU General Public License for more details. 

18# 

19# You should have received a copy of the GNU General Public License 

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

21from __future__ import annotations 

22 

23"""Module defining quantum graph classes and related methods. 

24 

25There could be different representations of the quantum graph depending 

26on the client needs. Presently this module contains graph implementation 

27which is based on requirements of command-line environment. In the future 

28we could add other implementations and methods to convert between those 

29representations. 

30""" 

31 

32# "exported" names 

33__all__ = ["QuantumGraph", "QuantumGraphTaskNodes", "QuantumIterData"] 

34 

35# ------------------------------- 

36# Imports of standard modules -- 

37# ------------------------------- 

38from itertools import chain 

39from dataclasses import dataclass 

40from typing import List, FrozenSet, Mapping 

41 

42# ----------------------------- 

43# Imports for other modules -- 

44# ----------------------------- 

45from .pipeline import TaskDef 

46from .pipeTools import orderPipeline 

47from lsst.daf.butler import Quantum, DatasetRef, DatasetType 

48from lsst.daf.butler.core.utils import NamedKeyDict 

49 

50# ---------------------------------- 

51# Local non-exported definitions -- 

52# ---------------------------------- 

53 

54# ------------------------ 

55# Exported definitions -- 

56# ------------------------ 

57 

58 

59@dataclass 

60class QuantumIterData: 

61 """Helper class for iterating over quanta in a graph. 

62 

63 The `QuantumGraph.traverse` method needs to return topologically ordered 

64 Quanta together with their dependencies. This class is used as a value 

65 for the iterator, it contains enumerated Quantum and its dependencies. 

66 """ 

67 

68 __slots__ = ["index", "quantum", "taskDef", "dependencies"] 

69 

70 index: int 

71 """Index of this Quantum, a unique but arbitrary integer.""" 

72 

73 quantum: Quantum 

74 """Quantum corresponding to a graph node.""" 

75 

76 taskDef: TaskDef 

77 """Task class to be run on this quantum, and corresponding label and 

78 config. 

79 """ 

80 

81 dependencies: FrozenSet(int) 

82 """Possibly empty set of indices of dependencies for this Quantum. 

83 Dependencies include other nodes in the graph; they do not reflect data 

84 already in butler (there are no graph nodes for those). 

85 """ 

86 

87 

88@dataclass 

89class QuantumGraphTaskNodes: 

90 """QuantumGraphTaskNodes represents a bunch of nodes in an quantum graph 

91 corresponding to a single task. 

92 

93 The node in quantum graph is represented by the `PipelineTask` and a 

94 single `~lsst.daf.butler.Quantum` instance. One possible representation 

95 of the graph is just a list of nodes without edges (edges can be deduced 

96 from nodes' quantum inputs and outputs if needed). That representation can 

97 be reduced to the list of PipelineTasks (or their corresponding TaskDefs) 

98 and the corresponding list of Quanta. This class is used in this reduced 

99 representation for a single task, and full `QuantumGraph` is a sequence of 

100 tinstances of this class for one or more tasks. 

101 

102 Different frameworks may use different graph representation, this 

103 representation was based mostly on requirements of command-line 

104 executor which does not need explicit edges information. 

105 """ 

106 

107 taskDef: TaskDef 

108 """Task defintion for this set of nodes.""" 

109 

110 quanta: List[Quantum] 

111 """List of quanta corresponding to the task.""" 

112 

113 initInputs: Mapping[DatasetType, DatasetRef] 

114 """Datasets that must be loaded or created to construct this task.""" 

115 

116 initOutputs: Mapping[DatasetType, DatasetRef] 

117 """Datasets that may be written after constructing this task.""" 

118 

119 

120class QuantumGraph(list): 

121 """QuantumGraph is a sequence of `QuantumGraphTaskNodes` objects. 

122 

123 Typically the order of the tasks in the list will be the same as the 

124 order of tasks in a pipeline (obviously depends on the code which 

125 constructs graph). 

126 

127 Parameters 

128 ---------- 

129 iterable : iterable of `QuantumGraphTaskNodes`, optional 

130 Initial sequence of per-task nodes. 

131 """ 

132 def __init__(self, iterable=None): 

133 list.__init__(self, iterable or []) 

134 self.initInputs = NamedKeyDict() 

135 self.initIntermediates = NamedKeyDict() 

136 self.initOutputs = NamedKeyDict() 

137 

138 initInputs: NamedKeyDict 

139 """Datasets that must be provided to construct one or more Tasks in this 

140 graph, and must be obtained from the data repository. 

141 

142 This is disjoint with both `initIntermediates` and `initOutputs`. 

143 """ 

144 

145 initIntermediates: NamedKeyDict 

146 """Datasets that must be provided to construct one or more Tasks in this 

147 graph, but are also produced after constructing a Task in this graph. 

148 

149 This is disjoint with both `initInputs` and `initOutputs`. 

150 """ 

151 

152 initOutputs: NamedKeyDict 

153 """Datasets that are produced after constructing a Task in this graph, 

154 and are not used to construct any other Task in this graph. 

155 

156 This is disjoint from both `initInputs` and `initIntermediates`. 

157 """ 

158 

159 def quanta(self): 

160 """Iterator over quanta in a graph. 

161 

162 Quanta are returned in unspecified order. 

163 

164 Yields 

165 ------ 

166 taskDef : `TaskDef` 

167 Task definition for a Quantum. 

168 quantum : `~lsst.daf.butler.Quantum` 

169 Single quantum. 

170 """ 

171 for taskNodes in self: 

172 taskDef = taskNodes.taskDef 

173 for quantum in taskNodes.quanta: 

174 yield taskDef, quantum 

175 

176 def quantaAsQgraph(self): 

177 """Iterator over quanta in a graph. 

178 

179 QuantumGraph containing individual quanta are returned. 

180 

181 Yields 

182 ------ 

183 graph : `QuantumGraph` 

184 """ 

185 for taskDef, quantum in self.quanta(): 

186 node = QuantumGraphTaskNodes(taskDef, [quantum], 

187 quantum.initInputs, quantum.outputs) 

188 graph = QuantumGraph([node]) 

189 yield graph 

190 

191 def countQuanta(self): 

192 """Return total count of quanta in a graph. 

193 

194 Returns 

195 ------- 

196 count : `int` 

197 Number of quanta in a graph. 

198 """ 

199 return sum(len(taskNodes.quanta) for taskNodes in self) 

200 

201 def traverse(self): 

202 """Return topologically ordered Quanta and their dependencies. 

203 

204 This method iterates over all Quanta in topological order, enumerating 

205 them during iteration. Returned `QuantumIterData` object contains 

206 Quantum instance, its ``index`` and the ``index`` of all its 

207 prerequsites (Quanta that produce inputs for this Quantum): 

208 

209 - the ``index`` values are generated by an iteration of a 

210 QuantumGraph, and are not intrinsic to the QuantumGraph 

211 - during iteration, each ID will appear in index before it ever 

212 appears in dependencies. 

213 

214 Yields 

215 ------ 

216 quantumData : `QuantumIterData` 

217 """ 

218 

219 def orderedTaskNodes(graph): 

220 """Return topologically ordered task nodes. 

221 

222 Yields 

223 ------ 

224 nodes : `QuantumGraphTaskNodes` 

225 """ 

226 # Tasks in a graph are probably topologically sorted already but there 

227 # is no guarantee for that. Just re-construct Pipeline and order tasks 

228 # in a pipeline using existing method. 

229 nodesMap = {id(item.taskDef): item for item in graph} 

230 pipeline = orderPipeline([item.taskDef for item in graph]) 

231 for taskDef in pipeline: 

232 yield nodesMap[id(taskDef)] 

233 

234 index = 0 

235 outputs = {} # maps (DatasetType.name, dataId) to its producing quantum index 

236 for nodes in orderedTaskNodes(self): 

237 for quantum in nodes.quanta: 

238 

239 # Find quantum dependencies (must be in `outputs` already) 

240 prereq = [] 

241 for dataRef in chain.from_iterable(quantum.predictedInputs.values()): 

242 # if data exists in butler then `id` is not None 

243 if dataRef.id is None: 

244 # Get the base name if this is a component 

245 name, component = dataRef.datasetType.nameAndComponent() 

246 key = (name, dataRef.dataId) 

247 try: 

248 prereq.append(outputs[key]) 

249 except KeyError: 

250 # The Quantum that makes our inputs is not in the graph, 

251 # this could happen if we run on a "split graph" which is 

252 # usually just one quantum. Check for number of Quanta 

253 # in a graph and ignore error if it's just one. 

254 # TODO: This code has to be removed or replaced with 

255 # something more generic 

256 if not (len(self) == 1 and len(self[0].quanta) == 1): 

257 raise 

258 

259 # Update `outputs` with this quantum outputs 

260 for dataRef in chain.from_iterable(quantum.outputs.values()): 

261 key = (dataRef.datasetType.name, dataRef.dataId) 

262 outputs[key] = index 

263 

264 yield QuantumIterData(index=index, quantum=quantum, taskDef=nodes.taskDef, 

265 dependencies=frozenset(prereq)) 

266 index += 1