Coverage for python/lsst/ctrl/mpexec/singleQuantumExecutor.py : 14%

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1# This file is part of ctrl_mpexec.
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/>.
22__all__ = ['SingleQuantumExecutor']
24# -------------------------------
25# Imports of standard modules --
26# -------------------------------
27import logging
28from itertools import chain
30# -----------------------------
31# Imports for other modules --
32# -----------------------------
33from .quantumGraphExecutor import QuantumExecutor
34from lsst.log import Log
35from lsst.obs.base import Instrument
36from lsst.pipe.base import ButlerQuantumContext
38# ----------------------------------
39# Local non-exported definitions --
40# ----------------------------------
42_LOG = logging.getLogger(__name__.partition(".")[2])
45class SingleQuantumExecutor(QuantumExecutor):
46 """Executor class which runs one Quantum at a time.
48 Parameters
49 ----------
50 butler : `~lsst.daf.butler.Butler`
51 Data butler.
52 taskFactory : `~lsst.pipe.base.TaskFactory`
53 Instance of a task factory.
54 skipExisting : `bool`, optional
55 If True then quanta with all existing outputs are not executed.
56 enableLsstDebug : `bool`, optional
57 Enable debugging with ``lsstDebug`` facility for a task.
58 """
59 def __init__(self, taskFactory, skipExisting=False, enableLsstDebug=False):
60 self.taskFactory = taskFactory
61 self.skipExisting = skipExisting
62 self.enableLsstDebug = enableLsstDebug
64 def execute(self, taskDef, quantum, butler):
65 # Docstring inherited from QuantumExecutor.execute
66 taskClass, config = taskDef.taskClass, taskDef.config
67 self.setupLogging(taskClass, config, quantum)
68 if self.skipExisting and self.quantumOutputsExist(quantum, butler):
69 _LOG.info("Quantum execution skipped due to existing outputs, "
70 f"task={taskClass.__name__} dataId={quantum.dataId}.")
71 return
72 self.updateQuantumInputs(quantum, butler)
74 # enable lsstDebug debugging
75 if self.enableLsstDebug:
76 try:
77 _LOG.debug("Will try to import debug.py")
78 import debug # noqa:F401
79 except ImportError:
80 _LOG.warn("No 'debug' module found.")
82 # initialize global state
83 self.initGlobals(quantum, butler)
85 task = self.makeTask(taskClass, config, butler)
86 self.runQuantum(task, quantum, taskDef, butler)
88 def setupLogging(self, taskClass, config, quantum):
89 """Configure logging system for execution of this task.
91 Ths method can setup logging to attach task- or
92 quantum-specific information to log messages. Potentially this can
93 take into accout some info from task configuration as well.
95 Parameters
96 ----------
97 taskClass : `type`
98 Sub-class of `~lsst.pipe.base.PipelineTask`.
99 config : `~lsst.pipe.base.PipelineTaskConfig`
100 Configuration object for this task
101 quantum : `~lsst.daf.butler.Quantum`
102 Single Quantum instance.
103 """
104 # include input dataIds into MDC
105 dataIds = set(ref.dataId for ref in chain.from_iterable(quantum.predictedInputs.values()))
106 if dataIds:
107 if len(dataIds) == 1:
108 Log.MDC("LABEL", str(dataIds.pop()))
109 else:
110 Log.MDC("LABEL", '[' + ', '.join([str(dataId) for dataId in dataIds]) + ']')
112 def quantumOutputsExist(self, quantum, butler):
113 """Decide whether this quantum needs to be executed.
115 Parameters
116 ----------
117 quantum : `~lsst.daf.butler.Quantum`
118 Quantum to check for existing outputs
119 butler : `~lsst.daf.butler.Butler`
120 Data butler.
122 Returns
123 -------
124 exist : `bool`
125 True if all quantum's outputs exist in a collection, False
126 otherwise.
128 Raises
129 ------
130 RuntimeError
131 Raised if some outputs exist and some not.
132 """
133 collection = butler.run
134 registry = butler.registry
136 existingRefs = []
137 missingRefs = []
138 for datasetRefs in quantum.outputs.values():
139 for datasetRef in datasetRefs:
140 ref = registry.findDataset(datasetRef.datasetType, datasetRef.dataId,
141 collections=butler.run)
142 if ref is None:
143 missingRefs.append(datasetRefs)
144 else:
145 existingRefs.append(datasetRefs)
146 if existingRefs and missingRefs:
147 # some outputs exist and same not, can't do a thing with that
148 raise RuntimeError(f"Registry inconsistency while checking for existing outputs:"
149 f" collection={collection} existingRefs={existingRefs}"
150 f" missingRefs={missingRefs}")
151 else:
152 return bool(existingRefs)
154 def makeTask(self, taskClass, config, butler):
155 """Make new task instance.
157 Parameters
158 ----------
159 taskClass : `type`
160 Sub-class of `~lsst.pipe.base.PipelineTask`.
161 config : `~lsst.pipe.base.PipelineTaskConfig`
162 Configuration object for this task
164 Returns
165 -------
166 task : `~lsst.pipe.base.PipelineTask`
167 Instance of ``taskClass`` type.
168 butler : `~lsst.daf.butler.Butler`
169 Data butler.
170 """
171 # call task factory for that
172 return self.taskFactory.makeTask(taskClass, config, None, butler)
174 def updateQuantumInputs(self, quantum, butler):
175 """Update quantum with extra information.
177 Some methods may require input DatasetRefs to have non-None
178 ``dataset_id``, but in case of intermediate dataset it may not be
179 filled during QuantumGraph construction. This method will retrieve
180 missing info from registry.
182 Parameters
183 ----------
184 quantum : `~lsst.daf.butler.Quantum`
185 Single Quantum instance.
186 butler : `~lsst.daf.butler.Butler`
187 Data butler.
188 """
189 for refsForDatasetType in quantum.predictedInputs.values():
190 newRefsForDatasetType = []
191 for ref in refsForDatasetType:
192 if ref.id is None:
193 resolvedRef = butler.registry.findDataset(ref.datasetType, ref.dataId,
194 collections=butler.collections)
195 if resolvedRef is None:
196 raise ValueError(
197 f"Cannot find {ref.datasetType.name} with id {ref.dataId} "
198 f"in collections {butler.collections}."
199 )
200 newRefsForDatasetType.append(resolvedRef)
201 _LOG.debug("Updating dataset ID for %s", ref)
202 else:
203 newRefsForDatasetType.append(ref)
204 refsForDatasetType[:] = newRefsForDatasetType
206 def runQuantum(self, task, quantum, taskDef, butler):
207 """Execute task on a single quantum.
209 Parameters
210 ----------
211 task : `~lsst.pipe.base.PipelineTask`
212 Task object.
213 quantum : `~lsst.daf.butler.Quantum`
214 Single Quantum instance.
215 taskDef : `~lsst.pipe.base.TaskDef`
216 Task definition structure.
217 butler : `~lsst.daf.butler.Butler`
218 Data butler.
219 """
220 # Create a butler that operates in the context of a quantum
221 butlerQC = ButlerQuantumContext(butler, quantum)
223 # Get the input and output references for the task
224 connectionInstance = task.config.connections.ConnectionsClass(config=task.config)
225 inputRefs, outputRefs = connectionInstance.buildDatasetRefs(quantum)
227 # Call task runQuantum() method. Any exception thrown by the task
228 # propagates to caller.
229 task.runQuantum(butlerQC, inputRefs, outputRefs)
231 if taskDef.metadataDatasetName is not None:
232 # DatasetRef has to be in the Quantum outputs, can lookup by name
233 try:
234 ref = quantum.outputs[taskDef.metadataDatasetName]
235 except LookupError as exc:
236 raise LookupError(
237 f"Quantum outputs is missing metadata dataset type {taskDef.metadataDatasetName},"
238 f" it could happen due to inconsistent options between Quantum generation"
239 f" and execution") from exc
240 butlerQC.put(task.getFullMetadata(), ref[0])
242 def initGlobals(self, quantum, butler):
243 """Initialize global state needed for task execution.
245 Parameters
246 ----------
247 quantum : `~lsst.daf.butler.Quantum`
248 Single Quantum instance.
249 butler : `~lsst.daf.butler.Butler`
250 Data butler.
252 Notes
253 -----
254 There is an issue with initializing filters singleton which is done
255 by instrument, to avoid requiring tasks to do it in runQuantum()
256 we do it here when any dataId has an instrument dimension. Also for
257 now we only allow single instrument, verify that all instrument
258 names in all dataIds are identical.
260 This will need revision when filter singleton disappears.
261 """
262 oneInstrument = None
263 for datasetRefs in chain(quantum.predictedInputs.values(), quantum.outputs.values()):
264 for datasetRef in datasetRefs:
265 dataId = datasetRef.dataId
266 instrument = dataId.get("instrument")
267 if instrument is not None:
268 if oneInstrument is not None:
269 assert instrument == oneInstrument, \
270 "Currently require that only one instrument is used per graph"
271 else:
272 oneInstrument = instrument
273 Instrument.fromName(instrument, butler.registry)