Coverage for python/lsst/ctrl/mpexec/singleQuantumExecutor.py : 15%

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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# -------------------------------
27from collections import defaultdict
28import logging
29from itertools import chain
30import time
32# -----------------------------
33# Imports for other modules --
34# -----------------------------
35from .quantumGraphExecutor import QuantumExecutor
36from lsst.log import Log
37from lsst.obs.base import Instrument
38from lsst.pipe.base import ButlerQuantumContext
39from lsst.daf.butler import Quantum
41# ----------------------------------
42# Local non-exported definitions --
43# ----------------------------------
45_LOG = logging.getLogger(__name__.partition(".")[2])
48class SingleQuantumExecutor(QuantumExecutor):
49 """Executor class which runs one Quantum at a time.
51 Parameters
52 ----------
53 butler : `~lsst.daf.butler.Butler`
54 Data butler.
55 taskFactory : `~lsst.pipe.base.TaskFactory`
56 Instance of a task factory.
57 skipExisting : `bool`, optional
58 If True then quanta with all existing outputs are not executed.
59 clobberPartialOutputs : `bool`, optional
60 If True then delete any partial outputs from quantum execution. If
61 complete outputs exists then exception is raise if ``skipExisting`` is
62 False.
63 enableLsstDebug : `bool`, optional
64 Enable debugging with ``lsstDebug`` facility for a task.
65 """
66 def __init__(self, taskFactory, skipExisting=False, clobberPartialOutputs=False, enableLsstDebug=False):
67 self.taskFactory = taskFactory
68 self.skipExisting = skipExisting
69 self.enableLsstDebug = enableLsstDebug
70 self.clobberPartialOutputs = clobberPartialOutputs
72 def execute(self, taskDef, quantum, butler):
74 startTime = time.time()
76 # Docstring inherited from QuantumExecutor.execute
77 self.setupLogging(taskDef, quantum)
78 taskClass, config = taskDef.taskClass, taskDef.config
80 # check whether to skip or delete old outputs
81 if self.checkExistingOutputs(quantum, butler, taskDef):
82 _LOG.info("Quantum execution skipped due to existing outputs, "
83 f"task={taskClass.__name__} dataId={quantum.dataId}.")
84 return
86 quantum = self.updatedQuantumInputs(quantum, butler)
88 # enable lsstDebug debugging
89 if self.enableLsstDebug:
90 try:
91 _LOG.debug("Will try to import debug.py")
92 import debug # noqa:F401
93 except ImportError:
94 _LOG.warn("No 'debug' module found.")
96 # initialize global state
97 self.initGlobals(quantum, butler)
99 # Ensure that we are executing a frozen config
100 config.freeze()
102 task = self.makeTask(taskClass, config, butler)
103 self.runQuantum(task, quantum, taskDef, butler)
105 stopTime = time.time()
106 _LOG.info("Execution of task '%s' on quantum %s took %.3f seconds",
107 taskDef.label, quantum.dataId, stopTime - startTime)
109 def setupLogging(self, taskDef, quantum):
110 """Configure logging system for execution of this task.
112 Ths method can setup logging to attach task- or
113 quantum-specific information to log messages. Potentially this can
114 take into account some info from task configuration as well.
116 Parameters
117 ----------
118 taskDef : `lsst.pipe.base.TaskDef`
119 The task definition.
120 quantum : `~lsst.daf.butler.Quantum`
121 Single Quantum instance.
122 """
123 # include quantum dataId and task label into MDC
124 label = taskDef.label
125 if quantum.dataId:
126 label += f":{quantum.dataId}"
127 Log.MDC("LABEL", label)
129 def checkExistingOutputs(self, quantum, butler, taskDef):
130 """Decide whether this quantum needs to be executed.
132 If only partial outputs exist then they are removed if
133 ``clobberPartialOutputs`` is True, otherwise an exception is raised.
135 Parameters
136 ----------
137 quantum : `~lsst.daf.butler.Quantum`
138 Quantum to check for existing outputs
139 butler : `~lsst.daf.butler.Butler`
140 Data butler.
141 taskDef : `~lsst.pipe.base.TaskDef`
142 Task definition structure.
144 Returns
145 -------
146 exist : `bool`
147 True if all quantum's outputs exist in a collection and
148 ``skipExisting`` is True, False otherwise.
150 Raises
151 ------
152 RuntimeError
153 Raised if some outputs exist and some not.
154 """
155 collection = butler.run
156 registry = butler.registry
158 existingRefs = []
159 missingRefs = []
160 for datasetRefs in quantum.outputs.values():
161 for datasetRef in datasetRefs:
162 ref = registry.findDataset(datasetRef.datasetType, datasetRef.dataId,
163 collections=butler.run)
164 if ref is None:
165 missingRefs.append(datasetRef)
166 else:
167 existingRefs.append(ref)
168 if existingRefs and missingRefs:
169 # some outputs exist and some don't, either delete existing ones or complain
170 _LOG.debug("Partial outputs exist for task %s dataId=%s collection=%s "
171 "existingRefs=%s missingRefs=%s",
172 taskDef, quantum.dataId, collection, existingRefs, missingRefs)
173 if self.clobberPartialOutputs:
174 _LOG.info("Removing partial outputs for task %s: %s", taskDef, existingRefs)
175 butler.pruneDatasets(existingRefs, disassociate=True, unstore=True, purge=True)
176 return False
177 else:
178 raise RuntimeError(f"Registry inconsistency while checking for existing outputs:"
179 f" collection={collection} existingRefs={existingRefs}"
180 f" missingRefs={missingRefs}")
181 elif existingRefs:
182 # complete outputs exist, this is fine only if skipExisting is set
183 return self.skipExisting
184 else:
185 # no outputs exist
186 return False
188 def makeTask(self, taskClass, config, butler):
189 """Make new task instance.
191 Parameters
192 ----------
193 taskClass : `type`
194 Sub-class of `~lsst.pipe.base.PipelineTask`.
195 config : `~lsst.pipe.base.PipelineTaskConfig`
196 Configuration object for this task
198 Returns
199 -------
200 task : `~lsst.pipe.base.PipelineTask`
201 Instance of ``taskClass`` type.
202 butler : `~lsst.daf.butler.Butler`
203 Data butler.
204 """
205 # call task factory for that
206 return self.taskFactory.makeTask(taskClass, config, None, butler)
208 def updatedQuantumInputs(self, quantum, butler):
209 """Update quantum with extra information, returns a new updated Quantum.
211 Some methods may require input DatasetRefs to have non-None
212 ``dataset_id``, but in case of intermediate dataset it may not be
213 filled during QuantumGraph construction. This method will retrieve
214 missing info from registry.
216 Parameters
217 ----------
218 quantum : `~lsst.daf.butler.Quantum`
219 Single Quantum instance.
220 butler : `~lsst.daf.butler.Butler`
221 Data butler.
223 Returns
224 -------
225 update : `~lsst.daf.butler.Quantum`
226 Updated Quantum instance
227 """
228 updatedInputs = defaultdict(list)
229 for key, refsForDatasetType in quantum.inputs.items():
230 newRefsForDatasetType = updatedInputs[key]
231 for ref in refsForDatasetType:
232 if ref.id is None:
233 resolvedRef = butler.registry.findDataset(ref.datasetType, ref.dataId,
234 collections=butler.collections)
235 if resolvedRef is None:
236 raise ValueError(
237 f"Cannot find {ref.datasetType.name} with id {ref.dataId} "
238 f"in collections {butler.collections}."
239 )
240 newRefsForDatasetType.append(resolvedRef)
241 _LOG.debug("Updating dataset ID for %s", ref)
242 else:
243 newRefsForDatasetType.append(ref)
244 return Quantum(taskName=quantum.taskName,
245 taskClass=quantum.taskClass,
246 dataId=quantum.dataId,
247 initInputs=quantum.initInputs,
248 inputs=updatedInputs,
249 outputs=quantum.outputs
250 )
252 def runQuantum(self, task, quantum, taskDef, butler):
253 """Execute task on a single quantum.
255 Parameters
256 ----------
257 task : `~lsst.pipe.base.PipelineTask`
258 Task object.
259 quantum : `~lsst.daf.butler.Quantum`
260 Single Quantum instance.
261 taskDef : `~lsst.pipe.base.TaskDef`
262 Task definition structure.
263 butler : `~lsst.daf.butler.Butler`
264 Data butler.
265 """
266 # Create a butler that operates in the context of a quantum
267 butlerQC = ButlerQuantumContext(butler, quantum)
269 # Get the input and output references for the task
270 inputRefs, outputRefs = taskDef.connections.buildDatasetRefs(quantum)
272 # Call task runQuantum() method. Any exception thrown by the task
273 # propagates to caller.
274 task.runQuantum(butlerQC, inputRefs, outputRefs)
276 if taskDef.metadataDatasetName is not None:
277 # DatasetRef has to be in the Quantum outputs, can lookup by name
278 try:
279 ref = quantum.outputs[taskDef.metadataDatasetName]
280 except LookupError as exc:
281 raise LookupError(
282 f"Quantum outputs is missing metadata dataset type {taskDef.metadataDatasetName},"
283 f" it could happen due to inconsistent options between Quantum generation"
284 f" and execution") from exc
285 butlerQC.put(task.getFullMetadata(), ref[0])
287 def initGlobals(self, quantum, butler):
288 """Initialize global state needed for task execution.
290 Parameters
291 ----------
292 quantum : `~lsst.daf.butler.Quantum`
293 Single Quantum instance.
294 butler : `~lsst.daf.butler.Butler`
295 Data butler.
297 Notes
298 -----
299 There is an issue with initializing filters singleton which is done
300 by instrument, to avoid requiring tasks to do it in runQuantum()
301 we do it here when any dataId has an instrument dimension. Also for
302 now we only allow single instrument, verify that all instrument
303 names in all dataIds are identical.
305 This will need revision when filter singleton disappears.
306 """
307 oneInstrument = None
308 for datasetRefs in chain(quantum.inputs.values(), quantum.outputs.values()):
309 for datasetRef in datasetRefs:
310 dataId = datasetRef.dataId
311 instrument = dataId.get("instrument")
312 if instrument is not None:
313 if oneInstrument is not None:
314 assert instrument == oneInstrument, \
315 "Currently require that only one instrument is used per graph"
316 else:
317 oneInstrument = instrument
318 Instrument.fromName(instrument, butler.registry)