Coverage for python/lsst/ctrl/mpexec/singleQuantumExecutor.py : 11%

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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 sys
31import time
33# -----------------------------
34# Imports for other modules --
35# -----------------------------
36from .quantumGraphExecutor import QuantumExecutor
37from lsst.log import Log
38from lsst.daf.base import PropertyList, PropertySet
39from lsst.obs.base import Instrument
40from lsst.pipe.base import (
41 AdjustQuantumHelper,
42 ButlerQuantumContext,
43 InvalidQuantumError,
44 NoWorkFound,
45 RepeatableQuantumError,
46 logInfo,
47)
48from lsst.daf.butler import Quantum
50# ----------------------------------
51# Local non-exported definitions --
52# ----------------------------------
54_LOG = logging.getLogger(__name__.partition(".")[2])
57class SingleQuantumExecutor(QuantumExecutor):
58 """Executor class which runs one Quantum at a time.
60 Parameters
61 ----------
62 butler : `~lsst.daf.butler.Butler`
63 Data butler.
64 taskFactory : `~lsst.pipe.base.TaskFactory`
65 Instance of a task factory.
66 skipExisting : `bool`, optional
67 If `True`, then quanta that succeeded will not be rerun.
68 clobberOutputs : `bool`, optional
69 If `True`, then existing outputs will be overwritten. If
70 `skipExisting` is also `True`, only outputs from failed quanta will
71 be overwritten.
72 enableLsstDebug : `bool`, optional
73 Enable debugging with ``lsstDebug`` facility for a task.
74 exitOnKnownError : `bool`, optional
75 If `True`, call `sys.exit` with the appropriate exit code for special
76 known exceptions, after printing a traceback, instead of letting the
77 exception propagate up to calling. This is always the behavior for
78 InvalidQuantumError.
79 """
80 def __init__(self, taskFactory, skipExisting=False, clobberOutputs=False, enableLsstDebug=False,
81 exitOnKnownError=False):
82 self.taskFactory = taskFactory
83 self.skipExisting = skipExisting
84 self.enableLsstDebug = enableLsstDebug
85 self.clobberOutputs = clobberOutputs
86 self.exitOnKnownError = exitOnKnownError
88 def execute(self, taskDef, quantum, butler):
90 startTime = time.time()
92 # Save detailed resource usage before task start to metadata.
93 quantumMetadata = PropertyList()
94 logInfo(None, "prep", metadata=quantumMetadata)
96 # Docstring inherited from QuantumExecutor.execute
97 self.setupLogging(taskDef, quantum)
98 taskClass, label, config = taskDef.taskClass, taskDef.label, taskDef.config
100 # check whether to skip or delete old outputs
101 if self.checkExistingOutputs(quantum, butler, taskDef):
102 _LOG.info("Skipping already-successful quantum for label=%s dataId=%s.", label, quantum.dataId)
103 return
104 try:
105 quantum = self.updatedQuantumInputs(quantum, butler, taskDef)
106 except NoWorkFound as exc:
107 _LOG.info("Nothing to do for task '%s' on quantum %s; saving metadata and skipping: %s",
108 taskDef.label, quantum.dataId, str(exc))
109 # Make empty metadata that looks something like what a do-nothing
110 # task would write (but we don't bother with empty nested
111 # PropertySets for subtasks). This is slightly duplicative with
112 # logic in pipe_base that we can't easily call from here; we'll fix
113 # this on DM-29761.
114 logInfo(None, "end", metadata=quantumMetadata)
115 fullMetadata = PropertySet()
116 fullMetadata[taskDef.label] = PropertyList()
117 fullMetadata["quantum"] = quantumMetadata
118 self.writeMetadata(quantum, fullMetadata, taskDef, butler)
119 return
121 # enable lsstDebug debugging
122 if self.enableLsstDebug:
123 try:
124 _LOG.debug("Will try to import debug.py")
125 import debug # noqa:F401
126 except ImportError:
127 _LOG.warn("No 'debug' module found.")
129 # initialize global state
130 self.initGlobals(quantum, butler)
132 # Ensure that we are executing a frozen config
133 config.freeze()
134 logInfo(None, "init", metadata=quantumMetadata)
135 task = self.makeTask(taskClass, label, config, butler)
136 logInfo(None, "start", metadata=quantumMetadata)
137 self.runQuantum(task, quantum, taskDef, butler)
138 logInfo(None, "end", metadata=quantumMetadata)
139 fullMetadata = task.getFullMetadata()
140 fullMetadata["quantum"] = quantumMetadata
141 self.writeMetadata(quantum, fullMetadata, taskDef, butler)
142 stopTime = time.time()
143 _LOG.info("Execution of task '%s' on quantum %s took %.3f seconds",
144 taskDef.label, quantum.dataId, stopTime - startTime)
146 def setupLogging(self, taskDef, quantum):
147 """Configure logging system for execution of this task.
149 Ths method can setup logging to attach task- or
150 quantum-specific information to log messages. Potentially this can
151 take into account some info from task configuration as well.
153 Parameters
154 ----------
155 taskDef : `lsst.pipe.base.TaskDef`
156 The task definition.
157 quantum : `~lsst.daf.butler.Quantum`
158 Single Quantum instance.
159 """
160 # include quantum dataId and task label into MDC
161 label = taskDef.label
162 if quantum.dataId:
163 label += f":{quantum.dataId}"
164 Log.MDC("LABEL", label)
166 def checkExistingOutputs(self, quantum, butler, taskDef):
167 """Decide whether this quantum needs to be executed.
169 If only partial outputs exist then they are removed if
170 ``clobberOutputs`` is True, otherwise an exception is raised.
172 Parameters
173 ----------
174 quantum : `~lsst.daf.butler.Quantum`
175 Quantum to check for existing outputs
176 butler : `~lsst.daf.butler.Butler`
177 Data butler.
178 taskDef : `~lsst.pipe.base.TaskDef`
179 Task definition structure.
181 Returns
182 -------
183 exist : `bool`
184 `True` if ``self.skipExisting`` is `True`, and a previous execution
185 of this quanta appears to have completed successfully (either
186 because metadata was written or all datasets were written).
187 `False` otherwise.
189 Raises
190 ------
191 RuntimeError
192 Raised if some outputs exist and some not.
193 """
194 collection = butler.run
195 registry = butler.registry
197 if self.skipExisting and taskDef.metadataDatasetName is not None:
198 # Metadata output exists; this is sufficient to assume the previous
199 # run was successful and should be skipped.
200 if (ref := butler.registry.findDataset(taskDef.metadataDatasetName, quantum.dataId)) is not None:
201 if butler.datastore.exists(ref):
202 return True
204 existingRefs = []
205 missingRefs = []
206 for datasetRefs in quantum.outputs.values():
207 for datasetRef in datasetRefs:
208 ref = registry.findDataset(datasetRef.datasetType, datasetRef.dataId,
209 collections=butler.run)
210 if ref is None:
211 missingRefs.append(datasetRef)
212 else:
213 if butler.datastore.exists(ref):
214 existingRefs.append(ref)
215 else:
216 missingRefs.append(datasetRef)
217 if existingRefs and missingRefs:
218 # some outputs exist and some don't, either delete existing ones or complain
219 _LOG.debug("Partial outputs exist for task %s dataId=%s collection=%s "
220 "existingRefs=%s missingRefs=%s",
221 taskDef, quantum.dataId, collection, existingRefs, missingRefs)
222 if self.clobberOutputs:
223 _LOG.info("Removing partial outputs for task %s: %s", taskDef, existingRefs)
224 butler.pruneDatasets(existingRefs, disassociate=True, unstore=True, purge=True)
225 return False
226 else:
227 raise RuntimeError(f"Registry inconsistency while checking for existing outputs:"
228 f" collection={collection} existingRefs={existingRefs}"
229 f" missingRefs={missingRefs}")
230 elif existingRefs:
231 # complete outputs exist, this is fine only if skipExisting is set
232 return self.skipExisting
233 else:
234 # no outputs exist
235 return False
237 def makeTask(self, taskClass, name, config, butler):
238 """Make new task instance.
240 Parameters
241 ----------
242 taskClass : `type`
243 Sub-class of `~lsst.pipe.base.PipelineTask`.
244 name : `str`
245 Name for this task.
246 config : `~lsst.pipe.base.PipelineTaskConfig`
247 Configuration object for this task
249 Returns
250 -------
251 task : `~lsst.pipe.base.PipelineTask`
252 Instance of ``taskClass`` type.
253 butler : `~lsst.daf.butler.Butler`
254 Data butler.
255 """
256 # call task factory for that
257 return self.taskFactory.makeTask(taskClass, name, config, None, butler)
259 def updatedQuantumInputs(self, quantum, butler, taskDef):
260 """Update quantum with extra information, returns a new updated Quantum.
262 Some methods may require input DatasetRefs to have non-None
263 ``dataset_id``, but in case of intermediate dataset it may not be
264 filled during QuantumGraph construction. This method will retrieve
265 missing info from registry.
267 Parameters
268 ----------
269 quantum : `~lsst.daf.butler.Quantum`
270 Single Quantum instance.
271 butler : `~lsst.daf.butler.Butler`
272 Data butler.
273 taskDef : `~lsst.pipe.base.TaskDef`
274 Task definition structure.
276 Returns
277 -------
278 update : `~lsst.daf.butler.Quantum`
279 Updated Quantum instance
280 """
281 anyChanges = False
282 updatedInputs = defaultdict(list)
283 for key, refsForDatasetType in quantum.inputs.items():
284 newRefsForDatasetType = updatedInputs[key]
285 for ref in refsForDatasetType:
286 if ref.id is None:
287 resolvedRef = butler.registry.findDataset(ref.datasetType, ref.dataId,
288 collections=butler.collections)
289 if resolvedRef is None:
290 _LOG.debug("No dataset found for %s", ref)
291 continue
292 else:
293 _LOG.debug("Updated dataset ID for %s", ref)
294 else:
295 resolvedRef = ref
296 # We need to ask datastore if the dataset actually exists
297 # because the Registry of a local "execution butler" cannot
298 # know this (because we prepopulate it with all of the datasets
299 # that might be created).
300 if butler.datastore.exists(resolvedRef):
301 newRefsForDatasetType.append(resolvedRef)
302 if len(newRefsForDatasetType) != len(refsForDatasetType):
303 anyChanges = True
304 # If we removed any input datasets, let the task check if it has enough
305 # to proceed and/or prune related datasets that it also doesn't
306 # need/produce anymore. It will raise NoWorkFound if it can't run,
307 # which we'll let propagate up. This is exactly what we run during QG
308 # generation, because a task shouldn't care whether an input is missing
309 # because some previous task didn't produce it, or because it just
310 # wasn't there during QG generation.
311 helper = AdjustQuantumHelper(updatedInputs, quantum.outputs)
312 if anyChanges:
313 helper.adjust_in_place(taskDef.connections, label=taskDef.label, data_id=quantum.dataId)
314 return Quantum(taskName=quantum.taskName,
315 taskClass=quantum.taskClass,
316 dataId=quantum.dataId,
317 initInputs=quantum.initInputs,
318 inputs=helper.inputs,
319 outputs=helper.outputs
320 )
322 def runQuantum(self, task, quantum, taskDef, butler):
323 """Execute task on a single quantum.
325 Parameters
326 ----------
327 task : `~lsst.pipe.base.PipelineTask`
328 Task object.
329 quantum : `~lsst.daf.butler.Quantum`
330 Single Quantum instance.
331 taskDef : `~lsst.pipe.base.TaskDef`
332 Task definition structure.
333 butler : `~lsst.daf.butler.Butler`
334 Data butler.
335 """
336 # Create a butler that operates in the context of a quantum
337 butlerQC = ButlerQuantumContext(butler, quantum)
339 # Get the input and output references for the task
340 inputRefs, outputRefs = taskDef.connections.buildDatasetRefs(quantum)
342 # Call task runQuantum() method. Catch a few known failure modes and
343 # translate them into specific
344 try:
345 task.runQuantum(butlerQC, inputRefs, outputRefs)
346 except NoWorkFound as err:
347 # Not an error, just an early exit.
348 _LOG.info("Task '%s' on quantum %s exited early: %s",
349 taskDef.label, quantum.dataId, str(err))
350 pass
351 except RepeatableQuantumError as err:
352 if self.exitOnKnownError:
353 _LOG.warning("Caught repeatable quantum error for %s (%s):", taskDef, quantum.dataId)
354 _LOG.warning(err, exc_info=True)
355 sys.exit(err.EXIT_CODE)
356 else:
357 raise
358 except InvalidQuantumError as err:
359 _LOG.fatal("Invalid quantum error for %s (%s): %s", taskDef, quantum.dataId)
360 _LOG.fatal(err, exc_info=True)
361 sys.exit(err.EXIT_CODE)
363 def writeMetadata(self, quantum, metadata, taskDef, butler):
364 if taskDef.metadataDatasetName is not None:
365 # DatasetRef has to be in the Quantum outputs, can lookup by name
366 try:
367 ref = quantum.outputs[taskDef.metadataDatasetName]
368 except LookupError as exc:
369 raise InvalidQuantumError(
370 f"Quantum outputs is missing metadata dataset type {taskDef.metadataDatasetName};"
371 f" this could happen due to inconsistent options between QuantumGraph generation"
372 f" and execution") from exc
373 butler.put(metadata, ref[0])
375 def initGlobals(self, quantum, butler):
376 """Initialize global state needed for task execution.
378 Parameters
379 ----------
380 quantum : `~lsst.daf.butler.Quantum`
381 Single Quantum instance.
382 butler : `~lsst.daf.butler.Butler`
383 Data butler.
385 Notes
386 -----
387 There is an issue with initializing filters singleton which is done
388 by instrument, to avoid requiring tasks to do it in runQuantum()
389 we do it here when any dataId has an instrument dimension. Also for
390 now we only allow single instrument, verify that all instrument
391 names in all dataIds are identical.
393 This will need revision when filter singleton disappears.
394 """
395 oneInstrument = None
396 for datasetRefs in chain(quantum.inputs.values(), quantum.outputs.values()):
397 for datasetRef in datasetRefs:
398 dataId = datasetRef.dataId
399 instrument = dataId.get("instrument")
400 if instrument is not None:
401 if oneInstrument is not None:
402 assert instrument == oneInstrument, \
403 "Currently require that only one instrument is used per graph"
404 else:
405 oneInstrument = instrument
406 Instrument.fromName(instrument, butler.registry)