21 from __future__
import annotations
23 """Module defining GraphBuilder class and related methods.
26 __all__ = [
'GraphBuilder']
32 from collections
import ChainMap
33 from contextlib
import contextmanager
34 from dataclasses
import dataclass
35 from typing
import Dict, Iterable, Iterator, List, Set
42 from .connections
import iterConnections
43 from .pipeline
import PipelineDatasetTypes, TaskDatasetTypes, TaskDef, Pipeline
44 from .graph
import QuantumGraph
45 from lsst.daf.butler
import (
54 from lsst.daf.butler.registry.queries.exprParser
import ParseError, ParserYacc, TreeVisitor
61 _LOG = logging.getLogger(__name__.partition(
".")[2])
64 class _DatasetDict(NamedKeyDict[DatasetType, Dict[DataCoordinate, DatasetRef]]):
65 """A custom dictionary that maps `DatasetType` to a nested dictionary of
66 the known `DatasetRef` instances of that type.
71 Positional arguments are forwarded to the `dict` constructor.
72 universe : `DimensionUniverse`
73 Universe of all possible dimensions.
75 def __init__(self, *args, universe: DimensionGraph):
81 universe: DimensionUniverse) -> _DatasetDict:
82 """Construct a dictionary from a flat iterable of `DatasetType` keys.
86 datasetTypes : `iterable` of `DatasetType`
87 DatasetTypes to use as keys for the dict. Values will be empty
89 universe : `DimensionUniverse`
90 Universe of all possible dimensions.
94 dictionary : `_DatasetDict`
95 A new `_DatasetDict` instance.
97 return cls({datasetType: {}
for datasetType
in datasetTypes}, universe=universe)
100 def fromSubset(cls, datasetTypes: Iterable[DatasetType], first: _DatasetDict, *rest: _DatasetDict
102 """Return a new dictionary by extracting items corresponding to the
103 given keys from one or more existing dictionaries.
107 datasetTypes : `iterable` of `DatasetType`
108 DatasetTypes to use as keys for the dict. Values will be obtained
109 by lookups against ``first`` and ``rest``.
110 first : `_DatasetDict`
111 Another dictionary from which to extract values.
113 Additional dictionaries from which to extract values.
117 dictionary : `_DatasetDict`
118 A new dictionary instance.
120 combined = ChainMap(first, *rest)
121 return cls({datasetType: combined[datasetType]
for datasetType
in datasetTypes},
122 universe=first.universe)
126 """The union of all dimensions used by all dataset types in this
127 dictionary, including implied dependencies (`DimensionGraph`).
132 return base.union(*[datasetType.dimensions
for datasetType
in self.keys()])
135 """Unpack nested single-element `DatasetRef` dicts into a new
136 mapping with `DatasetType` keys and `DatasetRef` values.
138 This method assumes that each nest contains exactly one item, as is the
139 case for all "init" datasets.
143 dictionary : `NamedKeyDict`
144 Dictionary mapping `DatasetType` to `DatasetRef`, with both
145 `DatasetType` instances and string names usable as keys.
147 def getOne(refs: Dict[DataCoordinate, DatasetRef]) -> DatasetRef:
150 return NamedKeyDict({datasetType: getOne(refs)
for datasetType, refs
in self.items()})
153 """Unpack nested multi-element `DatasetRef` dicts into a new
154 mapping with `DatasetType` keys and `set` of `DatasetRef` values.
158 dictionary : `NamedKeyDict`
159 Dictionary mapping `DatasetType` to `DatasetRef`, with both
160 `DatasetType` instances and string names usable as keys.
162 return NamedKeyDict({datasetType: list(refs.values())
for datasetType, refs
in self.items()})
164 def extract(self, datasetType: DatasetType, dataIds: Iterable[DataCoordinate]
165 ) -> Iterator[DatasetRef]:
166 """Iterate over the contained `DatasetRef` instances that match the
167 given `DatasetType` and data IDs.
171 datasetType : `DatasetType`
172 Dataset type to match.
173 dataIds : `Iterable` [ `DataCoordinate` ]
178 refs : `Iterator` [ `DatasetRef` ]
179 DatasetRef instances for which ``ref.datasetType == datasetType``
180 and ``ref.dataId`` is in ``dataIds``.
182 refs = self[datasetType]
183 return (refs[dataId]
for dataId
in dataIds)
187 """Helper class aggregating information about a `Quantum`, used when
188 constructing a `QuantumGraph`.
190 See `_PipelineScaffolding` for a top-down description of the full
191 scaffolding data structure.
195 task : _TaskScaffolding
196 Back-reference to the helper object for the `PipelineTask` this quantum
197 represents an execution of.
198 dataId : `DataCoordinate`
199 Data ID for this quantum.
201 def __init__(self, task: _TaskScaffolding, dataId: DataCoordinate):
204 self.
inputs = _DatasetDict.fromDatasetTypes(task.inputs.keys(), universe=dataId.universe)
205 self.
outputs = _DatasetDict.fromDatasetTypes(task.outputs.keys(), universe=dataId.universe)
206 self.
prerequisites = _DatasetDict.fromDatasetTypes(task.prerequisites.keys(),
207 universe=dataId.universe)
209 __slots__ = (
"task",
"dataId",
"inputs",
"outputs",
"prerequisites")
212 return f
"_QuantumScaffolding(taskDef={self.task.taskDef}, dataId={self.dataId}, ...)"
214 task: _TaskScaffolding
215 """Back-reference to the helper object for the `PipelineTask` this quantum
216 represents an execution of.
219 dataId: DataCoordinate
220 """Data ID for this quantum.
224 """Nested dictionary containing `DatasetRef` inputs to this quantum.
226 This is initialized to map each `DatasetType` to an empty dictionary at
227 construction. Those nested dictionaries are populated (with data IDs as
228 keys) with unresolved `DatasetRef` instances in
229 `_PipelineScaffolding.connectDataIds`.
232 outputs: _DatasetDict
233 """Nested dictionary containing `DatasetRef` outputs this quantum.
236 prerequisites: _DatasetDict
237 """Nested dictionary containing `DatasetRef` prerequisite inputs to this
242 """Transform the scaffolding object into a true `Quantum` instance.
247 An actual `Quantum` instance.
249 allInputs = self.
inputs.unpackMultiRefs()
255 allInputs = self.
task.taskDef.connections.adjustQuantum(allInputs)
257 taskName=self.
task.taskDef.taskName,
258 taskClass=self.
task.taskDef.taskClass,
260 initInputs=self.
task.initInputs.unpackSingleRefs(),
262 outputs=self.
outputs.unpackMultiRefs(),
268 """Helper class aggregating information about a `PipelineTask`, used when
269 constructing a `QuantumGraph`.
271 See `_PipelineScaffolding` for a top-down description of the full
272 scaffolding data structure.
277 Data structure that identifies the task class and its config.
278 parent : `_PipelineScaffolding`
279 The parent data structure that will hold the instance being
281 datasetTypes : `TaskDatasetTypes`
282 Data structure that categorizes the dataset types used by this task.
284 def __init__(self, taskDef: TaskDef, parent: _PipelineScaffolding, datasetTypes: TaskDatasetTypes):
285 universe = parent.dimensions.universe
287 self.
dimensions = DimensionGraph(universe, names=taskDef.connections.dimensions)
288 assert self.
dimensions.issubset(parent.dimensions)
291 self.
initInputs = _DatasetDict.fromSubset(datasetTypes.initInputs, parent.initInputs,
292 parent.initIntermediates)
293 self.
initOutputs = _DatasetDict.fromSubset(datasetTypes.initOutputs, parent.initIntermediates,
295 self.
inputs = _DatasetDict.fromSubset(datasetTypes.inputs, parent.inputs, parent.intermediates)
296 self.
outputs = _DatasetDict.fromSubset(datasetTypes.outputs, parent.intermediates, parent.outputs)
297 self.
prerequisites = _DatasetDict.fromSubset(datasetTypes.prerequisites, parent.prerequisites)
304 return f
"_TaskScaffolding(taskDef={self.taskDef}, ...)"
307 """Data structure that identifies the task class and its config
311 dimensions: DimensionGraph
312 """The dimensions of a single `Quantum` of this task (`DimensionGraph`).
315 initInputs: _DatasetDict
316 """Dictionary containing information about datasets used to construct this
317 task (`_DatasetDict`).
320 initOutputs: _DatasetDict
321 """Dictionary containing information about datasets produced as a
322 side-effect of constructing this task (`_DatasetDict`).
326 """Dictionary containing information about datasets used as regular,
327 graph-constraining inputs to this task (`_DatasetDict`).
330 outputs: _DatasetDict
331 """Dictionary containing information about datasets produced by this task
335 prerequisites: _DatasetDict
336 """Dictionary containing information about input datasets that must be
337 present in the repository before any Pipeline containing this task is run
341 quanta: Dict[DataCoordinate, _QuantumScaffolding]
342 """Dictionary mapping data ID to a scaffolding object for the Quantum of
343 this task with that data ID.
347 """Create a `set` of `Quantum` from the information in ``self``.
351 nodes : `set` of `Quantum
352 The `Quantum` elements corresponding to this task.
354 return set(q.makeQuantum()
for q
in self.
quanta.values())
359 """A helper data structure that organizes the information involved in
360 constructing a `QuantumGraph` for a `Pipeline`.
364 pipeline : `Pipeline`
365 Sequence of tasks from which a graph is to be constructed. Must
366 have nested task classes already imported.
367 universe : `DimensionUniverse`
368 Universe of all possible dimensions.
372 The scaffolding data structure contains nested data structures for both
373 tasks (`_TaskScaffolding`) and datasets (`_DatasetDict`). The dataset
374 data structures are shared between the pipeline-level structure (which
375 aggregates all datasets and categorizes them from the perspective of the
376 complete pipeline) and the individual tasks that use them as inputs and
379 `QuantumGraph` construction proceeds in four steps, with each corresponding
380 to a different `_PipelineScaffolding` method:
382 1. When `_PipelineScaffolding` is constructed, we extract and categorize
383 the DatasetTypes used by the pipeline (delegating to
384 `PipelineDatasetTypes.fromPipeline`), then use these to construct the
385 nested `_TaskScaffolding` and `_DatasetDict` objects.
387 2. In `connectDataIds`, we construct and run the "Big Join Query", which
388 returns related tuples of all dimensions used to identify any regular
389 input, output, and intermediate datasets (not prerequisites). We then
390 iterate over these tuples of related dimensions, identifying the subsets
391 that correspond to distinct data IDs for each task and dataset type,
392 and then create `_QuantumScaffolding` objects.
394 3. In `resolveDatasetRefs`, we run follow-up queries against all of the
395 dataset data IDs previously identified, transforming unresolved
396 DatasetRefs into resolved DatasetRefs where appropriate. We then look
397 up prerequisite datasets for all quanta.
399 4. In `makeQuantumGraph`, we construct a `QuantumGraph` from the lists of
400 per-task `_QuantumScaffolding` objects.
403 _LOG.debug(
"Initializing data structures for QuantumGraph generation.")
406 datasetTypes = PipelineDatasetTypes.fromPipeline(pipeline, registry=registry)
409 for attr
in (
"initInputs",
"initIntermediates",
"initOutputs",
410 "inputs",
"intermediates",
"outputs",
"prerequisites"):
411 setattr(self, attr, _DatasetDict.fromDatasetTypes(getattr(datasetTypes, attr),
412 universe=registry.dimensions))
416 self.
dimensions = self.inputs.dimensions.union(self.intermediates.dimensions,
417 self.outputs.dimensions)
423 if isinstance(pipeline, Pipeline):
424 pipeline = pipeline.toExpandedPipeline()
426 for taskDef, taskDatasetTypes
in zip(pipeline,
427 datasetTypes.byTask.values())]
432 return f
"_PipelineScaffolding(tasks={self.tasks}, ...)"
434 tasks: List[_TaskScaffolding]
435 """Scaffolding data structures for each task in the pipeline
436 (`list` of `_TaskScaffolding`).
439 initInputs: _DatasetDict
440 """Datasets consumed but not produced when constructing the tasks in this
441 pipeline (`_DatasetDict`).
444 initIntermediates: _DatasetDict
445 """Datasets that are both consumed and produced when constructing the tasks
446 in this pipeline (`_DatasetDict`).
449 initOutputs: _DatasetDict
450 """Datasets produced but not consumed when constructing the tasks in this
451 pipeline (`_DatasetDict`).
455 """Datasets that are consumed but not produced when running this pipeline
459 intermediates: _DatasetDict
460 """Datasets that are both produced and consumed when running this pipeline
464 outputs: _DatasetDict
465 """Datasets produced but not consumed when when running this pipeline
469 prerequisites: _DatasetDict
470 """Datasets that are consumed when running this pipeline and looked up
471 per-Quantum when generating the graph (`_DatasetDict`).
474 dimensions: DimensionGraph
475 """All dimensions used by any regular input, intermediate, or output
476 (not prerequisite) dataset; the set of dimension used in the "Big Join
477 Query" (`DimensionGraph`).
479 This is required to be a superset of all task quantum dimensions.
484 """Query for the data IDs that connect nodes in the `QuantumGraph`.
486 This method populates `_TaskScaffolding.dataIds` and
487 `_DatasetScaffolding.dataIds` (except for those in `prerequisites`).
491 registry : `lsst.daf.butler.Registry`
492 Registry for the data repository; used for all data ID queries.
493 collections : `lsst.daf.butler.CollectionSearch`
494 Object representing the collections to search for input datasets.
495 userQuery : `str`, optional
496 User-provided expression to limit the data IDs processed.
501 `lsst.daf.butler.registry.queries.DataCoordinateQueryResults`
502 An interface to a database temporary table containing all data IDs
503 that will appear in this `QuantumGraph`. Returned inside a
504 context manager, which will drop the temporary table at the end of
505 the `with` block in which this method is called.
507 _LOG.debug(
"Building query for data IDs.")
509 emptyDataId = DataCoordinate.makeEmpty(registry.dimensions)
510 for datasetType, refs
in itertools.chain(self.initInputs.items(),
511 self.initIntermediates.items(),
512 self.initOutputs.items()):
513 refs[emptyDataId] = DatasetRef(datasetType, emptyDataId)
518 _LOG.debug(
"Submitting data ID query and materializing results.")
520 datasets=list(self.inputs),
521 collections=collections,
523 ).materialize()
as commonDataIds:
524 _LOG.debug(
"Expanding data IDs.")
525 commonDataIds = commonDataIds.expanded()
526 _LOG.debug(
"Iterating over query results to associate quanta with datasets.")
530 for n, commonDataId
in enumerate(commonDataIds):
536 for datasetType, refs
in itertools.chain(self.inputs.items(), self.intermediates.items(),
537 self.outputs.items()):
538 datasetDataId = commonDataId.subset(datasetType.dimensions)
539 ref = refs.get(datasetDataId)
541 ref = DatasetRef(datasetType, datasetDataId)
542 refs[datasetDataId] = ref
543 refsForRow[datasetType.name] = ref
546 for task
in self.
tasks:
547 quantumDataId = commonDataId.subset(task.dimensions)
548 quantum = task.quanta.get(quantumDataId)
551 task.quanta[quantumDataId] = quantum
561 for datasetType
in task.inputs:
562 ref = refsForRow[datasetType.name]
563 quantum.inputs[datasetType.name][ref.dataId] = ref
564 for datasetType
in task.outputs:
565 ref = refsForRow[datasetType.name]
566 quantum.outputs[datasetType.name][ref.dataId] = ref
567 _LOG.debug(
"Finished processing %d rows from data ID query.", n)
571 """Perform follow up queries for each dataset data ID produced in
574 This method populates `_DatasetScaffolding.refs` (except for those in
579 registry : `lsst.daf.butler.Registry`
580 Registry for the data repository; used for all data ID queries.
581 collections : `lsst.daf.butler.CollectionSearch`
582 Object representing the collections to search for input datasets.
583 run : `str`, optional
584 Name of the `~lsst.daf.butler.CollectionType.RUN` collection for
585 output datasets, if it already exists.
587 `lsst.daf.butler.registry.queries.DataCoordinateQueryResults`
588 Result of a previous call to `connectDataIds`.
589 skipExisting : `bool`, optional
590 If `True` (default), a Quantum is not created if all its outputs
591 already exist in ``run``. Ignored if ``run`` is `None`.
596 Raised if an output dataset already exists in the output run
597 and ``skipExisting`` is `False`. The case where some but not all
598 of a quantum's outputs are present and ``skipExisting`` is `True`
599 cannot be identified at this stage, and is handled by `fillQuanta`
605 for datasetType, refs
in itertools.chain(self.initIntermediates.items(),
606 self.initOutputs.items(),
607 self.intermediates.items(),
608 self.outputs.items()):
609 _LOG.debug(
"Resolving %d datasets for intermediate and/or output dataset %s.",
610 len(refs), datasetType.name)
611 isInit = datasetType
in self.initIntermediates
or datasetType
in self.initOutputs
612 resolvedRefQueryResults = commonDataIds.subset(
613 datasetType.dimensions,
620 for resolvedRef
in resolvedRefQueryResults:
625 assert resolvedRef.dataId
in refs
626 if skipExisting
or isInit:
627 refs[resolvedRef.dataId] = resolvedRef
630 f
"output RUN collection '{run}' with data ID"
631 f
" {resolvedRef.dataId}.")
633 for datasetType, refs
in itertools.chain(self.initInputs.items(), self.inputs.items()):
634 _LOG.debug(
"Resolving %d datasets for input dataset %s.", len(refs), datasetType.name)
635 resolvedRefQueryResults = commonDataIds.subset(
636 datasetType.dimensions,
640 collections=collections,
643 dataIdsNotFoundYet = set(refs.keys())
644 for resolvedRef
in resolvedRefQueryResults:
645 dataIdsNotFoundYet.discard(resolvedRef.dataId)
646 refs[resolvedRef.dataId] = resolvedRef
647 if dataIdsNotFoundYet:
649 f
"{len(dataIdsNotFoundYet)} dataset(s) of type "
650 f
"'{datasetType.name}' was/were present in a previous "
651 f
"query, but could not be found now."
652 f
"This is either a logic bug in QuantumGraph generation "
653 f
"or the input collections have been modified since "
654 f
"QuantumGraph generation began."
658 for task
in self.
tasks:
660 "Applying resolutions and finding prerequisites for %d quanta of task with label '%s'.",
665 c.name: c.lookupFunction
666 for c
in iterConnections(task.taskDef.connections,
"prerequisiteInputs")
667 if c.lookupFunction
is not None
670 for quantum
in task.quanta.values():
677 if run
is not None and skipExisting:
680 for datasetType, originalRefs
in quantum.outputs.items():
681 for ref
in task.outputs.extract(datasetType, originalRefs.keys()):
682 if ref.id
is not None:
683 resolvedRefs.append(ref)
685 unresolvedRefs.append(ref)
689 f
"Quantum {quantum.dataId} of task with label "
690 f
"'{quantum.task.taskDef.label}' has some outputs that exist "
692 f
"and others that don't ({unresolvedRefs})."
697 dataIdsToSkip.append(quantum.dataId)
701 for datasetType, refs
in quantum.inputs.items():
702 for ref
in task.inputs.extract(datasetType, refs.keys()):
703 refs[ref.dataId] = ref
708 for datasetType
in task.prerequisites:
709 lookupFunction = lookupFunctions.get(datasetType.name)
710 if lookupFunction
is not None:
714 lookupFunction(datasetType, registry, quantum.dataId, collections)
716 elif (datasetType.isCalibration()
717 and datasetType.dimensions <= quantum.dataId.graph
718 and quantum.dataId.graph.temporal):
722 timespan = quantum.dataId.timespan
724 refs = [registry.findDataset(datasetType, quantum.dataId,
725 collections=collections,
733 refs = list(registry.queryDatasets(datasetType,
734 collections=collections,
735 dataId=quantum.dataId,
736 findFirst=
True).expanded())
737 quantum.prerequisites[datasetType].update({ref.dataId: ref
for ref
in refs
741 _LOG.debug(
"Pruning %d quanta for task with label '%s' because all of their outputs exist.",
742 len(dataIdsToSkip), task.taskDef.label)
743 for dataId
in dataIdsToSkip:
744 del task.quanta[dataId]
747 """Create a `QuantumGraph` from the quanta already present in
748 the scaffolding data structure.
752 graph : `QuantumGraph`
753 The full `QuantumGraph`.
755 graph =
QuantumGraph({task.taskDef: task.makeQuantumSet()
for task
in self.
tasks})
760 """Implementation of TreeVisitor which looks for instrument name
762 Instrument should be specified as a boolean expression
764 instrument = 'string'
765 'string' = instrument
767 so we only need to find a binary operator where operator is "=",
768 one side is a string literal and other side is an identifier.
769 All visit methods return tuple of (type, value), non-useful nodes
770 return None for both type and value.
781 return (
"str", value)
792 if name.lower() ==
"instrument":
793 return (
"id",
"instrument")
802 if lhs == (
"id",
"instrument")
and rhs[0] ==
"str":
804 elif rhs == (
"id",
"instrument")
and lhs[0] ==
"str":
817 def _findInstruments(queryStr):
818 """Get the names of any instrument named in the query string by searching
819 for "instrument = <value>" and similar patterns.
823 queryStr : `str` or None
824 The query string to search, or None if there is no query.
828 instruments : `list` [`str`]
829 The list of instrument names found in the query.
834 If the query expression can not be parsed.
838 parser = ParserYacc()
841 tree = parser.parse(queryStr)
842 except ParseError
as exc:
843 raise ValueError(f
"failed to parse query expression: {queryStr}")
from exc
845 return finder.instruments
854 """Base class for exceptions generated by graph builder.
859 class OutputExistsError(GraphBuilderError):
860 """Exception generated when output datasets already exist.
866 """Exception generated when a prerequisite dataset does not exist.
872 """GraphBuilder class is responsible for building task execution graph from
877 registry : `~lsst.daf.butler.Registry`
878 Data butler instance.
879 skipExisting : `bool`, optional
880 If `True` (default), a Quantum is not created if all its outputs
889 def makeGraph(self, pipeline, collections, run, userQuery):
890 """Create execution graph for a pipeline.
894 pipeline : `Pipeline`
895 Pipeline definition, task names/classes and their configs.
896 collections : `lsst.daf.butler.CollectionSearch`
897 Object representing the collections to search for input datasets.
898 run : `str`, optional
899 Name of the `~lsst.daf.butler.CollectionType.RUN` collection for
900 output datasets, if it already exists.
902 String which defines user-defined selection for registry, should be
903 empty or `None` if there is no restrictions on data selection.
907 graph : `QuantumGraph`
912 Raised when user expression cannot be parsed.
914 Raised when output datasets already exist.
916 Other exceptions types may be raised by underlying registry
921 instrument = pipeline.getInstrument()
922 if isinstance(instrument, str):
923 instrument = doImport(instrument)
924 instrumentName = instrument.getName()
if instrument
else None
927 with scaffolding.connectDataIds(self.
registry, collections, userQuery)
as commonDataIds:
928 scaffolding.resolveDatasetRefs(self.
registry, collections, run, commonDataIds,
930 return scaffolding.makeQuantumGraph()
933 def _verifyInstrumentRestriction(instrumentName, query):
934 """Add an instrument restriction to the query if it does not have one,
935 and verify that if given an instrument name that there are no other
936 instrument restrictions in the query.
940 instrumentName : `str`
941 The name of the instrument that should appear in the query.
948 The query string with the instrument added to it if needed.
953 If the pipeline names an instrument and the query contains more
954 than one instrument or the name of the instrument in the query does
955 not match the instrument named by the pipeline.
957 if not instrumentName:
959 queryInstruments = _findInstruments(query)
960 if len(queryInstruments) > 1:
961 raise RuntimeError(f
"When the pipeline has an instrument (\"{instrumentName}\") the query must "
962 "have zero instruments or one instrument that matches the pipeline. "
963 f
"Found these instruments in the query: {queryInstruments}.")
964 if not queryInstruments:
966 restriction = f
"instrument = '{instrumentName}'"
967 _LOG.debug(f
"Adding restriction \"{restriction}\" to query.")
968 query = f
"{restriction} AND ({query})" if query
else restriction
969 elif queryInstruments[0] != instrumentName:
972 raise RuntimeError(f
"The instrument named in the query (\"{queryInstruments[0]}\") does not "
973 f
"match the instrument named by the pipeline (\"{instrumentName}\")")