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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
23"""Module defining GraphBuilder class and related methods.
24"""
26__all__ = ['GraphBuilder']
28# -------------------------------
29# Imports of standard modules --
30# -------------------------------
31import itertools
32from collections import ChainMap
33from contextlib import contextmanager
34from dataclasses import dataclass
35from typing import Dict, Iterable, Iterator, List, Set
36import logging
39# -----------------------------
40# Imports for other modules --
41# -----------------------------
42from .connections import iterConnections
43from .pipeline import PipelineDatasetTypes, TaskDatasetTypes, TaskDef, Pipeline
44from .graph import QuantumGraph
45from lsst.daf.butler import (
46 DataCoordinate,
47 DatasetRef,
48 DatasetType,
49 DimensionGraph,
50 DimensionUniverse,
51 NamedKeyDict,
52 Quantum,
53)
54from lsst.daf.butler.registry.queries.exprParser import ParseError, ParserYacc, TreeVisitor
55from lsst.utils import doImport
57# ----------------------------------
58# Local non-exported definitions --
59# ----------------------------------
61_LOG = logging.getLogger(__name__.partition(".")[2])
64class _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.
68 Parameters
69 ----------
70 args
71 Positional arguments are forwarded to the `dict` constructor.
72 universe : `DimensionUniverse`
73 Universe of all possible dimensions.
74 """
75 def __init__(self, *args, universe: DimensionGraph):
76 super().__init__(*args)
77 self.universe = universe
79 @classmethod
80 def fromDatasetTypes(cls, datasetTypes: Iterable[DatasetType], *,
81 universe: DimensionUniverse) -> _DatasetDict:
82 """Construct a dictionary from a flat iterable of `DatasetType` keys.
84 Parameters
85 ----------
86 datasetTypes : `iterable` of `DatasetType`
87 DatasetTypes to use as keys for the dict. Values will be empty
88 dictionaries.
89 universe : `DimensionUniverse`
90 Universe of all possible dimensions.
92 Returns
93 -------
94 dictionary : `_DatasetDict`
95 A new `_DatasetDict` instance.
96 """
97 return cls({datasetType: {} for datasetType in datasetTypes}, universe=universe)
99 @classmethod
100 def fromSubset(cls, datasetTypes: Iterable[DatasetType], first: _DatasetDict, *rest: _DatasetDict
101 ) -> _DatasetDict:
102 """Return a new dictionary by extracting items corresponding to the
103 given keys from one or more existing dictionaries.
105 Parameters
106 ----------
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.
112 rest
113 Additional dictionaries from which to extract values.
115 Returns
116 -------
117 dictionary : `_DatasetDict`
118 A new dictionary instance.
119 """
120 combined = ChainMap(first, *rest)
121 return cls({datasetType: combined[datasetType] for datasetType in datasetTypes},
122 universe=first.universe)
124 @property
125 def dimensions(self) -> DimensionGraph:
126 """The union of all dimensions used by all dataset types in this
127 dictionary, including implied dependencies (`DimensionGraph`).
128 """
129 base = self.universe.empty
130 if len(self) == 0:
131 return base
132 return base.union(*[datasetType.dimensions for datasetType in self.keys()])
134 def unpackSingleRefs(self) -> NamedKeyDict[DatasetType, DatasetRef]:
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.
141 Returns
142 -------
143 dictionary : `NamedKeyDict`
144 Dictionary mapping `DatasetType` to `DatasetRef`, with both
145 `DatasetType` instances and string names usable as keys.
146 """
147 def getOne(refs: Dict[DataCoordinate, DatasetRef]) -> DatasetRef:
148 ref, = refs.values()
149 return ref
150 return NamedKeyDict({datasetType: getOne(refs) for datasetType, refs in self.items()})
152 def unpackMultiRefs(self) -> NamedKeyDict[DatasetType, DatasetRef]:
153 """Unpack nested multi-element `DatasetRef` dicts into a new
154 mapping with `DatasetType` keys and `set` of `DatasetRef` values.
156 Returns
157 -------
158 dictionary : `NamedKeyDict`
159 Dictionary mapping `DatasetType` to `DatasetRef`, with both
160 `DatasetType` instances and string names usable as keys.
161 """
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.
169 Parameters
170 ----------
171 datasetType : `DatasetType`
172 Dataset type to match.
173 dataIds : `Iterable` [ `DataCoordinate` ]
174 Data IDs to match.
176 Returns
177 -------
178 refs : `Iterator` [ `DatasetRef` ]
179 DatasetRef instances for which ``ref.datasetType == datasetType``
180 and ``ref.dataId`` is in ``dataIds``.
181 """
182 refs = self[datasetType]
183 return (refs[dataId] for dataId in dataIds)
186class _QuantumScaffolding:
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.
193 Parameters
194 ----------
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.
200 """
201 def __init__(self, task: _TaskScaffolding, dataId: DataCoordinate):
202 self.task = task
203 self.dataId = dataId
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")
211 def __repr__(self):
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.
217 """
219 dataId: DataCoordinate
220 """Data ID for this quantum.
221 """
223 inputs: _DatasetDict
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`.
230 """
232 outputs: _DatasetDict
233 """Nested dictionary containing `DatasetRef` outputs this quantum.
234 """
236 prerequisites: _DatasetDict
237 """Nested dictionary containing `DatasetRef` prerequisite inputs to this
238 quantum.
239 """
241 def makeQuantum(self) -> Quantum:
242 """Transform the scaffolding object into a true `Quantum` instance.
244 Returns
245 -------
246 quantum : `Quantum`
247 An actual `Quantum` instance.
248 """
249 allInputs = self.inputs.unpackMultiRefs()
250 allInputs.update(self.prerequisites.unpackMultiRefs())
251 # Give the task's Connections class an opportunity to remove some
252 # inputs, or complain if they are unacceptable.
253 # This will raise if one of the check conditions is not met, which is
254 # the intended behavior
255 allInputs = self.task.taskDef.connections.adjustQuantum(allInputs)
256 return Quantum(
257 taskName=self.task.taskDef.taskName,
258 taskClass=self.task.taskDef.taskClass,
259 dataId=self.dataId,
260 initInputs=self.task.initInputs.unpackSingleRefs(),
261 inputs=allInputs,
262 outputs=self.outputs.unpackMultiRefs(),
263 )
266@dataclass
267class _TaskScaffolding:
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.
274 Parameters
275 ----------
276 taskDef : `TaskDef`
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
280 constructed.
281 datasetTypes : `TaskDatasetTypes`
282 Data structure that categorizes the dataset types used by this task.
283 """
284 def __init__(self, taskDef: TaskDef, parent: _PipelineScaffolding, datasetTypes: TaskDatasetTypes):
285 universe = parent.dimensions.universe
286 self.taskDef = taskDef
287 self.dimensions = DimensionGraph(universe, names=taskDef.connections.dimensions)
288 assert self.dimensions.issubset(parent.dimensions)
289 # Initialize _DatasetDicts as subsets of the one or two
290 # corresponding dicts in the parent _PipelineScaffolding.
291 self.initInputs = _DatasetDict.fromSubset(datasetTypes.initInputs, parent.initInputs,
292 parent.initIntermediates)
293 self.initOutputs = _DatasetDict.fromSubset(datasetTypes.initOutputs, parent.initIntermediates,
294 parent.initOutputs)
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)
298 self.dataIds = set()
299 self.quanta = {}
301 def __repr__(self):
302 # Default dataclass-injected __repr__ gets caught in an infinite loop
303 # because of back-references.
304 return f"_TaskScaffolding(taskDef={self.taskDef}, ...)"
306 taskDef: TaskDef
307 """Data structure that identifies the task class and its config
308 (`TaskDef`).
309 """
311 dimensions: DimensionGraph
312 """The dimensions of a single `Quantum` of this task (`DimensionGraph`).
313 """
315 initInputs: _DatasetDict
316 """Dictionary containing information about datasets used to construct this
317 task (`_DatasetDict`).
318 """
320 initOutputs: _DatasetDict
321 """Dictionary containing information about datasets produced as a
322 side-effect of constructing this task (`_DatasetDict`).
323 """
325 inputs: _DatasetDict
326 """Dictionary containing information about datasets used as regular,
327 graph-constraining inputs to this task (`_DatasetDict`).
328 """
330 outputs: _DatasetDict
331 """Dictionary containing information about datasets produced by this task
332 (`_DatasetDict`).
333 """
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
338 (`_DatasetDict`).
339 """
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.
344 """
346 def makeQuantumSet(self) -> Set[Quantum]:
347 """Create a `set` of `Quantum` from the information in ``self``.
349 Returns
350 -------
351 nodes : `set` of `Quantum
352 The `Quantum` elements corresponding to this task.
353 """
354 return set(q.makeQuantum() for q in self.quanta.values())
357@dataclass
358class _PipelineScaffolding:
359 """A helper data structure that organizes the information involved in
360 constructing a `QuantumGraph` for a `Pipeline`.
362 Parameters
363 ----------
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.
370 Notes
371 -----
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
377 outputs.
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.
401 """
402 def __init__(self, pipeline, *, registry):
403 _LOG.debug("Initializing data structures for QuantumGraph generation.")
404 self.tasks = []
405 # Aggregate and categorize the DatasetTypes in the Pipeline.
406 datasetTypes = PipelineDatasetTypes.fromPipeline(pipeline, registry=registry)
407 # Construct dictionaries that map those DatasetTypes to structures
408 # that will (later) hold addiitonal information about them.
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))
413 # Aggregate all dimensions for all non-init, non-prerequisite
414 # DatasetTypes. These are the ones we'll include in the big join
415 # query.
416 self.dimensions = self.inputs.dimensions.union(self.intermediates.dimensions,
417 self.outputs.dimensions)
418 # Construct scaffolding nodes for each Task, and add backreferences
419 # to the Task from each DatasetScaffolding node.
420 # Note that there's only one scaffolding node for each DatasetType,
421 # shared by _PipelineScaffolding and all _TaskScaffoldings that
422 # reference it.
423 if isinstance(pipeline, Pipeline):
424 pipeline = pipeline.toExpandedPipeline()
425 self.tasks = [_TaskScaffolding(taskDef=taskDef, parent=self, datasetTypes=taskDatasetTypes)
426 for taskDef, taskDatasetTypes in zip(pipeline,
427 datasetTypes.byTask.values())]
429 def __repr__(self):
430 # Default dataclass-injected __repr__ gets caught in an infinite loop
431 # because of back-references.
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`).
437 """
439 initInputs: _DatasetDict
440 """Datasets consumed but not produced when constructing the tasks in this
441 pipeline (`_DatasetDict`).
442 """
444 initIntermediates: _DatasetDict
445 """Datasets that are both consumed and produced when constructing the tasks
446 in this pipeline (`_DatasetDict`).
447 """
449 initOutputs: _DatasetDict
450 """Datasets produced but not consumed when constructing the tasks in this
451 pipeline (`_DatasetDict`).
452 """
454 inputs: _DatasetDict
455 """Datasets that are consumed but not produced when running this pipeline
456 (`_DatasetDict`).
457 """
459 intermediates: _DatasetDict
460 """Datasets that are both produced and consumed when running this pipeline
461 (`_DatasetDict`).
462 """
464 outputs: _DatasetDict
465 """Datasets produced but not consumed when when running this pipeline
466 (`_DatasetDict`).
467 """
469 prerequisites: _DatasetDict
470 """Datasets that are consumed when running this pipeline and looked up
471 per-Quantum when generating the graph (`_DatasetDict`).
472 """
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.
480 """
482 @contextmanager
483 def connectDataIds(self, registry, collections, userQuery):
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`).
489 Parameters
490 ----------
491 registry : `lsst.daf.butler.Registry`
492 Registry for the data repository; used for all data ID queries.
493 collections
494 Expressions representing the collections to search for input
495 datasets. May be any of the types accepted by
496 `lsst.daf.butler.CollectionSearch.fromExpression`.
497 userQuery : `str`, optional
498 User-provided expression to limit the data IDs processed.
500 Returns
501 -------
502 commonDataIds : \
503 `lsst.daf.butler.registry.queries.DataCoordinateQueryResults`
504 An interface to a database temporary table containing all data IDs
505 that will appear in this `QuantumGraph`. Returned inside a
506 context manager, which will drop the temporary table at the end of
507 the `with` block in which this method is called.
508 """
509 _LOG.debug("Building query for data IDs.")
510 # Initialization datasets always have empty data IDs.
511 emptyDataId = DataCoordinate.makeEmpty(registry.dimensions)
512 for datasetType, refs in itertools.chain(self.initInputs.items(),
513 self.initIntermediates.items(),
514 self.initOutputs.items()):
515 refs[emptyDataId] = DatasetRef(datasetType, emptyDataId)
516 # Run one big query for the data IDs for task dimensions and regular
517 # inputs and outputs. We limit the query to only dimensions that are
518 # associated with the input dataset types, but don't (yet) try to
519 # obtain the dataset_ids for those inputs.
520 _LOG.debug("Submitting data ID query and materializing results.")
521 with registry.queryDataIds(self.dimensions,
522 datasets=list(self.inputs),
523 collections=collections,
524 where=userQuery,
525 ).materialize() as commonDataIds:
526 _LOG.debug("Expanding data IDs.")
527 commonDataIds = commonDataIds.expanded()
528 _LOG.debug("Iterating over query results to associate quanta with datasets.")
529 # Iterate over query results, populating data IDs for datasets and
530 # quanta and then connecting them to each other.
531 n = 0
532 for n, commonDataId in enumerate(commonDataIds):
533 # Create DatasetRefs for all DatasetTypes from this result row,
534 # noting that we might have created some already.
535 # We remember both those that already existed and those that we
536 # create now.
537 refsForRow = {}
538 for datasetType, refs in itertools.chain(self.inputs.items(), self.intermediates.items(),
539 self.outputs.items()):
540 datasetDataId = commonDataId.subset(datasetType.dimensions)
541 ref = refs.get(datasetDataId)
542 if ref is None:
543 ref = DatasetRef(datasetType, datasetDataId)
544 refs[datasetDataId] = ref
545 refsForRow[datasetType.name] = ref
546 # Create _QuantumScaffolding objects for all tasks from this
547 # result row, noting that we might have created some already.
548 for task in self.tasks:
549 quantumDataId = commonDataId.subset(task.dimensions)
550 quantum = task.quanta.get(quantumDataId)
551 if quantum is None:
552 quantum = _QuantumScaffolding(task=task, dataId=quantumDataId)
553 task.quanta[quantumDataId] = quantum
554 # Whether this is a new quantum or an existing one, we can
555 # now associate the DatasetRefs for this row with it. The
556 # fact that a Quantum data ID and a dataset data ID both
557 # came from the same result row is what tells us they
558 # should be associated.
559 # Many of these associates will be duplicates (because
560 # another query row that differed from this one only in
561 # irrelevant dimensions already added them), and we use
562 # sets to skip.
563 for datasetType in task.inputs:
564 ref = refsForRow[datasetType.name]
565 quantum.inputs[datasetType.name][ref.dataId] = ref
566 for datasetType in task.outputs:
567 ref = refsForRow[datasetType.name]
568 quantum.outputs[datasetType.name][ref.dataId] = ref
569 _LOG.debug("Finished processing %d rows from data ID query.", n)
570 yield commonDataIds
572 def resolveDatasetRefs(self, registry, collections, run, commonDataIds, *, skipExisting=True):
573 """Perform follow up queries for each dataset data ID produced in
574 `fillDataIds`.
576 This method populates `_DatasetScaffolding.refs` (except for those in
577 `prerequisites`).
579 Parameters
580 ----------
581 registry : `lsst.daf.butler.Registry`
582 Registry for the data repository; used for all data ID queries.
583 collections
584 Expressions representing the collections to search for input
585 datasets. May be any of the types accepted by
586 `lsst.daf.butler.CollectionSearch.fromExpression`.
587 run : `str`, optional
588 Name of the `~lsst.daf.butler.CollectionType.RUN` collection for
589 output datasets, if it already exists.
590 commonDataIds : \
591 `lsst.daf.butler.registry.queries.DataCoordinateQueryResults`
592 Result of a previous call to `connectDataIds`.
593 skipExisting : `bool`, optional
594 If `True` (default), a Quantum is not created if all its outputs
595 already exist in ``run``. Ignored if ``run`` is `None`.
597 Raises
598 ------
599 OutputExistsError
600 Raised if an output dataset already exists in the output run
601 and ``skipExisting`` is `False`. The case where some but not all
602 of a quantum's outputs are present and ``skipExisting`` is `True`
603 cannot be identified at this stage, and is handled by `fillQuanta`
604 instead.
605 """
606 # Look up [init] intermediate and output datasets in the output
607 # collection, if there is an output collection.
608 if run is not None:
609 for datasetType, refs in itertools.chain(self.initIntermediates.items(),
610 self.initOutputs.items(),
611 self.intermediates.items(),
612 self.outputs.items()):
613 _LOG.debug("Resolving %d datasets for intermediate and/or output dataset %s.",
614 len(refs), datasetType.name)
615 isInit = datasetType in self.initIntermediates or datasetType in self.initOutputs
616 resolvedRefQueryResults = commonDataIds.subset(
617 datasetType.dimensions,
618 unique=True
619 ).findDatasets(
620 datasetType,
621 collections=run,
622 findFirst=True
623 )
624 for resolvedRef in resolvedRefQueryResults:
625 # TODO: we could easily support per-DatasetType
626 # skipExisting and I could imagine that being useful - it's
627 # probably required in order to support writing initOutputs
628 # before QuantumGraph generation.
629 assert resolvedRef.dataId in refs
630 if skipExisting or isInit:
631 refs[resolvedRef.dataId] = resolvedRef
632 else:
633 raise OutputExistsError(f"Output dataset {datasetType.name} already exists in "
634 f"output RUN collection '{run}' with data ID"
635 f" {resolvedRef.dataId}.")
636 # Look up input and initInput datasets in the input collection(s).
637 for datasetType, refs in itertools.chain(self.initInputs.items(), self.inputs.items()):
638 _LOG.debug("Resolving %d datasets for input dataset %s.", len(refs), datasetType.name)
639 resolvedRefQueryResults = commonDataIds.subset(
640 datasetType.dimensions,
641 unique=True
642 ).findDatasets(
643 datasetType,
644 collections=collections,
645 findFirst=True
646 )
647 dataIdsNotFoundYet = set(refs.keys())
648 for resolvedRef in resolvedRefQueryResults:
649 dataIdsNotFoundYet.discard(resolvedRef.dataId)
650 refs[resolvedRef.dataId] = resolvedRef
651 if dataIdsNotFoundYet:
652 raise RuntimeError(
653 f"{len(dataIdsNotFoundYet)} dataset(s) of type "
654 f"'{datasetType.name}' was/were present in a previous "
655 f"query, but could not be found now."
656 f"This is either a logic bug in QuantumGraph generation "
657 f"or the input collections have been modified since "
658 f"QuantumGraph generation began."
659 )
660 # Copy the resolved DatasetRefs to the _QuantumScaffolding objects,
661 # replacing the unresolved refs there, and then look up prerequisites.
662 for task in self.tasks:
663 _LOG.debug(
664 "Applying resolutions and finding prerequisites for %d quanta of task with label '%s'.",
665 len(task.quanta),
666 task.taskDef.label
667 )
668 lookupFunctions = {
669 c.name: c.lookupFunction
670 for c in iterConnections(task.taskDef.connections, "prerequisiteInputs")
671 if c.lookupFunction is not None
672 }
673 dataIdsToSkip = []
674 for quantum in task.quanta.values():
675 # Process outputs datasets only if there is a run to look for
676 # outputs in and skipExisting is True. Note that if
677 # skipExisting is False, any output datasets that already exist
678 # would have already caused an exception to be raised.
679 # We never update the DatasetRefs in the quantum because those
680 # should never be resolved.
681 if run is not None and skipExisting:
682 resolvedRefs = []
683 unresolvedRefs = []
684 for datasetType, originalRefs in quantum.outputs.items():
685 for ref in task.outputs.extract(datasetType, originalRefs.keys()):
686 if ref.id is not None:
687 resolvedRefs.append(ref)
688 else:
689 unresolvedRefs.append(ref)
690 if resolvedRefs:
691 if unresolvedRefs:
692 raise OutputExistsError(
693 f"Quantum {quantum.dataId} of task with label "
694 f"'{quantum.task.taskDef.label}' has some outputs that exist "
695 f"({resolvedRefs}) "
696 f"and others that don't ({unresolvedRefs})."
697 )
698 else:
699 # All outputs are already present; skip this
700 # quantum and continue to the next.
701 dataIdsToSkip.append(quantum.dataId)
702 continue
703 # Update the input DatasetRefs to the resolved ones we already
704 # searched for.
705 for datasetType, refs in quantum.inputs.items():
706 for ref in task.inputs.extract(datasetType, refs.keys()):
707 refs[ref.dataId] = ref
708 # Look up prerequisite datasets in the input collection(s).
709 # These may have dimensions that extend beyond those we queried
710 # for originally, because we want to permit those data ID
711 # values to differ across quanta and dataset types.
712 for datasetType in task.prerequisites:
713 lookupFunction = lookupFunctions.get(datasetType.name)
714 if lookupFunction is not None:
715 # PipelineTask has provided its own function to do the
716 # lookup. This always takes precedence.
717 refs = list(
718 lookupFunction(datasetType, registry, quantum.dataId, collections)
719 )
720 elif (datasetType.isCalibration()
721 and datasetType.dimensions <= quantum.dataId.graph
722 and quantum.dataId.graph.temporal):
723 # This is a master calibration lookup, which we have to
724 # handle specially because the query system can't do a
725 # temporal join on a non-dimension-based timespan yet.
726 timespan = quantum.dataId.timespan
727 try:
728 refs = [registry.findDataset(datasetType, quantum.dataId,
729 collections=collections,
730 timespan=timespan)]
731 except KeyError:
732 # This dataset type is not present in the registry,
733 # which just means there are no datasets here.
734 refs = []
735 else:
736 # Most general case.
737 refs = list(registry.queryDatasets(datasetType,
738 collections=collections,
739 dataId=quantum.dataId,
740 findFirst=True).expanded())
741 quantum.prerequisites[datasetType].update({ref.dataId: ref for ref in refs
742 if ref is not None})
743 # Actually remove any quanta that we decided to skip above.
744 if dataIdsToSkip:
745 _LOG.debug("Pruning %d quanta for task with label '%s' because all of their outputs exist.",
746 len(dataIdsToSkip), task.taskDef.label)
747 for dataId in dataIdsToSkip:
748 del task.quanta[dataId]
750 def makeQuantumGraph(self):
751 """Create a `QuantumGraph` from the quanta already present in
752 the scaffolding data structure.
754 Returns
755 -------
756 graph : `QuantumGraph`
757 The full `QuantumGraph`.
758 """
759 graph = QuantumGraph({task.taskDef: task.makeQuantumSet() for task in self.tasks})
760 return graph
763class _InstrumentFinder(TreeVisitor):
764 """Implementation of TreeVisitor which looks for instrument name
766 Instrument should be specified as a boolean expression
768 instrument = 'string'
769 'string' = instrument
771 so we only need to find a binary operator where operator is "=",
772 one side is a string literal and other side is an identifier.
773 All visit methods return tuple of (type, value), non-useful nodes
774 return None for both type and value.
775 """
776 def __init__(self):
777 self.instruments = []
779 def visitNumericLiteral(self, value, node):
780 # do not care about numbers
781 return (None, None)
783 def visitStringLiteral(self, value, node):
784 # return type and value
785 return ("str", value)
787 def visitTimeLiteral(self, value, node):
788 # do not care about these
789 return (None, None)
791 def visitRangeLiteral(self, start, stop, stride, node):
792 # do not care about these
793 return (None, None)
795 def visitIdentifier(self, name, node):
796 if name.lower() == "instrument":
797 return ("id", "instrument")
798 return (None, None)
800 def visitUnaryOp(self, operator, operand, node):
801 # do not care about these
802 return (None, None)
804 def visitBinaryOp(self, operator, lhs, rhs, node):
805 if operator == "=":
806 if lhs == ("id", "instrument") and rhs[0] == "str":
807 self.instruments.append(rhs[1])
808 elif rhs == ("id", "instrument") and lhs[0] == "str":
809 self.instruments.append(lhs[1])
810 return (None, None)
812 def visitIsIn(self, lhs, values, not_in, node):
813 # do not care about these
814 return (None, None)
816 def visitParens(self, expression, node):
817 # do not care about these
818 return (None, None)
821def _findInstruments(queryStr):
822 """Get the names of any instrument named in the query string by searching
823 for "instrument = <value>" and similar patterns.
825 Parameters
826 ----------
827 queryStr : `str` or None
828 The query string to search, or None if there is no query.
830 Returns
831 -------
832 instruments : `list` [`str`]
833 The list of instrument names found in the query.
835 Raises
836 ------
837 ValueError
838 If the query expression can not be parsed.
839 """
840 if not queryStr:
841 return []
842 parser = ParserYacc()
843 finder = _InstrumentFinder()
844 try:
845 tree = parser.parse(queryStr)
846 except ParseError as exc:
847 raise ValueError(f"failed to parse query expression: {queryStr}") from exc
848 tree.visit(finder)
849 return finder.instruments
852# ------------------------
853# Exported definitions --
854# ------------------------
857class GraphBuilderError(Exception):
858 """Base class for exceptions generated by graph builder.
859 """
860 pass
863class OutputExistsError(GraphBuilderError):
864 """Exception generated when output datasets already exist.
865 """
866 pass
869class PrerequisiteMissingError(GraphBuilderError):
870 """Exception generated when a prerequisite dataset does not exist.
871 """
872 pass
875class GraphBuilder(object):
876 """GraphBuilder class is responsible for building task execution graph from
877 a Pipeline.
879 Parameters
880 ----------
881 registry : `~lsst.daf.butler.Registry`
882 Data butler instance.
883 skipExisting : `bool`, optional
884 If `True` (default), a Quantum is not created if all its outputs
885 already exist.
886 """
888 def __init__(self, registry, skipExisting=True):
889 self.registry = registry
890 self.dimensions = registry.dimensions
891 self.skipExisting = skipExisting
893 def makeGraph(self, pipeline, collections, run, userQuery):
894 """Create execution graph for a pipeline.
896 Parameters
897 ----------
898 pipeline : `Pipeline`
899 Pipeline definition, task names/classes and their configs.
900 collections
901 Expressions representing the collections to search for input
902 datasets. May be any of the types accepted by
903 `lsst.daf.butler.CollectionSearch.fromExpression`.
904 run : `str`, optional
905 Name of the `~lsst.daf.butler.CollectionType.RUN` collection for
906 output datasets, if it already exists.
907 userQuery : `str`
908 String which defines user-defined selection for registry, should be
909 empty or `None` if there is no restrictions on data selection.
911 Returns
912 -------
913 graph : `QuantumGraph`
915 Raises
916 ------
917 UserExpressionError
918 Raised when user expression cannot be parsed.
919 OutputExistsError
920 Raised when output datasets already exist.
921 Exception
922 Other exceptions types may be raised by underlying registry
923 classes.
924 """
925 scaffolding = _PipelineScaffolding(pipeline, registry=self.registry)
927 instrument = pipeline.getInstrument()
928 if isinstance(instrument, str):
929 instrument = doImport(instrument)
930 instrumentName = instrument.getName() if instrument else None
931 userQuery = self._verifyInstrumentRestriction(instrumentName, userQuery)
933 with scaffolding.connectDataIds(self.registry, collections, userQuery) as commonDataIds:
934 scaffolding.resolveDatasetRefs(self.registry, collections, run, commonDataIds,
935 skipExisting=self.skipExisting)
936 return scaffolding.makeQuantumGraph()
938 @staticmethod
939 def _verifyInstrumentRestriction(instrumentName, query):
940 """Add an instrument restriction to the query if it does not have one,
941 and verify that if given an instrument name that there are no other
942 instrument restrictions in the query.
944 Parameters
945 ----------
946 instrumentName : `str`
947 The name of the instrument that should appear in the query.
948 query : `str`
949 The query string.
951 Returns
952 -------
953 query : `str`
954 The query string with the instrument added to it if needed.
956 Raises
957 ------
958 RuntimeError
959 If the pipeline names an instrument and the query contains more
960 than one instrument or the name of the instrument in the query does
961 not match the instrument named by the pipeline.
962 """
963 if not instrumentName:
964 return query
965 queryInstruments = _findInstruments(query)
966 if len(queryInstruments) > 1:
967 raise RuntimeError(f"When the pipeline has an instrument (\"{instrumentName}\") the query must "
968 "have zero instruments or one instrument that matches the pipeline. "
969 f"Found these instruments in the query: {queryInstruments}.")
970 if not queryInstruments:
971 # There is not an instrument in the query, add it:
972 restriction = f"instrument = '{instrumentName}'"
973 _LOG.debug(f"Adding restriction \"{restriction}\" to query.")
974 query = f"{restriction} AND ({query})" if query else restriction # (there may not be a query)
975 elif queryInstruments[0] != instrumentName:
976 # Since there is an instrument in the query, it should match
977 # the instrument in the pipeline.
978 raise RuntimeError(f"The instrument named in the query (\"{queryInstruments[0]}\") does not "
979 f"match the instrument named by the pipeline (\"{instrumentName}\")")
980 return query