forked from mirrors/gecko-dev
7e084ff0ae
It is not at *all* clear how multiple optimizations for a single task should interact. No simple logical operation is right in all cases, and in fact in most imaginable cases the desired behavior turns out to be independent of all but one of the optimizations. For example, given both `seta` and `skip-unless-files-changed` optimizations, if SETA says to skip a test, it is low value and should be skipped regardless of what files have changed. But if SETA says to run a test, then it has likely been skipped in previous pushes, so it should be run regardless of what has changed in this push. This also adds a bit more output about optimization, that may be useful for anyone wondering why a particular job didn't run. MozReview-Commit-ID: 3OsvRnWjai4 --HG-- extra : rebase_source : 4ac6a5cc592f4210918c73e667f3b5dd50230894
326 lines
12 KiB
Python
326 lines
12 KiB
Python
# This Source Code Form is subject to the terms of the Mozilla Public
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# License, v. 2.0. If a copy of the MPL was not distributed with this
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# file, You can obtain one at http://mozilla.org/MPL/2.0/.
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from __future__ import absolute_import, print_function, unicode_literals
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import logging
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import os
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import yaml
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import copy
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from . import filter_tasks
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from .graph import Graph
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from .taskgraph import TaskGraph
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from .task import Task
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from .optimize import optimize_task_graph
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from .morph import morph
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from .util.python_path import find_object
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from .transforms.base import TransformSequence, TransformConfig
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from .util.verify import (
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verify_docs,
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verifications,
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)
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logger = logging.getLogger(__name__)
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class Kind(object):
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def __init__(self, name, path, config):
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self.name = name
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self.path = path
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self.config = config
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def _get_loader(self):
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try:
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loader = self.config['loader']
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except KeyError:
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raise KeyError("{!r} does not define `loader`".format(self.path))
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return find_object(loader)
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def load_tasks(self, parameters, loaded_tasks):
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loader = self._get_loader()
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config = copy.deepcopy(self.config)
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if 'parse-commit' in self.config:
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parse_commit = find_object(config['parse-commit'])
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config['args'] = parse_commit(parameters['message'])
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else:
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config['args'] = None
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kind_dependencies = config.get('kind-dependencies', [])
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kind_dependencies_tasks = [task for task in loaded_tasks
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if task.kind in kind_dependencies]
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inputs = loader(self.name, self.path, config, parameters, loaded_tasks)
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transforms = TransformSequence()
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for xform_path in config['transforms']:
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transform = find_object(xform_path)
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transforms.add(transform)
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# perform the transformations on the loaded inputs
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trans_config = TransformConfig(self.name, self.path, config, parameters,
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kind_dependencies_tasks)
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tasks = [Task(self.name,
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label=task_dict['label'],
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attributes=task_dict['attributes'],
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task=task_dict['task'],
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optimization=task_dict.get('optimization'),
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dependencies=task_dict.get('dependencies'))
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for task_dict in transforms(trans_config, inputs)]
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return tasks
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class TaskGraphGenerator(object):
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"""
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The central controller for taskgraph. This handles all phases of graph
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generation. The task is generated from all of the kinds defined in
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subdirectories of the generator's root directory.
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Access to the results of this generation, as well as intermediate values at
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various phases of generation, is available via properties. This encourages
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the provision of all generation inputs at instance construction time.
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"""
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# Task-graph generation is implemented as a Python generator that yields
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# each "phase" of generation. This allows some mach subcommands to short-
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# circuit generation of the entire graph by never completing the generator.
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def __init__(self, root_dir, parameters):
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"""
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@param root_dir: root directory, with subdirectories for each kind
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@param parameters: parameters for this task-graph generation
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@type parameters: dict
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"""
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self.root_dir = root_dir
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self.parameters = parameters
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self.verify_parameters(self.parameters)
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filters = parameters.get('filters', [])
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# Always add legacy target tasks method until we deprecate that API.
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if 'target_tasks_method' not in filters:
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filters.insert(0, 'target_tasks_method')
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self.filters = [filter_tasks.filter_task_functions[f] for f in filters]
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# this can be set up until the time the target task set is generated;
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# it defaults to parameters['target_tasks']
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self._target_tasks = parameters.get('target_tasks')
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# start the generator
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self._run = self._run()
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self._run_results = {}
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@property
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def full_task_set(self):
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"""
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The full task set: all tasks defined by any kind (a graph without edges)
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@type: TaskGraph
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"""
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return self._run_until('full_task_set')
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@property
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def full_task_graph(self):
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"""
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The full task graph: the full task set, with edges representing
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dependencies.
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@type: TaskGraph
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"""
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return self._run_until('full_task_graph')
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@property
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def target_task_set(self):
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"""
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The set of targetted tasks (a graph without edges)
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@type: TaskGraph
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"""
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return self._run_until('target_task_set')
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@property
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def target_task_graph(self):
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"""
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The set of targetted tasks and all of their dependencies
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@type: TaskGraph
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"""
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return self._run_until('target_task_graph')
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@property
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def optimized_task_graph(self):
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"""
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The set of targetted tasks and all of their dependencies; tasks that
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have been optimized out are either omitted or replaced with a Task
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instance containing only a task_id.
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@type: TaskGraph
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"""
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return self._run_until('optimized_task_graph')
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@property
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def label_to_taskid(self):
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"""
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A dictionary mapping task label to assigned taskId. This property helps
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in interpreting `optimized_task_graph`.
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@type: dictionary
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"""
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return self._run_until('label_to_taskid')
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@property
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def morphed_task_graph(self):
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"""
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The optimized task graph, with any subsequent morphs applied. This graph
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will have the same meaning as the optimized task graph, but be in a form
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more palatable to TaskCluster.
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@type: TaskGraph
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"""
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return self._run_until('morphed_task_graph')
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def _load_kinds(self):
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for path in os.listdir(self.root_dir):
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path = os.path.join(self.root_dir, path)
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if not os.path.isdir(path):
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continue
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kind_name = os.path.basename(path)
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kind_yml = os.path.join(path, 'kind.yml')
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if not os.path.exists(kind_yml):
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continue
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logger.debug("loading kind `{}` from `{}`".format(kind_name, path))
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with open(kind_yml) as f:
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config = yaml.load(f)
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yield Kind(kind_name, path, config)
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def _run(self):
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logger.info("Loading kinds")
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# put the kinds into a graph and sort topologically so that kinds are loaded
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# in post-order
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kinds = {kind.name: kind for kind in self._load_kinds()}
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self.verify_kinds(kinds)
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edges = set()
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for kind in kinds.itervalues():
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for dep in kind.config.get('kind-dependencies', []):
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edges.add((kind.name, dep, 'kind-dependency'))
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kind_graph = Graph(set(kinds), edges)
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logger.info("Generating full task set")
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all_tasks = {}
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for kind_name in kind_graph.visit_postorder():
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logger.debug("Loading tasks for kind {}".format(kind_name))
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kind = kinds[kind_name]
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new_tasks = kind.load_tasks(self.parameters, list(all_tasks.values()))
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for task in new_tasks:
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if task.label in all_tasks:
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raise Exception("duplicate tasks with label " + task.label)
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all_tasks[task.label] = task
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logger.info("Generated {} tasks for kind {}".format(len(new_tasks), kind_name))
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full_task_set = TaskGraph(all_tasks, Graph(set(all_tasks), set()))
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self.verify_attributes(all_tasks)
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self.verify_run_using()
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yield verifications('full_task_set', full_task_set)
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logger.info("Generating full task graph")
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edges = set()
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for t in full_task_set:
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for depname, dep in t.dependencies.iteritems():
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edges.add((t.label, dep, depname))
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full_task_graph = TaskGraph(all_tasks,
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Graph(full_task_set.graph.nodes, edges))
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logger.info("Full task graph contains %d tasks and %d dependencies" % (
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len(full_task_set.graph.nodes), len(edges)))
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yield verifications('full_task_graph', full_task_graph)
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logger.info("Generating target task set")
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target_task_set = TaskGraph(dict(all_tasks),
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Graph(set(all_tasks.keys()), set()))
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for fltr in self.filters:
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old_len = len(target_task_set.graph.nodes)
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target_tasks = set(fltr(target_task_set, self.parameters))
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target_task_set = TaskGraph(
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{l: all_tasks[l] for l in target_tasks},
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Graph(target_tasks, set()))
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logger.info('Filter %s pruned %d tasks (%d remain)' % (
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fltr.__name__,
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old_len - len(target_tasks),
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len(target_tasks)))
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yield verifications('target_task_set', target_task_set)
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logger.info("Generating target task graph")
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# include all docker-image build tasks here, in case they are needed for a graph morph
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docker_image_tasks = set(t.label for t in full_task_graph.tasks.itervalues()
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if t.attributes['kind'] == 'docker-image')
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target_graph = full_task_graph.graph.transitive_closure(target_tasks | docker_image_tasks)
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target_task_graph = TaskGraph(
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{l: all_tasks[l] for l in target_graph.nodes},
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target_graph)
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yield verifications('target_task_graph', target_task_graph)
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logger.info("Generating optimized task graph")
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do_not_optimize = set()
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if not self.parameters.get('optimize_target_tasks', True):
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do_not_optimize = target_task_set.graph.nodes
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optimized_task_graph, label_to_taskid = optimize_task_graph(target_task_graph,
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self.parameters,
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do_not_optimize)
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yield verifications('optimized_task_graph', optimized_task_graph)
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morphed_task_graph, label_to_taskid = morph(
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optimized_task_graph, label_to_taskid, self.parameters)
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yield 'label_to_taskid', label_to_taskid
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yield verifications('morphed_task_graph', morphed_task_graph)
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def _run_until(self, name):
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while name not in self._run_results:
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try:
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k, v = self._run.next()
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except StopIteration:
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raise AttributeError("No such run result {}".format(name))
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self._run_results[k] = v
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return self._run_results[name]
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def verify_parameters(self, parameters):
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parameters_dict = dict(**parameters)
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verify_docs(
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filename="parameters.rst",
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identifiers=parameters_dict.keys(),
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appearing_as="inline-literal"
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)
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def verify_kinds(self, kinds):
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verify_docs(
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filename="kinds.rst",
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identifiers=kinds.keys(),
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appearing_as="heading"
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)
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def verify_attributes(self, all_tasks):
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attribute_set = set()
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for label, task in all_tasks.iteritems():
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attribute_set.update(task.attributes.keys())
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verify_docs(
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filename="attributes.rst",
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identifiers=list(attribute_set),
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appearing_as="heading"
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)
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def verify_run_using(self):
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from .transforms.job import registry
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verify_docs(
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filename="transforms.rst",
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identifiers=registry.keys(),
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appearing_as="inline-literal"
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)
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