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56c96fa18d
We add the following command line options to Taskcluster try syntax: --spsProfile - enable profile mode. --rebuild-talos <N> - retrigger talos tests N times. --setenv <VAR>=<val> - add extra environments variables. --tag <TAG> - run tests only the tag TAG. --no-retry - doesn't retry failed jobs. We have a chicken-egg problem, as we first generate the full task graph and then parse the try message. But the graph generation step needs to know the try message to process the aforementioned options. The solution is to parse the message before graph generation and then pass the command line options to the transforms. Then, each transform can look at the option that interests it and process it accordingly. The message parse function is configured in kind.yml, which gives some flexibility for future implementations of alternative syntaxes. MozReview-Commit-ID: GPFdi0FD6Vn --HG-- extra : rebase_source : b992786158851f1099aedfce8669a163228edc51
289 lines
10 KiB
Python
289 lines
10 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 .optimize import optimize_task_graph
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from .util.python_path import find_object
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from .util.verify import (
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verify_docs,
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verify_task_graph_symbol,
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verify_gecko_v2_routes,
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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_impl_class(self):
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# load the class defined by implementation
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try:
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impl = self.config['implementation']
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except KeyError:
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raise KeyError("{!r} does not define implementation".format(self.path))
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return find_object(impl)
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def load_tasks(self, parameters, loaded_tasks):
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impl_class = self._get_impl_class()
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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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return impl_class.load_tasks(self.name, self.path, config,
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parameters, loaded_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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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 '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 dep, depname in t.get_dependencies(full_task_set):
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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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full_task_graph.for_each_task(verify_task_graph_symbol, scratch_pad={})
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full_task_graph.for_each_task(verify_gecko_v2_routes, scratch_pad={})
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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 '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 'target_task_set', target_task_set
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logger.info("Generating target task graph")
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target_graph = full_task_graph.graph.transitive_closure(target_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 '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 'label_to_taskid', label_to_taskid
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yield 'optimized_task_graph', optimized_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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