forked from mirrors/gecko-dev
1b90c899f3
The `always_target` attribute is a feature which I believe I previously mis-implemented. It doesn't belong in `generator.py` and should instead be implemented somewhere in `target_tasks.py`. I chose to wrap the registration decorator to guarantee backwards compatibility within Gecko. Though we may wish to move it out to a filter in the future. I'm making this change now to facilitate merging standalone and Gecko taskgraphs. The `always_target` feature will be removed from standalone (as it isn't being used and should consumers need something like it, they can roll their own implementations). With the feature removed from both Gecko and standalone in generator.py, this file will now be identical across both Taskgraphs and can therefore be removed from Gecko. Differential Revision: https://phabricator.services.mozilla.com/D159181
435 lines
15 KiB
Python
435 lines
15 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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import logging
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import os
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import copy
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import attr
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from taskgraph import filter_tasks
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from taskgraph.config import GraphConfig, load_graph_config
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from taskgraph.graph import Graph
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from taskgraph.morph import morph
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from taskgraph.optimize.base import optimize_task_graph
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from taskgraph.parameters import parameters_loader
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from taskgraph.task import Task
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from taskgraph.taskgraph import TaskGraph
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from taskgraph.transforms.base import TransformSequence, TransformConfig
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from taskgraph.util.python_path import find_object
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from taskgraph.util.yaml import load_yaml
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from .util.verify import verifications
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logger = logging.getLogger(__name__)
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class KindNotFound(Exception):
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"""
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Raised when trying to load kind from a directory without a kind.yml.
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"""
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@attr.s(frozen=True)
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class Kind:
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name = attr.ib(type=str)
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path = attr.ib(type=str)
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config = attr.ib(type=dict)
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graph_config = attr.ib(type=GraphConfig)
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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(f"{self.path!r} does not define `loader`")
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return find_object(loader)
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def load_tasks(self, parameters, loaded_tasks, write_artifacts):
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loader = self._get_loader()
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config = copy.deepcopy(self.config)
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kind_dependencies = config.get("kind-dependencies", [])
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kind_dependencies_tasks = {
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task.label: task for task in loaded_tasks if task.kind in kind_dependencies
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}
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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(
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self.name,
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self.path,
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config,
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parameters,
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kind_dependencies_tasks,
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self.graph_config,
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write_artifacts=write_artifacts,
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)
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tasks = [
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Task(
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self.name,
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label=task_dict["label"],
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description=task_dict["description"],
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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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soft_dependencies=task_dict.get("soft-dependencies"),
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if_dependencies=task_dict.get("if-dependencies"),
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)
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for task_dict in transforms(trans_config, inputs)
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]
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return tasks
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@classmethod
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def load(cls, root_dir, graph_config, kind_name):
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path = os.path.join(root_dir, kind_name)
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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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raise KindNotFound(kind_yml)
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logger.debug(f"loading kind `{kind_name}` from `{path}`")
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config = load_yaml(kind_yml)
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return cls(kind_name, path, config, graph_config)
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class TaskGraphGenerator:
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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__(
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self,
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root_dir,
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parameters,
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decision_task_id="DECISION-TASK",
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write_artifacts=False,
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):
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"""
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@param root_dir: root directory, with subdirectories for each kind
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@param paramaters: parameters for this task-graph generation, or callable
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taking a `GraphConfig` and returning parameters
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@type parameters: Union[Parameters, Callable[[GraphConfig], Parameters]]
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"""
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if root_dir is None:
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root_dir = "taskcluster/ci"
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self.root_dir = root_dir
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self._parameters = parameters
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self._decision_task_id = decision_task_id
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self._write_artifacts = write_artifacts
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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 parameters(self):
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"""
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The properties used for this graph.
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@type: Properties
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"""
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return self._run_until("parameters")
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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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@property
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def graph_config(self):
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"""
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The configuration for this graph.
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@type: TaskGraph
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"""
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return self._run_until("graph_config")
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def _load_kinds(self, graph_config, target_kind=None):
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if target_kind:
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# docker-image is an implicit dependency that never appears in
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# kind-dependencies.
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queue = [target_kind, "docker-image"]
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seen_kinds = set()
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while queue:
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kind_name = queue.pop()
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if kind_name in seen_kinds:
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continue
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seen_kinds.add(kind_name)
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kind = Kind.load(self.root_dir, graph_config, kind_name)
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yield kind
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queue.extend(kind.config.get("kind-dependencies", []))
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else:
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for kind_name in os.listdir(self.root_dir):
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try:
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yield Kind.load(self.root_dir, graph_config, kind_name)
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except KindNotFound:
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continue
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def _run(self):
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logger.info("Loading graph configuration.")
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graph_config = load_graph_config(self.root_dir)
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yield ("graph_config", graph_config)
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graph_config.register()
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# Initial verifications that don't depend on any generation state.
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verifications("initial")
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if callable(self._parameters):
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parameters = self._parameters(graph_config)
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else:
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parameters = self._parameters
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logger.info("Using {}".format(parameters))
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logger.debug("Dumping parameters:\n{}".format(repr(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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filters = [filter_tasks.filter_task_functions[f] for f in filters]
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yield self.verify("parameters", parameters)
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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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if parameters.get("target-kind"):
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target_kind = parameters["target-kind"]
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logger.info(
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"Limiting kinds to {target_kind} and dependencies".format(
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target_kind=target_kind
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)
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)
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kinds = {
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kind.name: kind
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for kind in self._load_kinds(graph_config, parameters.get("target-kind"))
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}
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verifications("kinds", kinds)
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edges = set()
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for kind in kinds.values():
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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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if parameters.get("target-kind"):
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kind_graph = kind_graph.transitive_closure({target_kind, "docker-image"})
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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(f"Loading tasks for kind {kind_name}")
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kind = kinds[kind_name]
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try:
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new_tasks = kind.load_tasks(
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parameters,
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list(all_tasks.values()),
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self._write_artifacts,
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)
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except Exception:
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logger.exception(f"Error loading tasks for kind {kind_name}:")
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raise
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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(f"Generated {len(new_tasks)} tasks for kind {kind_name}")
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full_task_set = TaskGraph(all_tasks, Graph(set(all_tasks), set()))
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yield self.verify("full_task_set", full_task_set, graph_config, parameters)
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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.items():
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edges.add((t.label, dep, depname))
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full_task_graph = TaskGraph(all_tasks, Graph(full_task_set.graph.nodes, edges))
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logger.info(
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"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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)
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yield self.verify("full_task_graph", full_task_graph, graph_config, parameters)
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logger.info("Generating target task set")
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target_task_set = TaskGraph(
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dict(all_tasks), Graph(set(all_tasks.keys()), set())
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)
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for fltr in filters:
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old_len = len(target_task_set.graph.nodes)
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target_tasks = set(fltr(target_task_set, parameters, graph_config))
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target_task_set = TaskGraph(
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{l: all_tasks[l] for l in target_tasks}, Graph(target_tasks, set())
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)
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logger.info(
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"Filter %s pruned %d tasks (%d remain)"
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% (fltr.__name__, old_len - len(target_tasks), len(target_tasks))
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)
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yield self.verify("target_task_set", target_task_set, graph_config, parameters)
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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 = {
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t.label
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for t in full_task_graph.tasks.values()
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if t.attributes["kind"] == "docker-image"
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}
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requested_tasks = target_tasks | docker_image_tasks
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target_graph = full_task_graph.graph.transitive_closure(requested_tasks)
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target_task_graph = TaskGraph(
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{l: all_tasks[l] for l in target_graph.nodes}, target_graph
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)
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yield self.verify(
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"target_task_graph", target_task_graph, graph_config, parameters
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)
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logger.info("Generating optimized task graph")
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existing_tasks = parameters.get("existing_tasks")
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do_not_optimize = set(parameters.get("do_not_optimize", []))
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if not parameters.get("optimize_target_tasks", True):
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do_not_optimize = set(target_task_set.graph.nodes).union(do_not_optimize)
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# this is used for testing experimental optimization strategies
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strategies = os.environ.get(
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"TASKGRAPH_OPTIMIZE_STRATEGIES", parameters.get("optimize_strategies")
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)
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if strategies:
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strategies = find_object(strategies)
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optimized_task_graph, label_to_taskid = optimize_task_graph(
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target_task_graph,
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requested_tasks,
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parameters,
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do_not_optimize,
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self._decision_task_id,
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existing_tasks=existing_tasks,
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strategy_override=strategies,
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)
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yield self.verify(
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"optimized_task_graph", optimized_task_graph, graph_config, parameters
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)
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morphed_task_graph, label_to_taskid = morph(
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optimized_task_graph, label_to_taskid, parameters, graph_config
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)
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yield "label_to_taskid", label_to_taskid
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yield self.verify(
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"morphed_task_graph", morphed_task_graph, graph_config, parameters
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)
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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 = next(self._run)
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except StopIteration:
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raise AttributeError(f"No such run result {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(self, name, obj, *args, **kwargs):
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verifications(name, obj, *args, **kwargs)
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return name, obj
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def load_tasks_for_kind(parameters, kind, root_dir=None):
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"""
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Get all the tasks of a given kind.
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This function is designed to be called from outside of taskgraph.
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"""
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# make parameters read-write
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parameters = dict(parameters)
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parameters["target-kind"] = kind
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parameters = parameters_loader(spec=None, strict=False, overrides=parameters)
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tgg = TaskGraphGenerator(root_dir=root_dir, parameters=parameters)
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return {
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task.task["metadata"]["name"]: task
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for task in tgg.full_task_set
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if task.kind == kind
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}
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