forked from mirrors/gecko-dev
400f6f88ad
In preparation for much more thorough optimization of task-graphs, this makes a few changes: * optimization is split into thre phases, with task removal in one phase (following dependency links) and task replacement in the next (in the reverse order). * optimization uses class instances instead of functions for optimizations; this allows different functions for different phases, and also leaves open the possibility of composing optimizations. * the replacement phase can also support removal; this is when utility tasks like symbol uploads can be optimized away iff their parent task is optimized. MozReview-Commit-ID: C5QznNpwqXn --HG-- extra : rebase_source : cf1654036041a64398a2cd38e35e8de4f3596ff9 extra : source : c25af2c111a5be4e3381d0b002641691d15fe4e8
84 lines
3.1 KiB
Python
84 lines
3.1 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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class Task(object):
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"""
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Representation of a task in a TaskGraph. Each Task has, at creation:
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- kind: the name of the task kind
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- label; the label for this task
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- attributes: a dictionary of attributes for this task (used for filtering)
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- task: the task definition (JSON-able dictionary)
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- optimization: optimization to apply to the task (see taskgraph.optimize)
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- dependencies: tasks this one depends on, in the form {name: label}, for example
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{'build': 'build-linux64/opt', 'docker-image': 'build-docker-image-desktop-test'}
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And later, as the task-graph processing proceeds:
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- task_id -- TaskCluster taskId under which this task will be created
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This class is just a convenience wraper for the data type and managing
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display, comparison, serialization, etc. It has no functionality of its own.
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"""
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def __init__(self, kind, label, attributes, task,
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optimization=None, dependencies=None):
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self.kind = kind
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self.label = label
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self.attributes = attributes
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self.task = task
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self.task_id = None
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self.attributes['kind'] = kind
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self.optimization = optimization
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self.dependencies = dependencies or {}
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def __eq__(self, other):
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return self.kind == other.kind and \
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self.label == other.label and \
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self.attributes == other.attributes and \
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self.task == other.task and \
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self.task_id == other.task_id and \
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self.optimization == other.optimization and \
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self.dependencies == other.dependencies
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def __repr__(self):
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return ('Task({kind!r}, {label!r}, {attributes!r}, {task!r}, '
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'optimization={optimization!r}, '
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'dependencies={dependencies!r})'.format(**self.__dict__))
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def to_json(self):
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rv = {
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'kind': self.kind,
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'label': self.label,
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'attributes': self.attributes,
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'dependencies': self.dependencies,
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'optimization': self.optimization,
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'task': self.task,
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}
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if self.task_id:
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rv['task_id'] = self.task_id
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return rv
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@classmethod
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def from_json(cls, task_dict):
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"""
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Given a data structure as produced by taskgraph.to_json, re-construct
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the original Task object. This is used to "resume" the task-graph
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generation process, for example in Action tasks.
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"""
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rv = cls(
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kind=task_dict['kind'],
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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['optimization'],
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dependencies=task_dict.get('dependencies'))
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if 'task_id' in task_dict:
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rv.task_id = task_dict['task_id']
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return rv
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