forked from mirrors/gecko-dev
710d91063a
Differential Revision: https://phabricator.services.mozilla.com/D14477 --HG-- extra : moz-landing-system : lando
201 lines
7.3 KiB
Python
201 lines
7.3 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 pytest
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import unittest
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from mozunit import main
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from taskgraph.generator import TaskGraphGenerator, Kind
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from taskgraph.optimize import OptimizationStrategy
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from taskgraph.util.templates import merge
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from taskgraph import (
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generator,
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graph,
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optimize as optimize_mod,
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target_tasks as target_tasks_mod,
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)
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def fake_loader(kind, path, config, parameters, loaded_tasks):
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for i in range(3):
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dependencies = {}
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if i >= 1:
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dependencies['prev'] = '{}-t-{}'.format(kind, i-1)
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task = {
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'kind': kind,
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'label': '{}-t-{}'.format(kind, i),
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'attributes': {'_tasknum': str(i)},
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'task': {'i': i},
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'dependencies': dependencies,
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}
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if 'job-defaults' in config:
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task = merge(config['job-defaults'], task)
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yield task
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class FakeKind(Kind):
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def _get_loader(self):
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return fake_loader
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def load_tasks(self, parameters, loaded_tasks):
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FakeKind.loaded_kinds.append(self.name)
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return super(FakeKind, self).load_tasks(parameters, loaded_tasks)
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class WithFakeKind(TaskGraphGenerator):
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def _load_kinds(self, graph_config):
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for kind_name, cfg in self.parameters['_kinds']:
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config = {
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'transforms': [],
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}
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if cfg:
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config.update(cfg)
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yield FakeKind(kind_name, '/fake', config, graph_config)
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def fake_load_graph_config(root_dir):
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return {'trust-domain': 'test-domain'}
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class FakeParameters(dict):
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strict = True
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class FakeOptimization(OptimizationStrategy):
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def __init__(self, mode, *args, **kwargs):
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super(FakeOptimization, self).__init__(*args, **kwargs)
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self.mode = mode
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def should_remove_task(self, task, params, arg):
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if self.mode == 'always':
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return True
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if self.mode == 'even':
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return task.task['i'] % 2 == 0
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if self.mode == 'odd':
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return task.task['i'] % 2 != 0
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return False
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class TestGenerator(unittest.TestCase):
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@pytest.fixture(autouse=True)
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def patch(self, monkeypatch):
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self.patch = monkeypatch
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def maketgg(self, target_tasks=None, kinds=[('_fake', [])], params=None):
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params = params or {}
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FakeKind.loaded_kinds = []
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self.target_tasks = target_tasks or []
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def target_tasks_method(full_task_graph, parameters, graph_config):
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return self.target_tasks
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def make_fake_strategies():
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return {mode: FakeOptimization(mode)
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for mode in ('always', 'never', 'even', 'odd')}
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target_tasks_mod._target_task_methods['test_method'] = target_tasks_method
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self.patch.setattr(optimize_mod, '_make_default_strategies', make_fake_strategies)
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parameters = FakeParameters({
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'_kinds': kinds,
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'target_tasks_method': 'test_method',
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'try_mode': None,
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})
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parameters.update(params)
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self.patch.setattr(generator, 'load_graph_config', fake_load_graph_config)
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return WithFakeKind('/root', parameters)
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def test_kind_ordering(self):
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"When task kinds depend on each other, they are loaded in postorder"
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self.tgg = self.maketgg(kinds=[
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('_fake3', {'kind-dependencies': ['_fake2', '_fake1']}),
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('_fake2', {'kind-dependencies': ['_fake1']}),
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('_fake1', {'kind-dependencies': []}),
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])
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self.tgg._run_until('full_task_set')
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self.assertEqual(FakeKind.loaded_kinds, ['_fake1', '_fake2', '_fake3'])
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def test_full_task_set(self):
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"The full_task_set property has all tasks"
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self.tgg = self.maketgg()
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self.assertEqual(self.tgg.full_task_set.graph,
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graph.Graph({'_fake-t-0', '_fake-t-1', '_fake-t-2'}, set()))
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self.assertEqual(sorted(self.tgg.full_task_set.tasks.keys()),
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sorted(['_fake-t-0', '_fake-t-1', '_fake-t-2']))
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def test_full_task_graph(self):
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"The full_task_graph property has all tasks, and links"
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self.tgg = self.maketgg()
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self.assertEqual(self.tgg.full_task_graph.graph,
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graph.Graph({'_fake-t-0', '_fake-t-1', '_fake-t-2'},
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{
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('_fake-t-1', '_fake-t-0', 'prev'),
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('_fake-t-2', '_fake-t-1', 'prev'),
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}))
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self.assertEqual(sorted(self.tgg.full_task_graph.tasks.keys()),
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sorted(['_fake-t-0', '_fake-t-1', '_fake-t-2']))
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def test_target_task_set(self):
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"The target_task_set property has the targeted tasks"
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self.tgg = self.maketgg(['_fake-t-1'])
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self.assertEqual(self.tgg.target_task_set.graph,
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graph.Graph({'_fake-t-1'}, set()))
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self.assertEqual(self.tgg.target_task_set.tasks.keys(),
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['_fake-t-1'])
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def test_target_task_graph(self):
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"The target_task_graph property has the targeted tasks and deps"
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self.tgg = self.maketgg(['_fake-t-1'])
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self.assertEqual(self.tgg.target_task_graph.graph,
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graph.Graph({'_fake-t-0', '_fake-t-1'},
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{('_fake-t-1', '_fake-t-0', 'prev')}))
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self.assertEqual(sorted(self.tgg.target_task_graph.tasks.keys()),
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sorted(['_fake-t-0', '_fake-t-1']))
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def test_always_target_tasks(self):
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"The target_task_graph includes tasks with 'always_target'"
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tgg_args = {
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'target_tasks': ['_fake-t-0', '_fake-t-1', '_ignore-t-0', '_ignore-t-1'],
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'kinds': [
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('_fake', {'job-defaults': {'optimization': {'odd': None}}}),
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('_ignore', {'job-defaults': {
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'attributes': {'always_target': True},
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'optimization': {'even': None},
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}}),
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],
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'params': {'optimize_target_tasks': False},
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}
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self.tgg = self.maketgg(**tgg_args)
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self.assertEqual(
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sorted(self.tgg.target_task_set.tasks.keys()),
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sorted(['_fake-t-0', '_fake-t-1', '_ignore-t-0', '_ignore-t-1']))
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self.assertEqual(
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sorted(self.tgg.target_task_graph.tasks.keys()),
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sorted(['_fake-t-0', '_fake-t-1', '_ignore-t-0', '_ignore-t-1', '_ignore-t-2']))
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self.assertEqual(
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sorted([t.label for t in self.tgg.optimized_task_graph.tasks.values()]),
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sorted(['_fake-t-0', '_fake-t-1', '_ignore-t-0', '_ignore-t-1']))
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def test_optimized_task_graph(self):
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"The optimized task graph contains task ids"
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self.tgg = self.maketgg(['_fake-t-2'])
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tid = self.tgg.label_to_taskid
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self.assertEqual(
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self.tgg.optimized_task_graph.graph,
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graph.Graph({tid['_fake-t-0'], tid['_fake-t-1'], tid['_fake-t-2']}, {
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(tid['_fake-t-1'], tid['_fake-t-0'], 'prev'),
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(tid['_fake-t-2'], tid['_fake-t-1'], 'prev'),
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}))
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if __name__ == '__main__':
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main()
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