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117 lines
4.8 KiB
117 lines
4.8 KiB
# Copyright (c) 2013 The Chromium OS Authors. All rights reserved.
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# Use of this source code is governed by a BSD-style license that can be
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# found in the LICENSE file.
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"""The framework stage that produces the next generation of tasks to run.
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Part of the Chrome build flags optimization.
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"""
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__author__ = 'yuhenglong@google.com (Yuheng Long)'
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import pipeline_process
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def Steering(cache, generations, input_queue, result_queue):
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"""The core method template that produces the next generation of tasks to run.
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This method waits for the results of the tasks from the previous generation.
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Upon the arrival of all these results, the method uses them to generate the
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next generation of tasks.
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The main logic of producing the next generation from previous generation is
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application specific. For example, in the genetic algorithm, a task is
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produced by combining two parents tasks, while in the hill climbing algorithm,
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a task is generated by its immediate neighbor. The method 'Next' is overridden
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in the concrete subclasses of the class Generation to produce the next
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application-specific generation. The steering method invokes the 'Next'
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method, produces the next generation and submits the tasks in this generation
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to the next stage, e.g., the build/compilation stage.
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Args:
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cache: It stores the experiments that have been conducted before. Used to
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avoid duplicate works.
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generations: The initial generations of tasks to be run.
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input_queue: The input results from the last stage of the framework. These
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results will trigger new iteration of the algorithm.
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result_queue: The output task queue for this pipeline stage. The new tasks
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generated by the steering algorithm will be sent to the next stage via
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this queue.
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"""
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# Generations that have pending tasks to be executed. Pending tasks are those
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# whose results are not ready. The tasks that have their results ready are
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# referenced to as ready tasks. Once there is no pending generation, the
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# algorithm terminates.
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waiting = generations
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# Record how many initial tasks there are. If there is no task at all, the
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# algorithm can terminate right away.
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num_tasks = 0
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# Submit all the tasks in the initial generations to the next stage of the
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# framework. The next stage can be the build/compilation stage.
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for generation in generations:
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# Only send the task that has not been performed before to the next stage.
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for task in [task for task in generation.Pool() if task not in cache]:
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result_queue.put(task)
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cache.add(task)
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num_tasks += 1
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# If there is no task to be executed at all, the algorithm returns right away.
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if not num_tasks:
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# Inform the next stage that there will be no more task.
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result_queue.put(pipeline_process.POISONPILL)
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return
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# The algorithm is done if there is no pending generation. A generation is
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# pending if it has pending task.
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while waiting:
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# Busy-waiting for the next task.
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if input_queue.empty():
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continue
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# If there is a task whose result is ready from the last stage of the
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# feedback loop, there will be one less pending task.
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task = input_queue.get()
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# Store the result of this ready task. Intermediate results can be used to
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# generate report for final result or be used to reboot from a crash from
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# the failure of any module of the framework.
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task.LogSteeringCost()
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# Find out which pending generation this ready task belongs to. This pending
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# generation will have one less pending task. The "next" expression iterates
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# the generations in waiting until the first generation whose UpdateTask
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# method returns true.
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generation = next(gen for gen in waiting if gen.UpdateTask(task))
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# If there is still any pending task, do nothing.
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if not generation.Done():
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continue
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# All the tasks in the generation are finished. The generation is ready to
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# produce the next generation.
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waiting.remove(generation)
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# Check whether a generation should generate the next generation.
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# A generation may not generate the next generation, e.g., because a
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# fixpoint has been reached, there has not been any improvement for a few
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# generations or a local maxima is reached.
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if not generation.IsImproved():
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continue
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for new_generation in generation.Next(cache):
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# Make sure that each generation should contain at least one task.
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assert new_generation.Pool()
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waiting.append(new_generation)
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# Send the tasks of the new generations to the next stage for execution.
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for new_task in new_generation.Pool():
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result_queue.put(new_task)
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cache.add(new_task)
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# Steering algorithm is finished and it informs the next stage that there will
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# be no more task.
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result_queue.put(pipeline_process.POISONPILL)
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