xref: /aosp_15_r20/external/toolchain-utils/bestflags/pipeline_process.py (revision 760c253c1ed00ce9abd48f8546f08516e57485fe)

# Copyright 2013 The ChromiumOS Authors
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Pipeline process that encapsulates the actual content.

Part of the Chrome build flags optimization.

The actual stages include the builder and the executor.
"""

__author__ = "[email protected] (Yuheng Long)"

import multiprocessing


# Pick an integer at random.
POISONPILL = 975


class PipelineProcess(multiprocessing.Process):
    """A process that encapsulates the actual content pipeline stage.

    The actual pipeline stage can be the builder or the tester.  This process
    continuously pull tasks from the queue until a poison pill is received.
    Once a job is received, it will hand it to the actual stage for processing.

    Each pipeline stage contains three modules.
    The first module continuously pulls task from the input queue. It searches the
    cache to check whether the task has encountered before. If so, duplicate
    computation can be avoided.
    The second module consists of a pool of workers that do the actual work, e.g.,
    the worker will compile the source code and get the image in the builder
    pipeline stage.
    The third module is a helper that put the result cost to the cost field of the
    duplicate tasks. For example, if two tasks are equivalent, only one task, say
    t1 will be executed and the other task, say t2 will not be executed. The third
    mode gets the result from t1, when it is available and set the cost of t2 to
    be the same as that of t1.
    """

    def __init__(
        self,
        num_processes,
        name,
        cache,
        stage,
        task_queue,
        helper,
        worker,
        result_queue,
    ):
        """Set up input/output queue and the actual method to be called.

        Args:
          num_processes: Number of helpers subprocessors this stage has.
          name: The name of this stage.
          cache: The computed tasks encountered before.
          stage: An int value that specifies the stage for this pipeline stage, for
            example, build stage or test stage. This value will be used to retrieve
            the keys in different stage. I.e., the flags set is the key in build
            stage and the checksum is the key in the test stage. The key is used to
            detect duplicates.
          task_queue: The input task queue for this pipeline stage.
          helper: The method hosted by the helper module to fill up the cost of the
            duplicate tasks.
          worker: The method hosted by the worker pools to do the actual work, e.g.,
            compile the image.
          result_queue: The output task queue for this pipeline stage.
        """

        multiprocessing.Process.__init__(self)

        self._name = name
        self._task_queue = task_queue
        self._result_queue = result_queue

        self._helper = helper
        self._worker = worker

        self._cache = cache
        self._stage = stage
        self._num_processes = num_processes

        # the queues used by the modules for communication
        manager = multiprocessing.Manager()
        self._helper_queue = manager.Queue()
        self._work_queue = manager.Queue()

    def run(self):
        """Busy pulling the next task from the queue for execution.

        Once a job is pulled, this stage invokes the actual stage method and submits
        the result to the next pipeline stage.

        The process will terminate on receiving the poison pill from previous stage.
        """

        # the worker pool
        work_pool = multiprocessing.Pool(self._num_processes)

        # the helper process
        helper_process = multiprocessing.Process(
            target=self._helper,
            args=(
                self._stage,
                self._cache,
                self._helper_queue,
                self._work_queue,
                self._result_queue,
            ),
        )
        helper_process.start()
        mycache = self._cache.keys()

        while True:
            task = self._task_queue.get()
            if task == POISONPILL:
                # Poison pill means shutdown
                self._result_queue.put(POISONPILL)
                break

            task_key = task.GetIdentifier(self._stage)
            if task_key in mycache:
                # The task has been encountered before. It will be sent to the helper
                # module for further processing.
                self._helper_queue.put(task)
            else:
                # Let the workers do the actual work.
                work_pool.apply_async(
                    self._worker,
                    args=(
                        self._stage,
                        task,
                        self._work_queue,
                        self._result_queue,
                    ),
                )
                mycache.append(task_key)

        # Shutdown the workers pool and the helper process.
        work_pool.close()
        work_pool.join()

        self._helper_queue.put(POISONPILL)
        helper_process.join()