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Diffstat (limited to 'bestflags/testing_batch.py')
-rw-r--r-- | bestflags/testing_batch.py | 450 |
1 files changed, 450 insertions, 0 deletions
diff --git a/bestflags/testing_batch.py b/bestflags/testing_batch.py new file mode 100644 index 00000000..ffe19448 --- /dev/null +++ b/bestflags/testing_batch.py @@ -0,0 +1,450 @@ +# Copyright (c) 2013 The Chromium OS Authors. All rights reserved. +# Use of this source code is governed by a BSD-style license that can be +# found in the LICENSE file. +"""Hill climbing unitest. + +Part of the Chrome build flags optimization. + +Test the best branching hill climbing algorithms, genetic algorithm and +iterative elimination algorithm. +""" + +__author__ = 'yuhenglong@google.com (Yuheng Long)' + +import multiprocessing +import random +import sys +import unittest + +import flags +from flags import Flag +from flags import FlagSet +from genetic_algorithm import GAGeneration +from genetic_algorithm import GATask +from hill_climb_best_neighbor import HillClimbingBestBranch +from iterative_elimination import IterativeEliminationFirstGeneration +import pipeline_process +from steering import Steering +from task import BUILD_STAGE +from task import Task +from task import TEST_STAGE + +# The number of flags be tested. +NUM_FLAGS = 5 + +# The value range of the flags. +FLAG_RANGES = 10 + +# The following variables are meta data for the Genetic Algorithm. +STOP_THRESHOLD = 20 +NUM_CHROMOSOMES = 10 +NUM_TRIALS = 20 +MUTATION_RATE = 0.03 + + +def _GenerateRandomRasks(specs): + """Generate a task that has random values. + + Args: + specs: A list of spec from which the flag set is created. + + Returns: + A set containing a task that has random values. + """ + + flag_set = [] + + for spec in specs: + numeric_flag_match = flags.Search(spec) + if numeric_flag_match: + # Numeric flags. + start = int(numeric_flag_match.group('start')) + end = int(numeric_flag_match.group('end')) + + value = random.randint(start - 1, end - 1) + if value != start - 1: + # If the value falls in the range, this flag is enabled. + flag_set.append(Flag(spec, value)) + else: + # Boolean flags. + if random.randint(0, 1): + flag_set.append(Flag(spec)) + + return set([Task(FlagSet(flag_set))]) + + +def _GenerateAllFlagsTasks(specs): + """Generate a task that all the flags are enable. + + All the boolean flags in the specs will be enabled and all the numeric flag + with have the largest legal value. + + Args: + specs: A list of spec from which the flag set is created. + + Returns: + A set containing a task that has all flags enabled. + """ + + flag_set = [] + + for spec in specs: + numeric_flag_match = flags.Search(spec) + + if numeric_flag_match: + value = (int(numeric_flag_match.group('end')) - 1) + else: + value = -1 + flag_set.append(Flag(spec, value)) + + return set([Task(FlagSet(flag_set))]) + + +def _GenerateNoFlagTask(): + return set([Task(FlagSet([]))]) + + +def GenerateRandomGATasks(specs, num_tasks, num_trials): + """Generate a set of tasks for the Genetic Algorithm. + + Args: + specs: A list of spec from which the flag set is created. + num_tasks: number of tasks that should be generated. + num_trials: the maximum number of tries should be attempted to generate the + set of tasks. + + Returns: + A set of randomly generated tasks. + """ + + tasks = set([]) + + total_trials = 0 + while len(tasks) < num_tasks and total_trials < num_trials: + new_flag = FlagSet([Flag(spec) for spec in specs if random.randint(0, 1)]) + new_task = GATask(new_flag) + + if new_task in tasks: + total_trials += 1 + else: + tasks.add(new_task) + total_trials = 0 + + return tasks + + +def _GenerateInitialFlags(specs, spec): + """Generate the flag_set of a task in the flag elimination algorithm. + + Set the value of all the flags to the largest value, except for the flag that + contains spec. + + For example, if the specs are [-finline-limit=[1-1000], -fstrict-aliasing] and + the spec is -finline-limit=[1-1000], then the result is + [-finline-limit=[1-1000]:-finline-limit=998, + -fstrict-aliasing:-fstrict-aliasing] + + Args: + specs: an array of specifications from which the result flag_set is created. + The flag_set contains one and only one flag that contain the specification + spec. + spec: The flag containing this spec should have a value that is smaller than + the highest value the flag can have. + + Returns: + An array of flags, each of which contains one spec in specs. All the values + of the flags are the largest values in specs, expect the one that contains + spec. + """ + + flag_set = [] + for other_spec in specs: + numeric_flag_match = flags.Search(other_spec) + # Found the spec in the array specs. + if other_spec == spec: + # Numeric flag will have a value that is smaller than the largest value + # and Boolean flag will be deleted. + if numeric_flag_match: + end = int(numeric_flag_match.group('end')) + flag_set.append(flags.Flag(other_spec, end - 2)) + + continue + + # other_spec != spec + if numeric_flag_match: + # numeric flag + end = int(numeric_flag_match.group('end')) + flag_set.append(flags.Flag(other_spec, end - 1)) + continue + + # boolean flag + flag_set.append(flags.Flag(other_spec)) + + return flag_set + + +def _GenerateAllIterativeEliminationTasks(specs): + """Generate the initial tasks for the negative flag elimination algorithm. + + Generate the base line task that turns on all the boolean flags and sets the + value to be the largest value for the numeric flag. + + For example, if the specs are [-finline-limit=[1-1000], -fstrict-aliasing], + the base line is [-finline-limit=[1-1000]:-finline-limit=999, + -fstrict-aliasing:-fstrict-aliasing] + + Generate a set of task, each turns off one of the flag or sets a value that is + smaller than the largest value for the flag. + + Args: + specs: an array of specifications from which the result flag_set is created. + + Returns: + An array containing one generation of the initial tasks for the negative + flag elimination algorithm. + """ + + # The set of tasks to be generated. + results = set([]) + flag_set = [] + + for spec in specs: + numeric_flag_match = flags.Search(spec) + if numeric_flag_match: + # Numeric flag. + end_value = int(numeric_flag_match.group('end')) + flag_set.append(flags.Flag(spec, end_value - 1)) + continue + + # Boolean flag. + flag_set.append(flags.Flag(spec)) + + # The base line task that set all the flags to their largest values. + parent_task = Task(flags.FlagSet(flag_set)) + results.add(parent_task) + + for spec in specs: + results.add(Task(flags.FlagSet(_GenerateInitialFlags(specs, spec)))) + + return [IterativeEliminationFirstGeneration(results, parent_task)] + + +def _ComputeCost(cost_func, specs, flag_set): + """Compute the mock cost of the flag_set using the input cost function. + + All the boolean flags in the specs will be enabled and all the numeric flag + with have the largest legal value. + + Args: + cost_func: The cost function which is used to compute the mock cost of a + dictionary of flags. + specs: All the specs that are used in the algorithm. This is used to check + whether certain flag is disabled in the flag_set dictionary. + flag_set: a dictionary of the spec and flag pairs. + + Returns: + The mock cost of the input dictionary of the flags. + """ + + values = [] + + for spec in specs: + # If a flag is enabled, its value is added. Otherwise a padding 0 is added. + values.append(flag_set[spec].GetValue() if spec in flag_set else 0) + + # The cost function string can use the values array. + return eval(cost_func) + + +def _GenerateTestFlags(num_flags, upper_bound, file_name): + """Generate a set of mock flags and write it to a configuration file. + + Generate a set of mock flags + + Args: + num_flags: Number of numeric flags to be generated. + upper_bound: The value of the upper bound of the range. + file_name: The configuration file name into which the mock flags are put. + """ + + with open(file_name, 'w') as output_file: + num_flags = int(num_flags) + upper_bound = int(upper_bound) + for i in range(num_flags): + output_file.write('%s=[1-%d]\n' % (i, upper_bound)) + + +def _TestAlgorithm(cost_func, specs, generations, best_result): + """Test the best result the algorithm should return. + + Set up the framework, run the input algorithm and verify the result. + + Args: + cost_func: The cost function which is used to compute the mock cost of a + dictionary of flags. + specs: All the specs that are used in the algorithm. This is used to check + whether certain flag is disabled in the flag_set dictionary. + generations: The initial generations to be evaluated. + best_result: The expected best result of the algorithm. If best_result is + -1, the algorithm may or may not return the best value. Therefore, no + assertion will be inserted. + """ + + # Set up the utilities to test the framework. + manager = multiprocessing.Manager() + input_queue = manager.Queue() + output_queue = manager.Queue() + pp_steer = multiprocessing.Process( + target=Steering, + args=(set(), generations, output_queue, input_queue)) + pp_steer.start() + + # The best result of the algorithm so far. + result = sys.maxint + + while True: + task = input_queue.get() + + # POISONPILL signal the ends of the algorithm. + if task == pipeline_process.POISONPILL: + break + + task.SetResult(BUILD_STAGE, (0, 0, 0, 0, 0)) + + # Compute the mock cost for the task. + task_result = _ComputeCost(cost_func, specs, task.GetFlags()) + task.SetResult(TEST_STAGE, task_result) + + # If the mock result of the current task is the best so far, set this + # result to be the best result. + if task_result < result: + result = task_result + + output_queue.put(task) + + pp_steer.join() + + # Only do this test when best_result is not -1. + if best_result != -1: + assert best_result == result + + +class MockAlgorithmsTest(unittest.TestCase): + """This class mock tests different steering algorithms. + + The steering algorithms are responsible for generating the next set of tasks + to run in each iteration. This class does a functional testing on the + algorithms. It mocks out the computation of the fitness function from the + build and test phases by letting the user define the fitness function. + """ + + def _GenerateFlagSpecifications(self): + """Generate the testing specifications.""" + + mock_test_file = 'scale_mock_test' + _GenerateTestFlags(NUM_FLAGS, FLAG_RANGES, mock_test_file) + return flags.ReadConf(mock_test_file) + + def testBestHillClimb(self): + """Test the best hill climb algorithm. + + Test whether it finds the best results as expected. + """ + + # Initiate the build/test command and the log directory. + Task.InitLogCommand(None, None, 'output') + + # Generate the testing specs. + specs = self._GenerateFlagSpecifications() + + # Generate the initial generations for a test whose cost function is the + # summation of the values of all the flags. + generation_tasks = _GenerateAllFlagsTasks(specs) + generations = [HillClimbingBestBranch(generation_tasks, set([]), specs)] + + # Test the algorithm. The cost function is the summation of all the values + # of all the flags. Therefore, the best value is supposed to be 0, i.e., + # when all the flags are disabled. + _TestAlgorithm('sum(values[0:len(values)])', specs, generations, 0) + + # This test uses a cost function that is the negative of the previous cost + # function. Therefore, the best result should be found in task with all the + # flags enabled. + cost_function = 'sys.maxint - sum(values[0:len(values)])' + all_flags = list(generation_tasks)[0].GetFlags() + cost = _ComputeCost(cost_function, specs, all_flags) + + # Generate the initial generations. + generation_tasks = _GenerateNoFlagTask() + generations = [HillClimbingBestBranch(generation_tasks, set([]), specs)] + + # Test the algorithm. The cost function is negative of the summation of all + # the values of all the flags. Therefore, the best value is supposed to be + # 0, i.e., when all the flags are disabled. + _TestAlgorithm(cost_function, specs, generations, cost) + + def testGeneticAlgorithm(self): + """Test the Genetic Algorithm. + + Do a functional testing here and see how well it scales. + """ + + # Initiate the build/test command and the log directory. + Task.InitLogCommand(None, None, 'output') + + # Generate the testing specs. + specs = self._GenerateFlagSpecifications() + # Initiate the build/test command and the log directory. + GAGeneration.InitMetaData(STOP_THRESHOLD, NUM_CHROMOSOMES, NUM_TRIALS, + specs, MUTATION_RATE) + + # Generate the initial generations. + generation_tasks = GenerateRandomGATasks(specs, NUM_CHROMOSOMES, NUM_TRIALS) + generations = [GAGeneration(generation_tasks, set([]), 0)] + + # Test the algorithm. + _TestAlgorithm('sum(values[0:len(values)])', specs, generations, -1) + cost_func = 'sys.maxint - sum(values[0:len(values)])' + _TestAlgorithm(cost_func, specs, generations, -1) + + def testIterativeElimination(self): + """Test the iterative elimination algorithm. + + Test whether it finds the best results as expected. + """ + + # Initiate the build/test command and the log directory. + Task.InitLogCommand(None, None, 'output') + + # Generate the testing specs. + specs = self._GenerateFlagSpecifications() + + # Generate the initial generations. The generation contains the base line + # task that turns on all the flags and tasks that each turn off one of the + # flags. + generations = _GenerateAllIterativeEliminationTasks(specs) + + # Test the algorithm. The cost function is the summation of all the values + # of all the flags. Therefore, the best value is supposed to be 0, i.e., + # when all the flags are disabled. + _TestAlgorithm('sum(values[0:len(values)])', specs, generations, 0) + + # This test uses a cost function that is the negative of the previous cost + # function. Therefore, the best result should be found in task with all the + # flags enabled. + all_flags_tasks = _GenerateAllFlagsTasks(specs) + cost_function = 'sys.maxint - sum(values[0:len(values)])' + # Compute the cost of the task that turns on all the flags. + all_flags = list(all_flags_tasks)[0].GetFlags() + cost = _ComputeCost(cost_function, specs, all_flags) + + # Test the algorithm. The cost function is negative of the summation of all + # the values of all the flags. Therefore, the best value is supposed to be + # 0, i.e., when all the flags are disabled. + # The concrete type of the generation decides how the next generation will + # be generated. + _TestAlgorithm(cost_function, specs, generations, cost) + + +if __name__ == '__main__': + unittest.main() |