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# 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 variations of the hill climbing algorithms.
"""
__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 hill_climb_best_neighbor import HillClimbingBestBranch
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
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:
result = flags.Search(spec)
if result:
# Numeric flags.
start = int(result.group('start'))
end = int(result.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:
result = flags.Search(spec)
value = (int(result.group('end')) - 1) if result else -1
flag_set.append(Flag(spec, value))
return set([Task(FlagSet(flag_set))])
def _GenerateNoFlagTask():
return set([Task(FlagSet([]))])
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.
"""
# 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()
assert best_result == result
class FlagAlgorithms(unittest.TestCase):
"""This class test the FlagSet class."""
def testBestHillClimb(self):
"""Test the equal method of the Class FlagSet.
Two FlagSet instances are equal if all their flags are equal.
"""
# Initiate the build/test command and the log directory.
Task.InitLogCommand(None, None, 'output')
# Generate the testing specs.
mock_test_file = 'scale_mock_test'
_GenerateTestFlags(NUM_FLAGS, FLAG_RANGES, mock_test_file)
specs = flags.ReadConf(mock_test_file)
# 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 = '-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)
if __name__ == '__main__':
unittest.main()
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