skia/tools/calmbench/ab.py at main · AndroidHighQualityCodeStudy/skia · GitHub

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#!/usr/bin/python

# encoding: utf-8

# Use of this source code is governed by a BSD-style license that can be found

# in the LICENSE file.

# This is an A/B test utility script used by calmbench.py

# For each bench, we get a distribution of min_ms measurements from nanobench.

# From that, we try to recover the 1/3 and 2/3 quantiles of the distribution.

# If range (1/3 quantile, 2/3 quantile) is completely disjoint between A and B,

# we report that as a regression.

# The more measurements we have for a bench, the more accurate our quantiles

# are. However, taking more measurements is time consuming. Hence we'll prune

# out benches and only take more measurements for benches whose current quantile

# ranges are disjoint.

# P.S. The current script is brute forcely translated from a ruby script. So it

# may be ugly...

from __future__ import print_function

import re

import os

import sys

import time

import json

import subprocess

import shlex

import multiprocessing

import traceback

from argparse import ArgumentParser

from multiprocessing import Process

from threading import Thread

from threading import Lock

from pdb import set_trace

HELP = """

\033[31mPlease call calmbench.py to drive this script if you're not doing so.

This script is not supposed to be used by itself. (At least, it's not easy to

use by itself. The calmbench bots may use this script directly.)

\033[0m

"""

FACTOR = 3 # lower/upper quantile factor

DIFF_T = 0.99 # different enough threshold

TERM = 10 # terminate after this no. of iterations without suspect changes

MAXTRY = 30 # max number of nanobench tries to narrow down suspects

UNITS = "ns µs ms s".split()

timesLock = Lock()

timesA = {}

timesB = {}

def parse_args():

parser = ArgumentParser(description=HELP)

parser.add_argument('outdir', type=str, help="output directory")

parser.add_argument('a', type=str, help="name of A")

parser.add_argument('b', type=str, help="name of B")

parser.add_argument('nano_a', type=str, help="path to A's nanobench binary")

parser.add_argument('nano_b', type=str, help="path to B's nanobench binary")

parser.add_argument('arg_a', type=str, help="args for A's nanobench run")

parser.add_argument('arg_b', type=str, help="args for B's nanobench run")

parser.add_argument('repeat', type=int, help="number of initial runs")

parser.add_argument('skip_b', type=str, help=("whether to skip running B"

" ('true' or 'false')"))

parser.add_argument('config', type=str, help="nanobenh config")

parser.add_argument('threads', type=int, help="number of threads to run")

parser.add_argument('noinit', type=str, help=("whether to skip running B"

" ('true' or 'false')"))

parser.add_argument('--concise', dest='concise', action="store_true",

help="If set, no verbose thread info will be printed.")

parser.set_defaults(concise=False)

# Additional args for bots

BHELP = "bot specific options"

parser.add_argument('--githash', type=str, default="", help=BHELP)

parser.add_argument('--keys', type=str, default=[], nargs='+', help=BHELP)

args = parser.parse_args()

args.skip_b = args.skip_b == "true"

args.noinit = args.noinit == "true"

if args.threads == -1:

args.threads = 1

if args.config in ["8888", "565"]: # multi-thread for CPU only

args.threads = max(1, multiprocessing.cpu_count() / 2)

return args

def append_dict_sorted_array(dict_array, key, value):

if key not in dict_array:

dict_array[key] = []

dict_array[key].append(value)

dict_array[key].sort()

def add_time(args, name, bench, t, unit):

normalized_t = t * 1000 ** UNITS.index(unit);

if name.startswith(args.a):

append_dict_sorted_array(timesA, bench, normalized_t)

else:

append_dict_sorted_array(timesB, bench, normalized_t)

def append_times_from_file(args, name, filename):

with open(filename) as f:

lines = f.readlines()

for line in lines:

items = line.split()

if len(items) > 10:

bench = items[10]

matches = re.search("([+-]?\d*.?\d+)(s|ms|µs|ns)", items[3])

if (not matches or items[9] != args.config):

continue

time_num = matches.group(1)

time_unit = matches.group(2)

add_time(args, name, bench, float(time_num), time_unit)

class ThreadWithException(Thread):

def __init__(self, target):

super(ThreadWithException, self).__init__(target = target)

self.exception = None

def run(self):

try:

self._Thread__target(*self._Thread__args, **self._Thread__kwargs)

except BaseException as e:

self.exception = e

def join(self, timeout=None):

super(ThreadWithException, self).join(timeout)

class ThreadRunner:

"""Simplest and stupidiest threaded executer."""

def __init__(self, args):

self.concise = args.concise

self.threads = []

def add(self, args, fn):

if len(self.threads) >= args.threads:

self.wait()

t = ThreadWithException(target = fn)

t.daemon = True

self.threads.append(t)

t.start()

def wait(self):

def spin():

i = 0

spinners = [". ", ".. ", "..."]

while len(self.threads) > 0:

timesLock.acquire()

sys.stderr.write(

"\r" + spinners[i % len(spinners)] +

" (%d threads running)" % len(self.threads) +

" \r" # spaces for erasing characters

)

timesLock.release()

time.sleep(0.5)

i += 1

if not self.concise:

ts = Thread(target = spin);

ts.start()

for t in self.threads:

t.join()

exceptions = []

for t in self.threads:

if t.exception:

exceptions.append(t.exception)

self.threads = []

if not self.concise:

ts.join()

if len(exceptions):

for exc in exceptions:

print(exc)

raise exceptions[0]

def split_arg(arg):

raw = shlex.split(arg)

result = []

for r in raw:

if '~' in r:

result.append(os.path.expanduser(r))

else:

result.append(r)

return result

def run(args, threadRunner, name, nano, arg, i):

def task():

file_i = "%s/%s.out%d" % (args.outdir, name, i)

should_run = not args.noinit and not (name == args.b and args.skip_b)

if i <= 0:

should_run = True # always run for suspects

if should_run:

if i > 0:

timesLock.acquire()

print("Init run %d for %s..." % (i, name))

timesLock.release()

subprocess.check_call(["touch", file_i])

with open(file_i, 'w') as f:

subprocess.check_call([nano] + split_arg(arg) +

["--config", args.config], stderr=f, stdout=f)

timesLock.acquire()

append_times_from_file(args, name, file_i)

timesLock.release()

threadRunner.add(args, task)

def init_run(args):

threadRunner = ThreadRunner(args)

for i in range(1, max(args.repeat, args.threads / 2) + 1):

run(args, threadRunner, args.a, args.nano_a, args.arg_a, i)

run(args, threadRunner, args.b, args.nano_b, args.arg_b, i)

threadRunner.wait()

def get_lower_upper(values):

i = max(0, (len(values) - 1) / FACTOR)

return values[i], values[-i - 1]

def different_enough(lower1, upper2):

return upper2 < DIFF_T * lower1

# TODO(liyuqian): we used this hacky criteria mainly because that I didn't have

# time to study more rigorous statistical tests. We should adopt a more rigorous

# test in the future.

def get_suspects():

suspects = []

for bench in timesA.keys():

if bench not in timesB:

continue

lowerA, upperA = get_lower_upper(timesA[bench])

lowerB, upperB = get_lower_upper(timesB[bench])

if different_enough(lowerA, upperB) or different_enough(lowerB, upperA):

suspects.append(bench)

return suspects

def process_bench_pattern(s):

if ".skp" in s: # skp bench won't match their exact names...

return "^\"" + s[0:(s.index(".skp") + 3)] + "\""

else:

return "^\"" + s + "\"$"

def suspects_arg(suspects):

patterns = map(process_bench_pattern, suspects)

return " --match " + (" ".join(patterns))

def median(array):

return array[len(array) / 2]

def regression(bench):

a = median(timesA[bench])

b = median(timesB[bench])

if (a == 0): # bad bench, just return no regression

return 1

return b / a

def percentage(x):

return (x - 1) * 100

def format_r(r):

return ('%6.2f' % percentage(r)) + "%"

def normalize_r(r):

if r > 1.0:

return r - 1.0

else:

return 1.0 - 1/r

def test():

args = parse_args()

init_run(args)

last_unchanged_iter = 0

last_suspect_number = -1

tryCnt = 0

it = 0

while tryCnt < MAXTRY:

it += 1

suspects = get_suspects()

if len(suspects) != last_suspect_number:

last_suspect_number = len(suspects)

last_unchanged_iter = it

if (len(suspects) == 0 or it - last_unchanged_iter >= TERM):

break

print("Number of suspects at iteration %d: %d" % (it, len(suspects)))

threadRunner = ThreadRunner(args)

for j in range(1, max(1, args.threads / 2) + 1):

run(args, threadRunner, args.a, args.nano_a,

args.arg_a + suspects_arg(suspects), -j)

run(args, threadRunner, args.b, args.nano_b,

args.arg_b + suspects_arg(suspects), -j)

tryCnt += 1

threadRunner.wait()

suspects = get_suspects()

if len(suspects) == 0:

print(("%s and %s does not seem to have significant " + \

"performance differences.") % (args.a, args.b))

else:

suspects.sort(key = regression)

print("%s (compared to %s) is likely" % (args.a, args.b))

for suspect in suspects:

r = regression(suspect)

if r < 1:

print("\033[31m %s slower in %s\033[0m" % (format_r(1/r), suspect))

else:

print("\033[32m %s faster in %s\033[0m" % (format_r(r), suspect))

with open("%s/bench_%s_%s.json" % (args.outdir, args.a, args.b), 'w') as f:

results = {}

for bench in timesA:

r = regression(bench) if bench in suspects else 1.0

results[bench] = {

args.config: {

"signed_regression": normalize_r(r),

"lower_quantile_ms": get_lower_upper(timesA[bench])[0] * 1e-6,

"upper_quantile_ms": get_lower_upper(timesA[bench])[1] * 1e-6,

"options": {

# TODO(liyuqian): let ab.py call nanobench with --outResultsFile so

# nanobench could generate the json for us that's exactly the same

# as that being used by perf bots. Currently, we cannot guarantee

# that bench is the name (e.g., bench may have additional resolution

# information appended after name).

"name": bench

}

output = {"results": results}

if args.githash:

output["gitHash"] = args.githash

if args.keys:

keys = {}

for i in range(len(args.keys) / 2):

keys[args.keys[i * 2]] = args.keys[i * 2 + 1]

output["key"] = keys

f.write(json.dumps(output, indent=4))

print(("\033[36mJSON results available in %s\033[0m" % f.name))

with open("%s/bench_%s_%s.csv" % (args.outdir, args.a, args.b), 'w') as out:

out.write(("bench, significant?, raw regresion, " +

"%(A)s quantile (ns), %(B)s quantile (ns), " +

"%(A)s (ns), %(B)s (ns)\n") % {'A': args.a, 'B': args.b})

for bench in suspects + timesA.keys():

if (bench not in timesA or bench not in timesB):

continue

ta = timesA[bench]

tb = timesB[bench]

out.write(

"%s, %s, %f, " % (bench, bench in suspects, regression(bench)) +

' '.join(map(str, get_lower_upper(ta))) + ", " +

' '.join(map(str, get_lower_upper(tb))) + ", " +

("%s, %s\n" % (' '.join(map(str, ta)), ' '.join(map(str, tb))))

)

print(("\033[36m" +

"Compared %d benches. " +

"%d of them seem to be significantly differrent." +

"\033[0m") %

(len([x for x in timesA if x in timesB]), len(suspects)))

print("\033[36mPlease see detailed bench results in %s\033[0m" % out.name)

if __name__ == "__main__":

try:

test()

except Exception as e:

print(e)

print(HELP)

traceback.print_exc()

raise e

skia/tools/calmbench/ab.py at main · AndroidHighQualityCodeStudy/skia · GitHub

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