9127ea3488
This should let perf.skia.org get the "name" keyword instead of "test". Bug: skia:7816 Change-Id: Icb2e9f7c6109a0cfb06499ee58f9fd0cc834e5c3 Reviewed-on: https://skia-review.googlesource.com/125540 Auto-Submit: Yuqian Li <liyuqian@google.com> Commit-Queue: Joe Gregorio <jcgregorio@google.com> Reviewed-by: Joe Gregorio <jcgregorio@google.com>
408 lines
12 KiB
Python
408 lines
12 KiB
Python
#!/usr/bin/python
|
|
# encoding: utf-8
|
|
|
|
# Copyright 2017 Google Inc.
|
|
#
|
|
# 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...
|
|
|
|
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
|