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407 lines
12 KiB
407 lines
12 KiB
#!/usr/bin/python
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# encoding: utf-8
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# Copyright 2017 Google Inc.
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#
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# Use of this source code is governed by a BSD-style license that can be found
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# in the LICENSE file.
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#
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# This is an A/B test utility script used by calmbench.py
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#
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# For each bench, we get a distribution of min_ms measurements from nanobench.
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# From that, we try to recover the 1/3 and 2/3 quantiles of the distribution.
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# If range (1/3 quantile, 2/3 quantile) is completely disjoint between A and B,
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# we report that as a regression.
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#
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# The more measurements we have for a bench, the more accurate our quantiles
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# are. However, taking more measurements is time consuming. Hence we'll prune
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# out benches and only take more measurements for benches whose current quantile
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# ranges are disjoint.
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#
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# P.S. The current script is brute forcely translated from a ruby script. So it
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# may be ugly...
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from __future__ import print_function
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import re
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import os
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import sys
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import time
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import json
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import subprocess
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import shlex
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import multiprocessing
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import traceback
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from argparse import ArgumentParser
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from multiprocessing import Process
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from threading import Thread
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from threading import Lock
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from pdb import set_trace
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HELP = """
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\033[31mPlease call calmbench.py to drive this script if you're not doing so.
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This script is not supposed to be used by itself. (At least, it's not easy to
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use by itself. The calmbench bots may use this script directly.)
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\033[0m
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"""
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FACTOR = 3 # lower/upper quantile factor
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DIFF_T = 0.99 # different enough threshold
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TERM = 10 # terminate after this no. of iterations without suspect changes
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MAXTRY = 30 # max number of nanobench tries to narrow down suspects
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UNITS = "ns µs ms s".split()
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timesLock = Lock()
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timesA = {}
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timesB = {}
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def parse_args():
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parser = ArgumentParser(description=HELP)
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parser.add_argument('outdir', type=str, help="output directory")
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parser.add_argument('a', type=str, help="name of A")
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parser.add_argument('b', type=str, help="name of B")
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parser.add_argument('nano_a', type=str, help="path to A's nanobench binary")
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parser.add_argument('nano_b', type=str, help="path to B's nanobench binary")
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parser.add_argument('arg_a', type=str, help="args for A's nanobench run")
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parser.add_argument('arg_b', type=str, help="args for B's nanobench run")
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parser.add_argument('repeat', type=int, help="number of initial runs")
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parser.add_argument('skip_b', type=str, help=("whether to skip running B"
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" ('true' or 'false')"))
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parser.add_argument('config', type=str, help="nanobenh config")
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parser.add_argument('threads', type=int, help="number of threads to run")
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parser.add_argument('noinit', type=str, help=("whether to skip running B"
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" ('true' or 'false')"))
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parser.add_argument('--concise', dest='concise', action="store_true",
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help="If set, no verbose thread info will be printed.")
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parser.set_defaults(concise=False)
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# Additional args for bots
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BHELP = "bot specific options"
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parser.add_argument('--githash', type=str, default="", help=BHELP)
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parser.add_argument('--keys', type=str, default=[], nargs='+', help=BHELP)
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args = parser.parse_args()
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args.skip_b = args.skip_b == "true"
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args.noinit = args.noinit == "true"
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if args.threads == -1:
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args.threads = 1
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if args.config in ["8888", "565"]: # multi-thread for CPU only
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args.threads = max(1, multiprocessing.cpu_count() / 2)
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return args
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def append_dict_sorted_array(dict_array, key, value):
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if key not in dict_array:
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dict_array[key] = []
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dict_array[key].append(value)
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dict_array[key].sort()
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def add_time(args, name, bench, t, unit):
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normalized_t = t * 1000 ** UNITS.index(unit);
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if name.startswith(args.a):
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append_dict_sorted_array(timesA, bench, normalized_t)
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else:
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append_dict_sorted_array(timesB, bench, normalized_t)
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def append_times_from_file(args, name, filename):
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with open(filename) as f:
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lines = f.readlines()
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for line in lines:
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items = line.split()
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if len(items) > 10:
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bench = items[10]
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matches = re.search("([+-]?\d*.?\d+)(s|ms|µs|ns)", items[3])
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if (not matches or items[9] != args.config):
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continue
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time_num = matches.group(1)
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time_unit = matches.group(2)
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add_time(args, name, bench, float(time_num), time_unit)
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class ThreadWithException(Thread):
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def __init__(self, target):
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super(ThreadWithException, self).__init__(target = target)
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self.exception = None
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def run(self):
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try:
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self._Thread__target(*self._Thread__args, **self._Thread__kwargs)
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except BaseException as e:
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self.exception = e
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def join(self, timeout=None):
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super(ThreadWithException, self).join(timeout)
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class ThreadRunner:
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"""Simplest and stupidiest threaded executer."""
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def __init__(self, args):
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self.concise = args.concise
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self.threads = []
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def add(self, args, fn):
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if len(self.threads) >= args.threads:
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self.wait()
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t = ThreadWithException(target = fn)
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t.daemon = True
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self.threads.append(t)
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t.start()
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def wait(self):
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def spin():
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i = 0
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spinners = [". ", ".. ", "..."]
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while len(self.threads) > 0:
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timesLock.acquire()
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sys.stderr.write(
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"\r" + spinners[i % len(spinners)] +
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" (%d threads running)" % len(self.threads) +
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" \r" # spaces for erasing characters
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)
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timesLock.release()
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time.sleep(0.5)
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i += 1
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if not self.concise:
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ts = Thread(target = spin);
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ts.start()
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for t in self.threads:
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t.join()
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exceptions = []
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for t in self.threads:
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if t.exception:
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exceptions.append(t.exception)
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self.threads = []
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if not self.concise:
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ts.join()
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if len(exceptions):
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for exc in exceptions:
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print(exc)
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raise exceptions[0]
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def split_arg(arg):
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raw = shlex.split(arg)
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result = []
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for r in raw:
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if '~' in r:
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result.append(os.path.expanduser(r))
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else:
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result.append(r)
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return result
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def run(args, threadRunner, name, nano, arg, i):
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def task():
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file_i = "%s/%s.out%d" % (args.outdir, name, i)
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should_run = not args.noinit and not (name == args.b and args.skip_b)
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if i <= 0:
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should_run = True # always run for suspects
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if should_run:
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if i > 0:
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timesLock.acquire()
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print("Init run %d for %s..." % (i, name))
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timesLock.release()
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subprocess.check_call(["touch", file_i])
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with open(file_i, 'w') as f:
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subprocess.check_call([nano] + split_arg(arg) +
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["--config", args.config], stderr=f, stdout=f)
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timesLock.acquire()
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append_times_from_file(args, name, file_i)
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timesLock.release()
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threadRunner.add(args, task)
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def init_run(args):
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threadRunner = ThreadRunner(args)
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for i in range(1, max(args.repeat, args.threads / 2) + 1):
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run(args, threadRunner, args.a, args.nano_a, args.arg_a, i)
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run(args, threadRunner, args.b, args.nano_b, args.arg_b, i)
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threadRunner.wait()
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def get_lower_upper(values):
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i = max(0, (len(values) - 1) / FACTOR)
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return values[i], values[-i - 1]
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def different_enough(lower1, upper2):
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return upper2 < DIFF_T * lower1
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# TODO(liyuqian): we used this hacky criteria mainly because that I didn't have
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# time to study more rigorous statistical tests. We should adopt a more rigorous
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# test in the future.
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def get_suspects():
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suspects = []
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for bench in timesA.keys():
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if bench not in timesB:
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continue
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lowerA, upperA = get_lower_upper(timesA[bench])
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lowerB, upperB = get_lower_upper(timesB[bench])
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if different_enough(lowerA, upperB) or different_enough(lowerB, upperA):
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suspects.append(bench)
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return suspects
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def process_bench_pattern(s):
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if ".skp" in s: # skp bench won't match their exact names...
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return "^\"" + s[0:(s.index(".skp") + 3)] + "\""
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else:
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return "^\"" + s + "\"$"
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def suspects_arg(suspects):
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patterns = map(process_bench_pattern, suspects)
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return " --match " + (" ".join(patterns))
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def median(array):
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return array[len(array) / 2]
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def regression(bench):
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a = median(timesA[bench])
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b = median(timesB[bench])
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if (a == 0): # bad bench, just return no regression
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return 1
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return b / a
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def percentage(x):
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return (x - 1) * 100
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def format_r(r):
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return ('%6.2f' % percentage(r)) + "%"
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def normalize_r(r):
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if r > 1.0:
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return r - 1.0
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else:
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return 1.0 - 1/r
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def test():
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args = parse_args()
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init_run(args)
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last_unchanged_iter = 0
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last_suspect_number = -1
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tryCnt = 0
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it = 0
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while tryCnt < MAXTRY:
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it += 1
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suspects = get_suspects()
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if len(suspects) != last_suspect_number:
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last_suspect_number = len(suspects)
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last_unchanged_iter = it
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if (len(suspects) == 0 or it - last_unchanged_iter >= TERM):
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break
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print("Number of suspects at iteration %d: %d" % (it, len(suspects)))
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threadRunner = ThreadRunner(args)
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for j in range(1, max(1, args.threads / 2) + 1):
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run(args, threadRunner, args.a, args.nano_a,
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args.arg_a + suspects_arg(suspects), -j)
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run(args, threadRunner, args.b, args.nano_b,
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args.arg_b + suspects_arg(suspects), -j)
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tryCnt += 1
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threadRunner.wait()
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suspects = get_suspects()
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if len(suspects) == 0:
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print(("%s and %s does not seem to have significant " + \
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"performance differences.") % (args.a, args.b))
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else:
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suspects.sort(key = regression)
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print("%s (compared to %s) is likely" % (args.a, args.b))
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for suspect in suspects:
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r = regression(suspect)
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if r < 1:
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print("\033[31m %s slower in %s\033[0m" % (format_r(1/r), suspect))
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else:
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print("\033[32m %s faster in %s\033[0m" % (format_r(r), suspect))
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with open("%s/bench_%s_%s.json" % (args.outdir, args.a, args.b), 'w') as f:
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results = {}
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for bench in timesA:
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r = regression(bench) if bench in suspects else 1.0
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results[bench] = {
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args.config: {
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"signed_regression": normalize_r(r),
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"lower_quantile_ms": get_lower_upper(timesA[bench])[0] * 1e-6,
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"upper_quantile_ms": get_lower_upper(timesA[bench])[1] * 1e-6,
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"options": {
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# TODO(liyuqian): let ab.py call nanobench with --outResultsFile so
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# nanobench could generate the json for us that's exactly the same
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# as that being used by perf bots. Currently, we cannot guarantee
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# that bench is the name (e.g., bench may have additional resolution
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# information appended after name).
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"name": bench
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}
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}
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}
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output = {"results": results}
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if args.githash:
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output["gitHash"] = args.githash
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if args.keys:
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keys = {}
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for i in range(len(args.keys) / 2):
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keys[args.keys[i * 2]] = args.keys[i * 2 + 1]
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output["key"] = keys
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f.write(json.dumps(output, indent=4))
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print(("\033[36mJSON results available in %s\033[0m" % f.name))
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with open("%s/bench_%s_%s.csv" % (args.outdir, args.a, args.b), 'w') as out:
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out.write(("bench, significant?, raw regresion, " +
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"%(A)s quantile (ns), %(B)s quantile (ns), " +
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"%(A)s (ns), %(B)s (ns)\n") % {'A': args.a, 'B': args.b})
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for bench in suspects + timesA.keys():
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if (bench not in timesA or bench not in timesB):
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continue
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ta = timesA[bench]
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tb = timesB[bench]
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out.write(
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"%s, %s, %f, " % (bench, bench in suspects, regression(bench)) +
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' '.join(map(str, get_lower_upper(ta))) + ", " +
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' '.join(map(str, get_lower_upper(tb))) + ", " +
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("%s, %s\n" % (' '.join(map(str, ta)), ' '.join(map(str, tb))))
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)
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print(("\033[36m" +
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"Compared %d benches. " +
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"%d of them seem to be significantly differrent." +
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"\033[0m") %
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(len([x for x in timesA if x in timesB]), len(suspects)))
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print("\033[36mPlease see detailed bench results in %s\033[0m" % out.name)
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if __name__ == "__main__":
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try:
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test()
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except Exception as e:
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print(e)
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print(HELP)
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traceback.print_exc()
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raise e
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