1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260
|
#!/usr/bin/env python3
#
# Copyright 2017 gRPC authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Computes the diff between two bm runs and outputs significant results """
import argparse
import collections
import json
import os
import subprocess
import sys
sys.path.append(os.path.join(os.path.dirname(sys.argv[0]), '..'))
import bm_constants
import bm_json
import bm_speedup
import tabulate
verbose = False
def _median(ary):
assert (len(ary))
ary = sorted(ary)
n = len(ary)
if n % 2 == 0:
return (ary[(n - 1) // 2] + ary[(n - 1) // 2 + 1]) / 2.0
else:
return ary[n // 2]
def _args():
argp = argparse.ArgumentParser(
description='Perform diff on microbenchmarks')
argp.add_argument('-t',
'--track',
choices=sorted(bm_constants._INTERESTING),
nargs='+',
default=sorted(bm_constants._INTERESTING),
help='Which metrics to track')
argp.add_argument('-b',
'--benchmarks',
nargs='+',
choices=bm_constants._AVAILABLE_BENCHMARK_TESTS,
default=bm_constants._AVAILABLE_BENCHMARK_TESTS,
help='Which benchmarks to run')
argp.add_argument(
'-l',
'--loops',
type=int,
default=20,
help=
'Number of times to loops the benchmarks. Must match what was passed to bm_run.py'
)
argp.add_argument('-r',
'--regex',
type=str,
default="",
help='Regex to filter benchmarks run')
argp.add_argument('-n', '--new', type=str, help='New benchmark name')
argp.add_argument('-o', '--old', type=str, help='Old benchmark name')
argp.add_argument('-v',
'--verbose',
type=bool,
help='Print details of before/after')
args = argp.parse_args()
global verbose
if args.verbose:
verbose = True
assert args.new
assert args.old
return args
def _maybe_print(str):
if verbose:
print(str)
class Benchmark:
def __init__(self):
self.samples = {
True: collections.defaultdict(list),
False: collections.defaultdict(list)
}
self.final = {}
self.speedup = {}
def add_sample(self, track, data, new):
for f in track:
if f in data:
self.samples[new][f].append(float(data[f]))
def process(self, track, new_name, old_name):
for f in sorted(track):
new = self.samples[True][f]
old = self.samples[False][f]
if not new or not old:
continue
mdn_diff = abs(_median(new) - _median(old))
_maybe_print('%s: %s=%r %s=%r mdn_diff=%r' %
(f, new_name, new, old_name, old, mdn_diff))
s = bm_speedup.speedup(new, old, 1e-5)
self.speedup[f] = s
if abs(s) > 3:
if mdn_diff > 0.5:
self.final[f] = '%+d%%' % s
return self.final.keys()
def skip(self):
return not self.final
def row(self, flds):
return [self.final[f] if f in self.final else '' for f in flds]
def speedup(self, name):
if name in self.speedup:
return self.speedup[name]
return None
def _read_json(filename, badjson_files, nonexistant_files):
stripped = ".".join(filename.split(".")[:-2])
try:
with open(filename) as f:
r = f.read()
return json.loads(r)
except IOError as e:
if stripped in nonexistant_files:
nonexistant_files[stripped] += 1
else:
nonexistant_files[stripped] = 1
return None
except ValueError as e:
print(r)
if stripped in badjson_files:
badjson_files[stripped] += 1
else:
badjson_files[stripped] = 1
return None
def fmt_dict(d):
return ''.join([" " + k + ": " + str(d[k]) + "\n" for k in d])
def diff(bms, loops, regex, track, old, new):
benchmarks = collections.defaultdict(Benchmark)
badjson_files = {}
nonexistant_files = {}
for bm in bms:
for loop in range(0, loops):
for line in subprocess.check_output([
'bm_diff_%s/opt/%s' % (old, bm), '--benchmark_list_tests',
'--benchmark_filter=%s' % regex
]).splitlines():
line = line.decode('UTF-8')
stripped_line = line.strip().replace("/", "_").replace(
"<", "_").replace(">", "_").replace(", ", "_")
js_new_opt = _read_json(
'%s.%s.opt.%s.%d.json' % (bm, stripped_line, new, loop),
badjson_files, nonexistant_files)
js_old_opt = _read_json(
'%s.%s.opt.%s.%d.json' % (bm, stripped_line, old, loop),
badjson_files, nonexistant_files)
for row in bm_json.expand_json(js_new_opt):
name = row['cpp_name']
if name.endswith('_mean') or name.endswith('_stddev'):
continue
benchmarks[name].add_sample(track, row, True)
for row in bm_json.expand_json(js_old_opt):
name = row['cpp_name']
if name.endswith('_mean') or name.endswith('_stddev'):
continue
benchmarks[name].add_sample(track, row, False)
really_interesting = set()
for name, bm in benchmarks.items():
_maybe_print(name)
really_interesting.update(bm.process(track, new, old))
fields = [f for f in track if f in really_interesting]
# figure out the significance of the changes... right now we take the 95%-ile
# benchmark delta %-age, and then apply some hand chosen thresholds
histogram = []
_NOISY = ["BM_WellFlushed"]
for name, bm in benchmarks.items():
if name in _NOISY:
print("skipping noisy benchmark '%s' for labelling evaluation" %
name)
if bm.skip():
continue
d = bm.speedup['cpu_time']
if d is None:
continue
histogram.append(d)
histogram.sort()
print("histogram of speedups: ", histogram)
if len(histogram) == 0:
significance = 0
else:
delta = histogram[int(len(histogram) * 0.95)]
mul = 1
if delta < 0:
delta = -delta
mul = -1
if delta < 2:
significance = 0
elif delta < 5:
significance = 1
elif delta < 10:
significance = 2
else:
significance = 3
significance *= mul
headers = ['Benchmark'] + fields
rows = []
for name in sorted(benchmarks.keys()):
if benchmarks[name].skip():
continue
rows.append([name] + benchmarks[name].row(fields))
note = None
if len(badjson_files):
note = 'Corrupt JSON data (indicates timeout or crash): \n%s' % fmt_dict(
badjson_files)
if len(nonexistant_files):
if note:
note += '\n\nMissing files (indicates new benchmark): \n%s' % fmt_dict(
nonexistant_files)
else:
note = '\n\nMissing files (indicates new benchmark): \n%s' % fmt_dict(
nonexistant_files)
if rows:
return tabulate.tabulate(rows, headers=headers,
floatfmt='+.2f'), note, significance
else:
return None, note, 0
if __name__ == '__main__':
args = _args()
diff, note = diff(args.benchmarks, args.loops, args.regex, args.track,
args.old, args.new, args.counters)
print('%s\n%s' % (note, diff if diff else "No performance differences"))
|