deps/libchdr/deps/zstd-1.5.5/tests/automated_benchmarking.py

   1 # ################################################################
   2 # Copyright (c) Meta Platforms, Inc. and affiliates.
   3 # All rights reserved.
   4 #
   5 # This source code is licensed under both the BSD-style license (found in the
   6 # LICENSE file in the root directory of this source tree) and the GPLv2 (found
   7 # in the COPYING file in the root directory of this source tree).
   8 # You may select, at your option, one of the above-listed licenses.
   9 # ##########################################################################
  10
  11 import argparse
  12 import glob
  13 import json
  14 import os
  15 import time
  16 import pickle as pk
  17 import subprocess
  18 import urllib.request
  19
  20
  21 GITHUB_API_PR_URL = "https://api.github.com/repos/facebook/zstd/pulls?state=open"
  22 GITHUB_URL_TEMPLATE = "https://github.com/{}/zstd"
  23 RELEASE_BUILD = {"user": "facebook", "branch": "dev", "hash": None}
  24
  25 # check to see if there are any new PRs every minute
  26 DEFAULT_MAX_API_CALL_FREQUENCY_SEC = 60
  27 PREVIOUS_PRS_FILENAME = "prev_prs.pk"
  28
  29 # Not sure what the threshold for triggering alarms should be
  30 # 1% regression sounds like a little too sensitive but the desktop
  31 # that I'm running it on is pretty stable so I think this is fine
  32 CSPEED_REGRESSION_TOLERANCE = 0.01
  33 DSPEED_REGRESSION_TOLERANCE = 0.01
  34
  35
  36 def get_new_open_pr_builds(prev_state=True):
  37     prev_prs = None
  38     if os.path.exists(PREVIOUS_PRS_FILENAME):
  39         with open(PREVIOUS_PRS_FILENAME, "rb") as f:
  40             prev_prs = pk.load(f)
  41     data = json.loads(urllib.request.urlopen(GITHUB_API_PR_URL).read().decode("utf-8"))
  42     prs = {
  43         d["url"]: {
  44             "user": d["user"]["login"],
  45             "branch": d["head"]["ref"],
  46             "hash": d["head"]["sha"].strip(),
  47         }
  48         for d in data
  49     }
  50     with open(PREVIOUS_PRS_FILENAME, "wb") as f:
  51         pk.dump(prs, f)
  52     if not prev_state or prev_prs == None:
  53         return list(prs.values())
  54     return [pr for url, pr in prs.items() if url not in prev_prs or prev_prs[url] != pr]
  55
  56
  57 def get_latest_hashes():
  58     tmp = subprocess.run(["git", "log", "-1"], stdout=subprocess.PIPE).stdout.decode(
  59         "utf-8"
  60     )
  61     sha1 = tmp.split("\n")[0].split(" ")[1]
  62     tmp = subprocess.run(
  63         ["git", "show", "{}^1".format(sha1)], stdout=subprocess.PIPE
  64     ).stdout.decode("utf-8")
  65     sha2 = tmp.split("\n")[0].split(" ")[1]
  66     tmp = subprocess.run(
  67         ["git", "show", "{}^2".format(sha1)], stdout=subprocess.PIPE
  68     ).stdout.decode("utf-8")
  69     sha3 = "" if len(tmp) == 0 else tmp.split("\n")[0].split(" ")[1]
  70     return [sha1.strip(), sha2.strip(), sha3.strip()]
  71
  72
  73 def get_builds_for_latest_hash():
  74     hashes = get_latest_hashes()
  75     for b in get_new_open_pr_builds(False):
  76         if b["hash"] in hashes:
  77             return [b]
  78     return []
  79
  80
  81 def clone_and_build(build):
  82     if build["user"] != None:
  83         github_url = GITHUB_URL_TEMPLATE.format(build["user"])
  84         os.system(
  85             """
  86             rm -rf zstd-{user}-{sha} &&
  87             git clone {github_url} zstd-{user}-{sha} &&
  88             cd zstd-{user}-{sha} &&
  89             {checkout_command}
  90             make -j &&
  91             cd ../
  92         """.format(
  93                 user=build["user"],
  94                 github_url=github_url,
  95                 sha=build["hash"],
  96                 checkout_command="git checkout {} &&".format(build["hash"])
  97                 if build["hash"] != None
  98                 else "",
  99             )
 100         )
 101         return "zstd-{user}-{sha}/zstd".format(user=build["user"], sha=build["hash"])
 102     else:
 103         os.system("cd ../ && make -j && cd tests")
 104         return "../zstd"
 105
 106
 107 def parse_benchmark_output(output):
 108     idx = [i for i, d in enumerate(output) if d == "MB/s"]
 109     return [float(output[idx[0] - 1]), float(output[idx[1] - 1])]
 110
 111
 112 def benchmark_single(executable, level, filename):
 113     return parse_benchmark_output((
 114         subprocess.run(
 115             [executable, "-qb{}".format(level), filename], stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
 116         )
 117         .stdout.decode("utf-8")
 118         .split(" ")
 119     ))
 120
 121
 122 def benchmark_n(executable, level, filename, n):
 123     speeds_arr = [benchmark_single(executable, level, filename) for _ in range(n)]
 124     cspeed, dspeed = max(b[0] for b in speeds_arr), max(b[1] for b in speeds_arr)
 125     print(
 126         "Bench (executable={} level={} filename={}, iterations={}):\n\t[cspeed: {} MB/s, dspeed: {} MB/s]".format(
 127             os.path.basename(executable),
 128             level,
 129             os.path.basename(filename),
 130             n,
 131             cspeed,
 132             dspeed,
 133         )
 134     )
 135     return (cspeed, dspeed)
 136
 137
 138 def benchmark(build, filenames, levels, iterations):
 139     executable = clone_and_build(build)
 140     return [
 141         [benchmark_n(executable, l, f, iterations) for f in filenames] for l in levels
 142     ]
 143
 144
 145 def benchmark_dictionary_single(executable, filenames_directory, dictionary_filename, level, iterations):
 146     cspeeds, dspeeds = [], []
 147     for _ in range(iterations):
 148         output = subprocess.run([executable, "-qb{}".format(level), "-D", dictionary_filename, "-r", filenames_directory], stdout=subprocess.PIPE).stdout.decode("utf-8").split(" ")
 149         cspeed, dspeed = parse_benchmark_output(output)
 150         cspeeds.append(cspeed)
 151         dspeeds.append(dspeed)
 152     max_cspeed, max_dspeed = max(cspeeds), max(dspeeds)
 153     print(
 154         "Bench (executable={} level={} filenames_directory={}, dictionary_filename={}, iterations={}):\n\t[cspeed: {} MB/s, dspeed: {} MB/s]".format(
 155             os.path.basename(executable),
 156             level,
 157             os.path.basename(filenames_directory),
 158             os.path.basename(dictionary_filename),
 159             iterations,
 160             max_cspeed,
 161             max_dspeed,
 162         )
 163     )
 164     return (max_cspeed, max_dspeed)
 165
 166
 167 def benchmark_dictionary(build, filenames_directory, dictionary_filename, levels, iterations):
 168     executable = clone_and_build(build)
 169     return [benchmark_dictionary_single(executable, filenames_directory, dictionary_filename, l, iterations) for l in levels]
 170
 171
 172 def parse_regressions_and_labels(old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build):
 173     cspeed_reg = (old_cspeed - new_cspeed) / old_cspeed
 174     dspeed_reg = (old_dspeed - new_dspeed) / old_dspeed
 175     baseline_label = "{}:{} ({})".format(
 176         baseline_build["user"], baseline_build["branch"], baseline_build["hash"]
 177     )
 178     test_label = "{}:{} ({})".format(
 179         test_build["user"], test_build["branch"], test_build["hash"]
 180     )
 181     return cspeed_reg, dspeed_reg, baseline_label, test_label
 182
 183
 184 def get_regressions(baseline_build, test_build, iterations, filenames, levels):
 185     old = benchmark(baseline_build, filenames, levels, iterations)
 186     new = benchmark(test_build, filenames, levels, iterations)
 187     regressions = []
 188     for j, level in enumerate(levels):
 189         for k, filename in enumerate(filenames):
 190             old_cspeed, old_dspeed = old[j][k]
 191             new_cspeed, new_dspeed = new[j][k]
 192             cspeed_reg, dspeed_reg, baseline_label, test_label = parse_regressions_and_labels(
 193                 old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build
 194             )
 195             if cspeed_reg > CSPEED_REGRESSION_TOLERANCE:
 196                 regressions.append(
 197                     "[COMPRESSION REGRESSION] (level={} filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format(
 198                         level,
 199                         filename,
 200                         baseline_label,
 201                         test_label,
 202                         old_cspeed,
 203                         new_cspeed,
 204                         cspeed_reg * 100.0,
 205                     )
 206                 )
 207             if dspeed_reg > DSPEED_REGRESSION_TOLERANCE:
 208                 regressions.append(
 209                     "[DECOMPRESSION REGRESSION] (level={} filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format(
 210                         level,
 211                         filename,
 212                         baseline_label,
 213                         test_label,
 214                         old_dspeed,
 215                         new_dspeed,
 216                         dspeed_reg * 100.0,
 217                     )
 218                 )
 219     return regressions
 220
 221 def get_regressions_dictionary(baseline_build, test_build, filenames_directory, dictionary_filename, levels, iterations):
 222     old = benchmark_dictionary(baseline_build, filenames_directory, dictionary_filename, levels, iterations)
 223     new = benchmark_dictionary(test_build, filenames_directory, dictionary_filename, levels, iterations)
 224     regressions = []
 225     for j, level in enumerate(levels):
 226         old_cspeed, old_dspeed = old[j]
 227         new_cspeed, new_dspeed = new[j]
 228         cspeed_reg, dspeed_reg, baesline_label, test_label = parse_regressions_and_labels(
 229             old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build
 230         )
 231         if cspeed_reg > CSPEED_REGRESSION_TOLERANCE:
 232             regressions.append(
 233                 "[COMPRESSION REGRESSION] (level={} filenames_directory={} dictionary_filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format(
 234                     level,
 235                     filenames_directory,
 236                     dictionary_filename,
 237                     baseline_label,
 238                     test_label,
 239                     old_cspeed,
 240                     new_cspeed,
 241                     cspeed_reg * 100.0,
 242                 )
 243             )
 244         if dspeed_reg > DSPEED_REGRESSION_TOLERANCE:
 245             regressions.append(
 246                 "[DECOMPRESSION REGRESSION] (level={} filenames_directory={} dictionary_filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format(
 247                     level,
 248                     filenames_directory,
 249                     dictionary_filename,
 250                     baseline_label,
 251                     test_label,
 252                     old_dspeed,
 253                     new_dspeed,
 254                     dspeed_reg * 100.0,
 255                 )
 256             )
 257         return regressions
 258
 259
 260 def main(filenames, levels, iterations, builds=None, emails=None, continuous=False, frequency=DEFAULT_MAX_API_CALL_FREQUENCY_SEC, dictionary_filename=None):
 261     if builds == None:
 262         builds = get_new_open_pr_builds()
 263     while True:
 264         for test_build in builds:
 265             if dictionary_filename == None:
 266                 regressions = get_regressions(
 267                     RELEASE_BUILD, test_build, iterations, filenames, levels
 268                 )
 269             else:
 270                 regressions = get_regressions_dictionary(
 271                     RELEASE_BUILD, test_build, filenames, dictionary_filename, levels, iterations
 272                 )
 273             body = "\n".join(regressions)
 274             if len(regressions) > 0:
 275                 if emails != None:
 276                     os.system(
 277                         """
 278                         echo "{}" | mutt -s "[zstd regression] caused by new pr" {}
 279                     """.format(
 280                             body, emails
 281                         )
 282                     )
 283                     print("Emails sent to {}".format(emails))
 284                 print(body)
 285         if not continuous:
 286             break
 287         time.sleep(frequency)
 288
 289
 290 if __name__ == "__main__":
 291     parser = argparse.ArgumentParser()
 292
 293     parser.add_argument("--directory", help="directory with files to benchmark", default="golden-compression")
 294     parser.add_argument("--levels", help="levels to test e.g. ('1,2,3')", default="1")
 295     parser.add_argument("--iterations", help="number of benchmark iterations to run", default="1")
 296     parser.add_argument("--emails", help="email addresses of people who will be alerted upon regression. Only for continuous mode", default=None)
 297     parser.add_argument("--frequency", help="specifies the number of seconds to wait before each successive check for new PRs in continuous mode", default=DEFAULT_MAX_API_CALL_FREQUENCY_SEC)
 298     parser.add_argument("--mode", help="'fastmode', 'onetime', 'current', or 'continuous' (see README.md for details)", default="current")
 299     parser.add_argument("--dict", help="filename of dictionary to use (when set, this dictionary will be used to compress the files provided inside --directory)", default=None)
 300
 301     args = parser.parse_args()
 302     filenames = args.directory
 303     levels = [int(l) for l in args.levels.split(",")]
 304     mode = args.mode
 305     iterations = int(args.iterations)
 306     emails = args.emails
 307     frequency = int(args.frequency)
 308     dictionary_filename = args.dict
 309
 310     if dictionary_filename == None:
 311         filenames = glob.glob("{}/**".format(filenames))
 312
 313     if (len(filenames) == 0):
 314         print("0 files found")
 315         quit()
 316
 317     if mode == "onetime":
 318         main(filenames, levels, iterations, frequency=frequenc, dictionary_filename=dictionary_filename)
 319     elif mode == "current":
 320         builds = [{"user": None, "branch": "None", "hash": None}]
 321         main(filenames, levels, iterations, builds, frequency=frequency, dictionary_filename=dictionary_filename)
 322     elif mode == "fastmode":
 323         builds = [{"user": "facebook", "branch": "release", "hash": None}]
 324         main(filenames, levels, iterations, builds, frequency=frequency, dictionary_filename=dictionary_filename)
 325     else:
 326         main(filenames, levels, iterations, None, emails, True, frequency=frequency, dictionary_filename=dictionary_filename)