| 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) |