| # ################################################################ |
| # Copyright (c) 2020-2020, Facebook, Inc. |
| # All rights reserved. |
| # |
| # This source code is licensed under both the BSD-style license (found in the |
| # LICENSE file in the root directory of this source tree) and the GPLv2 (found |
| # in the COPYING file in the root directory of this source tree). |
| # You may select, at your option, one of the above-listed licenses. |
| # ########################################################################## |
| |
| import argparse |
| import glob |
| import json |
| import os |
| import time |
| import pickle as pk |
| import subprocess |
| import urllib.request |
| |
| |
| GITHUB_API_PR_URL = "https://api.github.com/repos/facebook/zstd/pulls?state=open" |
| GITHUB_URL_TEMPLATE = "https://github.com/{}/zstd" |
| MASTER_BUILD = {"user": "facebook", "branch": "dev", "hash": None} |
| |
| # check to see if there are any new PRs every minute |
| DEFAULT_MAX_API_CALL_FREQUENCY_SEC = 60 |
| PREVIOUS_PRS_FILENAME = "prev_prs.pk" |
| |
| # Not sure what the threshold for triggering alarms should be |
| # 1% regression sounds like a little too sensitive but the desktop |
| # that I'm running it on is pretty stable so I think this is fine |
| CSPEED_REGRESSION_TOLERANCE = 0.01 |
| DSPEED_REGRESSION_TOLERANCE = 0.01 |
| |
| |
| def get_new_open_pr_builds(prev_state=True): |
| prev_prs = None |
| if os.path.exists(PREVIOUS_PRS_FILENAME): |
| with open(PREVIOUS_PRS_FILENAME, "rb") as f: |
| prev_prs = pk.load(f) |
| data = json.loads(urllib.request.urlopen(GITHUB_API_PR_URL).read().decode("utf-8")) |
| prs = { |
| d["url"]: { |
| "user": d["user"]["login"], |
| "branch": d["head"]["ref"], |
| "hash": d["head"]["sha"].strip(), |
| } |
| for d in data |
| } |
| with open(PREVIOUS_PRS_FILENAME, "wb") as f: |
| pk.dump(prs, f) |
| if not prev_state or prev_prs == None: |
| return list(prs.values()) |
| return [pr for url, pr in prs.items() if url not in prev_prs or prev_prs[url] != pr] |
| |
| |
| def get_latest_hashes(): |
| tmp = subprocess.run(["git", "log", "-1"], stdout=subprocess.PIPE).stdout.decode( |
| "utf-8" |
| ) |
| sha1 = tmp.split("\n")[0].split(" ")[1] |
| tmp = subprocess.run( |
| ["git", "show", "{}^1".format(sha1)], stdout=subprocess.PIPE |
| ).stdout.decode("utf-8") |
| sha2 = tmp.split("\n")[0].split(" ")[1] |
| tmp = subprocess.run( |
| ["git", "show", "{}^2".format(sha1)], stdout=subprocess.PIPE |
| ).stdout.decode("utf-8") |
| sha3 = "" if len(tmp) == 0 else tmp.split("\n")[0].split(" ")[1] |
| return [sha1.strip(), sha2.strip(), sha3.strip()] |
| |
| |
| def get_builds_for_latest_hash(): |
| hashes = get_latest_hashes() |
| for b in get_new_open_pr_builds(False): |
| if b["hash"] in hashes: |
| return [b] |
| return [] |
| |
| |
| def clone_and_build(build): |
| if build["user"] != None: |
| github_url = GITHUB_URL_TEMPLATE.format(build["user"]) |
| os.system( |
| """ |
| rm -rf zstd-{user}-{sha} && |
| git clone {github_url} zstd-{user}-{sha} && |
| cd zstd-{user}-{sha} && |
| {checkout_command} |
| make && |
| cd ../ |
| """.format( |
| user=build["user"], |
| github_url=github_url, |
| sha=build["hash"], |
| checkout_command="git checkout {} &&".format(build["hash"]) |
| if build["hash"] != None |
| else "", |
| ) |
| ) |
| return "zstd-{user}-{sha}/zstd".format(user=build["user"], sha=build["hash"]) |
| else: |
| os.system("cd ../ && make && cd tests") |
| return "../zstd" |
| |
| |
| def parse_benchmark_output(output): |
| idx = [i for i, d in enumerate(output) if d == "MB/s"] |
| return [float(output[idx[0] - 1]), float(output[idx[1] - 1])] |
| |
| |
| def benchmark_single(executable, level, filename): |
| return parse_benchmark_output(( |
| subprocess.run( |
| [executable, "-qb{}".format(level), filename], stderr=subprocess.PIPE |
| ) |
| .stderr.decode("utf-8") |
| .split(" ") |
| )) |
| |
| |
| def benchmark_n(executable, level, filename, n): |
| speeds_arr = [benchmark_single(executable, level, filename) for _ in range(n)] |
| cspeed, dspeed = max(b[0] for b in speeds_arr), max(b[1] for b in speeds_arr) |
| print( |
| "Bench (executable={} level={} filename={}, iterations={}):\n\t[cspeed: {} MB/s, dspeed: {} MB/s]".format( |
| os.path.basename(executable), |
| level, |
| os.path.basename(filename), |
| n, |
| cspeed, |
| dspeed, |
| ) |
| ) |
| return (cspeed, dspeed) |
| |
| |
| def benchmark(build, filenames, levels, iterations): |
| executable = clone_and_build(build) |
| return [ |
| [benchmark_n(executable, l, f, iterations) for f in filenames] for l in levels |
| ] |
| |
| |
| def benchmark_dictionary_single(executable, filenames_directory, dictionary_filename, level, iterations): |
| cspeeds, dspeeds = [], [] |
| for _ in range(iterations): |
| output = subprocess.run([executable, "-qb{}".format(level), "-D", dictionary_filename, "-r", filenames_directory], stderr=subprocess.PIPE).stderr.decode("utf-8").split(" ") |
| cspeed, dspeed = parse_benchmark_output(output) |
| cspeeds.append(cspeed) |
| dspeeds.append(dspeed) |
| max_cspeed, max_dspeed = max(cspeeds), max(dspeeds) |
| print( |
| "Bench (executable={} level={} filenames_directory={}, dictionary_filename={}, iterations={}):\n\t[cspeed: {} MB/s, dspeed: {} MB/s]".format( |
| os.path.basename(executable), |
| level, |
| os.path.basename(filenames_directory), |
| os.path.basename(dictionary_filename), |
| iterations, |
| max_cspeed, |
| max_dspeed, |
| ) |
| ) |
| return (max_cspeed, max_dspeed) |
| |
| |
| def benchmark_dictionary(build, filenames_directory, dictionary_filename, levels, iterations): |
| executable = clone_and_build(build) |
| return [benchmark_dictionary_single(executable, filenames_directory, dictionary_filename, l, iterations) for l in levels] |
| |
| |
| def parse_regressions_and_labels(old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build): |
| cspeed_reg = (old_cspeed - new_cspeed) / old_cspeed |
| dspeed_reg = (old_dspeed - new_dspeed) / old_dspeed |
| baseline_label = "{}:{} ({})".format( |
| baseline_build["user"], baseline_build["branch"], baseline_build["hash"] |
| ) |
| test_label = "{}:{} ({})".format( |
| test_build["user"], test_build["branch"], test_build["hash"] |
| ) |
| return cspeed_reg, dspeed_reg, baseline_label, test_label |
| |
| |
| def get_regressions(baseline_build, test_build, iterations, filenames, levels): |
| old = benchmark(baseline_build, filenames, levels, iterations) |
| new = benchmark(test_build, filenames, levels, iterations) |
| regressions = [] |
| for j, level in enumerate(levels): |
| for k, filename in enumerate(filenames): |
| old_cspeed, old_dspeed = old[j][k] |
| new_cspeed, new_dspeed = new[j][k] |
| cspeed_reg, dspeed_reg, baseline_label, test_label = parse_regressions_and_labels( |
| old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build |
| ) |
| if cspeed_reg > CSPEED_REGRESSION_TOLERANCE: |
| regressions.append( |
| "[COMPRESSION REGRESSION] (level={} filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format( |
| level, |
| filename, |
| baseline_label, |
| test_label, |
| old_cspeed, |
| new_cspeed, |
| cspeed_reg * 100.0, |
| ) |
| ) |
| if dspeed_reg > DSPEED_REGRESSION_TOLERANCE: |
| regressions.append( |
| "[DECOMPRESSION REGRESSION] (level={} filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format( |
| level, |
| filename, |
| baseline_label, |
| test_label, |
| old_dspeed, |
| new_dspeed, |
| dspeed_reg * 100.0, |
| ) |
| ) |
| return regressions |
| |
| def get_regressions_dictionary(baseline_build, test_build, filenames_directory, dictionary_filename, levels, iterations): |
| old = benchmark_dictionary(baseline_build, filenames_directory, dictionary_filename, levels, iterations) |
| new = benchmark_dictionary(test_build, filenames_directory, dictionary_filename, levels, iterations) |
| regressions = [] |
| for j, level in enumerate(levels): |
| old_cspeed, old_dspeed = old[j] |
| new_cspeed, new_dspeed = new[j] |
| cspeed_reg, dspeed_reg, baesline_label, test_label = parse_regressions_and_labels( |
| old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build |
| ) |
| if cspeed_reg > CSPEED_REGRESSION_TOLERANCE: |
| regressions.append( |
| "[COMPRESSION REGRESSION] (level={} filenames_directory={} dictionary_filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format( |
| level, |
| filenames_directory, |
| dictionary_filename, |
| baseline_label, |
| test_label, |
| old_cspeed, |
| new_cspeed, |
| cspeed_reg * 100.0, |
| ) |
| ) |
| if dspeed_reg > DSPEED_REGRESSION_TOLERANCE: |
| regressions.append( |
| "[DECOMPRESSION REGRESSION] (level={} filenames_directory={} dictionary_filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format( |
| level, |
| filenames_directory, |
| dictionary_filename, |
| baseline_label, |
| test_label, |
| old_dspeed, |
| new_dspeed, |
| dspeed_reg * 100.0, |
| ) |
| ) |
| return regressions |
| |
| |
| def main(filenames, levels, iterations, builds=None, emails=None, continuous=False, frequency=DEFAULT_MAX_API_CALL_FREQUENCY_SEC, dictionary_filename=None): |
| if builds == None: |
| builds = get_new_open_pr_builds() |
| while True: |
| for test_build in builds: |
| if dictionary_filename == None: |
| regressions = get_regressions( |
| MASTER_BUILD, test_build, iterations, filenames, levels |
| ) |
| else: |
| regressions = get_regressions_dictionary( |
| MASTER_BUILD, test_build, filenames, dictionary_filename, levels, iterations |
| ) |
| body = "\n".join(regressions) |
| if len(regressions) > 0: |
| if emails != None: |
| os.system( |
| """ |
| echo "{}" | mutt -s "[zstd regression] caused by new pr" {} |
| """.format( |
| body, emails |
| ) |
| ) |
| print("Emails sent to {}".format(emails)) |
| print(body) |
| if not continuous: |
| break |
| time.sleep(frequency) |
| |
| |
| if __name__ == "__main__": |
| parser = argparse.ArgumentParser() |
| |
| parser.add_argument("--directory", help="directory with files to benchmark", default="golden-compression") |
| parser.add_argument("--levels", help="levels to test eg ('1,2,3')", default="1") |
| parser.add_argument("--iterations", help="number of benchmark iterations to run", default="1") |
| parser.add_argument("--emails", help="email addresses of people who will be alerted upon regression. Only for continuous mode", default=None) |
| 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) |
| parser.add_argument("--mode", help="'fastmode', 'onetime', 'current', or 'continuous' (see README.md for details)", default="current") |
| parser.add_argument("--dict", help="filename of dictionary to use (when set, this dictioanry will be used to compress the files provided inside --directory)", default=None) |
| |
| args = parser.parse_args() |
| filenames = args.directory |
| levels = [int(l) for l in args.levels.split(",")] |
| mode = args.mode |
| iterations = int(args.iterations) |
| emails = args.emails |
| frequency = int(args.frequency) |
| dictionary_filename = args.dict |
| |
| if dictionary_filename == None: |
| filenames = glob.glob("{}/**".format(filenames)) |
| |
| if (len(filenames) == 0): |
| print("0 files found") |
| quit() |
| |
| if mode == "onetime": |
| main(filenames, levels, iterations, frequency=frequenc, dictionary_filename=dictionary_filename) |
| elif mode == "current": |
| builds = [{"user": None, "branch": "None", "hash": None}] |
| main(filenames, levels, iterations, builds, frequency=frequency, dictionary_filename=dictionary_filename) |
| elif mode == "fastmode": |
| builds = [{"user": "facebook", "branch": "master", "hash": None}] |
| main(filenames, levels, iterations, builds, frequency=frequency, dictionary_filename=dictionary_filename) |
| else: |
| main(filenames, levels, iterations, None, emails, True, frequency=frequency, dictionary_filename=dictionary_filename) |