# ################################################################ # Copyright (c) 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" RELEASE_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 -j && 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 -j && 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], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, ) .stdout.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], stdout=subprocess.PIPE).stdout.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( RELEASE_BUILD, test_build, iterations, filenames, levels ) else: regressions = get_regressions_dictionary( RELEASE_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 e.g. ('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 dictionary 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": "release", "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)