X-Git-Url: https://notaz.gp2x.de/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=deps%2Flibchdr%2Fdeps%2Fzstd-1.5.5%2Ftests%2Fautomated_benchmarking.py;fp=deps%2Flibchdr%2Fdeps%2Fzstd-1.5.5%2Ftests%2Fautomated_benchmarking.py;h=153e7db4c8491b9abd3f916b647e34b2e02cb3de;hb=648db22b0750712da893c306efcc8e4b2d3a4e3c;hp=0000000000000000000000000000000000000000;hpb=e2fb1389dc12376acb84e4993ed3b08760257252;p=pcsx_rearmed.git diff --git a/deps/libchdr/deps/zstd-1.5.5/tests/automated_benchmarking.py b/deps/libchdr/deps/zstd-1.5.5/tests/automated_benchmarking.py new file mode 100644 index 00000000..153e7db4 --- /dev/null +++ b/deps/libchdr/deps/zstd-1.5.5/tests/automated_benchmarking.py @@ -0,0 +1,326 @@ +# ################################################################ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# 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)