--- /dev/null
+# ################################################################
+# 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)
notaz.gp2x.de / pcsx_rearmed.git / blobdiff