iw5-mod/deps/zstd/tests/automated_benchmarking.py

327 lines
13 KiB
Python

# ################################################################
# 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)