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