1 files changed, 326 insertions, 0 deletions
diff --git a/src/zstd/tests/automated_benchmarking.py b/src/zstd/tests/automated_benchmarking.py
new file mode 100644
index 000000000..d0cfb1fbe
--- /dev/null
+++ b/src/zstd/tests/automated_benchmarking.py
@@ -0,0 +1,326 @@
+# ################################################################
+# Copyright (c) 2020-2020, 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"
+MASTER_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 &&
+            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 && 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], stderr=subprocess.PIPE
+        )
+        .stderr.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], stderr=subprocess.PIPE).stderr.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(
+                    MASTER_BUILD, test_build, iterations, filenames, levels
+                )
+            else:
+                regressions = get_regressions_dictionary(
+                    MASTER_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 eg ('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 dictioanry 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": "master", "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)