648db22b |
1 | # ################################################################ |
2 | # Copyright (c) Meta Platforms, Inc. and affiliates. |
3 | # All rights reserved. |
4 | # |
5 | # This source code is licensed under both the BSD-style license (found in the |
6 | # LICENSE file in the root directory of this source tree) and the GPLv2 (found |
7 | # in the COPYING file in the root directory of this source tree). |
8 | # You may select, at your option, one of the above-listed licenses. |
9 | # ########################################################################## |
10 | |
11 | import argparse |
12 | import glob |
13 | import json |
14 | import os |
15 | import time |
16 | import pickle as pk |
17 | import subprocess |
18 | import urllib.request |
19 | |
20 | |
21 | GITHUB_API_PR_URL = "https://api.github.com/repos/facebook/zstd/pulls?state=open" |
22 | GITHUB_URL_TEMPLATE = "https://github.com/{}/zstd" |
23 | RELEASE_BUILD = {"user": "facebook", "branch": "dev", "hash": None} |
24 | |
25 | # check to see if there are any new PRs every minute |
26 | DEFAULT_MAX_API_CALL_FREQUENCY_SEC = 60 |
27 | PREVIOUS_PRS_FILENAME = "prev_prs.pk" |
28 | |
29 | # Not sure what the threshold for triggering alarms should be |
30 | # 1% regression sounds like a little too sensitive but the desktop |
31 | # that I'm running it on is pretty stable so I think this is fine |
32 | CSPEED_REGRESSION_TOLERANCE = 0.01 |
33 | DSPEED_REGRESSION_TOLERANCE = 0.01 |
34 | |
35 | |
36 | def get_new_open_pr_builds(prev_state=True): |
37 | prev_prs = None |
38 | if os.path.exists(PREVIOUS_PRS_FILENAME): |
39 | with open(PREVIOUS_PRS_FILENAME, "rb") as f: |
40 | prev_prs = pk.load(f) |
41 | data = json.loads(urllib.request.urlopen(GITHUB_API_PR_URL).read().decode("utf-8")) |
42 | prs = { |
43 | d["url"]: { |
44 | "user": d["user"]["login"], |
45 | "branch": d["head"]["ref"], |
46 | "hash": d["head"]["sha"].strip(), |
47 | } |
48 | for d in data |
49 | } |
50 | with open(PREVIOUS_PRS_FILENAME, "wb") as f: |
51 | pk.dump(prs, f) |
52 | if not prev_state or prev_prs == None: |
53 | return list(prs.values()) |
54 | return [pr for url, pr in prs.items() if url not in prev_prs or prev_prs[url] != pr] |
55 | |
56 | |
57 | def get_latest_hashes(): |
58 | tmp = subprocess.run(["git", "log", "-1"], stdout=subprocess.PIPE).stdout.decode( |
59 | "utf-8" |
60 | ) |
61 | sha1 = tmp.split("\n")[0].split(" ")[1] |
62 | tmp = subprocess.run( |
63 | ["git", "show", "{}^1".format(sha1)], stdout=subprocess.PIPE |
64 | ).stdout.decode("utf-8") |
65 | sha2 = tmp.split("\n")[0].split(" ")[1] |
66 | tmp = subprocess.run( |
67 | ["git", "show", "{}^2".format(sha1)], stdout=subprocess.PIPE |
68 | ).stdout.decode("utf-8") |
69 | sha3 = "" if len(tmp) == 0 else tmp.split("\n")[0].split(" ")[1] |
70 | return [sha1.strip(), sha2.strip(), sha3.strip()] |
71 | |
72 | |
73 | def get_builds_for_latest_hash(): |
74 | hashes = get_latest_hashes() |
75 | for b in get_new_open_pr_builds(False): |
76 | if b["hash"] in hashes: |
77 | return [b] |
78 | return [] |
79 | |
80 | |
81 | def clone_and_build(build): |
82 | if build["user"] != None: |
83 | github_url = GITHUB_URL_TEMPLATE.format(build["user"]) |
84 | os.system( |
85 | """ |
86 | rm -rf zstd-{user}-{sha} && |
87 | git clone {github_url} zstd-{user}-{sha} && |
88 | cd zstd-{user}-{sha} && |
89 | {checkout_command} |
90 | make -j && |
91 | cd ../ |
92 | """.format( |
93 | user=build["user"], |
94 | github_url=github_url, |
95 | sha=build["hash"], |
96 | checkout_command="git checkout {} &&".format(build["hash"]) |
97 | if build["hash"] != None |
98 | else "", |
99 | ) |
100 | ) |
101 | return "zstd-{user}-{sha}/zstd".format(user=build["user"], sha=build["hash"]) |
102 | else: |
103 | os.system("cd ../ && make -j && cd tests") |
104 | return "../zstd" |
105 | |
106 | |
107 | def parse_benchmark_output(output): |
108 | idx = [i for i, d in enumerate(output) if d == "MB/s"] |
109 | return [float(output[idx[0] - 1]), float(output[idx[1] - 1])] |
110 | |
111 | |
112 | def benchmark_single(executable, level, filename): |
113 | return parse_benchmark_output(( |
114 | subprocess.run( |
115 | [executable, "-qb{}".format(level), filename], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, |
116 | ) |
117 | .stdout.decode("utf-8") |
118 | .split(" ") |
119 | )) |
120 | |
121 | |
122 | def benchmark_n(executable, level, filename, n): |
123 | speeds_arr = [benchmark_single(executable, level, filename) for _ in range(n)] |
124 | cspeed, dspeed = max(b[0] for b in speeds_arr), max(b[1] for b in speeds_arr) |
125 | print( |
126 | "Bench (executable={} level={} filename={}, iterations={}):\n\t[cspeed: {} MB/s, dspeed: {} MB/s]".format( |
127 | os.path.basename(executable), |
128 | level, |
129 | os.path.basename(filename), |
130 | n, |
131 | cspeed, |
132 | dspeed, |
133 | ) |
134 | ) |
135 | return (cspeed, dspeed) |
136 | |
137 | |
138 | def benchmark(build, filenames, levels, iterations): |
139 | executable = clone_and_build(build) |
140 | return [ |
141 | [benchmark_n(executable, l, f, iterations) for f in filenames] for l in levels |
142 | ] |
143 | |
144 | |
145 | def benchmark_dictionary_single(executable, filenames_directory, dictionary_filename, level, iterations): |
146 | cspeeds, dspeeds = [], [] |
147 | for _ in range(iterations): |
148 | output = subprocess.run([executable, "-qb{}".format(level), "-D", dictionary_filename, "-r", filenames_directory], stdout=subprocess.PIPE).stdout.decode("utf-8").split(" ") |
149 | cspeed, dspeed = parse_benchmark_output(output) |
150 | cspeeds.append(cspeed) |
151 | dspeeds.append(dspeed) |
152 | max_cspeed, max_dspeed = max(cspeeds), max(dspeeds) |
153 | print( |
154 | "Bench (executable={} level={} filenames_directory={}, dictionary_filename={}, iterations={}):\n\t[cspeed: {} MB/s, dspeed: {} MB/s]".format( |
155 | os.path.basename(executable), |
156 | level, |
157 | os.path.basename(filenames_directory), |
158 | os.path.basename(dictionary_filename), |
159 | iterations, |
160 | max_cspeed, |
161 | max_dspeed, |
162 | ) |
163 | ) |
164 | return (max_cspeed, max_dspeed) |
165 | |
166 | |
167 | def benchmark_dictionary(build, filenames_directory, dictionary_filename, levels, iterations): |
168 | executable = clone_and_build(build) |
169 | return [benchmark_dictionary_single(executable, filenames_directory, dictionary_filename, l, iterations) for l in levels] |
170 | |
171 | |
172 | def parse_regressions_and_labels(old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build): |
173 | cspeed_reg = (old_cspeed - new_cspeed) / old_cspeed |
174 | dspeed_reg = (old_dspeed - new_dspeed) / old_dspeed |
175 | baseline_label = "{}:{} ({})".format( |
176 | baseline_build["user"], baseline_build["branch"], baseline_build["hash"] |
177 | ) |
178 | test_label = "{}:{} ({})".format( |
179 | test_build["user"], test_build["branch"], test_build["hash"] |
180 | ) |
181 | return cspeed_reg, dspeed_reg, baseline_label, test_label |
182 | |
183 | |
184 | def get_regressions(baseline_build, test_build, iterations, filenames, levels): |
185 | old = benchmark(baseline_build, filenames, levels, iterations) |
186 | new = benchmark(test_build, filenames, levels, iterations) |
187 | regressions = [] |
188 | for j, level in enumerate(levels): |
189 | for k, filename in enumerate(filenames): |
190 | old_cspeed, old_dspeed = old[j][k] |
191 | new_cspeed, new_dspeed = new[j][k] |
192 | cspeed_reg, dspeed_reg, baseline_label, test_label = parse_regressions_and_labels( |
193 | old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build |
194 | ) |
195 | if cspeed_reg > CSPEED_REGRESSION_TOLERANCE: |
196 | regressions.append( |
197 | "[COMPRESSION REGRESSION] (level={} filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format( |
198 | level, |
199 | filename, |
200 | baseline_label, |
201 | test_label, |
202 | old_cspeed, |
203 | new_cspeed, |
204 | cspeed_reg * 100.0, |
205 | ) |
206 | ) |
207 | if dspeed_reg > DSPEED_REGRESSION_TOLERANCE: |
208 | regressions.append( |
209 | "[DECOMPRESSION REGRESSION] (level={} filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format( |
210 | level, |
211 | filename, |
212 | baseline_label, |
213 | test_label, |
214 | old_dspeed, |
215 | new_dspeed, |
216 | dspeed_reg * 100.0, |
217 | ) |
218 | ) |
219 | return regressions |
220 | |
221 | def get_regressions_dictionary(baseline_build, test_build, filenames_directory, dictionary_filename, levels, iterations): |
222 | old = benchmark_dictionary(baseline_build, filenames_directory, dictionary_filename, levels, iterations) |
223 | new = benchmark_dictionary(test_build, filenames_directory, dictionary_filename, levels, iterations) |
224 | regressions = [] |
225 | for j, level in enumerate(levels): |
226 | old_cspeed, old_dspeed = old[j] |
227 | new_cspeed, new_dspeed = new[j] |
228 | cspeed_reg, dspeed_reg, baesline_label, test_label = parse_regressions_and_labels( |
229 | old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build |
230 | ) |
231 | if cspeed_reg > CSPEED_REGRESSION_TOLERANCE: |
232 | regressions.append( |
233 | "[COMPRESSION REGRESSION] (level={} filenames_directory={} dictionary_filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format( |
234 | level, |
235 | filenames_directory, |
236 | dictionary_filename, |
237 | baseline_label, |
238 | test_label, |
239 | old_cspeed, |
240 | new_cspeed, |
241 | cspeed_reg * 100.0, |
242 | ) |
243 | ) |
244 | if dspeed_reg > DSPEED_REGRESSION_TOLERANCE: |
245 | regressions.append( |
246 | "[DECOMPRESSION REGRESSION] (level={} filenames_directory={} dictionary_filename={})\n\t{} -> {}\n\t{} -> {} ({:0.2f}%)".format( |
247 | level, |
248 | filenames_directory, |
249 | dictionary_filename, |
250 | baseline_label, |
251 | test_label, |
252 | old_dspeed, |
253 | new_dspeed, |
254 | dspeed_reg * 100.0, |
255 | ) |
256 | ) |
257 | return regressions |
258 | |
259 | |
260 | def main(filenames, levels, iterations, builds=None, emails=None, continuous=False, frequency=DEFAULT_MAX_API_CALL_FREQUENCY_SEC, dictionary_filename=None): |
261 | if builds == None: |
262 | builds = get_new_open_pr_builds() |
263 | while True: |
264 | for test_build in builds: |
265 | if dictionary_filename == None: |
266 | regressions = get_regressions( |
267 | RELEASE_BUILD, test_build, iterations, filenames, levels |
268 | ) |
269 | else: |
270 | regressions = get_regressions_dictionary( |
271 | RELEASE_BUILD, test_build, filenames, dictionary_filename, levels, iterations |
272 | ) |
273 | body = "\n".join(regressions) |
274 | if len(regressions) > 0: |
275 | if emails != None: |
276 | os.system( |
277 | """ |
278 | echo "{}" | mutt -s "[zstd regression] caused by new pr" {} |
279 | """.format( |
280 | body, emails |
281 | ) |
282 | ) |
283 | print("Emails sent to {}".format(emails)) |
284 | print(body) |
285 | if not continuous: |
286 | break |
287 | time.sleep(frequency) |
288 | |
289 | |
290 | if __name__ == "__main__": |
291 | parser = argparse.ArgumentParser() |
292 | |
293 | parser.add_argument("--directory", help="directory with files to benchmark", default="golden-compression") |
294 | parser.add_argument("--levels", help="levels to test e.g. ('1,2,3')", default="1") |
295 | parser.add_argument("--iterations", help="number of benchmark iterations to run", default="1") |
296 | parser.add_argument("--emails", help="email addresses of people who will be alerted upon regression. Only for continuous mode", default=None) |
297 | 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) |
298 | parser.add_argument("--mode", help="'fastmode', 'onetime', 'current', or 'continuous' (see README.md for details)", default="current") |
299 | 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) |
300 | |
301 | args = parser.parse_args() |
302 | filenames = args.directory |
303 | levels = [int(l) for l in args.levels.split(",")] |
304 | mode = args.mode |
305 | iterations = int(args.iterations) |
306 | emails = args.emails |
307 | frequency = int(args.frequency) |
308 | dictionary_filename = args.dict |
309 | |
310 | if dictionary_filename == None: |
311 | filenames = glob.glob("{}/**".format(filenames)) |
312 | |
313 | if (len(filenames) == 0): |
314 | print("0 files found") |
315 | quit() |
316 | |
317 | if mode == "onetime": |
318 | main(filenames, levels, iterations, frequency=frequenc, dictionary_filename=dictionary_filename) |
319 | elif mode == "current": |
320 | builds = [{"user": None, "branch": "None", "hash": None}] |
321 | main(filenames, levels, iterations, builds, frequency=frequency, dictionary_filename=dictionary_filename) |
322 | elif mode == "fastmode": |
323 | builds = [{"user": "facebook", "branch": "release", "hash": None}] |
324 | main(filenames, levels, iterations, builds, frequency=frequency, dictionary_filename=dictionary_filename) |
325 | else: |
326 | main(filenames, levels, iterations, None, emails, True, frequency=frequency, dictionary_filename=dictionary_filename) |