blob: 84dee5a551103626ca7a598c5839e026c1aa86f3 [file] [log] [blame]
#!/usr/bin/env python2
# Copyright 2017 The Chromium OS Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
# pylint: disable=cros-logging-import
"""Transforms skia benchmark results to ones that crosperf can understand."""
from __future__ import print_function
import itertools
import logging
import json
import sys
# Turn the logging level to INFO before importing other autotest
# code, to avoid having failed import logging messages confuse the
# test_droid user.
# All of the results we care about, by name.
# Each of these *must* end in _ns, _us, _ms, or _s, since all the metrics we
# collect (so far) are related to time, and we alter the results based on the
# suffix of these strings (so we don't have 0.000421ms per sample, for example)
'memset32_100000_640_480_nonrendering': 'memset_time_ms',
'path_equality_50%_640_480_nonrendering': 'path_equality_ns',
'sort_qsort_backward_640_480_nonrendering': 'qsort_us'
def _GetFamiliarName(name):
return r if r else name
def _IsResultInteresting(name):
return name in _RESULT_RENAMES
def _GetTimeMultiplier(label_name):
"""Given a time (in milliseconds), normalize it to what label_name expects.
"What label_name expects" meaning "we pattern match against the last few
non-space chars in label_name."
This expects the time unit to be separated from anything else by '_'.
ms_mul = 1000 * 1000.
endings = [('_ns', 1), ('_us', 1000), ('_ms', ms_mul), ('_s', ms_mul * 1000)]
for end, mul in endings:
if label_name.endswith(end):
return ms_mul / mul
raise ValueError('Unknown ending in "%s"; expecting one of %s' %
(label_name, [end for end, _ in endings]))
def _GetTimeDenom(ms):
"""Express times in a common time unit.
Given a list of times (in milliseconds), find a time unit in which
they can all be expressed.
Returns the unit name, and `ms` normalized to that time unit.
>>> _GetTimeDenom([1, 2, 3])
('ms', [1.0, 2.0, 3.0])
>>> _GetTimeDenom([.1, .2, .3])
('us', [100.0, 200.0, 300.0])
ms_mul = 1000 * 1000
units = [('us', 1000), ('ms', ms_mul), ('s', ms_mul * 1000)]
for name, mul in reversed(units):
normalized = [float(t) * ms_mul / mul for t in ms]
average = sum(normalized) / len(normalized)
if all(n > 0.1 for n in normalized) and average >= 1:
return name, normalized
normalized = [float(t) * ms_mul for t in ms]
return 'ns', normalized
def _TransformBenchmarks(raw_benchmarks):
# We get {"results": {"bench_name": Results}}
# where
# Results = {"config_name": {"samples": [float], etc.}}
# We want {"data": {"skia": [[BenchmarkData]]},
# "platforms": ["platform1, ..."]}
# where
# BenchmarkData = {"bench_name": bench_samples[N], ..., "retval": 0}
# Note that retval is awkward -- crosperf's JSON reporter reports the result
# as a failure if it's not there. Everything else treats it like a
# statistic...
benchmarks = raw_benchmarks['results']
results = []
for bench_name, bench_result in benchmarks.items():
for cfg_name, keyvals in bench_result.items():
# Some benchmarks won't have timing data (either it won't exist at all,
# or it'll be empty); skip them.
samples = keyvals.get('samples')
if not samples:
bench_name = '%s_%s' % (bench_name, cfg_name)
if not _IsResultInteresting(bench_name):
friendly_name = _GetFamiliarName(bench_name)
if len(results) < len(samples):
{'retval': 0} for _ in range(len(samples) - len(results)))
time_mul = _GetTimeMultiplier(friendly_name)
for sample, app in, results):
assert friendly_name not in app
app[friendly_name] = sample * time_mul
except (KeyError, ValueError) as e:
logging.error('While converting "%s" (key: %s): %s', bench_result,
bench_name, e)
# Realistically, [results] should be multiple results, where each entry in the
# list is the result for a different label. Because we only deal with one
# label at the moment, we need to wrap it in its own list.
return results
if __name__ == '__main__':
def _GetUserFile(argv):
if not argv or argv[0] == '-':
return sys.stdin
return open(argv[0])
def _Main():
with _GetUserFile(sys.argv[1:]) as in_file:
obj = json.load(in_file)
output = _TransformBenchmarks(obj)
json.dump(output, sys.stdout)