def test_skip_param_selection():
d = {'repo': 'foo'}
d.update(ASV_CONF_JSON)
conf = config.Config.from_json(d)
class DummyEnv(object):
name = 'env'
d = [{
'name': 'test_nonparam',
'params': [],
'version': '1'
}, {
'name': 'test_param',
'params': [['1', '2', '3']],
'param_names': ['n'],
'version': '1'
}]
results = Results.unnamed()
b = benchmarks.Benchmarks(conf, d,
[r'test_nonparam', r'test_param\([23]\)'])
results.add_result(
b['test_param'],
runner.BenchmarkResult(result=[1, 2, 3],
samples=[None] * 3,
number=[None] * 3,
errcode=0,
stderr='',
profile=None))
runner.skip_benchmarks(b, DummyEnv(), results)
assert results._results.get('test_nonparam') == None
assert results._results['test_param'] == [1, None, None]
def test_run_benchmarks(benchmarks_fixture, tmpdir):
conf, repo, envs, commit_hash = benchmarks_fixture
start_timestamp = datetime.datetime.utcnow()
b = benchmarks.Benchmarks.discover(conf, repo, envs, [commit_hash])
# Old results to append to
results = Results.unnamed()
name = 'time_examples.TimeSuite.time_example_benchmark_1'
results.add_result(b[name],
runner.BenchmarkResult(result=[1],
samples=[[42.0, 24.0]],
number=[1],
errcode=0,
stderr='',
profile=None),
record_samples=True)
# Run
runner.run_benchmarks(b,
envs[0],
results=results,
profile=True,
show_stderr=True,
append_samples=True,
record_samples=True)
times = ResultsWrapper(results, b)
end_timestamp = datetime.datetime.utcnow()
assert len(times) == len(b)
assert times[
'time_examples.TimeSuite.time_example_benchmark_1'].result != [None]
stats = results.get_result_stats(name, b[name]['params'])
assert isinstance(stats[0]['std'], float)
# The exact number of samples may vary if the calibration is not fully accurate
samples = results.get_result_samples(name, b[name]['params'])
assert len(samples[0]) >= 4
# Explicitly provided 'prev_samples` should come first
assert samples[0][:2] == [42.0, 24.0]
# Benchmarks that raise exceptions should have a time of "None"
assert times['time_secondary.TimeSecondary.time_exception'].result == [
None
]
assert times['subdir.time_subdir.time_foo'].result != [None]
if not ON_PYPY:
# XXX: the memory benchmarks don't work on Pypy, since asizeof
# is CPython-only
assert times['mem_examples.mem_list'].result[0] > 1000
assert times['time_secondary.track_value'].result == [42.0]
assert times['time_secondary.track_value'].profile is not None
assert isinstance(times['time_examples.time_with_warnings'].stderr,
type(''))
assert times['time_examples.time_with_warnings'].errcode != 0
assert times['time_examples.TimeWithBadTimer.time_it'].result == [0.0]
assert times['params_examples.track_param'].params == [[
"<class 'benchmark.params_examples.ClassOne'>",
"<class 'benchmark.params_examples.ClassTwo'>"
]]
assert times['params_examples.track_param'].result == [42, 42]
assert times['params_examples.mem_param'].params == [['10', '20'],
['2', '3']]
assert len(times['params_examples.mem_param'].result) == 2 * 2
assert times['params_examples.ParamSuite.track_value'].params == [[
"'a'", "'b'", "'c'"
]]
assert times['params_examples.ParamSuite.track_value'].result == [
1 + 0, 2 + 0, 3 + 0
]
assert isinstance(times['params_examples.TuningTest.time_it'].result[0],
float)
assert isinstance(times['params_examples.TuningTest.time_it'].result[1],
float)
assert isinstance(times['params_examples.time_skip'].result[0], float)
assert isinstance(times['params_examples.time_skip'].result[1], float)
assert util.is_nan(times['params_examples.time_skip'].result[2])
assert times['peakmem_examples.peakmem_list'].result[0] >= 4 * 2**20
assert times['cache_examples.ClassLevelSetup.track_example'].result == [
500
]
assert times['cache_examples.ClassLevelSetup.track_example2'].result == [
500
]
assert times['cache_examples.track_cache_foo'].result == [42]
assert times['cache_examples.track_cache_bar'].result == [12]
assert times['cache_examples.track_my_cache_foo'].result == [0]
assert times['cache_examples.ClassLevelSetupFail.track_fail'].result == [
None
]
assert 'raise RuntimeError()' in times[
'cache_examples.ClassLevelSetupFail.track_fail'].stderr
assert times[
'cache_examples.ClassLevelCacheTimeout.track_fail'].result == [None]
assert times[
'cache_examples.ClassLevelCacheTimeoutSuccess.track_success'].result == [
0
]
assert times['cache_examples.time_fail_second_run'].result == [None]
assert times['cache_examples.time_fail_second_run'].samples == [None]
profile_path = join(six.text_type(tmpdir), 'test.profile')
with open(profile_path, 'wb') as fd:
fd.write(times['time_secondary.track_value'].profile)
pstats.Stats(profile_path)
# Check for running setup on each repeat (one extra run from profile)
# The output would contain error messages if the asserts in the benchmark fail.
expected = ["<%d>" % j for j in range(1, 12)]
assert times['time_examples.TimeWithRepeat.time_it'].stderr.split(
) == expected
# Calibration of iterations should not rerun setup
expected = (['setup'] * 2, ['setup'] * 3)
assert times['time_examples.TimeWithRepeatCalibrate.time_it'].stderr.split(
) in expected
# Check tuple-form repeat attribute produced results
assert 2 <= len(times['time_examples.time_auto_repeat'].samples[0]) <= 4
# Check run time timestamps
for name, result in times.items():
assert result.started_at >= util.datetime_to_js_timestamp(
start_timestamp)
assert result.ended_at >= result.started_at
assert result.ended_at <= util.datetime_to_js_timestamp(end_timestamp)
