def process_benchmarks(log_files):
benchmarks = test_log_pb2.BenchmarkEntries()
for f in log_files:
content = gfile.GFile(f, "rb").read()
if benchmarks.MergeFromString(content) != len(content):
raise Exception("Failed parsing benchmark entry from %s" % f)
return benchmarks
def testReportBenchmark(self):
output_dir = self.get_temp_dir() + os.path.sep
os.environ['TEST_REPORT_FILE_PREFIX'] = output_dir
proto_file_path = os.path.join(output_dir,
'BenchmarkTest.testReportBenchmark')
if os.path.exists(proto_file_path):
os.remove(proto_file_path)
self.report_benchmark(iters=2000,
wall_time=1000,
name='testReportBenchmark',
metrics=[{
'name': 'metric_name_1',
'value': 0,
'min_value': 1
}, {
'name': 'metric_name_2',
'value': 90,
'min_value': 0,
'max_value': 95
}])
with open(proto_file_path, 'rb') as f:
benchmark_entries = test_log_pb2.BenchmarkEntries()
benchmark_entries.ParseFromString(f.read())
actual_result = json_format.MessageToDict(
benchmark_entries,
preserving_proto_field_name=True,
including_default_value_fields=True)['entry'][0]
os.remove(proto_file_path)
expected_result = {
'name':
'BenchmarkTest.testReportBenchmark',
# google.protobuf.json_format.MessageToDict() will convert
# int64 field to string.
'iters':
'2000',
'wall_time':
1000,
'cpu_time':
0,
'throughput':
0,
'extras': {},
'metrics': [{
'name': 'metric_name_1',
'value': 0,
'min_value': 1
}, {
'name': 'metric_name_2',
'value': 90,
'min_value': 0,
'max_value': 95
}]
}
self.assertEqual(2000, benchmark_entries.entry[0].iters)
self.assertDictEqual(expected_result, actual_result)
def process_benchmarks(log_files):
benchmarks = test_log_pb2.BenchmarkEntries()
for f in log_files:
content = tf.gfile.GFile(f).read()
entry = benchmarks.entry.add()
text_format.Merge(content, entry)
return benchmarks
def _global_report_benchmark(
name, iters=None, cpu_time=None, wall_time=None,
throughput=None, extras=None):
"""Method for recording a benchmark directly.
Args:
name: The BenchmarkEntry name.
iters: (optional) How many iterations were run
cpu_time: (optional) Total cpu time in seconds
wall_time: (optional) Total wall time in seconds
throughput: (optional) Throughput (in MB/s)
extras: (optional) Dict mapping string keys to additional benchmark info.
Raises:
TypeError: if extras is not a dict.
IOError: if the benchmark output file already exists.
"""
if extras is not None:
if not isinstance(extras, dict):
raise TypeError("extras must be a dict")
logging.info("Benchmark [%s] iters: %d, wall_time: %g, cpu_time: %g,"
"throughput: %g %s", name, iters if iters is not None else -1,
wall_time if wall_time is not None else -1, cpu_time if
cpu_time is not None else -1, throughput if
throughput is not None else -1, str(extras) if extras else "")
entries = test_log_pb2.BenchmarkEntries()
entry = entries.entry.add()
entry.name = name
if iters is not None:
entry.iters = iters
if cpu_time is not None:
entry.cpu_time = cpu_time
if wall_time is not None:
entry.wall_time = wall_time
if throughput is not None:
entry.throughput = throughput
if extras is not None:
for (k, v) in extras.items():
if isinstance(v, numbers.Number):
entry.extras[k].double_value = v
else:
entry.extras[k].string_value = str(v)
test_env = os.environ.get(TEST_REPORTER_TEST_ENV, None)
if test_env is None:
# Reporting was not requested, just print the proto
print(str(entries))
return
serialized_entry = entries.SerializeToString()
mangled_name = name.replace("/", "__")
output_path = "%s%s" % (test_env, mangled_name)
if gfile.Exists(output_path):
raise IOError("File already exists: %s" % output_path)
with gfile.GFile(output_path, "wb") as out:
out.write(serialized_entry)
def load_benchmarks(benchmarks_dir: str,
run_id: str = "") -> List[BenchmarkResult]:
"""Load benchmarks saved under `benchmarks_dir`."""
filenames = tf.io.gfile.listdir(benchmarks_dir)
entries = test_log_pb2.BenchmarkEntries()
for filename in filenames:
path = os.path.join(benchmarks_dir, filename)
with tf.io.gfile.GFile(path, "rb") as fp:
entries.MergeFromString(fp.read())
return [
BenchmarkResult.from_proto(e, run_id=run_id) for e in entries.entry # pylint: disable=no-member
]
def read_benchmark_result(benchmark_result_file_path):
from google.protobuf import json_format
from tensorflow.core.util import test_log_pb2
if not os.path.isfile(benchmark_result_file_path):
logging.error(
'Failed to read benchmark result because file {} does not exist'.
format(benchmark_result_file_path))
return {}
with open(benchmark_result_file_path, 'rb') as f:
benchmark_entries = test_log_pb2.BenchmarkEntries()
benchmark_entries.ParseFromString(f.read())
return json_format.MessageToDict(
benchmark_entries, preserving_proto_field_name=True)['entry'][0]
def read_benchmark_result(benchmark_result_file_path):
"""Read benchmark result from the protobuf file."""
from google.protobuf import json_format # pylint: disable=g-import-not-at-top
from tensorflow.core.util import test_log_pb2 # pylint: disable=g-import-not-at-top
if not os.path.isfile(benchmark_result_file_path):
logging.error('Failed to read benchmark result because '
'file %s does not exist', benchmark_result_file_path)
return {}
with open(benchmark_result_file_path, 'rb') as f:
benchmark_entries = test_log_pb2.BenchmarkEntries()
benchmark_entries.ParseFromString(f.read())
return json_format.MessageToDict(
benchmark_entries, preserving_proto_field_name=True)['entry'][0]
def _global_report_benchmark(name,
iters=None,
cpu_time=None,
wall_time=None,
throughput=None,
extras=None,
metrics=None):
"""Method for recording a benchmark directly.
Args:
name: The BenchmarkEntry name.
iters: (optional) How many iterations were run
cpu_time: (optional) Total cpu time in seconds
wall_time: (optional) Total wall time in seconds
throughput: (optional) Throughput (in MB/s)
extras: (optional) Dict mapping string keys to additional benchmark info.
metrics: (optional) A list of dict representing metrics generated by the
benchmark. Each dict should contain keys 'name' and'value'. A dict
can optionally contain keys 'min_value' and 'max_value'.
Raises:
TypeError: if extras is not a dict.
IOError: if the benchmark output file already exists.
"""
logging.info(
"Benchmark [%s] iters: %d, wall_time: %g, cpu_time: %g,"
"throughput: %g, extras: %s, metrics: %s", name,
iters if iters is not None else -1,
wall_time if wall_time is not None else -1,
cpu_time if cpu_time is not None else -1,
throughput if throughput is not None else -1,
str(extras) if extras else "None",
str(metrics) if metrics else "None")
entries = test_log_pb2.BenchmarkEntries()
entry = entries.entry.add()
entry.name = name
if iters is not None:
entry.iters = iters
if cpu_time is not None:
entry.cpu_time = cpu_time
if wall_time is not None:
entry.wall_time = wall_time
if throughput is not None:
entry.throughput = throughput
if extras is not None:
if not isinstance(extras, dict):
raise TypeError("extras must be a dict")
for (k, v) in extras.items():
if isinstance(v, numbers.Number):
entry.extras[k].double_value = v
else:
entry.extras[k].string_value = str(v)
if metrics is not None:
if not isinstance(metrics, list):
raise TypeError("metrics must be a list")
for metric in metrics:
if "name" not in metric:
raise TypeError("metric must has a 'name' field")
if "value" not in metric:
raise TypeError("metric must has a 'value' field")
metric_entry = entry.metrics.add()
metric_entry.name = metric["name"]
metric_entry.value = metric["value"]
if "min_value" in metric:
metric_entry.min_value.value = metric["min_value"]
if "max_value" in metric:
metric_entry.max_value.value = metric["max_value"]
test_env = os.environ.get(TEST_REPORTER_TEST_ENV, None)
if test_env is None:
# Reporting was not requested, just print the proto
print(str(entries))
return
serialized_entry = entries.SerializeToString()
mangled_name = name.replace("/", "__")
output_path = "%s%s" % (test_env, mangled_name)
if gfile.Exists(output_path):
raise IOError("File already exists: %s" % output_path)
with gfile.GFile(output_path, "wb") as out:
out.write(serialized_entry)