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# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utilities for measuring elapsed time."""
import contextlib
import logging
import threading
import time
from tensorboard.util import tb_logging
logger = tb_logging.get_logger()
def log_latency(region_name_or_function_to_decorate, log_level=None):
"""Log latency in a function or region.
Three usages are supported. As a decorator:
>>> @log_latency
... def function_1():
... pass
...
As a decorator with a custom label for the region:
>>> @log_latency("custom_label")
... def function_2():
... pass
...
As a context manager:
>>> def function_3():
... with log_latency("region_within_function"):
... pass
...
Args:
region_name_or_function_to_decorate: Either: a `str`, in which
case the result of this function may be used as either a
decorator or a context manager; or a callable, in which case
the result of this function is a decorated version of that
callable.
log_level: Optional integer logging level constant. Defaults to
`logging.INFO`.
Returns:
A decorated version of the input callable, or a dual
decorator/context manager with the input region name.
"""
if log_level is None:
log_level = logging.INFO
if isinstance(region_name_or_function_to_decorate, str):
region_name = region_name_or_function_to_decorate
return _log_latency(region_name, log_level)
else:
function_to_decorate = region_name_or_function_to_decorate
qualname = getattr(function_to_decorate, "__qualname__", None)
if qualname is None:
qualname = str(function_to_decorate)
decorator = _log_latency(qualname, log_level)
return decorator(function_to_decorate)
class _ThreadLocalStore(threading.local):
def __init__(self):
self.nesting_level = 0
_store = _ThreadLocalStore()
@contextlib.contextmanager
def _log_latency(name, log_level):
if not logger.isEnabledFor(log_level):
yield
return
start_level = _store.nesting_level
try:
started = time.time()
_store.nesting_level = start_level + 1
indent = (" " * 2) * start_level
thread = threading.current_thread()
prefix = "%s[%x]%s" % (thread.name, thread.ident, indent)
_log(log_level, "%s ENTER %s", prefix, name)
yield
finally:
_store.nesting_level = start_level
elapsed = time.time() - started
_log(
log_level,
"%s LEAVE %s - %0.6fs elapsed",
prefix,
name,
elapsed,
)
def _log(log_level, msg, *args):
# Forwarding method to ensure that all logging statements
# originating in this module have the same line number; if the
# "ENTER" log is on a line with 2-digit number and the "LEAVE" log
# is on a line with 3-digit number, the logs are misaligned and
# harder to read.
logger.log(log_level, msg, *args)
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