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"""
This module support timing of code blocks.
"""
# Copyright (c) 2021, NVIDIA CORPORATION. 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.
import time
import numpy as np
import torch
__all__ = ["NamedTimer"]
class NamedTimer(object):
"""
A timer class that supports multiple named timers.
A named timer can be used multiple times, in which case the average
dt will be returned.
A named timer cannot be started if it is already currently running.
Use case: measuring execution of multiple code blocks.
"""
_REDUCTION_TYPE = ["mean", "sum", "min", "max", "none"]
def __init__(self, reduction="mean", sync_cuda=False, buffer_size=-1):
"""
Args:
reduction (str): reduction over multiple timings of the same timer
(none - returns the list instead of a scalar)
sync_cuda (bool): if True torch.cuda.synchronize() is called for start/stop
buffer_size (int): if positive, limits the number of stored measures per name
"""
if reduction not in self._REDUCTION_TYPE:
raise ValueError(f"Unknown reduction={reduction} please use one of {self._REDUCTION_TYPE}")
self._reduction = reduction
self._sync_cuda = sync_cuda
self._buffer_size = buffer_size
self.reset()
def __getitem__(self, k):
return self.get(k)
@property
def buffer_size(self):
return self._buffer_size
@property
def _reduction_fn(self):
if self._reduction == "none":
fn = lambda x: x
else:
fn = getattr(np, self._reduction)
return fn
def reset(self, name=None):
"""
Resents all / specific timer
Args:
name (str): timer name to reset (if None all timers are reset)
"""
if name is None:
self.timers = {}
else:
self.timers[name] = {}
def start(self, name=""):
"""
Starts measuring a named timer.
Args:
name (str): timer name to start
"""
timer_data = self.timers.get(name, {})
if "start" in timer_data:
raise RuntimeError(f"Cannot start timer = '{name}' since it is already active")
# synchronize pytorch cuda execution if supported
if self._sync_cuda and torch.cuda.is_initialized():
torch.cuda.synchronize()
timer_data["start"] = time.time()
self.timers[name] = timer_data
def stop(self, name=""):
"""
Stops measuring a named timer.
Args:
name (str): timer name to stop
"""
timer_data = self.timers.get(name, None)
if (timer_data is None) or ("start" not in timer_data):
raise RuntimeError(f"Cannot end timer = '{name}' since it is not active")
# synchronize pytorch cuda execution if supported
if self._sync_cuda and torch.cuda.is_initialized():
torch.cuda.synchronize()
# compute dt and make timer inactive
dt = time.time() - timer_data.pop("start")
# store dt
timer_data["dt"] = timer_data.get("dt", []) + [dt]
# enforce buffer_size if positive
if self._buffer_size > 0:
timer_data["dt"] = timer_data["dt"][-self._buffer_size :]
self.timers[name] = timer_data
def active_timers(self):
"""
Return list of all active named timers
"""
return [k for k, v in self.timers.items() if ("start" in v)]
def get(self, name=""):
"""
Returns the value of a named timer
Args:
name (str): timer name to return
"""
dt_list = self.timers[name].get("dt", [])
return self._reduction_fn(dt_list)
def export(self):
"""
Exports a dictionary with average/all dt per named timer
"""
fn = self._reduction_fn
data = {k: fn(v["dt"]) for k, v in self.timers.items() if ("dt" in v)}
return data
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