src/python/m5/stats/gem5stats.py - public/gem5 - Git at Google

 # Copyright (c) 2021 The Regents of The University of California
 # All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions are
 # met: redistributions of source code must retain the above copyright
 # notice, this list of conditions and the following disclaimer;
 # redistributions in binary form must reproduce the above copyright
 # notice, this list of conditions and the following disclaimer in the
 # documentation and/or other materials provided with the distribution;
 # neither the name of the copyright holders nor the names of its
 # contributors may be used to endorse or promote products derived from
 # this software without specific prior written permission.
 #
 # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
 # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
 # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
 # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
 # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
 # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
 # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

 """
 This serves as the bridge between the gem5 statistics exposed via PyBind11 and
 the Python Stats model.
 """

 from datetime import datetime
 from typing import IO, List, Union

 import _m5.stats
 from m5.objects import *
 from m5.ext.pystats.group import *
 from m5.ext.pystats.simstat import *
 from m5.ext.pystats.statistic import *
 from m5.ext.pystats.storagetype import *

 class JsonOutputVistor():
     """
     This is a helper vistor class used to include a JSON output via the stats
     API (`src/python/m5/stats/__init__.py`).
     """
     file: str
     json_args: Dict

     def __init__(self, file: str, **kwargs):
         """
         Parameters
         ----------

         file: str
             The output file location in which the JSON will be dumped.

         kwargs: Dict[str, Any]
             Additional parameters to be passed to the `json.dumps` method.
         """

         self.file = file
         self.json_args = kwargs

     def dump(self, roots: Union[List[SimObject], Root]) -> None:
         """
         Dumps the stats of a simulation root (or list of roots) to the output
         JSON file specified in the JsonOutput constructor.

         WARNING: This dump assumes the statistics have already been prepared
         for the target root.

         Parameters
         ----------

         roots: Union[List[Root], Root]]
             The Root, or List of roots, whose stats are are to be dumped JSON.
         """

         with open(self.file, 'w') as fp:
             simstat = get_simstat(root=roots, prepare_stats=False)
             simstat.dump(fp=fp, **self.json_args)

 def get_stats_group(group: _m5.stats.Group) -> Group:
     """
     Translates a gem5 Group object into a Python stats Group object. A Python
     statistic Group object is a dictionary of labeled Statistic objects. Any
     gem5 object passed to this will have its `getStats()` and `getStatGroups`
     function called, and all the stats translated (inclusive of the stats
     further down the hierarchy).

     Parameters
     ----------
     group: _m5.stats.Group
         The gem5 _m5.stats.Group object to be translated to be a Python stats
         Group object. Typically this will be a gem5 SimObject.

     Returns
     -------
     Group
         The stats group object translated from the input gem5 object.
     """

     stats_dict = {}

     for stat in group.getStats():
         statistic = __get_statistic(stat)
         if statistic is not None:
             stats_dict[stat.name] = statistic

     for key in group.getStatGroups():
         stats_dict[key] = get_stats_group(group.getStatGroups()[key])

     return Group(**stats_dict)

 def __get_statistic(statistic: _m5.stats.Info) -> Optional[Statistic]:
     """
     Translates a _m5.stats.Info object into a Statistic object, to process
     statistics at the Python level.

     Parameters
     ----------
     statistic: Info
         The Info object to be translated to a Statistic object.

     Returns
     -------
     Optional[Statistic]
         The Statistic object of the Info object. Returns None if Info object
         cannot be translated.
     """

     assert(isinstance(statistic, _m5.stats.Info))
     statistic.prepare()

     if isinstance(statistic, _m5.stats.ScalarInfo):
         return __get_scaler(statistic)
     elif isinstance(statistic, _m5.stats.DistInfo):
         return __get_distribution(statistic)
     elif isinstance(statistic, _m5.stats.FormulaInfo):
         # We don't do anything with Formula's right now.
         # We may never do so, see https://gem5.atlassian.net/browse/GEM5-868.
         pass
     elif isinstance(statistic, _m5.stats.VectorInfo):
         return __get_vector(statistic)

     return None

 def __get_scaler(statistic: _m5.stats.ScalarInfo) -> Scalar:
     value = statistic.value
     unit = statistic.unit
     description = statistic.desc
     # ScalarInfo uses the C++ `double`.
     datatype = StorageType["f64"]

     return Scalar(
                   value=value,
                   unit=unit,
                   description=description,
                   datatype=datatype,
                  )

 def __get_distribution(statistic: _m5.stats.DistInfo) -> Distribution:
     unit = statistic.unit
     description = statistic.desc
     value = statistic.values
     bin_size = statistic.bucket_size
     min = statistic.min_val
     max = statistic.max_val
     num_bins = len(value)
     sum_val = statistic.sum
     sum_squared = statistic.squares
     underflow = statistic.underflow
     overflow = statistic.overflow
     logs = statistic.logs
     # DistInfo uses the C++ `double`.
     datatype = StorageType["f64"]

     return Distribution(
                         value=value,
                         min=min,
                         max=max,
                         num_bins=num_bins,
                         bin_size=bin_size,
                         sum = sum_val,
                         sum_squared = sum_squared,
                         underflow = underflow,
                         overflow = overflow,
                         logs = logs,
                         unit=unit,
                         description=description,
                         datatype=datatype,
                         )

 def __get_vector(statistic: _m5.stats.VectorInfo) -> Vector:
     to_add = dict()

     for index in range(statistic.size):
         # All the values in a Vector are Scalar values
         value = statistic.value[index]
         unit = statistic.unit
         description = statistic.subdescs[index]
         # ScalarInfo uses the C++ `double`.
         datatype = StorageType["f64"]

         # Sometimes elements within a vector are defined by their name. Other
         # times they have no name. When a name is not available, we name the
         # stat the index value.
         if str(statistic.subnames[index]):
             index_string = str(statistic.subnames[index])
         else:
             index_string = str(index)

         to_add[index_string] = Scalar(
                                       value=value,
                                       unit=unit,
                                       description=description,
                                       datatype=datatype,
                                       )

     return Vector(scalar_map=to_add)

 def _prepare_stats(group: _m5.stats.Group):
     """
     Prepares the statistics for dumping.
     """

     group.preDumpStats()

     for stat in group.getStats():
         stat.prepare()

     for child in group.getStatGroups().values():
         _prepare_stats(child)


 def get_simstat(root: Union[Root, List[SimObject]],
                 prepare_stats: bool = True) -> SimStat:
     """
     This function will return the SimStat object for a simulation. From the
     SimStat object all stats within the current gem5 simulation are present.

     Parameters
     ----------
     root: Union[Root, List[Root]]
         The root, or a list of Simobjects, of the simulation for translation to
         a SimStat object.

     prepare_stats: bool
         Dictates whether the stats are to be prepared prior to creating the
         SimStat object. By default this is 'True'.

     Returns
     -------
     SimStat
         The SimStat Object of the current simulation.

     """
     stats_map = {}
     creation_time = datetime.now()
     time_converstion = None # TODO https://gem5.atlassian.net/browse/GEM5-846
     final_tick = Root.getInstance().resolveStat("finalTick").value
     sim_ticks = Root.getInstance().resolveStat("simTicks").value
     simulated_begin_time = int(final_tick - sim_ticks)
     simulated_end_time = int(final_tick)

     if prepare_stats:
         _m5.stats.processDumpQueue()

     for r in root:
         if isinstance(r, Root):
             if prepare_stats:
                 _prepare_stats(r)
             for key in r.getStatGroups():
                 stats_map[key] = get_stats_group(r.getStatGroups()[key])
         elif isinstance(r, SimObject):
             if prepare_stats:
                 _prepare_stats(r)
             stats_map[r.name] = get_stats_group(r)
         else:
             raise TypeError("Object (" + str(r) + ") passed is neither Root "
                             "nor SimObject. " + __name__ + " only processes "
                             "Roots, SimObjects, or a list of Roots and/or "
                             "SimObjects.")


     return SimStat(
                    creation_time=creation_time,
                    time_conversion=time_converstion,
                    simulated_begin_time=simulated_begin_time,
                    simulated_end_time=simulated_end_time,
                    **stats_map,
                   )
	# Copyright (c) 2021 The Regents of The University of California
	# All rights reserved.
	#
	# Redistribution and use in source and binary forms, with or without
	# modification, are permitted provided that the following conditions are
	# met: redistributions of source code must retain the above copyright
	# notice, this list of conditions and the following disclaimer;
	# redistributions in binary form must reproduce the above copyright
	# notice, this list of conditions and the following disclaimer in the
	# documentation and/or other materials provided with the distribution;
	# neither the name of the copyright holders nor the names of its
	# contributors may be used to endorse or promote products derived from
	# this software without specific prior written permission.
	#
	# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
	# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
	# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
	# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
	# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
	# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
	# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
	# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
	# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
	# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
	# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

	"""
	This serves as the bridge between the gem5 statistics exposed via PyBind11 and
	the Python Stats model.
	"""

	from datetime import datetime
	from typing import IO, List, Union

	import _m5.stats
	from m5.objects import *
	from m5.ext.pystats.group import *
	from m5.ext.pystats.simstat import *
	from m5.ext.pystats.statistic import *
	from m5.ext.pystats.storagetype import *

	class JsonOutputVistor():
	"""
	This is a helper vistor class used to include a JSON output via the stats
	API (`src/python/m5/stats/__init__.py`).
	"""
	file: str
	json_args: Dict

	def __init__(self, file: str, **kwargs):
	"""
	Parameters
	----------

	file: str
	The output file location in which the JSON will be dumped.

	kwargs: Dict[str, Any]
	Additional parameters to be passed to the `json.dumps` method.
	"""

	self.file = file
	self.json_args = kwargs

	def dump(self, roots: Union[List[SimObject], Root]) -> None:
	"""
	Dumps the stats of a simulation root (or list of roots) to the output
	JSON file specified in the JsonOutput constructor.

	WARNING: This dump assumes the statistics have already been prepared
	for the target root.

	Parameters
	----------

	roots: Union[List[Root], Root]]
	The Root, or List of roots, whose stats are are to be dumped JSON.
	"""

	with open(self.file, 'w') as fp:
	simstat = get_simstat(root=roots, prepare_stats=False)
	simstat.dump(fp=fp, **self.json_args)

	def get_stats_group(group: _m5.stats.Group) -> Group:
	"""
	Translates a gem5 Group object into a Python stats Group object. A Python
	statistic Group object is a dictionary of labeled Statistic objects. Any
	gem5 object passed to this will have its `getStats()` and `getStatGroups`
	function called, and all the stats translated (inclusive of the stats
	further down the hierarchy).

	Parameters
	----------
	group: _m5.stats.Group
	The gem5 _m5.stats.Group object to be translated to be a Python stats
	Group object. Typically this will be a gem5 SimObject.

	Returns
	-------
	Group
	The stats group object translated from the input gem5 object.
	"""

	stats_dict = {}

	for stat in group.getStats():
	statistic = __get_statistic(stat)
	if statistic is not None:
	stats_dict[stat.name] = statistic

	for key in group.getStatGroups():
	stats_dict[key] = get_stats_group(group.getStatGroups()[key])

	return Group(**stats_dict)

	def __get_statistic(statistic: _m5.stats.Info) -> Optional[Statistic]:
	"""
	Translates a _m5.stats.Info object into a Statistic object, to process
	statistics at the Python level.

	Parameters
	----------
	statistic: Info
	The Info object to be translated to a Statistic object.

	Returns
	-------
	Optional[Statistic]
	The Statistic object of the Info object. Returns None if Info object
	cannot be translated.
	"""

	assert(isinstance(statistic, _m5.stats.Info))
	statistic.prepare()

	if isinstance(statistic, _m5.stats.ScalarInfo):
	return __get_scaler(statistic)
	elif isinstance(statistic, _m5.stats.DistInfo):
	return __get_distribution(statistic)
	elif isinstance(statistic, _m5.stats.FormulaInfo):
	# We don't do anything with Formula's right now.
	# We may never do so, see https://gem5.atlassian.net/browse/GEM5-868.
	pass
	elif isinstance(statistic, _m5.stats.VectorInfo):
	return __get_vector(statistic)

	return None

	def __get_scaler(statistic: _m5.stats.ScalarInfo) -> Scalar:
	value = statistic.value
	unit = statistic.unit
	description = statistic.desc
	# ScalarInfo uses the C++ `double`.
	datatype = StorageType["f64"]

	return Scalar(
	value=value,
	unit=unit,
	description=description,
	datatype=datatype,
	)

	def __get_distribution(statistic: _m5.stats.DistInfo) -> Distribution:
	unit = statistic.unit
	description = statistic.desc
	value = statistic.values
	bin_size = statistic.bucket_size
	min = statistic.min_val
	max = statistic.max_val
	num_bins = len(value)
	sum_val = statistic.sum
	sum_squared = statistic.squares
	underflow = statistic.underflow
	overflow = statistic.overflow
	logs = statistic.logs
	# DistInfo uses the C++ `double`.
	datatype = StorageType["f64"]

	return Distribution(
	value=value,
	min=min,
	max=max,
	num_bins=num_bins,
	bin_size=bin_size,
	sum = sum_val,
	sum_squared = sum_squared,
	underflow = underflow,
	overflow = overflow,
	logs = logs,
	unit=unit,
	description=description,
	datatype=datatype,
	)

	def __get_vector(statistic: _m5.stats.VectorInfo) -> Vector:
	to_add = dict()

	for index in range(statistic.size):
	# All the values in a Vector are Scalar values
	value = statistic.value[index]
	unit = statistic.unit
	description = statistic.subdescs[index]
	# ScalarInfo uses the C++ `double`.
	datatype = StorageType["f64"]

	# Sometimes elements within a vector are defined by their name. Other
	# times they have no name. When a name is not available, we name the
	# stat the index value.
	if str(statistic.subnames[index]):
	index_string = str(statistic.subnames[index])
	else:
	index_string = str(index)

	to_add[index_string] = Scalar(
	value=value,
	unit=unit,
	description=description,
	datatype=datatype,
	)

	return Vector(scalar_map=to_add)

	def _prepare_stats(group: _m5.stats.Group):
	"""
	Prepares the statistics for dumping.
	"""

	group.preDumpStats()

	for stat in group.getStats():
	stat.prepare()

	for child in group.getStatGroups().values():
	_prepare_stats(child)


	def get_simstat(root: Union[Root, List[SimObject]],
	prepare_stats: bool = True) -> SimStat:
	"""
	This function will return the SimStat object for a simulation. From the
	SimStat object all stats within the current gem5 simulation are present.

	Parameters
	----------
	root: Union[Root, List[Root]]
	The root, or a list of Simobjects, of the simulation for translation to
	a SimStat object.

	prepare_stats: bool
	Dictates whether the stats are to be prepared prior to creating the
	SimStat object. By default this is 'True'.

	Returns
	-------
	SimStat
	The SimStat Object of the current simulation.

	"""
	stats_map = {}
	creation_time = datetime.now()
	time_converstion = None # TODO https://gem5.atlassian.net/browse/GEM5-846
	final_tick = Root.getInstance().resolveStat("finalTick").value
	sim_ticks = Root.getInstance().resolveStat("simTicks").value
	simulated_begin_time = int(final_tick - sim_ticks)
	simulated_end_time = int(final_tick)

	if prepare_stats:
	_m5.stats.processDumpQueue()

	for r in root:
	if isinstance(r, Root):
	if prepare_stats:
	_prepare_stats(r)
	for key in r.getStatGroups():
	stats_map[key] = get_stats_group(r.getStatGroups()[key])
	elif isinstance(r, SimObject):
	if prepare_stats:
	_prepare_stats(r)
	stats_map[r.name] = get_stats_group(r)
	else:
	raise TypeError("Object (" + str(r) + ") passed is neither Root "
	"nor SimObject. " + __name__ + " only processes "
	"Roots, SimObjects, or a list of Roots and/or "
	"SimObjects.")



	return SimStat(
	creation_time=creation_time,
	time_conversion=time_converstion,
	simulated_begin_time=simulated_begin_time,
	simulated_end_time=simulated_end_time,
	**stats_map,
	)