def is_benchmark_supported(benchmark : Benchmark):
"""returns True if the provided benchmark is supported by the tool and if the given benchmark should appear on the generated benchmark list"""
if benchmark.is_pta() and benchmark.is_prism():
# The PTAs from Prism are not supported because either
# modest can't apply digital clocks semantic due to open constraints, or
# modest puts branch-rewards on the models (these are not supported by Storm)
return False
if benchmark.is_prism_inf() and benchmark.is_ctmc():
# Storm does not support the CTMCs with infinite state-spaces
return False
# do not include models with state space largern than 50 Mio
if benchmark.get_num_states_tweak() is not None and benchmark.get_num_states_tweak() > 50000000:
return False
# do not select dfts with a file parameter "R" that is set to true
if benchmark.is_galileo():
for p in benchmark.get_file_parameters():
if p["name"] == "R" and p["value"] == True:
return False
if benchmark.is_ctmc():
return True
return False
def get_invocations(benchmark: Benchmark):
"""
Returns a list of invocations that invoke the tool for the given benchmark.
It can be assumed that the current directory is the directory from which execute_invocations.py is executed.
For QCOMP 2020, this should return a list of invocations for all tracks in which the tool can take part. For each track an invocation with default settings has to be provided and in addition, an optimized setting (e.g., the fastest engine and/or solution technique for this benchmark) can be specified. Only information about the model type, the property type and the state space size are allowed to be used to tweak the parameters.
If this benchmark is not supported, an empty list has to be returned.
"""
if not is_benchmark_supported(benchmark):
return []
short = benchmark.get_model_short_name()
prop = benchmark.get_property_name()
prop_type = benchmark.get_short_property_type()
params = benchmark.get_parameter_values_string()
instance = short + "." + params
size = benchmark.get_num_states_tweak()
invocations = []
benchmark_settings = "--props " + benchmark.get_property_name()
if benchmark.get_open_parameter_def_string() != "":
benchmark_settings += " -E " + benchmark.get_open_parameter_def_string(
)
default_base = "modes/modest modes --unsafe --max-run-length 0 " + benchmark.get_janifilename(
) + " " + benchmark_settings + " -O out.txt Minimal"
#
# Track "probably-epsilon-correct"
#
precision = "5e-2"
default_cmd = default_base + " --width $PRECISION --relative-width"
if benchmark.is_dtmc() or benchmark.is_ctmc():
add_invocations(invocations, "probably-epsilon-correct",
default_cmd.replace("$PRECISION", precision))
#
# Track "often-epsilon-correct"
#
precision = "1e-3"
if benchmark.is_dtmc() or benchmark.is_ctmc():
add_invocations(invocations, "often-epsilon-correct",
default_cmd.replace("$PRECISION", precision))
#
# Track "often-epsilon-correct-10-min"
#
if benchmark.is_dtmc() or benchmark.is_ctmc():
default_cmd = default_base + " -N 2147483647"
else:
default_cmd = default_base + " --width 2e-2 --relative-width --lss Interruptible 1000000 -L 1000"
if benchmark.is_pta():
default_cmd += " --digital-clocks"
add_invocations(invocations, "often-epsilon-correct-10-min", default_cmd)
#
# Done
#
return invocations
def get_invocations(benchmark: Benchmark):
"""
Returns a list of invocations that invoke the tool for the given benchmark.
It can be assumed that the current directory is the directory from which execute_invocations.py is executed.
For QCOMP 2020, this should return a list of invocations for all tracks in which the tool can take part. For each track an invocation with default settings has to be provided and in addition, an optimized setting (e.g., the fastest engine and/or solution technique for this benchmark) can be specified. Only information about the model type, the property type and the state space size are allowed to be used to tweak the parameters.
If this benchmark is not supported, an empty list has to be returned.
"""
if not is_benchmark_supported(benchmark):
return []
# Gather options that are needed for this particular benchmark for any invocation of PRISM
benchmark_instance = get_prism_invocation_model_prop_instance(benchmark)
invocations = []
basic_args = "{}".format(prism_mem_args)
# epsilon-correct (all models but PTAs), default settings
if (benchmark.get_model_type() != "pta"):
# Use interval iteration generally (or uniformisation for time-bounded CTMCs)
default_args = "-ii"
# Choose engine heuristically
default_args += " -heuristic speed"
# Required precision (default anyway)
default_args += " -e 1e-6"
# Usual II settings when there is plenty of memory
default_args += " -ddextraactionvars 100"
# Increase maxiters (since QComp has a timeout anyway)
default_args += " -maxiters 1000000"
default_inv = Invocation()
default_inv.identifier = "default"
default_inv.track_id = "epsilon-correct"
default_inv.add_command(prism_bin + " " + basic_args + " " +
default_args + " " + benchmark_instance)
invocations.append(default_inv)
# epsilon-correct (all models but PTAs), specific settings
if (benchmark.get_model_type() != "pta"):
# Choose method/engine
# Use interval iteration generally (or uniformisation for time-bounded CTMCs)
if benchmark.get_model_short_name() == "haddad-monmege":
specific_args = "-exact"
elif (benchmark.get_num_states_tweak() == None
or benchmark.get_num_states_tweak() >= 20000000):
specific_args = "-ii -mtbdd"
else:
specific_args = "-ii -heuristic speed"
# Required precision (default anyway)
specific_args += " -e 1e-6"
# Usual II settings when there is plenty of memory
specific_args += " -ddextraactionvars 100"
# Increase maxiters (since QComp has a timeout anyway)
specific_args += " -maxiters 1000000"
specific_inv = Invocation()
specific_inv.identifier = "specific"
specific_inv.track_id = "epsilon-correct"
specific_inv.add_command(prism_bin + " " + basic_args + " " +
specific_args + " " + benchmark_instance)
invocations.append(specific_inv)
# often-epsilon-correct (all models), default settings
if (True):
# Choose engine heuristically
default_args = "-heuristic speed"
# Required precision (just use default 1e-6, as agreed for QComp'19)
default_args += " -e 1e-6"
# Increase maxiters (since QComp has a timeout anyway)
default_args += " -maxiters 1000000"
default_inv = Invocation()
default_inv.identifier = "default"
default_inv.track_id = "often-epsilon-correct"
default_inv.add_command(prism_bin + " " + basic_args + " " +
default_args + " " + benchmark_instance)
invocations.append(default_inv)
# often-epsilon-correct (all models), specific settings
if (True):
# Choose method/engine
if benchmark.get_model_short_name() == "haddad-monmege":
specific_args = "-exact"
elif (benchmark.get_num_states_tweak() == None
or benchmark.get_num_states_tweak() >= 20000000):
specific_args = "-mtbdd"
else:
specific_args = "-heuristic speed"
# Required precision (just use default 1e-6, as agreed for QComp'19)
specific_args += " -e 1e-6"
# Increase maxiters (since QComp has a timeout anyway)
specific_args += " -maxiters 1000000"
specific_inv = Invocation()
specific_inv.identifier = "specific"
specific_inv.track_id = "often-epsilon-correct"
specific_inv.add_command(prism_bin + " " + basic_args + " " +
specific_args + " " + benchmark_instance)
invocations.append(specific_inv)
# probably-epsilon-correct (all models but PTAs), default settings
if (benchmark.get_model_type() != "pta"):
# Use interval iteration generally (or uniformisation for time-bounded CTMCs)
if (benchmark.get_model_type() == "ctmc"
and benchmark.is_time_bounded_probabilistic_reachability()):
default_args = ""
else:
default_args = "-ii -e 5e-2"
# Choose engine heuristically
default_args += " -heuristic speed"
# Usual II settings when there is plenty of memory
default_args += " -ddextraactionvars 100"
# Increase maxiters (since QComp has a timeout anyway)
default_args += " -maxiters 1000000"
default_inv = Invocation()
default_inv.identifier = "default"
default_inv.track_id = "probably-epsilon-correct"
default_inv.add_command(prism_bin + " " + basic_args + " " +
default_args + " " + benchmark_instance)
invocations.append(default_inv)
# probably-epsilon-correct (all models but PTAs), specific settings
if (benchmark.get_model_type() != "pta"):
# Choose method/engine
# Use interval iteration generally (or uniformisation for time-bounded CTMCs)
if benchmark.get_model_short_name() == "haddad-monmege":
specific_args = "-exact"
elif (benchmark.get_model_type() == "ctmc"
and benchmark.is_time_bounded_probabilistic_reachability()):
specific_args = ""
elif (benchmark.get_num_states_tweak() == None
or benchmark.get_num_states_tweak() >= 20000000):
specific_args = "-ii -e 5e-2 -mtbdd"
else:
specific_args = "-ii -e 5e-2 -heuristic speed"
# Usual II settings when there is plenty of memory
specific_args += " -ddextraactionvars 100"
# Increase maxiters (since QComp has a timeout anyway)
specific_args += " -maxiters 1000000"
specific_inv = Invocation()
specific_inv.identifier = "specific"
specific_inv.track_id = "probably-epsilon-correct"
specific_inv.add_command(prism_bin + " " + basic_args + " " +
specific_args + " " + benchmark_instance)
invocations.append(specific_inv)
return invocations
def get_invocations(benchmark: Benchmark):
"""
Returns a list of invocations that invoke the tool for the given benchmark.
It can be assumed that the current directory is the directory from which execute_invocations.py is executed.
For QCOMP 2020, this should return a list of invocations for all tracks in which the tool can take part. For each track an invocation with default settings has to be provided and in addition, an optimized setting (e.g., the fastest engine and/or solution technique for this benchmark) can be specified. Only information about the model type, the property type and the state space size are allowed to be used to tweak the parameters.
If this benchmark is not supported, an empty list has to be returned.
"""
if not is_benchmark_supported(benchmark):
return []
prec = dict()
prec["epsilon-correct"] = "0.000001"
prec["probably-epsilon-correct"] = "0.05"
prec["often-epsilon-correct"] = "0.001"
prec["often-epsilon-correct-10-min"] = "0.001"
result = []
for track in prec.keys():
benchmark_settings = "./pet.sh reachability --precision {} --relative-error --only-result -m {} -p {} --property {}".format(
prec[track],
benchmark.get_prism_program_filename(),
benchmark.get_prism_property_filename(),
benchmark.get_property_name(),
)
if benchmark.get_open_parameter_def_string() != "":
benchmark_settings += " --const {}".format(
benchmark.get_open_parameter_def_string())
if ("haddad" in benchmark.get_prism_program_filename()
or "gathering" in benchmark.get_prism_program_filename()):
benchmark_settings = "./fix-syntax " + benchmark_settings
# default settings PET eps-corr
default_inv = Invocation()
default_inv.identifier = "default"
default_inv.note = "Default settings."
default_inv.track_id = track
default_inv.add_command(benchmark_settings)
result += [default_inv]
if (track == "epsilon-correct" or benchmark.get_model_type() == "ctmc"
or "haddad" in benchmark.get_prism_program_filename()
or "csma" in benchmark.get_prism_program_filename()
or "wlan" in benchmark.get_prism_program_filename()
or "gathering" in benchmark.get_prism_program_filename()):
# smc is prob eps correct, cannot handle ctmc and haddad monmege cannot be parsed by it
continue
if benchmark.get_num_states_tweak() is None:
# need this info
continue
smc_settings = "./smc.sh {} {} -prop {} -heuristic RTDP_ADJ -RTDP_ADJ_OPTS 1 -colourParams S:{},Av:10,e:{},d:0.05,p:0.05,post:64".format(
benchmark.get_prism_program_filename(),
benchmark.get_prism_property_filename(),
benchmark.get_property_name(),
benchmark.get_num_states_tweak(),
prec[track],
)
if benchmark.get_open_parameter_def_string() != "":
smc_settings += " -const {}".format(
benchmark.get_open_parameter_def_string())
if ("haddad" in benchmark.get_prism_program_filename()
or "gathering" in benchmark.get_prism_program_filename()):
smc_settings = "./fix-syntax " + smc_settings
# SMC invocations
SMC_inv = Invocation()
SMC_inv.identifier = "specific"
SMC_inv.note = "Statistical model checking with limited information (no transition probabilities)"
SMC_inv.track_id = track
SMC_inv.add_command(smc_settings)
result += [SMC_inv]
return result
def get_invocations(benchmark : Benchmark):
"""
Returns a list of invocations that invoke the tool for the given benchmark.
It can be assumed that the current directory is the directory from which execute_invocations.py is executed.
For QCOMP 2020, this should return a list of invocations for all tracks in which the tool can take part. For each track an invocation with default settings has to be provided and in addition, an optimized setting (e.g., the fastest engine and/or solution technique for this benchmark) can be specified. Only information about the model type, the property type and the state space size are allowed to be used to tweak the parameters.
If this benchmark is not supported, an empty list has to be returned.
"""
if not is_benchmark_supported(benchmark):
return []
short = benchmark.get_model_short_name()
prop = benchmark.get_property_name()
prop_type = benchmark.get_short_property_type()
params = benchmark.get_parameter_values_string()
instance = short + "." + params
size = benchmark.get_num_states_tweak()
invocations = []
benchmark_settings = ""
if benchmark.get_open_parameter_def_string() != "":
benchmark_settings += "-E " + benchmark.get_open_parameter_def_string() + " "
benchmark_settings += "--props " + benchmark.get_property_name()
default_base = "mcsta/modest mcsta " + benchmark.get_janifilename() + " " + benchmark_settings + " -O out.txt Minimal --unsafe --es"
specific_base = default_base + tweak_memory(benchmark) # specific settings
default_base += " -S Memory"
#
# Track "floating-point-correct"
#
skip = False
precision = "0"
default_cmd = default_base + " --no-partial-results"
specific_cmd = specific_base + " --no-partial-results"
if prop_type == "S" or (benchmark.is_ma() or benchmark.is_ctmc()) and benchmark.is_time_bounded_probabilistic_reachability():
# long-run average or time-bounded on MA/CTMC: no fp-exact algorithm available
skip = True
elif prop_type == "P":
# probabilistic reachability: try value iteration until fp-fixpoint
default_cmd += " --p0 --p1 --epsilon 0 --absolute-epsilon"
specific_cmd += " --p0 --p1 --epsilon 0 --absolute-epsilon"
elif prop_type == "E":
# expected reward: try value iteration until fp-fixpoint
default_cmd += " --epsilon 0 --absolute-epsilon"
specific_cmd += " --epsilon 0 --absolute-epsilon"
elif prop_type == "Pb":
# state elimination is fp-exact
default_cmd += " --reward-bounded-alg StateElimination"
if "-S Memory" in specific_cmd:
specific_cmd += " --reward-bounded-alg StateElimination"
else:
specific_cmd += " --epsilon 0 --absolute-epsilon"
if not skip:
add_invocations(invocations, "floating-point-correct", default_cmd, tweak(benchmark, specific_cmd))
#
# Track "epsilon-correct"
#
skip = False
precision = "1e-6"
default_cmd = default_base + " --no-partial-results"
specific_cmd = specific_base + " --no-partial-results"
if prop_type == "S":
# long-run average: default is the sound algorithm based on value iteration
default_cmd += " --width $PRECISION --relative-width"
specific_cmd += " --width $PRECISION --relative-width"
elif (benchmark.is_ma() or benchmark.is_ctmc()) and benchmark.is_time_bounded_probabilistic_reachability():
# time-bounded probability for CTMC and MA: default is sound Unif+
default_cmd += " --width $PRECISION --relative-width"
specific_cmd += " --width $PRECISION --relative-width"
elif benchmark.is_pta() and prop_type == "Pb":
# time-bounded reachability for PTA: state elimination recommended
default_cmd += " --reward-bounded-alg StateElimination"
if "-S Memory" in specific_cmd:
specific_cmd += " --reward-bounded-alg StateElimination"
else:
specific_cmd += " --alg IntervalIteration --width $PRECISION --relative-width"
elif prop_type == "Pb":
# reward-bounded probability: default is unsound VI, so need to change to II (SVI and OVI not yet implemented for this case)
default_cmd += " --alg IntervalIteration --width $PRECISION --relative-width"
specific_cmd += " --alg IntervalIteration --width $PRECISION --relative-width"
else:
# unbounded probability or expected reward: default is unsound VI, so need to change to OVI
default_cmd += " --alg OptimisticValueIteration --epsilon $PRECISION --width $PRECISION --relative-width"
specific_cmd += " --alg OptimisticValueIteration --epsilon $PRECISION --width $PRECISION --relative-width"
if prop_type == "P" and benchmark.is_dtmc() or benchmark.is_ctmc():
# for unbounded probabilities on DTMC and CTMC: use 0/1 preprocessing
default_cmd += " --p0 --p1"
specific_cmd += " --p0 --p1"
if not skip:
add_invocations(invocations, "epsilon-correct", default_cmd.replace("$PRECISION", precision), tweak(benchmark, specific_cmd).replace("$PRECISION", precision))
#
# Track "probably-epsilon-correct"
#
skip = False
precision = "5e-2"
if not skip:
add_invocations(invocations, "probably-epsilon-correct", default_cmd.replace("$PRECISION", precision), tweak(benchmark, specific_cmd).replace("$PRECISION", precision))
#
# Track "often-epsilon-correct"
#
skip = False
precision = "1e-3"
default_cmd = default_base + " --no-partial-results"
specific_cmd = specific_base + " --no-partial-results"
if prop_type == "S":
# long-run average: default is the sound algorithm based on value iteration
default_cmd += " --width $PRECISION --relative-width"
specific_cmd += " --width $PRECISION --relative-width"
elif (benchmark.is_ma() or benchmark.is_ctmc()) and benchmark.is_time_bounded_probabilistic_reachability():
# time-bounded probability for CTMC and MA: default is sound Unif+
default_cmd += " --width $PRECISION --relative-width"
specific_cmd += " --width $PRECISION --relative-width"
elif benchmark.is_pta() and prop_type == "Pb":
# time-bounded reachability for PTA: state elimination recommended
default_cmd += " --reward-bounded-alg StateElimination"
if "-S Memory" in specific_cmd:
specific_cmd += " --reward-bounded-alg StateElimination"
else:
specific_cmd += " --alg IntervalIteration --width $PRECISION --relative-width"
elif prop_type == "Pb":
# reward-bounded probability: default is unsound VI, which is okay here
pass
else:
# unbounded probability or expected reward: default is unsound VI, which is okay here
if prop_type == "P" and benchmark.is_dtmc() or benchmark.is_ctmc():
# for unbounded probabilities on DTMC and CTMC: use 0/1 preprocessing
default_cmd += " --p0 --p1"
specific_cmd += " --p0 --p1"
if not skip:
add_invocations(invocations, "often-epsilon-correct", default_cmd.replace("$PRECISION", precision), tweak(benchmark, specific_cmd).replace("$PRECISION", precision))
#
# Track "often-epsilon-correct-10-min"
#
skip = False
precision = "0" # so we just run for the full 10 minutes (or until we get an exact result)
default_cmd = default_base
specific_cmd = specific_base
if prop_type == "S":
skip = True
elif (benchmark.is_ma() or benchmark.is_ctmc()) and benchmark.is_time_bounded_probabilistic_reachability():
default_cmd += " --width 0"
specific_cmd += " --width 0"
elif prop_type == "Pb":
# reward-bounded reachability for DTMC, MDP, and PTA
default_cmd += " --reward-bounded-alg StateElimination"
if "-S Memory" in specific_cmd:
specific_cmd += " --reward-bounded-alg StateElimination"
else:
specific_cmd += " --epsilon 0"
else:
default_cmd += " --epsilon 0"
specific_cmd += " --epsilon 0"
if prop_type == "P" and benchmark.is_dtmc() or benchmark.is_ctmc():
# for unbounded probabilities on DTMC and CTMC: use 0/1 preprocessing
default_cmd += " --p0 --p1"
specific_cmd += " --p0 --p1"
if not skip:
add_invocations(invocations, "often-epsilon-correct-10-min", default_cmd, tweak(benchmark, specific_cmd))
#
# Done
#
return invocations
