def get_condition_change_enablers(self, influence_update, all_dts, cache):
""" Calculates if an if/else condition within the function can change its value """
logger.debug(
f"Calculating condition change time in '{influence_update._name}' in entity '{influence_update._parent._name}' ({influence_update._parent.__class__.__name__})"
)
solver = z3.Optimize(cache.ctx)
# build a mapping that shows the propagation of information to the influence/update source (what influences the guard)
if isinstance(influence_update, model.Influence):
modifier_map = self.get_modifier_map(
[influence_update.source, influence_update.target],
cache=False)
else:
read_ports = SH.get_accessed_ports(influence_update.function,
influence_update,
cache=False)
read_ports.append(influence_update.target)
modifier_map = self.get_modifier_map(read_ports, cache=False)
z3_vars = cache.z3_vars
# add the initial values for the sources of the dataflow
for port, modifiers in modifier_map.items():
# set default port value to the current value
pre_value = port.value #get_z3_value(port, port._name + "_0").translate(cache.ctx)
solver.add(z3_vars[port][port._name + "_0"] == pre_value)
if len(modifiers) == 0:
solver.add(
z3_vars[port][port._name] == z3_vars[port][port._name +
"_0"])
# create the constraints for updates and influences
for port, modifiers in modifier_map.items():
for modifier in modifiers:
if modifier != influence_update: # skip the one we're actually analysing, this should be already done in the modifier-map creation...
constraints = cache.z3_modifier_constraints[modifier]
solver.add(
constraints
) #[const.translate(ctx) for const in constraints])
conditionchanged_constraintset, additionals = cache.z3_conditionchanged_constraintsets[
influence_update]
solver.add(additionals) #[a.translate(ctx) for a in additionals])
min_dt, label = get_behaviour_change_dt_from_constraintset(
solver,
conditionchanged_constraintset,
z3_vars['dt'],
ctx=cache.ctx)
if min_dt is not None:
logger.info(
f"Minimum condition change times in '{influence_update._name}' in entity '{influence_update._parent._name}' ({influence_update._parent.__class__.__name__}) is {min_dt} (at label {label})"
)
ret = (to_python(min_dt), influence_update, label)
all_dts.append(ret)
def _get_update_function_value(self, update, time):
solver = z3.Solver()
# create the z3 variables
z3_vars = {}
# create the TIME UNIT for dt
z3_vars['dt'] = get_z3_var(self.timeunit, 'dt')
z3_vars['dt'].type = self.timeunit
solver.add(z3_vars['dt'] == time)
z3_vars[update.target] = {
update.target._name:
get_z3_variable(update.target, update.target._name)
}
accessed_ports = SH.get_accessed_ports(update.function, update)
for access in accessed_ports:
if access != update.target:
z3_vars[access] = {
access._name: get_z3_value(access, access._name)
}
z3_vars[access][access._name + "_0"] = get_z3_value(
access, access._name + "_0")
# convert function
conv = Z3Converter(z3_vars,
entity=update._parent,
container=update,
use_integer_and_real=self.default_to_integer_real)
conv.target = update.target
constraints = conv.to_z3(update.function)
if logger.getEffectiveLevel(
) <= logging.DEBUG: # only log if the level is appropriate, since z3's string-conversion takes ages
logger.debug(
f"{update._name} {update} constraints: { constraints }")
if SH.is_lambda(update.function):
# equation for lambda result
tgt = conv.z3_vars[update.target][update.target._name]
solver.add(tgt == constraints)
else:
solver.add(constraints)
if solver.check() == z3.sat:
new_target_value = solver.model()[z3_vars[update.target][
update.target._name]]
new_target_value.type = z3_vars[update.target][
update.target._name].type # remember to assign a type
return new_target_value
else:
raise Exception(
"Problem when calculating the target value of the influence")
def gen_Transition(obj, name="", parent=None, **kwargs):
returnlist = []
color = get_color(id(obj)) if kwargs["color_updates"] else "black"
label = ""
if kwargs["transition_labels"]:
guard_ast = SH.get_ast_body(obj.guard)
label = astor.to_source(guard_ast)
returnlist.append(
f"{id(obj.source)} -> {id(obj.target)} [label=\"{label}\"]")
accessed = SH.get_accessed_ports(obj.guard, obj)
for acc in accessed:
style = "dashed" if kwargs["show_transition_ports"] else "invis"
returnlist.append(
f"{id(acc)} -> {id(obj.target)} [style=\"{style}\" color=\"{color}\" ]"
)
return returnlist
def create_input_space(self):
""" create observation space and store it in local field"""
logger.debug(f"Creating observation space")
all_parameters = []
all_parameters.extend(
SH.get_accessed_ports(self.original_function,
self.modifier,
exclude_pre=True,
cache=False))
if isinstance(self.modifier, crest.Influence):
all_parameters.append(self.modifier.source)
# use openai's spaces for sampling, it's easier!
space_dict = dict()
for port in all_parameters:
portname = port._name
if port._parent != self.modifier._parent:
portname = f"{port._parent._name}.{portname}"
if "ranges" in self.kwargs:
valuerange = self.kwargs["ranges"].get(
port, self.kwargs["ranges"].get(portname, None))
else:
valuerange = None
space_dict[portname] = get_space_from_domain(
port.resource.domain, valuerange)
if isinstance(self.modifier, crest.Update):
low = 0
high = DEFAULT_SPREAD
if "ranges" in self.kwargs and "dt" in self.kwargs["ranges"]:
low = self.kwargs["ranges"].get("dt")[0]
high = self.kwargs["ranges"].get("dt")[1]
space_dict["dt"] = spaces.Box(low=low,
high=high,
shape=(1, ),
dtype=np.float)
assert GYM_INSTALLED, "Couldn't detect the gym package. Please ensure it is installed or run 'pip install gym'."
return spaces.Dict(spaces=space_dict)
def _get_transition_guard_value(self, transition):
solver = z3.Solver()
# create the z3 variables
z3_vars = {}
transition_ports = SH.get_accessed_ports(transition.guard, transition)
for port in transition_ports:
z3_vars[port] = {port._name: get_z3_value(port, port._name)}
z3_vars[port][port._name + "_0"] = get_z3_value(
port, port._name + "_0")
conv = Z3Converter(z3_vars,
entity=transition._parent,
container=transition,
use_integer_and_real=self.default_to_integer_real)
constraints = conv.to_z3(transition.guard)
solver.add(constraints)
return solver.check() == z3.sat
def gen_Update(obj, name="", parent=None, **kwargs):
returnlist = []
color = get_color(id(obj)) if kwargs["color_updates"] else "black"
label = ""
if kwargs["update_labels"]:
# print("There's an issue with the display of update-labels. Waiting for astor 0.6 to be available...")
# """ deactivate until astor 0.6 is available"""
func_ast = SH.get_ast_from_function_definition(obj.function)
label = astor.to_source(func_ast)
returnlist.append(
f"{id(obj.state)} -> {id(obj.target)} [style=\"dashed\" color=\"{color}\" label=\"{label}\"]"
)
accessed = SH.get_accessed_ports(obj.function, obj)
for acc in accessed:
style = "dashed" if kwargs["show_update_ports"] else "invis"
returnlist.append(
f"{id(acc)} -> {id(obj.target)} [style=\"{style}\" color=\"{color}\" ]"
)
return returnlist
def get_transition_time(self, transition, all_dts, cache):
"""
- we need to find a solution for the guard condition (e.g. using a theorem prover)
- guards are boolean expressions over port values
- ports are influenced by Influences starting at other ports (find recursively)
"""
logger.debug(
f"Calculating the transition time of '{transition._name}' in entity '{transition._parent._name}' ({transition._parent.__class__.__name__})"
)
solver = z3.Optimize(cache.ctx)
# find the ports that influence the transition
transition_ports = SH.get_accessed_ports(transition.guard,
transition,
cache=False)
# logger.debug(f"The transitions influencing ports are called: {[p._name for p in transition_ports]}")
# build a mapping that shows the propagation of information to the guard (what influences the guard)
modifier_map = self.get_modifier_map(transition_ports, cache=False)
z3_vars = cache.z3_vars
solver.add(z3_vars['dt'] >= 0)
# add the initial values for the sources of the dataflow
for port, modifiers in modifier_map.items():
# set default port value to the current value
pre_value = port.value #get_z3_value(port, port._name + "_0").translate(cache.ctx)
solver.add(z3_vars[port][port._name + "_0"] == pre_value)
if len(modifiers) == 0:
solver.add(
z3_vars[port][port._name] == z3_vars[port][port._name +
"_0"])
# create the constraints for updates and influences
for port, modifiers in modifier_map.items():
for modifier in modifiers:
constraints = cache.z3_modifier_constraints[modifier]
solver.add(constraints)
# guard_constraint = cache.z3_modifier_constraints[transition] if not isinstance(guardconst, bool)]
# # this is because we cannot add booleans directly to a z3.Optimize (it works for Solver)
# # the issue is here: https://github.com/Z3Prover/z3/issues/1736
# if isinstance(guard_constraint, bool):
# guard_constraint = z3.And(guard_constraint, ctx)
solver.add(cache.z3_modifier_constraints[transition])
objective = solver.minimize(z3_vars['dt']) # find minimal value of dt
check = solver.check()
# logger.debug("satisfiability: %s", check)
if solver.check() == z3.sat:
log_if_level(
logging.INFO,
f"Minimum time to enable transition '{transition._name}' in entity '{transition._parent._name}' ({transition._parent.__class__.__name__}) will be enabled in {to_python(objective.value())}"
)
# return (objective.value(), transition, as_epsilon_expr)
inf_coeff, numeric_coeff, eps_coeff = objective.lower_values()
ret = (Epsilon(numeric_coeff, eps_coeff), transition)
all_dts.append(ret)
elif check == z3.unknown:
log_if_level(
logging.WARNING,
f"The calculation of the minimum transition time for '{transition._name}' in entity '{transition._parent._name}' ({transition._parent.__class__.__name__}) was UNKNOWN. This usually happening in the presence of non-linear constraints. Do we have any?"
)
std_solver = z3.Solver()
std_solver.add(solver.assertions())
std_solver_check = std_solver.check()
if std_solver_check == z3.sat:
min_dt = std_solver.model()[z3_vars['dt']]
log_if_level(
logging.INFO,
f"We did get a solution using the standard solver though: {to_python(min_dt)} Assuming that this is the smallest solution. CAREFUL THIS MIGHT BE WRONG (especially when the transition is an inequality constraint)!!!"
)
ret = (to_python(min_dt), transition)
all_dts.append(ret)
elif std_solver_check == z3.unknown:
logger.error(
f"The standard solver was also not able to decide if there is a solution or not. The constraints are too hard!!!"
)
else:
logger.info(
"The standard solver says there is no solution to the constraints. This means we also couldn't minimize. Problem solved."
)
else:
logger.debug(
f"Constraint set to enable transition '{transition._name}' in entity '{transition._parent._name}' ({transition._parent.__class__.__name__}) is unsatisfiable."
)
def get_modifier_map(root_entity, port_list, cache=True):
"""Creates a dict that has ports as keys and a list of influences/updates that influence those ports as values."""
logger.debug(
f"creating modifier map for ports {[p._name +' (in: '+ p._parent._name+')' for p in port_list]}"
)
modifier_map = {port: list() for port in port_list}
map_change = True
all_updates = get_all_updates(root_entity)
all_influences = get_all_influences(root_entity)
while map_change:
map_change = False # initially we think there are no changes
for port, modifiers in modifier_map.copy().items(
): # iterate over a copy, so we can modify the original list
# we only look at ports that have no influences (it might be because there exist none, but that small overhead is okay for now)
if len(modifiers) == 0:
logger.debug(
f"trying to find modifiers for port '{port._name}' of entity '{port._parent._name} ({port._parent.__class__.__name__})'"
)
influences = [
inf for inf in all_influences if port == inf.target
]
modifier_map[port].extend(influences)
for inf in influences:
logger.debug(
f"'{port._name}' is modified by influence '{inf._name}'"
)
# this means influences is not empty, hence we change the map (probably)
map_change = True
if inf.source not in modifier_map:
modifier_map[inf.source] = list(
) # add an empty list, the next iteration will try to fill it
updates = [
up for up in all_updates
if port == up.target and up.state == up._parent.current
]
modifier_map[port].extend(updates)
for up in updates:
# logger.debug(f"'{port._name}' is modified by update '{up._name}'")
# read_ports = SH.get_read_ports_from_update(up.function, up) # +[up.target]
accessed_ports = SH.get_accessed_ports(up.function,
up,
exclude_pre=False,
cache=cache)
# logger.debug(f"'{up._name}' in '{up._parent._name}' reads the following ports: {[(p._name, p._parent._name) for p in accessed_ports]}")
for read_port in accessed_ports:
# this means there are updates and we change the map
map_change = True
if read_port not in modifier_map:
# logger.debug(f"adding {read_port._name} to modifier_map")
modifier_map[read_port] = list(
) # add an empty list, the next iteration will try to fill it
logger.debug(
f"the modifier map looks like this: \n{pformat(prettify_modifier_map(modifier_map))}"
)
return modifier_map
def get_transition_time(self, transition):
"""
- we need to find a solution for the guard condition (e.g. using a theorem prover)
- guards are boolean expressions over port values
- ports are influenced by Influences starting at other ports (find recursively)
"""
logger.debug(
f"Calculating the transition time of '{transition._name}' in entity '{transition._parent._name}' ({transition._parent.__class__.__name__})"
)
solver = z3.Optimize()
# find the ports that influence the transition
transition_ports = SH.get_accessed_ports(transition.guard, transition)
# logger.debug(f"The transitions influencing ports are called: {[p._name for p in transition_ports]}")
# build a mapping that shows the propagation of information to the guard (what influences the guard)
modifier_map = self.get_modifier_map(transition_ports)
z3var_constraints, z3_vars = self.get_z3_vars(modifier_map)
solver.add(z3var_constraints)
solver.add(z3_vars['dt'] >= 0)
# check if there are actually any timed behaviour changes
any_modifier_uses_dt = False
for port, modifiers in modifier_map.items():
for modifier in modifiers:
any_modifier_uses_dt = any_modifier_uses_dt or uses_dt_variable(
modifier)
if not any_modifier_uses_dt:
currently_enabled = transition.guard(api.get_parent(transition))
return (0, transition) if currently_enabled else None
# create the constraints for updates and influences
for port, modifiers in modifier_map.items():
for modifier in modifiers:
constraints = self._get_constraints_from_modifier(
modifier, z3_vars)
solver.add(constraints)
# for port, modifiers in modifier_map.items():
# write pre value
# logger.debug(f"adding constraints for transition guard: {transition._name}")
conv = Z3Converter(z3_vars,
entity=transition._parent,
container=transition,
use_integer_and_real=self.use_integer_and_real)
guard_constraint = conv.to_z3(transition.guard)
# this is because we cannot add booleans directly to a z3.Optimize (it works for Solver)
# the issue is here: https://github.com/Z3Prover/z3/issues/1736
if isinstance(guard_constraint, bool):
guard_constraint = z3.And(guard_constraint)
solver.add(guard_constraint)
# import pprint;pprint.pprint(z3_vars)
# log_if_level(logging.DEBUG, f"Constraints handed to solver:\n{solver}")
objective = solver.minimize(z3_vars['dt']) # find minimal value of dt
check = solver.check()
# logger.debug("satisfiability: %s", check)
if solver.check() == z3.sat:
log_if_level(
logging.INFO,
f"Minimum time to enable transition '{transition._name}' in entity '{transition._parent._name}' ({transition._parent.__class__.__name__}) will be enabled in {to_python(objective.value())}"
)
# return (objective.value(), transition, as_epsilon_expr)
inf_coeff, numeric_coeff, eps_coeff = objective.lower_values()
return (Epsilon(numeric_coeff, eps_coeff), transition)
elif check == z3.unknown:
log_if_level(
logging.WARNING,
f"The calculation of the minimum transition time for '{transition._name}' in entity '{transition._parent._name}' ({transition._parent.__class__.__name__}) was UNKNOWN. This usually happening in the presence of non-linear constraints. Do we have any?"
)
std_solver = z3.Solver()
std_solver.add(solver.assertions())
std_solver_check = std_solver.check()
if std_solver_check == z3.sat:
min_dt = std_solver.model()[z3_vars['dt']]
log_if_level(
logging.INFO,
f"We did get a solution using the standard solver though: {to_python(min_dt)} Assuming that this is the smallest solution. CAREFUL THIS MIGHT BE WRONG (especially when the transition is an inequality constraint)!!!"
)
return (to_python(min_dt), transition)
elif std_solver_check == z3.unknown:
logger.error(
f"The standard solver was also not able to decide if there is a solution or not. The constraints are too hard!!!"
)
return None
else:
logger.info(
"The standard solver says there is no solution to the constraints. This means we also couldn't minimize. Problem solved."
)
return None
else:
logger.debug(
f"Constraint set to enable transition '{transition._name}' in entity '{transition._parent._name}' ({transition._parent.__class__.__name__}) is unsatisfiable."
)
return None
def get_condition_change_enablers(self, influence_update):
""" Calculates if an if/else condition within the function can change its value """
logger.debug(
f"Calculating condition change time in '{influence_update._name}' in entity '{influence_update._parent._name}' ({influence_update._parent.__class__.__name__})"
)
solver = z3.Optimize()
# build a mapping that shows the propagation of information to the influence/update source (what influences the guard)
if isinstance(influence_update, Influence):
modifier_map = self.get_modifier_map(
[influence_update.source, influence_update.target])
else:
read_ports = SH.get_accessed_ports(influence_update.function,
influence_update)
read_ports.append(influence_update.target)
modifier_map = self.get_modifier_map(read_ports)
z3var_constraints, z3_vars = self.get_z3_vars(modifier_map)
solver.add(z3var_constraints)
# NOTE: we do not add a "dt >= 0" or "dt == 0" constraint here, because it would break the solving
# check if there are actually any timed behaviour changes
any_modifier_uses_dt = False
for port, modifiers in modifier_map.items():
for modifier in modifiers:
if modifier != influence_update: # skip the one we're actually analysing, this should be already done in the modifier-map creation...
any_modifier_uses_dt = any_modifier_uses_dt or uses_dt_variable(
modifier)
influence_update_uses_dt = uses_dt_variable(influence_update)
if not influence_update_uses_dt and not any_modifier_uses_dt:
return None # nobody uses dt, no point in trying to find out when things will change...
# create the constraints for updates and influences
for port, modifiers in modifier_map.items():
for modifier in modifiers:
if modifier != influence_update: # skip the one we're actually analysing, this should be already done in the modifier-map creation...
constraints = self._get_constraints_from_modifier(
modifier, z3_vars)
solver.add(constraints)
# solver.push() # backup
# if it's an influence, we need to add the source param equation
if isinstance(influence_update, Influence):
z3_src = z3_vars[influence_update.source][
influence_update.source._name]
params = SH.get_param_names(influence_update.function)
param_key = params[0] + "_" + str(id(influence_update))
z3_param = get_z3_variable(influence_update.source, params[0],
str(id(influence_update)))
log_if_level(
logging.DEBUG,
f"adding param: z3_vars[{param_key}] = {params[0]}_0 : {z3_param} "
)
z3_vars[param_key] = {params[0] + "_0": z3_param}
log_if_level(
logging.DEBUG,
f"influence entry constraint: {z3_src} == {z3_param}")
solver.add(z3_src == z3_param)
# solver.push()
if not hasattr(influence_update,
"_cached_z3_behaviour_change_constraints"):
conv = Z3ConditionChangeCalculator(
z3_vars,
entity=influence_update._parent,
container=influence_update,
use_integer_and_real=self.use_integer_and_real)
influence_update._cached_z3_behaviour_change_constraints = conv.calculate_constraints(
influence_update.function)
constraints = influence_update._cached_z3_behaviour_change_constraints
min_dt, label = get_behaviour_change_dt_from_constraintset(
solver, constraints, z3_vars['dt'])
if min_dt is not None:
logger.info(
f"Minimum condition change times in '{influence_update._name}' in entity '{influence_update._parent._name}' ({influence_update._parent.__class__.__name__}) is {min_dt} (at label {label})"
)
return (to_python(min_dt), influence_update, label)
else:
return None
