"""Classes and helper functions for PTA and other automata.""" from functions import AnalyticFunction, NormalizationFunction from utils import is_numeric import itertools import numpy as np import json import queue import yaml def _dict_to_list(input_dict: dict) -> list: return [input_dict[x] for x in sorted(input_dict.keys())] class SimulationResult: """ Duration, Energy, and state/parameter results from PTA.simulate on a single run. :param duration: run duration in s :param duration_mae: Mean Absolute Error of duration, assuming cycle-perfect delay/sleep calls :param duration_mape: Mean Absolute Percentage Error of duration, assuming cycle-perfect delay/sleep caals :param energy: run energy in J :param energy_mae: Mean Absolute Error of energy :param energy_mape: Mean Absolute Percentage Error of energy :param end_state: Final `State` of run :param parameters: Final parameters of run :param mean_power: mean power during run in W """ def __init__(self, duration: float, energy: float, end_state, parameters, duration_mae: float = None, energy_mae: float = None): u""" Create a new SimulationResult. :param duration: run duration in µs :param duration_mae: Mean Absolute Error of duration in µs, default None :param energy: run energy in pJ :param energy_mae: Mean Absolute Error of energy in pJ, default None :param end_state: Final `State` after simulation run :param parameters: Parameter values after simulation run """ self.duration = duration * 1e-6 self.duration_mae = duration_mae * 1e-6 self.duration_mape = self.duration_mae * 100 / self.duration self.energy = energy * 1e-12 self.energy_mae = energy_mae * 1e-12 self.energy_mape = self.energy_mae * 100 / self.energy self.end_state = end_state self.parameters = parameters self.mean_power = self.energy / self.duration class PTAAttribute: def __init__(self, value: float = 0, function: AnalyticFunction = None, value_error=None, function_error=None): self.value = value self.function = function self.value_error = value_error self.function_error = function_error def __repr__(self): if self.function is not None: return 'PTAATtribute<{:.0f}, {}>'.format(self.value, self.function._model_str) return 'PTAATtribute<{:.0f}, None>'.format(self.value) def eval(self, param_dict=dict(), args=list()): param_list = _dict_to_list(param_dict) if self.function and self.function.is_predictable(param_list): return self.function.eval(param_list, args) return self.value def eval_mae(self, param_dict=dict(), args=list()): param_list = _dict_to_list(param_dict) if self.function and self.function.is_predictable(param_list): return self.function_error['mae'] return self.value_error['mae'] def to_json(self): ret = { 'static': self.value, 'static_error': self.value_error, } if self.function: ret['function'] = { 'raw': self.function._model_str, 'regression_args': list(self.function._regression_args) } ret['function_error'] = self.function_error return ret @classmethod def from_json(cls, json_input: dict, parameters: dict): ret = cls() if 'static' in json_input: ret.value = json_input['static'] if 'static_error' in json_input: ret.value_error = json_input['static_error'] if 'function' in json_input: ret.function = AnalyticFunction(json_input['function']['raw'], parameters, 0, regression_args=json_input['function']['regression_args']) if 'function_error' in json_input: ret.function_error = json_input['function_error'] return ret @classmethod def from_json_maybe(cls, json_wrapped: dict, attribute: str, parameters: dict): if attribute in json_wrapped: return cls.from_json(json_wrapped[attribute], parameters) return cls() def _json_function_to_analytic_function(base, attribute: str, parameters: list): if attribute in base and 'function' in base[attribute]: base = base[attribute]['function'] return AnalyticFunction(base['raw'], parameters, 0, regression_args=base['regression_args']) return None class State: """A single PTA state.""" def __init__(self, name: str, power: PTAAttribute = PTAAttribute(), power_function: AnalyticFunction = None): u""" Create a new PTA state. :param name: state name :param power: state power PTAAttribute in µW, default static 0 / parameterized None :param power_function: Legacy support """ self.name = name self.power = power self.outgoing_transitions = {} if type(self.power) is float or type(self.power) is int: self.power = PTAAttribute(self.power) if power_function is not None: if type(power_function) is AnalyticFunction: self.power.function = power_function else: raise ValueError('power_function must be None or AnalyticFunction') def __repr__(self): return 'State<{:s}, {}>'.format(self.name, self.power) def add_outgoing_transition(self, new_transition: object): """Add a new outgoing transition.""" self.outgoing_transitions[new_transition.name] = new_transition def get_energy(self, duration: float, param_dict: dict = {}) -> float: u""" Return energy spent in state in pJ. :param duration: duration in µs :param param_dict: current parameters :returns: energy spent in pJ """ return self.power.eval(param_dict) * duration def set_random_energy_model(self, static_model=True): u"""Set a random static state power between 0 µW and 50 mW.""" self.power.value = int(np.random.sample() * 50000) def get_transition(self, transition_name: str) -> object: """ Return Transition object for outgoing transtion transition_name. :param transition_name: transition name :returns: `Transition` object """ try: return self.outgoing_transitions[transition_name] except KeyError: raise ValueError('State {} has no outgoing transition called {}'.format(self.name, transition_name)) from None def has_interrupt_transitions(self) -> bool: """Return whether this state has any outgoing interrupt transitions.""" for trans in self.outgoing_transitions.values(): if trans.is_interrupt: return True return False def get_next_interrupt(self, parameters: dict) -> object: """ Return the outgoing interrupt transition with the lowet timeout. Must only be called if has_interrupt_transitions returned true. :param parameters: current parameter values :returns: Transition object """ interrupts = filter(lambda x: x.is_interrupt, self.outgoing_transitions.values()) interrupts = sorted(interrupts, key=lambda x: x.get_timeout(parameters)) return interrupts[0] def dfs(self, depth: int, with_arguments=False, trace_filter=None, sleep=0): """ Return a generator object for depth-first search over all outgoing transitions. :param depth: search depth :param with_arguments: perform dfs with function+argument transitions instead of just function transitions. :param trace_filter: list of lists. Each sub-list is a trace. Only traces matching one of the provided sub-lists are returned. E.g. trace_filter = [['init', 'foo'], ['init', 'bar']] will only return traces with init as first and foo or bar as second element. trace_filter = [['init', 'foo', '$'], ['init', 'bar', '$']] will only return the traces ['init', 'foo'] and ['init', 'bar']. Note that `trace_filter` takes precedence over `depth`: traces matching `trace_filter` are generated even if their length exceeds `depth` :param sleep: if set and non-zero: include sleep pseudo-states with us duration For the [['init', 'foo', '$'], ['init', 'bar', '$']] example above, sleep=10 results in [(None, 10), 'init', (None, 10), 'foo'] and [(None, 10), 'init', (None, 10), 'bar'] :returns: Generator object for depth-first search. Each access yields a list of (Transition, (arguments)) elements describing a single run through the PTA. """ # TODO parametergewahrer Trace-Filter, z.B. "setHeaterDuration nur wenn bme680 power mode => FORCED und GAS_ENABLED" # A '$' entry in trace_filter indicates that the trace should (successfully) terminate here regardless of `depth`. if trace_filter is not None and next(filter(lambda x: x == '$', map(lambda x: x[0], trace_filter)), None) is not None: yield [] # there may be other entries in trace_filter that still yield results. if depth == 0: for trans in self.outgoing_transitions.values(): if trace_filter is not None and len(list(filter(lambda x: x == trans.name, map(lambda x: x[0], trace_filter)))) == 0: continue if with_arguments: if trans.argument_combination == 'cartesian': for args in itertools.product(*trans.argument_values): if sleep: yield [(None, sleep), (trans, args)] else: yield [(trans, args)] else: for args in zip(*trans.argument_values): if sleep: yield [(None, sleep), (trans, args)] else: yield [(trans, args)] else: if sleep: yield [(None, sleep), (trans,)] else: yield [(trans,)] else: for trans in self.outgoing_transitions.values(): if trace_filter is not None and next(filter(lambda x: x == trans.name, map(lambda x: x[0], trace_filter)), None) is None: continue if trace_filter is not None: new_trace_filter = map(lambda x: x[1:], filter(lambda x: x[0] == trans.name, trace_filter)) new_trace_filter = list(filter(len, new_trace_filter)) if len(new_trace_filter) == 0: new_trace_filter = None else: new_trace_filter = None for suffix in trans.destination.dfs(depth - 1, with_arguments=with_arguments, trace_filter=new_trace_filter, sleep=sleep): if with_arguments: if trans.argument_combination == 'cartesian': for args in itertools.product(*trans.argument_values): if sleep: new_suffix = [(None, sleep), (trans, args)] else: new_suffix = [(trans, args)] new_suffix.extend(suffix) yield new_suffix else: if len(trans.argument_values): arg_values = zip(*trans.argument_values) else: arg_values = [tuple()] for args in arg_values: if sleep: new_suffix = [(None, sleep), (trans, args)] else: new_suffix = [(trans, args)] new_suffix.extend(suffix) yield new_suffix else: if sleep: new_suffix = [(None, sleep), (trans,)] else: new_suffix = [(trans,)] new_suffix.extend(suffix) yield new_suffix def to_json(self) -> dict: """Return JSON encoding of this state object.""" ret = { 'name': self.name, 'power': self.power.to_json() } return ret class Transition: u""" A single PTA transition with one origin and one destination state. :param name: transition name, corresponds to driver function name :param origin: origin `State` :param destination: destination `State` :param energy: static energy needed to execute this transition, in pJ :param energy_function: parameterized transition energy `AnalyticFunction`, returning pJ :param duration: transition duration, in µs :param duration_function: parameterized duration `AnalyticFunction`, returning µs :param timeout: transition timeout, in µs. Only set for interrupt transitions. :param timeout_function: parameterized transition timeout `AnalyticFunction`, in µs. Only set for interrupt transitions. :param is_interrupt: Is this an interrupt transition? :param arguments: list of function argument names :param argument_values: list of argument values used for benchmark generation. Each entry is a list of values for the corresponding argument :param argument_combination: During benchmark generation, should arguments be combined via `cartesian` or `zip`? :param param_update_function: Setter for parameters after a transition. Gets current parameter dict and function argument values as arguments, must return the new parameter dict :param arg_to_param_map: dict mapping argument index to the name of the parameter affected by its value :param set_param: dict mapping parameter name to their value (set as side-effect of executing the transition, not parameter-dependent) :param return_value_handlers: todo :param codegen: todo """ def __init__(self, orig_state: State, dest_state: State, name: str, energy: PTAAttribute = PTAAttribute(), energy_function: AnalyticFunction = None, duration: PTAAttribute = PTAAttribute(), duration_function: AnalyticFunction = None, timeout: PTAAttribute = PTAAttribute(), timeout_function: AnalyticFunction = None, is_interrupt: bool = False, arguments: list = [], argument_values: list = [], argument_combination: str = 'cartesian', # or 'zip' param_update_function=None, arg_to_param_map: dict = None, set_param=None, return_value_handlers: list = [], codegen=dict()): """ Create a new transition between two PTA states. :param orig_state: origin `State` :param dest_state: destination `State` :param name: transition name, typically the same as a driver/library function name """ self.name = name self.origin = orig_state self.destination = dest_state self.energy = energy self.duration = duration self.timeout = timeout self.is_interrupt = is_interrupt self.arguments = arguments.copy() self.argument_values = argument_values.copy() self.argument_combination = argument_combination self.param_update_function = param_update_function self.arg_to_param_map = arg_to_param_map self.set_param = set_param self.return_value_handlers = return_value_handlers self.codegen = codegen if type(self.energy) is float or type(self.energy) is int: self.energy = PTAAttribute(self.energy) if energy_function is not None: if type(energy_function) is AnalyticFunction: self.energy.function = energy_function if type(self.duration) is float or type(self.duration) is int: self.duration = PTAAttribute(self.duration) if duration_function is not None: if type(duration_function) is AnalyticFunction: self.duration.function = duration_function if type(self.timeout) is float or type(self.timeout) is int: self.timeout = PTAAttribute(self.timeout) if timeout_function is not None: if type(timeout_function) is AnalyticFunction: self.timeout.function = timeout_function for handler in self.return_value_handlers: if 'formula' in handler: handler['formula'] = NormalizationFunction(handler['formula']) def get_duration(self, param_dict: dict = {}, args: list = []) -> float: u""" Return transition duration in µs. :param param_dict: current parameter values :param args: function arguments :returns: transition duration in µs """ return self.duration.eval(param_dict, args) def get_energy(self, param_dict: dict = {}, args: list = []) -> float: u""" Return transition energy cost in pJ. :param param_dict: current parameter values :param args: function arguments """ return self.energy.eval(param_dict, args) def set_random_energy_model(self, static_model=True): self.energy.value = int(np.random.sample() * 50000) self.duration.value = int(np.random.sample() * 50000) if self.is_interrupt: self.timeout.value = int(np.random.sample() * 50000) def get_timeout(self, param_dict: dict = {}) -> float: u""" Return transition timeout in µs. Returns 0 if the transition does not have a timeout. :param param_dict: current parameter values :param args: function arguments """ return self.timeout.eval(param_dict) def get_params_after_transition(self, param_dict: dict, args: list = []) -> dict: """ Return the new parameter dict after taking this transition. parameter values may be affected by this transition's update function, it's argument-to-param map, and its set_param settings. Does not normalize parameter values. """ if self.param_update_function: return self.param_update_function(param_dict, args) ret = param_dict.copy() if self.arg_to_param_map: for k, v in self.arg_to_param_map.items(): ret[v] = args[k] if self.set_param: for k, v in self.set_param.items(): ret[k] = v return ret def to_json(self) -> dict: """Return JSON encoding of this transition object.""" ret = { 'name': self.name, 'origin': self.origin.name, 'destination': self.destination.name, 'is_interrupt': self.is_interrupt, 'arguments': self.arguments, 'argument_values': self.argument_values, 'argument_combination': self.argument_combination, 'arg_to_param_map': self.arg_to_param_map, 'set_param': self.set_param, 'duration': self.duration.to_json(), 'energy': self.energy.to_json(), 'timeout': self.timeout.to_json() } return ret def _json_get_static(base, attribute: str): if attribute in base: return base[attribute]['static'] return 0 class PTA: """ A parameterized priced timed automaton. Suitable for simulation, model storage, and (soon) benchmark generation. :param state: dict mapping state name to `State` object :param accepting_states: list of accepting state names :param parameters: current parameters :param parameter_normalization: dict mapping driver API parameter values to hardware values, e.g. a bitrate register value to an actual bitrate in kbit/s. Each parameter key has in turn a dict value. Supported entries: `enum`: Mapping of enum descriptors (eys) to parameter values. Note that the mapping is not required to correspond to the driver API. `formula`: NormalizationFunction mapping an argument or return value (passed as `param`) to a parameter value. :param codegen: TODO :param initial_param_values: TODO :param transitions: list of `Transition` objects """ def __init__(self, state_names: list = [], accepting_states: list = None, parameters: list = [], initial_param_values: list = None, codegen: dict = {}, parameter_normalization: dict = None): """ Return a new PTA object. :param state_names: names of PTA states. Note that the PTA always contains an initial UNINITIALIZED state, regardless of the content of state_names. :param accepting_states: names of accepting states. By default, all states are accepting :param parameters: names of PTA parameters :param initial_param_values: initial value for each parameter :param instance: class used for generated C++ code :param header: header include path for C++ class definition :param parameter_normalization: dict mapping driver API parameter values to hardware values, e.g. a bitrate register value to an actual bitrate in kbit/s. Each parameter key has in turn a dict value. Supported entries: `enum`: maps enum descriptors (keys) to parameter values. Note that the mapping is not required to correspond to the driver API. `formula`: maps an argument or return value (passed as `param`) to a parameter value. Must be a string describing a valid python lambda function. NumPy is available as `np`. """ self.state = dict([[state_name, State(state_name)] for state_name in state_names]) self.accepting_states = accepting_states.copy() if accepting_states else None self.parameters = parameters.copy() self.parameter_normalization = parameter_normalization self.codegen = codegen if initial_param_values: self.initial_param_values = initial_param_values.copy() else: self.initial_param_values = [None for x in self.parameters] self.transitions = [] if 'UNINITIALIZED' not in state_names: self.state['UNINITIALIZED'] = State('UNINITIALIZED') if self.parameter_normalization: for normalization_spec in self.parameter_normalization.values(): if 'formula' in normalization_spec: normalization_spec['formula'] = NormalizationFunction(normalization_spec['formula']) def normalize_parameter(self, parameter_name: str, parameter_value) -> float: """ Return normalized parameter. Normalization refers to anything specified in the model's `parameter_normalization` section, e.g. enum -> int translation or argument -> parameter value formulas. :param parameter_name: parameter name. :param parameter_value: parameter value. """ if parameter_value is not None and self.parameter_normalization is not None and parameter_name in self.parameter_normalization: if 'enum' in self.parameter_normalization[parameter_name] and parameter_value in self.parameter_normalization[parameter_name]['enum']: return self.parameter_normalization[parameter_name]['enum'][parameter_value] if 'formula' in self.parameter_normalization[parameter_name]: normalization_formula = self.parameter_normalization[parameter_name]['formula'] return normalization_formula.eval(parameter_value) return parameter_value def normalize_parameters(self, param_dict) -> dict: """ Return normalized parameters. Normalization refers to anything specified in the model's `parameter_normalization` section, e.g. enum -> int translation or argument -> parameter value formulas. :param param_dict: non-normalized parameters. """ if self.parameter_normalization is None: return param_dict.copy() normalized_param = param_dict.copy() for parameter, value in param_dict.items(): normalized_param[parameter] = self.normalize_parameter(parameter, value) return normalized_param @classmethod def from_file(cls, model_file: str): """Return PTA loaded from the provided JSON or YAML file.""" with open(model_file, 'r') as f: if '.json' in model_file: return cls.from_json(json.load(f)) else: return cls.from_yaml(yaml.safe_load(f)) @classmethod def from_json(cls, json_input: dict): """ Return a PTA created from the provided JSON data. Compatible with the to_json method. """ if 'transition' in json_input: return cls.from_legacy_json(json_input) kwargs = dict() for key in ('state_names', 'parameters', 'initial_param_values', 'accepting_states'): if key in json_input: kwargs[key] = json_input[key] pta = cls(**kwargs) for name, state in json_input['state'].items(): pta.add_state(name, power=PTAAttribute.from_json_maybe(state, 'power', pta.parameters)) for transition in json_input['transitions']: kwargs = dict() for key in ['arguments', 'argument_values', 'argument_combination', 'is_interrupt', 'set_param']: if key in transition: kwargs[key] = transition[key] # arg_to_param_map uses integer indices. This is not supported by JSON if 'arg_to_param_map' in transition: kwargs['arg_to_param_map'] = dict() for arg_index, param_name in transition['arg_to_param_map'].items(): kwargs['arg_to_param_map'][int(arg_index)] = param_name origins = transition['origin'] if type(origins) != list: origins = [origins] for origin in origins: pta.add_transition(origin, transition['destination'], transition['name'], duration=PTAAttribute.from_json_maybe(transition, 'duration', pta.parameters), energy=PTAAttribute.from_json_maybe(transition, 'energy', pta.parameters), timeout=PTAAttribute.from_json_maybe(transition, 'timeout', pta.parameters), **kwargs) return pta @classmethod def from_legacy_json(cls, json_input: dict): """ Return a PTA created from the provided JSON data. Compatible with the legacy dfatool/perl format. """ kwargs = { 'parameters': list(), 'initial_param_values': list(), } for param in sorted(json_input['parameter'].keys()): kwargs['parameters'].append(param) kwargs['initial_param_values'].append(json_input['parameter'][param]['default']) pta = cls(**kwargs) for name, state in json_input['state'].items(): pta.add_state(name, power=PTAAttribute(value=float(state['power']['static']))) for trans_name in sorted(json_input['transition'].keys()): transition = json_input['transition'][trans_name] destination = transition['destination'] arguments = list() argument_values = list() is_interrupt = False if transition['level'] == 'epilogue': is_interrupt = True if type(destination) == list: destination = destination[0] for arg in transition['parameters']: arguments.append(arg['name']) argument_values.append(arg['values']) for origin in transition['origins']: pta.add_transition(origin, destination, trans_name, arguments=arguments, argument_values=argument_values, is_interrupt=is_interrupt) return pta @classmethod def from_yaml(cls, yaml_input: dict): """Return a PTA created from the YAML DFA format (passed as dict).""" kwargs = dict() if 'parameters' in yaml_input: kwargs['parameters'] = yaml_input['parameters'] if 'initial_param_values' in yaml_input: kwargs['initial_param_values'] = yaml_input['initial_param_values'] if 'states' in yaml_input: kwargs['state_names'] = yaml_input['states'] # else: set to UNINITIALIZED by class constructor if 'codegen' in yaml_input: kwargs['codegen'] = yaml_input['codegen'] if 'parameter_normalization' in yaml_input: kwargs['parameter_normalization'] = yaml_input['parameter_normalization'] pta = cls(**kwargs) for trans_name in sorted(yaml_input['transition'].keys()): kwargs = dict() transition = yaml_input['transition'][trans_name] arguments = list() argument_values = list() arg_to_param_map = dict() if 'arguments' in transition: for i, argument in enumerate(transition['arguments']): arguments.append(argument['name']) argument_values.append(argument['values']) if 'parameter' in argument: arg_to_param_map[i] = argument['parameter'] if 'argument_combination' in transition: kwargs['argument_combination'] = transition['argument_combination'] if 'set_param' in transition: kwargs['set_param'] = transition['set_param'] if 'is_interrupt' in transition: kwargs['is_interrupt'] = transition['is_interrupt'] if 'return_value' in transition: kwargs['return_value_handlers'] = transition['return_value'] if 'codegen' in transition: kwargs['codegen'] = transition['codegen'] if 'loop' in transition: for state_name in transition['loop']: pta.add_transition(state_name, state_name, trans_name, arguments=arguments, argument_values=argument_values, arg_to_param_map=arg_to_param_map, **kwargs) else: if 'src' not in transition: transition['src'] = ['UNINITIALIZED'] if 'dst' not in transition: transition['dst'] = 'UNINITIALIZED' for origin in transition['src']: pta.add_transition(origin, transition['dst'], trans_name, arguments=arguments, argument_values=argument_values, arg_to_param_map=arg_to_param_map, **kwargs) return pta def to_json(self) -> dict: """ Return JSON encoding of this PTA. Compatible with the from_json method. """ ret = { 'parameters': self.parameters, 'initial_param_values': self.initial_param_values, 'state': dict([[state.name, state.to_json()] for state in self.state.values()]), 'transitions': [trans.to_json() for trans in self.transitions], 'accepting_states': self.accepting_states, } return ret def add_state(self, state_name: str, **kwargs): """ Add a new state. See the State() documentation for acceptable arguments. """ if 'power_function' in kwargs and type(kwargs['power_function']) != AnalyticFunction and kwargs['power_function'] is not None: kwargs['power_function'] = AnalyticFunction(kwargs['power_function'], self.parameters, 0) self.state[state_name] = State(state_name, **kwargs) def add_transition(self, orig_state: str, dest_state: str, function_name: str, **kwargs): """ Add function_name as new transition from orig_state to dest_state. :param orig_state: origin state name. Must be known to PTA :param dest_state: destination state name. Must be known to PTA. :param function_name: function name :param kwargs: see Transition() documentation """ orig_state = self.state[orig_state] dest_state = self.state[dest_state] for key in ('duration_function', 'energy_function', 'timeout_function'): if key in kwargs and kwargs[key] is not None and type(kwargs[key]) != AnalyticFunction: kwargs[key] = AnalyticFunction(kwargs[key], self.parameters, 0) new_transition = Transition(orig_state, dest_state, function_name, **kwargs) self.transitions.append(new_transition) orig_state.add_outgoing_transition(new_transition) def get_transition_id(self, transition: Transition) -> int: """Return PTA-specific ID of transition.""" return self.transitions.index(transition) def get_state_names(self): """Return lexically sorted list of PTA state names.""" return sorted(self.state.keys()) def get_state_id(self, state: State) -> int: """Return PTA-specific ID of state.""" return self.get_state_names().index(state.name) def get_unique_transitions(self): """ Return list of PTA transitions without duplicates. I.e., each transition name only occurs once, even if it has several entries due to multiple origin states and/or overloading. """ seen_transitions = set() ret_transitions = list() for transition in self.transitions: if transition.name not in seen_transitions: ret_transitions.append(transition) seen_transitions.add(transition.name) return ret_transitions def get_unique_transition_id(self, transition: Transition) -> int: """ Return PTA-specific ID of transition in unique transition list. The followinng condition holds: ` max_index = max(map(lambda t: pta.get_unique_transition_id(t), pta.get_unique_transitions())) max_index == len(pta.get_unique_transitions) - 1 ` """ return self.get_unique_transitions().index(transition) def get_initial_param_dict(self): return dict([[self.parameters[i], self.initial_param_values[i]] for i in range(len(self.parameters))]) def set_random_energy_model(self, static_model=True): u""" Set random power/energy/duration/timeout for all states and transitions. Values in µW/pJ/µs are chosen from a uniform [0 .. 50000] distribution. Only sets the static model at the moment. """ for state in self.state.values(): state.set_random_energy_model(static_model) for transition in self.transitions: transition.set_random_energy_model(static_model) def get_most_expensive_state(self): max_state = None for state in self.state.values(): if state.name != 'UNINITIALIZED' and (max_state is None or state.power.value > max_state.power.value): max_state = state return max_state def get_least_expensive_state(self): min_state = None for state in self.state.values(): if state.name != 'UNINITIALIZED' and (min_state is None or state.power.value < min_state.power.value): min_state = state return min_state def min_duration_until_energy_overflow(self, energy_granularity=1e-12, max_energy_value=2 ** 32 - 1): """ Return minimum duration (in s) until energy counter overflow during online accounting. :param energy_granularity: granularity of energy counter variable in J, i.e., how many Joules does an increment of one in the energy counter represent. Default: 1e-12 J = 1 pJ :param max_energy_value: maximum raw value in energy variable. Default: 2^32 - 1 """ max_power_state = self.get_most_expensive_state() if max_power_state.has_interrupt_transitions(): raise RuntimeWarning('state with maximum power consumption has outgoing interrupt transitions, results will be inaccurate') # convert from µW to W max_power = max_power_state.power.value * 1e-6 min_duration = max_energy_value * energy_granularity / max_power return min_duration def max_duration_until_energy_overflow(self, energy_granularity=1e-12, max_energy_value=2 ** 32 - 1): """ Return maximum duration (in s) until energy counter overflow during online accounting. :param energy_granularity: granularity of energy counter variable in J, i.e., how many Joules does an increment of one in the energy counter represent. Default: 1e-12 J = 1 pJ :param max_energy_value: maximum raw value in energy variable. Default: 2^32 - 1 """ min_power_state = self.get_least_expensive_state() if min_power_state.has_interrupt_transitions(): raise RuntimeWarning('state with maximum power consumption has outgoing interrupt transitions, results will be inaccurate') # convert from µW to W min_power = min_power_state.power.value * 1e-6 max_duration = max_energy_value * energy_granularity / min_power return max_duration def shrink_argument_values(self): """ Throw away all but two values for each numeric argument of each transition. This is meant to speed up an initial PTA-based benchmark by reducing the parameter space while still gaining insights in the effect (or lack thereof) or individual parameters on hardware behaviour. Parameters with non-numeric values (anything containing neither numbers nor enums) are left as-is, as they may be distinct toggles whose effect cannot be estimated when they are left out. """ for transition in self.transitions: for i, argument in enumerate(transition.arguments): if len(transition.argument_values[i]) <= 2: continue if transition.argument_combination == 'zip': continue values_are_numeric = True for value in transition.argument_values[i]: if not is_numeric(self.normalize_parameter(transition.arg_to_param_map[i], value)): values_are_numeric = False if values_are_numeric and len(transition.argument_values[i]) > 2: transition.argument_values[i] = [transition.argument_values[i][0], transition.argument_values[i][-1]] def _dfs_with_param(self, generator, param_dict): for trace in generator: param = param_dict.copy() ret = list() for elem in trace: transition, arguments = elem if transition is not None: param = transition.get_params_after_transition(param, arguments) ret.append((transition, arguments, self.normalize_parameters(param))) else: # parameters have already been normalized ret.append((transition, arguments, param)) yield ret def bfs(self, depth: int = 10, orig_state: str = 'UNINITIALIZED', param_dict: dict = None, with_parameters: bool = False, transition_filter=None, state_filter=None): """ Return a generator object for breadth-first search of traces starting at orig_state. Each trace consists of a list of tuples describing the corresponding transition and (if enabled) arguments and parameters. When both with_arguments and with_parameters are True, each transition is a (Transition object, argument list, parameter dict) tuple. Note that the parameter dict refers to parameter values _after_ passing the corresponding transition. Although this may seem odd at first, it is useful when analyzing measurements: Properties of the state following this transition may be affected by the parameters set by the transition, so it is useful to have those readily available. A trace is (at the moment) a list of alternating states and transition, both starting and ending with a state. Does not yield the no-operation trace consisting only of `orig_state`. If `orig_state` has no outgoing transitions, the output is empty. :param orig_state: initial state for breadth-first search :param depth: search depth, default 10 :param param_dict: initial parameter values :param with_arguments: perform dfs with argument values :param with_parameters: include parameters in trace? :param transition_filter: If set, only follow a transition if transition_filter(transition object) returns true. Default None. :param state_iflter: If set, only follow a state if state_filter(state_object) returns true. Default None. """ state_queue = queue.Queue() state_queue.put((list(), self.state[orig_state])) while not state_queue.empty(): trace, state = state_queue.get() if len(trace) > depth: return if state_filter is None or state_filter(state): for transition in state.outgoing_transitions.values(): if transition_filter is None or transition_filter(transition): new_trace = trace.copy() new_trace.append((transition,)) yield new_trace state_queue.put((new_trace, transition.destination)) def dfs(self, depth: int = 10, orig_state: str = 'UNINITIALIZED', param_dict: dict = None, with_parameters: bool = False, **kwargs): """ Return a generator object for depth-first search starting at orig_state. :param depth: search depth, default 10 :param orig_state: initial state for depth-first search :param param_dict: initial parameter values :param with_arguments: perform dfs with argument values :param with_parameters: include parameters in trace? :param trace_filter: list of lists. Each sub-list is a trace. Only traces matching one of the provided sub-lists are returned. :param sleep: sleep duration between states in us. If None or 0, no sleep pseudo-transitions will be included in the trace. Each trace consists of a list of tuples describing the corresponding transition and (if enabled) arguments and parameters. When both with_arguments and with_parameters are True, each transition is a (Transition object, argument list, parameter dict) tuple. Note that the parameter dict refers to parameter values _after_ passing the corresponding transition. Although this may seem odd at first, it is useful when analyzing measurements: Properties of the state following this transition may be affected by the parameters set by the transition, so it is useful to have those readily available. """ if with_parameters and not param_dict: param_dict = self.get_initial_param_dict() if with_parameters and 'with_arguments' not in kwargs: raise ValueError("with_parameters = True requires with_arguments = True") if self.accepting_states: generator = filter(lambda x: x[-1][0].destination.name in self.accepting_states, self.state[orig_state].dfs(depth, **kwargs)) else: generator = self.state[orig_state].dfs(depth, **kwargs) if with_parameters: return self._dfs_with_param(generator, param_dict) else: return generator def simulate(self, trace: list, orig_state: str = 'UNINITIALIZED', orig_param=None, accounting=None): u""" Simulate a single run through the PTA and return total energy, duration, final state, and resulting parameters. :param trace: list of (function name, arg1, arg2, ...) tuples representing the individual transitions, or list of (Transition, argument tuple, parameter) tuples originating from dfs. The tuple (None, duration) represents a sleep time between states in us :param orig_state: origin state, default UNINITIALIZED :param orig_param: initial parameters, default: `self.initial_param_values` :param accounting: EnergyAccounting object, default empty :returns: SimulationResult with duration in s, total energy in J, end state, and final parameters """ total_duration = 0. total_duration_mae = 0. total_energy = 0. total_energy_error = 0. if type(orig_state) is State: state = orig_state else: state = self.state[orig_state] if orig_param: param_dict = orig_param.copy() else: param_dict = dict([[self.parameters[i], self.initial_param_values[i]] for i in range(len(self.parameters))]) for function in trace: if isinstance(function[0], Transition): function_name = function[0].name function_args = function[1] else: function_name = function[0] function_args = function[1:] if function_name is None or function_name == '_': duration = function_args[0] total_energy += state.get_energy(duration, param_dict) if state.power.value_error is not None: total_energy_error += (duration * state.power.eval_mae(param_dict, function_args))**2 total_duration += duration # assumption: sleep is near-exact and does not contribute to the duration error if accounting is not None: accounting.sleep(duration) else: transition = state.get_transition(function_name) total_duration += transition.duration.eval(param_dict, function_args) if transition.duration.value_error is not None: total_duration_mae += transition.duration.eval_mae(param_dict, function_args)**2 total_energy += transition.get_energy(param_dict, function_args) if transition.energy.value_error is not None: total_energy_error += transition.energy.eval_mae(param_dict, function_args)**2 param_dict = transition.get_params_after_transition(param_dict, function_args) state = transition.destination if accounting is not None: accounting.pass_transition(transition) while (state.has_interrupt_transitions()): transition = state.get_next_interrupt(param_dict) duration = transition.get_timeout(param_dict) total_duration += duration total_energy += state.get_energy(duration, param_dict) if accounting is not None: accounting.sleep(duration) accounting.pass_transition(transition) param_dict = transition.get_params_after_transition(param_dict) state = transition.destination return SimulationResult(total_duration, total_energy, state, param_dict, duration_mae=np.sqrt(total_duration_mae), energy_mae=np.sqrt(total_energy_error)) def update(self, static_model, param_model, static_error=None, analytic_error=None): for state in self.state.values(): if state.name != 'UNINITIALIZED': try: state.power.value = static_model(state.name, 'power') if static_error is not None: state.power.value_error = static_error[state.name]['power'] if param_model(state.name, 'power'): state.power.function = param_model(state.name, 'power')['function'] if analytic_error is not None: state.power.function_error = analytic_error[state.name]['power'] except KeyError: print('[W] skipping model update of state {} due to missing data'.format(state.name)) pass for transition in self.transitions: try: transition.duration.value = static_model(transition.name, 'duration') if param_model(transition.name, 'duration'): transition.duration.function = param_model(transition.name, 'duration')['function'] if analytic_error is not None: transition.duration.function_error = analytic_error[transition.name]['duration'] transition.energy.value = static_model(transition.name, 'energy') if param_model(transition.name, 'energy'): transition.energy.function = param_model(transition.name, 'energy')['function'] if analytic_error is not None: transition.energy.function_error = analytic_error[transition.name]['energy'] if transition.is_interrupt: transition.timeout.value = static_model(transition.name, 'timeout') if param_model(transition.name, 'timeout'): transition.timeout.function = param_model(transition.name, 'timeout')['function'] if analytic_error is not None: transition.timeout.function_error = analytic_error[transition.name]['timeout'] if static_error is not None: transition.duration.value_error = static_error[transition.name]['duration'] transition.energy.value_error = static_error[transition.name]['energy'] transition.timeout.value_error = static_error[transition.name]['timeout'] except KeyError: print('[W] skipping model update of transition {} due to missing data'.format(transition.name)) pass