Module tinytroupe.control
Simulation controlling mechanisms.
Expand source code
"""
Simulation controlling mechanisms.
"""
import json
import os
import tempfile
import threading
import traceback
import tinytroupe
import tinytroupe.utils as utils
import uuid
import logging
logger = logging.getLogger("tinytroupe")
# to protect from race conditions when running in parallel
concurrent_execution_lock = threading.Lock()
class Simulation:
STATUS_STOPPED = "stopped"
STATUS_STARTED = "started"
def __init__(self, id="default", cached_trace:list=None):
self.id = id
self.agents = []
self.name_to_agent = {} # {agent_name: agent, ...}
self.environments = []
self.factories = [] # e.g., TinyPersonFactory instances
self.name_to_factory = {} # {factory_name: factory, ...}
self.name_to_environment = {} # {environment_name: environment, ...}
self.status = Simulation.STATUS_STOPPED
self.cache_path = f"./tinytroupe-{id}.cache.json" # default cache path
# should we always automatically checkpoint at the every transaction?
self.auto_checkpoint = False
# whether there are changes not yet saved to the cache file
self.has_unsaved_cache_changes = False
# whether the agent is under a transaction or not, used for managing
# simulation caching later
self._under_transaction = {None: False}
# whether the agent is under a parallel transactions segment or not, used for managing
# simulation caching later
self._under_parallel_transactions = False
# Cache chain mechanism.
#
# stores a list of simulation states.
# Each state is a tuple (prev_node_hash, event_hash, event_output, state), where prev_node_hash is a hash of the previous node in this chain,
# if any, event_hash is a hash of the event that triggered the transition to this state, if any, event_output is the output of the event,
# if any, and state is the actual complete state that resulted.
if cached_trace is None:
self.cached_trace = []
else:
self.cached_trace = cached_trace
self.cache_misses = 0
self.cache_hits = 0
# Execution chain mechanism.
#
# The actual, current, execution trace. Each state is a tuple (prev_node_hash, event_hash, state), where prev_node_hash is a hash
# of the previous node in this chain, if any, event_hash is a hash of the event that triggered the transition to this state, if any,
# event_output is the output of the event, if any, and state is the actual complete state that resulted.
self.execution_trace = []
def begin(self, cache_path:str=None, auto_checkpoint:bool=False):
"""
Marks the start of the simulation being controlled.
Args:
cache_path (str): The path to the cache file. If not specified,
defaults to the default cache path defined in the class.
auto_checkpoint (bool, optional): Whether to automatically checkpoint at the end of each transaction. Defaults to False.
"""
logger.debug(f"Starting simulation, cache_path={cache_path}, auto_checkpoint={auto_checkpoint}.")
# local import to avoid circular dependencies
from tinytroupe.agent import TinyPerson
from tinytroupe.environment import TinyWorld
from tinytroupe.factory.tiny_factory import TinyFactory
from tinytroupe.factory.tiny_person_factory import TinyPersonFactory
if self.status == Simulation.STATUS_STOPPED:
self.status = Simulation.STATUS_STARTED
else:
raise ValueError("Simulation is already started.")
if cache_path is not None:
self.cache_path = cache_path
# should we automatically checkpoint?
self.auto_checkpoint = auto_checkpoint
# clear the agents, environments and other simulated entities, we'll track them from now on
TinyPerson.clear_agents()
TinyWorld.clear_environments()
TinyFactory.clear_factories()
TinyPersonFactory.clear_factories()
# All automated fresh ids will start from 0 again for this simulation
utils.reset_fresh_id()
# load the cache file, if any
if self.cache_path is not None:
self._load_cache_file(self.cache_path)
def end(self):
"""
Marks the end of the simulation being controlled.
"""
logger.debug("Ending simulation.")
if self.status == Simulation.STATUS_STARTED:
self.status = Simulation.STATUS_STOPPED
self.checkpoint()
else:
raise ValueError("Simulation is already stopped.")
def checkpoint(self):
"""
Saves current simulation trace to a file.
"""
logger.debug("Checkpointing simulation state...")
# save the cache file
if self.has_unsaved_cache_changes:
self._save_cache_file(self.cache_path)
else:
logger.debug("No unsaved cache changes to save to file.")
def add_agent(self, agent):
"""
Adds an agent to the simulation.
"""
if agent.name in self.name_to_agent:
raise ValueError(f"Agent names must be unique, but '{agent.name}' is already defined.")
agent.simulation_id = self.id
self.agents.append(agent)
self.name_to_agent[agent.name] = agent
def add_environment(self, environment):
"""
Adds an environment to the simulation.
"""
if environment.name in self.name_to_environment:
raise ValueError(f"Environment names must be unique, but '{environment.name}' is already defined.")
environment.simulation_id = self.id
self.environments.append(environment)
self.name_to_environment[environment.name] = environment
def add_factory(self, factory):
"""
Adds a factory to the simulation.
"""
if factory.name in self.name_to_factory:
raise ValueError(f"Factory names must be unique, but '{factory.name}' is already defined.")
factory.simulation_id = self.id
self.factories.append(factory)
self.name_to_factory[factory.name] = factory
###################################################################################################
# Cache and execution chain mechanisms
###################################################################################################
def _execution_trace_position(self) -> int:
"""
Returns the current position in the execution trace, or -1 if the execution trace is empty.
"""
return len(self.execution_trace) - 1
def _function_call_hash(self, function_name, *args, **kwargs) -> int:
"""
Computes the hash of the given function call.
"""
# if functions are passed as arguments to the function, there's the problem that their
# string representation always changes due to memory position (e.g., <function my_function at 0x7f8d1a7b7d30>).
# so we need to remove the changing suffix in those cases, while preserving the function name if it exists.
# positional arguments
# covnerts to a list of string representations first
args_str = list(map(str, args))
for i, arg in enumerate(args):
if callable(arg):
args_str[i] = arg.__name__
# keyword arguments
# converts to a list of string representations first
kwargs_str = {k: str(v) for k, v in kwargs.items()}
for k, v in kwargs.items():
if callable(v):
kwargs_str[k] = v.__name__
# then, convert to a single string, to obtain a unique hash
event = str((function_name, args_str, kwargs_str))
# TODO actually compute a short hash of the event string, e.g., using SHA256 ?
# event_hash = utils.custom_hash(event)
return event
def _skip_execution_with_cache(self):
"""
Skips the current execution, assuming there's a cached state at the same position.
"""
assert len(self.cached_trace) > self._execution_trace_position() + 1, "There's no cached state at the current execution position."
self.execution_trace.append(self.cached_trace[self._execution_trace_position() + 1])
def _is_transaction_event_cached(self, event_hash, parallel=False) -> bool:
"""
Checks whether the given event hash matches the corresponding cached one, if any.
If there's no corresponding cached state, returns True.
"""
if not parallel:
# there's cache that could be used
if len(self.cached_trace) > self._execution_trace_position() + 1:
if self._execution_trace_position() >= -1:
# here's a graphical depiction of the logic:
#
# Cache: c0:(c_prev_node_hash_0, c_event_hash_0, _, c_state_0) ------------------> c1:(c_prev_node_hash_1, c_event_hash_1, _, c_state_1) -> ...
# Execution: e0:(e_prev_node_hash_0, e_event_hash_0, _, e_state_0) -<being computed>-> e1:(e_prev_node_hash_1, <being computed>, <being computed>, <being computed>)
# position = 0 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Must satisfy:
# - event_hash == c_event_hash_1
# - hash(e0) == c_prev_node_hash_1
try:
event_hash_match = event_hash == self.cached_trace[self._execution_trace_position() + 1][1]
except Exception as e:
logger.error(f"Error while checking event hash match: {e}")
event_hash_match = False
prev_node_match = True # TODO implement real check
return event_hash_match and prev_node_match
else:
raise ValueError("Execution trace position is invalid, must be >= -1, but is ", self._execution_trace_position())
else: # no cache to use
return False
else: # parallel
if len(self.cached_trace) >= self._execution_trace_position():
if self._execution_trace_position() >= 0:
# parallel stores ignore order, so we need to check instead whether the event hash is a key in the parallel store,
# regardless of the order of the events generated the data therein.
if isinstance(self.cached_trace[self._execution_trace_position()], dict):
event_hash_match = event_hash in self.cached_trace[self._execution_trace_position()].keys()
else:
event_hash_match = False
prev_node_match = True # TODO implement real check
return event_hash_match and prev_node_match
else:
raise ValueError("Execution trace position is invalid, must be >= 0, but is ", self._execution_trace_position())
def _get_cached_parallel_value(self, event_hash, key):
parallel_store = self.cached_trace[self._execution_trace_position()]
value = parallel_store[event_hash][key]
return value
def _drop_cached_trace_suffix(self):
"""
Drops the cached trace suffix starting at the current execution trace position. This effectively
refreshes the cache to the current execution state and starts building a new cache from there.
"""
self.cached_trace = self.cached_trace[:self._execution_trace_position()+1]
def _add_to_execution_trace(self, state: dict, event_hash: int, event_output, parallel=False):
"""
Adds a state to the execution_trace list and computes the appropriate hash.
The computed hash is compared to the hash of the cached trace at the same position,
and if they don't match, the execution is aborted. Similarly, the event_hash is compared
to the hash of the event in the cached trace at the same position, and if they don't match, the execution
is aborted.
"""
# Compute the hash of the previous execution pair, if any
previous_hash = None
if not parallel:
# Create a tuple of (hash, state) and append it to the execution_trace list
self.execution_trace.append((previous_hash, event_hash, event_output, state))
else:
with concurrent_execution_lock:
# state is not stored in parallel segments, only outputs
self.execution_trace[-1][event_hash] = {"prev_node_hash": previous_hash,
"encoded_output": event_output}
def _add_to_cache_trace(self, state: dict, event_hash: int, event_output, parallel=False):
"""
Adds a state to the cached_trace list and computes the appropriate hash.
"""
# Compute the hash of the previous cached pair, if any
previous_hash = None
if self.cached_trace:
previous_hash = utils.custom_hash(self.cached_trace[-1])
if not parallel:
# Create a tuple of (hash, state) and append it to the cached_trace list
self.cached_trace.append((previous_hash, event_hash, event_output, state))
else:
with concurrent_execution_lock:
# state is not stored in parallel segments, only outputs
self.cached_trace[-1][event_hash] = {"prev_node_hash": previous_hash,
"encoded_output": event_output}
self.has_unsaved_cache_changes = True
def _load_cache_file(self, cache_path:str):
"""
Loads the cache file from the given path.
"""
try:
self.cached_trace = json.load(open(cache_path, "r"))
except FileNotFoundError:
logger.info(f"Cache file not found on path: {cache_path}.")
self.cached_trace = []
def _save_cache_file(self, cache_path:str):
"""
Saves the cache file to the given path. Always overwrites.
"""
logger.debug(f"Now saving cache file to {cache_path}.")
try:
# Create a temporary file
with tempfile.NamedTemporaryFile('w', delete=False) as temp:
json.dump(self.cached_trace, temp, indent=4)
# Replace the original file with the temporary file
os.replace(temp.name, cache_path)
except Exception as e:
traceback_string = ''.join(traceback.format_tb(e.__traceback__))
logger.error(f"An error occurred while saving the cache file: {e}\nTraceback:\n{traceback_string}")
self.has_unsaved_cache_changes = False
###################################################################################################
# Transactional control
###################################################################################################
#
# Regular sequential transactions
#
def begin_transaction(self, id=None):
"""
Starts a transaction.
"""
with concurrent_execution_lock:
self._under_transaction[id] = True
self._clear_communications_buffers() # TODO <----------------------------------------------------------------
def end_transaction(self, id=None):
"""
Ends a transaction.
"""
with concurrent_execution_lock:
self._under_transaction[id] = False
def is_under_transaction(self, id=None):
"""
Checks if the agent is under a transaction.
"""
with concurrent_execution_lock:
return self._under_transaction.get(id, False)
def _clear_communications_buffers(self):
"""
Cleans the communications buffers of all agents and environments.
"""
for agent in self.agents:
agent.clear_communications_buffer()
for environment in self.environments:
environment.clear_communications_buffer()
#
# Parallel transactions
#
def begin_parallel_transactions(self):
"""
Starts parallel transactions.
"""
with concurrent_execution_lock:
self._under_parallel_transactions = True
# add a new parallel segment to the execution and cache traces
self.execution_trace.append({})
self.cached_trace.append({})
def end_parallel_transactions(self):
"""
Ends parallel transactions.
"""
self._under_parallel_transactions = False
def is_under_parallel_transactions(self):
"""
Checks if the agent is under parallel transactions.
"""
return self._under_parallel_transactions
###################################################################################################
# Simulation state handling
###################################################################################################
def _encode_simulation_state(self) -> dict:
"""
Encodes the current simulation state, including agents, environments, and other
relevant information.
"""
state = {}
# Encode agents
state["agents"] = []
for agent in self.agents:
state["agents"].append(agent.encode_complete_state())
# Encode environments
state["environments"] = []
for environment in self.environments:
state["environments"].append(environment.encode_complete_state())
# Encode factories
state["factories"] = []
for factory in self.factories:
state["factories"].append(factory.encode_complete_state())
return state
def _decode_simulation_state(self, state: dict):
"""
Decodes the given simulation state, including agents, environments, and other
relevant information.
Args:
state (dict): The state to decode.
"""
# local import to avoid circular dependencies
from tinytroupe.agent import TinyPerson
from tinytroupe.environment import TinyWorld
logger.debug(f"Decoding simulation state: {state['factories']}")
logger.debug(f"Registered factories: {self.name_to_factory}")
logger.debug(f"Registered agents: {self.name_to_agent}")
logger.debug(f"Registered environments: {self.name_to_environment}")
# Decode factories
for factory_state in state["factories"]:
factory = self.name_to_factory[factory_state["name"]]
factory.decode_complete_state(factory_state)
# Decode environments
###self.environments = []
for environment_state in state["environments"]:
try:
environment = self.name_to_environment[environment_state["name"]]
environment.decode_complete_state(environment_state)
if TinyWorld.communication_display:
environment.pop_and_display_latest_communications()
except Exception as e:
raise ValueError(f"Environment {environment_state['name']} is not in the simulation, thus cannot be decoded there.") from e
# Decode agents (if they were not already decoded by the environment)
####self.agents = []
for agent_state in state["agents"]:
try:
agent = self.name_to_agent[agent_state["name"]]
agent.decode_complete_state(agent_state)
# The agent has not yet been decoded because it is not in any environment. So, decode it.
if agent.environment is None:
if TinyPerson.communication_display:
agent.pop_and_display_latest_communications()
except Exception as e:
raise ValueError(f"Agent {agent_state['name']} is not in the simulation, thus cannot be decoded there.") from e
class Transaction:
def __init__(self, obj_under_transaction, simulation, function, *args, **kwargs):
# local import to avoid circular dependencies
from tinytroupe.agent import TinyPerson
from tinytroupe.environment import TinyWorld
from tinytroupe.factory.tiny_factory import TinyFactory
self.obj_under_transaction = obj_under_transaction
self.simulation = simulation
self.function_name = function.__name__
self.function = function
self.args = args
self.kwargs = kwargs
#
# If we have an ongoing simulation, set the simulation id of the object under transaction if it is not already set.
#
if simulation is not None:
if hasattr(obj_under_transaction, 'simulation_id') and obj_under_transaction.simulation_id is not None:
if obj_under_transaction.simulation_id != simulation.id:
raise ValueError(f"Object {obj_under_transaction} is already captured by a different simulation (id={obj_under_transaction.simulation_id}), \
and cannot be captured by simulation id={simulation.id}.")
logger.debug(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Object {obj_under_transaction} is already captured by simulation {simulation.id}.")
else:
# if is a TinyPerson, add the agent to the simulation
if isinstance(obj_under_transaction, TinyPerson):
simulation.add_agent(obj_under_transaction)
logger.debug(f">>>>>>>>>>>>>>>>>>>>>>> Added agent {obj_under_transaction} to simulation {simulation.id}.")
# if is a TinyWorld, add the environment to the simulation
elif isinstance(obj_under_transaction, TinyWorld):
simulation.add_environment(obj_under_transaction)
# if is a TinyFactory, add the factory to the simulation
elif isinstance(obj_under_transaction, TinyFactory):
simulation.add_factory(obj_under_transaction)
logger.debug(f">>>>>>>>>>>>>>>>>>>>>>> Added factory {obj_under_transaction} to simulation {simulation.id}.")
else:
raise ValueError(f"Object {obj_under_transaction} (type = {type(obj_under_transaction)}) is not a TinyPerson or TinyWorld instance, and cannot be captured by the simulation.")
def execute(self, begin_parallel=False, parallel_id=None):
output = None
# Transaction caching will only operate if there is a simulation and it is started
if self.simulation is None or self.simulation.status == Simulation.STATUS_STOPPED:
# Compute the function and return it, no caching, since the simulation is not started
output = self.function(*self.args, **self.kwargs)
elif self.simulation.status == Simulation.STATUS_STARTED:
# Compute the event hash
event_hash = self.simulation._function_call_hash(self.function_name, *self.args, **self.kwargs)
# Sequential and parallel transactions are handled in different ways
if begin_parallel:
self.simulation.begin_parallel_transactions()
# CACHED? Check if the event hash is in the cache
if self.simulation._is_transaction_event_cached(event_hash,
parallel=self.simulation.is_under_parallel_transactions()):
self.simulation.cache_hits += 1
# Restore the full state and return the cached output
logger.info(f"Skipping execution of {self.function_name} with args {self.args} and kwargs {self.kwargs} because it is already cached.")
# SEQUENTIAL
if not self.simulation.is_under_parallel_transactions():
self.simulation._skip_execution_with_cache()
state = self.simulation.cached_trace[self.simulation._execution_trace_position()][3] # state
self.simulation._decode_simulation_state(state)
# Output encoding/decoding is used to preserve references to TinyPerson and TinyWorld instances
# mainly. Scalar values (int, float, str, bool) and composite values (list, dict) are
# encoded/decoded as is.
encoded_output = self.simulation.cached_trace[self.simulation._execution_trace_position()][2] # output
output = self._decode_function_output(encoded_output)
# PARALLEL
else: # is under parallel transactions
# in parallel segments, state is not restored, only outputs
encoded_output = self.simulation._get_cached_parallel_value(event_hash, "encoded_output")
output = self._decode_function_output(encoded_output)
else: # not cached
if not begin_parallel:
# in case of beginning a parallel segment, we don't want to count it as a cache miss,
# since the segment itself will not be cached, but rather the events within it.
self.simulation.cache_misses += 1
if not self.simulation.is_under_transaction(id=parallel_id) and not begin_parallel:
# BEGIN SEQUENTIAL TRANSACTION ###############################################################
#
# if this is the beginning of a parallel segment, we don't need to begin a transaction, since
# we want to allow additional transactions within the parallel segment (i.e., one-level reentrancy).
if not begin_parallel:
self.simulation.begin_transaction(id=parallel_id)
# Compute the function and encode the relevant output and simulation state
output = self.function(*self.args, **self.kwargs)
self._save_output_with_simulation_state(event_hash, output)
# END TRANSACTION #################################################################
if not begin_parallel:
self.simulation.end_transaction(id=parallel_id)
else: # already under transaction (thus, now a reentrant transaction) OR beginning a parallel segment
# NOTES:
#
# - Reentrant sequential transactions are not cached, since what matters is the final result of
# the top-level transaction.
#
# - The event that starts the parallel transactions segment WILL NOT itself be cached, since
# it is not part of the parallel segment, but rather the beginning of it. This event will be
# reconstructed during runtime from the parallel events within the segment.
output = self.function(*self.args, **self.kwargs)
if begin_parallel:
self.simulation.end_parallel_transactions()
# execute an ad-hoc Transaction to save the simulation state AFTER the parallel segment is done.
Transaction(self.obj_under_transaction, self.simulation, lambda: True).execute(begin_parallel=False, parallel_id=parallel_id)
else:
raise ValueError(f"Simulation status is invalid at this point: {self.simulation.status}")
# Checkpoint if needed
logger.debug(f"Will attempt to checkpoint simulation state after transaction execution.")
if self.simulation is not None and self.simulation.auto_checkpoint:
logger.debug("Auto-checkpointing simulation state after transaction execution.")
self.simulation.checkpoint()
# after all the transaction is done, return the output - the client will never know about all the complexity we've
# gone through to get here.
return output
def _save_output_with_simulation_state(self, event_hash, output):
encoded_output = self._encode_function_output(output)
state = self.simulation._encode_simulation_state()
# immediately drop the cached trace suffix, since we are starting a new execution from this point on.
# in the case of parallel transactions, this will drop everything _after_ the current parallel segment
# (which itself occupies one position only, with a dictionary of event hashes and their outputs).
self.simulation._drop_cached_trace_suffix()
# Cache the result and update the current execution trace. If this is a parallel transaction, the
# cache and execution traces will be updated in a different way.
self.simulation._add_to_cache_trace(state, event_hash, encoded_output,
parallel=self.simulation.is_under_parallel_transactions())
self.simulation._add_to_execution_trace(state, event_hash, encoded_output,
parallel=self.simulation.is_under_parallel_transactions())
def _encode_function_output(self, output) -> dict:
"""
Encodes the given function output.
"""
# local import to avoid circular dependencies
from tinytroupe.agent import TinyPerson
from tinytroupe.environment import TinyWorld
from tinytroupe.factory.tiny_factory import TinyFactory
# if the output is a supported object, encode it
if output is None:
return None
elif isinstance(output, TinyPerson):
return {"type": "TinyPersonRef", "name": output.name}
elif isinstance(output, TinyWorld):
return {"type": "TinyWorldRef", "name": output.name}
elif isinstance(output, TinyFactory):
return {"type": "TinyFactoryRef", "name": output.name}
elif isinstance(output, list):
encoded_list = []
for item in output:
if isinstance(item, TinyPerson):
encoded_list.append({"type": "TinyPersonRef", "name": item.name})
elif isinstance(item, TinyWorld):
encoded_list.append({"type": "TinyWorldRef", "name": item.name})
elif isinstance(item, TinyFactory):
encoded_list.append({"type": "TinyFactoryRef", "name": item.name})
else:
encoded_list.append({"type": "JSON", "value": item})
return {"type": "List", "value": encoded_list}
elif isinstance(output, (int, float, str, bool, dict, tuple)):
return {"type": "JSON", "value": output}
else:
raise ValueError(f"Unsupported output type: {type(output)}")
def _decode_function_output(self, encoded_output: dict):
"""
Decodes the given encoded function output.
"""
# local import to avoid circular dependencies
from tinytroupe.agent import TinyPerson
from tinytroupe.environment import TinyWorld
from tinytroupe.factory.tiny_factory import TinyFactory
if encoded_output is None:
return None
elif encoded_output["type"] == "TinyPersonRef":
return TinyPerson.get_agent_by_name(encoded_output["name"])
elif encoded_output["type"] == "TinyWorldRef":
return TinyWorld.get_environment_by_name(encoded_output["name"])
elif encoded_output["type"] == "TinyFactoryRef":
return TinyFactory.get_factory_by_name(encoded_output["name"])
elif encoded_output["type"] == "List":
decoded_list = []
for item in encoded_output["value"]:
if item["type"] == "TinyPersonRef":
decoded_list.append(TinyPerson.get_agent_by_name(item["name"]))
elif item["type"] == "TinyWorldRef":
decoded_list.append(TinyWorld.get_environment_by_name(item["name"]))
elif item["type"] == "TinyFactoryRef":
decoded_list.append(TinyFactory.get_factory_by_name(item["name"]))
else:
decoded_list.append(item["value"])
return decoded_list
elif encoded_output["type"] == "JSON":
return encoded_output["value"]
else:
raise ValueError(f"Unsupported output type: {encoded_output['type']}")
def transactional(parallel=False):
"""
A helper decorator that makes a function simulation-transactional.
"""
def decorator(func):
def wrapper(*args, **kwargs):
obj_under_transaction = args[0]
simulation = current_simulation()
obj_sim_id = obj_under_transaction.simulation_id if hasattr(obj_under_transaction, 'simulation_id') else None
logger.debug(f"-----------------------------------------> Transaction: {func.__name__} with args {args[1:]} and kwargs {kwargs} under simulation {obj_sim_id}, parallel={parallel}.")
parallel_id = str(threading.current_thread())
transaction = Transaction(obj_under_transaction, simulation, func, *args, **kwargs)
result = transaction.execute(begin_parallel=parallel, parallel_id=parallel_id)
return result
return wrapper
return decorator
class SkipTransaction(Exception):
pass
class CacheOutOfSync(Exception):
"""
Raised when a cached and the corresponding freshly executed elements are out of sync.
"""
pass
class ExecutionCached(Exception):
"""
Raised when a proposed execution is already cached.
"""
pass
###################################################################################################
# Convenience functions
###################################################################################################
def reset():
"""
Resets the entire simulation control state.
"""
global _current_simulations, _current_simulation_id
_current_simulations = {"default": None}
# TODO Currently, only one simulation can be started at a time. In future versions, this should be
# changed to allow multiple simulations to be started at the same time, e.g., for fast
# analyses through parallelization.
_current_simulation_id = None
def _simulation(id="default"):
global _current_simulations
if _current_simulations[id] is None:
_current_simulations[id] = Simulation()
return _current_simulations[id]
def begin(cache_path=None, id="default", auto_checkpoint=False):
"""
Marks the start of the simulation being controlled.
"""
global _current_simulation_id
if _current_simulation_id is None:
_simulation(id).begin(cache_path, auto_checkpoint)
_current_simulation_id = id
else:
raise ValueError(f"Simulation is already started under id {_current_simulation_id}. Currently only one simulation can be started at a time.")
def end(id="default"):
"""
Marks the end of the simulation being controlled.
"""
global _current_simulation_id
_simulation(id).end()
_current_simulation_id = None
def checkpoint(id="default"):
"""
Saves current simulation state.
"""
_simulation(id).checkpoint()
def current_simulation():
"""
Returns the current simulation.
"""
global _current_simulation_id
if _current_simulation_id is not None:
return _simulation(_current_simulation_id)
else:
return None
def cache_hits(id="default"):
"""
Returns the number of cache hits.
"""
return _simulation(id).cache_hits
def cache_misses(id="default"):
"""
Returns the number of cache misses.
"""
return _simulation(id).cache_misses
reset() # initialize the control stateFunctions
def begin(cache_path=None, id='default', auto_checkpoint=False)-
Marks the start of the simulation being controlled.
Expand source code
def begin(cache_path=None, id="default", auto_checkpoint=False): """ Marks the start of the simulation being controlled. """ global _current_simulation_id if _current_simulation_id is None: _simulation(id).begin(cache_path, auto_checkpoint) _current_simulation_id = id else: raise ValueError(f"Simulation is already started under id {_current_simulation_id}. Currently only one simulation can be started at a time.") def cache_hits(id='default')-
Returns the number of cache hits.
Expand source code
def cache_hits(id="default"): """ Returns the number of cache hits. """ return _simulation(id).cache_hits def cache_misses(id='default')-
Returns the number of cache misses.
Expand source code
def cache_misses(id="default"): """ Returns the number of cache misses. """ return _simulation(id).cache_misses def checkpoint(id='default')-
Saves current simulation state.
Expand source code
def checkpoint(id="default"): """ Saves current simulation state. """ _simulation(id).checkpoint() def current_simulation()-
Returns the current simulation.
Expand source code
def current_simulation(): """ Returns the current simulation. """ global _current_simulation_id if _current_simulation_id is not None: return _simulation(_current_simulation_id) else: return None def end(id='default')-
Marks the end of the simulation being controlled.
Expand source code
def end(id="default"): """ Marks the end of the simulation being controlled. """ global _current_simulation_id _simulation(id).end() _current_simulation_id = None def reset()-
Resets the entire simulation control state.
Expand source code
def reset(): """ Resets the entire simulation control state. """ global _current_simulations, _current_simulation_id _current_simulations = {"default": None} # TODO Currently, only one simulation can be started at a time. In future versions, this should be # changed to allow multiple simulations to be started at the same time, e.g., for fast # analyses through parallelization. _current_simulation_id = None def transactional(parallel=False)-
A helper decorator that makes a function simulation-transactional.
Expand source code
def transactional(parallel=False): """ A helper decorator that makes a function simulation-transactional. """ def decorator(func): def wrapper(*args, **kwargs): obj_under_transaction = args[0] simulation = current_simulation() obj_sim_id = obj_under_transaction.simulation_id if hasattr(obj_under_transaction, 'simulation_id') else None logger.debug(f"-----------------------------------------> Transaction: {func.__name__} with args {args[1:]} and kwargs {kwargs} under simulation {obj_sim_id}, parallel={parallel}.") parallel_id = str(threading.current_thread()) transaction = Transaction(obj_under_transaction, simulation, func, *args, **kwargs) result = transaction.execute(begin_parallel=parallel, parallel_id=parallel_id) return result return wrapper return decorator
Classes
class CacheOutOfSync (*args, **kwargs)-
Raised when a cached and the corresponding freshly executed elements are out of sync.
Expand source code
class CacheOutOfSync(Exception): """ Raised when a cached and the corresponding freshly executed elements are out of sync. """ passAncestors
- builtins.Exception
- builtins.BaseException
class ExecutionCached (*args, **kwargs)-
Raised when a proposed execution is already cached.
Expand source code
class ExecutionCached(Exception): """ Raised when a proposed execution is already cached. """ passAncestors
- builtins.Exception
- builtins.BaseException
class Simulation (id='default', cached_trace: list = None)-
Expand source code
class Simulation: STATUS_STOPPED = "stopped" STATUS_STARTED = "started" def __init__(self, id="default", cached_trace:list=None): self.id = id self.agents = [] self.name_to_agent = {} # {agent_name: agent, ...} self.environments = [] self.factories = [] # e.g., TinyPersonFactory instances self.name_to_factory = {} # {factory_name: factory, ...} self.name_to_environment = {} # {environment_name: environment, ...} self.status = Simulation.STATUS_STOPPED self.cache_path = f"./tinytroupe-{id}.cache.json" # default cache path # should we always automatically checkpoint at the every transaction? self.auto_checkpoint = False # whether there are changes not yet saved to the cache file self.has_unsaved_cache_changes = False # whether the agent is under a transaction or not, used for managing # simulation caching later self._under_transaction = {None: False} # whether the agent is under a parallel transactions segment or not, used for managing # simulation caching later self._under_parallel_transactions = False # Cache chain mechanism. # # stores a list of simulation states. # Each state is a tuple (prev_node_hash, event_hash, event_output, state), where prev_node_hash is a hash of the previous node in this chain, # if any, event_hash is a hash of the event that triggered the transition to this state, if any, event_output is the output of the event, # if any, and state is the actual complete state that resulted. if cached_trace is None: self.cached_trace = [] else: self.cached_trace = cached_trace self.cache_misses = 0 self.cache_hits = 0 # Execution chain mechanism. # # The actual, current, execution trace. Each state is a tuple (prev_node_hash, event_hash, state), where prev_node_hash is a hash # of the previous node in this chain, if any, event_hash is a hash of the event that triggered the transition to this state, if any, # event_output is the output of the event, if any, and state is the actual complete state that resulted. self.execution_trace = [] def begin(self, cache_path:str=None, auto_checkpoint:bool=False): """ Marks the start of the simulation being controlled. Args: cache_path (str): The path to the cache file. If not specified, defaults to the default cache path defined in the class. auto_checkpoint (bool, optional): Whether to automatically checkpoint at the end of each transaction. Defaults to False. """ logger.debug(f"Starting simulation, cache_path={cache_path}, auto_checkpoint={auto_checkpoint}.") # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld from tinytroupe.factory.tiny_factory import TinyFactory from tinytroupe.factory.tiny_person_factory import TinyPersonFactory if self.status == Simulation.STATUS_STOPPED: self.status = Simulation.STATUS_STARTED else: raise ValueError("Simulation is already started.") if cache_path is not None: self.cache_path = cache_path # should we automatically checkpoint? self.auto_checkpoint = auto_checkpoint # clear the agents, environments and other simulated entities, we'll track them from now on TinyPerson.clear_agents() TinyWorld.clear_environments() TinyFactory.clear_factories() TinyPersonFactory.clear_factories() # All automated fresh ids will start from 0 again for this simulation utils.reset_fresh_id() # load the cache file, if any if self.cache_path is not None: self._load_cache_file(self.cache_path) def end(self): """ Marks the end of the simulation being controlled. """ logger.debug("Ending simulation.") if self.status == Simulation.STATUS_STARTED: self.status = Simulation.STATUS_STOPPED self.checkpoint() else: raise ValueError("Simulation is already stopped.") def checkpoint(self): """ Saves current simulation trace to a file. """ logger.debug("Checkpointing simulation state...") # save the cache file if self.has_unsaved_cache_changes: self._save_cache_file(self.cache_path) else: logger.debug("No unsaved cache changes to save to file.") def add_agent(self, agent): """ Adds an agent to the simulation. """ if agent.name in self.name_to_agent: raise ValueError(f"Agent names must be unique, but '{agent.name}' is already defined.") agent.simulation_id = self.id self.agents.append(agent) self.name_to_agent[agent.name] = agent def add_environment(self, environment): """ Adds an environment to the simulation. """ if environment.name in self.name_to_environment: raise ValueError(f"Environment names must be unique, but '{environment.name}' is already defined.") environment.simulation_id = self.id self.environments.append(environment) self.name_to_environment[environment.name] = environment def add_factory(self, factory): """ Adds a factory to the simulation. """ if factory.name in self.name_to_factory: raise ValueError(f"Factory names must be unique, but '{factory.name}' is already defined.") factory.simulation_id = self.id self.factories.append(factory) self.name_to_factory[factory.name] = factory ################################################################################################### # Cache and execution chain mechanisms ################################################################################################### def _execution_trace_position(self) -> int: """ Returns the current position in the execution trace, or -1 if the execution trace is empty. """ return len(self.execution_trace) - 1 def _function_call_hash(self, function_name, *args, **kwargs) -> int: """ Computes the hash of the given function call. """ # if functions are passed as arguments to the function, there's the problem that their # string representation always changes due to memory position (e.g., <function my_function at 0x7f8d1a7b7d30>). # so we need to remove the changing suffix in those cases, while preserving the function name if it exists. # positional arguments # covnerts to a list of string representations first args_str = list(map(str, args)) for i, arg in enumerate(args): if callable(arg): args_str[i] = arg.__name__ # keyword arguments # converts to a list of string representations first kwargs_str = {k: str(v) for k, v in kwargs.items()} for k, v in kwargs.items(): if callable(v): kwargs_str[k] = v.__name__ # then, convert to a single string, to obtain a unique hash event = str((function_name, args_str, kwargs_str)) # TODO actually compute a short hash of the event string, e.g., using SHA256 ? # event_hash = utils.custom_hash(event) return event def _skip_execution_with_cache(self): """ Skips the current execution, assuming there's a cached state at the same position. """ assert len(self.cached_trace) > self._execution_trace_position() + 1, "There's no cached state at the current execution position." self.execution_trace.append(self.cached_trace[self._execution_trace_position() + 1]) def _is_transaction_event_cached(self, event_hash, parallel=False) -> bool: """ Checks whether the given event hash matches the corresponding cached one, if any. If there's no corresponding cached state, returns True. """ if not parallel: # there's cache that could be used if len(self.cached_trace) > self._execution_trace_position() + 1: if self._execution_trace_position() >= -1: # here's a graphical depiction of the logic: # # Cache: c0:(c_prev_node_hash_0, c_event_hash_0, _, c_state_0) ------------------> c1:(c_prev_node_hash_1, c_event_hash_1, _, c_state_1) -> ... # Execution: e0:(e_prev_node_hash_0, e_event_hash_0, _, e_state_0) -<being computed>-> e1:(e_prev_node_hash_1, <being computed>, <being computed>, <being computed>) # position = 0 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # Must satisfy: # - event_hash == c_event_hash_1 # - hash(e0) == c_prev_node_hash_1 try: event_hash_match = event_hash == self.cached_trace[self._execution_trace_position() + 1][1] except Exception as e: logger.error(f"Error while checking event hash match: {e}") event_hash_match = False prev_node_match = True # TODO implement real check return event_hash_match and prev_node_match else: raise ValueError("Execution trace position is invalid, must be >= -1, but is ", self._execution_trace_position()) else: # no cache to use return False else: # parallel if len(self.cached_trace) >= self._execution_trace_position(): if self._execution_trace_position() >= 0: # parallel stores ignore order, so we need to check instead whether the event hash is a key in the parallel store, # regardless of the order of the events generated the data therein. if isinstance(self.cached_trace[self._execution_trace_position()], dict): event_hash_match = event_hash in self.cached_trace[self._execution_trace_position()].keys() else: event_hash_match = False prev_node_match = True # TODO implement real check return event_hash_match and prev_node_match else: raise ValueError("Execution trace position is invalid, must be >= 0, but is ", self._execution_trace_position()) def _get_cached_parallel_value(self, event_hash, key): parallel_store = self.cached_trace[self._execution_trace_position()] value = parallel_store[event_hash][key] return value def _drop_cached_trace_suffix(self): """ Drops the cached trace suffix starting at the current execution trace position. This effectively refreshes the cache to the current execution state and starts building a new cache from there. """ self.cached_trace = self.cached_trace[:self._execution_trace_position()+1] def _add_to_execution_trace(self, state: dict, event_hash: int, event_output, parallel=False): """ Adds a state to the execution_trace list and computes the appropriate hash. The computed hash is compared to the hash of the cached trace at the same position, and if they don't match, the execution is aborted. Similarly, the event_hash is compared to the hash of the event in the cached trace at the same position, and if they don't match, the execution is aborted. """ # Compute the hash of the previous execution pair, if any previous_hash = None if not parallel: # Create a tuple of (hash, state) and append it to the execution_trace list self.execution_trace.append((previous_hash, event_hash, event_output, state)) else: with concurrent_execution_lock: # state is not stored in parallel segments, only outputs self.execution_trace[-1][event_hash] = {"prev_node_hash": previous_hash, "encoded_output": event_output} def _add_to_cache_trace(self, state: dict, event_hash: int, event_output, parallel=False): """ Adds a state to the cached_trace list and computes the appropriate hash. """ # Compute the hash of the previous cached pair, if any previous_hash = None if self.cached_trace: previous_hash = utils.custom_hash(self.cached_trace[-1]) if not parallel: # Create a tuple of (hash, state) and append it to the cached_trace list self.cached_trace.append((previous_hash, event_hash, event_output, state)) else: with concurrent_execution_lock: # state is not stored in parallel segments, only outputs self.cached_trace[-1][event_hash] = {"prev_node_hash": previous_hash, "encoded_output": event_output} self.has_unsaved_cache_changes = True def _load_cache_file(self, cache_path:str): """ Loads the cache file from the given path. """ try: self.cached_trace = json.load(open(cache_path, "r")) except FileNotFoundError: logger.info(f"Cache file not found on path: {cache_path}.") self.cached_trace = [] def _save_cache_file(self, cache_path:str): """ Saves the cache file to the given path. Always overwrites. """ logger.debug(f"Now saving cache file to {cache_path}.") try: # Create a temporary file with tempfile.NamedTemporaryFile('w', delete=False) as temp: json.dump(self.cached_trace, temp, indent=4) # Replace the original file with the temporary file os.replace(temp.name, cache_path) except Exception as e: traceback_string = ''.join(traceback.format_tb(e.__traceback__)) logger.error(f"An error occurred while saving the cache file: {e}\nTraceback:\n{traceback_string}") self.has_unsaved_cache_changes = False ################################################################################################### # Transactional control ################################################################################################### # # Regular sequential transactions # def begin_transaction(self, id=None): """ Starts a transaction. """ with concurrent_execution_lock: self._under_transaction[id] = True self._clear_communications_buffers() # TODO <---------------------------------------------------------------- def end_transaction(self, id=None): """ Ends a transaction. """ with concurrent_execution_lock: self._under_transaction[id] = False def is_under_transaction(self, id=None): """ Checks if the agent is under a transaction. """ with concurrent_execution_lock: return self._under_transaction.get(id, False) def _clear_communications_buffers(self): """ Cleans the communications buffers of all agents and environments. """ for agent in self.agents: agent.clear_communications_buffer() for environment in self.environments: environment.clear_communications_buffer() # # Parallel transactions # def begin_parallel_transactions(self): """ Starts parallel transactions. """ with concurrent_execution_lock: self._under_parallel_transactions = True # add a new parallel segment to the execution and cache traces self.execution_trace.append({}) self.cached_trace.append({}) def end_parallel_transactions(self): """ Ends parallel transactions. """ self._under_parallel_transactions = False def is_under_parallel_transactions(self): """ Checks if the agent is under parallel transactions. """ return self._under_parallel_transactions ################################################################################################### # Simulation state handling ################################################################################################### def _encode_simulation_state(self) -> dict: """ Encodes the current simulation state, including agents, environments, and other relevant information. """ state = {} # Encode agents state["agents"] = [] for agent in self.agents: state["agents"].append(agent.encode_complete_state()) # Encode environments state["environments"] = [] for environment in self.environments: state["environments"].append(environment.encode_complete_state()) # Encode factories state["factories"] = [] for factory in self.factories: state["factories"].append(factory.encode_complete_state()) return state def _decode_simulation_state(self, state: dict): """ Decodes the given simulation state, including agents, environments, and other relevant information. Args: state (dict): The state to decode. """ # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld logger.debug(f"Decoding simulation state: {state['factories']}") logger.debug(f"Registered factories: {self.name_to_factory}") logger.debug(f"Registered agents: {self.name_to_agent}") logger.debug(f"Registered environments: {self.name_to_environment}") # Decode factories for factory_state in state["factories"]: factory = self.name_to_factory[factory_state["name"]] factory.decode_complete_state(factory_state) # Decode environments ###self.environments = [] for environment_state in state["environments"]: try: environment = self.name_to_environment[environment_state["name"]] environment.decode_complete_state(environment_state) if TinyWorld.communication_display: environment.pop_and_display_latest_communications() except Exception as e: raise ValueError(f"Environment {environment_state['name']} is not in the simulation, thus cannot be decoded there.") from e # Decode agents (if they were not already decoded by the environment) ####self.agents = [] for agent_state in state["agents"]: try: agent = self.name_to_agent[agent_state["name"]] agent.decode_complete_state(agent_state) # The agent has not yet been decoded because it is not in any environment. So, decode it. if agent.environment is None: if TinyPerson.communication_display: agent.pop_and_display_latest_communications() except Exception as e: raise ValueError(f"Agent {agent_state['name']} is not in the simulation, thus cannot be decoded there.") from eClass variables
var STATUS_STARTEDvar STATUS_STOPPED
Methods
def add_agent(self, agent)-
Adds an agent to the simulation.
Expand source code
def add_agent(self, agent): """ Adds an agent to the simulation. """ if agent.name in self.name_to_agent: raise ValueError(f"Agent names must be unique, but '{agent.name}' is already defined.") agent.simulation_id = self.id self.agents.append(agent) self.name_to_agent[agent.name] = agent def add_environment(self, environment)-
Adds an environment to the simulation.
Expand source code
def add_environment(self, environment): """ Adds an environment to the simulation. """ if environment.name in self.name_to_environment: raise ValueError(f"Environment names must be unique, but '{environment.name}' is already defined.") environment.simulation_id = self.id self.environments.append(environment) self.name_to_environment[environment.name] = environment def add_factory(self, factory)-
Adds a factory to the simulation.
Expand source code
def add_factory(self, factory): """ Adds a factory to the simulation. """ if factory.name in self.name_to_factory: raise ValueError(f"Factory names must be unique, but '{factory.name}' is already defined.") factory.simulation_id = self.id self.factories.append(factory) self.name_to_factory[factory.name] = factory def begin(self, cache_path: str = None, auto_checkpoint: bool = False)-
Marks the start of the simulation being controlled.
Args
cache_path:str- The path to the cache file. If not specified, defaults to the default cache path defined in the class.
auto_checkpoint:bool, optional- Whether to automatically checkpoint at the end of each transaction. Defaults to False.
Expand source code
def begin(self, cache_path:str=None, auto_checkpoint:bool=False): """ Marks the start of the simulation being controlled. Args: cache_path (str): The path to the cache file. If not specified, defaults to the default cache path defined in the class. auto_checkpoint (bool, optional): Whether to automatically checkpoint at the end of each transaction. Defaults to False. """ logger.debug(f"Starting simulation, cache_path={cache_path}, auto_checkpoint={auto_checkpoint}.") # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld from tinytroupe.factory.tiny_factory import TinyFactory from tinytroupe.factory.tiny_person_factory import TinyPersonFactory if self.status == Simulation.STATUS_STOPPED: self.status = Simulation.STATUS_STARTED else: raise ValueError("Simulation is already started.") if cache_path is not None: self.cache_path = cache_path # should we automatically checkpoint? self.auto_checkpoint = auto_checkpoint # clear the agents, environments and other simulated entities, we'll track them from now on TinyPerson.clear_agents() TinyWorld.clear_environments() TinyFactory.clear_factories() TinyPersonFactory.clear_factories() # All automated fresh ids will start from 0 again for this simulation utils.reset_fresh_id() # load the cache file, if any if self.cache_path is not None: self._load_cache_file(self.cache_path) def begin_parallel_transactions(self)-
Starts parallel transactions.
Expand source code
def begin_parallel_transactions(self): """ Starts parallel transactions. """ with concurrent_execution_lock: self._under_parallel_transactions = True # add a new parallel segment to the execution and cache traces self.execution_trace.append({}) self.cached_trace.append({}) def begin_transaction(self, id=None)-
Starts a transaction.
Expand source code
def begin_transaction(self, id=None): """ Starts a transaction. """ with concurrent_execution_lock: self._under_transaction[id] = True self._clear_communications_buffers() # TODO <---------------------------------------------------------------- def checkpoint(self)-
Saves current simulation trace to a file.
Expand source code
def checkpoint(self): """ Saves current simulation trace to a file. """ logger.debug("Checkpointing simulation state...") # save the cache file if self.has_unsaved_cache_changes: self._save_cache_file(self.cache_path) else: logger.debug("No unsaved cache changes to save to file.") def end(self)-
Marks the end of the simulation being controlled.
Expand source code
def end(self): """ Marks the end of the simulation being controlled. """ logger.debug("Ending simulation.") if self.status == Simulation.STATUS_STARTED: self.status = Simulation.STATUS_STOPPED self.checkpoint() else: raise ValueError("Simulation is already stopped.") def end_parallel_transactions(self)-
Ends parallel transactions.
Expand source code
def end_parallel_transactions(self): """ Ends parallel transactions. """ self._under_parallel_transactions = False def end_transaction(self, id=None)-
Ends a transaction.
Expand source code
def end_transaction(self, id=None): """ Ends a transaction. """ with concurrent_execution_lock: self._under_transaction[id] = False def is_under_parallel_transactions(self)-
Checks if the agent is under parallel transactions.
Expand source code
def is_under_parallel_transactions(self): """ Checks if the agent is under parallel transactions. """ return self._under_parallel_transactions def is_under_transaction(self, id=None)-
Checks if the agent is under a transaction.
Expand source code
def is_under_transaction(self, id=None): """ Checks if the agent is under a transaction. """ with concurrent_execution_lock: return self._under_transaction.get(id, False)
class SkipTransaction (*args, **kwargs)-
Common base class for all non-exit exceptions.
Expand source code
class SkipTransaction(Exception): passAncestors
- builtins.Exception
- builtins.BaseException
class Transaction (obj_under_transaction, simulation, function, *args, **kwargs)-
Expand source code
class Transaction: def __init__(self, obj_under_transaction, simulation, function, *args, **kwargs): # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld from tinytroupe.factory.tiny_factory import TinyFactory self.obj_under_transaction = obj_under_transaction self.simulation = simulation self.function_name = function.__name__ self.function = function self.args = args self.kwargs = kwargs # # If we have an ongoing simulation, set the simulation id of the object under transaction if it is not already set. # if simulation is not None: if hasattr(obj_under_transaction, 'simulation_id') and obj_under_transaction.simulation_id is not None: if obj_under_transaction.simulation_id != simulation.id: raise ValueError(f"Object {obj_under_transaction} is already captured by a different simulation (id={obj_under_transaction.simulation_id}), \ and cannot be captured by simulation id={simulation.id}.") logger.debug(f">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Object {obj_under_transaction} is already captured by simulation {simulation.id}.") else: # if is a TinyPerson, add the agent to the simulation if isinstance(obj_under_transaction, TinyPerson): simulation.add_agent(obj_under_transaction) logger.debug(f">>>>>>>>>>>>>>>>>>>>>>> Added agent {obj_under_transaction} to simulation {simulation.id}.") # if is a TinyWorld, add the environment to the simulation elif isinstance(obj_under_transaction, TinyWorld): simulation.add_environment(obj_under_transaction) # if is a TinyFactory, add the factory to the simulation elif isinstance(obj_under_transaction, TinyFactory): simulation.add_factory(obj_under_transaction) logger.debug(f">>>>>>>>>>>>>>>>>>>>>>> Added factory {obj_under_transaction} to simulation {simulation.id}.") else: raise ValueError(f"Object {obj_under_transaction} (type = {type(obj_under_transaction)}) is not a TinyPerson or TinyWorld instance, and cannot be captured by the simulation.") def execute(self, begin_parallel=False, parallel_id=None): output = None # Transaction caching will only operate if there is a simulation and it is started if self.simulation is None or self.simulation.status == Simulation.STATUS_STOPPED: # Compute the function and return it, no caching, since the simulation is not started output = self.function(*self.args, **self.kwargs) elif self.simulation.status == Simulation.STATUS_STARTED: # Compute the event hash event_hash = self.simulation._function_call_hash(self.function_name, *self.args, **self.kwargs) # Sequential and parallel transactions are handled in different ways if begin_parallel: self.simulation.begin_parallel_transactions() # CACHED? Check if the event hash is in the cache if self.simulation._is_transaction_event_cached(event_hash, parallel=self.simulation.is_under_parallel_transactions()): self.simulation.cache_hits += 1 # Restore the full state and return the cached output logger.info(f"Skipping execution of {self.function_name} with args {self.args} and kwargs {self.kwargs} because it is already cached.") # SEQUENTIAL if not self.simulation.is_under_parallel_transactions(): self.simulation._skip_execution_with_cache() state = self.simulation.cached_trace[self.simulation._execution_trace_position()][3] # state self.simulation._decode_simulation_state(state) # Output encoding/decoding is used to preserve references to TinyPerson and TinyWorld instances # mainly. Scalar values (int, float, str, bool) and composite values (list, dict) are # encoded/decoded as is. encoded_output = self.simulation.cached_trace[self.simulation._execution_trace_position()][2] # output output = self._decode_function_output(encoded_output) # PARALLEL else: # is under parallel transactions # in parallel segments, state is not restored, only outputs encoded_output = self.simulation._get_cached_parallel_value(event_hash, "encoded_output") output = self._decode_function_output(encoded_output) else: # not cached if not begin_parallel: # in case of beginning a parallel segment, we don't want to count it as a cache miss, # since the segment itself will not be cached, but rather the events within it. self.simulation.cache_misses += 1 if not self.simulation.is_under_transaction(id=parallel_id) and not begin_parallel: # BEGIN SEQUENTIAL TRANSACTION ############################################################### # # if this is the beginning of a parallel segment, we don't need to begin a transaction, since # we want to allow additional transactions within the parallel segment (i.e., one-level reentrancy). if not begin_parallel: self.simulation.begin_transaction(id=parallel_id) # Compute the function and encode the relevant output and simulation state output = self.function(*self.args, **self.kwargs) self._save_output_with_simulation_state(event_hash, output) # END TRANSACTION ################################################################# if not begin_parallel: self.simulation.end_transaction(id=parallel_id) else: # already under transaction (thus, now a reentrant transaction) OR beginning a parallel segment # NOTES: # # - Reentrant sequential transactions are not cached, since what matters is the final result of # the top-level transaction. # # - The event that starts the parallel transactions segment WILL NOT itself be cached, since # it is not part of the parallel segment, but rather the beginning of it. This event will be # reconstructed during runtime from the parallel events within the segment. output = self.function(*self.args, **self.kwargs) if begin_parallel: self.simulation.end_parallel_transactions() # execute an ad-hoc Transaction to save the simulation state AFTER the parallel segment is done. Transaction(self.obj_under_transaction, self.simulation, lambda: True).execute(begin_parallel=False, parallel_id=parallel_id) else: raise ValueError(f"Simulation status is invalid at this point: {self.simulation.status}") # Checkpoint if needed logger.debug(f"Will attempt to checkpoint simulation state after transaction execution.") if self.simulation is not None and self.simulation.auto_checkpoint: logger.debug("Auto-checkpointing simulation state after transaction execution.") self.simulation.checkpoint() # after all the transaction is done, return the output - the client will never know about all the complexity we've # gone through to get here. return output def _save_output_with_simulation_state(self, event_hash, output): encoded_output = self._encode_function_output(output) state = self.simulation._encode_simulation_state() # immediately drop the cached trace suffix, since we are starting a new execution from this point on. # in the case of parallel transactions, this will drop everything _after_ the current parallel segment # (which itself occupies one position only, with a dictionary of event hashes and their outputs). self.simulation._drop_cached_trace_suffix() # Cache the result and update the current execution trace. If this is a parallel transaction, the # cache and execution traces will be updated in a different way. self.simulation._add_to_cache_trace(state, event_hash, encoded_output, parallel=self.simulation.is_under_parallel_transactions()) self.simulation._add_to_execution_trace(state, event_hash, encoded_output, parallel=self.simulation.is_under_parallel_transactions()) def _encode_function_output(self, output) -> dict: """ Encodes the given function output. """ # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld from tinytroupe.factory.tiny_factory import TinyFactory # if the output is a supported object, encode it if output is None: return None elif isinstance(output, TinyPerson): return {"type": "TinyPersonRef", "name": output.name} elif isinstance(output, TinyWorld): return {"type": "TinyWorldRef", "name": output.name} elif isinstance(output, TinyFactory): return {"type": "TinyFactoryRef", "name": output.name} elif isinstance(output, list): encoded_list = [] for item in output: if isinstance(item, TinyPerson): encoded_list.append({"type": "TinyPersonRef", "name": item.name}) elif isinstance(item, TinyWorld): encoded_list.append({"type": "TinyWorldRef", "name": item.name}) elif isinstance(item, TinyFactory): encoded_list.append({"type": "TinyFactoryRef", "name": item.name}) else: encoded_list.append({"type": "JSON", "value": item}) return {"type": "List", "value": encoded_list} elif isinstance(output, (int, float, str, bool, dict, tuple)): return {"type": "JSON", "value": output} else: raise ValueError(f"Unsupported output type: {type(output)}") def _decode_function_output(self, encoded_output: dict): """ Decodes the given encoded function output. """ # local import to avoid circular dependencies from tinytroupe.agent import TinyPerson from tinytroupe.environment import TinyWorld from tinytroupe.factory.tiny_factory import TinyFactory if encoded_output is None: return None elif encoded_output["type"] == "TinyPersonRef": return TinyPerson.get_agent_by_name(encoded_output["name"]) elif encoded_output["type"] == "TinyWorldRef": return TinyWorld.get_environment_by_name(encoded_output["name"]) elif encoded_output["type"] == "TinyFactoryRef": return TinyFactory.get_factory_by_name(encoded_output["name"]) elif encoded_output["type"] == "List": decoded_list = [] for item in encoded_output["value"]: if item["type"] == "TinyPersonRef": decoded_list.append(TinyPerson.get_agent_by_name(item["name"])) elif item["type"] == "TinyWorldRef": decoded_list.append(TinyWorld.get_environment_by_name(item["name"])) elif item["type"] == "TinyFactoryRef": decoded_list.append(TinyFactory.get_factory_by_name(item["name"])) else: decoded_list.append(item["value"]) return decoded_list elif encoded_output["type"] == "JSON": return encoded_output["value"] else: raise ValueError(f"Unsupported output type: {encoded_output['type']}")Methods
def execute(self, begin_parallel=False, parallel_id=None)-
Expand source code
def execute(self, begin_parallel=False, parallel_id=None): output = None # Transaction caching will only operate if there is a simulation and it is started if self.simulation is None or self.simulation.status == Simulation.STATUS_STOPPED: # Compute the function and return it, no caching, since the simulation is not started output = self.function(*self.args, **self.kwargs) elif self.simulation.status == Simulation.STATUS_STARTED: # Compute the event hash event_hash = self.simulation._function_call_hash(self.function_name, *self.args, **self.kwargs) # Sequential and parallel transactions are handled in different ways if begin_parallel: self.simulation.begin_parallel_transactions() # CACHED? Check if the event hash is in the cache if self.simulation._is_transaction_event_cached(event_hash, parallel=self.simulation.is_under_parallel_transactions()): self.simulation.cache_hits += 1 # Restore the full state and return the cached output logger.info(f"Skipping execution of {self.function_name} with args {self.args} and kwargs {self.kwargs} because it is already cached.") # SEQUENTIAL if not self.simulation.is_under_parallel_transactions(): self.simulation._skip_execution_with_cache() state = self.simulation.cached_trace[self.simulation._execution_trace_position()][3] # state self.simulation._decode_simulation_state(state) # Output encoding/decoding is used to preserve references to TinyPerson and TinyWorld instances # mainly. Scalar values (int, float, str, bool) and composite values (list, dict) are # encoded/decoded as is. encoded_output = self.simulation.cached_trace[self.simulation._execution_trace_position()][2] # output output = self._decode_function_output(encoded_output) # PARALLEL else: # is under parallel transactions # in parallel segments, state is not restored, only outputs encoded_output = self.simulation._get_cached_parallel_value(event_hash, "encoded_output") output = self._decode_function_output(encoded_output) else: # not cached if not begin_parallel: # in case of beginning a parallel segment, we don't want to count it as a cache miss, # since the segment itself will not be cached, but rather the events within it. self.simulation.cache_misses += 1 if not self.simulation.is_under_transaction(id=parallel_id) and not begin_parallel: # BEGIN SEQUENTIAL TRANSACTION ############################################################### # # if this is the beginning of a parallel segment, we don't need to begin a transaction, since # we want to allow additional transactions within the parallel segment (i.e., one-level reentrancy). if not begin_parallel: self.simulation.begin_transaction(id=parallel_id) # Compute the function and encode the relevant output and simulation state output = self.function(*self.args, **self.kwargs) self._save_output_with_simulation_state(event_hash, output) # END TRANSACTION ################################################################# if not begin_parallel: self.simulation.end_transaction(id=parallel_id) else: # already under transaction (thus, now a reentrant transaction) OR beginning a parallel segment # NOTES: # # - Reentrant sequential transactions are not cached, since what matters is the final result of # the top-level transaction. # # - The event that starts the parallel transactions segment WILL NOT itself be cached, since # it is not part of the parallel segment, but rather the beginning of it. This event will be # reconstructed during runtime from the parallel events within the segment. output = self.function(*self.args, **self.kwargs) if begin_parallel: self.simulation.end_parallel_transactions() # execute an ad-hoc Transaction to save the simulation state AFTER the parallel segment is done. Transaction(self.obj_under_transaction, self.simulation, lambda: True).execute(begin_parallel=False, parallel_id=parallel_id) else: raise ValueError(f"Simulation status is invalid at this point: {self.simulation.status}") # Checkpoint if needed logger.debug(f"Will attempt to checkpoint simulation state after transaction execution.") if self.simulation is not None and self.simulation.auto_checkpoint: logger.debug("Auto-checkpointing simulation state after transaction execution.") self.simulation.checkpoint() # after all the transaction is done, return the output - the client will never know about all the complexity we've # gone through to get here. return output