76 lines
3.2 KiB
Python
76 lines
3.2 KiB
Python
from pathos.multiprocessing import ProcessingPool as Pool
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from SimCAD.utils import flatten
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from SimCAD.configuration import Processor
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from SimCAD.configuration.utils import TensorFieldReport
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from SimCAD.engine.simulation import Executor as SimExecutor
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class ExecutionMode:
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single_proc = 'single_proc'
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multi_proc = 'multi_proc'
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class ExecutionContext:
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def __init__(self, context=ExecutionMode.multi_proc):
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self.name = context
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self.method = None
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def single_proc_exec(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns):
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l = [simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns]
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simulation, states_list, config, env_processes, T, N = list(map(lambda x: x.pop(), l))
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result = simulation(states_list, config, env_processes, T, N)
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return flatten(result)
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def parallelize_simulations(fs, states_list, configs, env_processes, Ts, Ns):
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l = list(zip(fs, states_list, configs, env_processes, Ts, Ns))
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with Pool(len(configs)) as p:
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results = p.map(lambda t: t[0](t[1], t[2], t[3], t[4], t[5]), l)
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return results
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if context == 'single_proc':
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self.method = single_proc_exec
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elif context == 'multi_proc':
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self.method = parallelize_simulations
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class Executor:
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def __init__(self, exec_context, configs):
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self.SimExecutor = SimExecutor
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self.exec_method = exec_context.method
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self.exec_context = exec_context.name
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self.configs = configs
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self.main = self.execute
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def execute(self):
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config_proc = Processor()
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create_tensor_field = TensorFieldReport(config_proc).create_tensor_field
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print(self.exec_context+": "+str(self.configs))
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states_lists, Ts, Ns, eps, configs_structs, env_processes_list, mechanisms, simulation_execs = \
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[], [], [], [], [], [], [], []
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config_idx = 0
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for x in self.configs:
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states_lists.append([x.state_dict])
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Ts.append(x.sim_config['T'])
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Ns.append(x.sim_config['N'])
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eps.append(list(x.exogenous_states.values()))
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configs_structs.append(config_proc.generate_config(x.state_dict, x.mechanisms, eps[config_idx]))
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env_processes_list.append(x.env_processes)
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mechanisms.append(x.mechanisms)
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simulation_execs.append(SimExecutor(x.behavior_ops).simulation)
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config_idx += 1
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if self.exec_context == ExecutionMode.single_proc:
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tensor_field = create_tensor_field(mechanisms.pop(), eps.pop())
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result = self.exec_method(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
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return result, tensor_field
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elif self.exec_context == ExecutionMode.multi_proc:
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if len(self.configs) > 1:
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simulations = self.exec_method(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
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results = []
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for result, mechanism, ep in list(zip(simulations, mechanisms, eps)):
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results.append((flatten(result), create_tensor_field(mechanism, ep)))
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return results
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