vspursuer/vspursuer.py

186 lines
6.5 KiB
Python
Executable file

#!/usr/bin/env python3
# NOTE: Perhaps we should use process_cpu_count but that's not available to all Python versions
from os import cpu_count
from typing import Dict, Iterator, Optional, Tuple
from queue import Empty as QueueEmpty
import argparse
import multiprocessing as mp
from logic import Conjunction, Disjunction, Negation, Implication, Operation
from model import Model, ModelFunction
from parse_magic import SourceFile, parse_matrices
from vsp import has_vsp, VSP_Result
def restructure_solutions(solutions: Iterator[Tuple[Model, Dict[Operation, ModelFunction]]], skip_to: Optional[str]) -> \
Iterator[Tuple[Model, ModelFunction, Optional[ModelFunction], Optional[ModelFunction], Optional[ModelFunction]]]:
"""
When subprocess gets spawned, the logical operations will
have a different memory address than what's expected in interpretation.
Therefore, we need to pass the model functions directly instead.
While we're at it, filter out models until we get to --skip-to
if applicable.
"""
start_processing = skip_to is None
for model, interpretation in solutions:
# If skip_to is defined, then don't process models
# until then.
if not start_processing and model.name != skip_to:
continue
start_processing = True
# NOTE: Implication must be defined, the rest may not
impfunction = interpretation[Implication]
mconjunction = interpretation.get(Conjunction)
mdisjunction = interpretation.get(Disjunction)
mnegation = interpretation.get(Negation)
yield (model, impfunction, mconjunction, mdisjunction, mnegation)
def has_vsp_plus_model(model, impfunction, mconjunction, mdisjunction, mnegation) -> Tuple[Optional[Model], VSP_Result]:
"""
Wrapper which also stores the models along with its vsp result
"""
vsp_result = has_vsp(model, impfunction, mconjunction, mdisjunction, mnegation)
# NOTE: Memory optimization - Don't return model if it doens't have VSP
model = model if vsp_result.has_vsp else None
return (model, vsp_result)
def worker_vsp(task_queue: mp.Queue, result_queue: mp.Queue):
"""
Worker process which processes models from the task
queue and adds the result to the result_queue.
Adds the sentinal value None when exception occurs and when there's
no more to process.
"""
try:
while True:
task = task_queue.get()
# If sentinal value, break
if task is None:
break
(model, impfunction, mconjunction, mdisjunction, mnegation) = task
result = has_vsp_plus_model(model, impfunction, mconjunction, mdisjunction, mnegation)
result_queue.put(result)
finally:
# Either an exception occured or the worker finished
# Push sentinal value
result_queue.put(None)
def worker_parser(data_file_path: str, num_sentinal_values: int, task_queue: mp.Queue, skip_to: Optional[str]):
"""
Function which parses the MaGIC file
and adds models to the task_queue.
Intended to be deployed with a dedicated process.
"""
try:
data_file = open(data_file_path, "r")
solutions = parse_matrices(SourceFile(data_file))
solutions = restructure_solutions(solutions, skip_to)
while True:
try:
item = next(solutions)
task_queue.put(item)
except StopIteration:
break
finally:
data_file.close()
for _ in range(num_sentinal_values):
task_queue.put(None)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="VSP Checker")
parser.add_argument("--verbose", action='store_true', help="Print out all parsed matrices")
parser.add_argument("-i", type=str, help="Path to MaGIC ugly data file")
parser.add_argument("-c", type=int, help="Number of CPUs to use. Default: MAX - 2.")
parser.add_argument("--skip-to", type=str, help="Skip until a model name is found and process from then onwards.")
args = vars(parser.parse_args())
data_file_path = args.get("i")
if data_file_path is None:
data_file_path = input("Path to MaGIC Ugly Data File: ")
num_cpu = args.get("c")
if num_cpu is None:
num_cpu = max(cpu_count() - 2, 1)
# Set up parallel verification
num_tested = 0
num_has_vsp = 0
num_workers = max(num_cpu - 1, 1)
# Create queues
task_queue = mp.Queue(maxsize=1000)
result_queue = mp.Queue()
# Create dedicated process to parse the MaGIC file
process_parser = mp.Process(target=worker_parser, args=(data_file_path, num_workers, task_queue, args.get("skip_to")))
process_parser.start()
# Create dedicated processes which check VSP
process_workers = []
for _ in range(num_workers):
p = mp.Process(target=worker_vsp, args=(task_queue, result_queue))
process_workers.append(p)
p.start()
# Check results and add new tasks until finished
result_sentinal_count = 0
while True:
# Read a result
try:
result = result_queue.get(True, 60)
except QueueEmpty:
if all((not p.is_alive() for p in process_workers)):
# All workers finished without us receiving all the
# sentinal values.
break
task_queue_size = 0
try:
task_queue_size = task_queue.qsize()
except NotImplementedError:
# MacOS doesn't implement this
pass
if task_queue_size == 0 and not process_parser.is_alive():
# For Linux/Windows this means that the process_parser
# died before sending the sentinal values.
# For Mac, this doesn't guarentee anything but might
# as well push more sentinal values.
for _ in range(num_workers):
task_queue.put(None)
# When we receive None, it means a child process has finished
if result is None:
result_sentinal_count += 1
# If all workers have finished break
if result_sentinal_count == len(process_workers):
break
continue
# Process result
model, vsp_result = result
print(vsp_result)
num_tested += 1
if vsp_result.has_vsp:
print(model)
if vsp_result.has_vsp:
num_has_vsp += 1
print(f"Tested {num_tested} models, {num_has_vsp} of which satisfy VSP")