forked from s444417/ProjektAI
311 lines
8.5 KiB
Python
311 lines
8.5 KiB
Python
"""
|
|
* Experimental *
|
|
|
|
Like the map function, but can use a pool of threads.
|
|
|
|
Really easy to use threads. eg. tmap(f, alist)
|
|
|
|
If you know how to use the map function, you can use threads.
|
|
"""
|
|
|
|
__author__ = "Rene Dudfield"
|
|
__version__ = "0.3.0"
|
|
__license__ = 'Python license'
|
|
|
|
import traceback, sys
|
|
|
|
from pygame.compat import geterror
|
|
|
|
if sys.version_info[0] == 3:
|
|
from queue import Queue
|
|
from queue import Empty
|
|
elif (sys.version_info[0] == 2 and sys.version_info[1] < 5):
|
|
from Py25Queue import Queue
|
|
from Py25Queue import Empty
|
|
else:
|
|
# use up to date version
|
|
from Queue import Queue
|
|
from Queue import Empty
|
|
|
|
import threading
|
|
Thread = threading.Thread
|
|
|
|
STOP = object()
|
|
FINISH = object()
|
|
|
|
# DONE_ONE = object()
|
|
# DONE_TWO = object()
|
|
|
|
# a default worker queue.
|
|
_wq = None
|
|
|
|
# if we are using threads or not. This is the number of workers.
|
|
_use_workers = 0
|
|
|
|
# Set this to the maximum for the amount of Cores/CPUs
|
|
# Note, that the tests early out.
|
|
# So it should only test the best number of workers +2
|
|
MAX_WORKERS_TO_TEST = 64
|
|
|
|
|
|
|
|
def init(number_of_workers = 0):
|
|
""" Does a little test to see if threading is worth it.
|
|
Sets up a global worker queue if it's worth it.
|
|
|
|
Calling init() is not required, but is generally better to do.
|
|
"""
|
|
global _wq, _use_workers
|
|
|
|
if number_of_workers:
|
|
_use_workers = number_of_workers
|
|
else:
|
|
_use_workers = benchmark_workers()
|
|
|
|
# if it is best to use zero workers, then use that.
|
|
_wq = WorkerQueue(_use_workers)
|
|
|
|
|
|
|
|
|
|
def quit():
|
|
""" cleans up everything.
|
|
"""
|
|
global _wq, _use_workers
|
|
_wq.stop()
|
|
_wq = None
|
|
_use_workers = False
|
|
|
|
|
|
def benchmark_workers(a_bench_func = None, the_data = None):
|
|
""" does a little test to see if workers are at all faster.
|
|
Returns the number of workers which works best.
|
|
Takes a little bit of time to run, so you should only really call
|
|
it once.
|
|
You can pass in benchmark data, and functions if you want.
|
|
a_bench_func - f(data)
|
|
the_data - data to work on.
|
|
"""
|
|
global _use_workers
|
|
|
|
#TODO: try and make this scale better with slower/faster cpus.
|
|
# first find some variables so that using 0 workers takes about 1.0 seconds.
|
|
# then go from there.
|
|
|
|
|
|
# note, this will only work with pygame 1.8rc3+
|
|
# replace the doit() and the_data with something that releases the GIL
|
|
|
|
|
|
import pygame
|
|
import pygame.transform
|
|
import time
|
|
|
|
if not a_bench_func:
|
|
def doit(x):
|
|
return pygame.transform.scale(x, (544, 576))
|
|
else:
|
|
doit = a_bench_func
|
|
|
|
if not the_data:
|
|
thedata = []
|
|
for x in range(10):
|
|
thedata.append(pygame.Surface((155,155), 0, 32))
|
|
else:
|
|
thedata = the_data
|
|
|
|
best = time.time() + 100000000
|
|
best_number = 0
|
|
last_best = -1
|
|
|
|
for num_workers in range(0, MAX_WORKERS_TO_TEST):
|
|
|
|
wq = WorkerQueue(num_workers)
|
|
t1 = time.time()
|
|
for xx in range(20):
|
|
print ("active count:%s" % threading.activeCount())
|
|
results = tmap(doit, thedata, worker_queue = wq)
|
|
t2 = time.time()
|
|
|
|
wq.stop()
|
|
|
|
|
|
total_time = t2 - t1
|
|
print ("total time num_workers:%s: time:%s:" % (num_workers, total_time))
|
|
|
|
if total_time < best:
|
|
last_best = best_number
|
|
best_number =num_workers
|
|
best = total_time
|
|
|
|
if num_workers - best_number > 1:
|
|
# We tried to add more, but it didn't like it.
|
|
# so we stop with testing at this number.
|
|
break
|
|
|
|
|
|
return best_number
|
|
|
|
|
|
|
|
|
|
class WorkerQueue(object):
|
|
|
|
def __init__(self, num_workers = 20):
|
|
self.queue = Queue()
|
|
self.pool = []
|
|
self._setup_workers(num_workers)
|
|
|
|
def _setup_workers(self, num_workers):
|
|
""" Sets up the worker threads
|
|
NOTE: undefined behaviour if you call this again.
|
|
"""
|
|
self.pool = []
|
|
|
|
for _ in range(num_workers):
|
|
self.pool.append(Thread(target=self.threadloop))
|
|
|
|
for a_thread in self.pool:
|
|
a_thread.setDaemon(True)
|
|
a_thread.start()
|
|
|
|
|
|
def do(self, f, *args, **kwArgs):
|
|
""" puts a function on a queue for running later.
|
|
"""
|
|
self.queue.put((f, args, kwArgs))
|
|
|
|
|
|
def stop(self):
|
|
""" Stops the WorkerQueue, waits for all of the threads to finish up.
|
|
"""
|
|
self.queue.put(STOP)
|
|
for thread in self.pool:
|
|
thread.join()
|
|
|
|
|
|
def threadloop(self): #, finish = False):
|
|
""" Loops until all of the tasks are finished.
|
|
"""
|
|
while True:
|
|
args = self.queue.get()
|
|
if args is STOP:
|
|
self.queue.put(STOP)
|
|
self.queue.task_done()
|
|
break
|
|
else:
|
|
try:
|
|
args[0](*args[1], **args[2])
|
|
finally:
|
|
# clean up the queue, raise the exception.
|
|
self.queue.task_done()
|
|
#raise
|
|
|
|
|
|
def wait(self):
|
|
""" waits until all tasks are complete.
|
|
"""
|
|
self.queue.join()
|
|
|
|
class FuncResult:
|
|
""" Used for wrapping up a function call so that the results are stored
|
|
inside the instances result attribute.
|
|
"""
|
|
def __init__(self, f, callback = None, errback = None):
|
|
""" f - is the function we that we call
|
|
callback(result) - this is called when the function(f) returns
|
|
errback(exception) - this is called when the function(f) raises
|
|
an exception.
|
|
"""
|
|
self.f = f
|
|
self.exception = None
|
|
self.callback = callback
|
|
self.errback = errback
|
|
|
|
def __call__(self, *args, **kwargs):
|
|
|
|
#we try to call the function here. If it fails we store the exception.
|
|
try:
|
|
self.result = self.f(*args, **kwargs)
|
|
if self.callback:
|
|
self.callback(self.result)
|
|
except Exception:
|
|
self.exception = geterror()
|
|
if self.errback:
|
|
self.errback(self.exception)
|
|
|
|
|
|
def tmap(f, seq_args, num_workers = 20, worker_queue = None, wait = True, stop_on_error = True):
|
|
""" like map, but uses a thread pool to execute.
|
|
num_workers - the number of worker threads that will be used. If pool
|
|
is passed in, then the num_workers arg is ignored.
|
|
worker_queue - you can optionally pass in an existing WorkerQueue.
|
|
wait - True means that the results are returned when everything is finished.
|
|
False means that we return the [worker_queue, results] right away instead.
|
|
results, is returned as a list of FuncResult instances.
|
|
stop_on_error -
|
|
"""
|
|
|
|
if worker_queue:
|
|
wq = worker_queue
|
|
else:
|
|
# see if we have a global queue to work with.
|
|
if _wq:
|
|
wq = _wq
|
|
else:
|
|
if num_workers == 0:
|
|
return map(f, seq_args)
|
|
|
|
wq = WorkerQueue(num_workers)
|
|
|
|
# we short cut it here if the number of workers is 0.
|
|
# normal map should be faster in this case.
|
|
if len(wq.pool) == 0:
|
|
return map(f, seq_args)
|
|
|
|
#print ("queue size:%s" % wq.queue.qsize())
|
|
|
|
|
|
#TODO: divide the data (seq_args) into even chunks and
|
|
# then pass each thread a map(f, equal_part(seq_args))
|
|
# That way there should be less locking, and overhead.
|
|
|
|
|
|
|
|
results = []
|
|
for sa in seq_args:
|
|
results.append(FuncResult(f))
|
|
wq.do(results[-1], sa)
|
|
|
|
|
|
#wq.stop()
|
|
|
|
if wait:
|
|
#print ("wait")
|
|
wq.wait()
|
|
#print ("after wait")
|
|
#print ("queue size:%s" % wq.queue.qsize())
|
|
if wq.queue.qsize():
|
|
raise Exception("buggy threadmap")
|
|
# if we created a worker queue, we need to stop it.
|
|
if not worker_queue and not _wq:
|
|
#print ("stoping")
|
|
wq.stop()
|
|
if wq.queue.qsize():
|
|
um = wq.queue.get()
|
|
if not um is STOP:
|
|
raise Exception("buggy threadmap")
|
|
|
|
|
|
# see if there were any errors. If so raise the first one. This matches map behaviour.
|
|
# TODO: the traceback doesn't show up nicely.
|
|
# NOTE: TODO: we might want to return the results anyway? This should be an option.
|
|
if stop_on_error:
|
|
error_ones = list(filter(lambda x:x.exception, results))
|
|
if error_ones:
|
|
raise error_ones[0].exception
|
|
|
|
return map(lambda x:x.result, results)
|
|
else:
|
|
return [wq, results]
|