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multithreading.py
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510 lines (438 loc) · 18.7 KB
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#!/usr/bin/env python3
""" Multithreading/processing utils for faceswap """
import logging
import multiprocessing as mp
from multiprocessing.sharedctypes import RawArray
from ctypes import c_float
import queue as Queue
import sys
import threading
import numpy as np
from lib.logger import LOG_QUEUE, set_root_logger
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
_launched_processes = set() # pylint: disable=invalid-name
def total_cpus():
""" Return total number of cpus """
return mp.cpu_count()
class ConsumerBuffer():
""" Memory buffer for consuming """
def __init__(self, dispatcher, index, data):
logger.trace("Initializing %s: (dispatcher: '%s', index: %s, data: %s)",
self.__class__.__name__, dispatcher, index, data)
self._data = data
self._id = index
self._dispatcher = dispatcher
logger.trace("Initialized %s", self.__class__.__name__)
def get(self):
""" Return Data """
return self._data
def free(self):
""" Return Free """
self._dispatcher.free(self._id)
def __enter__(self):
""" On Enter """
return self.get()
def __exit__(self, *args):
""" On Exit """
self.free()
class WorkerBuffer():
""" Memory buffer for working """
def __init__(self, index, data, stop_event, queue):
logger.trace("Initializing %s: (index: '%s', data: %s, stop_event: %s, queue: %s)",
self.__class__.__name__, index, data, stop_event, queue)
self._id = index
self._data = data
self._stop_event = stop_event
self._queue = queue
logger.trace("Initialized %s", self.__class__.__name__)
def get(self):
""" Return Data """
return self._data
def ready(self):
""" Worker Ready """
if self._stop_event.is_set():
return
self._queue.put(self._id)
def __enter__(self):
""" On Enter """
return self.get()
def __exit__(self, *args):
""" On Exit """
self.ready()
class FixedProducerDispatcher():
"""
Runs the given method in N subprocesses
and provides fixed size shared memory to the method.
This class is designed for endless running worker processes
filling the provided memory with data,
like preparing trainingsdata for neural network training.
As soon as one worker finishes all worker are shutdown.
Example:
# Producer side
def do_work(memory_gen):
for memory_wrap in memory_gen:
# alternative memory_wrap.get and memory_wrap.ready can be used
with memory_wrap as memory:
input, exp_result = prepare_batch(...)
memory[0][:] = input
memory[1][:] = exp_result
# Consumer side
batch_size = 64
height = width = 256
batch_shapes = (batch_size, height, width, 3)
dispatcher = FixedProducerDispatcher(do_work, shapes=[batch_shapes, batch_shapes])
for batch_wrapper in dispatcher:
# alternative batch_wrapper.get and batch_wrapper.free can be used
with batch_wrapper as batch:
send_batch_to_trainer(batch)
"""
CTX = mp.get_context("spawn")
EVENT = CTX.Event
def __init__(self, method, shapes, in_queue, out_queue,
args=tuple(), kwargs={}, ctype=c_float, workers=1, buffers=None):
logger.debug("Initializing %s: (method: '%s', shapes: %s, ctype: %s, workers: %s, "
"buffers: %s)", self.__class__.__name__, method, shapes, ctype, workers,
buffers)
logger.trace("args: %s, kwargs: %s", args, kwargs)
if buffers is None:
buffers = workers * 2
else:
assert buffers >= 2 and buffers > workers
self.name = "%s_FixedProducerDispatcher" % str(method)
self._target_func = method
self._shapes = shapes
self._stop_event = self.EVENT()
self._buffer_tokens = in_queue
for i in range(buffers):
self._buffer_tokens.put(i)
self._result_tokens = out_queue
worker_data, self.data = self._create_data(shapes, ctype, buffers)
proc_args = {
'data': worker_data,
'stop_event': self._stop_event,
'target': self._target_func,
'buffer_tokens': self._buffer_tokens,
'result_tokens': self._result_tokens,
'dtype': np.dtype(ctype),
'shapes': shapes,
'log_queue': LOG_QUEUE,
'log_level': logger.getEffectiveLevel(),
'args': args,
'kwargs': kwargs
}
self._worker = tuple(self._create_worker(proc_args) for _ in range(workers))
self._open_worker = len(self._worker)
logger.debug("Initialized %s", self.__class__.__name__)
@staticmethod
def _np_from_shared(shared, shapes, dtype):
""" Numpy array from shared memory """
arrs = []
offset = 0
np_data = np.frombuffer(shared, dtype=dtype)
for shape in shapes:
count = np.prod(shape)
arrs.append(np_data[offset:offset+count].reshape(shape))
offset += count
return arrs
def _create_data(self, shapes, ctype, buffers):
""" Create data """
buffer_size = int(sum(np.prod(x) for x in shapes))
dtype = np.dtype(ctype)
data = tuple(RawArray(ctype, buffer_size) for _ in range(buffers))
np_data = tuple(self._np_from_shared(arr, shapes, dtype) for arr in data)
return data, np_data
def _create_worker(self, kwargs):
""" Create Worker """
return self.CTX.Process(target=self._runner, kwargs=kwargs)
def free(self, index):
""" Free memory """
if self._stop_event.is_set():
return
if isinstance(index, ConsumerBuffer):
index = index.index
self._buffer_tokens.put(index)
def __iter__(self):
""" Iterator """
return self
def __next__(self):
""" Next item """
return self.next()
def next(self, block=True, timeout=None):
"""
Yields ConsumerBuffer filled by the worker.
Will raise StopIteration if no more elements are available OR any worker is finished.
Will raise queue.Empty when block is False and no element is available.
The returned data is safe until ConsumerBuffer.free() is called or the
with context is left. If you plan to hold on to it after that make a copy.
This method is thread safe.
"""
if self._stop_event.is_set():
raise StopIteration
i = self._result_tokens.get(block=block, timeout=timeout)
if i is None:
self._open_worker -= 1
raise StopIteration
if self._stop_event.is_set():
raise StopIteration
return ConsumerBuffer(self, i, self.data[i])
def start(self):
""" Start Workers """
for process in self._worker:
process.start()
_launched_processes.add(self)
def is_alive(self):
""" Check workers are alive """
for worker in self._worker:
if worker.is_alive():
return True
return False
def join(self):
""" Join Workers """
self.stop()
while self._open_worker:
if self._result_tokens.get() is None:
self._open_worker -= 1
while True:
try:
self._buffer_tokens.get(block=False, timeout=0.01)
except Queue.Empty:
break
for worker in self._worker:
worker.join()
def stop(self):
""" Stop Workers """
self._stop_event.set()
for _ in range(self._open_worker):
self._buffer_tokens.put(None)
def is_shutdown(self):
""" Check if stop event is set """
return self._stop_event.is_set()
@classmethod
def _runner(cls, data=None, stop_event=None, target=None,
buffer_tokens=None, result_tokens=None, dtype=None,
shapes=None, log_queue=None, log_level=None,
args=None, kwargs=None):
""" Shared Memory Object runner """
# Fork inherits the queue handler, so skip registration with "fork"
set_root_logger(log_level, queue=log_queue)
logger.debug("FixedProducerDispatcher worker for %s started", str(target))
np_data = [cls._np_from_shared(d, shapes, dtype) for d in data]
def get_free_slot():
while not stop_event.is_set():
i = buffer_tokens.get()
if stop_event.is_set() or i is None or i == "EOF":
break
yield WorkerBuffer(i, np_data[i], stop_event, result_tokens)
args = tuple((get_free_slot(),)) + tuple(args)
try:
target(*args, **kwargs)
except Exception as ex:
logger.exception(ex)
stop_event.set()
result_tokens.put(None)
logger.debug("FixedProducerDispatcher worker for %s shutdown", str(target))
class PoolProcess():
""" Pool multiple processes """
def __init__(self, method, in_queue, out_queue, *args, processes=None, **kwargs):
self._name = method.__qualname__
logger.debug("Initializing %s: (target: '%s', processes: %s)",
self.__class__.__name__, self._name, processes)
self.procs = self.set_procs(processes)
ctx = mp.get_context("spawn")
self.pool = ctx.Pool(processes=self.procs,
initializer=set_root_logger,
initargs=(logger.getEffectiveLevel(), LOG_QUEUE))
self._method = method
self._kwargs = self.build_target_kwargs(in_queue, out_queue, kwargs)
self._args = args
logger.debug("Initialized %s: '%s'", self.__class__.__name__, self._name)
@staticmethod
def build_target_kwargs(in_queue, out_queue, kwargs):
""" Add standard kwargs to passed in kwargs list """
kwargs["in_queue"] = in_queue
kwargs["out_queue"] = out_queue
return kwargs
def set_procs(self, processes):
""" Set the number of processes to use """
processes = mp.cpu_count() if processes is None else processes
running_processes = len(mp.active_children())
avail_processes = max(mp.cpu_count() - running_processes, 1)
processes = min(avail_processes, processes)
logger.verbose("Processing '%s' in %s processes", self._name, processes)
return processes
def start(self):
""" Run the processing pool """
logging.debug("Pooling Processes: (target: '%s', args: %s, kwargs: %s)",
self._name, self._args, self._kwargs)
for idx in range(self.procs):
logger.debug("Adding process %s of %s to mp.Pool '%s'",
idx + 1, self.procs, self._name)
self.pool.apply_async(self._method, args=self._args, kwds=self._kwargs)
_launched_processes.add(self.pool)
logging.debug("Pooled Processes: '%s'", self._name)
def join(self):
""" Join the process """
logger.debug("Joining Pooled Process: '%s'", self._name)
self.pool.close()
self.pool.join()
_launched_processes.remove(self.pool)
logger.debug("Joined Pooled Process: '%s'", self._name)
class SpawnProcess(mp.context.SpawnProcess):
""" Process in spawnable context
Must be spawnable to share CUDA across processes """
def __init__(self, target, in_queue, out_queue, *args, **kwargs):
name = target.__qualname__
logger.debug("Initializing %s: (target: '%s', args: %s, kwargs: %s)",
self.__class__.__name__, name, args, kwargs)
ctx = mp.get_context("spawn")
self.event = ctx.Event()
self.error = ctx.Event()
kwargs = self.build_target_kwargs(in_queue, out_queue, kwargs)
super().__init__(target=target, name=name, args=args, kwargs=kwargs)
self.daemon = True
logger.debug("Initialized %s: '%s'", self.__class__.__name__, name)
def build_target_kwargs(self, in_queue, out_queue, kwargs):
""" Add standard kwargs to passed in kwargs list """
kwargs["event"] = self.event
kwargs["error"] = self.error
kwargs["log_init"] = set_root_logger
kwargs["log_queue"] = LOG_QUEUE
kwargs["log_level"] = logger.getEffectiveLevel()
kwargs["in_queue"] = in_queue
kwargs["out_queue"] = out_queue
return kwargs
def run(self):
""" Add logger to spawned process """
logger_init = self._kwargs["log_init"]
log_queue = self._kwargs["log_queue"]
log_level = self._kwargs["log_level"]
logger_init(log_level, log_queue)
super().run()
def start(self):
""" Add logging to start function """
logger.debug("Spawning Process: (name: '%s', args: %s, kwargs: %s, daemon: %s)",
self._name, self._args, self._kwargs, self.daemon)
super().start()
_launched_processes.add(self)
logger.debug("Spawned Process: (name: '%s', PID: %s)", self._name, self.pid)
def join(self, timeout=None):
""" Add logging to join function """
logger.debug("Joining Process: (name: '%s', PID: %s)", self._name, self.pid)
super().join(timeout=timeout)
if self in _launched_processes:
_launched_processes.remove(self)
logger.debug("Joined Process: (name: '%s', PID: %s)", self._name, self.pid)
class FSThread(threading.Thread):
""" Subclass of thread that passes errors back to parent """
def __init__(self, group=None, target=None, name=None, # pylint: disable=too-many-arguments
args=(), kwargs=None, *, daemon=None):
super().__init__(group=group, target=target, name=name,
args=args, kwargs=kwargs, daemon=daemon)
self.err = None
def run(self):
try:
if self._target:
self._target(*self._args, **self._kwargs)
except Exception as err: # pylint: disable=broad-except
self.err = sys.exc_info()
logger.debug("Error in thread (%s): %s", self._name, str(err))
finally:
# Avoid a refcycle if the thread is running a function with
# an argument that has a member that points to the thread.
del self._target, self._args, self._kwargs
class MultiThread():
""" Threading for IO heavy ops
Catches errors in thread and rethrows to parent """
def __init__(self, target, *args, thread_count=1, name=None, **kwargs):
self._name = name if name else target.__name__
logger.debug("Initializing %s: (target: '%s', thread_count: %s)",
self.__class__.__name__, self._name, thread_count)
logger.trace("args: %s, kwargs: %s", args, kwargs)
self.daemon = True
self._thread_count = thread_count
self._threads = list()
self._target = target
self._args = args
self._kwargs = kwargs
logger.debug("Initialized %s: '%s'", self.__class__.__name__, self._name)
@property
def has_error(self):
""" Return true if a thread has errored, otherwise false """
return any(thread.err for thread in self._threads)
@property
def errors(self):
""" Return a list of thread errors """
return [thread.err for thread in self._threads]
def check_and_raise_error(self):
""" Checks for errors in thread and raises them in caller """
if not self.has_error:
return
logger.debug("Thread error caught: %s", self.errors)
error = self.errors[0]
raise error[1].with_traceback(error[2])
def start(self):
""" Start a thread with the given method and args """
logger.debug("Starting thread(s): '%s'", self._name)
for idx in range(self._thread_count):
name = "{}_{}".format(self._name, idx)
logger.debug("Starting thread %s of %s: '%s'",
idx + 1, self._thread_count, name)
thread = FSThread(name=name,
target=self._target,
args=self._args,
kwargs=self._kwargs)
thread.daemon = self.daemon
thread.start()
self._threads.append(thread)
logger.debug("Started all threads '%s': %s", self._name, len(self._threads))
def join(self):
""" Join the running threads, catching and re-raising any errors """
logger.debug("Joining Threads: '%s'", self._name)
for thread in self._threads:
logger.debug("Joining Thread: '%s'", thread._name) # pylint: disable=protected-access
thread.join()
if thread.err:
logger.error("Caught exception in thread: '%s'",
thread._name) # pylint: disable=protected-access
raise thread.err[1].with_traceback(thread.err[2])
logger.debug("Joined all Threads: '%s'", self._name)
class BackgroundGenerator(threading.Thread):
""" Run a queue in the background. From:
https://stackoverflow.com/questions/7323664/ """
# See below why prefetch count is flawed
def __init__(self, generator, prefetch=1):
threading.Thread.__init__(self)
self.queue = Queue.Queue(maxsize=prefetch)
self.generator = generator
self.daemon = True
self.start()
def run(self):
""" Put until queue size is reached.
Note: put blocks only if put is called while queue has already
reached max size => this makes 2 prefetched items! One in the
queue, one waiting for insertion! """
for item in self.generator:
self.queue.put(item)
self.queue.put(None)
def iterator(self):
""" Iterate items out of the queue """
while True:
next_item = self.queue.get()
if next_item is None:
break
yield next_item
def terminate_processes():
""" Join all active processes on unexpected shutdown
If the process is doing long running work, make sure you
have a mechanism in place to terminate this work to avoid
long blocks
"""
logger.debug("Processes to join: %s", [process
for process in _launched_processes
if isinstance(process, mp.pool.Pool)
or process.is_alive()])
for process in list(_launched_processes):
if isinstance(process, mp.pool.Pool):
process.terminate()
if isinstance(process, mp.pool.Pool) or process.is_alive():
process.join()