def _pin_memory_loop(in_queue, out_queue, done_event, pin_memory_param,
device_id):
"""
This is copied from dataloader. It uses a different `pin_memory()`.
It'd probably be best to merge.
"""
if pin_memory_param:
torch.cuda.set_device(device_id)
while True:
try:
r = in_queue.get()
except Exception:
if done_event.is_set():
return
raise
if r is None or done_event.is_set():
break
if isinstance(r[1], ExceptionWrapper):
out_queue.put(r)
continue
idx, batch = r
try:
if pin_memory_param:
batch = pin_memory(batch)
except Exception:
out_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
out_queue.put((idx, batch))
def _ms_loop(dataset, index_queue, data_queue, collate_fn, scale, seed,
init_fn, worker_id):
global _use_shared_memory
_use_shared_memory = True
_set_worker_signal_handlers()
torch.set_num_threads(1)
torch.manual_seed(seed)
while True:
r = index_queue.get()
if r is None:
break
idx, batch_indices = r
try:
idx_scale = 0
if len(scale) > 1 and dataset.train:
idx_scale = random.randrange(0, len(scale))
dataset.set_scale(idx_scale)
samples = collate_fn([dataset[i] for i in batch_indices])
samples.append(idx_scale)
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
def _worker_loop(index_queue, data_queue, done_event,
seed, init_fn, worker_id, cnt):
# See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on the
# logic of this function.
try:
global _use_shared_memory
_use_shared_memory = True
# Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
# module's handlers are executed after Python returns from C low-level
# handlers, likely when the same fatal signal happened again already.
# https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
signal_handling._set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
torch.manual_seed(seed)
data_queue.cancel_join_thread()
if init_fn is not None:
init_fn(worker_id)
watchdog = ManagerWatchdog()
while watchdog.is_alive():
try:
r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
except queue.Empty:
continue
if r is None:
# Received the final signal
assert done_event.is_set()
return
elif done_event.is_set():
# Done event is set. But I haven't received the final signal
# (None) yet. I will keep continuing until get it, and skip the
# processing steps.
continue
idx, batch_indices = r
try:
samples = cnt.increment(batch_indices)
# if cnt.val.value % interval == 0:
# print('change')
# dataset.transform(np.random.choice(transform_fns))
# samples = collate_fn([dataset[i] for i in batch_indices])
# print(cnt.val.value)
except Exception:
# It is important that we don't store exc_info in a variable,
# see NOTE [ Python Traceback Reference Cycle Problem ]
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
del samples
except KeyboardInterrupt:
# Main process will raise KeyboardInterrupt anyways.
pass
def run(self):
# Disable polluting stderr with errors that are supposed to happen.
sys.stderr = open(os.devnull, "w")
try:
super(ErrorTrackingProcess, self).run()
self._cconn.send(None)
except Exception:
self._cconn.send(ExceptionWrapper(sys.exc_info()))
raise
def run(self):
if HAS_FAULTHANDLER:
faulthandler.enable()
if not IS_WINDOWS:
# windows does not have faulthandler.register
faulthandler.register(signal.SIGUSR1, chain=True)
if self.disable_stderr:
# Disable polluting stderr with errors that are supposed to happen.
sys.stderr = open(os.devnull, "w")
try:
super(ErrorTrackingProcess, self).run()
self._cconn.send(None)
except Exception:
self._cconn.send(ExceptionWrapper(sys.exc_info()))
raise
def _ms_loop(dataset, index_queue, data_queue, done_event, collate_fn, scale,
seed, init_fn, worker_id):
try:
collate._use_shared_memory = True
signal_handling._set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
torch.manual_seed(seed)
data_queue.cancel_join_thread()
if init_fn is not None:
init_fn(worker_id)
watchdog = ManagerWatchdog()
while watchdog.is_alive():
try:
r = index_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
except queue.Empty:
continue
if r is None:
assert done_event.is_set()
return
elif done_event.is_set():
continue
idx, batch_indices = r
try:
idx_scale = 0
if len(scale) > 1 and dataset.train:
idx_scale = random.randrange(0, len(scale))
dataset.set_scale(idx_scale)
samples = collate_fn([dataset[i] for i in batch_indices])
##make
samples.append(idx_scale)
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
del samples
except KeyboardInterrupt:
pass
def _worker_loop(
data_reader,
batch_queue,
data_queue,
global_done_event,
worker_done_event,
seed,
init_fn,
worker_id,
):
# Intialize C side signal handlers for SIGBUS and SIGSEGV. Python signal
# module's handlers are executed after Python returns from C low-level
# handlers, likely when the same fatal signal happened again already.
# https://docs.python.org/3/library/signal.html Sec. 18.8.1.1
_set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
# TODO: numpy doesn't take seed bigger than INT32
# np.random.seed(seed)
torch.manual_seed(seed)
# Do not wait for putting thread to join when this worker exits. Otherwise,
# this worker may always be waiting to put and doesn't check batch_queue
# and global_done_event for termination signal.
data_queue.cancel_join_thread()
if init_fn is not None:
init_fn(worker_id)
watchdog = ManagerWatchdog()
shard = data_reader.get_shard(worker_id)
shard_itr = iter(shard)
shard_done = False
while True:
if shard_done:
# Wait until the main thread acknowledge the WorkerDone message or
# it signals shutdown.
if (not watchdog.is_alive() or global_done_event.is_set()
or worker_done_event.wait(0.1)):
break
continue
try:
idx = batch_queue.get(timeout=MANAGER_STATUS_CHECK_INTERVAL)
except queue.Empty:
if watchdog.is_alive() and not global_done_event.is_set():
continue
else:
break
# use global_done_event so that we can get faster exiting signal even if there
# are still batches in batch_queue
if idx is None or global_done_event.is_set():
break
try:
samples = next(shard_itr)
except StopIteration:
# Signal to the main thread that this worker has run out of data.
# The worker cannot exit immediately because the queue might not be
# flushed immediately.
data_queue.put((idx, WorkerDone(worker_id)))
shard_done = True
except Exception:
data_queue.put((idx, ExceptionWrapper(sys.exc_info())))
else:
data_queue.put((idx, samples))
del samples
