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 _worker_loop(input_queue, output_queue, done_event, init_fn, worker_id):
try:
# Initialize 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 had already happened
# again.
# https://docs.python.org/3/library/signal.html#execution-of-python-signal-handlers
signal_handling._set_worker_signal_handlers()
torch.set_num_threads(1)
watchdog = ManagerWatchdog()
global _worker_info
_worker_info = WorkerInfo(id=worker_id)
init_exception = None
try:
if init_fn is not None:
init_fn(worker_id)
except Exception:
init_exception = ExceptionWrapper(where=f'in process {worker_id}')
if watchdog.is_alive():
output_queue.put((None, init_exception))
while watchdog.is_alive():
try:
func = input_queue.get(timeout=MP_STATUS_CHECK_INTERVAL)
except queue.Empty:
continue
if func is None:
# Received the final signal
break
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
try:
result = func()
except Exception:
# It is important that we don't store exc_info in a variable.
# `ExceptionWrapper` does the correct thing.
# See NOTE [ Python Traceback Reference Cycle Problem ]
result = ExceptionWrapper(where=f'in process {worker_id}')
output_queue.put(result)
del result, func # save memory
except KeyboardInterrupt:
# Main process will raise KeyboardInterrupt anyways.
pass
output_queue.cancel_join_thread()
output_queue.close()
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
def _custom_worker_loop(dataset_kind, dataset, index_queue, data_queue,
done_event, auto_collation, collate_fn, drop_last,
seed, init_fn, worker_id, num_workers):
# See NOTE [ Data Loader Multiprocessing Shutdown Logic ] for details on the
# logic of this function.
try:
# 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 had already happened
# again.
# https://docs.python.org/3/library/signal.html#execution-of-python-signal-handlers
signal_handling._set_worker_signal_handlers()
torch.set_num_threads(1)
random.seed(seed)
torch.manual_seed(seed)
global _worker_info
_worker_info = WorkerInfo(id=worker_id,
num_workers=num_workers,
seed=seed,
dataset=dataset)
init_exception = None
try:
if init_fn is not None:
init_fn(worker_id)
fetcher = _CustomDatasetKind.create_fetcher(
dataset_kind, dataset, auto_collation, collate_fn, drop_last)
except Exception:
init_exception = ExceptionWrapper(
where="in DataLoader worker process {}".format(worker_id))
# When using Iterable mode, some worker can exit earlier than others due
# to the IterableDataset behaving differently for different workers.
# When such things happen, an `_IterableDatasetStopIteration` object is
# sent over to the main process with the ID of this worker, so that the
# main process won't send more tasks to this worker, and will send
# `None` to this worker to properly exit it.
#
# Note that we cannot set `done_event` from a worker as it is shared
# among all processes. Instead, we set the `iteration_end` flag to
# signify that the iterator is exhausted. When either `done_event` or
# `iteration_end` is set, we skip all processing step and just wait for
# `None`.
iteration_end = False
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() or iteration_end
break
elif done_event.is_set() or iteration_end:
# `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, index = r
if init_exception is not None:
data = init_exception
init_exception = None
else:
try:
data = fetcher.fetch(index)
except Exception as e:
if isinstance(e, StopIteration
) and dataset_kind == _DatasetKind.Iterable:
data = _IterableDatasetStopIteration(worker_id)
# Set `iteration_end`
# (1) to save future `next(...)` calls, and
# (2) to avoid sending multiple `_IterableDatasetStopIteration`s.
iteration_end = True
else:
# It is important that we don't store exc_info in a variable.
# `ExceptionWrapper` does the correct thing.
# See NOTE [ Python Traceback Reference Cycle Problem ]
data = ExceptionWrapper(
where="in DataLoader worker process {}".format(
worker_id))
data_queue.put((idx, data))
del data, idx, index, r # save memory
except KeyboardInterrupt:
# Main process will raise KeyboardInterrupt anyways.
pass
if done_event.is_set():
data_queue.cancel_join_thread()
data_queue.close()
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
