def _deserialize_pickle5_data(self, data):
try:
in_band, buffers = unpack_pickle5_buffers(data)
if len(buffers) > 0:
obj = pickle.loads(in_band, buffers=buffers)
else:
obj = pickle.loads(in_band)
# cloudpickle does not provide error types
except pickle.pickle.PicklingError:
raise DeserializationError()
return obj
def _deserialize_object(self, data, metadata, object_id):
if metadata:
if metadata == ray_constants.PICKLE5_BUFFER_METADATA:
if not self.use_pickle:
raise ValueError("Receiving pickle5 serialized objects "
"while the serialization context is "
"using pyarrow as the backend.")
try:
in_band, buffers = unpack_pickle5_buffers(data)
if len(buffers) > 0:
obj = pickle.loads(in_band, buffers=buffers)
else:
obj = pickle.loads(in_band)
# cloudpickle does not provide error types
except pickle.pickle.PicklingError:
raise DeserializationError()
# Check that there are no ObjectIDs serialized in arguments
# that are inlined.
if object_id.is_nil():
assert len(self.get_and_clear_contained_object_ids()) == 0
else:
worker = ray.worker.global_worker
worker.core_worker.add_contained_object_ids(
object_id,
self.get_and_clear_contained_object_ids(),
)
return obj
# Check if the object should be returned as raw bytes.
if metadata == ray_constants.RAW_BUFFER_METADATA:
if data is None:
return b""
return data.to_pybytes()
# Otherwise, return an exception object based on
# the error type.
error_type = int(metadata)
if error_type == ErrorType.Value("WORKER_DIED"):
return RayWorkerError()
elif error_type == ErrorType.Value("ACTOR_DIED"):
return RayActorError()
elif error_type == ErrorType.Value("OBJECT_UNRECONSTRUCTABLE"):
return UnreconstructableError(ray.ObjectID(object_id.binary()))
else:
assert error_type != ErrorType.Value("OBJECT_IN_PLASMA"), \
"Tried to get object that has been promoted to plasma."
assert False, "Unrecognized error type " + str(error_type)
elif data:
raise ValueError("non-null object should always have metadata")
else:
# Object isn't available in plasma. This should never be returned
# to the user. We should only reach this line if this object was
# deserialized as part of a list, and another object in the list
# throws an exception.
return plasma.ObjectNotAvailable
def _deserialize_object_from_arrow(self, data, metadata, object_id):
if metadata:
if metadata == ray_constants.PICKLE5_BUFFER_METADATA:
if not self.use_pickle:
raise ValueError("Receiving pickle5 serialized objects "
"while the serialization context is "
"using pyarrow as the backend.")
try:
in_band, buffers = unpack_pickle5_buffers(data)
if len(buffers) > 0:
return pickle.loads(in_band, buffers=buffers)
else:
return pickle.loads(in_band)
# cloudpickle does not provide error types
except pickle.pickle.PicklingError:
raise DeserializationError()
# Check if the object should be returned as raw bytes.
if metadata == ray_constants.RAW_BUFFER_METADATA:
if data is None:
return b""
return data.to_pybytes()
# Otherwise, return an exception object based on
# the error type.
error_type = int(metadata)
if error_type == ErrorType.Value("WORKER_DIED"):
return RayWorkerError()
elif error_type == ErrorType.Value("ACTOR_DIED"):
return RayActorError()
elif error_type == ErrorType.Value("OBJECT_UNRECONSTRUCTABLE"):
return UnreconstructableError(ray.ObjectID(object_id.binary()))
else:
assert error_type != ErrorType.Value("OBJECT_IN_PLASMA"), \
"Tried to get object that has been promoted to plasma."
assert False, "Unrecognized error type " + str(error_type)
elif data:
if self.use_pickle:
raise ValueError("Receiving plasma serialized objects "
"while the serialization context is "
"using pickle5 as the backend.")
try:
# If data is not empty, deserialize the object.
return pyarrow.deserialize(data, self.pyarrow_context)
except pyarrow.DeserializationCallbackError:
raise DeserializationError()
else:
# Object isn't available in plasma. This should never be returned
# to the user. We should only reach this line if this object was
# deserialized as part of a list, and another object in the list
# throws an exception.
return plasma.ObjectNotAvailable
def _deserialize_pickle5_data(self, data):
if not self.use_pickle:
raise ValueError("Receiving pickle5 serialized objects "
"while the serialization context is "
"using a custom raw backend.")
try:
in_band, buffers = unpack_pickle5_buffers(data)
if len(buffers) > 0:
obj = pickle.loads(in_band, buffers=buffers)
else:
obj = pickle.loads(in_band)
# cloudpickle does not provide error types
except pickle.pickle.PicklingError:
raise DeserializationError()
return obj