def add_and_sub_plaintext(job_id, idx, data_list):
_, all_parties = session_init(job_id, idx)
with SPDZ():
if idx == 0:
x = FixedPointTensor.from_source("x", data_list[0])
else:
x = FixedPointTensor.from_source("x", all_parties[0])
y = data_list[1]
a = (x + y).get()
a1 = (y + x).get()
b = (x - y).get()
b1 = (y - x).get()
return a, a1, b, b1
def _test_spdz(self):
table_list = []
table_int_data_x, table_float_data_x = None, None
table_int_data_y, table_float_data_y = None, None
if self.local_party.role == "guest":
table_int_data_x = session.parallelize(
self.int_data_x,
include_key=False,
partition=self.data_partition)
table_int_data_x = table_int_data_x.mapValues(
lambda x: np.array([x]))
table_float_data_x = session.parallelize(
self.float_data_x,
include_key=False,
partition=self.data_partition)
table_float_data_x = table_float_data_x.mapValues(
lambda x: np.array([x]))
else:
table_int_data_y = session.parallelize(
self.int_data_y,
include_key=False,
partition=self.data_partition)
table_int_data_y = table_int_data_y.mapValues(
lambda y: np.array([y]))
table_float_data_y = session.parallelize(
self.float_data_y,
include_key=False,
partition=self.data_partition)
table_float_data_y = table_float_data_y.mapValues(
lambda y: np.array([y]))
for tensor_type in ["numpy", "table"]:
table = PrettyTable()
table.set_style(ORGMODE)
field_name = [
"DataType", "One time consumption",
f"{self.data_num} times consumption", "relative acc",
"log2 acc", "operations per second"
]
self._summary["field_name"] = field_name
table.field_names = field_name
with SPDZ(local_party=self.local_party,
all_parties=self.parties) as spdz:
for op_type in self.op_test_list:
start_time = time.time()
for epoch in range(self.test_round):
LOGGER.info(
f"test spdz, tensor_type: {tensor_type}, op_type: {op_type}, epoch: {epoch}"
)
tag = "_".join([tensor_type, op_type, str(epoch)])
spdz.set_flowid(tag)
if self.local_party.role == "guest":
if tensor_type == "table":
if op_type.startswith("int"):
fixed_point_x = TableTensor.from_source(
"int_x_" + tag, table_int_data_x)
fixed_point_y = TableTensor.from_source(
"int_y_" + tag, self.other_party)
else:
fixed_point_x = TableTensor.from_source(
"float_x_" + tag, table_float_data_x)
fixed_point_y = TableTensor.from_source(
"float_y_" + tag, self.other_party)
else:
if op_type.startswith("int"):
fixed_point_x = NumpyTensor.from_source(
"int_x_" + tag, self.int_data_x)
fixed_point_y = NumpyTensor.from_source(
"int_y_" + tag, self.other_party)
else:
fixed_point_x = NumpyTensor.from_source(
"float_x_" + tag, self.float_data_x)
fixed_point_y = NumpyTensor.from_source(
"float_y_" + tag, self.other_party)
else:
if tensor_type == "table":
if op_type.startswith("int"):
fixed_point_y = TableTensor.from_source(
"int_y_" + tag, table_int_data_y)
fixed_point_x = TableTensor.from_source(
"int_x_" + tag, self.other_party)
else:
fixed_point_y = TableTensor.from_source(
"float_y_" + tag, table_float_data_y)
fixed_point_x = TableTensor.from_source(
"float_x_" + tag, self.other_party)
else:
if op_type.startswith("int"):
fixed_point_y = NumpyTensor.from_source(
"int_y_" + tag, self.int_data_y)
fixed_point_x = NumpyTensor.from_source(
"int_x_" + tag, self.other_party)
else:
fixed_point_y = NumpyTensor.from_source(
"float_y_" + tag, self.float_data_y)
fixed_point_x = NumpyTensor.from_source(
"float_x_" + tag, self.other_party)
ret = self.calculate_ret(op_type, tensor_type,
fixed_point_x, fixed_point_y)
total_time = time.time() - start_time
self.output_table(op_type, table, tensor_type, total_time,
ret)
table_list.append(table)
self.tracker.log_component_summary(self._summary)
for table in table_list:
LOGGER.info(table)