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test_dp_controller.py
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import unittest
import torch
from torch.nn import Module
from torch.optim import Adam
from torch.utils.data import DataLoader, Dataset
from unittest.mock import patch, MagicMock
from fedbiomed.common.models import TorchModel
from fedbiomed.common.optimizers.generic_optimizers import NativeTorchOptimizer
from fedbiomed.common.privacy import DPController
from fedbiomed.common.exceptions import FedbiomedDPControllerError
class TestDPController(unittest.TestCase):
# Fake Dataset
class DS(Dataset):
def __getitem__(self):
return 1, 1
def __len__(self):
return 1, 1
def setUp(self) -> None:
self.dp_args_c = {"type": "central", "sigma": 0.1, "clip": 0.1}
self.dp_args_l = {"type": "local", "sigma": 0.1, "clip": 0.1}
self.dpc = DPController(self.dp_args_c)
self.dpl = DPController(self.dp_args_l)
self.patcher_privacy_engine = patch("opacus.PrivacyEngine.__init__", MagicMock(return_value=None))
self.patcher_privacy_engine_make_private = patch("opacus.PrivacyEngine.make_private")
self.patcher_module_validator = patch("opacus.validators.ModuleValidator.is_valid")
self.patcher_module_validator_fix = patch("opacus.validators.ModuleValidator.fix")
self.privacy_engine = self.patcher_privacy_engine.start()
self.privacy_engine_make_private = self.patcher_privacy_engine_make_private.start()
self.module_validator = self.patcher_module_validator.start()
self.module_validator_fix = self.patcher_module_validator_fix.start()
self.privacy_engine_make_private.return_value = (None, None, None)
self.module_validator.return_value = None
self.module_validator_fix.return_value = None
pass
def tearDown(self) -> None:
self.patcher_privacy_engine.stop()
self.patcher_module_validator.stop()
self.patcher_module_validator_fix.stop()
pass
def test_dep_controller_01_init_1(self):
dp_controller = DPController()
self.assertEqual(dp_controller._dp_args, {}, "_dp_args is not none")
self.assertFalse(dp_controller._is_active, "DPController is active where it should be inactive")
def test_dep_controller_02_init_2(self):
"""Tests builds DP controller with CENTRAL DP """
dp_args = {"type": "central", "sigma": 0.1, "clip": 0.1}
dp_controller = DPController(dp_args)
self.assertTrue("sigma_CDP" in dp_controller._dp_args, "Sigma CDP is not set when DP type is central")
self.assertEqual(dp_controller._dp_args["sigma"], 0.0, "Sigma is not set when DP type is central")
self.assertEqual(dp_controller._dp_args["clip"], dp_args["clip"], "Sigma is not set when DP type is central")
self.assertEqual(dp_controller._dp_args["type"], dp_args["type"], "Sigma is not set when DP type is central")
def test_dep_controller_03_init_3(self):
"""Tests builds DP controller with LOCAL DP """
dp_args = {"type": "local", "sigma": 0.1, "clip": 0.1}
dp_controller = DPController(dp_args)
self.assertFalse("sigma_CDP" in dp_controller._dp_args, "Sigma CDP is set when DP type is local")
self.assertEqual(dp_controller._dp_args["sigma"], dp_args["sigma"], "Sigma is not set when DP type is local")
self.assertEqual(dp_controller._dp_args["clip"], dp_args["clip"], "Sigma is not set when DP type is local")
self.assertEqual(dp_controller._dp_args["type"], dp_args["type"], "Sigma is not set when DP type is local")
def test_dep_controller_04_init_4(self):
"""Tests builds DP controller with invalid arguments """
# Invalid DP type
dp_args = {"type": "invalid", "sigma": 0.1, "clip": 0.1}
with self.assertRaises(FedbiomedDPControllerError):
DPController(dp_args)
# Invalid sigma
dp_args = {"type": "local", "sigma": 0, "clip": 0.1}
with self.assertRaises(FedbiomedDPControllerError):
DPController(dp_args)
# Invalid clip
dp_args = {"type": "local", "sigma": 0.1, "clip": 0}
with self.assertRaises(FedbiomedDPControllerError):
DPController(dp_args)
def test_dep_controller_05_validate_and_fix_model(self):
"""Tests builds DP controller with invalid arguments """
model = MagicMock()
self.module_validator.return_value = True
self.dpl._is_active = True
self.dpl.validate_and_fix_model(model)
self.module_validator.assert_called_once_with(model)
self.module_validator.return_value = False
self.dpl.validate_and_fix_model(model)
self.module_validator_fix.assert_called_once_with(model)
@patch('fedbiomed.common.privacy.DPController.validate_and_fix_model')
def test_dep_controller_06_before_training(self, validate_and_fix):
"""Tests before training method with different scenarios"""
#model_false = MagicMock()
opt_false = MagicMock()
loader_false = MagicMock()
model = Module()
model_wrapper = MagicMock(spec=TorchModel)
model_wrapper.model = model
opt = Adam([torch.zeros([2, 4])])
loader = DataLoader(TestDPController.DS())
optim_wrapper = NativeTorchOptimizer(model_wrapper, opt)
with self.assertRaises(FedbiomedDPControllerError):
self.dpl.before_training(opt_false, loader)
with self.assertRaises(FedbiomedDPControllerError):
self.dpl.before_training(optim_wrapper, loader_false)
validate_and_fix.return_value = model
self.privacy_engine_make_private.side_effect = Exception
with self.assertRaises(FedbiomedDPControllerError):
self.dpl.before_training(optim_wrapper, loader)
self.privacy_engine_make_private.side_effect = None
self.privacy_engine_make_private.reset_mock()
self.dpl.before_training(optim_wrapper, loader)
self.privacy_engine_make_private.assert_called_once_with(
module=model,
optimizer=opt,
data_loader=loader,
noise_multiplier=self.dp_args_l.get('sigma'),
max_grad_norm=self.dp_args_l.get('clip'))
def test_dep_controller_07_post_process_dp(self):
"""Tests before training method with different scenarios"""
params = {"a_module.": torch.zeros([2, 4]), "b_module.": torch.zeros([2, 4])}
# Post processes with DPL
p = self.dpl._postprocess_dp(params)
self.assertTrue('module' not in list(p.keys())[0], "`module tag is not properly removed from private "
"end-model`")
# Post processes with DPC
p = self.dpc._postprocess_dp(params)
self.assertTrue('module' not in list(p.keys())[0], "`module tag is not properly removed from private "
"end-model`")
@patch('fedbiomed.common.privacy.DPController._postprocess_dp')
def test_dep_controller_08_after_training(self, postprocess):
"""Tests before training method with different scenarios"""
postprocess.return_value = "POSTPROCESS"
params = {"a_module.": torch.zeros([2, 4]), "b_module.": torch.zeros([2, 4])}
# Post processes with DPL
p = self.dpl.after_training(params)
self.assertEqual(p, "POSTPROCESS")
# Post processes with DPC
p = self.dpc.after_training(params)
self.assertEqual(p, "POSTPROCESS")
if __name__ == '__main__': # pragma: no cover
unittest.main()