before rebase, refactor mechanisms implementation.

rename mech._decay_policy to mech._noise_update.

after rebase, rename norm_clip to norm_bound.

change mechanismsfactory to noisemechanismsfactory in test.

11

12

13

1414141414141414141414141414

15

example/mnist_demo/lenet5_config.py

@@ -32,7 +32,7 @@ mnist_cfg = edict({
     'data_path': './MNIST_unzip',  # the path of training and testing data set
     'dataset_sink_mode': False,  # whether deliver all training data to device one time
     'micro_batches': 16,  # the number of small batches split from an original batch
-    'norm_clip': 1.0,  # the clip bound of the gradients of model's training parameters
+    'norm_bound': 1.0,  # the clip bound of the gradients of model's training parameters
     'initial_noise_multiplier': 0.5,  # the initial multiplication coefficient of the noise added to training
     # parameters' gradients
     'noise_mechanisms': 'AdaGaussian',  # the method of adding noise in gradients while training

example/mnist_demo/lenet5_dp.py

@@ -115,8 +115,9 @@ if __name__ == "__main__":
     # or 'AdaGaussian', in which noise would be decayed with 'AdaGaussian'
     # mechanism while be constant with 'Gaussian' mechanism.
     noise_mech = NoiseMechanismsFactory().create(cfg.noise_mechanisms,
-                                                 norm_bound=cfg.norm_clip,
-                                                 initial_noise_multiplier=cfg.initial_noise_multiplier)
+                                                 norm_bound=cfg.norm_bound,
+                                                 initial_noise_multiplier=cfg.initial_noise_multiplier,
+                                                 noise_update='Exp')
     # Create a factory class of clip mechanisms, this method is to adaptive clip
     # gradients while training, decay_policy support 'Linear' and 'Geometric',
     # learning_rate is the learning rate to update clip_norm,
@@ -136,11 +137,11 @@ if __name__ == "__main__":
     rdp_monitor = PrivacyMonitorFactory.create('rdp',
                                                num_samples=60000,
                                                batch_size=cfg.batch_size,
-                                               initial_noise_multiplier=cfg.initial_noise_multiplier*cfg.norm_clip,
+                                               initial_noise_multiplier=cfg.initial_noise_multiplier*cfg.norm_bound,
                                                per_print_times=10)
     # Create the DP model for training.
     model = DPModel(micro_batches=cfg.micro_batches,
-                    norm_clip=cfg.norm_clip,
+                    norm_bound=cfg.norm_bound,
                     noise_mech=noise_mech,
                     clip_mech=clip_mech,
                     network=network,

example/mnist_demo/lenet5_dp_pynative_mode.py → example/mnist_demo/lenet5_dp_pynative_model.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-python lenet5_dp_pynative_mode.py --data_path /YourDataPath --micro_batches=2
+python lenet5_dp_pynative_model.py --data_path /YourDataPath --micro_batches=2
 """
 import os
 
@@ -32,6 +32,7 @@ import mindspore.common.dtype as mstype
 from mindarmour.diff_privacy import DPModel
 from mindarmour.diff_privacy import PrivacyMonitorFactory
 from mindarmour.diff_privacy import DPOptimizerClassFactory
+from mindarmour.diff_privacy import ClipMechanismsFactory
 from mindarmour.utils.logger import LogUtil
 from lenet5_net import LeNet5
 from lenet5_config import mnist_cfg as cfg
@@ -108,21 +109,35 @@ if __name__ == "__main__":
     # means that the privacy protection effect is weak. Mechanisms can be 'Gaussian' or 'AdaGaussian', in which noise
     # would be decayed with 'AdaGaussian' mechanism while be constant with 'Gaussian' mechanism.
     dp_opt = DPOptimizerClassFactory(micro_batches=cfg.micro_batches)
-    dp_opt.set_mechanisms(cfg.mechanisms,
-                          norm_bound=cfg.norm_clip,
-                          initial_noise_multiplier=cfg.initial_noise_multiplier)
+    dp_opt.set_mechanisms(cfg.noise_mechanisms,
+                          norm_bound=cfg.norm_bound,
+                          initial_noise_multiplier=cfg.initial_noise_multiplier,
+                          noise_update='Exp')
+    # Create a factory class of clip mechanisms, this method is to adaptive clip
+    # gradients while training, decay_policy support 'Linear' and 'Geometric',
+    # learning_rate is the learning rate to update clip_norm,
+    # target_unclipped_quantile is the target quantile of norm clip,
+    # fraction_stddev is the stddev of Gaussian normal which used in
+    # empirical_fraction, the formula is
+    # $empirical_fraction + N(0, fraction_stddev)$.
+    clip_mech = ClipMechanismsFactory().create(cfg.clip_mechanisms,
+                                               decay_policy=cfg.clip_decay_policy,
+                                               learning_rate=cfg.clip_learning_rate,
+                                               target_unclipped_quantile=cfg.target_unclipped_quantile,
+                                               fraction_stddev=cfg.fraction_stddev)
     net_opt = dp_opt.create('Momentum')(params=network.trainable_params(), learning_rate=cfg.lr, momentum=cfg.momentum)
     # Create a monitor for DP training. The function of the monitor is to compute and print the privacy budget(eps
     # and delta) while training.
     rdp_monitor = PrivacyMonitorFactory.create('rdp',
                                                num_samples=60000,
                                                batch_size=cfg.batch_size,
-                                               initial_noise_multiplier=cfg.initial_noise_multiplier*cfg.norm_clip,
+                                               initial_noise_multiplier=cfg.initial_noise_multiplier*cfg.norm_bound,
                                                per_print_times=10)
     # Create the DP model for training.
     model = DPModel(micro_batches=cfg.micro_batches,
-                    norm_clip=cfg.norm_clip,
-                    mech=None,
+                    norm_bound=cfg.norm_bound,
+                    noise_mech=None,
+                    clip_mech=clip_mech,
                     network=network,
                     loss_fn=net_loss,
                     optimizer=net_opt,

mindarmour/diff_privacy/__init__.py

@@ -2,7 +2,7 @@
 This module provide Differential Privacy feature to protect user privacy.
 """
 from .mechanisms.mechanisms import NoiseGaussianRandom
-from .mechanisms.mechanisms import AdaGaussianRandom
+from .mechanisms.mechanisms import NoiseAdaGaussianRandom
 from .mechanisms.mechanisms import AdaClippingWithGaussianRandom
 from .mechanisms.mechanisms import NoiseMechanismsFactory
 from .mechanisms.mechanisms import ClipMechanismsFactory
@@ -11,7 +11,7 @@ from .optimizer.optimizer import DPOptimizerClassFactory
 from .train.model import DPModel
 
 __all__ = ['NoiseGaussianRandom',
-           'AdaGaussianRandom',
+           'NoiseAdaGaussianRandom',
            'AdaClippingWithGaussianRandom',
            'NoiseMechanismsFactory',
            'ClipMechanismsFactory',

mindarmour/diff_privacy/mechanisms/mechanisms.py

@@ -19,6 +19,7 @@ from abc import abstractmethod
 from mindspore import Tensor
 from mindspore.nn import Cell
 from mindspore.ops import operations as P
+from mindspore.ops.composite import normal
 from mindspore.common.parameter import Parameter
 from mindspore.common import dtype as mstype
 
@@ -55,7 +56,7 @@ class ClipMechanismsFactory:
         Examples:
             >>> decay_policy = 'Linear'
             >>> beta = Tensor(0.5, mstype.float32)
-            >>> norm_clip = Tensor(1.0, mstype.float32)
+            >>> norm_bound = Tensor(1.0, mstype.float32)
             >>> beta_stddev = 0.1
             >>> learning_rate = 0.1
             >>> target_unclipped_quantile = 0.3
@@ -65,7 +66,7 @@ class ClipMechanismsFactory:
             >>>                          learning_rate=learning_rate,
             >>>                          target_unclipped_quantile=target_unclipped_quantile,
             >>>                          fraction_stddev=beta_stddev)
-            >>> next_norm_clip = ada_clip(beta, norm_clip)
+            >>> next_norm_bound = ada_clip(beta, norm_bound)
 
         """
         if mech_name == 'Gaussian':
@@ -81,25 +82,32 @@ class NoiseMechanismsFactory:
         pass
 
     @staticmethod
-    def create(policy, *args, **kwargs):
+    def create(mech_name='Gaussian', norm_bound=0.5, initial_noise_multiplier=1.5, seed=0, noise_decay_rate=6e-6,
+               noise_update=None):
         """
         Args:
-            policy(str): Noise generated strategy, could be 'Gaussian' or
+            mech_name(str): Noise generated strategy, could be 'Gaussian' or
                 'AdaGaussian'. Noise would be decayed with 'AdaGaussian' mechanism
                 while be constant with 'Gaussian' mechanism.
-            args(Union[float, str]): Parameters used for creating noise
-                mechanisms.
-            kwargs(Union[float, str]): Parameters used for creating noise
-                mechanisms.
+            norm_bound(float): Clipping bound for the l2 norm of the gradients.
+            initial_noise_multiplier(float): Ratio of the standard deviation of
+                Gaussian noise divided by the norm_bound, which will be used to
+                calculate privacy spent.
+            seed(int): Original random seed, if seed=0 random normal will use secure
+                random number. IF seed!=0 random normal will generate values using
+                given seed.
+            noise_decay_rate(float): Hyper parameter for controlling the noise decay.
+            noise_update(str): Mechanisms parameters update policy. Default: None, no
+                parameters need update.
 
         Raises:
-            NameError: `policy` must be in ['Gaussian', 'AdaGaussian'].
+            NameError: `mech_name` must be in ['Gaussian', 'AdaGaussian'].
 
         Returns:
             Mechanisms, class of noise generated Mechanism.
 
         Examples:
-            >>> norm_clip = 1.0
+            >>> norm_bound = 1.0
             >>> initial_noise_multiplier = 0.01
             >>> network = LeNet5()
             >>> batch_size = 32
@@ -107,7 +115,7 @@ class NoiseMechanismsFactory:
             >>> epochs = 1
             >>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
             >>> noise_mech = NoiseMechanismsFactory().create('Gaussian',
-            >>>                                              norm_bound=norm_clip,
+            >>>                                              norm_bound=norm_bound,
             >>>                                              initial_noise_multiplier=initial_noise_multiplier)
             >>> clip_mech = ClipMechanismsFactory().create('Gaussian',
             >>>                                            decay_policy='Linear',
@@ -118,7 +126,7 @@ class NoiseMechanismsFactory:
             >>>                       momentum=0.9)
             >>> model = DPModel(micro_batches=2,
             >>>                 clip_mech=clip_mech,
-            >>>                 norm_clip=norm_clip,
+            >>>                 norm_bound=norm_bound,
             >>>                 noise_mech=noise_mech,
             >>>                 network=network,
             >>>                 loss_fn=loss,
@@ -129,15 +137,22 @@ class NoiseMechanismsFactory:
             >>> ms_ds.set_dataset_size(batch_size * batches)
             >>> model.train(epochs, ms_ds, dataset_sink_mode=False)
         """
-        if policy == 'Gaussian':
-            return NoiseGaussianRandom(*args, **kwargs)
-        if policy == 'AdaGaussian':
-            return AdaGaussianRandom(*args, **kwargs)
+        if mech_name == 'Gaussian':
+            return NoiseGaussianRandom(norm_bound=norm_bound,
+                                       initial_noise_multiplier=initial_noise_multiplier,
+                                       seed=seed,
+                                       noise_update=noise_update)
+        if mech_name == 'AdaGaussian':
+            return NoiseAdaGaussianRandom(norm_bound=norm_bound,
+                                          initial_noise_multiplier=initial_noise_multiplier,
+                                          seed=seed,
+                                          noise_decay_rate=noise_decay_rate,
+                                          noise_update=noise_update)
         raise NameError("The {} is not implement, please choose "
-                        "['Gaussian', 'AdaGaussian']".format(policy))
+                        "['Gaussian', 'AdaGaussian']".format(mech_name))
 
 
-class Mechanisms(Cell):
+class _Mechanisms(Cell):
     """
     Basic class of noise generated mechanism.
     """
@@ -149,21 +164,19 @@ class Mechanisms(Cell):
         """
 
 
-class NoiseGaussianRandom(Mechanisms):
+class NoiseGaussianRandom(_Mechanisms):
     """
     Gaussian noise generated mechanism.
 
     Args:
         norm_bound(float): Clipping bound for the l2 norm of the gradients.
-            Default: 0.5.
         initial_noise_multiplier(float): Ratio of the standard deviation of
             Gaussian noise divided by the norm_bound, which will be used to
-            calculate privacy spent. Default: 1.5.
+            calculate privacy spent.
         seed(int): Original random seed, if seed=0 random normal will use secure
             random number. IF seed!=0 random normal will generate values using
-            given seed. Default: 0.
-        policy(str): Mechanisms parameters update policy. Default: None, no
-            parameters need update.
+            given seed.
+        noise_update(str): Mechanisms parameters update policy. Default: None.
 
     Returns:
         Tensor, generated noise with shape like given gradients.
@@ -172,24 +185,25 @@ class NoiseGaussianRandom(Mechanisms):
         >>> gradients = Tensor([0.2, 0.9], mstype.float32)
         >>> norm_bound = 0.5
         >>> initial_noise_multiplier = 1.5
-        >>> net = NoiseGaussianRandom(norm_bound, initial_noise_multiplier)
+        >>> seed = 0
+        >>> noise_update = None
+        >>> net = NoiseGaussianRandom(norm_bound, initial_noise_multiplier, seed, noise_update)
         >>> res = net(gradients)
         >>> print(res)
     """
 
-    def __init__(self, norm_bound=0.5, initial_noise_multiplier=1.5, seed=0,
-                 policy=None):
+    def __init__(self, norm_bound, initial_noise_multiplier, seed, noise_update=None):
         super(NoiseGaussianRandom, self).__init__()
         self._norm_bound = check_value_positive('norm_bound', norm_bound)
         self._norm_bound = Tensor(norm_bound, mstype.float32)
-        self._initial_noise_multiplier = check_value_positive(
-            'initial_noise_multiplier',
+        self._initial_noise_multiplier = check_value_positive('initial_noise_multiplier',
                                                               initial_noise_multiplier)
-        self._initial_noise_multiplier = Tensor(initial_noise_multiplier,
-                                                mstype.float32)
+        self._initial_noise_multiplier = Tensor(initial_noise_multiplier, mstype.float32)
         self._mean = Tensor(0, mstype.float32)
-        self._normal = P.Normal(seed=seed)
-        self._decay_policy = policy
+        if noise_update is not None:
+            raise ValueError('noise_update must be None in GaussianRandom class, but got {}.'.format(noise_update))
+        self._noise_update = noise_update
+        self._seed = seed
 
     def construct(self, gradients):
         """
@@ -203,26 +217,25 @@ class NoiseGaussianRandom(Mechanisms):
         """
         shape = P.Shape()(gradients)
         stddev = P.Mul()(self._norm_bound, self._initial_noise_multiplier)
-        noise = self._normal(shape, self._mean, stddev)
+        noise = normal(shape, self._mean, stddev, self._seed)
         return noise
 
 
-class AdaGaussianRandom(Mechanisms):
+class NoiseAdaGaussianRandom(NoiseGaussianRandom):
     """
     Adaptive Gaussian noise generated mechanism. Noise would be decayed with
-    training. Decay mode could be 'Time' mode or 'Step' mode.
+    training. Decay mode could be 'Time' mode, 'Step' mode, 'Exp' mode.
 
     Args:
         norm_bound(float): Clipping bound for the l2 norm of the gradients.
-            Default: 1.0.
         initial_noise_multiplier(float): Ratio of the standard deviation of
             Gaussian noise divided by the norm_bound, which will be used to
-            calculate privacy spent. Default: 1.5.
+            calculate privacy spent.
+        seed(int): Original random seed, if seed=0 random normal will use secure
+            random number. IF seed!=0 random normal will generate values using
+            given seed.
         noise_decay_rate(float): Hyper parameter for controlling the noise decay.
-            Default: 6e-4.
-        decay_policy(str): Noise decay strategy include 'Step' and 'Time'.
-            Default: 'Time'.
-        seed(int): Original random seed. Default: 0.
+        noise_update(str): Noise decay strategy include 'Step', 'Time', 'Exp'.
 
     Returns:
         Tensor, generated noise with shape like given gradients.
@@ -231,56 +244,27 @@ class AdaGaussianRandom(Mechanisms):
         >>> gradients = Tensor([0.2, 0.9], mstype.float32)
         >>> norm_bound = 1.0
         >>> initial_noise_multiplier = 1.5
+        >>> seed = 0
         >>> noise_decay_rate = 6e-4
-        >>> decay_policy = "Time"
-        >>> net = AdaGaussianRandom(norm_bound, initial_noise_multiplier,
-        >>>                         noise_decay_rate, decay_policy)
+        >>> noise_update = "Time"
+        >>> net = NoiseAdaGaussianRandom(norm_bound, initial_noise_multiplier, seed, noise_decay_rate, noise_update)
         >>> res = net(gradients)
         >>> print(res)
     """
 
-    def __init__(self, norm_bound=1.0, initial_noise_multiplier=1.5,
-                 noise_decay_rate=6e-4, decay_policy='Time', seed=0):
-        super(AdaGaussianRandom, self).__init__()
-        norm_bound = check_value_positive('norm_bound', norm_bound)
-        initial_noise_multiplier = check_value_positive(
-            'initial_noise_multiplier',
-            initial_noise_multiplier)
-        self._norm_bound = Tensor(norm_bound, mstype.float32)
-
-        initial_noise_multiplier = Tensor(initial_noise_multiplier,
-                                          mstype.float32)
-        self._initial_noise_multiplier = Parameter(initial_noise_multiplier,
-                                                   name='initial_noise_multiplier')
-        self._noise_multiplier = Parameter(initial_noise_multiplier,
+    def __init__(self, norm_bound, initial_noise_multiplier, seed, noise_decay_rate, noise_update):
+        super(NoiseAdaGaussianRandom, self).__init__(norm_bound=norm_bound,
+                                                     initial_noise_multiplier=initial_noise_multiplier,
+                                                     seed=seed)
+        self._noise_multiplier = Parameter(self._initial_noise_multiplier,
                                            name='noise_multiplier')
-        self._mean = Tensor(0, mstype.float32)
-        noise_decay_rate = check_param_type('noise_decay_rate',
-                                            noise_decay_rate, float)
+        noise_decay_rate = check_param_type('noise_decay_rate', noise_decay_rate, float)
         check_param_in_range('noise_decay_rate', noise_decay_rate, 0.0, 1.0)
         self._noise_decay_rate = Tensor(noise_decay_rate, mstype.float32)
-        if decay_policy not in ['Time', 'Step', 'Exp']:
-            raise NameError("The decay_policy must be in ['Time', 'Step', 'Exp'], but "
-                            "get {}".format(decay_policy))
-        self._decay_policy = decay_policy
-        self._mul = P.Mul()
-        self._normal = P.Normal(seed=seed)
-
-    def construct(self, gradients):
-        """
-        Generate adaptive Gaussian noise.
-
-        Args:
-            gradients(Tensor): The gradients.
-
-        Returns:
-            Tensor, generated noise with shape like given gradients.
-        """
-        shape = P.Shape()(gradients)
-        noise = self._normal(shape, self._mean,
-                             self._mul(self._noise_multiplier,
-                                       self._norm_bound))
-        return noise
+        if noise_update not in ['Time', 'Step', 'Exp']:
+            raise NameError("The noise_update must be in ['Time', 'Step', 'Exp'], but "
+                            "get {}".format(noise_update))
+        self._noise_update = noise_update
 
 
 class _MechanismsParamsUpdater(Cell):
@@ -288,7 +272,7 @@ class _MechanismsParamsUpdater(Cell):
     Update mechanisms parameters, the parameters will refresh in train period.
 
     Args:
-        policy(str): Pass in by the mechanisms class, mechanisms parameters
+        noise_update(str): Pass in by the mechanisms class, mechanisms parameters
             update policy.
         decay_rate(Tensor): Pass in by the mechanisms class, hyper parameter for
             controlling the decay size.
@@ -300,9 +284,9 @@ class _MechanismsParamsUpdater(Cell):
     Returns:
         Tuple, next params value.
     """
-    def __init__(self, policy, decay_rate, cur_noise_multiplier, init_noise_multiplier):
+    def __init__(self, noise_update, decay_rate, cur_noise_multiplier, init_noise_multiplier):
         super(_MechanismsParamsUpdater, self).__init__()
-        self._policy = policy
+        self._noise_update = noise_update
         self._decay_rate = decay_rate
         self._cur_noise_multiplier = cur_noise_multiplier
         self._init_noise_multiplier = init_noise_multiplier
@@ -322,27 +306,27 @@ class _MechanismsParamsUpdater(Cell):
         Returns:
             Tuple, next step parameters value.
         """
-        if self._policy == 'Time':
+        if self._noise_update == 'Time':
             temp = self._div(self._init_noise_multiplier, self._cur_noise_multiplier)
             temp = self._add(temp, self._decay_rate)
             next_noise_multiplier = self._assign(self._cur_noise_multiplier,
                                                  self._div(self._init_noise_multiplier, temp))
-        elif self._policy == 'Step':
+        elif self._noise_update == 'Step':
             temp = self._sub(self._one, self._decay_rate)
             next_noise_multiplier = self._assign(self._cur_noise_multiplier,
                                                  self._mul(temp, self._cur_noise_multiplier))
         else:
             next_noise_multiplier = self._assign(self._cur_noise_multiplier,
-                                                 self._div(self._one, self._exp(self._one)))
+                                                 self._div(self._cur_noise_multiplier, self._exp(self._decay_rate)))
         return next_noise_multiplier
 
 
 class AdaClippingWithGaussianRandom(Cell):
     """
     Adaptive clipping. If `decay_policy` is 'Linear', the update formula is
-    $ norm_clip = norm_clip - learning_rate*(beta-target_unclipped_quantile)$.
+    $ norm_bound = norm_bound - learning_rate*(beta-target_unclipped_quantile)$.
     `decay_policy` is 'Geometric', the update formula is
-    $ norm_clip = norm_clip*exp(-learning_rate*(empirical_fraction-target_unclipped_quantile))$.
+    $ norm_bound = norm_bound*exp(-learning_rate*(empirical_fraction-target_unclipped_quantile))$.
     where beta is the empirical fraction of samples with the value at most
     `target_unclipped_quantile`.
 
@@ -363,7 +347,7 @@ class AdaClippingWithGaussianRandom(Cell):
     Examples:
         >>> decay_policy = 'Linear'
         >>> beta = Tensor(0.5, mstype.float32)
-        >>> norm_clip = Tensor(1.0, mstype.float32)
+        >>> norm_bound = Tensor(1.0, mstype.float32)
         >>> beta_stddev = 0.01
         >>> learning_rate = 0.001
         >>> target_unclipped_quantile = 0.9
@@ -371,7 +355,7 @@ class AdaClippingWithGaussianRandom(Cell):
         >>>                                          learning_rate=learning_rate,
         >>>                                          target_unclipped_quantile=target_unclipped_quantile,
         >>>                                          fraction_stddev=beta_stddev)
-        >>> next_norm_clip = ada_clip(beta, norm_clip)
+        >>> next_norm_bound = ada_clip(beta, norm_bound)
 
     """
 
@@ -400,26 +384,26 @@ class AdaClippingWithGaussianRandom(Cell):
         self._sub = P.Sub()
         self._mul = P.Mul()
         self._exp = P.Exp()
-        self._normal = P.Normal(seed=seed)
+        self._seed = seed
 
-    def construct(self, empirical_fraction, norm_clip):
+    def construct(self, empirical_fraction, norm_bound):
         """
-        Update value of norm_clip.
+        Update value of norm_bound.
 
         Args:
             empirical_fraction(Tensor): empirical fraction of samples with the
                 value at most `target_unclipped_quantile`.
-            norm_clip(Tensor): Clipping bound for the l2 norm of the gradients.
+            norm_bound(Tensor): Clipping bound for the l2 norm of the gradients.
 
         Returns:
             Tensor, generated noise with shape like given gradients.
         """
-        fraction_noise = self._normal((1,), self._zero, self._fraction_stddev)
+        fraction_noise = normal((1,), self._zero, self._fraction_stddev, self._seed)
         empirical_fraction = self._add(empirical_fraction, fraction_noise)
         if self._decay_policy == 'Linear':
             grad_clip = self._sub(empirical_fraction,
                                   self._target_unclipped_quantile)
-            next_norm_clip = self._sub(norm_clip,
+            next_norm_bound = self._sub(norm_bound,
                                         self._mul(self._learning_rate, grad_clip))
 
         # decay_policy == 'Geometric'
@@ -427,5 +411,5 @@ class AdaClippingWithGaussianRandom(Cell):
             grad_clip = self._sub(empirical_fraction,
                                   self._target_unclipped_quantile)
             grad_clip = self._exp(self._mul(-self._learning_rate, grad_clip))
-            next_norm_clip = self._mul(norm_clip, grad_clip)
-        return next_norm_clip
+            next_norm_bound = self._mul(norm_bound, grad_clip)
+        return next_norm_bound

mindarmour/diff_privacy/optimizer/optimizer.py

@@ -127,8 +127,8 @@ class DPOptimizerClassFactory:
                 self._micro_float = Tensor(micro_batches, mstype.float32)
 
                 self._mech_param_updater = None
-                if self._mech is not None and self._mech._decay_policy is not None:
-                    self._mech_param_updater = _MechanismsParamsUpdater(policy=self._mech._decay_policy,
+                if self._mech is not None and self._mech._noise_update is not None:
+                    self._mech_param_updater = _MechanismsParamsUpdater(noise_update=self._mech._noise_update,
                                                                         decay_rate=self._mech._noise_decay_rate,
                                                                         cur_noise_multiplier=
                                                                         self._mech._noise_multiplier,

mindarmour/diff_privacy/train/model.py

@@ -75,7 +75,7 @@ class DPModel(Model):
     Args:
         micro_batches (int): The number of small batches split from an original
             batch. Default: 2.
-        norm_clip (float): Use to clip the bound, if set 1, will retun the
+        norm_bound (float): Use to clip the bound, if set 1, will return the
             original data. Default: 1.0.
         noise_mech (Mechanisms): The object can generate the different type of
             noise. Default: None.
@@ -83,7 +83,7 @@ class DPModel(Model):
             Default: None.
 
     Examples:
-        >>> norm_clip = 1.0
+        >>> norm_bound = 1.0
         >>> initial_noise_multiplier = 0.01
         >>> network = LeNet5()
         >>> batch_size = 32
@@ -93,7 +93,7 @@ class DPModel(Model):
         >>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
         >>> factory_opt = DPOptimizerClassFactory(micro_batches=micro_batches)
         >>> factory_opt.set_mechanisms('Gaussian',
-        >>>                            norm_bound=norm_clip,
+        >>>                            norm_bound=norm_bound,
         >>>                            initial_noise_multiplier=initial_noise_multiplier)
         >>> net_opt = factory_opt.create('Momentum')(network.trainable_params(),
         >>>                                          learning_rate=0.1, momentum=0.9)
@@ -103,7 +103,7 @@ class DPModel(Model):
         >>>                                            target_unclipped_quantile=0.9,
         >>>                                            fraction_stddev=0.01)
         >>> model = DPModel(micro_batches=micro_batches,
-        >>>                 norm_clip=norm_clip,
+        >>>                 norm_bound=norm_bound,
         >>>                 clip_mech=clip_mech,
         >>>                 noise_mech=None,
         >>>                 network=network,
@@ -116,17 +116,18 @@ class DPModel(Model):
         >>> model.train(epochs, ms_ds, dataset_sink_mode=False)
     """
 
-    def __init__(self, micro_batches=2, norm_clip=1.0, noise_mech=None,
+    def __init__(self, micro_batches=2, norm_bound=1.0, noise_mech=None,
                  clip_mech=None, **kwargs):
         if micro_batches:
             self._micro_batches = check_int_positive('micro_batches',
                                                      micro_batches)
         else:
             self._micro_batches = None
-        norm_clip = check_param_type('norm_clip', norm_clip, float)
-        norm_clip = check_value_positive('norm_clip', norm_clip)
-        norm_clip = Tensor(norm_clip, mstype.float32)
-        self._norm_clip = Parameter(norm_clip, 'norm_clip')
+        norm_bound = check_param_type('norm_bound', norm_bound, float)
+        norm_bound = check_value_positive('norm_bound', norm_bound)
+        norm_bound = Tensor(norm_bound, mstype.float32)
+        self._norm_bound = Parameter(norm_bound, 'norm_bound')
+
         if noise_mech is not None and "DPOptimizer" in kwargs['optimizer'].__class__.__name__:
             msg = 'DPOptimizer is not supported while noise_mech is not None'
             LOGGER.error(TAG, msg)
@@ -219,14 +220,14 @@ class DPModel(Model):
                                                          optimizer,
                                                          scale_update_cell=update_cell,
                                                          micro_batches=self._micro_batches,
-                                                         norm_clip=self._norm_clip,
+                                                         norm_bound=self._norm_bound,
                                                          clip_mech=self._clip_mech,
                                                          noise_mech=self._noise_mech).set_train()
                 return network
 
         network = _TrainOneStepCell(network,
                                     optimizer,
-                                    self._norm_clip,
+                                    self._norm_bound,
                                     loss_scale,
                                     micro_batches=self._micro_batches,
                                     clip_mech=self._clip_mech,
@@ -347,7 +348,7 @@ class _TrainOneStepWithLossScaleCell(Cell):
             Default: None.
         micro_batches (int): The number of small batches split from an original
             batch. Default: None.
-        norm_clip (Tensor): Use to clip the bound, if set 1, will return the
+        norm_bound (Tensor): Use to clip the bound, if set 1, will return the
             original data. Default: 1.0.
         noise_mech (Mechanisms): The object can generate the different type of
             noise. Default: None.
@@ -366,7 +367,7 @@ class _TrainOneStepWithLossScaleCell(Cell):
     """
 
     def __init__(self, network, optimizer, scale_update_cell=None,
-                 micro_batches=None, norm_clip=1.0, noise_mech=None,
+                 micro_batches=None, norm_bound=1.0, noise_mech=None,
                  clip_mech=None):
         super(_TrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False)
         self.network = network
@@ -405,15 +406,13 @@ class _TrainOneStepWithLossScaleCell(Cell):
         self.loss_scale = None
         self.loss_scaling_manager = scale_update_cell
         if scale_update_cell:
-            self.loss_scale = Parameter(
-                Tensor(scale_update_cell.get_loss_scale(),
-                       dtype=mstype.float32),
+            self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
                                         name="loss_scale")
         self.add_flags(has_effect=True)
 
         # dp params
         self._micro_batches = micro_batches
-        self._norm_clip = norm_clip
+        self._norm_bound = norm_bound
         self._split = P.Split(0, self._micro_batches)
         self._clip_by_global_norm = _ClipGradients()
         self._noise_mech = noise_mech
@@ -433,9 +432,9 @@ class _TrainOneStepWithLossScaleCell(Cell):
         self._cast = P.Cast()
 
         self._noise_mech_param_updater = None
-        if self._noise_mech is not None and self._noise_mech._decay_policy is not None:
+        if self._noise_mech is not None and self._noise_mech._noise_update is not None:
             self._noise_mech_param_updater = _MechanismsParamsUpdater(
-                policy=self._noise_mech._decay_policy,
+                noise_update=self._noise_mech._noise_update,
                 decay_rate=self._noise_mech._noise_decay_rate,
                 cur_noise_multiplier=
                 self._noise_mech._noise_multiplier,
@@ -477,10 +476,10 @@ class _TrainOneStepWithLossScaleCell(Cell):
                                    self._reduce_sum(self._square_all(grad)))
         norm_grad = self._sqrt(square_sum)
         beta = self._add(beta,
-                         self._cast(self._less(norm_grad, self._norm_clip),
+                         self._cast(self._less(norm_grad, self._norm_bound),
                                     mstype.float32))
         record_grad = self._clip_by_global_norm(record_grad, GRADIENT_CLIP_TYPE,
-                                                self._norm_clip)
+                                                self._norm_bound)
         grads = record_grad
         total_loss = loss
         for i in range(1, self._micro_batches):
@@ -497,12 +496,12 @@ class _TrainOneStepWithLossScaleCell(Cell):
                                        self._reduce_sum(self._square_all(grad)))
             norm_grad = self._sqrt(square_sum)
             beta = self._add(beta,
-                             self._cast(self._less(norm_grad, self._norm_clip),
+                             self._cast(self._less(norm_grad, self._norm_bound),
                                         mstype.float32))
 
             record_grad = self._clip_by_global_norm(record_grad,
                                                     GRADIENT_CLIP_TYPE,
-                                                    self._norm_clip)
+                                                    self._norm_bound)
             grads = self._tuple_add(grads, record_grad)
             total_loss = P.TensorAdd()(total_loss, loss)
         loss = P.Div()(total_loss, self._micro_float)
@@ -552,8 +551,8 @@ class _TrainOneStepWithLossScaleCell(Cell):
         ret = (loss, cond, scaling_sens)
 
         if self._clip_mech is not None:
-            next_norm_clip = self._clip_mech(beta, self._norm_clip)
-            P.assign(self._norm_clip, next_norm_clip)
+            next_norm_bound = self._clip_mech(beta, self._norm_bound)
+            P.assign(self._norm_bound, next_norm_bound)
         return F.depend(ret, opt)
 
 
@@ -573,7 +572,7 @@ class _TrainOneStepCell(Cell):
             propagation. Default value is 1.0.
         micro_batches (int): The number of small batches split from an original
             batch. Default: None.
-        norm_clip (Tensor): Use to clip the bound, if set 1, will return the
+        norm_bound (Tensor): Use to clip the bound, if set 1, will return the
             original data. Default: 1.0.
         noise_mech (Mechanisms): The object can generate the different type
             of noise. Default: None.
@@ -586,7 +585,7 @@ class _TrainOneStepCell(Cell):
         Tensor, a scalar Tensor with shape :math:`()`.
     """
 
-    def __init__(self, network, optimizer, norm_clip=1.0, sens=1.0,
+    def __init__(self, network, optimizer, norm_bound=1.0, sens=1.0,
                  micro_batches=None,
                  noise_mech=None, clip_mech=None):
         super(_TrainOneStepCell, self).__init__(auto_prefix=False)
@@ -616,7 +615,7 @@ class _TrainOneStepCell(Cell):
             LOGGER.error(TAG, msg)
             raise ValueError(msg)
         self._micro_batches = micro_batches
-        self._norm_clip = norm_clip
+        self._norm_bound = norm_bound
         self._split = P.Split(0, self._micro_batches)
         self._clip_by_global_norm = _ClipGradients()
         self._noise_mech = noise_mech
@@ -637,9 +636,9 @@ class _TrainOneStepCell(Cell):
         self._micro_float = Tensor(micro_batches, mstype.float32)
 
         self._noise_mech_param_updater = None
-        if self._noise_mech is not None and self._noise_mech._decay_policy is not None:
+        if self._noise_mech is not None and self._noise_mech._noise_update is not None:
             self._noise_mech_param_updater = _MechanismsParamsUpdater(
-                policy=self._noise_mech._decay_policy,
+                noise_update=self._noise_mech._noise_update,
                 decay_rate=self._noise_mech._noise_decay_rate,
                 cur_noise_multiplier=
                 self._noise_mech._noise_multiplier,
@@ -664,11 +663,11 @@ class _TrainOneStepCell(Cell):
                                    self._reduce_sum(self._square_all(grad)))
         norm_grad = self._sqrt(square_sum)
         beta = self._add(beta,
-                         self._cast(self._less(norm_grad, self._norm_clip),
+                         self._cast(self._less(norm_grad, self._norm_bound),
                                     mstype.float32))
 
         record_grad = self._clip_by_global_norm(record_grad, GRADIENT_CLIP_TYPE,
-                                                self._norm_clip)
+                                                self._norm_bound)
         grads = record_grad
         total_loss = loss
         for i in range(1, self._micro_batches):
@@ -683,12 +682,12 @@ class _TrainOneStepCell(Cell):
                                        self._reduce_sum(self._square_all(grad)))
             norm_grad = self._sqrt(square_sum)
             beta = self._add(beta,
-                             self._cast(self._less(norm_grad, self._norm_clip),
+                             self._cast(self._less(norm_grad, self._norm_bound),
                                         mstype.float32))
 
             record_grad = self._clip_by_global_norm(record_grad,
                                                     GRADIENT_CLIP_TYPE,
-                                                    self._norm_clip)
+                                                    self._norm_bound)
             grads = self._tuple_add(grads, record_grad)
             total_loss = P.TensorAdd()(total_loss, loss)
         loss = self._div(total_loss, self._micro_float)
@@ -712,8 +711,8 @@ class _TrainOneStepCell(Cell):
             grads = self.grad_reducer(grads)
 
         if self._clip_mech is not None:
-            next_norm_clip = self._clip_mech(beta, self._norm_clip)
-            self._norm_clip = self._assign(self._norm_clip, next_norm_clip)
-            loss = F.depend(loss, next_norm_clip)
+            next_norm_bound = self._clip_mech(beta, self._norm_bound)
+            self._norm_bound = self._assign(self._norm_bound, next_norm_bound)
+            loss = F.depend(loss, next_norm_bound)
 
         return F.depend(loss, self.optimizer(grads))

mindarmour/fuzzing/model_coverage_metrics.py

@@ -63,14 +63,14 @@ class ModelCoverageMetrics:
         self._model = check_model('model', model, Model)
         self._segmented_num = check_int_positive('segmented_num', segmented_num)
         self._neuron_num = check_int_positive('neuron_num', neuron_num)
-        if self._neuron_num > 1e+10:
+        if self._neuron_num >= 1e+10:
             msg = 'neuron_num should be less than 1e+10, otherwise a MemoryError' \
                   'would occur'
             LOGGER.error(TAG, msg)
         train_dataset = check_numpy_param('train_dataset', train_dataset)
-        self._lower_bounds = [np.inf]*neuron_num
-        self._upper_bounds = [-np.inf]*neuron_num
-        self._var = [0]*neuron_num
+        self._lower_bounds = [np.inf]*self._neuron_num
+        self._upper_bounds = [-np.inf]*self._neuron_num
+        self._var = [0]*self._neuron_num
         self._main_section_hits = [[0 for _ in range(self._segmented_num)] for _ in
                                    range(self._neuron_num)]
         self._lower_corner_hits = [0]*self._neuron_num

requirements.txt

 numpy >= 1.17.0
 scipy >= 1.3.3
-matplotlib >= 3.1.3
+matplotlib >= 3.2.1
 Pillow >= 2.0.0
 pytest >= 4.3.1
 wheel >= 0.32.0

setup.py

@@ -104,7 +104,7 @@ setup(
     install_requires=[
         'scipy >= 1.3.3',
         'numpy >= 1.17.0',
-        'matplotlib >= 3.1.3',
+        'matplotlib >= 3.2.1',
         'Pillow >= 2.0.0'
     ],
     classifiers=[

tests/ut/python/diff_privacy/test_mechanisms.py

@@ -19,8 +19,7 @@ import pytest
 from mindspore import context
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindarmour.diff_privacy import NoiseGaussianRandom
-from mindarmour.diff_privacy import AdaGaussianRandom
+from mindarmour.diff_privacy import NoiseAdaGaussianRandom
 from mindarmour.diff_privacy import AdaClippingWithGaussianRandom
 from mindarmour.diff_privacy import NoiseMechanismsFactory
 from mindarmour.diff_privacy import ClipMechanismsFactory
@@ -30,71 +29,77 @@ from mindarmour.diff_privacy import ClipMechanismsFactory
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
 @pytest.mark.component_mindarmour
-def test_graph_gaussian():
+def test_graph_factory():
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     grad = Tensor([0.3, 0.2, 0.4], mstype.float32)
     norm_bound = 1.0
     initial_noise_multiplier = 0.1
-    net = NoiseGaussianRandom(norm_bound, initial_noise_multiplier)
-    res = net(grad)
-    print(res)
+    alpha = 0.5
+    noise_update = 'Step'
+    factory = NoiseMechanismsFactory()
+    noise_mech = factory.create('Gaussian',
+                                norm_bound,
+                                initial_noise_multiplier)
+    noise = noise_mech(grad)
+    print('Gaussian noise: ', noise)
+    ada_noise_mech = factory.create('AdaGaussian',
+                                    norm_bound,
+                                    initial_noise_multiplier,
+                                    noise_decay_rate=alpha,
+                                    noise_update=noise_update)
+    ada_noise = ada_noise_mech(grad)
+    print('ada noise: ', ada_noise)
 
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
 @pytest.mark.component_mindarmour
-def test_pynative_gaussian():
+def test_pynative_factory():
     context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
     grad = Tensor([0.3, 0.2, 0.4], mstype.float32)
     norm_bound = 1.0
     initial_noise_multiplier = 0.1
-    net = NoiseGaussianRandom(norm_bound, initial_noise_multiplier)
-    res = net(grad)
-    print(res)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
-@pytest.mark.component_mindarmour
-def test_graph_ada_gaussian():
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    grad = Tensor([0.3, 0.2, 0.4], mstype.float32)
-    norm_bound = 1.0
-    initial_noise_multiplier = 0.1
     alpha = 0.5
-    decay_policy = 'Step'
-    net = AdaGaussianRandom(norm_bound, initial_noise_multiplier,
-                            noise_decay_rate=alpha, decay_policy=decay_policy)
-    res = net(grad)
-    print(res)
+    noise_update = 'Step'
+    factory = NoiseMechanismsFactory()
+    noise_mech = factory.create('Gaussian',
+                                norm_bound,
+                                initial_noise_multiplier)
+    noise = noise_mech(grad)
+    print('Gaussian noise: ', noise)
+    ada_noise_mech = factory.create('AdaGaussian',
+                                    norm_bound,
+                                    initial_noise_multiplier,
+                                    noise_decay_rate=alpha,
+                                    noise_update=noise_update)
+    ada_noise = ada_noise_mech(grad)
+    print('ada noise: ', ada_noise)
 
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
 @pytest.mark.component_mindarmour
-def test_graph_factory():
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+def test_pynative_gaussian():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
     grad = Tensor([0.3, 0.2, 0.4], mstype.float32)
     norm_bound = 1.0
     initial_noise_multiplier = 0.1
     alpha = 0.5
-    decay_policy = 'Step'
-    noise_mechanism = NoiseMechanismsFactory()
-    noise_construct = noise_mechanism.create('Gaussian',
+    noise_update = 'Step'
+    factory = NoiseMechanismsFactory()
+    noise_mech = factory.create('Gaussian',
                                 norm_bound,
                                 initial_noise_multiplier)
-    noise = noise_construct(grad)
+    noise = noise_mech(grad)
     print('Gaussian noise: ', noise)
-    ada_mechanism = NoiseMechanismsFactory()
-    ada_noise_construct = ada_mechanism.create('AdaGaussian',
+    ada_noise_mech = factory.create('AdaGaussian',
                                     norm_bound,
                                     initial_noise_multiplier,
                                     noise_decay_rate=alpha,
-                                               decay_policy=decay_policy)
-    ada_noise = ada_noise_construct(grad)
+                                    noise_update=noise_update)
+    ada_noise = ada_noise_mech(grad)
     print('ada noise: ', ada_noise)
 
 
@@ -102,16 +107,19 @@ def test_graph_factory():
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
 @pytest.mark.component_mindarmour
-def test_pynative_ada_gaussian():
-    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+def test_graph_ada_gaussian():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     grad = Tensor([0.3, 0.2, 0.4], mstype.float32)
     norm_bound = 1.0
     initial_noise_multiplier = 0.1
-    alpha = 0.5
-    decay_policy = 'Step'
-    net = AdaGaussianRandom(norm_bound, initial_noise_multiplier,
-                            noise_decay_rate=alpha, decay_policy=decay_policy)
-    res = net(grad)
+    noise_decay_rate = 0.5
+    noise_update = 'Step'
+    ada_noise_mech = NoiseAdaGaussianRandom(norm_bound,
+                                            initial_noise_multiplier,
+                                            seed=0,
+                                            noise_decay_rate=noise_decay_rate,
+                                            noise_update=noise_update)
+    res = ada_noise_mech(grad)
     print(res)
 
 
@@ -119,47 +127,40 @@ def test_pynative_ada_gaussian():
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
 @pytest.mark.component_mindarmour
-def test_pynative_factory():
+def test_pynative_ada_gaussian():
     context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
     grad = Tensor([0.3, 0.2, 0.4], mstype.float32)
     norm_bound = 1.0
     initial_noise_multiplier = 0.1
-    alpha = 0.5
-    decay_policy = 'Step'
-    noise_mechanism = NoiseMechanismsFactory()
-    noise_construct = noise_mechanism.create('Gaussian',
-                                             norm_bound,
-                                             initial_noise_multiplier)
-    noise = noise_construct(grad)
-    print('Gaussian noise: ', noise)
-    ada_mechanism = NoiseMechanismsFactory()
-    ada_noise_construct = ada_mechanism.create('AdaGaussian',
-                                               norm_bound,
+    noise_decay_rate = 0.5
+    noise_update = 'Step'
+    ada_noise_mech = NoiseAdaGaussianRandom(norm_bound,
                                             initial_noise_multiplier,
-                                               noise_decay_rate=alpha,
-                                               decay_policy=decay_policy)
-    ada_noise = ada_noise_construct(grad)
-    print('ada noise: ', ada_noise)
+                                            seed=0,
+                                            noise_decay_rate=noise_decay_rate,
+                                            noise_update=noise_update)
+    res = ada_noise_mech(grad)
+    print(res)
 
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
 @pytest.mark.component_mindarmour
-def test_pynative_exponential():
-    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+def test_graph_exponential():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     grad = Tensor([0.3, 0.2, 0.4], mstype.float32)
     norm_bound = 1.0
     initial_noise_multiplier = 0.1
     alpha = 0.5
-    decay_policy = 'Exp'
-    ada_mechanism = NoiseMechanismsFactory()
-    ada_noise_construct = ada_mechanism.create('AdaGaussian',
+    noise_update = 'Exp'
+    factory = NoiseMechanismsFactory()
+    ada_noise = factory.create('AdaGaussian',
                                norm_bound,
                                initial_noise_multiplier,
                                noise_decay_rate=alpha,
-                                               decay_policy=decay_policy)
-    ada_noise = ada_noise_construct(grad)
+                               noise_update=noise_update)
+    ada_noise = ada_noise(grad)
     print('ada noise: ', ada_noise)
 
 
@@ -167,20 +168,20 @@ def test_pynative_exponential():
 @pytest.mark.platform_x86_ascend_training
 @pytest.mark.env_onecard
 @pytest.mark.component_mindarmour
-def test_graph_exponential():
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+def test_pynative_exponential():
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
     grad = Tensor([0.3, 0.2, 0.4], mstype.float32)
     norm_bound = 1.0
     initial_noise_multiplier = 0.1
     alpha = 0.5
-    decay_policy = 'Exp'
-    ada_mechanism = NoiseMechanismsFactory()
-    ada_noise_construct = ada_mechanism.create('AdaGaussian',
+    noise_update = 'Exp'
+    factory = NoiseMechanismsFactory()
+    ada_noise = factory.create('AdaGaussian',
                                norm_bound,
                                initial_noise_multiplier,
                                noise_decay_rate=alpha,
-                                               decay_policy=decay_policy)
-    ada_noise = ada_noise_construct(grad)
+                               noise_update=noise_update)
+    ada_noise = ada_noise(grad)
     print('ada noise: ', ada_noise)
 
 
@@ -192,7 +193,7 @@ def test_ada_clip_gaussian_random_pynative():
     context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
     decay_policy = 'Linear'
     beta = Tensor(0.5, mstype.float32)
-    norm_clip = Tensor(1.0, mstype.float32)
+    norm_bound = Tensor(1.0, mstype.float32)
     beta_stddev = 0.1
     learning_rate = 0.1
     target_unclipped_quantile = 0.3
@@ -201,8 +202,8 @@ def test_ada_clip_gaussian_random_pynative():
                                              target_unclipped_quantile=target_unclipped_quantile,
                                              fraction_stddev=beta_stddev,
                                              seed=1)
-    next_norm_clip = ada_clip(beta, norm_clip)
-    print('Liner next norm clip:', next_norm_clip)
+    next_norm_bound = ada_clip(beta, norm_bound)
+    print('Liner next norm clip:', next_norm_bound)
 
     decay_policy = 'Geometric'
     ada_clip = AdaClippingWithGaussianRandom(decay_policy=decay_policy,
@@ -210,8 +211,8 @@ def test_ada_clip_gaussian_random_pynative():
                                              target_unclipped_quantile=target_unclipped_quantile,
                                              fraction_stddev=beta_stddev,
                                              seed=1)
-    next_norm_clip = ada_clip(beta, norm_clip)
-    print('Geometric next norm clip:', next_norm_clip)
+    next_norm_bound = ada_clip(beta, norm_bound)
+    print('Geometric next norm clip:', next_norm_bound)
 
 
 @pytest.mark.level0
@@ -222,7 +223,7 @@ def test_ada_clip_gaussian_random_graph():
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     decay_policy = 'Linear'
     beta = Tensor(0.5, mstype.float32)
-    norm_clip = Tensor(1.0, mstype.float32)
+    norm_bound = Tensor(1.0, mstype.float32)
     beta_stddev = 0.1
     learning_rate = 0.1
     target_unclipped_quantile = 0.3
@@ -231,8 +232,8 @@ def test_ada_clip_gaussian_random_graph():
                                              target_unclipped_quantile=target_unclipped_quantile,
                                              fraction_stddev=beta_stddev,
                                              seed=1)
-    next_norm_clip = ada_clip(beta, norm_clip)
-    print('Liner next norm clip:', next_norm_clip)
+    next_norm_bound = ada_clip(beta, norm_bound)
+    print('Liner next norm clip:', next_norm_bound)
 
     decay_policy = 'Geometric'
     ada_clip = AdaClippingWithGaussianRandom(decay_policy=decay_policy,
@@ -240,8 +241,8 @@ def test_ada_clip_gaussian_random_graph():
                                              target_unclipped_quantile=target_unclipped_quantile,
                                              fraction_stddev=beta_stddev,
                                              seed=1)
-    next_norm_clip = ada_clip(beta, norm_clip)
-    print('Geometric next norm clip:', next_norm_clip)
+    next_norm_bound = ada_clip(beta, norm_bound)
+    print('Geometric next norm clip:', next_norm_bound)
 
 
 @pytest.mark.level0
@@ -252,18 +253,18 @@ def test_pynative_clip_mech_factory():
     context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
     decay_policy = 'Linear'
     beta = Tensor(0.5, mstype.float32)
-    norm_clip = Tensor(1.0, mstype.float32)
+    norm_bound = Tensor(1.0, mstype.float32)
     beta_stddev = 0.1
     learning_rate = 0.1
     target_unclipped_quantile = 0.3
-    clip_mechanism = ClipMechanismsFactory()
-    ada_clip = clip_mechanism.create('Gaussian',
+    factory = ClipMechanismsFactory()
+    ada_clip = factory.create('Gaussian',
                               decay_policy=decay_policy,
                               learning_rate=learning_rate,
                               target_unclipped_quantile=target_unclipped_quantile,
                               fraction_stddev=beta_stddev)
-    next_norm_clip = ada_clip(beta, norm_clip)
-    print('next_norm_clip: ', next_norm_clip)
+    next_norm_bound = ada_clip(beta, norm_bound)
+    print('next_norm_bound: ', next_norm_bound)
 
 
 @pytest.mark.level0
@@ -274,15 +275,15 @@ def test_graph_clip_mech_factory():
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     decay_policy = 'Linear'
     beta = Tensor(0.5, mstype.float32)
-    norm_clip = Tensor(1.0, mstype.float32)
+    norm_bound = Tensor(1.0, mstype.float32)
     beta_stddev = 0.1
     learning_rate = 0.1
     target_unclipped_quantile = 0.3
-    clip_mechanism = ClipMechanismsFactory()
-    ada_clip = clip_mechanism.create('Gaussian',
+    factory = ClipMechanismsFactory()
+    ada_clip = factory.create('Gaussian',
                               decay_policy=decay_policy,
                               learning_rate=learning_rate,
                               target_unclipped_quantile=target_unclipped_quantile,
                               fraction_stddev=beta_stddev)
-    next_norm_clip = ada_clip(beta, norm_clip)
-    print('next_norm_clip: ', next_norm_clip)
+    next_norm_bound = ada_clip(beta, norm_bound)
+    print('next_norm_bound: ', next_norm_bound)

tests/ut/python/diff_privacy/test_model_train.py

@@ -46,7 +46,7 @@ def dataset_generator(batch_size, batches):
 @pytest.mark.component_mindarmour
 def test_dp_model_with_pynative_mode():
     context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
-    norm_clip = 1.0
+    norm_bound = 1.0
     initial_noise_multiplier = 0.01
     network = LeNet5()
     batch_size = 32
@@ -56,7 +56,7 @@ def test_dp_model_with_pynative_mode():
     loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
     factory_opt = DPOptimizerClassFactory(micro_batches=micro_batches)
     factory_opt.set_mechanisms('Gaussian',
-                               norm_bound=norm_clip,
+                               norm_bound=norm_bound,
                                initial_noise_multiplier=initial_noise_multiplier)
     net_opt = factory_opt.create('Momentum')(network.trainable_params(),
                                              learning_rate=0.1, momentum=0.9)
@@ -66,7 +66,7 @@ def test_dp_model_with_pynative_mode():
                                                target_unclipped_quantile=0.9,
                                                fraction_stddev=0.01)
     model = DPModel(micro_batches=micro_batches,
-                    norm_clip=norm_clip,
+                    norm_bound=norm_bound,
                     clip_mech=clip_mech,
                     noise_mech=None,
                     network=network,
@@ -86,7 +86,7 @@ def test_dp_model_with_pynative_mode():
 @pytest.mark.component_mindarmour
 def test_dp_model_with_graph_mode():
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    norm_clip = 1.0
+    norm_bound = 1.0
     initial_noise_multiplier = 0.01
     network = LeNet5()
     batch_size = 32
@@ -94,7 +94,7 @@ def test_dp_model_with_graph_mode():
     epochs = 1
     loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
     noise_mech = NoiseMechanismsFactory().create('Gaussian',
-                                                 norm_bound=norm_clip,
+                                                 norm_bound=norm_bound,
                                                  initial_noise_multiplier=initial_noise_multiplier)
     clip_mech = ClipMechanismsFactory().create('Gaussian',
                                                decay_policy='Linear',
@@ -105,7 +105,7 @@ def test_dp_model_with_graph_mode():
                           momentum=0.9)
     model = DPModel(micro_batches=2,
                     clip_mech=clip_mech,
-                    norm_clip=norm_clip,
+                    norm_bound=norm_bound,
                     noise_mech=noise_mech,
                     network=network,
                     loss_fn=loss,
@@ -124,22 +124,25 @@ def test_dp_model_with_graph_mode():
 @pytest.mark.component_mindarmour
 def test_dp_model_with_graph_mode_ada_gaussian():
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    norm_clip = 1.0
+    norm_bound = 1.0
     initial_noise_multiplier = 0.01
     network = LeNet5()
     batch_size = 32
     batches = 128
     epochs = 1
+    alpha = 0.8
     loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
     noise_mech = NoiseMechanismsFactory().create('AdaGaussian',
-                                                 norm_bound=norm_clip,
-                                                 initial_noise_multiplier=initial_noise_multiplier)
+                                                 norm_bound=norm_bound,
+                                                 initial_noise_multiplier=initial_noise_multiplier,
+                                                 noise_decay_rate=alpha,
+                                                 noise_update='Exp')
     clip_mech = None
     net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.1,
                           momentum=0.9)
     model = DPModel(micro_batches=2,
                     clip_mech=clip_mech,
-                    norm_clip=norm_clip,
+                    norm_bound=norm_bound,
                     noise_mech=noise_mech,
                     network=network,
                     loss_fn=loss,

tests/ut/python/diff_privacy/test_optimizer.py

@@ -34,9 +34,9 @@ def test_optimizer():
     momentum = 0.9
     micro_batches = 2
     loss = nn.SoftmaxCrossEntropyWithLogits()
-    gaussian_mech = DPOptimizerClassFactory(micro_batches)
-    gaussian_mech.set_mechanisms('Gaussian', norm_bound=1.5, initial_noise_multiplier=5.0)
-    net_opt = gaussian_mech.create('SGD')(params=network.trainable_params(), learning_rate=lr,
+    factory = DPOptimizerClassFactory(micro_batches)
+    factory.set_mechanisms('Gaussian', norm_bound=1.5, initial_noise_multiplier=5.0)
+    net_opt = factory.create('SGD')(params=network.trainable_params(), learning_rate=lr,
                                     momentum=momentum)
     _ = Model(network, loss_fn=loss, optimizer=net_opt, metrics=None)
 
@@ -52,9 +52,9 @@ def test_optimizer_gpu():
     momentum = 0.9
     micro_batches = 2
     loss = nn.SoftmaxCrossEntropyWithLogits()
-    gaussian_mech = DPOptimizerClassFactory(micro_batches)
-    gaussian_mech.set_mechanisms('Gaussian', norm_bound=1.5, initial_noise_multiplier=5.0)
-    net_opt = gaussian_mech.create('SGD')(params=network.trainable_params(), learning_rate=lr,
+    factory = DPOptimizerClassFactory(micro_batches)
+    factory.set_mechanisms('Gaussian', norm_bound=1.5, initial_noise_multiplier=5.0)
+    net_opt = factory.create('SGD')(params=network.trainable_params(), learning_rate=lr,
                                     momentum=momentum)
     _ = Model(network, loss_fn=loss, optimizer=net_opt, metrics=None)
 
@@ -70,8 +70,8 @@ def test_optimizer_cpu():
     momentum = 0.9
     micro_batches = 2
     loss = nn.SoftmaxCrossEntropyWithLogits()
-    gaussian_mech = DPOptimizerClassFactory(micro_batches)
-    gaussian_mech.set_mechanisms('Gaussian', norm_bound=1.5, initial_noise_multiplier=5.0)
-    net_opt = gaussian_mech.create('SGD')(params=network.trainable_params(), learning_rate=lr,
+    factory = DPOptimizerClassFactory(micro_batches)
+    factory.set_mechanisms('Gaussian', norm_bound=1.5, initial_noise_multiplier=5.0)
+    net_opt = factory.create('SGD')(params=network.trainable_params(), learning_rate=lr,
                                     momentum=momentum)
     _ = Model(network, loss_fn=loss, optimizer=net_opt, metrics=None)