!33 Add value-range check for parameter 'noise_decay_rate' of AdaGaussianRandom Class.

Merge pull request !33 from jxlang910/master

example/mnist_demo/lenet5_config.py

@@ -19,14 +19,22 @@ network config setting, will be used in train.py
 from easydict import EasyDict as edict
 
 mnist_cfg = edict({
-    'num_classes': 10,
-    'lr': 0.01,
-    'momentum': 0.9,
-    'epoch_size': 10,
-    'batch_size': 256,
-    'buffer_size': 1000,
-    'image_height': 32,
-    'image_width': 32,
-    'save_checkpoint_steps': 234,
-    'keep_checkpoint_max': 10,
+    'num_classes': 10,  # the number of classes of model's output
+    'lr': 0.01,  # the learning rate of model's optimizer
+    'momentum': 0.9,  # the momentum value of model's optimizer
+    'epoch_size': 10,  # training epochs
+    'batch_size': 256,  # batch size for training
+    'image_height': 32,  # the height of training samples
+    'image_width': 32,  # the width of training samples
+    'save_checkpoint_steps': 234,  # the interval steps for saving checkpoint file of the model
+    'keep_checkpoint_max': 10,  # the maximum number of checkpoint files would be saved
+    'device_target': 'Ascend',  # device used
+    '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': 32,  # the number of small batches split from an original batch
+    'l2_norm_bound': 1.0,  # the clip bound of the gradients of model's training parameters
+    'initial_noise_multiplier': 1.5,  # the initial multiplication coefficient of the noise added to training
+    # parameters' gradients
+    'mechanisms': 'AdaGaussian',  # the method of adding noise in gradients while training
+    'optimizer': 'Momentum'  # the base optimizer used for Differential privacy training
 })

example/mnist_demo/lenet5_dp_model_train.py

@@ -15,7 +15,6 @@
 python lenet5_dp_model_train.py --data_path /YourDataPath --micro_batches=2
 """
 import os
-import argparse
 
 import mindspore.nn as nn
 from mindspore import context
@@ -87,21 +86,7 @@ def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1,
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
-    parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
-                        help='device where the code will be implemented (default: Ascend)')
-    parser.add_argument('--data_path', type=str, default="./MNIST_unzip",
-                        help='path where the dataset is saved')
-    parser.add_argument('--dataset_sink_mode', type=bool, default=False, help='dataset_sink_mode is False or True')
-    parser.add_argument('--micro_batches', type=int, default=32,
-                        help='optional, if use differential privacy, need to set micro_batches')
-    parser.add_argument('--l2_norm_bound', type=float, default=1.0,
-                        help='optional, if use differential privacy, need to set l2_norm_bound')
-    parser.add_argument('--initial_noise_multiplier', type=float, default=1.5,
-                        help='optional, if use differential privacy, need to set initial_noise_multiplier')
-    args = parser.parse_args()
-
-    context.set_context(mode=context.PYNATIVE_MODE, device_target=args.device_target)
+    context.set_context(mode=context.PYNATIVE_MODE, device_target=cfg.device_target)
 
     network = LeNet5()
     net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
@@ -111,27 +96,41 @@ if __name__ == "__main__":
                                  directory='./trained_ckpt_file/',
                                  config=config_ck)
 
-    ds_train = generate_mnist_dataset(os.path.join(args.data_path, "train"),
+    # get training dataset
+    ds_train = generate_mnist_dataset(os.path.join(cfg.data_path, "train"),
                                       cfg.batch_size,
                                       cfg.epoch_size)
 
-    if args.micro_batches and cfg.batch_size % args.micro_batches != 0:
+    if cfg.micro_batches and cfg.batch_size % cfg.micro_batches != 0:
         raise ValueError("Number of micro_batches should divide evenly batch_size")
-    gaussian_mech = DPOptimizerClassFactory(args.micro_batches)
-    gaussian_mech.set_mechanisms('Gaussian',
-                                 norm_bound=args.l2_norm_bound,
-                                 initial_noise_multiplier=args.initial_noise_multiplier)
-    net_opt = gaussian_mech.create('Momentum')(params=network.trainable_params(),
+    # Create a factory class of DP optimizer
+    gaussian_mech = DPOptimizerClassFactory(cfg.micro_batches)
+
+    # Set the method of adding noise in gradients while training. Initial_noise_multiplier is suggested to be greater
+    # than 1.0, otherwise the privacy budget would be huge, which 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.
+    gaussian_mech.set_mechanisms(cfg.mechanisms,
+                                 norm_bound=cfg.l2_norm_bound,
+                                 initial_noise_multiplier=cfg.initial_noise_multiplier)
+
+    # Wrap the base optimizer for DP training. Momentum optimizer is suggested for LenNet5.
+    net_opt = gaussian_mech.create(cfg.optimizer)(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=args.initial_noise_multiplier*
-                                               args.l2_norm_bound,
-                                               per_print_times=10)
-    model = DPModel(micro_batches=args.micro_batches,
-                    norm_clip=args.l2_norm_bound,
+                                               initial_noise_multiplier=cfg.initial_noise_multiplier*
+                                               cfg.l2_norm_bound,
+                                               per_print_times=50)
+
+    # Create the DP model for training.
+    model = DPModel(micro_batches=cfg.micro_batches,
+                    norm_clip=cfg.l2_norm_bound,
                     dp_mech=gaussian_mech.mech,
                     network=network,
                     loss_fn=net_loss,
@@ -140,12 +139,12 @@ if __name__ == "__main__":
 
     LOGGER.info(TAG, "============== Starting Training ==============")
     model.train(cfg['epoch_size'], ds_train, callbacks=[ckpoint_cb, LossMonitor(), rdp_monitor],
-                dataset_sink_mode=args.dataset_sink_mode)
+                dataset_sink_mode=cfg.dataset_sink_mode)
 
     LOGGER.info(TAG, "============== Starting Testing ==============")
     ckpt_file_name = 'trained_ckpt_file/checkpoint_lenet-10_234.ckpt'
     param_dict = load_checkpoint(ckpt_file_name)
     load_param_into_net(network, param_dict)
-    ds_eval = generate_mnist_dataset(os.path.join(args.data_path, 'test'), batch_size=cfg.batch_size)
+    ds_eval = generate_mnist_dataset(os.path.join(cfg.data_path, 'test'), batch_size=cfg.batch_size)
     acc = model.eval(ds_eval, dataset_sink_mode=False)
     LOGGER.info(TAG, "============== Accuracy: %s  ==============", acc)

mindarmour/diff_privacy/mechanisms/mechanisms.py

@@ -24,6 +24,7 @@ from mindspore.common import dtype as mstype
 
 from mindarmour.utils._check_param import check_param_type
 from mindarmour.utils._check_param import check_value_positive
+from mindarmour.utils._check_param import check_param_in_range
 
 
 class MechanismsFactory:
@@ -37,7 +38,8 @@ class MechanismsFactory:
         """
         Args:
             policy(str): Noise generated strategy, could be 'Gaussian' or
-                'AdaGaussian'. Default: 'AdaGaussian'.
+                'AdaGaussian'. Noise would be decayed with 'AdaGaussian' mechanism while
+                be constant with 'Gaussian' mechanism. Default: 'AdaGaussian'.
             args(Union[float, str]): Parameters used for creating noise
                 mechanisms.
             kwargs(Union[float, str]): Parameters used for creating noise
@@ -115,7 +117,8 @@ class GaussianRandom(Mechanisms):
 
 class AdaGaussianRandom(Mechanisms):
     """
-    Adaptive Gaussian noise generated mechanism.
+    Adaptive Gaussian noise generated mechanism. Noise would be decayed with training. Decay mode could be 'Time'
+    mode or 'Step' mode.
 
     Args:
         norm_bound(float): Clipping bound for the l2 norm of the gradients.
@@ -123,7 +126,7 @@ class AdaGaussianRandom(Mechanisms):
         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: 5.0.
-        alpha(float): Hyperparameter for controlling the noise decay.
+        noise_decay_rate(float): Hyperparameter for controlling the noise decay.
             Default: 6e-4.
         decay_policy(str): Noise decay strategy include 'Step' and 'Time'.
             Default: 'Time'.
@@ -135,16 +138,16 @@ class AdaGaussianRandom(Mechanisms):
         >>> shape = (3, 2, 4)
         >>> norm_bound = 1.0
         >>> initial_noise_multiplier = 0.1
-        >>> alpha = 0.5
+        >>> noise_decay_rate = 0.5
         >>> decay_policy = "Time"
         >>> net = AdaGaussianRandom(norm_bound, initial_noise_multiplier,
-        >>>                         alpha, decay_policy)
+        >>>                         noise_decay_rate, decay_policy)
         >>> res = net(shape)
         >>> print(res)
     """
 
     def __init__(self, norm_bound=1.5, initial_noise_multiplier=5.0,
-                 alpha=6e-4, decay_policy='Time'):
+                 noise_decay_rate=6e-4, decay_policy='Time'):
         super(AdaGaussianRandom, self).__init__()
         initial_noise_multiplier = check_value_positive('initial_noise_multiplier',
                                                         initial_noise_multiplier)
@@ -156,8 +159,9 @@ class AdaGaussianRandom(Mechanisms):
         norm_bound = check_value_positive('norm_bound', norm_bound)
         self._norm_bound = Tensor(np.array(norm_bound, np.float32))
 
-        alpha = check_param_type('alpha', alpha, float)
-        self._alpha = Tensor(np.array(alpha, np.float32))
+        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(np.array(noise_decay_rate, np.float32))
 
         if decay_policy not in ['Time', 'Step']:
             raise NameError("The decay_policy must be in ['Time', 'Step'], but "
@@ -176,12 +180,12 @@ class AdaGaussianRandom(Mechanisms):
         if self._decay_policy == 'Time':
             temp = self._div(self._initial_noise_multiplier,
                              self._noise_multiplier)
-            temp = self._add(temp, self._alpha)
+            temp = self._add(temp, self._noise_decay_rate)
             temp = self._div(self._initial_noise_multiplier, temp)
             self._noise_multiplier = Parameter(temp, name='noise_multiplier')
         else:
             one = Tensor(1, self._dtype)
-            temp = self._sub(one, self._alpha)
+            temp = self._sub(one, self._noise_decay_rate)
             temp = self._mul(temp, self._noise_multiplier)
             self._noise_multiplier = Parameter(temp, name='noise_multiplier')
 

mindarmour/diff_privacy/monitor/monitor.py

@@ -20,7 +20,7 @@ from mindspore.train.callback import Callback
 
 from mindarmour.utils.logger import LogUtil
 from mindarmour.utils._check_param import check_int_positive, \
-    check_value_positive
+    check_value_positive, check_param_in_range, check_param_type
 
 LOGGER = LogUtil.get_instance()
 TAG = 'DP monitor'
@@ -40,7 +40,8 @@ class PrivacyMonitorFactory:
         Create a privacy monitor class.
 
         Args:
-            policy (str): Monitor policy, 'rdp' is supported by now.
+            policy (str): Monitor policy, 'rdp' is supported by now. RDP means R'enyi differential privacy,
+                which computed based on R'enyi divergence.
             args (Union[int, float, numpy.ndarray, list, str]): Parameters
                 used for creating a privacy monitor.
             kwargs (Union[int, float, numpy.ndarray, list, str]): Keyword
@@ -70,7 +71,7 @@ class RDPMonitor(Callback):
         num_samples (int): The total number of samples in training data sets.
         batch_size (int): The number of samples in a batch while training.
         initial_noise_multiplier (Union[float, int]): The initial
-            multiplier of added noise. Default: 1.5.
+            multiplier of the noise added to training parameters' gradients. Default: 1.5.
         max_eps (Union[float, int, None]): The maximum acceptable epsilon
             budget for DP training. Default: 10.0.
         target_delta (Union[float, int, None]): Target delta budget for DP
@@ -137,11 +138,8 @@ class RDPMonitor(Callback):
             LOGGER.error(TAG, msg)
             raise ValueError(msg)
         if noise_decay_rate is not None:
-            check_value_positive('noise_decay_rate', noise_decay_rate)
-            if noise_decay_rate >= 1:
-                msg = 'Noise decay rate must be less than 1'
-                LOGGER.error(TAG, msg)
-                raise ValueError(msg)
+            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)
         check_int_positive('per_print_times', per_print_times)
 
         self._total_echo_privacy = None

mindarmour/diff_privacy/optimizer/optimizer.py

@@ -27,7 +27,7 @@ class DPOptimizerClassFactory:
     Factory class of Optimizer.
 
     Args:
-        micro_batches (int): The number of small batches split from an origianl batch. Default: 2.
+        micro_batches (int): The number of small batches split from an original batch. Default: 2.
 
     Returns:
         Optimizer, Optimizer class

mindarmour/diff_privacy/train/model.py

@@ -70,7 +70,7 @@ class DPModel(Model):
     This class is overload mindspore.train.model.Model.
 
     Args:
-        micro_batches (int): The number of small batches split from an origianl batch. Default: 2.
+        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 original data. Default: 1.0.
         dp_mech (Mechanisms): The object can generate the different type of noise. Default: None.
 

tests/ut/python/diff_privacy/test_mechanisms.py

@@ -45,10 +45,10 @@ def test_ada_gaussian():
     shape = (3, 2, 4)
     norm_bound = 1.0
     initial_noise_multiplier = 0.1
-    alpha = 0.5
+    noise_decay_rate = 0.5
     decay_policy = "Step"
     net = AdaGaussianRandom(norm_bound, initial_noise_multiplier,
-                            alpha, decay_policy)
+                            noise_decay_rate, decay_policy)
     res = net(shape)
     print(res)
 
@@ -58,7 +58,7 @@ def test_factory():
     shape = (3, 2, 4)
     norm_bound = 1.0
     initial_noise_multiplier = 0.1
-    alpha = 0.5
+    noise_decay_rate = 0.5
     decay_policy = "Step"
     noise_mechanism = MechanismsFactory()
     noise_construct = noise_mechanism.create('Gaussian',
@@ -70,7 +70,7 @@ def test_factory():
     ada_noise_construct = ada_mechanism.create('AdaGaussian',
                                                norm_bound,
                                                initial_noise_multiplier,
-                                               alpha,
+                                               noise_decay_rate,
                                                decay_policy)
     ada_noise = ada_noise_construct(shape)
     print('ada noise: ', ada_noise)