diff --git a/example/mnist_demo/lenet5_config.py b/example/mnist_demo/lenet5_config.py index 604199e..69e8efa 100644 --- a/example/mnist_demo/lenet5_config.py +++ b/example/mnist_demo/lenet5_config.py @@ -30,9 +30,9 @@ mnist_cfg = edict({ '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　 + '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 - 'l2_norm_bound': 1.0, # the clip bound of the gradients of model's training parameters + 'norm_clip': 1.0, # the clip bound of the gradients of model's training parameters 'initial_noise_multiplier': 0.2, # the initial multiplication coefficient of the noise added to training # parameters' gradients 'mechanisms': 'AdaGaussian', # the method of adding noise in gradients while training diff --git a/example/mnist_demo/lenet5_dp.py b/example/mnist_demo/lenet5_dp.py index abd1b59..c86bf1b 100644 --- a/example/mnist_demo/lenet5_dp.py +++ b/example/mnist_demo/lenet5_dp.py @@ -108,7 +108,7 @@ 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. mech = MechanismsFactory().create(cfg.mechanisms, - norm_bound=cfg.l2_norm_bound, + norm_bound=cfg.norm_clip, initial_noise_multiplier=cfg.initial_noise_multiplier) net_opt = nn.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 @@ -116,11 +116,11 @@ if __name__ == "__main__": rdp_monitor = PrivacyMonitorFactory.create('rdp', num_samples=60000, batch_size=cfg.batch_size, - initial_noise_multiplier=cfg.initial_noise_multiplier*cfg.l2_norm_bound, + initial_noise_multiplier=cfg.initial_noise_multiplier*cfg.norm_clip, per_print_times=10) # Create the DP model for training. model = DPModel(micro_batches=cfg.micro_batches, - norm_clip=cfg.l2_norm_bound, + norm_clip=cfg.norm_clip, mech=mech, network=network, loss_fn=net_loss, diff --git a/example/mnist_demo/lenet5_dp_pynative_mode.py b/example/mnist_demo/lenet5_dp_pynative_mode.py index 74ef5ee..625869e 100644 --- a/example/mnist_demo/lenet5_dp_pynative_mode.py +++ b/example/mnist_demo/lenet5_dp_pynative_mode.py @@ -109,7 +109,7 @@ if __name__ == "__main__": # 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.l2_norm_bound, + norm_bound=cfg.norm_clip, initial_noise_multiplier=cfg.initial_noise_multiplier) 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 @@ -117,11 +117,11 @@ if __name__ == "__main__": rdp_monitor = PrivacyMonitorFactory.create('rdp', num_samples=60000, batch_size=cfg.batch_size, - initial_noise_multiplier=cfg.initial_noise_multiplier*cfg.l2_norm_bound, + initial_noise_multiplier=cfg.initial_noise_multiplier*cfg.norm_clip, per_print_times=10) # Create the DP model for training. model = DPModel(micro_batches=cfg.micro_batches, - norm_clip=cfg.l2_norm_bound, + norm_clip=cfg.norm_clip, mech=None, network=network, loss_fn=net_loss, diff --git a/mindarmour/diff_privacy/train/model.py b/mindarmour/diff_privacy/train/model.py index b0d372f..67f75c9 100644 --- a/mindarmour/diff_privacy/train/model.py +++ b/mindarmour/diff_privacy/train/model.py @@ -93,7 +93,7 @@ class DPModel(Model): >>> loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) >>> net_opt = Momentum(params=net.trainable_params(), learning_rate=0.01, momentum=0.9) >>> mech = MechanismsFactory().create('Gaussian', - >>> norm_bound=args.l2_norm_bound, + >>> norm_bound=args.norm_clip, >>> initial_noise_multiplier=args.initial_noise_multiplier) >>> model = DPModel(micro_batches=2, >>> norm_clip=1.0, @@ -111,8 +111,8 @@ class DPModel(Model): self._micro_batches = check_int_positive('micro_batches', micro_batches) else: self._micro_batches = None - float_norm_clip = check_param_type('l2_norm_clip', norm_clip, float) - self._norm_clip = check_value_positive('l2_norm_clip', float_norm_clip) + norm_clip = check_param_type('norm_clip', norm_clip, float) + self._norm_clip = check_value_positive('norm_clip', norm_clip) if mech is not None and "DPOptimizer" in kwargs['optimizer'].__class__.__name__: raise ValueError('DPOptimizer is not supported while mech is not None') if mech is None: @@ -180,14 +180,14 @@ class DPModel(Model): optimizer, scale_update_cell=update_cell, micro_batches=self._micro_batches, - l2_norm_clip=self._norm_clip, + norm_clip=self._norm_clip, mech=self._mech).set_train() return network network = _TrainOneStepCell(network, optimizer, loss_scale, micro_batches=self._micro_batches, - l2_norm_clip=self._norm_clip, + norm_clip=self._norm_clip, mech=self._mech).set_train() return network @@ -300,7 +300,7 @@ class _TrainOneStepWithLossScaleCell(Cell): optimizer (Cell): Optimizer for updating the weights. scale_update_cell(Cell): The loss scaling update logic cell. Default: None. micro_batches (int): The number of small batches split from an original batch. Default: None. - l2_norm_clip (float): Use to clip the bound, if set 1, will return the original data. Default: 1.0. + norm_clip (float): Use to clip the bound, if set 1, will return the original data. Default: 1.0. mech (Mechanisms): The object can generate the different type of noise. Default: None. Inputs: @@ -316,7 +316,7 @@ class _TrainOneStepWithLossScaleCell(Cell): - **loss_scale** (Tensor) - Tensor with shape :math:`()`. """ - def __init__(self, network, optimizer, scale_update_cell=None, micro_batches=None, l2_norm_clip=1.0, mech=None): + def __init__(self, network, optimizer, scale_update_cell=None, micro_batches=None, norm_clip=1.0, mech=None): super(_TrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False) self.network = network self.network.set_grad() @@ -358,8 +358,8 @@ class _TrainOneStepWithLossScaleCell(Cell): # dp params self._micro_batches = micro_batches - float_norm_clip = check_param_type('l2_norm_clip', l2_norm_clip, float) - self._l2_norm = check_value_positive('l2_norm_clip', float_norm_clip) + norm_clip = check_param_type('norm_clip', norm_clip, float) + self._l2_norm = check_value_positive('norm_clip', norm_clip) self._split = P.Split(0, self._micro_batches) self._clip_by_global_norm = _ClipGradients() self._mech = mech @@ -452,7 +452,7 @@ class _TrainOneStepCell(Cell): optimizer (Cell): Optimizer for updating the weights. sens (Number): The scaling number to be filled as the input of back propagation. Default value is 1.0. micro_batches (int): The number of small batches split from an original batch. Default: None. - l2_norm_clip (float): Use to clip the bound, if set 1, will return the original data. Default: 1.0. + norm_clip (float): Use to clip the bound, if set 1, will return the original data. Default: 1.0. mech (Mechanisms): The object can generate the different type of noise. Default: None. Inputs: @@ -463,7 +463,7 @@ class _TrainOneStepCell(Cell): Tensor, a scalar Tensor with shape :math:`()`. """ - def __init__(self, network, optimizer, sens=1.0, micro_batches=None, l2_norm_clip=1.0, mech=None): + def __init__(self, network, optimizer, sens=1.0, micro_batches=None, norm_clip=1.0, mech=None): super(_TrainOneStepCell, self).__init__(auto_prefix=False) self.network = network self.network.set_grad() @@ -484,8 +484,8 @@ class _TrainOneStepCell(Cell): # dp params self._micro_batches = micro_batches - float_norm_clip = check_param_type('l2_norm_clip', l2_norm_clip, float) - self._l2_norm = check_value_positive('l2_norm_clip', float_norm_clip) + norm_clip = check_param_type('norm_clip', norm_clip, float) + self._l2_norm = check_value_positive('norm_clip', norm_clip) self._split = P.Split(0, self._micro_batches) self._clip_by_global_norm = _ClipGradients() self._mech = mech diff --git a/tests/ut/python/diff_privacy/test_model_train.py b/tests/ut/python/diff_privacy/test_model_train.py index 86422d2..88bd942 100644 --- a/tests/ut/python/diff_privacy/test_model_train.py +++ b/tests/ut/python/diff_privacy/test_model_train.py @@ -43,7 +43,7 @@ def dataset_generator(batch_size, batches): @pytest.mark.component_mindarmour def test_dp_model_pynative_mode(): context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend") - l2_norm_bound = 1.0 + norm_clip = 1.0 initial_noise_multiplier = 0.01 network = LeNet5() batch_size = 32 @@ -53,11 +53,11 @@ def test_dp_model_pynative_mode(): loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) factory_opt = DPOptimizerClassFactory(micro_batches=micro_batches) factory_opt.set_mechanisms('Gaussian', - norm_bound=l2_norm_bound, + norm_bound=norm_clip, initial_noise_multiplier=initial_noise_multiplier) net_opt = factory_opt.create('Momentum')(network.trainable_params(), learning_rate=0.1, momentum=0.9) model = DPModel(micro_batches=micro_batches, - norm_clip=l2_norm_bound, + norm_clip=norm_clip, mech=None, network=network, loss_fn=loss, @@ -75,7 +75,7 @@ def test_dp_model_pynative_mode(): @pytest.mark.component_mindarmour def test_dp_model_with_graph_mode(): context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") - l2_norm_bound = 1.0 + norm_clip = 1.0 initial_noise_multiplier = 0.01 network = LeNet5() batch_size = 32 @@ -83,11 +83,11 @@ def test_dp_model_with_graph_mode(): epochs = 1 loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True) mech = MechanismsFactory().create('Gaussian', - norm_bound=l2_norm_bound, + norm_bound=norm_clip, initial_noise_multiplier=initial_noise_multiplier) net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.1, momentum=0.9) model = DPModel(micro_batches=2, - norm_clip=l2_norm_bound, + norm_clip=norm_clip, mech=mech, network=network, loss_fn=loss,