Add DP-Monitor module

mindarmour/diff_privacy/monitor/monitor.py

+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Monitor module of differential privacy training. """
+import math
+import numpy as np
+from scipy import special
+
+from mindspore.train.callback import Callback
+
+from mindarmour.utils.logger import LogUtil
+from mindarmour.utils._check_param import check_int_positive, \
+    check_value_positive
+
+LOGGER = LogUtil.get_instance()
+TAG = 'DP monitor'
+
+
+class PrivacyMonitorFactory:
+    """
+    Factory class of DP training's privacy monitor.
+    """
+
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def create(policy, *args, **kwargs):
+        """
+        Create a privacy monitor class.
+
+        Args:
+            policy (str): Monitor policy, 'rdp' is supported by now.
+            args (Union[int, float, numpy.ndarray, list, str]): Parameters
+                used for creating a privacy monitor.
+            kwargs (Union[int, float, numpy.ndarray, list, str]): Keyword
+                parameters used for creating a privacy monitor.
+
+        Returns:
+            PrivacyMonitor, a privacy monitor.
+
+        Examples:
+            >>> rdp = PrivacyMonitorFactory.create(policy='rdp',
+            >>> num_samples=60000, batch_size=32)
+        """
+        if policy == 'rdp':
+            return RDPMonitor(*args, **kwargs)
+        raise ValueError("Only RDP-policy is supported by now")
+
+
+class RDPMonitor(Callback):
+    """
+    Compute the privacy budget of DP training based on Renyi differential
+    privacy theory.
+
+    Reference: `Rényi Differential Privacy of the Sampled Gaussian Mechanism
+    <https://arxiv.org/abs/1908.10530>`_
+
+    Args:
+        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: 0.4.
+        max_eps (Union[float, int, None]): The maximum acceptable epsilon
+            budget for DP training. Default: 3.0.
+        target_delta (Union[float, int, None]): Target delta budget for DP
+            training. Default: 1e-5.
+        max_delta (Union[float, int, None]): The maximum acceptable delta
+            budget for DP training. Max_delta must be less than 1 and
+            suggested to be less than 1e-3, otherwise overflow would be
+            encountered. Default: None.
+        target_eps (Union[float, int, None]): Target epsilon budget for DP
+            training. Default: None.
+        orders (Union[None, list[int, float]]): Finite orders used for
+            computing rdp, which must be greater than 1.
+        noise_decay_mode (str): Decay mode of adding noise while training,
+            which can be 'no_decay', 'time' or 'step'. Default: 'step'.
+        noise_decay_rate (Union[float, None]): Decay rate of noise while
+            training. Default: 6e-4.
+        per_print_times　(int): The interval steps of computing and printing
+            the privacy budget. Default: 50.
+
+    Examples:
+        >>> rdp = PrivacyMonitorFactory.create(policy='rdp',
+        >>> num_samples=60000, batch_size=32)
+        >>> network = Net()
+        >>> net_loss = nn.SoftmaxCrossEntropyWithLogits()
+        >>> net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
+        >>> model = Model(network, net_loss, net_opt)
+        >>> model.train(epochs, ds, callbacks=[rdp], dataset_sink_mode=False)
+    """
+
+    def __init__(self, num_samples, batch_size, initial_noise_multiplier=0.4,
+                 max_eps=3.0, target_delta=1e-5, max_delta=None,
+                 target_eps=None, orders=None, noise_decay_mode='step',
+                 noise_decay_rate=6e-4, per_print_times=50):
+        super(RDPMonitor, self).__init__()
+        check_int_positive('num_samples', num_samples)
+        check_int_positive('batch_size', batch_size)
+        if batch_size >= num_samples:
+            msg = 'Batch_size must be less than num_samples.'
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
+        check_value_positive('initial_noise_multiplier',
+                             initial_noise_multiplier)
+        if max_eps is not None:
+            check_value_positive('max_eps', max_eps)
+        if target_delta is not None:
+            check_value_positive('target_delta', target_delta)
+        if max_delta is not None:
+            check_value_positive('max_delta', max_delta)
+            if max_delta >= 1:
+                msg = 'max_delta must be less than 1.'
+                LOGGER.error(TAG, msg)
+                raise ValueError(msg)
+        if target_eps is not None:
+            check_value_positive('target_eps', target_eps)
+        if orders is not None:
+            for item in orders:
+                check_value_positive('order', item)
+                if item <= 1:
+                    msg = 'orders must be greater than 1'
+                    LOGGER.error(TAG, msg)
+                    raise ValueError(msg)
+        if noise_decay_mode not in ('no_decay', 'step', 'time'):
+            msg = 'Noise decay mode must be in (no_decay, step, time)'
+            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)
+        check_int_positive('per_print_times', per_print_times)
+
+        self._total_echo_privacy = None
+        self._num_samples = num_samples
+        self._batch_size = batch_size
+        self._initial_noise_multiplier = initial_noise_multiplier
+        self._max_eps = max_eps
+        self._target_delta = target_delta
+        self._max_delta = max_delta
+        self._target_eps = target_eps
+        self._orders = orders
+        self._noise_decay_mode = noise_decay_mode
+        self._noise_decay_rate = noise_decay_rate
+        self._rdp = 0
+        self._per_print_times = per_print_times
+
+    def max_epoch_suggest(self):
+        """
+        Estimate the maximum training epochs to satisfy the predefined
+        privacy budget.
+
+        Returns:
+            int, the recommended maximum training epochs.
+
+        Examples:
+            >>> rdp = PrivacyMonitorFactory.create(policy='rdp',
+            >>> num_samples=60000, batch_size=32)
+            >>> suggest_epoch = rdp.max_epoch_suggest()
+        """
+        epoch = 1
+        while epoch < 10000:
+            steps = self._num_samples // self._batch_size
+            eps, delta = self._compute_privacy_steps(
+                list(np.arange((epoch - 1) * steps, epoch * steps + 1)))
+            if self._max_eps is not None:
+                if eps <= self._max_eps:
+                    epoch += 1
+                else:
+                    break
+            if self._max_delta is not None:
+                if delta <= self._max_delta:
+                    epoch += 1
+                else:
+                    break
+        self._rdp = 0
+        return epoch
+
+    def step_end(self, run_context):
+        """
+        Compute privacy budget after each training step.
+
+        Args:
+            run_context (RunContext): Include some information of the model.
+        """
+        cb_params = run_context.original_args()
+        cur_step = cb_params.cur_step_num
+        cur_step_in_epoch = (cb_params.cur_step_num - 1) % \
+                            cb_params.batch_num + 1
+
+        if cb_params.cur_step_num % self._per_print_times == 0:
+            steps = np.arange(cur_step - self._per_print_times, cur_step + 1)
+            eps, delta = self._compute_privacy_steps(list(steps))
+            if np.isnan(eps) or np.isinf(eps) or np.isnan(delta) or np.isinf(
+                    delta):
+                msg = 'epoch: {} step: {}, invalid eps, terminating ' \
+                      'training.'.format(
+                          cb_params.cur_epoch_num, cur_step_in_epoch)
+                LOGGER.error(TAG, msg)
+                raise ValueError(msg)
+            if np.isnan(delta) or np.isinf(delta):
+                msg = 'epoch: {} step: {}, invalid delta, terminating ' \
+                      'training.'.format(
+                          cb_params.cur_epoch_num, cur_step_in_epoch)
+                LOGGER.error(TAG, msg)
+                raise ValueError(msg)
+            print("epoch: %s step: %s, delta is %s, eps is %s" % (
+                cb_params.cur_epoch_num, cur_step_in_epoch, delta, eps))
+
+    def _compute_privacy_steps(self, steps):
+        """
+        Compute privacy budget corresponding to steps.
+
+        Args:
+            steps (list): Training steps.
+
+        Returns:
+            float, privacy budget.
+        """
+        if self._target_eps is None and self._target_delta is None:
+            msg = 'target eps and target delta cannot both be None'
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
+
+        if self._target_eps is not None and self._target_delta is not None:
+            msg = 'One of target eps and target delta must be None'
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
+
+        if self._orders is None:
+            self._orders = (
+                [1.005, 1.01, 1.02, 1.08, 1.2, 2, 5, 10, 20, 40, 80])
+
+        sampling_rate = self._batch_size / self._num_samples
+        noise_step = self._initial_noise_multiplier
+
+        if self._noise_decay_mode == 'no_decay':
+            self._rdp += self._compute_rdp(sampling_rate, noise_step) * len(
+                steps)
+        else:
+            if self._noise_decay_rate is None:
+                msg = 'noise_decay_rate in decay-mode cannot be None'
+                LOGGER.error(TAG, msg)
+                raise ValueError(msg)
+
+            if self._noise_decay_mode == 'time':
+                noise_step = [self._initial_noise_multiplier / (
+                    1 + self._noise_decay_rate * step) for step in steps]
+
+            elif self._noise_decay_mode == 'step':
+                noise_step = [self._initial_noise_multiplier * (
+                    1 - self._noise_decay_rate) ** step for step in steps]
+            self._rdp += sum(
+                [self._compute_rdp(sampling_rate, noise) for noise in
+                 noise_step])
+        eps, delta = self._compute_privacy_budget(self._rdp)
+
+        return eps, delta
+
+    def _compute_rdp(self, q, noise):
+        """
+        Compute rdp according to sampling rate, added noise and Renyi
+        divergence orders.
+
+        Args:
+            q (float): Sampling rate of each batch of samples.
+            noise (float): Noise multiplier.
+
+        Returns:
+            float or numpy.ndarray, rdp values.
+        """
+        rdp = np.array(
+            [_compute_rdp_order(q, noise, order) for order in self._orders])
+        return rdp
+
+    def _compute_privacy_budget(self, rdp):
+        """
+        Compute delta or eps for given rdp.
+
+        Args:
+            rdp (Union[float, numpy.ndarray]): Renyi differential privacy.
+
+        Returns:
+            float, delta budget or eps budget.
+        """
+        if self._target_eps is not None:
+            delta = self._compute_delta(rdp)
+            return self._target_eps, delta
+        eps = self._compute_eps(rdp)
+        return eps, self._target_delta
+
+    def _compute_delta(self, rdp):
+        """
+        Compute delta for given rdp and eps.
+
+        Args:
+            rdp (Union[float, numpy.ndarray]): Renyi differential privacy.
+
+        Returns:
+            float, delta budget.
+        """
+        orders = np.atleast_1d(self._orders)
+        rdps = np.atleast_1d(rdp)
+        if len(orders) != len(rdps):
+            msg = 'rdp lists and orders list must have the same length.'
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
+
+        deltas = np.exp((rdps - self._target_eps) * (orders - 1))
+        min_delta = min(deltas)
+        return min(min_delta, 1.)
+
+    def _compute_eps(self, rdp):
+        """
+        Compute eps for given rdp and delta.
+
+        Args:
+            rdp (Union[float, numpy.ndarray]): Renyi differential privacy.
+
+        Returns:
+            float, eps budget.
+        """
+        orders = np.atleast_1d(self._orders)
+        rdps = np.atleast_1d(rdp)
+        if len(orders) != len(rdps):
+            msg = 'rdp lists and orders list must have the same length.'
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
+        eps = rdps - math.log(self._target_delta) / (orders - 1)
+        return min(eps)
+
+
+def _compute_rdp_order(q, sigma, alpha):
+    """
+    Compute rdp for each order.
+
+    Args:
+        q (float): Sampling probability.
+        sigma (float): Noise multiplier.
+        alpha: The order used for computing rdp.
+
+    Returns:
+        float, rdp value.
+    """
+    if float(alpha).is_integer():
+        log_integrate = -np.inf
+        for k in range(alpha + 1):
+            term_k = (math.log(
+                special.binom(alpha, k)) + k * math.log(q) + (
+                    alpha - k) * math.log(
+                        1 - q)) + (k * k - k) / (2 * (sigma ** 2))
+            log_integrate = _log_add(log_integrate, term_k)
+        return float(log_integrate) / (alpha - 1)
+    log_part_0, log_part_1 = -np.inf, -np.inf
+    k = 0
+    z0 = sigma ** 2 * math.log(1 / q - 1) + 1 / 2
+    while True:
+        bi_coef = special.binom(alpha, k)
+        log_coef = math.log(abs(bi_coef))
+        j = alpha - k
+
+        term_k_part_0 = log_coef + k * math.log(q) + j * math.log(1 - q) + (
+            k * k - k) / (2 * (sigma ** 2)) + special.log_ndtr(
+                (z0 - k) / sigma)
+
+        term_k_part_1 = log_coef + j * math.log(q) + k * math.log(1 - q) + (
+            j * j - j) / (2 * (sigma ** 2)) + special.log_ndtr(
+                (j - z0) / sigma)
+
+        if bi_coef > 0:
+            log_part_0 = _log_add(log_part_0, term_k_part_0)
+            log_part_1 = _log_add(log_part_1, term_k_part_1)
+        else:
+            log_part_0 = _log_subtract(log_part_0, term_k_part_0)
+            log_part_1 = _log_subtract(log_part_1, term_k_part_1)
+
+        k += 1
+        if max(term_k_part_0, term_k_part_1) < -30:
+            break
+
+    return _log_add(log_part_0, log_part_1) / (alpha - 1)
+
+
+def _log_add(x, y):
+    """
+    Add x and y in log space.
+    """
+    if x == -np.inf:
+        return y
+    if y == -np.inf:
+        return x
+    return max(x, y) + math.log1p(math.exp(-abs(x - y)))
+
+
+def _log_subtract(x, y):
+    """
+    Subtract y from x in log space, x must be greater than y.
+    """
+    if x <= y:
+        msg = 'The antilog of log functions must be positive'
+        LOGGER.error(TAG, msg)
+        raise ValueError(msg)
+    if y == -np.inf:
+        return x
+    return math.log1p(math.exp(y - x)) + x

tests/ut/python/diff_privacy/test_monitor.py

+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+DP-Monitor test.
+"""
+import pytest
+import numpy as np
+
+import mindspore.nn as nn
+import mindspore.dataset as ds
+from mindspore.train import Model
+import mindspore.context as context
+from mindspore.model_zoo.lenet import LeNet5
+
+from mindarmour.diff_privacy.monitor.monitor import PrivacyMonitorFactory
+from mindarmour.utils.logger import LogUtil
+
+LOGGER = LogUtil.get_instance()
+TAG = 'DP-Monitor Test'
+
+
+def dataset_generator(batch_size, batches):
+    data = np.random.random((batches * batch_size, 1, 32, 32)).astype(
+        np.float32)
+    label = np.random.randint(0, 10, batches * batch_size).astype(np.int32)
+    for i in range(batches):
+        yield data[i * batch_size: (i + 1) * batch_size], \
+              label[i * batch_size: (i + 1) * batch_size]
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_card
+@pytest.mark.component_mindarmour
+def test_dp_monitor():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    batch_size = 16
+    batches = 128
+    epochs = 1
+    rdp = PrivacyMonitorFactory.create(policy='rdp', num_samples=60000,
+                                       batch_size=batch_size,
+                                       initial_noise_multiplier=0.4,
+                                       noise_decay_rate=6e-5)
+    suggest_epoch = rdp.max_epoch_suggest()
+    LOGGER.info(TAG, 'The recommended maximum training epochs is: %s',
+                suggest_epoch)
+    network = LeNet5()
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True,
+                                                reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
+
+    model = Model(network, net_loss, net_opt)
+
+    LOGGER.info(TAG, "============== Starting Training ==============")
+    ds1 = ds.GeneratorDataset(dataset_generator(batch_size, batches),
+                              ["data", "label"])
+    ds1.set_dataset_size(batch_size * batches)
+    model.train(epochs, ds1, callbacks=[rdp], dataset_sink_mode=False)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_inference
+@pytest.mark.env_card
+@pytest.mark.component_mindarmour
+def test_dp_monitor_gpu():
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+    batch_size = 16
+    batches = 128
+    epochs = 1
+    rdp = PrivacyMonitorFactory.create(policy='rdp', num_samples=60000,
+                                       batch_size=batch_size,
+                                       initial_noise_multiplier=0.4,
+                                       noise_decay_rate=6e-5)
+    suggest_epoch = rdp.max_epoch_suggest()
+    LOGGER.info(TAG, 'The recommended maximum training epochs is: %s',
+                suggest_epoch)
+    network = LeNet5()
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True,
+                                                reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
+
+    model = Model(network, net_loss, net_opt)
+
+    LOGGER.info(TAG, "============== Starting Training ==============")
+    ds1 = ds.GeneratorDataset(dataset_generator(batch_size, batches),
+                              ["data", "label"])
+    ds1.set_dataset_size(batch_size * batches)
+    model.train(epochs, ds1, callbacks=[rdp], dataset_sink_mode=False)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_card
+@pytest.mark.component_mindarmour
+def test_dp_monitor_cpu():
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    batch_size = 16
+    batches = 128
+    epochs = 1
+    rdp = PrivacyMonitorFactory.create(policy='rdp', num_samples=60000,
+                                       batch_size=batch_size,
+                                       initial_noise_multiplier=0.4,
+                                       noise_decay_rate=6e-5)
+    suggest_epoch = rdp.max_epoch_suggest()
+    LOGGER.info(TAG, 'The recommended maximum training epochs is: %s',
+                suggest_epoch)
+    network = LeNet5()
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True,
+                                                reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), 0.01, 0.9)
+
+    model = Model(network, net_loss, net_opt)
+
+    LOGGER.info(TAG, "============== Starting Training ==============")
+    ds1 = ds.GeneratorDataset(dataset_generator(batch_size, batches),
+                              ["data", "label"])
+    ds1.set_dataset_size(batch_size * batches)
+    model.train(epochs, ds1, callbacks=[rdp], dataset_sink_mode=False)