d5dcfec745
set_denominator was added so that the batch size doesn't need to be specified before constructing the optimizer, but it breaks the DPQuery abstraction. Now the optimizer uses a GaussianSumQuery instead of GaussianAverageQuery, and normalization by batch size is done inside the optimizer. Also instead of creating all DPQueries with a PrivacyLedger and then wrapping with QueryWithLedger, it is now sufficient to create the queries with no ledger and QueryWithLedger will construct the ledger and pass it to all inner queries. PiperOrigin-RevId: 251462353
241 lines
9.3 KiB
Python
241 lines
9.3 KiB
Python
# Copyright 2019, The TensorFlow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for differentially private optimizers."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from absl.testing import parameterized
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import mock
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import numpy as np
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import tensorflow as tf
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from privacy.analysis import privacy_ledger
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from privacy.dp_query import gaussian_query
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from privacy.optimizers import dp_optimizer
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class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
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def _loss(self, val0, val1):
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"""Loss function that is minimized at the mean of the input points."""
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return 0.5 * tf.reduce_sum(tf.squared_difference(val0, val1), axis=1)
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# Parameters for testing: optimizer, num_microbatches, expected answer.
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@parameterized.named_parameters(
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('DPGradientDescent 1', dp_optimizer.DPGradientDescentOptimizer, 1,
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[-2.5, -2.5]),
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('DPGradientDescent 2', dp_optimizer.DPGradientDescentOptimizer, 2,
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[-2.5, -2.5]),
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('DPGradientDescent 4', dp_optimizer.DPGradientDescentOptimizer, 4,
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[-2.5, -2.5]),
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('DPAdagrad 1', dp_optimizer.DPAdagradOptimizer, 1, [-2.5, -2.5]),
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('DPAdagrad 2', dp_optimizer.DPAdagradOptimizer, 2, [-2.5, -2.5]),
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('DPAdagrad 4', dp_optimizer.DPAdagradOptimizer, 4, [-2.5, -2.5]),
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('DPAdam 1', dp_optimizer.DPAdamOptimizer, 1, [-2.5, -2.5]),
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('DPAdam 2', dp_optimizer.DPAdamOptimizer, 2, [-2.5, -2.5]),
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('DPAdam 4', dp_optimizer.DPAdamOptimizer, 4, [-2.5, -2.5]))
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def testBaseline(self, cls, num_microbatches, expected_answer):
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with self.cached_session() as sess:
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var0 = tf.Variable([1.0, 2.0])
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data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]])
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dp_sum_query = gaussian_query.GaussianSumQuery(1.0e9, 0.0)
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dp_sum_query = privacy_ledger.QueryWithLedger(
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dp_sum_query, 1e6, num_microbatches / 1e6)
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opt = cls(
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dp_sum_query,
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num_microbatches=num_microbatches,
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learning_rate=2.0)
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self.evaluate(tf.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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# Expected gradient is sum of differences divided by number of
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# microbatches.
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gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
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grads_and_vars = sess.run(gradient_op)
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self.assertAllCloseAccordingToType(expected_answer, grads_and_vars[0][0])
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@parameterized.named_parameters(
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('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
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('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
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('DPAdam', dp_optimizer.DPAdamOptimizer))
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def testClippingNorm(self, cls):
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with self.cached_session() as sess:
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var0 = tf.Variable([0.0, 0.0])
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data0 = tf.Variable([[3.0, 4.0], [6.0, 8.0]])
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dp_sum_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
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dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query, 1e6, 1 / 1e6)
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opt = cls(dp_sum_query, num_microbatches=1, learning_rate=2.0)
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self.evaluate(tf.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([0.0, 0.0], self.evaluate(var0))
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# Expected gradient is sum of differences.
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gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
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grads_and_vars = sess.run(gradient_op)
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self.assertAllCloseAccordingToType([-0.6, -0.8], grads_and_vars[0][0])
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@parameterized.named_parameters(
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('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
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('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
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('DPAdam', dp_optimizer.DPAdamOptimizer))
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def testNoiseMultiplier(self, cls):
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with self.cached_session() as sess:
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var0 = tf.Variable([0.0])
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data0 = tf.Variable([[0.0]])
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dp_sum_query = gaussian_query.GaussianSumQuery(4.0, 8.0)
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dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query, 1e6, 1 / 1e6)
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opt = cls(dp_sum_query, num_microbatches=1, learning_rate=2.0)
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self.evaluate(tf.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([0.0], self.evaluate(var0))
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gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
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grads = []
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for _ in range(1000):
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grads_and_vars = sess.run(gradient_op)
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grads.append(grads_and_vars[0][0])
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# Test standard deviation is close to l2_norm_clip * noise_multiplier.
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self.assertNear(np.std(grads), 2.0 * 4.0, 0.5)
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@mock.patch.object(tf, 'logging')
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def testComputeGradientsOverrideWarning(self, mock_logging):
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class SimpleOptimizer(tf.train.Optimizer):
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def compute_gradients(self):
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return 0
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dp_optimizer.make_optimizer_class(SimpleOptimizer)
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mock_logging.warning.assert_called_once_with(
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'WARNING: Calling make_optimizer_class() on class %s that overrides '
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'method compute_gradients(). Check to ensure that '
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'make_optimizer_class() does not interfere with overridden version.',
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'SimpleOptimizer')
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def testEstimator(self):
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"""Tests that DP optimizers work with tf.estimator."""
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def linear_model_fn(features, labels, mode):
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preds = tf.keras.layers.Dense(
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1, activation='linear', name='dense').apply(features['x'])
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vector_loss = tf.squared_difference(labels, preds)
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scalar_loss = tf.reduce_mean(vector_loss)
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dp_sum_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
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dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query, 1e6, 1 / 1e6)
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optimizer = dp_optimizer.DPGradientDescentOptimizer(
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dp_sum_query,
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num_microbatches=1,
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learning_rate=1.0)
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global_step = tf.train.get_global_step()
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train_op = optimizer.minimize(loss=vector_loss, global_step=global_step)
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return tf.estimator.EstimatorSpec(
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mode=mode, loss=scalar_loss, train_op=train_op)
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linear_regressor = tf.estimator.Estimator(model_fn=linear_model_fn)
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true_weights = np.array([[-5], [4], [3], [2]]).astype(np.float32)
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true_bias = 6.0
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train_data = np.random.normal(scale=3.0, size=(200, 4)).astype(np.float32)
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train_labels = np.matmul(train_data,
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true_weights) + true_bias + np.random.normal(
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scale=0.1, size=(200, 1)).astype(np.float32)
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train_input_fn = tf.estimator.inputs.numpy_input_fn(
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x={'x': train_data},
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y=train_labels,
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batch_size=20,
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num_epochs=10,
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shuffle=True)
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linear_regressor.train(input_fn=train_input_fn, steps=100)
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self.assertAllClose(
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linear_regressor.get_variable_value('dense/kernel'),
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true_weights,
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atol=1.0)
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@parameterized.named_parameters(
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('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
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('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
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('DPAdam', dp_optimizer.DPAdamOptimizer))
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def testUnrollMicrobatches(self, cls):
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with self.cached_session() as sess:
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var0 = tf.Variable([1.0, 2.0])
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data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]])
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num_microbatches = 4
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dp_sum_query = gaussian_query.GaussianSumQuery(1.0e9, 0.0)
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dp_sum_query = privacy_ledger.QueryWithLedger(
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dp_sum_query, 1e6, num_microbatches / 1e6)
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opt = cls(
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dp_sum_query,
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num_microbatches=num_microbatches,
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learning_rate=2.0,
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unroll_microbatches=True)
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self.evaluate(tf.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([1.0, 2.0], self.evaluate(var0))
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# Expected gradient is sum of differences divided by number of
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# microbatches.
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gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
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grads_and_vars = sess.run(gradient_op)
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self.assertAllCloseAccordingToType([-2.5, -2.5], grads_and_vars[0][0])
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@parameterized.named_parameters(
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('DPGradientDescent', dp_optimizer.DPGradientDescentGaussianOptimizer),
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('DPAdagrad', dp_optimizer.DPAdagradGaussianOptimizer),
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('DPAdam', dp_optimizer.DPAdamGaussianOptimizer))
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def testDPGaussianOptimizerClass(self, cls):
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with self.cached_session() as sess:
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var0 = tf.Variable([0.0])
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data0 = tf.Variable([[0.0]])
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opt = cls(
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l2_norm_clip=4.0,
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noise_multiplier=2.0,
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num_microbatches=1,
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learning_rate=2.0)
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self.evaluate(tf.global_variables_initializer())
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# Fetch params to validate initial values
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self.assertAllClose([0.0], self.evaluate(var0))
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gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
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grads = []
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for _ in range(1000):
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grads_and_vars = sess.run(gradient_op)
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grads.append(grads_and_vars[0][0])
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# Test standard deviation is close to l2_norm_clip * noise_multiplier.
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self.assertNear(np.std(grads), 2.0 * 4.0, 0.5)
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if __name__ == '__main__':
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tf.test.main()
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