Tests for Eager mode.
PiperOrigin-RevId: 236382269
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Steve Chien
A. Unique TensorFlower
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2 changed files with 146 additions and 10 deletions
136
privacy/optimizers/dp_optimizer_eager_test.py
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privacy/optimizers/dp_optimizer_eager_test.py
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# 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 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.optimizers import dp_optimizer
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from privacy.optimizers import gaussian_query
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class DPOptimizerEagerTest(tf.test.TestCase, parameterized.TestCase):
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def setUp(self):
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tf.enable_eager_execution()
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super(DPOptimizerEagerTest, self).setUp()
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def _loss_fn(self, val0, val1):
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return 0.5 * tf.reduce_sum(tf.squared_difference(val0, val1), axis=1)
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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 tf.GradientTape(persistent=True) as gradient_tape:
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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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ledger = privacy_ledger.PrivacyLedger(1e6, num_microbatches / 1e6, 50, 50)
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dp_average_query = gaussian_query.GaussianAverageQuery(
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1.0e9, 0.0, num_microbatches, ledger)
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dp_average_query = privacy_ledger.QueryWithLedger(dp_average_query,
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ledger)
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opt = cls(
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dp_average_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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grads_and_vars = opt.compute_gradients(
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lambda: self._loss_fn(var0, data0), [var0],
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gradient_tape=gradient_tape)
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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 tf.GradientTape(persistent=True) as gradient_tape:
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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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ledger = privacy_ledger.PrivacyLedger(1e6, 1 / 1e6, 50, 50)
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dp_average_query = gaussian_query.GaussianAverageQuery(1.0, 0.0, 1)
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dp_average_query = privacy_ledger.QueryWithLedger(dp_average_query,
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ledger)
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opt = cls(dp_average_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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grads_and_vars = opt.compute_gradients(
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lambda: self._loss_fn(var0, data0), [var0],
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gradient_tape=gradient_tape)
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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 tf.GradientTape(persistent=True) as gradient_tape:
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var0 = tf.Variable([0.0])
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data0 = tf.Variable([[0.0]])
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ledger = privacy_ledger.PrivacyLedger(1e6, 1 / 1e6, 5000, 5000)
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dp_average_query = gaussian_query.GaussianAverageQuery(4.0, 8.0, 1)
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dp_average_query = privacy_ledger.QueryWithLedger(dp_average_query,
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ledger)
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opt = cls(dp_average_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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grads = []
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for _ in range(1000):
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grads_and_vars = opt.compute_gradients(
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lambda: self._loss_fn(var0, data0), [var0],
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gradient_tape=gradient_tape)
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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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@ -27,13 +27,12 @@ from privacy.optimizers import dp_optimizer
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from privacy.optimizers import gaussian_query
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def loss(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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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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@ -71,7 +70,7 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
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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(loss(data0, var0), [var0])
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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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@ -96,7 +95,7 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
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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(loss(data0, var0), [var0])
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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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@ -120,7 +119,7 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
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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(loss(data0, var0), [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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@ -216,7 +215,7 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
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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(loss(data0, var0), [var0])
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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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@ -239,7 +238,7 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
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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(loss(data0, var0), [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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@ -248,5 +247,6 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
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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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