Tests for Eager mode.

PiperOrigin-RevId: 236382269

privacy/optimizers/dp_optimizer_eager_test.py

@@ -0,0 +1,136 @@
+# Copyright 2019, The TensorFlow Authors.
+#
+# 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.
+"""Tests for differentially private optimizers."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl.testing import parameterized
+import numpy as np
+import tensorflow as tf
+
+from privacy.analysis import privacy_ledger
+from privacy.optimizers import dp_optimizer
+from privacy.optimizers import gaussian_query
+
+
+class DPOptimizerEagerTest(tf.test.TestCase, parameterized.TestCase):
+
+  def setUp(self):
+    tf.enable_eager_execution()
+    super(DPOptimizerEagerTest, self).setUp()
+
+  def _loss_fn(self, val0, val1):
+    return 0.5 * tf.reduce_sum(tf.squared_difference(val0, val1), axis=1)
+
+  @parameterized.named_parameters(
+      ('DPGradientDescent 1', dp_optimizer.DPGradientDescentOptimizer, 1,
+       [-2.5, -2.5]),
+      ('DPGradientDescent 2', dp_optimizer.DPGradientDescentOptimizer, 2,
+       [-2.5, -2.5]),
+      ('DPGradientDescent 4', dp_optimizer.DPGradientDescentOptimizer, 4,
+       [-2.5, -2.5]),
+      ('DPAdagrad 1', dp_optimizer.DPAdagradOptimizer, 1, [-2.5, -2.5]),
+      ('DPAdagrad 2', dp_optimizer.DPAdagradOptimizer, 2, [-2.5, -2.5]),
+      ('DPAdagrad 4', dp_optimizer.DPAdagradOptimizer, 4, [-2.5, -2.5]),
+      ('DPAdam 1', dp_optimizer.DPAdamOptimizer, 1, [-2.5, -2.5]),
+      ('DPAdam 2', dp_optimizer.DPAdamOptimizer, 2, [-2.5, -2.5]),
+      ('DPAdam 4', dp_optimizer.DPAdamOptimizer, 4, [-2.5, -2.5]))
+  def testBaseline(self, cls, num_microbatches, expected_answer):
+    with tf.GradientTape(persistent=True) as gradient_tape:
+      var0 = tf.Variable([1.0, 2.0])
+      data0 = tf.Variable([[3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [-1.0, 0.0]])
+
+      ledger = privacy_ledger.PrivacyLedger(1e6, num_microbatches / 1e6, 50, 50)
+      dp_average_query = gaussian_query.GaussianAverageQuery(
+          1.0e9, 0.0, num_microbatches, ledger)
+      dp_average_query = privacy_ledger.QueryWithLedger(dp_average_query,
+                                                        ledger)
+
+      opt = cls(
+          dp_average_query,
+          num_microbatches=num_microbatches,
+          learning_rate=2.0)
+
+      self.evaluate(tf.global_variables_initializer())
+      # Fetch params to validate initial values
+      self.assertAllClose([1.0, 2.0], self.evaluate(var0))
+
+      # Expected gradient is sum of differences divided by number of
+      # microbatches.
+      grads_and_vars = opt.compute_gradients(
+          lambda: self._loss_fn(var0, data0), [var0],
+          gradient_tape=gradient_tape)
+      self.assertAllCloseAccordingToType(expected_answer, grads_and_vars[0][0])
+
+  @parameterized.named_parameters(
+      ('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
+      ('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
+      ('DPAdam', dp_optimizer.DPAdamOptimizer))
+  def testClippingNorm(self, cls):
+    with tf.GradientTape(persistent=True) as gradient_tape:
+      var0 = tf.Variable([0.0, 0.0])
+      data0 = tf.Variable([[3.0, 4.0], [6.0, 8.0]])
+
+      ledger = privacy_ledger.PrivacyLedger(1e6, 1 / 1e6, 50, 50)
+      dp_average_query = gaussian_query.GaussianAverageQuery(1.0, 0.0, 1)
+      dp_average_query = privacy_ledger.QueryWithLedger(dp_average_query,
+                                                        ledger)
+
+      opt = cls(dp_average_query, num_microbatches=1, learning_rate=2.0)
+
+      self.evaluate(tf.global_variables_initializer())
+      # Fetch params to validate initial values
+      self.assertAllClose([0.0, 0.0], self.evaluate(var0))
+
+      # Expected gradient is sum of differences.
+      grads_and_vars = opt.compute_gradients(
+          lambda: self._loss_fn(var0, data0), [var0],
+          gradient_tape=gradient_tape)
+      self.assertAllCloseAccordingToType([-0.6, -0.8], grads_and_vars[0][0])
+
+  @parameterized.named_parameters(
+      ('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
+      ('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
+      ('DPAdam', dp_optimizer.DPAdamOptimizer))
+  def testNoiseMultiplier(self, cls):
+    with tf.GradientTape(persistent=True) as gradient_tape:
+      var0 = tf.Variable([0.0])
+      data0 = tf.Variable([[0.0]])
+
+      ledger = privacy_ledger.PrivacyLedger(1e6, 1 / 1e6, 5000, 5000)
+      dp_average_query = gaussian_query.GaussianAverageQuery(4.0, 8.0, 1)
+      dp_average_query = privacy_ledger.QueryWithLedger(dp_average_query,
+                                                        ledger)
+
+      opt = cls(dp_average_query, num_microbatches=1, learning_rate=2.0)
+
+      self.evaluate(tf.global_variables_initializer())
+      # Fetch params to validate initial values
+      self.assertAllClose([0.0], self.evaluate(var0))
+
+      grads = []
+      for _ in range(1000):
+        grads_and_vars = opt.compute_gradients(
+            lambda: self._loss_fn(var0, data0), [var0],
+            gradient_tape=gradient_tape)
+        grads.append(grads_and_vars[0][0])
+
+      # Test standard deviation is close to l2_norm_clip * noise_multiplier.
+      self.assertNear(np.std(grads), 2.0 * 4.0, 0.5)
+
+
+if __name__ == '__main__':
+  tf.test.main()

privacy/optimizers/dp_optimizer_test.py

@@ -27,13 +27,12 @@ from privacy.optimizers import dp_optimizer
 from privacy.optimizers import gaussian_query
 
 
-def loss(val0, val1):
-  """Loss function that is minimized at the mean of the input points."""
-  return 0.5 * tf.reduce_sum(tf.squared_difference(val0, val1), axis=1)
-
-
 class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
 
+  def _loss(self, val0, val1):
+    """Loss function that is minimized at the mean of the input points."""
+    return 0.5 * tf.reduce_sum(tf.squared_difference(val0, val1), axis=1)
+
   # Parameters for testing: optimizer, num_microbatches, expected answer.
   @parameterized.named_parameters(
       ('DPGradientDescent 1', dp_optimizer.DPGradientDescentOptimizer, 1,
@@ -71,7 +70,7 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
 
       # Expected gradient is sum of differences divided by number of
       # microbatches.
-      gradient_op = opt.compute_gradients(loss(data0, var0), [var0])
+      gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
       grads_and_vars = sess.run(gradient_op)
       self.assertAllCloseAccordingToType(expected_answer, grads_and_vars[0][0])
 
@@ -96,7 +95,7 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
       self.assertAllClose([0.0, 0.0], self.evaluate(var0))
 
       # Expected gradient is sum of differences.
-      gradient_op = opt.compute_gradients(loss(data0, var0), [var0])
+      gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
       grads_and_vars = sess.run(gradient_op)
       self.assertAllCloseAccordingToType([-0.6, -0.8], grads_and_vars[0][0])
 
@@ -120,7 +119,7 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
       # Fetch params to validate initial values
       self.assertAllClose([0.0], self.evaluate(var0))
 
-      gradient_op = opt.compute_gradients(loss(data0, var0), [var0])
+      gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
       grads = []
       for _ in range(1000):
         grads_and_vars = sess.run(gradient_op)
@@ -216,7 +215,7 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
 
       # Expected gradient is sum of differences divided by number of
       # microbatches.
-      gradient_op = opt.compute_gradients(loss(data0, var0), [var0])
+      gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
       grads_and_vars = sess.run(gradient_op)
       self.assertAllCloseAccordingToType([-2.5, -2.5], grads_and_vars[0][0])
 
@@ -239,7 +238,7 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
       # Fetch params to validate initial values
       self.assertAllClose([0.0], self.evaluate(var0))
 
-      gradient_op = opt.compute_gradients(loss(data0, var0), [var0])
+      gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
       grads = []
       for _ in range(1000):
         grads_and_vars = sess.run(gradient_op)
@@ -248,5 +247,6 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
       # Test standard deviation is close to l2_norm_clip * noise_multiplier.
       self.assertNear(np.std(grads), 2.0 * 4.0, 0.5)
 
+
 if __name__ == '__main__':
   tf.test.main()