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]])
dp_sum_query = gaussian_query.GaussianSumQuery(1.0e9, 0.0)
dp_sum_query = privacy_ledger.QueryWithLedger(
dp_sum_query, 1e6, num_microbatches / 1e6)
opt = cls(dp_sum_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])
def __init__(
self,
l2_norm_clip,
noise_multiplier,
num_microbatches=None,
ledger=None,
unroll_microbatches=False,
*args, # pylint: disable=keyword-arg-before-vararg
**kwargs):
self._l2_norm_clip = l2_norm_clip
self._noise_multiplier = noise_multiplier
self._num_microbatches = num_microbatches
self._base_optimizer_class = cls
dp_sum_query = gaussian_query.GaussianSumQuery(
l2_norm_clip, l2_norm_clip * noise_multiplier)
if ledger:
dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query,
ledger=ledger)
super(DPGaussianOptimizerClass, self).__init__(
dp_sum_query,
num_microbatches,
unroll_microbatches,
*args,
**kwargs)
def testUnrollMicrobatches(self, cls):
with self.cached_session() as sess:
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]])
num_microbatches = 4
dp_sum_query = gaussian_query.GaussianSumQuery(1.0e9, 0.0)
dp_sum_query = privacy_ledger.QueryWithLedger(
dp_sum_query, 1e6, num_microbatches / 1e6)
opt = cls(
dp_sum_query,
num_microbatches=num_microbatches,
learning_rate=2.0,
unroll_microbatches=True)
self.evaluate(tf.compat.v1.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.
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])
def linear_model_fn(features, labels, mode):
preds = tf.keras.layers.Dense(
1, activation='linear', name='dense').apply(features['x'])
vector_loss = tf.math.squared_difference(labels, preds)
scalar_loss = tf.reduce_mean(input_tensor=vector_loss)
dp_sum_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query, 1e6, 1 / 1e6)
optimizer = dp_optimizer.DPGradientDescentOptimizer(
dp_sum_query,
num_microbatches=1,
learning_rate=1.0)
global_step = tf.compat.v1.train.get_global_step()
train_op = optimizer.minimize(loss=vector_loss, global_step=global_step)
return tf.estimator.EstimatorSpec(
mode=mode, loss=scalar_loss, train_op=train_op)
def test_ledger(self):
record1 = tf.constant([8.5])
record2 = tf.constant([-7.25])
population_size = tf.Variable(0)
selection_probability = tf.Variable(1.0)
query = quantile_adaptive_clip_sum_query.QuantileAdaptiveClipSumQuery(
initial_l2_norm_clip=10.0,
noise_multiplier=1.0,
target_unclipped_quantile=0.0,
learning_rate=1.0,
clipped_count_stddev=0.0,
expected_num_records=2.0,
geometric_update=False)
query = privacy_ledger.QueryWithLedger(query, population_size,
selection_probability)
# First sample.
tf.assign(population_size, 10)
tf.assign(selection_probability, 0.1)
_, global_state = test_utils.run_query(query, [record1, record2])
expected_queries = [[10.0, 10.0], [0.5, 0.0]]
formatted = query.ledger.get_formatted_ledger_eager()
sample_1 = formatted[0]
self.assertAllClose(sample_1.population_size, 10.0)
self.assertAllClose(sample_1.selection_probability, 0.1)
self.assertAllClose(sample_1.queries, expected_queries)
# Second sample.
tf.assign(population_size, 20)
tf.assign(selection_probability, 0.2)
test_utils.run_query(query, [record1, record2], global_state)
formatted = query.ledger.get_formatted_ledger_eager()
sample_1, sample_2 = formatted
self.assertAllClose(sample_1.population_size, 10.0)
self.assertAllClose(sample_1.selection_probability, 0.1)
self.assertAllClose(sample_1.queries, expected_queries)
expected_queries_2 = [[9.0, 9.0], [0.5, 0.0]]
self.assertAllClose(sample_2.population_size, 20.0)
self.assertAllClose(sample_2.selection_probability, 0.2)
self.assertAllClose(sample_2.queries, expected_queries_2)
def testClippingNorm(self, cls):
with self.cached_session() as sess:
var0 = tf.Variable([0.0, 0.0])
data0 = tf.Variable([[3.0, 4.0], [6.0, 8.0]])
dp_sum_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query, 1e6, 1 / 1e6)
opt = cls(dp_sum_query, num_microbatches=1, learning_rate=2.0)
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([0.0, 0.0], self.evaluate(var0))
# Expected gradient is sum of differences.
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])
def test_nested_query(self):
population_size = tf.Variable(0)
selection_probability = tf.Variable(1.0)
query1 = gaussian_query.GaussianAverageQuery(l2_norm_clip=4.0,
sum_stddev=2.0,
denominator=5.0)
query2 = gaussian_query.GaussianAverageQuery(l2_norm_clip=5.0,
sum_stddev=1.0,
denominator=5.0)
query = nested_query.NestedQuery([query1, query2])
query = privacy_ledger.QueryWithLedger(query, population_size,
selection_probability)
record1 = [1.0, [12.0, 9.0]]
record2 = [5.0, [1.0, 2.0]]
# First sample.
tf.compat.v1.assign(population_size, 10)
tf.compat.v1.assign(selection_probability, 0.1)
test_utils.run_query(query, [record1, record2])
expected_queries = [[4.0, 2.0], [5.0, 1.0]]
formatted = query.ledger.get_formatted_ledger_eager()
sample_1 = formatted[0]
self.assertAllClose(sample_1.population_size, 10.0)
self.assertAllClose(sample_1.selection_probability, 0.1)
self.assertAllClose(sorted(sample_1.queries), sorted(expected_queries))
# Second sample.
tf.compat.v1.assign(population_size, 20)
tf.compat.v1.assign(selection_probability, 0.2)
test_utils.run_query(query, [record1, record2])
formatted = query.ledger.get_formatted_ledger_eager()
sample_1, sample_2 = formatted
self.assertAllClose(sample_1.population_size, 10.0)
self.assertAllClose(sample_1.selection_probability, 0.1)
self.assertAllClose(sorted(sample_1.queries), sorted(expected_queries))
self.assertAllClose(sample_2.population_size, 20.0)
self.assertAllClose(sample_2.selection_probability, 0.2)
self.assertAllClose(sorted(sample_2.queries), sorted(expected_queries))
def __init__(
self,
l2_norm_clip,
exponents,
noise_variance,
num_microbatches=None,
ledger=None,
unroll_microbatches=False,
*args, # pylint: disable=keyword-arg-before-vararg
**kwargs):
dp_sum_query = dp_fixedvariance_query.GeneralSumQuery(
l2_norm_clip, exponents, noise_variance)
if ledger:
dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query,
ledger=ledger)
super(DP_fixedvarianceoptimizerClass,
self).__init__(dp_sum_query, num_microbatches,
unroll_microbatches, *args, **kwargs)
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]])
dp_sum_query = gaussian_query.GaussianSumQuery(1.0, 0.0)
dp_sum_query = privacy_ledger.QueryWithLedger(
dp_sum_query, 1e6, 1 / 1e6)
opt = cls(dp_sum_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])
def __init__(
self,
l2_norm_clip,
noise_multiplier,
num_microbatches=None,
ledger=None,
unroll_microbatches=False,
*args, # pylint: disable=keyword-arg-before-vararg
**kwargs):
"""Initializes the `DPGaussianOptimizerClass`.
Args:
l2_norm_clip: Clipping norm (max L2 norm of per microbatch gradients).
noise_multiplier: Ratio of the standard deviation to the clipping norm.
num_microbatches: Number of microbatches into which each minibatch is
split. If `None`, will default to the size of the minibatch, and
per-example gradients will be computed.
ledger: Defaults to `None`. An instance of `tf_privacy.PrivacyLedger`.
unroll_microbatches: If true, processes microbatches within a Python
loop instead of a `tf.while_loop`. Can be used if using a
`tf.while_loop` raises an exception.
*args: These will be passed on to the base class `__init__` method.
**kwargs: These will be passed on to the base class `__init__` method.
"""
self._l2_norm_clip = l2_norm_clip
self._noise_multiplier = noise_multiplier
self._num_microbatches = num_microbatches
self._base_optimizer_class = cls
dp_sum_query = gaussian_query.GaussianSumQuery(
l2_norm_clip, l2_norm_clip * noise_multiplier)
if ledger:
dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query,
ledger=ledger)
super(DPGaussianOptimizerClass,
self).__init__(dp_sum_query, num_microbatches,
unroll_microbatches, *args, **kwargs)
def testNoiseMultiplier(self, cls):
with self.cached_session() as sess:
var0 = tf.Variable([0.0])
data0 = tf.Variable([[0.0]])
dp_sum_query = gaussian_query.GaussianSumQuery(4.0, 8.0)
dp_sum_query = privacy_ledger.QueryWithLedger(dp_sum_query, 1e6, 1 / 1e6)
opt = cls(dp_sum_query, num_microbatches=1, learning_rate=2.0)
self.evaluate(tf.compat.v1.global_variables_initializer())
# Fetch params to validate initial values
self.assertAllClose([0.0], self.evaluate(var0))
gradient_op = opt.compute_gradients(self._loss(data0, var0), [var0])
grads = []
for _ in range(1000):
grads_and_vars = sess.run(gradient_op)
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)
def test_sum_query(self):
record1 = tf.constant([2.0, 0.0])
record2 = tf.constant([-1.0, 1.0])
population_size = tf.Variable(0)
selection_probability = tf.Variable(1.0)
query = gaussian_query.GaussianSumQuery(
l2_norm_clip=10.0, stddev=0.0)
query = privacy_ledger.QueryWithLedger(
query, population_size, selection_probability)
# First sample.
tf.assign(population_size, 10)
tf.assign(selection_probability, 0.1)
test_utils.run_query(query, [record1, record2])
expected_queries = [[10.0, 0.0]]
formatted = query.ledger.get_formatted_ledger_eager()
sample_1 = formatted[0]
self.assertAllClose(sample_1.population_size, 10.0)
self.assertAllClose(sample_1.selection_probability, 0.1)
self.assertAllClose(sample_1.queries, expected_queries)
# Second sample.
tf.assign(population_size, 20)
tf.assign(selection_probability, 0.2)
test_utils.run_query(query, [record1, record2])
formatted = query.ledger.get_formatted_ledger_eager()
sample_1, sample_2 = formatted
self.assertAllClose(sample_1.population_size, 10.0)
self.assertAllClose(sample_1.selection_probability, 0.1)
self.assertAllClose(sample_1.queries, expected_queries)
self.assertAllClose(sample_2.population_size, 20.0)
self.assertAllClose(sample_2.selection_probability, 0.2)
self.assertAllClose(sample_2.queries, expected_queries)
def testNoiseMultiplier(self, cls):
with tf.GradientTape(persistent=True) as gradient_tape:
var0 = tf.Variable([0.0])
data0 = tf.Variable([[0.0]])
dp_sum_query = gaussian_query.GaussianSumQuery(4.0, 8.0)
dp_sum_query = privacy_ledger.QueryWithLedger(
dp_sum_query, 1e6, 1 / 1e6)
opt = cls(dp_sum_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)
def __init__(
self,
l2_norm_clip,
noise_multiplier,
scalars,
num_microbatches=None,
ledger=None,
unroll_microbatches=False,
*args, # pylint: disable=keyword-arg-before-vararg
**kwargs):
query1 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[0], l2_norm_clip[0] * noise_multiplier[0],
scalars[0])
query2 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[1], l2_norm_clip[1] * noise_multiplier[1],
scalars[1])
query3 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[2], l2_norm_clip[2] * noise_multiplier[2],
scalars[2])
query4 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[3], l2_norm_clip[3] * noise_multiplier[3],
scalars[3])
query5 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[4], l2_norm_clip[4] * noise_multiplier[4],
scalars[4])
query6 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[5], l2_norm_clip[5] * noise_multiplier[5],
scalars[5])
query7 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[6], l2_norm_clip[6] * noise_multiplier[6],
scalars[6])
query8 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[7], l2_norm_clip[7] * noise_multiplier[7],
scalars[7])
query9 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[8], l2_norm_clip[8] * noise_multiplier[8],
scalars[8])
query10 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[9], l2_norm_clip[9] * noise_multiplier[9],
scalars[9])
query11 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[10], l2_norm_clip[10] * noise_multiplier[10],
scalars[10])
query12 = gaussian_query.GaussianAverageQuery(
l2_norm_clip[11], l2_norm_clip[11] * noise_multiplier[11],
scalars[11])
dp_nested_query = nested_query.NestedQuery([
query1, query2, query3, query4, query5, query6, query7, query8,
query9, query10, query11, query12
])
#dp_nested_query = nested_query.NestedQuery([query1, query2, query3])
if ledger:
dp_nested_query = privacy_ledger.QueryWithLedger(
dp_nested_query, ledger=ledger)
super(DPMultiGaussianOptimizerClass,
self).__init__(dp_nested_query, num_microbatches,
unroll_microbatches, *args, **kwargs)
