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Add DP versions of v1 FTRL optimizer.

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PiperOrigin-RevId: 553186886
This commit is contained in:
Steve Chien 2023-08-02 10:30:04 -07:00 committed by A. Unique TensorFlower
parent b7e9709ff7
commit a32e6ae5d0
2 changed files with 63 additions and 20 deletions
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7
tensorflow_privacy/privacy/optimizers/dp_optimizer.py
View file

@ -41,7 +41,7 @@ def make_optimizer_class(cls):
'make_optimizer_class() does not interfere with overridden version.',
cls.__name__)
class DPOptimizerClass(cls): # pylint: disable=empty-docstring
class DPOptimizerClass(cls): # pylint: disable=missing-class-docstring
__doc__ = ("""Differentially private subclass of `{base_class}`.
You can use this as a differentially private replacement for
@ -278,7 +278,7 @@ def make_gaussian_optimizer_class(cls):
A subclass of `cls` using DP-SGD with Gaussian averaging.
"""
class DPGaussianOptimizerClass(make_optimizer_class(cls)): # pylint: disable=empty-docstring
class DPGaussianOptimizerClass(make_optimizer_class(cls)): # pylint: disable=missing-class-docstring
__doc__ = ("""DP subclass of `{}`.
You can use this as a differentially private replacement for
@ -372,16 +372,19 @@ def make_gaussian_optimizer_class(cls):
AdagradOptimizer = tf.compat.v1.train.AdagradOptimizer
AdamOptimizer = tf.compat.v1.train.AdamOptimizer
FtrlOptimizer = tf.compat.v1.train.FtrlOptimizer
GradientDescentOptimizer = tf.compat.v1.train.GradientDescentOptimizer
RMSPropOptimizer = tf.compat.v1.train.RMSPropOptimizer
DPAdagradOptimizer = make_optimizer_class(AdagradOptimizer)
DPAdamOptimizer = make_optimizer_class(AdamOptimizer)
DPFtrlOptimizer = make_optimizer_class(FtrlOptimizer)
DPGradientDescentOptimizer = make_optimizer_class(GradientDescentOptimizer)
DPRMSPropOptimizer = make_optimizer_class(RMSPropOptimizer)
DPAdagradGaussianOptimizer = make_gaussian_optimizer_class(AdagradOptimizer)
DPAdamGaussianOptimizer = make_gaussian_optimizer_class(AdamOptimizer)
DPFtrlGaussianOptimizer = make_gaussian_optimizer_class(FtrlOptimizer)
DPGradientDescentGaussianOptimizer = make_gaussian_optimizer_class(
GradientDescentOptimizer)
DPRMSPropGaussianOptimizer = make_gaussian_optimizer_class(RMSPropOptimizer)

76
tensorflow_privacy/privacy/optimizers/dp_optimizer_test.py
View file

@ -57,22 +57,51 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
# Parameters for testing: optimizer, num_microbatches, expected answer.
@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]),
(
'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]),
('DPRMSPropOptimizer 1', dp_optimizer.DPRMSPropOptimizer, 1,
[-2.5, -2.5]), ('DPRMSPropOptimizer 2', dp_optimizer.DPRMSPropOptimizer,
2, [-2.5, -2.5]),
('DPRMSPropOptimizer 4', dp_optimizer.DPRMSPropOptimizer, 4, [-2.5, -2.5])
(
'DPRMSPropOptimizer 1',
dp_optimizer.DPRMSPropOptimizer,
1,
[-2.5, -2.5],
),
(
'DPRMSPropOptimizer 2',
dp_optimizer.DPRMSPropOptimizer,
2,
[-2.5, -2.5],
),
(
'DPRMSPropOptimizer 4',
dp_optimizer.DPRMSPropOptimizer,
4,
[-2.5, -2.5],
),
('DPFtrl 1', dp_optimizer.DPFtrlOptimizer, 1, [-2.5, -2.5]),
('DPFtrl 2', dp_optimizer.DPFtrlOptimizer, 2, [-2.5, -2.5]),
('DPFtrl 4', dp_optimizer.DPFtrlOptimizer, 4, [-2.5, -2.5]),
)
def testBaseline(self, cls, num_microbatches, expected_answer):
with self.cached_session() as sess:
@ -98,7 +127,9 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
('DPAdam', dp_optimizer.DPAdamOptimizer),
('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer))
('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer),
('DPFtrlOptimizer', dp_optimizer.DPFtrlOptimizer),
)
def testClippingNorm(self, cls):
with self.cached_session() as sess:
var0 = tf.Variable([0.0, 0.0])
@ -158,7 +189,9 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
('DPGradientDescent 4', dp_optimizer.DPGradientDescentOptimizer, 4),
('DPAdagrad', dp_optimizer.DPAdagradOptimizer, 1),
('DPAdam', dp_optimizer.DPAdamOptimizer, 1),
('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer, 1))
('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer, 1),
('DPFtrl', dp_optimizer.DPFtrlOptimizer, 1),
)
def testNoiseMultiplier(self, cls, num_microbatches):
with self.cached_session() as sess:
var0 = tf.Variable([0.0])
@ -212,10 +245,11 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
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)
return tf_estimator.EstimatorSpec( # pylint: disable=g-deprecated-tf-checker
mode=mode, loss=scalar_loss, train_op=train_op
)
linear_regressor = tf_estimator.Estimator(model_fn=linear_model_fn)
linear_regressor = tf_estimator.Estimator(model_fn=linear_model_fn) # pylint: disable=g-deprecated-tf-checker
true_weights = np.array([[-5], [4], [3], [2]]).astype(np.float32)
true_bias = 6.0
train_data = np.random.normal(scale=3.0, size=(200, 4)).astype(np.float32)
@ -240,7 +274,9 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
('DPAdam', dp_optimizer.DPAdamOptimizer),
('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer))
('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer),
('DPFtrl', dp_optimizer.DPFtrlOptimizer),
)
def testUnrollMicrobatches(self, cls):
with self.cached_session() as sess:
var0 = tf.Variable([1.0, 2.0])
@ -270,7 +306,9 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
('DPGradientDescent', dp_optimizer.DPGradientDescentGaussianOptimizer),
('DPAdagrad', dp_optimizer.DPAdagradGaussianOptimizer),
('DPAdam', dp_optimizer.DPAdamGaussianOptimizer),
('DPRMSPropOptimizer', dp_optimizer.DPRMSPropGaussianOptimizer))
('DPRMSPropOptimizer', dp_optimizer.DPRMSPropGaussianOptimizer),
('DPFtrl', dp_optimizer.DPFtrlGaussianOptimizer),
)
def testDPGaussianOptimizerClass(self, cls):
with self.cached_session() as sess:
var0 = tf.Variable([0.0])
@ -299,7 +337,9 @@ class DPOptimizerTest(tf.test.TestCase, parameterized.TestCase):
('DPGradientDescent', dp_optimizer.DPGradientDescentOptimizer),
('DPAdagrad', dp_optimizer.DPAdagradOptimizer),
('DPAdam', dp_optimizer.DPAdamOptimizer),
('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer))
('DPRMSPropOptimizer', dp_optimizer.DPRMSPropOptimizer),
('DPFtrl', dp_optimizer.DPFtrlOptimizer),
)
def testAssertOnNoCallOfComputeGradients(self, cls):
dp_sum_query = gaussian_query.GaussianSumQuery(1.0e9, 0.0)
opt = cls(dp_sum_query, num_microbatches=1, learning_rate=1.0)