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tensorflow_privacy/privacy/bolton/optimizers_test.py
Christopher Choquette Choo 71c4a11eb9 Fixing new pylint errors.
2019-07-27 14:14:05 -04:00

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# 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.
"""Unit testing for optimizers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
import tensorflow as tf
from tensorflow.python import ops as _ops
from tensorflow.python.framework import test_util
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras import losses
from tensorflow.python.keras.initializers import constant
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.optimizer_v2.optimizer_v2 import OptimizerV2
from tensorflow.python.keras.regularizers import L1L2
from tensorflow.python.platform import test
from privacy.bolton import optimizers as opt
from privacy.bolton.losses import StrongConvexMixin
class TestModel(Model): # pylint: disable=abstract-method
"""Bolton episilon-delta model.
Uses 4 key steps to achieve privacy guarantees:
1. Adds noise to weights after training (output perturbation).
2. Projects weights to R after each batch
3. Limits learning rate
4. Use a strongly convex loss function (see compile)
For more details on the strong convexity requirements, see:
Bolt-on Differential Privacy for Scalable Stochastic Gradient
Descent-based Analytics by Xi Wu et. al.
"""
def __init__(self, n_outputs=2, input_shape=(16,), init_value=2):
"""Constructor.
Args:
n_outputs: number of output neurons
epsilon: level of privacy guarantee
noise_distribution: distribution to pull weight perturbations from
weights_initializer: initializer for weights
seed: random seed to use
dtype: data type to use for tensors
"""
super(TestModel, self).__init__(name='bolton', dynamic=False)
self.n_outputs = n_outputs
self.layer_input_shape = input_shape
self.output_layer = tf.keras.layers.Dense(
self.n_outputs,
input_shape=self.layer_input_shape,
kernel_regularizer=L1L2(l2=1),
kernel_initializer=constant(init_value),
)
class TestLoss(losses.Loss, StrongConvexMixin):
"""Test loss function for testing Bolton model."""
def __init__(self, reg_lambda, C, radius_constant, name='test'):
super(TestLoss, self).__init__(name=name)
self.reg_lambda = reg_lambda
self.C = C # pylint: disable=invalid-name
self.radius_constant = radius_constant
def radius(self):
"""Radius, R, of the hypothesis space W.
W is a convex set that forms the hypothesis space.
Returns: radius
"""
return _ops.convert_to_tensor_v2(self.radius_constant, dtype=tf.float32)
def gamma(self):
"""Returns strongly convex parameter, gamma."""
return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
def beta(self, class_weight): # pylint: disable=unused-argument
"""Smoothness, beta.
Args:
class_weight: the class weights as scalar or 1d tensor, where its
dimensionality is equal to the number of outputs.
Returns:
Beta
"""
return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
def lipchitz_constant(self, class_weight): # pylint: disable=unused-argument
"""Lipchitz constant, L.
Args:
class_weight: class weights used
Returns: L
"""
return _ops.convert_to_tensor_v2(1, dtype=tf.float32)
def call(self, y_true, y_pred):
"""Loss function that is minimized at the mean of the input points."""
return 0.5 * tf.reduce_sum(
tf.math.squared_difference(y_true, y_pred),
axis=1
)
def max_class_weight(self, class_weight, dtype=tf.float32):
"""the maximum weighting in class weights (max value) as a scalar tensor.
Args:
class_weight: class weights used
dtype: the data type for tensor conversions.
Returns:
maximum class weighting as tensor scalar
"""
if class_weight is None:
return 1
raise NotImplementedError('')
def kernel_regularizer(self):
"""Returns the kernel_regularizer to be used.
Any subclass should override this method if they want a kernel_regularizer
(if required for the loss function to be StronglyConvex.
"""
return L1L2(l2=self.reg_lambda)
class TestOptimizer(OptimizerV2):
"""Optimizer used for testing the Bolton optimizer"""
def __init__(self):
super(TestOptimizer, self).__init__('test')
self.not_private = 'test'
self.iterations = tf.constant(1, dtype=tf.float32)
self._iterations = tf.constant(1, dtype=tf.float32)
def _compute_gradients(self, loss, var_list, grad_loss=None):
return 'test'
def get_config(self):
return 'test'
def from_config(self, config, custom_objects=None):
return 'test'
def _create_slots(self):
return 'test'
def _resource_apply_dense(self, grad, handle):
return 'test'
def _resource_apply_sparse(self, grad, handle, indices):
return 'test'
def get_updates(self, loss, params):
return 'test'
def apply_gradients(self, grads_and_vars, name=None):
return 'test'
def minimize(self, loss, var_list, grad_loss=None, name=None):
return 'test'
def get_gradients(self, loss, params):
return 'test'
def limit_learning_rate(self):
return 'test'
class BoltonOptimizerTest(keras_parameterized.TestCase):
"""Bolton Optimizer tests."""
@test_util.run_all_in_graph_and_eager_modes
@parameterized.named_parameters([
{'testcase_name': 'getattr',
'fn': '__getattr__',
'args': ['dtype'],
'result': tf.float32,
'test_attr': None},
{'testcase_name': 'project_weights_to_r',
'fn': 'project_weights_to_r',
'args': ['dtype'],
'result': None,
'test_attr': ''},
])
def test_fn(self, fn, args, result, test_attr):
"""test that a fn of Bolton optimizer is working as expected.
Args:
fn: method of Optimizer to test
args: args to optimizer fn
result: the expected result
test_attr: None if the fn returns the test result. Otherwise, this is
the attribute of Bolton to check against result with.
"""
tf.random.set_seed(1)
loss = TestLoss(1, 1, 1)
bolton = opt.Bolton(TestOptimizer(), loss)
model = TestModel(1)
model.layers[0].kernel = \
model.layers[0].kernel_initializer((model.layer_input_shape[0],
model.n_outputs))
bolton._is_init = True # pylint: disable=protected-access
bolton.layers = model.layers
bolton.epsilon = 2
bolton.noise_distribution = 'laplace'
bolton.n_outputs = 1
bolton.n_samples = 1
res = getattr(bolton, fn, None)(*args)
if test_attr is not None:
res = getattr(bolton, test_attr, None)
if hasattr(res, 'numpy') and hasattr(result, 'numpy'): # both tensors/not
res = res.numpy()
result = result.numpy()
self.assertEqual(res, result)
@test_util.run_all_in_graph_and_eager_modes
@parameterized.named_parameters([
{'testcase_name': '1 value project to r=1',
'r': 1,
'init_value': 2,
'shape': (1,),
'n_out': 1,
'result': [[1]]},
{'testcase_name': '2 value project to r=1',
'r': 1,
'init_value': 2,
'shape': (2,),
'n_out': 1,
'result': [[0.707107], [0.707107]]},
{'testcase_name': '1 value project to r=2',
'r': 2,
'init_value': 3,
'shape': (1,),
'n_out': 1,
'result': [[2]]},
{'testcase_name': 'no project',
'r': 2,
'init_value': 1,
'shape': (1,),
'n_out': 1,
'result': [[1]]},
])
def test_project(self, r, shape, n_out, init_value, result):
"""test that a fn of Bolton optimizer is working as expected.
Args:
r: Radius value for StrongConvex loss function.
shape: input_dimensionality
n_out: output dimensionality
init_value: the initial value for 'constant' kernel initializer
result: the expected output after projection.fFF
"""
tf.random.set_seed(1)
@tf.function
def project_fn(r):
loss = TestLoss(1, 1, r)
bolton = opt.Bolton(TestOptimizer(), loss)
model = TestModel(n_out, shape, init_value)
model.compile(bolton, loss)
model.layers[0].kernel = \
model.layers[0].kernel_initializer((model.layer_input_shape[0],
model.n_outputs))
bolton._is_init = True # pylint: disable=protected-access
bolton.layers = model.layers
bolton.epsilon = 2
bolton.noise_distribution = 'laplace'
bolton.n_outputs = 1
bolton.n_samples = 1
bolton.project_weights_to_r()
return _ops.convert_to_tensor_v2(bolton.layers[0].kernel, tf.float32)
res = project_fn(r)
self.assertAllClose(res, result)
@test_util.run_all_in_graph_and_eager_modes
@parameterized.named_parameters([
{'testcase_name': 'normal values',
'epsilon': 2,
'noise': 'laplace',
'class_weights': 1},
])
def test_context_manager(self, noise, epsilon, class_weights):
"""Tests the context manager functionality of the optimizer.
Args:
noise: noise distribution to pick
epsilon: epsilon privacy parameter to use
class_weights: class_weights to use
"""
@tf.function
def test_run():
loss = TestLoss(1, 1, 1)
bolton = opt.Bolton(TestOptimizer(), loss)
model = TestModel(1, (1,), 1)
model.compile(bolton, loss)
model.layers[0].kernel = \
model.layers[0].kernel_initializer((model.layer_input_shape[0],
model.n_outputs))
with bolton(noise, epsilon, model.layers, class_weights, 1, 1) as _:
pass
return _ops.convert_to_tensor_v2(bolton.epsilon, dtype=tf.float32)
epsilon = test_run()
self.assertEqual(epsilon.numpy(), -1)
@parameterized.named_parameters([
{'testcase_name': 'invalid noise',
'epsilon': 1,
'noise': 'not_valid',
'err_msg': 'Detected noise distribution: not_valid not one of:'},
{'testcase_name': 'invalid epsilon',
'epsilon': -1,
'noise': 'laplace',
'err_msg': 'Detected epsilon: -1. Valid range is 0 < epsilon <inf'},
])
def test_context_domains(self, noise, epsilon, err_msg):
"""Tests the context domains.
Args:
noise: noise distribution to pick
epsilon: epsilon privacy parameter to use
err_msg: the expected error message
"""
@tf.function
def test_run(noise, epsilon):
loss = TestLoss(1, 1, 1)
bolton = opt.Bolton(TestOptimizer(), loss)
model = TestModel(1, (1,), 1)
model.compile(bolton, loss)
model.layers[0].kernel = \
model.layers[0].kernel_initializer((model.layer_input_shape[0],
model.n_outputs))
with bolton(noise, epsilon, model.layers, 1, 1, 1) as _:
pass
with self.assertRaisesRegexp(ValueError, err_msg): # pylint: disable=deprecated-method
test_run(noise, epsilon)
@parameterized.named_parameters([
{'testcase_name': 'fn: get_noise',
'fn': 'get_noise',
'args': [1, 1],
'err_msg': 'ust be called from within the optimizer\'s context'},
])
def test_not_in_context(self, fn, args, err_msg):
"""Tests that the expected functions raise errors when not in context.
Args:
fn: the function to test
args: the arguments for said function
err_msg: expected error message
"""
@tf.function
def test_run(fn, args):
loss = TestLoss(1, 1, 1)
bolton = opt.Bolton(TestOptimizer(), loss)
model = TestModel(1, (1,), 1)
model.compile(bolton, loss)
model.layers[0].kernel = \
model.layers[0].kernel_initializer((model.layer_input_shape[0],
model.n_outputs))
getattr(bolton, fn)(*args)
with self.assertRaisesRegexp(Exception, err_msg): # pylint: disable=deprecated-method
test_run(fn, args)
@parameterized.named_parameters([
{'testcase_name': 'fn: get_updates',
'fn': 'get_updates',
'args': [0, 0]},
{'testcase_name': 'fn: get_config',
'fn': 'get_config',
'args': []},
{'testcase_name': 'fn: from_config',
'fn': 'from_config',
'args': [0]},
{'testcase_name': 'fn: _resource_apply_dense',
'fn': '_resource_apply_dense',
'args': [1, 1]},
{'testcase_name': 'fn: _resource_apply_sparse',
'fn': '_resource_apply_sparse',
'args': [1, 1, 1]},
{'testcase_name': 'fn: apply_gradients',
'fn': 'apply_gradients',
'args': [1]},
{'testcase_name': 'fn: minimize',
'fn': 'minimize',
'args': [1, 1]},
{'testcase_name': 'fn: _compute_gradients',
'fn': '_compute_gradients',
'args': [1, 1]},
{'testcase_name': 'fn: get_gradients',
'fn': 'get_gradients',
'args': [1, 1]},
])
def test_rerouted_function(self, fn, args):
"""Tests rerouted function.
Tests that a method of the internal optimizer is correctly routed from
the Bolton instance to the internal optimizer instance (TestOptimizer,
here).
Args:
fn: fn to test
args: arguments to that fn
"""
loss = TestLoss(1, 1, 1)
optimizer = TestOptimizer()
bolton = opt.Bolton(optimizer, loss)
model = TestModel(3)
model.compile(optimizer, loss)
model.layers[0].kernel = \
model.layers[0].kernel_initializer((model.layer_input_shape[0],
model.n_outputs))
model.layers[0].kernel = \
model.layers[0].kernel_initializer((model.layer_input_shape[0],
model.n_outputs))
bolton._is_init = True # pylint: disable=protected-access
bolton.layers = model.layers
bolton.epsilon = 2
bolton.noise_distribution = 'laplace'
bolton.n_outputs = 1
bolton.n_samples = 1
self.assertEqual(
getattr(bolton, fn, lambda: 'fn not found')(*args),
'test'
)
@parameterized.named_parameters([
{'testcase_name': 'fn: project_weights_to_r',
'fn': 'project_weights_to_r',
'args': []},
{'testcase_name': 'fn: get_noise',
'fn': 'get_noise',
'args': [1, 1]},
])
def test_not_reroute_fn(self, fn, args):
"""Test function is not rerouted.
Test that a fn that should not be rerouted to the internal optimizer is
in fact not rerouted.
Args:
fn: fn to test
args: arguments to that fn
"""
@tf.function
def test_run(fn, args):
loss = TestLoss(1, 1, 1)
bolton = opt.Bolton(TestOptimizer(), loss)
model = TestModel(1, (1,), 1)
model.compile(bolton, loss)
model.layers[0].kernel = \
model.layers[0].kernel_initializer((model.layer_input_shape[0],
model.n_outputs))
bolton._is_init = True # pylint: disable=protected-access
bolton.noise_distribution = 'laplace'
bolton.epsilon = 1
bolton.layers = model.layers
bolton.class_weights = 1
bolton.n_samples = 1
bolton.batch_size = 1
bolton.n_outputs = 1
res = getattr(bolton, fn, lambda: 'test')(*args)
if res != 'test':
res = 1
else:
res = 0
return _ops.convert_to_tensor_v2(res, dtype=tf.float32)
self.assertNotEqual(test_run(fn, args), 0)
@parameterized.named_parameters([
{'testcase_name': 'attr: _iterations',
'attr': '_iterations'}
])
def test_reroute_attr(self, attr):
"""Test a function is rerouted.
Test that attribute of internal optimizer is correctly rerouted to the
internal optimizer.
Args:
attr: attribute to test
"""
loss = TestLoss(1, 1, 1)
internal_optimizer = TestOptimizer()
optimizer = opt.Bolton(internal_optimizer, loss)
self.assertEqual(getattr(optimizer, attr),
getattr(internal_optimizer, attr))
@parameterized.named_parameters([
{'testcase_name': 'attr does not exist',
'attr': '_not_valid'}
])
def test_attribute_error(self, attr):
"""Test rerouting of attributes.
Test that attribute of internal optimizer is correctly rerouted to the
internal optimizer
Args:
attr: attribute to test
"""
loss = TestLoss(1, 1, 1)
internal_optimizer = TestOptimizer()
optimizer = opt.Bolton(internal_optimizer, loss)
with self.assertRaises(AttributeError):
getattr(optimizer, attr)
class SchedulerTest(keras_parameterized.TestCase):
"""GammaBeta Scheduler tests"""
@parameterized.named_parameters([
{'testcase_name': 'not in context',
'err_msg': 'Please initialize the GammaBetaDecreasingStep Learning Rate'
' Scheduler'
}
])
def test_bad_call(self, err_msg):
""" test that attribute of internal optimizer is correctly rerouted to
the internal optimizer
Args:
err_msg: The expected error message from the scheduler bad call.
"""
scheduler = opt.GammaBetaDecreasingStep()
with self.assertRaisesRegexp(Exception, err_msg): # pylint: disable=deprecated-method
scheduler(1)
@parameterized.named_parameters([
{'testcase_name': 'step 1',
'step': 1,
'res': 0.5},
{'testcase_name': 'step 2',
'step': 2,
'res': 0.5},
{'testcase_name': 'step 3',
'step': 3,
'res': 0.333333333},
])
def test_call(self, step, res):
"""Test call.
Test that attribute of internal optimizer is correctly rerouted to the
internal optimizer
Args:
step: step number to 'GammaBetaDecreasingStep' 'Scheduler'.
res: expected result from call to 'GammaBetaDecreasingStep' 'Scheduler'.
"""
beta = _ops.convert_to_tensor_v2(2, dtype=tf.float32)
gamma = _ops.convert_to_tensor_v2(1, dtype=tf.float32)
scheduler = opt.GammaBetaDecreasingStep()
scheduler.initialize(beta, gamma)
step = _ops.convert_to_tensor_v2(step, dtype=tf.float32)
lr = scheduler(step)
self.assertAllClose(lr.numpy(), res)
if __name__ == '__main__':
test.main()
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