forked from 626_privacy/tensorflow_privacy
578 lines
18 KiB
Python
578 lines
18 KiB
Python
# 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
|
|
input_shape:
|
|
init_value:
|
|
"""
|
|
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_arg, radius_constant, name='test'):
|
|
super(TestLoss, self).__init__(name=name)
|
|
self.reg_lambda = reg_lambda
|
|
self.C = C_arg # 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.
|
|
"""
|
|
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 attribute of internal opt correctly rerouted to the internal opt.
|
|
|
|
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()
|