101 lines
4.2 KiB
Python
101 lines
4.2 KiB
Python
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# Copyright 2018, The TensorFlow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Differentially private optimizers for TensorFlow."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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import privacy.optimizers.gaussian_average_query as ph
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def make_optimizer_class(cls):
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"""Constructs a DP optimizer class from an existing one."""
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if (tf.train.Optimizer.compute_gradients.__code__ is
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not cls.compute_gradients.__code__):
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tf.logging.warning(
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'WARNING: Calling make_optimizer_class() on class %s that overrides '
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'method compute_gradients(). Check to ensure that '
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'make_optimizer_class() does not interfere with overridden version.',
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cls.__name__)
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class DPOptimizerClass(cls):
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"""Differentially private subclass of given class cls."""
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def __init__(self, l2_norm_clip, noise_multiplier, num_microbatches, *args,
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**kwargs):
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super(DPOptimizerClass, self).__init__(*args, **kwargs)
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stddev = l2_norm_clip * noise_multiplier
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self._num_microbatches = num_microbatches
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self._privacy_helper = ph.GaussianAverageQuery(l2_norm_clip, stddev,
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num_microbatches)
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self._ph_global_state = self._privacy_helper.initial_global_state()
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def compute_gradients(self,
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loss,
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var_list,
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gate_gradients=tf.train.Optimizer.GATE_OP,
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aggregation_method=None,
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colocate_gradients_with_ops=False,
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grad_loss=None):
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# Note: it would be closer to the correct i.i.d. sampling of records if
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# we sampled each microbatch from the appropriate binomial distribution,
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# although that still wouldn't be quite correct because it would be
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# sampling from the dataset without replacement.
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microbatches_losses = tf.reshape(loss, [self._num_microbatches, -1])
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sample_params = (
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self._privacy_helper.derive_sample_params(self._ph_global_state))
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def process_microbatch(i, sample_state):
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"""Process one microbatch (record) with privacy helper."""
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grads, _ = zip(*super(cls, self).compute_gradients(
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tf.gather(microbatches_losses, [i]), var_list, gate_gradients,
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aggregation_method, colocate_gradients_with_ops, grad_loss))
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grads_list = list(grads)
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sample_state = self._privacy_helper.accumulate_record(
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sample_params, sample_state, grads_list)
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return [tf.add(i, 1), sample_state]
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i = tf.constant(0)
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if var_list is None:
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var_list = (
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tf.trainable_variables() + tf.get_collection(
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tf.GraphKeys.TRAINABLE_RESOURCE_VARIABLES))
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sample_state = self._privacy_helper.initial_sample_state(
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self._ph_global_state, var_list)
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# Use of while_loop here requires that sample_state be a nested structure
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# of tensors. In general, we would prefer to allow it to be an arbitrary
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# opaque type.
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_, final_state = tf.while_loop(
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lambda i, _: tf.less(i, self._num_microbatches), process_microbatch,
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[i, sample_state])
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final_grads, self._ph_global_state = (
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self._privacy_helper.get_noised_average(final_state,
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self._ph_global_state))
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return zip(final_grads, var_list)
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return DPOptimizerClass
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DPAdagradOptimizer = make_optimizer_class(tf.train.AdagradOptimizer)
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DPAdamOptimizer = make_optimizer_class(tf.train.AdamOptimizer)
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DPGradientDescentOptimizer = make_optimizer_class(
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tf.train.GradientDescentOptimizer)
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