-
Notifications
You must be signed in to change notification settings - Fork 453
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Sparsity Preserving DP-SGD in TF Privacy
Refactor utilities for adding noise into separate utility file. See https://research.google/blog/sparsity-preserving-differentially-private-training/ for more details on the algorithm. PiperOrigin-RevId: 660475009
- Loading branch information
1 parent
fc6f1dc
commit cabf75a
Showing
7 changed files
with
203 additions
and
158 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
115 changes: 115 additions & 0 deletions
115
tensorflow_privacy/privacy/fast_gradient_clipping/noise_utils.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,115 @@ | ||
| # Copyright 2024, 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. | ||
| """Utility functions that help in adding noise to gradients.""" | ||
|
|
||
| from collections.abc import Sequence | ||
| from typing import Literal, Optional | ||
|
|
||
| from absl import logging | ||
| import tensorflow as tf | ||
|
|
||
|
|
||
| def _infer_loss_reduction_type(model: tf.keras.Model): | ||
| """Infers what type of loss reduction is being performed.""" | ||
| model_loss = model.loss | ||
| if isinstance(model_loss, tf.keras.losses.Loss): | ||
| return model_loss.reduction | ||
| elif isinstance(model.loss, dict): | ||
| reductions = set() | ||
| compiled_loss = model.compiled_loss | ||
| if compiled_loss is None: | ||
| raise ValueError('Model must be compiled for adding noise') | ||
| new_config_list = compiled_loss.get_config()['losses'] | ||
| for loss_config in new_config_list: | ||
| reductions.add(loss_config['config']['reduction']) | ||
| if len(reductions) > 1: | ||
| raise ValueError( | ||
| 'Reductions in models with multiple losses must all be the same' | ||
| ) | ||
| return reductions.pop() | ||
| else: | ||
| raise ValueError( | ||
| 'Unsupported type for adding noise: {}'.format(type(model_loss)) | ||
| ) | ||
|
|
||
|
|
||
| def add_aggregate_noise( | ||
| clipped_grads: list[tf.Tensor], | ||
| batch_size: tf.Tensor, | ||
| l2_norm_clip: float, | ||
| noise_multiplier: float, | ||
| loss_reduction: Optional[Literal['mean', 'sum']] = None, | ||
| loss_model: Optional[tf.keras.Model] = None, | ||
| ) -> Sequence[tf.Tensor]: | ||
| """Adds noise to a collection of clipped gradients. | ||
| The magnitude of the noise depends on the aggregation strategy of the | ||
| input model's loss function. | ||
| Args: | ||
| clipped_grads: A list of `tf.Tensor`s representing the clipped gradients. | ||
| batch_size: The batch size. Used for normalizing the noise when | ||
| `loss_reduction` is 'sum'. | ||
| l2_norm_clip: Clipping norm (max L2 norm of each gradient). | ||
| noise_multiplier: Ratio of the standard deviation to the clipping norm. | ||
| loss_reduction: An string description of how the loss is reduced over | ||
| examples. Currently supports 'mean' and 'sum'. If `None`, then the | ||
| aggregation type must be inferred from `input_model.loss`. | ||
| loss_model: An optional `tf.keras.Model` used to infer the loss reduction | ||
| strategy from if `loss_reduction` is `None`. | ||
| Returns: | ||
| A list of tensors containing the clipped gradients, but with the right | ||
| amount of Gaussian noise added to them (depending on the reduction | ||
| strategy of the loss function). | ||
| Raises: | ||
| ValueError: If both `loss_model` and `loss_reduction` are `None` or if | ||
| they are both not `None`. | ||
| """ | ||
| if loss_reduction is None and loss_model is None: | ||
| raise ValueError( | ||
| 'Exactly one of `loss_reduction` and `loss_model` must be populated.' | ||
| ' Instead, both arguments were `None`.' | ||
| ) | ||
| if loss_reduction is not None and loss_model is not None: | ||
| raise ValueError( | ||
| 'Exactly one of `loss_reduction` and `loss_model` must be populated.' | ||
| ' Instead, both arguments were not `None`.' | ||
| ) | ||
|
|
||
| if loss_reduction is None and loss_model is not None: | ||
| implicit_mean_reductions = [ | ||
| tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE, | ||
| tf.keras.losses.Reduction.AUTO, | ||
| ] | ||
| model_reduction = _infer_loss_reduction_type(loss_model) | ||
| loss_reduction = ( | ||
| 'mean' if model_reduction in implicit_mean_reductions else 'sum' | ||
| ) | ||
| if model_reduction == tf.keras.losses.Reduction.AUTO: | ||
| logging.info( | ||
| 'Assuming that the model loss reduction is `SUM_OVER_BATCH_SIZE`.' | ||
| ) | ||
|
|
||
| scale = l2_norm_clip | ||
| if loss_reduction == 'mean': | ||
| scale /= tf.cast(batch_size, tf.float32) | ||
|
|
||
| def add_noise(g): | ||
| return g + tf.random.normal( | ||
| tf.shape(g), mean=0.0, stddev=noise_multiplier * scale | ||
| ) | ||
|
|
||
| return tf.nest.map_structure(add_noise, clipped_grads) |
Oops, something went wrong.