Add a parameter to the noise function that explicitly specifies the l…

…oss reduction type.

PiperOrigin-RevId: 583143642

tensorflow_privacy/privacy/fast_gradient_clipping/BUILD

@@ -38,6 +38,7 @@ py_test(
     name = "gradient_clipping_utils_test",
     srcs = ["gradient_clipping_utils_test.py"],
     python_version = "PY3",
+    shard_count = 8,
     srcs_version = "PY3",
     deps = [
         ":gradient_clipping_utils",

tensorflow_privacy/privacy/fast_gradient_clipping/gradient_clipping_utils.py

@@ -14,7 +14,7 @@
 """Utility functions that help in the computation of per-example gradient norms."""
 
 from collections.abc import Sequence, Set
-from typing import Any, Optional
+from typing import Any, Literal, Optional
 
 from absl import logging
 import tensorflow as tf
@@ -145,37 +145,66 @@ def all_trainable_layers_are_registered(
 
 
 def add_aggregate_noise(
-    input_model: tf.keras.Model,
     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:
-    input_model: The `tf.keras.Model` to obtain the layers from.
     clipped_grads: A list of `tf.Tensor`s representing the clipped gradients.
-    batch_size: The batch size, used for normalizing the noise, when the loss
-      reduction is AUTO or SUM_OVER_BATCH_SIZE.
+    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 = loss_model.loss.reduction
+    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 input_model.loss.reduction in [
-      tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE,
-      tf.keras.losses.Reduction.AUTO,
-  ]:
-    if input_model.loss.reduction == tf.keras.losses.Reduction.AUTO:
-      logging.info('Assuming that the loss reduction is `SUM_OVER_BATCH_SIZE`.')
+  if loss_reduction == 'mean':
     scale /= tf.cast(batch_size, tf.float32)
 
   def add_noise(g):

tensorflow_privacy/privacy/fast_gradient_clipping/gradient_clipping_utils_test.py

@@ -15,6 +15,7 @@
 from typing import Any
 
 from absl.testing import parameterized
+import numpy as np
 import tensorflow as tf
 from tensorflow_privacy.privacy.fast_gradient_clipping import gradient_clipping_utils
 
@@ -134,6 +135,60 @@ def test_outputs_are_consistent(
     self.assertAllClose(computed_outputs, true_outputs)
 
 
+class AddAggregateNoise(tf.test.TestCase, parameterized.TestCase):
+
+  @parameterized.product(
+      l2_norm_clip=[3.0, 5.0],
+      noise_multiplier=[2.0, 4.0],
+      batch_size=[1, 2, 10],
+      model_fn_reduction=[None, 'auto', 'sum_over_batch_size', 'sum'],
+      noise_fn_reduction=[None, 'mean', 'sum'],
+  )
+  def test_noise_is_computed_correctly(
+      self,
+      l2_norm_clip,
+      noise_multiplier,
+      batch_size,
+      model_fn_reduction,
+      noise_fn_reduction,
+  ):
+    # Skip invalid combinations.
+    if model_fn_reduction is None and noise_fn_reduction is None:
+      return
+    if model_fn_reduction is not None and noise_fn_reduction is not None:
+      return
+    # Make an simple model container for storing the loss.
+    if model_fn_reduction is not None:
+      linear_model = tf.keras.Sequential([tf.keras.layers.Dense(1)])
+      linear_model.compile(
+          loss=tf.keras.losses.MeanSquaredError(reduction=model_fn_reduction)
+      )
+    else:
+      linear_model = None
+    # The main computation is done on a deterministic dummy vector.
+    num_units = 100
+    clipped_grads = [
+        tf.expand_dims(np.arange(num_units, dtype=np.float32), axis=-1)
+    ]
+    noised_grads = gradient_clipping_utils.add_aggregate_noise(
+        clipped_grads,
+        batch_size,
+        l2_norm_clip,
+        noise_multiplier,
+        noise_fn_reduction,
+        linear_model,
+    )
+    # The only measure that varies is the standard deviation of the variation.
+    scale = (
+        1.0
+        if noise_fn_reduction == 'sum' or model_fn_reduction == 'sum'
+        else 1.0 / batch_size
+    )
+    computed_std = np.std(noised_grads[0] - clipped_grads[0])
+    expected_std = l2_norm_clip * noise_multiplier * scale
+    self.assertNear(computed_std, expected_std, 0.1 * expected_std)
+
+
 class GenerateOutputsUsingCoreKerasLayers(
     tf.test.TestCase, parameterized.TestCase
 ):

tensorflow_privacy/privacy/keras_models/BUILD

@@ -25,6 +25,7 @@ py_test(
     name = "dp_keras_model_test",
     srcs = ["dp_keras_model_test.py"],
     python_version = "PY3",
+    shard_count = 16,
     srcs_version = "PY3",
     deps = [
         "//tensorflow_privacy/privacy/fast_gradient_clipping:layer_registry",

tensorflow_privacy/privacy/keras_models/dp_keras_model.py

@@ -264,11 +264,12 @@ def train_step(self, data):
         output_metrics[_PRIVATIZED_LOSS_NAME] = clipping_loss
         if self._noise_multiplier > 0:
           grads = gradient_clipping_utils.add_aggregate_noise(
-              self,
               clipped_grads,
               num_microbatches,
               self._l2_norm_clip,
               self._noise_multiplier,
+              loss_reduction=None,
+              loss_model=self,
           )
         else:
           grads = clipped_grads