Sparsity Preserving DP-SGD in TF Privacy

Add function to merge varname_to_contribution_count_fn maps from different layers.

See https://research.google/blog/sparsity-preserving-differentially-private-training/ for more details on the algorithm.

PiperOrigin-RevId: 660525767

tensorflow_privacy/privacy/fast_gradient_clipping/BUILD

@@ -46,6 +46,8 @@ py_library(
         ":common_manip_utils",
         ":layer_registry",
         ":type_aliases",
+        "//tensorflow_privacy/privacy/sparsity_preserving_noise:layer_registry",
+        "//tensorflow_privacy/privacy/sparsity_preserving_noise:type_aliases",
     ],
 )
 
@@ -55,7 +57,11 @@ py_test(
     python_version = "PY3",
     shard_count = 8,
     srcs_version = "PY3",
-    deps = [":gradient_clipping_utils"],
+    deps = [
+        ":gradient_clipping_utils",
+        ":layer_registry",
+        "//tensorflow_privacy/privacy/sparsity_preserving_noise:layer_registry",
+    ],
 )
 
 py_library(

tensorflow_privacy/privacy/fast_gradient_clipping/clip_grads.py

@@ -164,6 +164,7 @@ def compute_gradient_norms(
   registry_generator_fn = gradient_clipping_utils.get_registry_generator_fn(
       tape=tape,
       layer_registry=layer_registry,
+      sparse_noise_layer_registry=None,
       num_microbatches=num_microbatches,
   )
   layer_grad_vars, generator_outputs_list = (

tensorflow_privacy/privacy/fast_gradient_clipping/clip_grads_test.py

@@ -132,6 +132,7 @@ def _run_model_forward_backward_pass(
   registry_generator_fn = gradient_clipping_utils.get_registry_generator_fn(
       tape=tape,
       layer_registry=layer_registry.make_default_layer_registry(),
+      sparse_noise_layer_registry=None,
       num_microbatches=None,
   )
   layer_grad_vars, registry_fn_outputs_list = (

tensorflow_privacy/privacy/fast_gradient_clipping/gradient_clipping_utils.py

@@ -22,13 +22,18 @@
 from tensorflow_privacy.privacy.fast_gradient_clipping import common_manip_utils
 from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry as lr
 from tensorflow_privacy.privacy.fast_gradient_clipping import type_aliases
+from tensorflow_privacy.privacy.sparsity_preserving_noise import layer_registry as snlr
+from tensorflow_privacy.privacy.sparsity_preserving_noise import type_aliases as sn_type_aliases
 
 
 @dataclasses.dataclass(frozen=True)
 class RegistryGeneratorFunctionOutput:
   layer_id: str
   layer_vars: Optional[Sequence[tf.Variable]]
   layer_sqr_norm_fn: Optional[type_aliases.SquareNormFunction]
+  varname_to_count_contribution_fn: Optional[
+      dict[str, sn_type_aliases.ContributionCountHistogramFn]
+  ]
   layer_trainable_weights: Optional[Sequence[tf.Variable]]
 
 
@@ -46,6 +51,7 @@ def has_internal_compute_graph(input_object: Any):
 def get_registry_generator_fn(
     tape: tf.GradientTape,
     layer_registry: lr.LayerRegistry,
+    sparse_noise_layer_registry: snlr.LayerRegistry,
     num_microbatches: Optional[type_aliases.BatchSize] = None,
 ) -> Optional[Callable[..., Tuple[tf.Tensor, RegistryGeneratorFunctionOutput]]]:
   """Creates the generator function for `model_forward_backward_pass()`.
@@ -58,6 +64,10 @@ def get_registry_generator_fn(
       `output` is the pre-activator tensor, `sqr_grad_norms` is related to the
       squared norms of a layer's pre-activation tensor, and `vars` are relevant
       trainable
+    sparse_noise_layer_registry: A `LayerRegistry` instance containing functions
+      that help compute contribution counts for sparse noise. See
+      `tensorflow_privacy.privacy.sparsity_preserving_noise.layer_registry` for
+      more details.
     num_microbatches: An optional number or scalar `tf.Tensor` for the number of
       microbatches. If not None, indicates that the loss is grouped into
       num_microbatches (in this case, the batch dimension needs to be a multiple
@@ -83,6 +93,16 @@ def registry_generator_fn(layer_instance, args, kwargs):
               'be used for efficient gradient clipping.'
               % layer_instance.__class__.__name__
           )
+        varname_to_count_contribution_fn = None
+        if sparse_noise_layer_registry and sparse_noise_layer_registry.is_elem(
+            layer_instance
+        ):
+          count_contribution_registry_fn = sparse_noise_layer_registry.lookup(
+              layer_instance
+          )
+          varname_to_count_contribution_fn = count_contribution_registry_fn(
+              layer_instance, args, kwargs, num_microbatches
+          )
         registry_fn = layer_registry.lookup(layer_instance)
         (layer_vars, layer_outputs, layer_sqr_norm_fn) = registry_fn(
             layer_instance, args, kwargs, tape, num_microbatches
@@ -91,6 +111,7 @@ def registry_generator_fn(layer_instance, args, kwargs):
             layer_id=str(id(layer_instance)),
             layer_vars=layer_vars,
             layer_sqr_norm_fn=layer_sqr_norm_fn,
+            varname_to_count_contribution_fn=varname_to_count_contribution_fn,
             layer_trainable_weights=layer_instance.trainable_weights,
         )
       else:

tensorflow_privacy/privacy/fast_gradient_clipping/gradient_clipping_utils_test.py

@@ -17,6 +17,8 @@
 from absl.testing import parameterized
 import tensorflow as tf
 from tensorflow_privacy.privacy.fast_gradient_clipping import gradient_clipping_utils
+from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry as lr
+from tensorflow_privacy.privacy.sparsity_preserving_noise import layer_registry as snlr
 
 
 # ==============================================================================
@@ -175,5 +177,92 @@ def test_new_custom_layer_spec(self):
       )
 
 
+class RegistryGeneratorFnTest(tf.test.TestCase, parameterized.TestCase):
+
+  def _get_sparse_layer_registry(self):
+    def count_contribution_fn(_):
+      return None
+
+    def registry_fn(*_):
+      return {'var': count_contribution_fn}
+
+    registry = snlr.LayerRegistry()
+    registry.insert(tf.keras.layers.Embedding, registry_fn)
+    return registry, count_contribution_fn
+
+  def _get_layer_registry(self):
+    var = tf.Variable(1.0)
+    output = tf.ones((1, 1))
+
+    def sqr_norm_fn(_):
+      return None
+
+    def registry_fn(*_):
+      return [var], output, sqr_norm_fn
+
+    registry = lr.LayerRegistry()
+    registry.insert(tf.keras.layers.Embedding, registry_fn)
+    registry.insert(tf.keras.layers.Dense, registry_fn)
+    return registry, var, output, sqr_norm_fn
+
+  def test_registry_generator_fn(self):
+    inputs = tf.constant([[0, 1]])
+    model = tf.keras.Sequential([
+        tf.keras.layers.Embedding(10, 1),
+        tf.keras.layers.Dense(1),
+    ])
+
+    sparse_layer_registry, count_contribution_fn = (
+        self._get_sparse_layer_registry()
+    )
+    layer_registry, var, output, sqr_norm_fn = self._get_layer_registry()
+    registry_generator_fn = gradient_clipping_utils.get_registry_generator_fn(
+        tape=tf.GradientTape(),
+        layer_registry=layer_registry,
+        sparse_noise_layer_registry=sparse_layer_registry,
+        num_microbatches=None,
+    )
+    embedding_layer = model.layers[0]
+    out, embedding_registry_generator_fn_output = registry_generator_fn(
+        embedding_layer,
+        [inputs],
+        {},
+    )
+    expected_embedding_registry_generator_fn_output = (
+        gradient_clipping_utils.RegistryGeneratorFunctionOutput(
+            layer_id=str(id(embedding_layer)),
+            layer_vars=[var],
+            layer_sqr_norm_fn=sqr_norm_fn,
+            varname_to_count_contribution_fn={'var': count_contribution_fn},
+            layer_trainable_weights=embedding_layer.trainable_weights,
+        )
+    )
+    self.assertEqual(
+        embedding_registry_generator_fn_output,
+        expected_embedding_registry_generator_fn_output,
+    )
+    self.assertEqual(out, output)
+    dense_layer = model.layers[1]
+    out, dense_registry_generator_fn_output = registry_generator_fn(
+        dense_layer,
+        [inputs],
+        {},
+    )
+    expected_dense_registry_generator_fn_output = (
+        gradient_clipping_utils.RegistryGeneratorFunctionOutput(
+            layer_id=str(id(dense_layer)),
+            layer_vars=[var],
+            layer_sqr_norm_fn=sqr_norm_fn,
+            varname_to_count_contribution_fn=None,
+            layer_trainable_weights=dense_layer.trainable_weights,
+        )
+    )
+    self.assertEqual(
+        dense_registry_generator_fn_output,
+        expected_dense_registry_generator_fn_output,
+    )
+    self.assertEqual(out, output)
+
+
 if __name__ == '__main__':
   tf.test.main()

tensorflow_privacy/privacy/keras_models/dp_keras_model.py

@@ -280,6 +280,7 @@ def train_step(self, data):
             gradient_clipping_utils.get_registry_generator_fn(
                 tape=tape,
                 layer_registry=self._layer_registry,
+                sparse_noise_layer_registry=None,
                 num_microbatches=num_microbatches,
             )
         )

tensorflow_privacy/privacy/sparsity_preserving_noise/BUILD

@@ -5,12 +5,19 @@ licenses(["notice"])
 py_library(
     name = "sparse_noise_utils",
     srcs = ["sparse_noise_utils.py"],
+    deps = [
+        ":type_aliases",
+        "//tensorflow_privacy/privacy/fast_gradient_clipping:gradient_clipping_utils",
+    ],
 )
 
 py_test(
     name = "sparse_noise_utils_test",
     srcs = ["sparse_noise_utils_test.py"],
-    deps = [":sparse_noise_utils"],
+    deps = [
+        ":sparse_noise_utils",
+        "//tensorflow_privacy/privacy/fast_gradient_clipping:gradient_clipping_utils",
+    ],
 )
 
 py_library(

tensorflow_privacy/privacy/sparsity_preserving_noise/sparse_noise_utils.py

@@ -16,10 +16,13 @@
 For more details on the algorithm, refer to https://arxiv.org/abs/2311.08357.
 """
 
+import collections
 from typing import Mapping, Optional, Sequence
 
 from scipy import stats
 import tensorflow as tf
+from tensorflow_privacy.privacy.fast_gradient_clipping import gradient_clipping_utils
+from tensorflow_privacy.privacy.sparsity_preserving_noise import type_aliases
 import tensorflow_probability as tfp
 
 
@@ -288,15 +291,60 @@ def add_sparse_gradient_noise(
   )
 
 
+def extract_varname_to_contribution_counts_fns(
+    registry_fn_outputs_list: Sequence[
+        gradient_clipping_utils.RegistryGeneratorFunctionOutput
+    ],
+    trainable_vars: Sequence[tf.Variable],
+) -> dict[str, type_aliases.ContributionCountHistogramFn]:
+  """Extracts a map of contribution count fns from generator outputs.
+
+  Args:
+    registry_fn_outputs_list: A list of `RegistryGeneratorFunctionOutput`
+      instances returned by
+      `gradient_clipping_utils.model_forward_backward_pass`.
+    trainable_vars: A list of trainable variables.
+
+  Returns:
+    A `dict` from varname to contribution counts functions
+  """
+  if trainable_vars is not None:
+    # Create a set using `ref()` for fast set membership check. tf.Variable
+    # itself is not hashable.
+    trainable_vars = set([v.ref() for v in trainable_vars])
+
+  varname_to_contribution_counts_fns = collections.defaultdict(list)
+  for registry_fn_output in registry_fn_outputs_list:
+    if trainable_vars is None or any(
+        w.ref() in trainable_vars
+        for w in registry_fn_output.layer_trainable_weights
+    ):
+      if registry_fn_output.varname_to_count_contribution_fn is not None:
+        duplicate_varnames = set(
+            registry_fn_output.varname_to_count_contribution_fn.keys()
+        ) & set(varname_to_contribution_counts_fns.keys())
+        if duplicate_varnames:
+          raise ValueError(
+              'Duplicate varnames: {duplicate_varnames} found in contribution'
+              ' counts functions.'
+          )
+        varname_to_contribution_counts_fns.update(
+            registry_fn_output.varname_to_count_contribution_fn
+        )
+  return varname_to_contribution_counts_fns
+
+
 def get_contribution_counts(
-    trainable_vars: list[tf.Variable],
-    grads: list[tf.Tensor],
-    varname_to_contribution_counts_fns: Mapping[str, tf.SparseTensor],
-) -> list[tf.Tensor | None]:
+    trainable_vars: Sequence[tf.Variable],
+    grads: Sequence[tf.Tensor],
+    varname_to_contribution_counts_fns: Mapping[
+        str, type_aliases.ContributionCountHistogramFn
+    ],
+) -> list[type_aliases.ContributionCountHistogram | None]:
   """Gets the contribution counts for each variable in the Model.
 
   Args:
-    trainable_vars: A list of the trainable variables in the Model.
+    trainable_vars: A list of trainable variables.
     grads: A corresponding list of gradients for each trainable variable.
     varname_to_contribution_counts_fns: A mapping from variable name to a list
       of functions to get the contribution counts for that variable.