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/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],
+) -> Mapping[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
+    ],
+) -> Sequence[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.

tensorflow_privacy/privacy/sparsity_preserving_noise/sparse_noise_utils_test.py

@@ -17,6 +17,7 @@
 import numpy as np
 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 sparse_noise_utils
 
 
@@ -436,6 +437,96 @@ def test_add_sparse_noise_with_noise(self):
         np.all(np.not_equal(noised_grad_valid_indices, grad.values.numpy()))
     )
 
+  def test_extract_varname_to_contribution_counts_fns(self):
+    def fn1(_):
+      return 1.0
+
+    def fn2(_):
+      return 2.0
+
+    var1 = tf.Variable(tf.ones((1, 2)), name='var1')
+    var2 = tf.Variable(tf.ones((1, 2)), name='var2')
+    var3 = tf.Variable(tf.ones((1, 2)), name='var3')
+
+    registry_fn_outputs_list = [
+        gradient_clipping_utils.RegistryGeneratorFunctionOutput(
+            layer_id='layer1',
+            layer_vars=[var1],
+            layer_sqr_norm_fn=None,
+            layer_trainable_weights=[var1],
+            varname_to_count_contribution_fn=None,
+        ),
+        gradient_clipping_utils.RegistryGeneratorFunctionOutput(
+            layer_id='layer2',
+            layer_vars=[var2],
+            layer_sqr_norm_fn=None,
+            layer_trainable_weights=[var2],
+            varname_to_count_contribution_fn={
+                'var2:0': [fn2],
+            },
+        ),
+        gradient_clipping_utils.RegistryGeneratorFunctionOutput(
+            layer_id='layer3',
+            layer_vars=[var3],
+            layer_sqr_norm_fn=None,
+            layer_trainable_weights=[var3],
+            varname_to_count_contribution_fn={
+                'var3:0': [fn1],
+            },
+        ),
+    ]
+    expected_varname_to_contribution_counts_fns = {
+        'var2:0': [fn2],
+        'var3:0': [fn1],
+    }
+    varname_to_contribution_counts_fns = (
+        sparse_noise_utils.extract_varname_to_contribution_counts_fns(
+            registry_fn_outputs_list,
+            trainable_vars=None,
+        )
+    )
+    self.assertEqual(
+        varname_to_contribution_counts_fns,
+        expected_varname_to_contribution_counts_fns,
+    )
+
+  def test_extract_varname_to_contribution_counts_fns_duplicate_varnames(self):
+    def fn1(_):
+      return 1.0
+
+    def fn2(_):
+      return 2.0
+
+    var1 = tf.Variable(tf.ones((1, 2)), name='var1')
+    var2 = tf.Variable(tf.ones((1, 2)), name='var1')
+
+    registry_fn_outputs_list = [
+        gradient_clipping_utils.RegistryGeneratorFunctionOutput(
+            layer_id='layer1',
+            layer_vars=[var1],
+            layer_sqr_norm_fn=None,
+            layer_trainable_weights=[var1],
+            varname_to_count_contribution_fn={
+                'var1:0': [fn1],
+            },
+        ),
+        gradient_clipping_utils.RegistryGeneratorFunctionOutput(
+            layer_id='layer2',
+            layer_vars=[var2],
+            layer_sqr_norm_fn=None,
+            layer_trainable_weights=[var2],
+            varname_to_count_contribution_fn={
+                'var1:0': [fn2],
+            },
+        ),
+    ]
+
+    with self.assertRaises(ValueError):
+      sparse_noise_utils.extract_varname_to_contribution_counts_fns(
+          registry_fn_outputs_list,
+          trainable_vars=None,
+      )
+
 
 if __name__ == '__main__':
   tf.test.main()

tensorflow_privacy/privacy/sparsity_preserving_noise/type_aliases.py

@@ -22,7 +22,7 @@
 SparseGradient = tf.IndexedSlices
 ContributionCountHistogram = tf.SparseTensor
 ContributionCountHistogramFn = Callable[
-    [SparseGradient], Mapping[str, ContributionCountHistogram]
+    [SparseGradient], ContributionCountHistogram
 ]
 NumMicrobatches = int | tf.Tensor
 SparsityPreservingNoiseLayerRegistryFunction = Callable[