Improve the readability of the fast gradient clipping library.

PiperOrigin-RevId: 566961891

tensorflow_privacy/privacy/fast_gradient_clipping/clip_grads.py

@@ -21,7 +21,8 @@ of the approach given in https://arxiv.org/pdf/2009.03106.pdf (see the
 `compute_gradient_norms()` function).
 """
 
-from typing import List, Optional, Tuple
+from collections.abc import Sequence
+from typing import Optional
 
 import tensorflow as tf
 from tensorflow_privacy.privacy.fast_gradient_clipping import common_manip_utils
@@ -74,7 +75,7 @@ def compute_gradient_norms(
     weight_batch: Optional[tf.Tensor] = None,
     per_example_loss_fn: Optional[type_aliases.LossFn] = None,
     num_microbatches: Optional[type_aliases.BatchSize] = None,
-    trainable_vars: Optional[List[tf.Variable]] = None,
+    trainable_vars: Optional[Sequence[tf.Variable]] = None,
 ):
   """Computes the per-example loss gradient norms for given data.
 
@@ -219,7 +220,7 @@ def compute_clipped_gradients_and_outputs(
     weight_batch: Optional[tf.Tensor] = None,
     num_microbatches: Optional[type_aliases.BatchSize] = None,
     clipping_loss: Optional[type_aliases.LossFn] = None,
-) -> Tuple[List[tf.Tensor], tf.Tensor, tf.Tensor]:
+) -> tuple[Sequence[tf.Tensor], tf.Tensor, tf.Tensor]:
   """Computes the per-example clipped loss gradient and other useful outputs.
 
   Given a batch of observations `(x_batch, y_batch, weight_batch)`, the main

tensorflow_privacy/privacy/fast_gradient_clipping/clip_grads_test.py

@@ -12,7 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, Optional, Text, Tuple
+from collections.abc import Mapping, Sequence
+from typing import Any, Optional
 
 from absl.testing import parameterized
 import tensorflow as tf
@@ -22,9 +23,6 @@ from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry
 from tensorflow_privacy.privacy.fast_gradient_clipping import type_aliases
 
 
-# ==============================================================================
-# Helper functions and classes.
-# ==============================================================================
 class DoubleDense(tf.keras.layers.Layer):
   """Generates two dense layers nested together."""
 
@@ -40,8 +38,8 @@ class DoubleDense(tf.keras.layers.Layer):
 
 def double_dense_layer_computation(
     layer_instance: tf.keras.layers.Layer,
-    input_args: Tuple[Any, ...],
-    input_kwargs: Dict[Text, Any],
+    input_args: Sequence[Any],
+    input_kwargs: Mapping[str, Any],
     tape: tf.GradientTape,
     num_microbatches: Optional[int],
 ) -> type_aliases.RegistryFunctionOutput:
@@ -61,9 +59,6 @@ def double_dense_layer_computation(
   return [vars1, vars2], outputs, sqr_norm_fn
 
 
-# ==============================================================================
-# Main tests.
-# ==============================================================================
 class DirectWeightsTest(tf.test.TestCase, parameterized.TestCase):
 
   @parameterized.product(

tensorflow_privacy/privacy/fast_gradient_clipping/common_test_utils.py

@@ -13,7 +13,8 @@
 # limitations under the License.
 """A collection of common utility functions for unit testing."""
 
-from typing import Callable, List, Optional, Tuple
+from collections.abc import Callable, MutableSequence, Sequence
+from typing import Optional
 
 import numpy as np
 import tensorflow as tf
@@ -108,8 +109,8 @@ def compute_true_gradient_norms(
 def get_model_from_generator(
     model_generator: type_aliases.ModelGenerator,
     layer_generator: type_aliases.LayerGenerator,
-    input_dims: List[int],
-    output_dims: List[int],
+    input_dims: Sequence[int],
+    output_dims: Sequence[int],
     is_eager: bool,
 ) -> tf.keras.Model:
   """Creates a simple model from input specifications."""
@@ -171,8 +172,8 @@ def get_computed_and_true_norms_from_model(
 def get_computed_and_true_norms(
     model_generator: type_aliases.ModelGenerator,
     layer_generator: type_aliases.LayerGenerator,
-    input_dims: List[int],
-    output_dims: List[int],
+    input_dims: Sequence[int],
+    output_dims: Sequence[int],
     per_example_loss_fn: Optional[Callable[[tf.Tensor, tf.Tensor], tf.Tensor]],
     num_microbatches: Optional[int],
     is_eager: bool,
@@ -181,7 +182,7 @@ def get_computed_and_true_norms(
     rng_seed: int = 777,
     registry: layer_registry.LayerRegistry = None,
     partial: bool = False,
-) -> Tuple[tf.Tensor, tf.Tensor]:
+) -> tuple[tf.Tensor, tf.Tensor]:
   """Obtains the true and computed gradient norms for a model and batch input.
 
   Helpful testing wrapper function used to avoid code duplication.
@@ -245,8 +246,8 @@ def reshape_and_sum(tensor: tf.Tensor) -> tf.Tensor:
 # ==============================================================================
 def make_one_layer_functional_model(
     layer_generator: type_aliases.LayerGenerator,
-    input_dims: List[int],
-    output_dims: List[int],
+    input_dims: Sequence[int],
+    output_dims: Sequence[int],
 ) -> tf.keras.Model:
   """Creates a 1-layer sequential model."""
   inputs = tf.keras.Input(shape=input_dims)
@@ -258,8 +259,8 @@ def make_one_layer_functional_model(
 
 def make_two_layer_functional_model(
     layer_generator: type_aliases.LayerGenerator,
-    input_dims: List[int],
-    output_dims: List[int],
+    input_dims: Sequence[int],
+    output_dims: Sequence[int],
 ) -> tf.keras.Model:
   """Creates a 2-layer sequential model."""
   inputs = tf.keras.Input(shape=input_dims)
@@ -272,8 +273,8 @@ def make_two_layer_functional_model(
 
 def make_two_tower_model(
     layer_generator: type_aliases.LayerGenerator,
-    input_dims: List[int],
-    output_dims: List[int],
+    input_dims: Sequence[int],
+    output_dims: Sequence[int],
 ) -> tf.keras.Model:
   """Creates a 2-layer 2-input functional model."""
   inputs1 = tf.keras.Input(shape=input_dims)
@@ -290,8 +291,8 @@ def make_two_tower_model(
 
 def make_bow_model(
     layer_generator: type_aliases.LayerGenerator,
-    input_dims: List[int],
-    output_dims: List[int],
+    input_dims: Sequence[int],
+    output_dims: Sequence[int],
 ) -> tf.keras.Model:
   """Creates a simple embedding bow model."""
   inputs = tf.keras.Input(shape=input_dims, dtype=tf.int32)
@@ -315,8 +316,8 @@ def make_bow_model(
 
 def make_dense_bow_model(
     layer_generator: type_aliases.LayerGenerator,
-    input_dims: List[int],
-    output_dims: List[int],
+    input_dims: Sequence[int],
+    output_dims: Sequence[int],
 ) -> tf.keras.Model:
   """Creates an embedding bow model with a `Dense` layer."""
   inputs = tf.keras.Input(shape=input_dims, dtype=tf.int32)
@@ -341,8 +342,8 @@ def make_dense_bow_model(
 
 def make_weighted_bow_model(
     layer_generator: type_aliases.LayerGenerator,
-    input_dims: List[int],
-    output_dims: List[int],
+    input_dims: MutableSequence[int],
+    output_dims: MutableSequence[int],
 ) -> tf.keras.Model:
   """Creates a weighted embedding bow model."""
   # NOTE: This model only accepts dense input tensors.
@@ -353,10 +354,9 @@ def make_weighted_bow_model(
   emb_layer = layer_generator(input_dims, output_dims)
   if len(output_dims) != 1:
     raise ValueError('Expected `output_dims` to be of size 1.')
-  output_dim = output_dims[0]
   feature_embs = emb_layer(inputs)
   # Use deterministic weights to avoid seeding issues on TPUs.
-  feature_shape = input_dims + [output_dim]
+  feature_shape = input_dims + output_dims
   feature_weights = tf.expand_dims(
       tf.reshape(
           tf.range(np.product(feature_shape), dtype=tf.float32),

tensorflow_privacy/privacy/fast_gradient_clipping/gradient_clipping_utils.py

@@ -13,7 +13,8 @@
 # limitations under the License.
 """Utility functions that help in the computation of per-example gradient norms."""
 
-from typing import Any, List, Optional, Set, Tuple
+from collections.abc import Sequence, Set
+from typing import Any, Optional
 
 from absl import logging
 import tensorflow as tf
@@ -36,7 +37,7 @@ def model_forward_pass(
     input_model: tf.keras.Model,
     inputs: type_aliases.PackedTensors,
     generator_fn: type_aliases.GeneratorFunction = None,
-) -> Tuple[type_aliases.PackedTensors, List[Any]]:
+) -> tuple[type_aliases.PackedTensors, Sequence[Any]]:
   """Does a forward pass of a model and returns useful intermediates.
 
   NOTE: the graph traversal algorithm is an adaptation of the logic in the
@@ -149,7 +150,7 @@ def add_aggregate_noise(
     batch_size: tf.Tensor,
     l2_norm_clip: float,
     noise_multiplier: float,
-) -> List[tf.Tensor]:
+) -> Sequence[tf.Tensor]:
   """Adds noise to a collection of clipped gradients.
 
   The magnitude of the noise depends on the aggregation strategy of the

tensorflow_privacy/privacy/fast_gradient_clipping/gradient_clipping_utils_test.py

@@ -16,7 +16,6 @@ from typing import Any
 
 from absl.testing import parameterized
 import tensorflow as tf
-
 from tensorflow_privacy.privacy.fast_gradient_clipping import gradient_clipping_utils
 
 
@@ -90,7 +89,7 @@ class ModelForwardPassTest(tf.test.TestCase, parameterized.TestCase):
     num_dims = 3
     num_inputs = 1 if input_packing_type is None else 2
     num_outputs = 1 if output_packing_type is None else 2
-    sample_inputs = [tf.keras.Input((num_dims,)) for i in range(num_inputs)]
+    sample_inputs = [tf.keras.Input((num_dims,)) for _ in range(num_inputs)]
     temp_sum = tf.stack(sample_inputs, axis=0)
     sample_outputs = [
         tf.multiply(temp_sum, float(i + 1.0)) for i in range(num_outputs)

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/dense.py

@@ -13,7 +13,8 @@
 # limitations under the License.
 """Fast clipping function for `tf.keras.layers.Dense`."""
 
-from typing import Any, Mapping, Tuple, Union
+from collections.abc import Mapping, Sequence
+from typing import Any, Optional
 import tensorflow as tf
 from tensorflow_privacy.privacy.fast_gradient_clipping import common_manip_utils
 from tensorflow_privacy.privacy.fast_gradient_clipping import type_aliases
@@ -21,10 +22,10 @@ from tensorflow_privacy.privacy.fast_gradient_clipping import type_aliases
 
 def dense_layer_computation(
     layer_instance: tf.keras.layers.Dense,
-    input_args: Tuple[Any, ...],
+    input_args: Sequence[Any],
     input_kwargs: Mapping[str, Any],
     tape: tf.GradientTape,
-    num_microbatches: Union[tf.Tensor, None] = None,
+    num_microbatches: Optional[tf.Tensor] = None,
 ) -> type_aliases.RegistryFunctionOutput:
   """Registry function for `tf.keras.layers.Dense`.
 

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/dense_test.py

@@ -19,11 +19,7 @@ from tensorflow_privacy.privacy.fast_gradient_clipping import layer_registry
 from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import dense
 
 
-# ==============================================================================
-# Helper functions.
-# ==============================================================================
 def get_dense_layer_generators():
-
   def sigmoid_dense_layer(units):
     return tf.keras.layers.Dense(units, activation='sigmoid')
 
@@ -50,14 +46,12 @@ def get_dense_layer_registries():
   }
 
 
-# ==============================================================================
-# Main tests.
-# ==============================================================================
 class GradNormTest(tf.test.TestCase, parameterized.TestCase):
 
   def setUp(self):
     super().setUp()
     self.strategy = tf.distribute.get_strategy()
+    self.using_tpu = False
 
   @parameterized.product(
       model_name=list(get_dense_model_generators().keys()),
@@ -112,8 +106,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
       )
 
     # TPUs can only run `tf.function`-decorated functions.
-    using_tpu = isinstance(self.strategy, tf.distribute.TPUStrategy)
-    if using_tpu:
+    if self.using_tpu:
       test_op = tf.function(test_op, jit_compile=True, autograph=False)
 
     # TPUs use lower precision than CPUs, so we relax our criterion.
@@ -127,8 +120,8 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
     # which is a reasonable level of error for computing gradient norms.
     # Other trials also give an absolute (resp. relative) error of around
     # 0.05 (resp. 0.0015).
-    rtol = 1e-2 if using_tpu else 1e-3
-    atol = 1e-1 if using_tpu else 1e-2
+    rtol = 1e-2 if self.using_tpu else 1e-3
+    atol = 1e-1 if self.using_tpu else 1e-2
 
     for x_batch, weight_batch in zip(x_batches, weight_batches):
       batch_size = x_batch.shape[0]
@@ -139,7 +132,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
           test_op, args=(x_batch, weight_batch)
       )
       # TPUs return replica contexts, which must be unwrapped.
-      if using_tpu:
+      if self.using_tpu:
         common_test_utils.assert_replica_values_are_close(self, computed_norms)
         common_test_utils.assert_replica_values_are_close(self, true_norms)
         computed_norms = computed_norms.values[0]

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/dense_tpu_test.py

@@ -20,9 +20,10 @@ from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import
 class GradNormTpuTest(dense_test.GradNormTest):
 
   def setUp(self):
-    super().setUp()
+    super(dense_test.GradNormTest, self).setUp()
     self.strategy = ctu.create_tpu_strategy()
     self.assertIn('TPU', self.strategy.extended.worker_devices[0])
+    self.using_tpu = True
 
 
 if __name__ == '__main__':

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/embedding.py

@@ -13,7 +13,8 @@
 # limitations under the License.
 """Fast clipping function for `tf.keras.layers.Embedding`."""
 
-from typing import Any, Mapping, Tuple, Union
+from collections.abc import Mapping, Sequence
+from typing import Any, Optional
 import tensorflow as tf
 from tensorflow_privacy.privacy.fast_gradient_clipping import type_aliases
 from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import registry_function_utils
@@ -21,10 +22,10 @@ from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import
 
 def embedding_layer_computation(
     layer_instance: tf.keras.layers.Embedding,
-    input_args: Tuple[Any, ...],
+    input_args: Sequence[Any],
     input_kwargs: Mapping[str, Any],
     tape: tf.GradientTape,
-    num_microbatches: Union[tf.Tensor, None] = None,
+    num_microbatches: Optional[tf.Tensor] = None,
 ) -> type_aliases.RegistryFunctionOutput:
   """Registry function for `tf.keras.layers.Embedding`.
 

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/embedding_test.py

@@ -20,9 +20,6 @@ from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import
 from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import embedding
 
 
-# ==============================================================================
-# Helper functions.
-# ==============================================================================
 def get_embedding_model_generators():
   return {
       'bow1': common_test_utils.make_bow_model,
@@ -59,14 +56,12 @@ def get_embedding_layer_registries():
   }
 
 
-# ==============================================================================
-# Main tests.
-# ==============================================================================
 class GradNormTest(tf.test.TestCase, parameterized.TestCase):
 
   def setUp(self):
     super().setUp()
     self.strategy = tf.distribute.get_strategy()
+    self.using_tpu = False
 
   # TODO(weiweikong): Test sparse input tensors when the GitHub CI environment
   # supports them for embeddings.
@@ -101,13 +96,12 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
 
     # The following are invalid test combinations and, hence, are skipped.
     batch_size = embed_indices.shape[0]
-    using_tpu = isinstance(self.strategy, tf.distribute.TPUStrategy)
     if (
         (num_microbatches is not None and batch_size % num_microbatches != 0)
         or (model_name == 'weighted_bow1' and is_ragged)
         or (
             # Current clipping ops do not have corresponding TPU kernels.
-            using_tpu
+            self.using_tpu
             and is_ragged
         )
     ):
@@ -139,7 +133,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
       )
 
     # TPUs can only run `tf.function`-decorated functions.
-    if using_tpu:
+    if self.using_tpu:
       test_op = tf.function(test_op, autograph=False)
 
     # Set up the device ops and run the test.
@@ -147,7 +141,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
         test_op, args=(embed_indices,)
     )
     # TPUs return replica contexts, which must be unwrapped.
-    if using_tpu:
+    if self.using_tpu:
       common_test_utils.assert_replica_values_are_close(self, computed_norms)
       common_test_utils.assert_replica_values_are_close(self, true_norms)
       computed_norms = computed_norms.values[0]

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/embedding_tpu_test.py

@@ -20,9 +20,10 @@ from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import
 class GradNormTpuTest(embedding_test.GradNormTest):
 
   def setUp(self):
-    super().setUp()
+    super(embedding_test.GradNormTest, self).setUp()
     self.strategy = common_test_utils.create_tpu_strategy()
     self.assertIn('TPU', self.strategy.extended.worker_devices[0])
+    self.using_tpu = True
 
 
 if __name__ == '__main__':

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization.py

@@ -13,21 +13,19 @@
 # limitations under the License.
 """Fast clipping function for `tf.keras.layers.LayerNormalization`."""
 
-from typing import Any, Mapping, Tuple, Union
+from collections.abc import Mapping, Sequence
+from typing import Any, Optional
 import tensorflow as tf
 from tensorflow_privacy.privacy.fast_gradient_clipping import common_manip_utils
 from tensorflow_privacy.privacy.fast_gradient_clipping import type_aliases
 
 
-# ==============================================================================
-# Supported Keras layers
-# ==============================================================================
 def layer_normalization_computation(
     layer_instance: tf.keras.layers.LayerNormalization,
-    input_args: Tuple[Any, ...],
+    input_args: Sequence[Any],
     input_kwargs: Mapping[str, Any],
     tape: tf.GradientTape,
-    num_microbatches: Union[tf.Tensor, None] = None,
+    num_microbatches: Optional[tf.Tensor] = None,
 ) -> type_aliases.RegistryFunctionOutput:
   """Registry function for `tf.keras.layers.LayerNormalization`.
 

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization_test.py

@@ -21,9 +21,6 @@ from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import
 from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import layer_normalization
 
 
-# ==============================================================================
-# Helper functions.
-# ==============================================================================
 def get_layer_norm_layer_generators():
   return {
       'defaults': lambda x: tf.keras.layers.LayerNormalization(axis=x),
@@ -73,14 +70,12 @@ def get_layer_norm_registries():
   }
 
 
-# ==============================================================================
-# Main tests.
-# ==============================================================================
 class GradNormTest(tf.test.TestCase, parameterized.TestCase):
 
   def setUp(self):
     super().setUp()
     self.strategy = tf.distribute.get_strategy()
+    self.using_tpu = False
 
   @parameterized.product(
       model_name=list(get_layer_norm_model_generators().keys()),
@@ -130,14 +125,13 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
       )
 
     # TPUs can only run `tf.function`-decorated functions.
-    using_tpu = isinstance(self.strategy, tf.distribute.TPUStrategy)
-    if using_tpu:
+    if self.using_tpu:
       test_op = tf.function(test_op, jit_compile=True, autograph=False)
 
     # TPUs use lower precision than CPUs, so we relax our criterion (see
     # `dense_test.py` for additional discussions).
-    rtol = 1e-2 if using_tpu else 1e-3
-    atol = 1e-1 if using_tpu else 1e-2
+    rtol = 1e-2 if self.using_tpu else 1e-3
+    atol = 1e-1 if self.using_tpu else 1e-2
 
     # Each batched input is a reshape of a `tf.range()` call.
     batch_size = 2
@@ -148,7 +142,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
     # Set up the device ops and run the test.
     computed_norms, true_norms = self.strategy.run(test_op, args=(x_batch,))
     # TPUs return replica contexts, which must be unwrapped.
-    if using_tpu:
+    if self.using_tpu:
       common_test_utils.assert_replica_values_are_close(self, computed_norms)
       common_test_utils.assert_replica_values_are_close(self, true_norms)
       computed_norms = computed_norms.values[0]

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization_tpu_test.py

@@ -20,9 +20,10 @@ from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import
 class GradNormTpuTest(layer_normalization_test.GradNormTest):
 
   def setUp(self):
-    super().setUp()
+    super(layer_normalization_test.GradNormTest, self).setUp()
     self.strategy = ctu.create_tpu_strategy()
     self.assertIn('TPU', self.strategy.extended.worker_devices[0])
+    self.using_tpu = True
 
 
 if __name__ == '__main__':

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/nlp_on_device_embedding.py

@@ -13,7 +13,8 @@
 # limitations under the License.
 """Fast clipping function for `tfm.nlp.layers.OnDeviceEmbedding`."""
 
-from typing import Any, Dict, Optional, Tuple
+from collections.abc import Mapping, Sequence
+from typing import Any, Optional
 import tensorflow as tf
 from tensorflow_privacy.privacy.fast_gradient_clipping import type_aliases
 from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import registry_function_utils
@@ -21,8 +22,8 @@ from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import
 
 def nlp_on_device_embedding_layer_computation(
     layer_instance: tf.keras.layers.Layer,
-    input_args: Tuple[Any, ...],
-    input_kwargs: Dict[str, Any],
+    input_args: Sequence[Any],
+    input_kwargs: Mapping[str, Any],
     tape: tf.GradientTape,
     num_microbatches: Optional[tf.Tensor] = None,
 ) -> type_aliases.RegistryFunctionOutput:

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/nlp_on_device_embedding_test.py

@@ -21,9 +21,6 @@ from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import
 from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import nlp_on_device_embedding
 
 
-# ==============================================================================
-# Helper functions.
-# ==============================================================================
 def get_nlp_on_device_embedding_model_generators():
   return {
       'bow1': common_test_utils.make_bow_model,
@@ -49,30 +46,26 @@ def get_nlp_on_device_embedding_layer_registries():
   dbl_registry.insert(tf.keras.layers.Dense, dense.dense_layer_computation)
   dbl_registry.insert(
       tfm.nlp.layers.OnDeviceEmbedding,
-      nlp_on_device_embedding.nlp_on_device_embedding_layer_computation
+      nlp_on_device_embedding.nlp_on_device_embedding_layer_computation,
   )
   return {
       'embed_and_dense': dbl_registry,
   }
 
 
-# ==============================================================================
-# Main tests.
-# ==============================================================================
 class GradNormTest(tf.test.TestCase, parameterized.TestCase):
 
   def setUp(self):
     super().setUp()
     self.strategy = tf.distribute.get_strategy()
+    self.using_tpu = False
 
   # TODO(weiweikong): Test sparse input tensors when the GitHub CI environment
   # supports them for embeddings.
   @parameterized.product(
       input_data=get_nlp_on_device_embedding_inputs(),
       scale_factor=[None, 0.5, 1.0],
-      model_name=list(
-          get_nlp_on_device_embedding_model_generators().keys()
-      ),
+      model_name=list(get_nlp_on_device_embedding_model_generators().keys()),
       output_dim=[2],
       layer_registry_name=list(
           get_nlp_on_device_embedding_layer_registries().keys()
@@ -97,7 +90,6 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
 
     # The following are invalid test combinations and, hence, are skipped.
     batch_size = embed_indices.shape[0]
-    using_tpu = isinstance(self.strategy, tf.distribute.TPUStrategy)
     if num_microbatches is not None and batch_size % num_microbatches != 0:
       return
 
@@ -106,9 +98,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
 
       def embed_layer_generator(_, output_dims):
         return tfm.nlp.layers.OnDeviceEmbedding(
-            10,
-            *output_dims,
-            scale_factor=scale_factor
+            10, *output_dims, scale_factor=scale_factor
         )
 
       model = common_test_utils.get_model_from_generator(
@@ -137,7 +127,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
       )
 
     # TPUs can only run `tf.function`-decorated functions.
-    if using_tpu:
+    if self.using_tpu:
       test_op = tf.function(test_op, autograph=False)
 
     # Set up the device ops and run the test.
@@ -145,7 +135,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
         test_op, args=(embed_indices,)
     )
     # TPUs return replica contexts, which must be unwrapped.
-    if using_tpu:
+    if self.using_tpu:
       common_test_utils.assert_replica_values_are_close(self, computed_norms)
       common_test_utils.assert_replica_values_are_close(self, true_norms)
       computed_norms = computed_norms.values[0]

tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/nlp_on_device_embedding_tpu_test.py

@@ -20,9 +20,10 @@ from tensorflow_privacy.privacy.fast_gradient_clipping.registry_functions import
 class GradNormTpuTest(nlp_on_device_embedding_test.GradNormTest):
 
   def setUp(self):
-    super().setUp()
+    super(nlp_on_device_embedding_test.GradNormTest, self).setUp()
     self.strategy = common_test_utils.create_tpu_strategy()
     self.assertIn('TPU', self.strategy.extended.worker_devices[0])
+    self.using_tpu = True
 
 
 if __name__ == '__main__':

tensorflow_privacy/privacy/fast_gradient_clipping/type_aliases.py

@@ -13,12 +13,13 @@
 # limitations under the License.
 """A collection of type aliases used throughout the clipping library."""
 
-from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Union
+from collections.abc import Callable, Iterable, Mapping, Sequence
+from typing import Any, Optional, Union
 import tensorflow as tf
 
 
 # Tensorflow aliases.
-PackedTensors = Union[tf.Tensor, Iterable[tf.Tensor], Dict[str, tf.Tensor]]
+PackedTensors = Union[tf.Tensor, Iterable[tf.Tensor], Mapping[str, tf.Tensor]]
 
 InputTensors = PackedTensors
 
@@ -31,12 +32,12 @@ LossFn = Callable[..., tf.Tensor]
 # Layer Registry aliases.
 SquareNormFunction = Callable[[OutputTensors], tf.Tensor]
 
-RegistryFunctionOutput = Tuple[Any, OutputTensors, SquareNormFunction]
+RegistryFunctionOutput = tuple[Any, OutputTensors, SquareNormFunction]
 
 RegistryFunction = Callable[
     [
         Any,
-        Tuple[Any, ...],
+        tuple[Any, ...],
         Mapping[str, Any],
         tf.GradientTape,
         Union[tf.Tensor, None],
@@ -45,11 +46,11 @@ RegistryFunction = Callable[
 ]
 
 # Clipping aliases.
-GeneratorFunction = Optional[Callable[[Any, Tuple, Dict], Tuple[Any, Any]]]
+GeneratorFunction = Optional[Callable[[Any, tuple, Mapping], tuple[Any, Any]]]
 
 # Testing aliases.
 LayerGenerator = Callable[[int, int], tf.keras.layers.Layer]
 
 ModelGenerator = Callable[
-    [LayerGenerator, List[int], List[int]], tf.keras.Model
+    [LayerGenerator, Sequence[int], Sequence[int]], tf.keras.Model
 ]