diff --git a/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/BUILD b/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/BUILD
index 358d8c3..e2da95d 100644
--- a/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/BUILD
+++ b/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/BUILD
@@ -102,7 +102,10 @@ py_library(
     name = "layer_normalization",
     srcs = ["layer_normalization.py"],
     srcs_version = "PY3",
-    deps = ["//tensorflow_privacy/privacy/fast_gradient_clipping:type_aliases"],
+    deps = [
+        "//tensorflow_privacy/privacy/fast_gradient_clipping:common_manip_utils",
+        "//tensorflow_privacy/privacy/fast_gradient_clipping:type_aliases",
+    ],
 )
 
 py_test(
diff --git a/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization.py b/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization.py
index 86aca3c..91338a9 100644
--- a/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization.py
+++ b/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization.py
@@ -15,18 +15,13 @@
 
 from typing import Any, Mapping, Tuple, Union
 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 _sqr_norm_fn(grads):
-  stacked_grads = tf.stack(grads, axis=-1)
-  reduction_axes = tf.range(1, tf.rank(stacked_grads))
-  return tf.reduce_sum(tf.square(stacked_grads), axis=reduction_axes)
-
-
 def layer_normalization_computation(
     layer_instance: tf.keras.layers.LayerNormalization,
     input_args: Tuple[Any, ...],
@@ -51,9 +46,6 @@ def layer_normalization_computation(
     See `dense_layer_computation()` in `dense.py`.
   """
   del input_kwargs  # Unused in layer normaliztion calls.
-  if num_microbatches is not None:
-    raise NotImplementedError("Microbatching is not currently supported.")
-
   # To make sure the watched variables (beta, gamma) generate per-example
   # gradients, we need to convert trainable variables from shape [S] to
   # [batch_size, S] via duplication to `tf.shape(inputs)` via broadcasting.
@@ -86,4 +78,14 @@ def layer_normalization_computation(
   layer_instance.gamma = orig_gamma
   layer_instance.beta = orig_beta
 
-  return base_vars, outputs, _sqr_norm_fn
+  def sqr_norm_fn(grads):
+    stacked_grads = tf.stack(grads, axis=-1)
+    if num_microbatches is not None:
+      stacked_grads = common_manip_utils.maybe_add_microbatch_axis(
+          grads, num_microbatches
+      )
+      stacked_grads = tf.reduce_sum(stacked_grads, axis=1)
+    reduction_axes = tf.range(1, tf.rank(stacked_grads))
+    return tf.reduce_sum(tf.square(stacked_grads), axis=reduction_axes)
+
+  return base_vars, outputs, sqr_norm_fn
diff --git a/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization_test.py b/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization_test.py
index 8e55214..c2b6429 100644
--- a/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization_test.py
+++ b/tensorflow_privacy/privacy/fast_gradient_clipping/registry_functions/layer_normalization_test.py
@@ -87,6 +87,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
       layer_name=list(get_layer_norm_layer_generators().keys()),
       parameter_tuple=get_layer_norm_parameter_tuples(),
       layer_registry_name=list(get_layer_norm_registries().keys()),
+      num_microbatches=[None, 2],
       is_eager=[True, False],
   )
   def test_gradient_norms_on_various_models(
@@ -95,6 +96,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
       layer_name,
       parameter_tuple,
       layer_registry_name,
+      num_microbatches,
       is_eager,
   ):
     # Parse inputs to generate test data.
@@ -121,7 +123,7 @@ class GradNormTest(tf.test.TestCase, parameterized.TestCase):
       return common_test_utils.get_computed_and_true_norms_from_model(
           model=model,
           per_example_loss_fn=None,
-          num_microbatches=None,
+          num_microbatches=num_microbatches,
           x_batch=[x_batch, x_batch] if model_name == 'tower2' else x_batch,
           weight_batch=None,
           registry=get_layer_norm_registries()[layer_registry_name],