diff --git a/flax/linen/normalization.py b/flax/linen/normalization.py
index f5b24628bb..f2777f4911 100644
--- a/flax/linen/normalization.py
+++ b/flax/linen/normalization.py
@@ -434,11 +434,13 @@ class LayerNorm(Module):
   use_fast_variance: bool = True
 
   @compact
-  def __call__(self, x):
+  def __call__(self, x, mask=None):
     """Applies layer normalization on the input.
 
     Args:
       x: the inputs
+      mask: Binary array of shape broadcastable to `inputs` tensor, indicating
+        the positions for which the mean and variance should be computed.
 
     Returns:
       Normalized inputs (the same shape as inputs).
@@ -450,6 +452,7 @@ def __call__(self, x):
       self.axis_name,
       self.axis_index_groups,
       use_fast_variance=self.use_fast_variance,
+      mask=mask,
     )
 
     return _normalize(
@@ -525,11 +528,13 @@ class RMSNorm(Module):
   axis_index_groups: Any = None
 
   @compact
-  def __call__(self, x):
+  def __call__(self, x, mask=None):
     """Applies layer normalization on the input.
 
     Args:
       x: the inputs
+      mask: Binary array of shape broadcastable to `inputs` tensor, indicating
+        the positions for which the mean and variance should be computed.
 
     Returns:
       Normalized inputs (the same shape as inputs).
@@ -541,6 +546,7 @@ def __call__(self, x):
       self.axis_name,
       self.axis_index_groups,
       use_mean=False,
+      mask=mask,
     )
 
     return _normalize(
@@ -625,13 +631,15 @@ class GroupNorm(Module):
   use_fast_variance: bool = True
 
   @compact
-  def __call__(self, x):
+  def __call__(self, x, mask=None):
     """Applies group normalization to the input (arxiv.org/abs/1803.08494).
 
     Args:
       x: the input of shape N...C, where N is a batch dimension and C is a
         channels dimensions. `...` represents an arbitrary number of extra
         dimensions that are used to accumulate statistics over.
+      mask: Binary array of shape broadcastable to `inputs` tensor, indicating
+        the positions for which the mean and variance should be computed.
 
     Returns:
       Normalized inputs (the same shape as inputs).
@@ -670,6 +678,9 @@ def __call__(self, x):
     group_size = x.shape[-1] // num_groups
     group_shape = x.shape[:-1] + (num_groups, group_size)
 
+    if mask is not None:
+      mask = mask.reshape(mask.shape[:-1] + (num_groups, group_size))
+
     mean, var = _compute_stats(
       x.reshape(group_shape),
       reduction_axes,
@@ -677,6 +688,7 @@ def __call__(self, x):
       self.axis_name,
       self.axis_index_groups,
       use_fast_variance=self.use_fast_variance,
+      mask=mask,
     )
     mean = jnp.repeat(mean, group_size, axis=-1)
     var = jnp.repeat(var, group_size, axis=-1)
diff --git a/tests/linen/linen_test.py b/tests/linen/linen_test.py
index 07a582e37b..20962b5a0a 100644
--- a/tests/linen/linen_test.py
+++ b/tests/linen/linen_test.py
@@ -137,6 +137,59 @@ def test_pooling_no_batch_dims(self):
 
 
 class NormalizationTest(parameterized.TestCase):
+  def test_layer_norm_mask(self):
+    key = random.key(0)
+    keys = random.split(key)
+    x = random.normal(keys[0], (3, 4, 5))
+    m = random.choice(keys[1], 2, x.shape).astype(bool)
+    m = m.at[..., :2].set(True)  # guarantee at least 2 elements
+    x = jnp.where(m, x, jnp.nan)
+
+    module = nn.LayerNorm()
+    y, w = module.init_with_output(key, x, m)
+
+    z = y.mean(-1, where=m)
+    np.testing.assert_allclose(z, 0, atol=1e-4)
+
+    z = y.var(-1, where=m)
+    np.testing.assert_allclose(z, 1, atol=1e-4)
+
+  def test_rms_norm_mask(self):
+    key = random.key(0)
+    keys = random.split(key)
+    x = random.normal(keys[0], (3, 4, 5))
+    m = random.choice(keys[1], 2, x.shape).astype(bool)
+    m = m.at[..., :1].set(True)  # guarantee at least 1 element
+    x = jnp.where(m, x, jnp.nan)
+
+    module = nn.RMSNorm()
+    y, w = module.init_with_output(key, x, m)
+
+    z = np.square(y).mean(-1, where=m)
+    np.testing.assert_allclose(z, 1, atol=1e-4)
+
+  def test_group_norm_mask(self):
+    key = random.key(0)
+    keys = random.split(key)
+    x = random.normal(keys[0], (13, 3, 5, 7 * 11))
+    m = random.choice(keys[1], 2, x.shape).astype(bool)
+    m = m.at[..., :2].set(True)  # guarantee at least 2 elements
+    x = jnp.where(m, x, jnp.nan)
+
+    module = nn.GroupNorm(7, use_bias=False, use_scale=False)
+    y, w = module.init_with_output(key, x, m)
+
+    yr = y.reshape((13, 3, 5, 7, 11))
+    mr = m.reshape((13, 3, 5, 7, 11))
+
+    axes = list(range(1, x.ndim - 1)) + [-1]
+
+    z = yr.mean(axes, where=mr)
+    np.testing.assert_allclose(z, 0, atol=1e-4)
+
+    z = yr.var(axes, where=mr)
+    np.testing.assert_allclose(z, 1, atol=1e-4)
+
   @parameterized.parameters({'test_mask': True}, {'test_mask': False})
   def test_batch_norm(self, test_mask):
     rng = random.key(0)