diff --git a/docs/source/networks.rst b/docs/source/networks.rst
index f9375f1e97..556bf12d50 100644
--- a/docs/source/networks.rst
+++ b/docs/source/networks.rst
@@ -248,6 +248,12 @@ Blocks
 .. autoclass:: monai.apps.reconstruction.networks.blocks.varnetblock.VarNetBlock
    :members:
 
+`Attention utilities`
+~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: monai.networks.blocks.attention_utils
+.. autofunction:: monai.networks.blocks.attention_utils.get_rel_pos
+.. autofunction:: monai.networks.blocks.attention_utils.add_decomposed_rel_pos
+
 N-Dim Fourier Transform
 ~~~~~~~~~~~~~~~~~~~~~~~~
 .. automodule:: monai.networks.blocks.fft_utils_t
diff --git a/monai/networks/blocks/attention_utils.py b/monai/networks/blocks/attention_utils.py
index a9fd6c89ae..8c9002a16e 100644
--- a/monai/networks/blocks/attention_utils.py
+++ b/monai/networks/blocks/attention_utils.py
@@ -19,7 +19,8 @@
 def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
     """
     Get relative positional embeddings according to the relative positions of
-        query and key sizes.
+    query and key sizes.
+
     Args:
         q_size (int): size of query q.
         k_size (int): size of key k.
@@ -51,10 +52,36 @@ def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor
 def add_decomposed_rel_pos(
     attn: torch.Tensor, q: torch.Tensor, rel_pos_lst: nn.ParameterList, q_size: Tuple, k_size: Tuple
 ) -> torch.Tensor:
-    """
-    Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
-    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py   # noqa B950
+    r"""
+    Calculate decomposed Relative Positional Embeddings from mvitv2 implementation:
+    https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py
+
     Only 2D and 3D are supported.
+
+    Encoding the relative position of tokens in the attention matrix: tokens spaced a distance
+    `d` apart will have the same embedding value (unlike absolute positional embedding).
+
+    .. math::
+        Attn_{logits}(Q, K) = (QK^{T} + E_{rel})*scale
+
+    where
+
+    .. math::
+        E_{ij}^{(rel)} = Q_{i}.R_{p(i), p(j)}
+
+    with :math:`R_{p(i), p(j)} \in R^{dim}` and :math:`p(i), p(j)`,
+    respectively spatial positions of element :math:`i` and :math:`j`
+
+    When using "decomposed" relative positional embedding, positional embedding is defined ("decomposed") as follow:
+
+    .. math::
+        R_{p(i), p(j)} = R^{d1}_{d1(i), d1(j)} + ... + R^{dn}_{dn(i), dn(j)}
+
+    with :math:`n = 1...dim`
+
+    Decomposed relative positional embedding reduces the complexity from :math:`\mathcal{O}(d1*...*dn)` to
+    :math:`\mathcal{O}(d1+...+dn)` compared with classical relative positional embedding.
+
     Args:
         attn (Tensor): attention map.
         q (Tensor): query q in the attention layer with shape (B, s_dim_1 * ... * s_dim_n, C).
@@ -63,7 +90,7 @@ def add_decomposed_rel_pos(
         k_size (Tuple): spatial sequence size of key k with (k_dim_1, ...,  k_dim_n).
 
     Returns:
-        attn (Tensor): attention map with added relative positional embeddings.
+        attn (Tensor): attention logits with added relative positional embeddings.
     """
     rh = get_rel_pos(q_size[0], k_size[0], rel_pos_lst[0])
     rw = get_rel_pos(q_size[1], k_size[1], rel_pos_lst[1])