diff --git a/torch/nn/functional.py b/torch/nn/functional.py
index 703e8b962a43..2909f9b36ef3 100644
--- a/torch/nn/functional.py
+++ b/torch/nn/functional.py
@@ -3126,7 +3126,7 @@ def multi_head_attention_forward(query,                  # type: Tensor
         - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
           the embedding dimension.
         - key_padding_mask: :math:`(N, S)`, ByteTensor, where N is the batch size, S is the source sequence length.
-        - attn_mask: :math:`(L, L)` where L is the target sequence length.
+        - attn_mask: :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
 
         Outputs:
         - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
diff --git a/torch/nn/modules/activation.py b/torch/nn/modules/activation.py
index 37f0fbaf6307..3c33ca6a2b61 100644
--- a/torch/nn/modules/activation.py
+++ b/torch/nn/modules/activation.py
@@ -764,7 +764,7 @@ class MultiheadAttention(Module):
         - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
           the embedding dimension.
         - key_padding_mask: :math:`(N, S)`, ByteTensor, where N is the batch size, S is the source sequence length.
-        - attn_mask: :math:`(L, L)` where L is the target sequence length.
+        - attn_mask: :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
 
         - Outputs:
         - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,