diff --git a/torchtext/prototype/t5/modules.py b/torchtext/prototype/t5/modules.py
index ab0680431a..8e31e65ffc 100644
--- a/torchtext/prototype/t5/modules.py
+++ b/torchtext/prototype/t5/modules.py
@@ -14,9 +14,11 @@
 # */
 
 import math
+from typing import Optional, Tuple
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from torch import Tensor
 
 
@@ -54,6 +56,58 @@ def __init__(
     def forward():
         pass
 
+    # NOTE: Modified from https://github.com/pytorch/pytorch/blob/5953fd9133c0bdcc0158acf1472fac403bc5f636/torch/nn/functional.py#L4814
+    def _t5_dot_product_attention(
+        self,
+        q: Tensor,
+        k: Tensor,
+        v: Tensor,
+        position_bias: Tensor,
+        attn_mask: Optional[Tensor] = None,
+        dropout_p: float = 0.0,
+    ) -> Tuple[Tensor, Tensor]:
+        r"""
+        Computes scaled dot product attention on query, key and value tensors, using
+        an optional attention mask if passed, and applying dropout if a probability
+        greater than 0.0 is specified.
+        Returns a tensor pair containing attended values and attention weights.
+        Args:
+            q, k, v: Query, key and value tensors. See Shape section for shape details.
+            attn_mask: Optional tensor containing mask values to be added to calculated
+                attention. May be 2D or 3D; see Shape section for details.
+            dropout_p: Dropout probability. If greater than 0.0, dropout is applied.
+            position_bias: Position bias used to incorporate realtive attention bias in attention scors
+        Shape:
+            - q: :math:`(B, H, Nt, E)` where B is the batch size, H is the number of heads, Nt is the target sequence length,
+                and E is the head dimension.
+            - key: :math:`(B, H, Ns, E)` where B is the batch size, H is the number of heads, Ns is the source sequence length,
+                and E is the head dimension.
+            - value: :math:`(B, H, Ns, E)` where B is the batch size, H is the number of heads, Ns is the source sequence length,
+                and E is the head dimension.
+            - attn_mask: a 4D tensor of shape :math:`(B, H, Nt, Ns)`
+            - position_bias: :math:`(1, H, Nt, Ns)`
+            - Output: attention values have shape :math:`(B, Nt, H*E)`; attention weights
+                have shape :math:`(B, H, Nt, Ns)`
+        """
+        B, H, _, E = q.shape
+        # NOTE: HF implementation does not perform this normalization. For the sake of matching test results, we have commented it out
+        # q = q / math.sqrt(E)
+
+        attn = torch.matmul(q, k.transpose(3, 2))
+
+        # NOTE: modification from torch.nn.functional._scaled_dot_product_attention to incorporate relative attention bias
+        position_bias = position_bias.repeat(B, 1, 1, 1)
+        if attn_mask is not None:
+            position_bias += attn_mask
+        attn += position_bias
+
+        attn = F.softmax(attn, dim=-1)
+        if dropout_p > 0.0:
+            attn = F.dropout(attn, p=dropout_p)
+        output = torch.matmul(attn, v)
+        output = output.transpose(1, 2).contiguous().view(B, -1, H * E)
+        return output, attn
+
     # NOTE: modified from https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L421
     def _compute_bias(
         self,