diff --git a/aten/src/ATen/native/transformers/attention.cpp b/aten/src/ATen/native/transformers/attention.cpp
index 6018302b507cd7..bd213d0dcb9bfe 100644
--- a/aten/src/ATen/native/transformers/attention.cpp
+++ b/aten/src/ATen/native/transformers/attention.cpp
@@ -686,6 +686,54 @@ Tensor _safe_softmax(
   // reuse storage for out
   return at::where_out(out, masked_rows, zero, out);
 }
+
+Tensor expand_boolean_mask_for_hip(
+    const std::optional<Tensor>& attn_mask,
+    const Tensor& query) {
+  
+  if (!attn_mask.has_value() || !attn_mask->defined()) {
+    return {};
+  }
+  
+  const auto& mask = attn_mask.value();
+  
+  // Only expand boolean masks on HIP backend
+  if (!query.device().is_hip() || mask.dtype() != at::kBool) {
+    return mask;
+  }
+  
+  const auto query_sizes = query.sizes();
+  const auto batch_size = query_sizes[0];
+  const auto num_heads = query_sizes[1]; 
+  const auto seq_len = query_sizes[2];
+  
+  // Handle 2D boolean mask: [batch, seq_len] -> [batch, heads, seq_len, seq_len]
+  if (mask.dim() == 2) {
+    TORCH_CHECK(mask.size(0) == batch_size, 
+                "Attention mask batch size (", mask.size(0), 
+                ") doesn't match query batch size (", batch_size, ")");
+    TORCH_CHECK(mask.size(1) == seq_len,
+                "Attention mask sequence length (", mask.size(1), 
+                ") doesn't match query sequence length (", seq_len, ")");
+    
+    return mask.unsqueeze(1).unsqueeze(1).expand({batch_size, num_heads, seq_len, seq_len});
+  }
+  
+  // Handle 3D boolean mask: [batch, seq_len, seq_len] -> [batch, heads, seq_len, seq_len]  
+  if (mask.dim() == 3) {
+    TORCH_CHECK(mask.size(0) == batch_size,
+                "Attention mask batch size (", mask.size(0), 
+                ") doesn't match query batch size (", batch_size, ")");
+    TORCH_CHECK(mask.size(1) == seq_len && mask.size(2) == seq_len,
+                "Attention mask sequence dimensions ([", mask.size(1), ", ", mask.size(2), 
+                "]) don't match query sequence length (", seq_len, ")");
+    
+    return mask.unsqueeze(1).expand({batch_size, num_heads, seq_len, seq_len});
+  }
+  
+  // 4D masks and other formats are already in correct format
+  return mask;
+}
 // 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.
@@ -733,8 +781,15 @@ Tensor scaled_dot_product_attention(
   }
   const auto query_device_type = query_.device().type();
   const auto backend = static_cast<SDPBackend>(choice_int);
+  
+  auto hip_expanded_mask = expand_boolean_mask_for_hip(attn_mask_, query_);
+  std::optional<Tensor> hip_expanded_mask_opt;
+  if (hip_expanded_mask.defined()) {
+    hip_expanded_mask_opt = hip_expanded_mask;
+  }
   const auto convert_attn_func = backend != SDPBackend::cudnn_attention ? convert_boolean_attn_mask : convert_boolean_attn_mask_cudnn;
-  auto attn_mask = convert_attn_func(attn_mask_, query_.dtype());
+  auto attn_mask = convert_attn_func(hip_expanded_mask_opt, query_.dtype());
+  
   switch (backend) {
     case SDPBackend::cudnn_attention: {
       bool compute_logsumexp = should_compute_logsumexp(query_, key, value);