diff --git a/modules/sub_quadratic_attention.py b/modules/sub_quadratic_attention.py
index f80c1600..cc38debd 100644
--- a/modules/sub_quadratic_attention.py
+++ b/modules/sub_quadratic_attention.py
@@ -201,23 +201,15 @@ def efficient_dot_product_attention(
             key=key,
             value=value,
         )
-    
-    # slices of res tensor are mutable, modifications made
-    # to the slices will affect the original tensor.
-    # if output of compute_query_chunk_attn function has same number of
-    # dimensions as input query tensor, we initialize tensor like this:
-    num_query_chunks = int(np.ceil(q_tokens / query_chunk_size))
-    query_shape = get_query_chunk(0).shape
-    res_shape = (query_shape[0], query_shape[1] * num_query_chunks, *query_shape[2:])
-    res_dtype = get_query_chunk(0).dtype
-    res = torch.zeros(res_shape, dtype=res_dtype)
 
-    for i in range(num_query_chunks):
+    res = torch.zeros_like(query)
+    for i in range(math.ceil(q_tokens / query_chunk_size)):
         attn_scores = compute_query_chunk_attn(
             query=get_query_chunk(i * query_chunk_size),
             key=key,
             value=value,
         )
-        res[:, i * query_chunk_size:(i + 1) * query_chunk_size, :] = attn_scores
+
+        res[:, i * query_chunk_size:i * query_chunk_size + attn_scores.shape[1], :] = attn_scores
 
     return res