diff --git a/python/tvm/topi/cuda/sort.py b/python/tvm/topi/cuda/sort.py
index 445b0212426d1..1e919ab6d7c7d 100644
--- a/python/tvm/topi/cuda/sort.py
+++ b/python/tvm/topi/cuda/sort.py
@@ -26,10 +26,6 @@
 from .. import tag
 
 
-def ceil_div(a, b):
-    return (a + b - 1) // b
-
-
 def swap(arr, axis):
     """ swap arr[axis] and arr[-1] """
     return arr[:axis] + [arr[-1]] + arr[axis + 1 : -1] + [arr[axis]]
@@ -145,7 +141,6 @@ def ceil_div(a, b):
         ib.scope_attr(bz, "thread_extent", nthread_bz)
         idx = (by * shape[axis] + tid) * axis_mul_after + bz
         with ib.if_scope(tid < shape[axis]):
-            idx = (by * shape[axis] + tid) * axis_mul_after + bz
             values_out[idx] = data[idx]
             if indices_out is not None:
                 indices_out[idx] = tvm.tir.generic.cast(tid, indices_out.dtype)
@@ -153,8 +148,13 @@ def ceil_div(a, b):
     ## we are looping over the array doing mergesort from the bottom up.
     ## The outer loop runs on the host and launches a cuda kernel for each iteration
     ## of the algorithm.
-    ## The basic idea is that at iteration 0, each thread does sort on 2 elements. On iteration 1, each thread merges 2 sorted arrays of 2 elements, to deal with 4 total elements. On iteration 2, each thread merges 2 sorted arrays of 4 elements, to deal with 8 total elements. On iteration 3, each thread deals with 16 elements, etc
-    ## On the final iteration of the algorithm, one thread will merge two sorted lists to sort the entire array
+    ## The basic idea is that at iteration 0, each thread does sort on 2 elements.
+    ## On iteration 1, each thread merges 2 sorted arrays of 2 elements,
+    ## to deal with 4 total elements.
+    ## On iteration 2, each thread merges 2 sorted arrays of 4 elements,
+    ## to deal with 8 total elements. On iteration 3, each thread deals with 16 elements, etc
+    ## On the final iteration of the algorithm, one thread will merge two sorted lists
+    ## to sort the entire array
     lim = tvm.tir.generic.cast(
         tvm.tir.ceil(tvm.tir.log2(tvm.tir.generic.cast(shape[axis], "float64"))), "int64"
     )