diff --git a/python/cudf/cudf/tests/test_hash_vocab.py b/python/cudf/cudf/tests/test_hash_vocab.py
index e081119ff89..c98b92f7083 100644
--- a/python/cudf/cudf/tests/test_hash_vocab.py
+++ b/python/cudf/cudf/tests/test_hash_vocab.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 import filecmp
 import os
 import warnings
@@ -21,9 +21,7 @@ def test_correct_bert_base_vocab_hash(datadir, tmpdir):
 
     groundtruth_path = os.path.join(datadir, "vocab-hash.txt")
     output_path = tmpdir.join("cudf-vocab-hash.txt")
-    with warnings.catch_warnings():
-        # See https://github.com/rapidsai/cudf/issues/12403
-        warnings.simplefilter(action="ignore", category=RuntimeWarning)
-        hash_vocab(vocab_path, output_path)
+    warnings.simplefilter(action="ignore", category=RuntimeWarning)
+    hash_vocab(vocab_path, output_path)
 
     assert filecmp.cmp(output_path, groundtruth_path, shallow=False)
diff --git a/python/cudf/cudf/tests/test_sorting.py b/python/cudf/cudf/tests/test_sorting.py
index 618c4f30bd9..449f21721f4 100644
--- a/python/cudf/cudf/tests/test_sorting.py
+++ b/python/cudf/cudf/tests/test_sorting.py
@@ -107,7 +107,8 @@ def test_series_argsort(nelem, dtype, asc):
     if asc:
         expected = np.argsort(sr.to_numpy(), kind="mergesort")
     else:
-        expected = np.argsort(sr.to_numpy() * -1, kind="mergesort")
+        # -1 multiply works around missing desc sort (may promote to float64)
+        expected = np.argsort(sr.to_numpy() * np.int8(-1), kind="mergesort")
     np.testing.assert_array_equal(expected, res.to_numpy())
 
 
diff --git a/python/cudf/cudf/utils/hash_vocab_utils.py b/python/cudf/cudf/utils/hash_vocab_utils.py
index ef078ed8c5d..babe4be2715 100644
--- a/python/cudf/cudf/utils/hash_vocab_utils.py
+++ b/python/cudf/cudf/utils/hash_vocab_utils.py
@@ -7,8 +7,8 @@
 
 # Coefficients ranges for inner hash - This are important to set to be
 # large so that we have randomness in the bottom bits when modding
-A_SECOND_LEVEL_POW = np.uint8(48)
-B_SECOND_LEVEL_POW = np.uint8(7)
+A_SECOND_LEVEL_POW = np.uint64(48)
+B_SECOND_LEVEL_POW = np.uint64(7)
 
 A_LBOUND_SECOND_LEVEL_HASH = 2**16
 A_HBOUND_SECOND_LEVEL_HASH = 2**A_SECOND_LEVEL_POW
@@ -23,11 +23,11 @@
 
 
 # Shifts for bit packing
-A_SECOND_LEVEL_SHIFT_AMT = np.uint8(64 - A_SECOND_LEVEL_POW)
-B_SECOND_LEVEL_SHIFT_AMT = np.uint8(
+A_SECOND_LEVEL_SHIFT_AMT = np.uint64(64 - A_SECOND_LEVEL_POW)
+B_SECOND_LEVEL_SHIFT_AMT = np.uint64(
     64 - A_SECOND_LEVEL_POW - B_SECOND_LEVEL_POW
 )
-BITS_FOR_INNER_TABLE_SIZE = np.uint8(8)
+BITS_FOR_INNER_TABLE_SIZE = np.uint64(8)
 
 NOT_FOUND = -1
 
@@ -94,7 +94,8 @@ def _find_hash_for_internal(hash_bin):
 
     while True:
         a = np.random.randint(
-            A_LBOUND_SECOND_LEVEL_HASH, A_HBOUND_SECOND_LEVEL_HASH
+            A_LBOUND_SECOND_LEVEL_HASH,
+            A_HBOUND_SECOND_LEVEL_HASH,
         )
         b = np.random.randint(
             B_LBOUND_SECOND_LEVEL_HASH, B_HBOUND_SECOND_LEVEL_HASH
@@ -130,13 +131,13 @@ def _perfect_hash(integers, max_constant):
         bin_length = len(internal_table)
         max_bin_length = max(bin_length, max_bin_length)
         internal_table_coeffs[i] = (
-            coeff_a << A_SECOND_LEVEL_SHIFT_AMT
-            | coeff_b << B_SECOND_LEVEL_SHIFT_AMT
-            | bin_length
-        )
-        offset_into_flattened_table[i + 1] = (
-            offset_into_flattened_table[i] + bin_length
+            np.uint64(coeff_a) << A_SECOND_LEVEL_SHIFT_AMT
+            | np.uint64(coeff_b) << B_SECOND_LEVEL_SHIFT_AMT
+            | np.uint64(bin_length)
         )
+        offset_into_flattened_table[i + 1] = offset_into_flattened_table[
+            i
+        ] + np.uint64(bin_length)
         flattened_bins.extend(internal_table)
 
     print(