diff --git a/tensorflow/compiler/xla/python/dlpack.cc b/tensorflow/compiler/xla/python/dlpack.cc
index a67358497a6..774def3a72c 100644
--- a/tensorflow/compiler/xla/python/dlpack.cc
+++ b/tensorflow/compiler/xla/python/dlpack.cc
@@ -102,6 +102,7 @@ StatusOr<DLDataType> PrimitiveTypeToDLDataType(PrimitiveType type) {
     case BF16:
       return DLDataType{kDLBfloat, 16, 1};
     case PRED:
+      return DLDataType{kDLUInt, 8, 1};
     case C64:
     case C128:
     default:
diff --git a/tensorflow/compiler/xla/python/xla_client_test.py b/tensorflow/compiler/xla/python/xla_client_test.py
index bca3ca5907f..197da05f94c 100644
--- a/tensorflow/compiler/xla/python/xla_client_test.py
+++ b/tensorflow/compiler/xla/python/xla_client_test.py
@@ -57,7 +57,7 @@ def TestFactory(xla_backend, cloud_tpu=False):
     float_dtypes = [np.float32]
     complex_dtypes = [np.complex64]
     standard_dtypes = int_dtypes + float_dtypes + complex_dtypes + [np.bool_]
-  dlpack_dtypes = int_dtypes + float_dtypes
+  dlpack_dtypes = int_dtypes + float_dtypes + [np.bool_]
 
   class ComputationTest(parameterized.TestCase):
     """Base class for running an XLA Computation through the local client."""
@@ -1949,14 +1949,18 @@ def TestFactory(xla_backend, cloud_tpu=False):
                                     for take_ownership in [False, True])
     # pyformat: enable
     def testRoundTrip(self, dtype, shape, take_ownership):
-      x = np.array(np.random.rand(*shape) * 100, dtype=dtype)
+      if dtype == np.bool_:
+        x = np.random.randint(0, 2, size=shape).astype(np.bool_)
+      else:
+        x = np.array(np.random.rand(*shape) * 100, dtype=dtype)
       buffer = self.backend.buffer_from_pyval(x)
       dlt = xla_client._xla.buffer_to_dlpack_managed_tensor(
           buffer, take_ownership=take_ownership)
       del buffer  # Free "buffer" to make sure dlt retains ownership.
       self.assertEqual(type(dlt).__name__, "PyCapsule")
       y = xla_client._xla.dlpack_managed_tensor_to_buffer(dlt, self.backend)
-      np.testing.assert_array_equal(x, y.to_py())
+      np.testing.assert_array_equal(
+          x.astype(np.uint8) if x == np.bool_ else x, y.to_py())
 
     def testTensorsCanBeConsumedOnceOnly(self):
       x = np.array(np.random.rand(3, 4, 5, 6), dtype=np.float32)