diff --git a/tests/python/unittest/test_codegen_rocm.py b/tests/python/unittest/test_codegen_rocm.py
new file mode 100644
index 000000000000..2077372cd5b6
--- /dev/null
+++ b/tests/python/unittest/test_codegen_rocm.py
@@ -0,0 +1,54 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+import tvm
+import numpy as np
+
+
+def test_rocm_cross_thread_reduction():
+    if not tvm.rocm(0).exist or not tvm.module.enabled("rocm"):
+        print("skip because rocm is not enabled..")
+        return
+
+    # based on the reduction tutorial
+    n = tvm.var("n")
+    m = tvm.var("m")
+    A = tvm.placeholder((n, m), name='A')
+    k = tvm.reduce_axis((0, m), "k")
+    B = tvm.compute((n,), lambda i: tvm.sum(A[i, k], axis=k), name="B")
+    s = tvm.create_schedule(B.op)
+    ko, ki = s[B].split(B.op.reduce_axis[0], factor=16)
+    BF = s.rfactor(B, ki)
+    xo, xi = s[B].split(s[B].op.axis[0], factor=32)
+    s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
+    s[B].bind(xi, tvm.thread_axis("threadIdx.y"))
+    tx = tvm.thread_axis("threadIdx.x")
+    s[B].bind(s[B].op.reduce_axis[0], tx)
+    s[BF].compute_at(s[B], s[B].op.reduce_axis[0])
+    s[B].set_store_predicate(tx.var.equal(0))
+    frocm = tvm.build(s, [A, B], "rocm")
+
+    nn = 128
+    ctx = tvm.rocm(0)
+    a = tvm.nd.array(np.random.uniform(size=(nn, nn)).astype(A.dtype), ctx)
+    b = tvm.nd.array(np.zeros(nn, dtype=B.dtype), ctx)
+    frocm(a, b)
+    tvm.testing.assert_allclose(
+      b.asnumpy(),  np.sum(a.asnumpy(), axis=1), rtol=1e-4)
+
+
+if __name__ == "__main__":
+    test_rocm_cross_thread_reduction()