diff --git a/tests/python/unittest/test_tir_schedule_reduction.py b/tests/python/unittest/test_tir_schedule_reduction.py
new file mode 100644
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+++ b/tests/python/unittest/test_tir_schedule_reduction.py
@@ -0,0 +1,222 @@
+# 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.
+# pylint: disable=missing-function-docstring,missing-module-docstring
+import numpy as np
+import tvm
+import tvm.testing
+from tvm import tir
+from tvm.script import ty
+
+# pylint: disable=no-member,invalid-name,unused-variable
+
+
+@tvm.script.tir
+def transformed_matmul(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
+    A = tir.match_buffer(a, [128, 128])
+    B = tir.match_buffer(b, [128, 128])
+    C = tir.match_buffer(c, [128, 128])
+
+    for i0, i1, i2_outer, i2_inner_outer, i2_inner_inner in tir.grid(128, 128, 4, 8, 4):
+        with tir.block([128, 128, tir.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
+            tir.bind(vi, i0)
+            tir.bind(vj, i1)
+            tir.bind(vk, (((i2_outer*32) + (i2_inner_outer*4)) + i2_inner_inner))
+            tir.reads([C[vi, vj], A[vi, vk], B[vj, vk]])
+            tir.writes([C[vi, vj]])
+            with tir.init():
+                C[vi, vj] = 0.0
+            C[vi, vj] = (C[vi, vj] + (A[vi, vk]*B[vj, vk]))
+
+
+@tvm.script.tir
+def matmul_rfactor(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
+    A = tir.match_buffer(a, [128, 128])
+    B = tir.match_buffer(b, [128, 128])
+    C = tir.match_buffer(c, [128, 128])
+    C_rf = tir.alloc_buffer([4, 128, 128])
+
+    for i0, i1, i2_outer, i2_inner_outer, i2_inner_inner in tir.grid(128, 128, 4, 8, 4):
+        with tir.block([4, 128, 128, tir.reduce_axis(0, 4), tir.reduce_axis(0, 8)], "update_rf") as [vi2_inner_inner, vi, vj, vi2_outer, vi2_inner_outer]:
+            tir.bind(vi2_inner_inner, i2_inner_inner)
+            tir.bind(vi, i0)
+            tir.bind(vj, i1)
+            tir.bind(vi2_outer, i2_outer)
+            tir.bind(vi2_inner_outer, i2_inner_outer)
+            with tir.init():
+                C_rf[vi2_inner_inner, vi, vj] = 0.0
+            C_rf[vi2_inner_inner, vi, vj] = (C_rf[vi2_inner_inner, vi, vj] + (A[vi, (((vi2_outer*32) + (vi2_inner_outer*4)) + vi2_inner_inner)]*B[vj, (((vi2_outer*32) + (vi2_inner_outer*4)) + vi2_inner_inner)]))
+
+    for i0_1, i1_1, i2_inner_inner_1 in tir.grid(128, 128, 4):
+        with tir.block([128, 128, tir.reduce_axis(0, 4)], "update") as [vi_1, vj_1, vi2_inner_inner_1]:
+            tir.bind(vi_1, i0_1)
+            tir.bind(vj_1, i1_1)
+            tir.bind(vi2_inner_inner_1, i2_inner_inner_1)
+            with tir.init():
+                C[vi_1, vj_1] = 0.0
+            C[vi_1, vj_1] = (C[vi_1, vj_1] + C_rf[vi2_inner_inner_1, vi_1, vj_1])
+
+
+@tvm.script.tir
+def square_sum(a: ty.handle, c: ty.handle) -> None:
+    A = tir.match_buffer(a, [16, 256, 256])
+    C = tir.match_buffer(c, [16])
+
+    with tir.block([16, tir.reduce_axis(0, 256), tir.reduce_axis(0, 256)], "C") as [b, i, j]:
+        with tir.init():
+            C[b] = 0.0
+        C[b] = C[b] + A[b, i, j] * A[b, i, j]
+
+
+@tvm.script.tir
+def square_sum_rfactor(a: ty.handle, c: ty.handle) -> None:
+    A = tir.match_buffer(a, [16, 256, 256])
+    C = tir.match_buffer(c, [16])
+    C_rf = tir.alloc_buffer([16, 256])
+
+    for i0, i1, i2 in tir.grid(16, 256, 256):
+        with tir.block([256, 16, tir.reduce_axis(0, 256)], "C_rf") as [vi2, b, i]:
+            tir.bind(vi2, i2)
+            tir.bind(b, i0)
+            tir.bind(i, i1)
+            with tir.init():
+                C_rf[b, vi2] = 0.0
+            C_rf[b, vi2] = (C_rf[b, vi2] + (A[b, i, vi2]*A[b, i, vi2]))
+
+    for i0_1, i2_1 in tir.grid(16, 256):
+        with tir.block([16, tir.reduce_axis(0, 256)], "C") as [b_1, vi2_1]:
+            tir.bind(b_1, i0_1)
+            tir.bind(vi2_1, i2_1)
+            with tir.init():
+                C[b_1] = 0.0
+            C[b_1] = (C[b_1] + C_rf[b_1, vi2_1])
+
+
+@tvm.script.tir
+def transformed_square_sum_square_root(a: ty.handle, d: ty.handle) -> None:
+    A = tir.match_buffer(a, [16, 256, 256])
+    D = tir.match_buffer(d, [16])
+    C = tir.alloc_buffer([16])
+
+    for i0, i1_i2_fused_outer, i1_i2_fused_inner in tir.grid(16, 65536, 1):
+        with tir.block([16, tir.reduce_axis(0, 256), tir.reduce_axis(0, 256)], "C") as [b, i, j]:
+            tir.bind(b, i0)
+            tir.bind(i, tir.floordiv(i1_i2_fused_outer, 256))
+            tir.bind(j, tir.floormod(i1_i2_fused_outer, 256))
+            tir.reads([C[b], A[b, i, j]])
+            tir.writes([C[b]])
+            with tir.init():
+                C[b] = 0.0
+            C[b] = (C[b] + (A[b, i, j]*A[b, i, j]))
+    for i0_1 in tir.serial(0, 16):
+        with tir.block([16], "D") as [b_1]:
+            tir.bind(b_1, i0_1)
+            tir.reads([C[b_1]])
+            tir.writes([D[b_1]])
+            D[b_1] = tir.sqrt(C[b_1], dtype="float32")
+
+
+@tvm.script.tir
+def square_sum_square_root_rfactor(a: ty.handle, d: ty.handle) -> None:
+    A = tir.match_buffer(a, [16, 256, 256])
+    D = tir.match_buffer(d, [16])
+    C = tir.alloc_buffer([16])
+    C_rf = tir.alloc_buffer([1, 16])
+
+    for i0, i1_i2_fused_outer, i1_i2_fused_inner in tir.grid(16, 65536, 1):
+        with tir.block([1, 16, tir.reduce_axis(0, 256), tir.reduce_axis(0, 256)], "C_rf") as [vi1_i2_fused_inner, b, i, j]:
+            tir.bind(vi1_i2_fused_inner, i1_i2_fused_inner)
+            tir.bind(b, i0)
+            tir.bind(i, tir.floordiv(i1_i2_fused_outer, 256))
+            tir.bind(j, tir.floormod(i1_i2_fused_outer, 256))
+            with tir.init():
+                C_rf[vi1_i2_fused_inner, b] = 0.0
+            C_rf[vi1_i2_fused_inner, b] = (C_rf[vi1_i2_fused_inner, b] + (A[b, i, j]*A[b, i, j]))
+
+    for i0_1, i1_i2_fused_inner_1 in tir.grid(16, 1):
+        with tir.block([16, tir.reduce_axis(0, 1)], "C") as [b_1, vi1_i2_fused_inner_1]:
+            tir.bind(b_1, i0_1)
+            tir.bind(vi1_i2_fused_inner_1, i1_i2_fused_inner_1)
+            with tir.init():
+                C[b_1] = 0.0
+            C[b_1] = (C[b_1] + C_rf[vi1_i2_fused_inner_1, b_1])
+
+    for i0_2 in tir.serial(0, 16):
+        with tir.block([16], "D") as [b_2]:
+            tir.bind(b_2, i0_2)
+            D[b_2] = tir.sqrt(C[b_2], dtype="float32")
+
+
+# pylint: enable=no-member,invalid-name,unused-variable
+
+
+def test_reduction_rfactor_matmul():
+    s = tir.Schedule(transformed_matmul, debug_mode=True)
+    C = s.get_block("update")
+    _, _, _, _, kii = s.get_loops(C)
+    rf_block = s.rfactor(kii, 0)
+    tvm.ir.assert_structural_equal(s.mod["main"], matmul_rfactor)
+    assert s.get(rf_block).same_as(s.get(s.get_block("update_rf")))
+
+    f = tvm.build(s.mod["main"], target="llvm")
+    a_np = np.random.uniform(size=(128, 128)).astype("float32")
+    b_np = np.random.uniform(size=(128, 128)).astype("float32")
+    a = tvm.nd.array(a_np)
+    b = tvm.nd.array(b_np)
+    c = tvm.nd.array(np.zeros((128, 128), dtype="float32"))
+    f(a, b, c)
+    c_np = np.matmul(a_np, b_np.T)
+    tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-4, atol=1e-4)
+
+
+def test_reduction_rfactor_square_sum():
+    s = tir.Schedule(square_sum, debug_mode=True)
+    C = s.get_block("C")
+    _, _, j = s.get_loops(C)
+    rf_block = s.rfactor(j, 1)
+    tvm.ir.assert_structural_equal(s.mod["main"], square_sum_rfactor)
+    assert s.get(rf_block).same_as(s.get(s.get_block("C_rf")))
+
+    f = tvm.build(s.mod["main"], target="llvm")
+    a_np = np.random.uniform(size=(16, 256, 256)).astype("float32")
+    a = tvm.nd.array(a_np)
+    c = tvm.nd.array(np.zeros((16,), dtype="float32"))
+    f(a, c)
+    c_np = np.sum(a_np * a_np, axis=(1, 2))
+    tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-4, atol=1e-4)
+
+
+def test_reduction_rfactor_square_sum_square_root():
+    s = tir.Schedule(transformed_square_sum_square_root, debug_mode=True)
+    C = s.get_block("C")
+    _, _, fi = s.get_loops(C)
+    rf_block = s.rfactor(fi, 0)
+    tvm.ir.assert_structural_equal(s.mod["main"], square_sum_square_root_rfactor)
+    assert s.get(rf_block).same_as(s.get(s.get_block("C_rf")))
+
+    f = tvm.build(s.mod["main"], target="llvm")
+    a_np = np.random.uniform(size=(16, 256, 256)).astype("float32")
+    a = tvm.nd.array(a_np)
+    c = tvm.nd.array(np.zeros((16,), dtype="float32"))
+    f(a, c)
+    c_np = np.sqrt(np.sum(a_np * a_np, axis=(1, 2)))
+    tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-4, atol=1e-4)
+
+
+if __name__ == "__main__":
+    test_reduction_rfactor_matmul()
+    test_reduction_rfactor_square_sum()
+    test_reduction_rfactor_square_sum_square_root()
\ No newline at end of file