diff --git a/ascend/examples/pytest_ut/test_nested3.py b/ascend/examples/pytest_ut/test_nested3.py
new file mode 100755
index 0000000000000000000000000000000000000000..ade5d113e5242f64373986d1f57f8a4779661721
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+++ b/ascend/examples/pytest_ut/test_nested3.py
@@ -0,0 +1,66 @@
+import torch
+import triton
+import triton.language as tl
+import torch_npu
+
+
+@triton.jit
+def nested3(in_ptr, out_ptr, stride_m, stride_n):
+    offs_am = tl.arange(0, 2)
+    offs_an = tl.arange(0, 2)
+    a_ptrs = in_ptr + (offs_am[:, None] * stride_m +
+                        offs_an[None, :] * stride_n)
+
+    offs_cm = tl.arange(0, 2)
+    offs_cn = tl.arange(0, 2)
+    c_ptrs = out_ptr + stride_m * offs_cm[:, None] + stride_n * offs_cn[
+        None, :]
+
+    # linearize memory visit case
+    for i in range(0, 2):
+        a1 = tl.load(a_ptrs)
+        for j in range(0, 2):
+            a_ptrs += 2 * stride_n
+            a2 = tl.load(a_ptrs)
+            for k in range(0, 2):
+                a_ptrs += 2 * stride_n
+                a3 = tl.load(a_ptrs)
+                tl.store(c_ptrs, a1)
+                c_ptrs += 2 * stride_n
+
+                tl.store(c_ptrs, a2)
+                c_ptrs += 2 * stride_n
+                tl.store(c_ptrs, a3)
+                c_ptrs += 2 * stride_n
+        a_ptrs += 2 * stride_n
+        
+
+def test_nested3():
+    n_rows = 4
+    n_cols = 48
+    expected = torch.tensor([[ 0,  1,  2,  3,  4,  5,  0,  1,  2,  3,  6,  7,  0,  1,
+          8,  9, 10, 11,  0,  1,  8,  9, 12, 13, 14, 15, 16, 17,
+         18, 19, 14, 15, 16, 17, 20, 21, 14, 15, 22, 23, 24, 25,
+         14, 15, 22, 23, 26, 27],
+        [48, 49, 50, 51, 52, 53, 48, 49, 50, 51, 54, 55, 48, 49,
+         56, 57, 58, 59, 48, 49, 56, 57, 60, 61, 62, 63, 64, 65,
+         66, 67, 62, 63, 64, 65, 68, 69, 62, 63, 70, 71, 72, 73,
+         62, 63, 70, 71, 74, 75],
+        [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+          0,  0,  0,  0,  0,  0],
+        [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+          0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
+          0,  0,  0,  0,  0,  0]], dtype=torch.int32).npu()
+
+    x = torch.arange(0, n_rows * n_cols, dtype=torch.int32).npu()
+    x = x.reshape([n_rows, n_cols])
+    output = torch.zeros([n_rows, n_cols], device=x.device, dtype=x.dtype)
+    grid = lambda meta: (n_cols // 4,)
+
+    nested3[grid](x, output, x.stride(0), x.stride(1))
+
+    assert torch.equal(output, expected)
+    print("Pass!")
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