diff --git a/python/test/unit/language/test_core.py b/python/test/unit/language/test_core.py
index de4caf574acd..11981f54c328 100644
--- a/python/test/unit/language/test_core.py
+++ b/python/test/unit/language/test_core.py
@@ -1543,7 +1543,53 @@ def test_store_op(M, src_layout, device='cuda'):
 
     pgm = store_kernel[(1, 1, 1)](x_tri, y_tri)
     y_ref = x
+    np.testing.assert_allclose(y_ref, y_tri.cpu().numpy(), rtol=0.01, atol=1e-3)
+
+
+layouts = [
+    BlockedLayout([1, 4], [1, 32], [4, 1], [1, 0]),
+    BlockedLayout([1, 4], [1, 32], [2, 2], [1, 0]),
+    MmaLayout(version=(2, 0), warps_per_cta=[4, 1])
+]
+
+
+@pytest.mark.parametrize("M", [64, 128, 256])
+@pytest.mark.parametrize("src_layout", layouts)
+@pytest.mark.parametrize("dst_layout", layouts)
+@pytest.mark.parametrize("src_dim", [0, 1])
+@pytest.mark.parametrize("dst_dim", [0, 1])
+def test_convert1d(M, src_layout, dst_layout, src_dim, dst_dim, device='cuda'):
+    ir = f"""
+    #dst = {dst_layout}
+    #src = {src_layout}
+    module attributes {{"triton_gpu.num-warps" = 4 : i32}} {{
+        tt.func public @kernel(%arg0: !tt.ptr<i32> {{tt.divisibility = 16 : i32}}, %arg1: !tt.ptr<i32> {{tt.divisibility = 16 : i32}}) {{
+            %0 = tt.splat %arg0 : (!tt.ptr<i32>) -> tensor<{M}x!tt.ptr<i32>, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>>
+            %1 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>>
+            %2 = tt.addptr %0, %1 : tensor<{M}x!tt.ptr<i32>, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>>, tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>>
+            %3 = tt.load %2 {{cache = 1 : i32, evict = 1 : i32, isVolatile = false}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>>
+            %4 = tt.splat %arg1 : (!tt.ptr<i32>) -> tensor<{M}x!tt.ptr<i32>, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>>
+            %5 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>>
+            %6 = tt.addptr %4, %5 : tensor<{M}x!tt.ptr<i32>, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>>, tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>>
+            %7 = triton_gpu.convert_layout %3 : (tensor<{M}xi32, #triton_gpu.slice<{{dim = {src_dim}, parent = #src}}>>) -> tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>>
+            tt.store %6, %7 : tensor<{M}xi32, #triton_gpu.slice<{{dim = {dst_dim}, parent = #dst}}>>
+            tt.return
+        }}
+    }}
+    """
+    import tempfile
+    with tempfile.NamedTemporaryFile(mode='w', suffix='.ttgir') as f:
+        f.write(ir)
+        f.flush()
+        kernel = triton.compile(f.name)
 
+    rs = RandomState(17)
+    x = rs.randint(0, 4, (M, )).astype('int32')
+    y = np.zeros((M, ), dtype='int32')
+    x_tri = torch.tensor(x, device=device)
+    y_tri = torch.tensor(y, device=device)
+    pgm = kernel[(1, 1, 1)](x_tri, y_tri)
+    y_ref = x
     np.testing.assert_allclose(y_ref, y_tri.cpu().numpy(), rtol=0.01, atol=1e-3)