diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_3.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_3.py
index 23ad1a821c43c..3ace9a72f769b 100644
--- a/test/ir/pir/cinn/sub_graphs/test_sub_graph_3.py
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_3.py
@@ -48,8 +48,8 @@ def forward(
         var_9 = var_8.reshape((0, 16, 49))
         var_10 = paddle.tensor.linalg.transpose(var_1, perm=[0, 1, 3, 2])
         var_11 = paddle.tensor.linalg.matmul(var_5, var_10)
-        var_12 = var_11.__mul__(0.25)
-        var_13 = var_12.__add__(var_9)
+        var_12 = var_11 * 0.25
+        var_13 = var_12 + var_9
         var_14 = paddle.nn.functional.activation.softmax(var_13)
         var_15 = paddle.tensor.linalg.matmul(var_14, var_2)
         var_16 = paddle.tensor.linalg.transpose(var_15, perm=[0, 2, 1, 3])
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_31.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_31.py
new file mode 100644
index 0000000000000..c335a9df27a8a
--- /dev/null
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_31.py
@@ -0,0 +1,80 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+# repo: PaddleClas
+# model: ppcls^configs^ImageNet^DPN^DPN98
+# api:paddle.tensor.manipulation.split||api:paddle.tensor.math.add||api:paddle.tensor.manipulation.concat
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        var_0,  # (shape: [22, 1056, 14, 14], dtype: paddle.float32, stop_gradient: False)
+        var_1,  # (shape: [22, 1024, 14, 14], dtype: paddle.float32, stop_gradient: False)
+        var_2,  # (shape: [22, 288, 14, 14], dtype: paddle.float32, stop_gradient: False)
+    ):
+        var_3, var_4 = paddle.tensor.manipulation.split(
+            var_0, num_or_sections=[1024, 32], axis=1
+        )
+        var_5 = paddle.tensor.math.add(x=var_1, y=var_3)
+        var_6 = paddle.tensor.manipulation.concat([var_2, var_4], axis=1)
+        return var_5, var_6
+
+
+class TestLayer(unittest.TestCase):
+    def setUp(self):
+        self.inputs = (
+            paddle.rand(shape=[22, 1056, 14, 14], dtype=paddle.float32),
+            paddle.rand(shape=[22, 1024, 14, 14], dtype=paddle.float32),
+            paddle.rand(shape=[22, 288, 14, 14], dtype=paddle.float32),
+        )
+        self.net = LayerCase()
+
+    def train(self, net, to_static, with_prim=False, with_cinn=False):
+        if to_static:
+            paddle.set_flags({'FLAGS_prim_all': with_prim})
+            if with_cinn:
+                build_strategy = paddle.static.BuildStrategy()
+                build_strategy.build_cinn_pass = True
+                net = paddle.jit.to_static(
+                    net, build_strategy=build_strategy, full_graph=True
+                )
+            else:
+                net = paddle.jit.to_static(net, full_graph=True)
+        paddle.seed(123)
+        outs = net(*self.inputs)
+        return outs
+
+    # NOTE prim + cinn lead to error
+    def test_ast_prim_cinn(self):
+        st_out = self.train(self.net, to_static=True)
+        cinn_out = self.train(
+            self.net, to_static=True, with_prim=True, with_cinn=False
+        )
+        for st, cinn in zip(
+            paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
+        ):
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_32.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_32.py
new file mode 100644
index 0000000000000..5dc014a873d05
--- /dev/null
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_32.py
@@ -0,0 +1,79 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+# repo: PaddleClas
+# model: ppcls^configs^ImageNet^ResNeSt^ResNeSt50
+# api:paddle.tensor.manipulation.reshape||api:paddle.tensor.linalg.transpose||api:paddle.nn.functional.activation.softmax||api:paddle.tensor.manipulation.reshape
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        var_0,  # (shape: [22, 1024, 1, 1], dtype: paddle.float32, stop_gradient: True)
+    ):
+        var_1 = paddle.tensor.manipulation.reshape(
+            x=var_0, shape=[22, 1, 2, 512]
+        )
+        var_2 = paddle.tensor.linalg.transpose(x=var_1, perm=[0, 2, 1, 3])
+        var_3 = paddle.nn.functional.activation.softmax(var_2, axis=1)
+        var_4 = paddle.tensor.manipulation.reshape(
+            x=var_3, shape=[22, 1024, 1, 1]
+        )
+        return var_4
+
+
+class TestLayer(unittest.TestCase):
+    def setUp(self):
+        self.inputs = (
+            paddle.rand(shape=[22, 1024, 1, 1], dtype=paddle.float32),
+        )
+        self.net = LayerCase()
+
+    def train(self, net, to_static, with_prim=False, with_cinn=False):
+        if to_static:
+            paddle.set_flags({'FLAGS_prim_all': with_prim})
+            if with_cinn:
+                build_strategy = paddle.static.BuildStrategy()
+                build_strategy.build_cinn_pass = True
+                net = paddle.jit.to_static(
+                    net, build_strategy=build_strategy, full_graph=True
+                )
+            else:
+                net = paddle.jit.to_static(net, full_graph=True)
+        paddle.seed(123)
+        outs = net(*self.inputs)
+        return outs
+
+    # NOTE prim + cinn lead to error
+    def test_ast_prim_cinn(self):
+        st_out = self.train(self.net, to_static=True)
+        cinn_out = self.train(
+            self.net, to_static=True, with_prim=True, with_cinn=False
+        )
+        for st, cinn in zip(
+            paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
+        ):
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_33.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_33.py
new file mode 100644
index 0000000000000..95b55b9bfe331
--- /dev/null
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_33.py
@@ -0,0 +1,102 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+# repo: PaddleClas
+# model: ppcls^configs^ImageNet^RedNet^RedNet38
+# api:paddle.nn.functional.conv._conv_nd||method:reshape||method:unsqueeze||api:paddle.nn.functional.common.unfold||method:reshape||method:__mul__||method:sum||method:reshape
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.parameter_0 = self.create_parameter(
+            shape=[784],
+            dtype=paddle.float32,
+        )
+        self.parameter_1 = self.create_parameter(
+            shape=[784, 64, 1, 1],
+            dtype=paddle.float32,
+        )
+
+    def forward(
+        self,
+        var_0,  # (shape: [10, 64, 14, 14], dtype: paddle.float32, stop_gradient: False)
+        var_1,  # (shape: [10, 256, 14, 14], dtype: paddle.float32, stop_gradient: False)
+    ):
+        var_2 = paddle.nn.functional.conv._conv_nd(
+            var_0,
+            self.parameter_1,
+            bias=self.parameter_0,
+            stride=[1, 1],
+            padding=[0, 0],
+            padding_algorithm='EXPLICIT',
+            dilation=[1, 1],
+            groups=1,
+            data_format='NCHW',
+            channel_dim=1,
+            op_type='conv2d',
+            use_cudnn=True,
+        )
+        var_3 = var_2.reshape((10, 16, 49, 14, 14))
+        var_4 = var_3.unsqueeze(2)
+        var_5 = paddle.nn.functional.common.unfold(var_1, 7, 1, 3, 1)
+        var_6 = var_5.reshape((10, 16, 16, 49, 14, 14))
+        var_7 = var_4 * var_6
+        var_8 = var_7.sum(axis=3)
+        var_9 = var_8.reshape((10, 256, 14, 14))
+        return var_9
+
+
+class TestLayer(unittest.TestCase):
+    def setUp(self):
+        self.inputs = (
+            paddle.rand(shape=[10, 64, 14, 14], dtype=paddle.float32),
+            paddle.rand(shape=[10, 256, 14, 14], dtype=paddle.float32),
+        )
+        self.net = LayerCase()
+
+    def train(self, net, to_static, with_prim=False, with_cinn=False):
+        if to_static:
+            paddle.set_flags({'FLAGS_prim_all': with_prim})
+            if with_cinn:
+                build_strategy = paddle.static.BuildStrategy()
+                build_strategy.build_cinn_pass = True
+                net = paddle.jit.to_static(
+                    net, build_strategy=build_strategy, full_graph=True
+                )
+            else:
+                net = paddle.jit.to_static(net, full_graph=True)
+        paddle.seed(123)
+        outs = net(*self.inputs)
+        return outs
+
+    # NOTE prim + cinn lead to error
+    def test_ast_prim_cinn(self):
+        st_out = self.train(self.net, to_static=True)
+        cinn_out = self.train(
+            self.net, to_static=True, with_prim=True, with_cinn=False
+        )
+        for st, cinn in zip(
+            paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
+        ):
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_34.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_34.py
new file mode 100644
index 0000000000000..c4ce15aed780a
--- /dev/null
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_34.py
@@ -0,0 +1,76 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+# repo: PaddleClas
+# model: ppcls^configs^ImageNet^HRNet^HRNet_W32_C
+# api:paddle.nn.functional.common.upsample||api:paddle.tensor.math.add
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        var_0,  # (shape: [10, 32, 14, 14], dtype: paddle.float32, stop_gradient: False)
+        var_1,  # (shape: [10, 32, 56, 56], dtype: paddle.float32, stop_gradient: False)
+    ):
+        var_2 = paddle.nn.functional.common.upsample(
+            var_0, scale_factor=4, mode='nearest'
+        )
+        var_3 = paddle.tensor.math.add(x=var_1, y=var_2)
+        return var_2, var_3
+
+
+class TestLayer(unittest.TestCase):
+    def setUp(self):
+        self.inputs = (
+            paddle.rand(shape=[10, 32, 14, 14], dtype=paddle.float32),
+            paddle.rand(shape=[10, 32, 56, 56], dtype=paddle.float32),
+        )
+        self.net = LayerCase()
+
+    def train(self, net, to_static, with_prim=False, with_cinn=False):
+        if to_static:
+            paddle.set_flags({'FLAGS_prim_all': with_prim})
+            if with_cinn:
+                build_strategy = paddle.static.BuildStrategy()
+                build_strategy.build_cinn_pass = True
+                net = paddle.jit.to_static(
+                    net, build_strategy=build_strategy, full_graph=True
+                )
+            else:
+                net = paddle.jit.to_static(net, full_graph=True)
+        paddle.seed(123)
+        outs = net(*self.inputs)
+        return outs
+
+    def test_ast_prim_cinn(self):
+        st_out = self.train(self.net, to_static=True)
+        cinn_out = self.train(
+            self.net, to_static=True, with_prim=True, with_cinn=True
+        )
+        for st, cinn in zip(
+            paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
+        ):
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_35.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_35.py
new file mode 100644
index 0000000000000..78311b8c6a05e
--- /dev/null
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_35.py
@@ -0,0 +1,110 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+# repo: PaddleClas
+# model: ppcls^configs^ImageNet^CSWinTransformer^CSWinTransformer_base_384
+# api:paddle.nn.functional.conv._conv_nd||method:flatten||method:transpose||api:paddle.nn.functional.norm.layer_norm
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.parameter_0 = self.create_parameter(
+            shape=[96],
+            dtype=paddle.float32,
+        )
+        self.parameter_1 = self.create_parameter(
+            shape=[96],
+            dtype=paddle.float32,
+        )
+        self.parameter_2 = self.create_parameter(
+            shape=[96, 3, 7, 7],
+            dtype=paddle.float32,
+        )
+        self.parameter_3 = self.create_parameter(
+            shape=[96],
+            dtype=paddle.float32,
+        )
+
+    def forward(
+        self,
+        var_0,  # (shape: [4, 3, 384, 384], dtype: paddle.float32, stop_gradient: True)
+    ):
+        var_1 = paddle.nn.functional.conv._conv_nd(
+            var_0,
+            self.parameter_2,
+            bias=self.parameter_3,
+            stride=[4, 4],
+            padding=[2, 2],
+            padding_algorithm='EXPLICIT',
+            dilation=[1, 1],
+            groups=1,
+            data_format='NCHW',
+            channel_dim=1,
+            op_type='conv2d',
+            use_cudnn=True,
+        )
+        var_2 = var_1.flatten(start_axis=2, stop_axis=-1)
+        var_3 = var_2.transpose([0, 2, 1])
+        var_4 = paddle.nn.functional.norm.layer_norm(
+            var_3,
+            normalized_shape=[96],
+            weight=self.parameter_1,
+            bias=self.parameter_0,
+            epsilon=1e-05,
+        )
+        return var_4
+
+
+class TestLayer(unittest.TestCase):
+    def setUp(self):
+        self.inputs = (
+            paddle.rand(shape=[4, 3, 384, 384], dtype=paddle.float32),
+        )
+        self.net = LayerCase()
+
+    def train(self, net, to_static, with_prim=False, with_cinn=False):
+        if to_static:
+            paddle.set_flags({'FLAGS_prim_all': with_prim})
+            if with_cinn:
+                build_strategy = paddle.static.BuildStrategy()
+                build_strategy.build_cinn_pass = True
+                net = paddle.jit.to_static(
+                    net, build_strategy=build_strategy, full_graph=True
+                )
+            else:
+                net = paddle.jit.to_static(net, full_graph=True)
+        paddle.seed(123)
+        outs = net(*self.inputs)
+        return outs
+
+    # NOTE prim + cinn lead to error
+    def test_ast_prim_cinn(self):
+        st_out = self.train(self.net, to_static=True)
+        cinn_out = self.train(
+            self.net, to_static=True, with_prim=False, with_cinn=False
+        )
+        for st, cinn in zip(
+            paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
+        ):
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_36.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_36.py
new file mode 100644
index 0000000000000..10e7eacac4c14
--- /dev/null
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_36.py
@@ -0,0 +1,96 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+# repo: PaddleClas
+# model: ppcls^configs^ImageNet^CSWinTransformer^CSWinTransformer_base_384
+# api:paddle.nn.functional.norm.layer_norm||api:paddle.nn.functional.common.linear||method:chunk
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.parameter_0 = self.create_parameter(
+            shape=[288],
+            dtype=paddle.float32,
+        )
+        self.parameter_1 = self.create_parameter(
+            shape=[96, 288],
+            dtype=paddle.float32,
+        )
+        self.parameter_2 = self.create_parameter(
+            shape=[96],
+            dtype=paddle.float32,
+        )
+        self.parameter_3 = self.create_parameter(
+            shape=[96],
+            dtype=paddle.float32,
+        )
+
+    def forward(
+        self,
+        var_0,  # (shape: [6, 9216, 96], dtype: paddle.float32, stop_gradient: False)
+    ):
+        var_1 = paddle.nn.functional.norm.layer_norm(
+            var_0,
+            normalized_shape=[96],
+            weight=self.parameter_2,
+            bias=self.parameter_3,
+            epsilon=1e-05,
+        )
+        var_2 = paddle.nn.functional.common.linear(
+            x=var_1, weight=self.parameter_1, bias=self.parameter_0, name=None
+        )
+        var_3, var_4, var_5 = var_2.chunk(3, axis=-1)
+        return var_3, var_4, var_5
+
+
+class TestLayer(unittest.TestCase):
+    def setUp(self):
+        self.inputs = (paddle.rand(shape=[6, 9216, 96], dtype=paddle.float32),)
+        self.net = LayerCase()
+
+    def train(self, net, to_static, with_prim=False, with_cinn=False):
+        if to_static:
+            paddle.set_flags({'FLAGS_prim_all': with_prim})
+            if with_cinn:
+                build_strategy = paddle.static.BuildStrategy()
+                build_strategy.build_cinn_pass = True
+                net = paddle.jit.to_static(
+                    net, build_strategy=build_strategy, full_graph=True
+                )
+            else:
+                net = paddle.jit.to_static(net, full_graph=True)
+        paddle.seed(123)
+        outs = net(*self.inputs)
+        return outs
+
+    # NOTE prim + cinn lead to error
+    def test_ast_prim_cinn(self):
+        st_out = self.train(self.net, to_static=True)
+        cinn_out = self.train(
+            self.net, to_static=True, with_prim=False, with_cinn=False
+        )
+        for st, cinn in zip(
+            paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
+        ):
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_37.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_37.py
new file mode 100644
index 0000000000000..6b6858d673bb6
--- /dev/null
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_37.py
@@ -0,0 +1,85 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+# repo: PaddleClas
+# model: ppcls^configs^ImageNet^CSWinTransformer^CSWinTransformer_base_384
+# api:paddle.nn.functional.norm.layer_norm||api:paddle.tensor.stat.mean
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.parameter_0 = self.create_parameter(
+            shape=[768],
+            dtype=paddle.float32,
+        )
+        self.parameter_1 = self.create_parameter(
+            shape=[768],
+            dtype=paddle.float32,
+        )
+
+    def forward(
+        self,
+        var_0,  # (shape: [4, 144, 768], dtype: paddle.float32, stop_gradient: False)
+    ):
+        var_1 = paddle.nn.functional.norm.layer_norm(
+            var_0,
+            normalized_shape=[768],
+            weight=self.parameter_0,
+            bias=self.parameter_1,
+            epsilon=1e-05,
+        )
+        var_2 = paddle.tensor.stat.mean(var_1, axis=1)
+        return var_2
+
+
+class TestLayer(unittest.TestCase):
+    def setUp(self):
+        self.inputs = (paddle.rand(shape=[4, 144, 768], dtype=paddle.float32),)
+        self.net = LayerCase()
+
+    def train(self, net, to_static, with_prim=False, with_cinn=False):
+        if to_static:
+            paddle.set_flags({'FLAGS_prim_all': with_prim})
+            if with_cinn:
+                build_strategy = paddle.static.BuildStrategy()
+                build_strategy.build_cinn_pass = True
+                net = paddle.jit.to_static(
+                    net, build_strategy=build_strategy, full_graph=True
+                )
+            else:
+                net = paddle.jit.to_static(net, full_graph=True)
+        paddle.seed(123)
+        outs = net(*self.inputs)
+        return outs
+
+    # NOTE prim + cinn lead to error
+    def test_ast_prim_cinn(self):
+        st_out = self.train(self.net, to_static=True)
+        cinn_out = self.train(
+            self.net, to_static=True, with_prim=False, with_cinn=False
+        )
+        for st, cinn in zip(
+            paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
+        ):
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_38.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_38.py
new file mode 100644
index 0000000000000..c9b53869aa8cb
--- /dev/null
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_38.py
@@ -0,0 +1,73 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+# repo: PaddleClas
+# model: ppcls^configs^ImageNet^CSWinTransformer^CSWinTransformer_base_384
+# method:reshape||method:transpose||method:reshape
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        var_0,  # (shape: [4, 48, 96, 96], dtype: paddle.float32, stop_gradient: False)
+    ):
+        var_1 = var_0.reshape([4, 48, 1, 96, 96, 1])
+        var_2 = var_1.transpose([0, 2, 4, 3, 5, 1])
+        var_3 = var_2.reshape([-1, 96, 48])
+        return var_3
+
+
+class TestLayer(unittest.TestCase):
+    def setUp(self):
+        self.inputs = (
+            paddle.rand(shape=[4, 48, 96, 96], dtype=paddle.float32),
+        )
+        self.net = LayerCase()
+
+    def train(self, net, to_static, with_prim=False, with_cinn=False):
+        if to_static:
+            paddle.set_flags({'FLAGS_prim_all': with_prim})
+            if with_cinn:
+                build_strategy = paddle.static.BuildStrategy()
+                build_strategy.build_cinn_pass = True
+                net = paddle.jit.to_static(
+                    net, build_strategy=build_strategy, full_graph=True
+                )
+            else:
+                net = paddle.jit.to_static(net, full_graph=True)
+        paddle.seed(123)
+        outs = net(*self.inputs)
+        return outs
+
+    def test_ast_prim_cinn(self):
+        st_out = self.train(self.net, to_static=True)
+        cinn_out = self.train(
+            self.net, to_static=True, with_prim=True, with_cinn=True
+        )
+        for st, cinn in zip(
+            paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
+        ):
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_39.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_39.py
new file mode 100644
index 0000000000000..c2cfa2786670d
--- /dev/null
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_39.py
@@ -0,0 +1,73 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+# repo: PaddleClas
+# model: ppcls^configs^ImageNet^CSWinTransformer^CSWinTransformer_base_384
+# api:paddle.tensor.creation.to_tensor||method:reshape||method:transpose||method:reshape
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class LayerCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        var_0,  # (shape: [12, 288, 192], dtype: paddle.float32, stop_gradient: False)
+    ):
+        var_1 = paddle.tensor.creation.to_tensor(6, 'int32')
+        var_2 = var_0.reshape([var_1, 2, 1, 12, 24, 192])
+        var_3 = var_2.transpose([0, 1, 3, 2, 4, 5])
+        var_4 = var_3.reshape([var_1, 24, 24, 192])
+        return var_4
+
+
+class TestLayer(unittest.TestCase):
+    def setUp(self):
+        self.inputs = (paddle.rand(shape=[12, 288, 192], dtype=paddle.float32),)
+        self.net = LayerCase()
+
+    def train(self, net, to_static, with_prim=False, with_cinn=False):
+        if to_static:
+            paddle.set_flags({'FLAGS_prim_all': with_prim})
+            if with_cinn:
+                build_strategy = paddle.static.BuildStrategy()
+                build_strategy.build_cinn_pass = True
+                net = paddle.jit.to_static(
+                    net, build_strategy=build_strategy, full_graph=True
+                )
+            else:
+                net = paddle.jit.to_static(net, full_graph=True)
+        paddle.seed(123)
+        outs = net(*self.inputs)
+        return outs
+
+    # NOTE prim + cinn lead to error
+    def test_ast_prim_cinn(self):
+        st_out = self.train(self.net, to_static=True)
+        cinn_out = self.train(
+            self.net, to_static=True, with_prim=True, with_cinn=False
+        )
+        for st, cinn in zip(
+            paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
+        ):
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_7.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_7.py
index 8341736299761..da5d0491b84b6 100644
--- a/test/ir/pir/cinn/sub_graphs/test_sub_graph_7.py
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_7.py
@@ -51,8 +51,8 @@ def forward(
         var_12 = paddle.tensor.linalg.transpose(var_11, (1, 0))
         var_13 = var_12.reshape((0, 49, 49))
         var_14 = paddle.tensor.linalg.matmul(var_6, var_9)
-        var_15 = var_14.__mul__(0.25)
-        var_16 = var_15.__add__(var_13)
+        var_15 = var_14 * 0.25
+        var_16 = var_15 + var_13
         var_17 = paddle.nn.functional.activation.softmax(var_16)
         var_18 = paddle.tensor.linalg.matmul(var_17, var_8)
         var_19 = paddle.tensor.linalg.transpose(var_18, perm=[0, 2, 1, 3])
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_9.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_9.py
index 073175ced160b..abd5282e9d1b7 100644
--- a/test/ir/pir/cinn/sub_graphs/test_sub_graph_9.py
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_9.py
@@ -48,8 +48,8 @@ def forward(
         var_9 = var_8.reshape((0, 49, 196))
         var_10 = paddle.tensor.linalg.transpose(var_1, perm=[0, 1, 3, 2])
         var_11 = paddle.tensor.linalg.matmul(var_5, var_10)
-        var_12 = var_11.__mul__(0.25)
-        var_13 = var_12.__add__(var_9)
+        var_12 = var_11 * 0.25
+        var_13 = var_12 + var_9
         var_14 = paddle.nn.functional.activation.softmax(var_13)
         var_15 = paddle.tensor.linalg.matmul(var_14, var_2)
         var_16 = paddle.tensor.linalg.transpose(var_15, perm=[0, 2, 1, 3])
diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_chunk.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_chunk.py
new file mode 100644
index 0000000000000..6b9d3e3c94557
--- /dev/null
+++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_chunk.py
@@ -0,0 +1,72 @@
+# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+# repo: PaddleClas
+# model: ppcls^configs^ImageNet^CSWinTransformer^CSWinTransformer_base_384
+# method:chunk
+import unittest
+
+import numpy as np
+
+import paddle
+
+
+class ChunkCase(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        var_0,  # (shape: [10, 2304, 192], dtype: paddle.float32, stop_gradient: False)
+    ):
+        var_1, var_2 = var_0.chunk(2, axis=-1)
+        return var_1, var_2
+
+
+class TestChunk(unittest.TestCase):
+    def setUp(self):
+        self.inputs = (
+            paddle.rand(shape=[10, 2304, 192], dtype=paddle.float32),
+        )
+        self.net = ChunkCase()
+
+    def train(self, net, to_static, with_prim=False, with_cinn=False):
+        if to_static:
+            paddle.set_flags({'FLAGS_prim_all': with_prim})
+            if with_cinn:
+                build_strategy = paddle.static.BuildStrategy()
+                build_strategy.build_cinn_pass = True
+                net = paddle.jit.to_static(
+                    net, build_strategy=build_strategy, full_graph=True
+                )
+            else:
+                net = paddle.jit.to_static(net, full_graph=True)
+        paddle.seed(123)
+        outs = net(*self.inputs)
+        return outs
+
+    # NOTE prim + cinn lead to error
+    def test_ast_prim_cinn(self):
+        st_out = self.train(self.net, to_static=True)
+        cinn_out = self.train(
+            self.net, to_static=True, with_prim=True, with_cinn=False
+        )
+        for st, cinn in zip(
+            paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
+        ):
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+
+
+if __name__ == '__main__':
+    unittest.main()