diff --git a/source/tests/pt/model/test_ener_model.py b/source/tests/pt/model/test_ener_model.py
new file mode 100644
index 0000000000..9d6a4b6f8f
--- /dev/null
+++ b/source/tests/pt/model/test_ener_model.py
@@ -0,0 +1,66 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import unittest
+
+from deepmd.pt.model.atomic_model.energy_atomic_model import (
+    DPEnergyAtomicModel,
+)
+from deepmd.pt.model.descriptor.se_a import (
+    DescrptSeA,
+)
+from deepmd.pt.model.model.ener_model import (
+    EnergyModel,
+)
+from deepmd.pt.model.task.ener import (
+    EnergyFittingNet,
+)
+
+from .utils import (
+    AtomicModelTestCase,
+    ModelTestCase,
+)
+
+
+class TestEnerModel(unittest.TestCase, ModelTestCase):
+    def setUp(self) -> None:
+        self.expected_rcut = 5.0
+        self.expected_type_map = ["foo", "bar"]
+        self.expected_dim_fparam = 0
+        self.expected_dim_aparam = 0
+        self.expected_sel_type = [0, 1]
+        self.expected_aparam_nall = False
+        self.expected_model_output_type = ["energy", "mask"]
+        self.expected_sel = [8, 12]
+        ds = DescrptSeA(
+            rcut=self.expected_rcut,
+            rcut_smth=self.expected_rcut / 2,
+            sel=self.expected_sel,
+        )
+        ft = EnergyFittingNet(
+            ntypes=len(self.expected_type_map),
+            dim_descrpt=ds.get_dim_out(),
+            mixed_types=ds.mixed_types(),
+        )
+        self.module = EnergyModel(ds, ft, type_map=self.expected_type_map)
+
+
+class TestEnerAtomicModel(unittest.TestCase, AtomicModelTestCase):
+    def setUp(self) -> None:
+        self.expected_rcut = 5.0
+        self.expected_type_map = ["foo", "bar"]
+        self.expected_dim_fparam = 0
+        self.expected_dim_aparam = 0
+        self.expected_sel_type = [0, 1]
+        self.expected_aparam_nall = False
+        self.expected_model_output_type = ["energy", "mask"]
+        self.expected_sel = [8, 12]
+        ds = DescrptSeA(
+            rcut=self.expected_rcut,
+            rcut_smth=self.expected_rcut / 2,
+            sel=self.expected_sel,
+        )
+        ft = EnergyFittingNet(
+            ntypes=len(self.expected_type_map),
+            dim_descrpt=ds.get_dim_out(),
+            mixed_types=ds.mixed_types(),
+        )
+        self.module = DPEnergyAtomicModel(ds, ft, type_map=self.expected_type_map)
diff --git a/source/tests/pt/model/utils.py b/source/tests/pt/model/utils.py
new file mode 100644
index 0000000000..aed6f8248c
--- /dev/null
+++ b/source/tests/pt/model/utils.py
@@ -0,0 +1,241 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+"""Common test case."""
+
+from typing import (
+    List,
+)
+
+import numpy as np
+import torch
+
+from deepmd.pt.utils.nlist import (
+    extend_input_and_build_neighbor_list,
+)
+from deepmd.pt.utils.utils import (
+    to_torch_tensor,
+)
+
+
+class CommonTestCase:
+    """Common test case."""
+
+    module: torch.nn.Module
+    """Module to test."""
+
+    @property
+    def script_module(self):
+        return torch.jit.script(self.module)
+
+    @property
+    def deserialized_module(self):
+        return self.module.deserialize(self.module.serialize())
+
+    @property
+    def modules_to_test(self):
+        return [self.module, self.script_module, self.deserialized_module]
+
+    def test_jit(self):
+        self.script_module
+
+
+class ModelTestCase(CommonTestCase):
+    """Common test case for model."""
+
+    expected_type_map: List[str]
+    """Expected type map."""
+    expected_rcut: float
+    """Expected cut-off radius."""
+    expected_dim_fparam: int
+    """Expected number (dimension) of frame parameters."""
+    expected_dim_aparam: int
+    """Expected number (dimension) of atomic parameters."""
+    expected_sel_type: List[int]
+    """Expected selected atom types."""
+    expected_aparam_nall: bool
+    """Expected shape of atomic parameters."""
+    expected_model_output_type: List[str]
+    """Expected output type for the model."""
+    expected_sel: List[int]
+    """Expected number of neighbors."""
+
+    def test_get_type_map(self):
+        """Test get_type_map."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_type_map(), self.expected_type_map)
+
+    def test_get_rcut(self):
+        """Test get_rcut."""
+        for module in self.modules_to_test:
+            self.assertAlmostEqual(module.get_rcut(), self.expected_rcut)
+
+    def test_get_dim_fparam(self):
+        """Test get_dim_fparam."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_dim_fparam(), self.expected_dim_fparam)
+
+    def test_get_dim_aparam(self):
+        """Test get_dim_aparam."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_dim_aparam(), self.expected_dim_aparam)
+
+    def test_get_sel_type(self):
+        """Test get_sel_type."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_sel_type(), self.expected_sel_type)
+
+    def test_is_aparam_nall(self):
+        """Test is_aparam_nall."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.is_aparam_nall(), self.expected_aparam_nall)
+
+    def test_model_output_type(self):
+        """Test model_output_type."""
+        for module in self.modules_to_test:
+            self.assertEqual(
+                module.model_output_type(), self.expected_model_output_type
+            )
+
+    def test_get_nnei(self):
+        """Test get_nnei."""
+        expected_nnei = sum(self.expected_sel)
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_nnei(), expected_nnei)
+
+    def test_get_ntypes(self):
+        """Test get_ntypes."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_ntypes(), len(self.expected_type_map))
+
+    def test_forward(self):
+        """Test forward and forward_lower."""
+        nf = 1
+        nloc = 3
+        coord = to_torch_tensor(
+            np.array(
+                [
+                    [0, 0, 0],
+                    [0, 1, 0],
+                    [0, 0, 1],
+                ],
+                dtype=np.float64,
+            ).reshape([nf, -1])
+        )
+        atype = to_torch_tensor(np.array([0, 0, 1], dtype=int).reshape([nf, -1]))
+        cell = to_torch_tensor(6.0 * np.eye(3).reshape([nf, 9]))
+        coord_ext, atype_ext, mapping, nlist = extend_input_and_build_neighbor_list(
+            coord,
+            atype,
+            self.expected_rcut,
+            self.expected_sel,
+            mixed_types=True,
+            box=cell,
+        )
+        ret = []
+        ret_lower = []
+        for module in self.modules_to_test:
+            ret.append(module(coord, atype, cell))
+            ret_lower.append(module.forward_lower(coord_ext, atype_ext, nlist))
+        for r in ret[1:]:
+            torch.testing.assert_close(ret[0], r)
+        for r in ret_lower[1:]:
+            torch.testing.assert_close(ret_lower[0], r)
+        same_keys = set(ret[0].keys()) & set(ret_lower[0].keys())
+        self.assertTrue(same_keys)
+        for key in same_keys:
+            torch.testing.assert_close(ret[0][key], ret_lower[0][key])
+
+
+class AtomicModelTestCase(CommonTestCase):
+    """Common test case for atomic model."""
+
+    expected_type_map: List[str]
+    """Expected type map."""
+    expected_rcut: float
+    """Expected cut-off radius."""
+    expected_dim_fparam: int
+    """Expected number (dimension) of frame parameters."""
+    expected_dim_aparam: int
+    """Expected number (dimension) of atomic parameters."""
+    expected_sel_type: List[int]
+    """Expected selected atom types."""
+    expected_aparam_nall: bool
+    """Expected shape of atomic parameters."""
+    expected_model_output_type: List[str]
+    """Expected output type for the model."""
+    expected_sel: List[int]
+    """Expected number of neighbors."""
+
+    @property
+    def modules_to_test(self):
+        return [self.module, self.deserialized_module]
+
+    def test_get_type_map(self):
+        """Test get_type_map."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_type_map(), self.expected_type_map)
+
+    def test_get_rcut(self):
+        """Test get_rcut."""
+        for module in self.modules_to_test:
+            self.assertAlmostEqual(module.get_rcut(), self.expected_rcut)
+
+    def test_get_dim_fparam(self):
+        """Test get_dim_fparam."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_dim_fparam(), self.expected_dim_fparam)
+
+    def test_get_dim_aparam(self):
+        """Test get_dim_aparam."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_dim_aparam(), self.expected_dim_aparam)
+
+    def test_get_sel_type(self):
+        """Test get_sel_type."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_sel_type(), self.expected_sel_type)
+
+    def test_is_aparam_nall(self):
+        """Test is_aparam_nall."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.is_aparam_nall(), self.expected_aparam_nall)
+
+    def test_get_nnei(self):
+        """Test get_nnei."""
+        expected_nnei = sum(self.expected_sel)
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_nnei(), expected_nnei)
+
+    def test_get_ntypes(self):
+        """Test get_ntypes."""
+        for module in self.modules_to_test:
+            self.assertEqual(module.get_ntypes(), len(self.expected_type_map))
+
+    def test_forward(self):
+        """Test forward and forward_lower."""
+        nf = 1
+        nloc = 3
+        coord = to_torch_tensor(
+            np.array(
+                [
+                    [0, 0, 0],
+                    [0, 1, 0],
+                    [0, 0, 1],
+                ],
+                dtype=np.float64,
+            ).reshape([nf, -1])
+        )
+        atype = to_torch_tensor(np.array([0, 0, 1], dtype=int).reshape([nf, -1]))
+        cell = to_torch_tensor(6.0 * np.eye(3).reshape([nf, 9]))
+        coord_ext, atype_ext, mapping, nlist = extend_input_and_build_neighbor_list(
+            coord,
+            atype,
+            self.expected_rcut,
+            self.expected_sel,
+            mixed_types=True,
+            box=cell,
+        )
+        ret_lower = []
+        for module in self.modules_to_test:
+            ret_lower.append(module.forward_common_atomic(coord_ext, atype_ext, nlist))
+        for r in ret_lower[1:]:
+            torch.testing.assert_close(ret_lower[0], r)