ut: add null test (#3391)

test the cases: 
1. system only has one atom
2. system has two atoms that are far away from each other. 

In each cases, the force and virial predictions should be zero and
energy should be a valid float.

---------

Co-authored-by: Han Wang <[email protected]>

source/tests/pt/model/test_null_input.py

@@ -0,0 +1,145 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import copy
+import unittest
+
+import numpy as np
+import torch
+
+from deepmd.pt.infer.deep_eval import (
+    eval_model,
+)
+from deepmd.pt.model.model import (
+    get_model,
+    get_zbl_model,
+)
+from deepmd.pt.utils import (
+    env,
+)
+from deepmd.pt.utils.utils import (
+    to_numpy_array,
+)
+
+from .test_permutation import (
+    model_dpa1,
+    model_dpa2,
+    model_hybrid,
+    model_se_e2_a,
+    model_zbl,
+)
+
+dtype = torch.float64
+
+
+class NullTest:
+    def test_nloc_1(
+        self,
+    ):
+        natoms = 1
+        # torch.manual_seed(1000)
+        cell = torch.rand([3, 3], dtype=dtype, device=env.DEVICE)
+        # large box to exclude images
+        cell = (cell + cell.T) + 100.0 * torch.eye(3, device=env.DEVICE)
+        coord = torch.rand([natoms, 3], dtype=dtype, device=env.DEVICE)
+        atype = torch.tensor([0], dtype=torch.int32, device=env.DEVICE)
+        e0, f0, v0 = eval_model(
+            self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype
+        )
+        ret0 = {
+            "energy": e0.squeeze(0),
+            "force": f0.squeeze(0),
+            "virial": v0.squeeze(0),
+        }
+        prec = 1e-10
+        expect_e_shape = [1]
+        expect_f = torch.zeros([natoms, 3], dtype=dtype, device=env.DEVICE)
+        expect_v = torch.zeros([9], dtype=dtype, device=env.DEVICE)
+        self.assertEqual(list(ret0["energy"].shape), expect_e_shape)
+        self.assertFalse(np.isnan(to_numpy_array(ret0["energy"])[0]))
+        torch.testing.assert_close(ret0["force"], expect_f, rtol=prec, atol=prec)
+        if not hasattr(self, "test_virial") or self.test_virial:
+            torch.testing.assert_close(ret0["virial"], expect_v, rtol=prec, atol=prec)
+
+    def test_nloc_2_far(
+        self,
+    ):
+        natoms = 2
+        cell = torch.rand([3, 3], dtype=dtype, device=env.DEVICE)
+        # large box to exclude images
+        cell = (cell + cell.T) + 3000.0 * torch.eye(3, device=env.DEVICE)
+        coord = torch.rand([1, 3], dtype=dtype, device=env.DEVICE)
+        # 2 far-away atoms
+        coord = torch.cat([coord, coord + 100.0], dim=0)
+        atype = torch.tensor([0, 2], dtype=torch.int32, device=env.DEVICE)
+        e0, f0, v0 = eval_model(
+            self.model, coord.unsqueeze(0), cell.unsqueeze(0), atype
+        )
+        ret0 = {
+            "energy": e0.squeeze(0),
+            "force": f0.squeeze(0),
+            "virial": v0.squeeze(0),
+        }
+        prec = 1e-10
+        expect_e_shape = [1]
+        expect_f = torch.zeros([natoms, 3], dtype=dtype, device=env.DEVICE)
+        expect_v = torch.zeros([9], dtype=dtype, device=env.DEVICE)
+        self.assertEqual(list(ret0["energy"].shape), expect_e_shape)
+        self.assertFalse(np.isnan(to_numpy_array(ret0["energy"])[0]))
+        torch.testing.assert_close(ret0["force"], expect_f, rtol=prec, atol=prec)
+        if not hasattr(self, "test_virial") or self.test_virial:
+            torch.testing.assert_close(ret0["virial"], expect_v, rtol=prec, atol=prec)
+
+
+class TestEnergyModelSeA(unittest.TestCase, NullTest):
+    def setUp(self):
+        model_params = copy.deepcopy(model_se_e2_a)
+        self.type_split = False
+        self.model = get_model(model_params).to(env.DEVICE)
+
+
+class TestEnergyModelDPA1(unittest.TestCase, NullTest):
+    def setUp(self):
+        model_params = copy.deepcopy(model_dpa1)
+        self.type_split = True
+        self.model = get_model(model_params).to(env.DEVICE)
+
+
+class TestEnergyModelDPA2(unittest.TestCase, NullTest):
+    def setUp(self):
+        model_params = copy.deepcopy(model_dpa2)
+        self.type_split = True
+        self.model = get_model(model_params).to(env.DEVICE)
+
+
+class TestForceModelDPA2(unittest.TestCase, NullTest):
+    def setUp(self):
+        model_params = copy.deepcopy(model_dpa2)
+        model_params["fitting_net"]["type"] = "direct_force_ener"
+        self.type_split = True
+        self.test_virial = False
+        self.model = get_model(model_params).to(env.DEVICE)
+
+
+@unittest.skip("hybrid not supported at the moment")
+class TestEnergyModelHybrid(unittest.TestCase, NullTest):
+    def setUp(self):
+        model_params = copy.deepcopy(model_hybrid)
+        self.type_split = True
+        self.model = get_model(model_params).to(env.DEVICE)
+
+
+@unittest.skip("hybrid not supported at the moment")
+class TestForceModelHybrid(unittest.TestCase, NullTest):
+    def setUp(self):
+        model_params = copy.deepcopy(model_hybrid)
+        model_params["fitting_net"]["type"] = "direct_force_ener"
+        self.type_split = True
+        self.test_virial = False
+        self.model = get_model(model_params).to(env.DEVICE)
+
+
+@unittest.skip("FAILED at the moment")
+class TestEnergyModelZBL(unittest.TestCase, NullTest):
+    def setUp(self):
+        model_params = copy.deepcopy(model_zbl)
+        self.type_split = False
+        self.model = get_zbl_model(model_params).to(env.DEVICE)