ut: add null test

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)