fix(test): make unit tests deterministic

This PR makes all unit tests deterministic. We have found non-deterministic unit tests have more disadvantages than advantages.
Read more: https://softwareengineering.stackexchange.com/questions/221632/testing-deterministic-or-non-deterministic

Signed-off-by: Jinzhe Zeng <[email protected]>

source/tests/common/dpmodel/test_descriptor_dpa1.py

@@ -19,7 +19,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 4))
         dstd = rng.normal(size=(self.nt, nnei, 4))

source/tests/common/dpmodel/test_descriptor_dpa2.py

@@ -23,7 +23,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 4))
         dstd = rng.normal(size=(self.nt, nnei, 4))

source/tests/common/dpmodel/test_descriptor_hybrid.py

@@ -29,7 +29,7 @@ def setUp(self):
     def test_get_parameters(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 4))
         dstd = rng.normal(size=(self.nt, nnei, 4))
@@ -72,7 +72,7 @@ def test_get_parameters(
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 4))
         dstd = rng.normal(size=(self.nt, nnei, 4))

source/tests/common/dpmodel/test_descriptor_se_e2_a.py

@@ -19,7 +19,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 4))
         dstd = rng.normal(size=(self.nt, nnei, 4))

source/tests/common/dpmodel/test_descriptor_se_r.py

@@ -19,7 +19,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 1))
         dstd = rng.normal(size=(self.nt, nnei, 1))

source/tests/common/dpmodel/test_descriptor_se_t.py

@@ -19,7 +19,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 4))
         dstd = rng.normal(size=(self.nt, nnei, 4))

source/tests/common/dpmodel/test_dp_model.py

@@ -50,7 +50,7 @@ def test_self_consistency(
         np.testing.assert_allclose(ret0["energy_redu"], ret1["energy_redu"])
 
     def test_prec_consistency(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         ds = DescrptSeA(
             self.rcut,
@@ -102,7 +102,7 @@ def setUp(self):
         TestCaseSingleFrameWithoutNlist.setUp(self)
 
     def test_prec_consistency(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc = self.atype.shape
         ds = DescrptSeA(
             self.rcut,

source/tests/common/dpmodel/test_env_mat.py

@@ -19,7 +19,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 4))
         dstd = rng.normal(size=(self.nt, nnei, 4))

source/tests/common/dpmodel/test_fitting_invar_fitting.py

@@ -23,7 +23,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         ds = DescrptSeA(self.rcut, self.rcut_smth, self.sel)
         dd = ds.call(self.coord_ext, self.atype_ext, self.nlist)
@@ -102,7 +102,7 @@ def test_mask(self):
     def test_self_exception(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         ds = DescrptSeA(self.rcut, self.rcut_smth, self.sel)
         dd = ds.call(self.coord_ext, self.atype_ext, self.nlist)
@@ -160,7 +160,7 @@ def test_get_set(self):
             3,
             1,
         )
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         foo = rng.normal([3, 4])
         for ii in [
             "bias_atom_e",

source/tests/common/dpmodel/test_neighbor_stat.py

@@ -11,16 +11,17 @@
 
 
 def gen_sys(nframes):
+    rng = np.random.default_rng(20240604)
     natoms = 1000
     data = {}
     X, Y, Z = np.mgrid[0:2:3j, 0:2:3j, 0:2:3j]
     positions = np.vstack([X.ravel(), Y.ravel(), Z.ravel()]).T  # + 0.1
     data["coords"] = np.repeat(positions[np.newaxis, :, :], nframes, axis=0)
-    data["forces"] = np.random.default_rng().random([nframes, natoms, 3])
+    data["forces"] = rng.random([nframes, natoms, 3])
     data["cells"] = np.array([3.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 3.0]).reshape(
         1, 3, 3
     )
-    data["energies"] = np.random.default_rng().random([nframes, 1])
+    data["energies"] = rng.random([nframes, 1])
     data["atom_names"] = ["TYPE"]
     data["atom_numbs"] = [27]
     data["atom_types"] = np.repeat(0, 27)

source/tests/common/dpmodel/test_region.py

@@ -19,7 +19,7 @@ def setUp(self):
         self.prec = 1e-8
 
     def test_inter_to_phys(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         inter = rng.normal(size=[4, 5, 3, 3])
         phys = inter2phys(inter, self.cell)
         for ii in range(4):

source/tests/common/dpmodel/test_update_sel.py

@@ -23,7 +23,7 @@ def update_sel(jdata):
 class TestTrain(unittest.TestCase):
     def setUp(self) -> None:
         self.update_sel = UpdateSel()
-        self.mock_min_nbor_dist = random.random()
+        self.mock_min_nbor_dist = random.Random(20240604).random()
         return super().setUp()
 
     @patch("deepmd.dpmodel.utils.update_sel.UpdateSel.get_nbor_stat")

source/tests/consistent/test_activation.py

@@ -33,7 +33,9 @@
 class TestActivationFunctionConsistent(unittest.TestCase):
     def setUp(self):
         (self.activation,) = self.param
-        self.random_input = np.random.default_rng().normal(scale=10, size=(10, 10))
+        self.random_input = np.random.default_rng(20240604).normal(
+            scale=10, size=(10, 10)
+        )
         self.ref = get_activation_fn_dp(self.activation)(self.random_input)
 
     @unittest.skipUnless(INSTALLED_TF, "TensorFlow is not installed")

source/tests/pt/model/test_autodiff.py

@@ -58,11 +58,12 @@ def test(
         places = 5
         delta = 1e-5
         natoms = 5
-        cell = torch.rand([3, 3], dtype=dtype, device="cpu")
+        generator = torch.Generator(device="cpu").manual_seed(20240604)
+        cell = torch.rand([3, 3], dtype=dtype, device="cpu", generator=generator)
         cell = (cell + cell.T) + 5.0 * torch.eye(3, device="cpu")
-        coord = torch.rand([natoms, 3], dtype=dtype, device="cpu")
+        coord = torch.rand([natoms, 3], dtype=dtype, device="cpu", generator=generator)
         coord = torch.matmul(coord, cell)
-        spin = torch.rand([natoms, 3], dtype=dtype, device="cpu")
+        spin = torch.rand([natoms, 3], dtype=dtype, device="cpu", generator=generator)
         atype = torch.IntTensor([0, 0, 0, 1, 1])
         # assumes input to be numpy tensor
         coord = coord.numpy()
@@ -129,9 +130,10 @@ def test(
         places = 5
         delta = 1e-4
         natoms = 5
-        cell = torch.rand([3, 3], dtype=dtype, device="cpu")
+        generator = torch.Generator(device="cpu").manual_seed(20240604)
+        cell = torch.rand([3, 3], dtype=dtype, device="cpu", generator=generator)
         cell = (cell) + 5.0 * torch.eye(3, device="cpu")
-        coord = torch.rand([natoms, 3], dtype=dtype, device="cpu")
+        coord = torch.rand([natoms, 3], dtype=dtype, device="cpu", generator=generator)
         coord = torch.matmul(coord, cell)
         atype = torch.IntTensor([0, 0, 0, 1, 1])
         # assumes input to be numpy tensor

source/tests/pt/model/test_descriptor_hybrid.py

@@ -56,7 +56,7 @@ def test_jit(
     def test_get_parameters(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         ddsub0 = DescrptSeA(
             rcut=self.rcut,

source/tests/pt/model/test_descriptor_se_r.py

@@ -37,7 +37,7 @@ def setUp(self):
     def test_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         _, _, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 1))
         dstd = rng.normal(size=(self.nt, nnei, 1))
@@ -107,7 +107,7 @@ def test_consistency(
                 )
 
     def test_load_stat(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         _, _, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 1))
         dstd = rng.normal(size=(self.nt, nnei, 1))
@@ -157,7 +157,7 @@ def test_load_stat(self):
     def test_jit(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         _, _, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 1))
         dstd = rng.normal(size=(self.nt, nnei, 1))

source/tests/pt/model/test_dipole_fitting.py

@@ -57,7 +57,7 @@ def finite_difference(f, x, a, delta=1e-6):
 class TestDipoleFitting(unittest.TestCase, TestCaseSingleFrameWithNlist):
     def setUp(self):
         TestCaseSingleFrameWithNlist.setUp(self)
-        self.rng = np.random.default_rng()
+        self.rng = np.random.default_rng(20240604)
         self.nf, self.nloc, _ = self.nlist.shape
         self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE)
 
@@ -147,11 +147,16 @@ def setUp(self) -> None:
         self.rcut_smth = 0.5
         self.sel = [46, 92, 4]
         self.nf = 1
-        self.coord = 2 * torch.rand([self.natoms, 3], dtype=dtype, device=env.DEVICE)
+        generator = torch.Generator(device="cpu").manual_seed(20240604)
+        self.coord = 2 * torch.rand(
+            [self.natoms, 3], dtype=dtype, device=env.DEVICE, generator=generator
+        )
         self.shift = torch.tensor([4, 4, 4], dtype=dtype, device=env.DEVICE)
         self.atype = torch.tensor([0, 0, 0, 1, 1], dtype=torch.int32, device=env.DEVICE)
         self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE)
-        self.cell = torch.rand([3, 3], dtype=dtype, device=env.DEVICE)
+        self.cell = torch.rand(
+            [3, 3], dtype=dtype, device=env.DEVICE, generator=generator
+        )
         self.cell = (self.cell + self.cell.T) + 5.0 * torch.eye(3, device=env.DEVICE)
 
     def test_rot(self):
@@ -160,7 +165,7 @@ def test_rot(self):
         coord_rot = torch.matmul(self.coord, rmat)
         # use larger cell to rotate only coord and shift to the center of cell
         cell_rot = 10.0 * torch.eye(3, dtype=dtype, device=env.DEVICE)
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         for nfp, nap in itertools.product(
             [0, 3],
             [0, 4],
@@ -304,8 +309,11 @@ def setUp(self):
         self.rcut_smth = 0.5
         self.sel = [46, 92, 4]
         self.nf = 1
-        self.coord = 2 * torch.rand([self.natoms, 3], dtype=dtype, device=env.DEVICE)
-        cell = torch.rand([3, 3], dtype=dtype, device=env.DEVICE)
+        generator = torch.Generator(device="cpu").manual_seed(20240604)
+        self.coord = 2 * torch.rand(
+            [self.natoms, 3], dtype=dtype, device=env.DEVICE, generator=generator
+        )
+        cell = torch.rand([3, 3], dtype=dtype, device=env.DEVICE, generator=generator)
         self.cell = (cell + cell.T) + 5.0 * torch.eye(3, device=env.DEVICE)
         self.atype = torch.IntTensor([0, 0, 0, 1, 1], device="cpu").to(env.DEVICE)
         self.dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE)

source/tests/pt/model/test_dp_atomic_model.py

@@ -75,7 +75,7 @@ def test_self_consistency(self):
             )
 
     def test_dp_consistency(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         ds = DPDescrptSeA(
             self.rcut,

source/tests/pt/model/test_dp_model.py

@@ -130,7 +130,7 @@ def test_dp_consistency(self):
         md0 = DPEnergyModel(ds, ft, type_map=type_map)
         md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE)
 
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         fparam = rng.normal(size=[self.nf, nfp])
         aparam = rng.normal(size=[self.nf, nloc, nap])
         args0 = [self.coord, self.atype, self.cell]
@@ -169,7 +169,7 @@ def test_dp_consistency_nopbc(self):
         md0 = DPEnergyModel(ds, ft, type_map=type_map)
         md1 = EnergyModel.deserialize(md0.serialize()).to(env.DEVICE)
 
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         fparam = rng.normal(size=[self.nf, nfp])
         aparam = rng.normal(size=[self.nf, self.nloc, nap])
         args0 = [self.coord, self.atype]
@@ -190,7 +190,7 @@ def test_dp_consistency_nopbc(self):
         )
 
     def test_prec_consistency(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc = self.atype.shape
         ds = DPDescrptSeA(
             self.rcut,
@@ -292,7 +292,7 @@ def test_self_consistency(self):
         )
 
     def test_dp_consistency(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         ds = DPDescrptSeA(
             self.rcut,
@@ -325,7 +325,7 @@ def test_dp_consistency(self):
         )
 
     def test_prec_consistency(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         ds = DPDescrptSeA(
             self.rcut,

source/tests/pt/model/test_dpa1.py

@@ -170,7 +170,7 @@ def test_consistency(
     def test_jit(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 4))
         dstd = rng.normal(size=(self.nt, nnei, 4))

source/tests/pt/model/test_ener_fitting.py

@@ -34,7 +34,7 @@ def setUp(self):
     def test_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         dd0 = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE)
         rd0, _, _, _, _ = dd0(
@@ -102,7 +102,7 @@ def test_consistency(
     def test_new_old(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         dd = DescrptSeA(self.rcut, self.rcut_smth, self.sel).to(env.DEVICE)
         rd0, _, _, _, _ = dd(
@@ -176,7 +176,7 @@ def test_get_set(self):
             3,
             1,
         )
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         foo = rng.normal([3, 4])
         for ii in [
             "bias_atom_e",

source/tests/pt/model/test_ener_spin_model.py

@@ -59,11 +59,12 @@ def setUp(self):
         natoms = 5
         self.ntypes = 3  # ["O", "H", "B"] for test
         self.cell = 4.0 * torch.eye(3, dtype=dtype, device=env.DEVICE).unsqueeze(0)
+        generator = torch.Generator(device="cpu").manual_seed(20240604)
         self.coord = 3.0 * torch.rand(
-            [natoms, 3], dtype=dtype, device=env.DEVICE
+            [natoms, 3], dtype=dtype, device=env.DEVICE, generator=generator
         ).unsqueeze(0)
         self.spin = 0.5 * torch.rand(
-            [natoms, 3], dtype=dtype, device=env.DEVICE
+            [natoms, 3], dtype=dtype, device=env.DEVICE, generator=generator
         ).unsqueeze(0)
         self.atype = torch.tensor(
             [0, 0, 0, 1, 1], dtype=torch.int64, device=env.DEVICE

source/tests/pt/model/test_env_mat.py

@@ -155,7 +155,7 @@ def setUp(self):
     def test_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(20240604)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 4))
         dstd = rng.normal(size=(self.nt, nnei, 4))