fix(test): make unit tests deterministic (deepmodeling#3856)

Fix deepmodeling#3847.
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

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **Tests**
- Introduced a global seed for consistent random number generation
across various test files.
- Updated the initialization of random tensors to include a generator
parameter seeded with the global seed.
- Adjusted epsilon values and setup methods in specific test cases for
accuracy and consistency.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jinzhe Zeng <[email protected]>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

source/tests/common/dpmodel/test_descriptor_dpa1.py

@@ -7,6 +7,9 @@
     DescrptDPA1,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .case_single_frame_with_nlist import (
     TestCaseSingleFrameWithNlist,
 )
@@ -19,7 +22,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         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

@@ -11,6 +11,9 @@
     RepinitArgs,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .case_single_frame_with_nlist import (
     TestCaseSingleFrameWithNlist,
 )
@@ -23,7 +26,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         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

@@ -16,6 +16,9 @@
     DescrptSeR,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .case_single_frame_with_nlist import (
     TestCaseSingleFrameWithNlist,
 )
@@ -29,7 +32,7 @@ def setUp(self):
     def test_get_parameters(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         nf, nloc, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 4))
         dstd = rng.normal(size=(self.nt, nnei, 4))
@@ -72,7 +75,7 @@ def test_get_parameters(
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         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

@@ -7,6 +7,9 @@
     DescrptSeA,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .case_single_frame_with_nlist import (
     TestCaseSingleFrameWithNlist,
 )
@@ -19,7 +22,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         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

@@ -7,6 +7,9 @@
     DescrptSeR,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .case_single_frame_with_nlist import (
     TestCaseSingleFrameWithNlist,
 )
@@ -19,7 +22,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         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

@@ -7,6 +7,9 @@
     DescrptSeT,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .case_single_frame_with_nlist import (
     TestCaseSingleFrameWithNlist,
 )
@@ -19,7 +22,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         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

@@ -13,6 +13,9 @@
     EnergyModel,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .case_single_frame_with_nlist import (
     TestCaseSingleFrameWithNlist,
     TestCaseSingleFrameWithoutNlist,
@@ -50,7 +53,7 @@ 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(GLOBAL_SEED)
         nf, nloc, nnei = self.nlist.shape
         ds = DescrptSeA(
             self.rcut,
@@ -102,7 +105,7 @@ def setUp(self):
         TestCaseSingleFrameWithoutNlist.setUp(self)
 
     def test_prec_consistency(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         nf, nloc = self.atype.shape
         ds = DescrptSeA(
             self.rcut,

source/tests/common/dpmodel/test_env_mat.py

@@ -7,6 +7,9 @@
     EnvMat,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .case_single_frame_with_nlist import (
     TestCaseSingleFrameWithNlist,
 )
@@ -19,7 +22,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         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

@@ -11,6 +11,9 @@
     InvarFitting,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .case_single_frame_with_nlist import (
     TestCaseSingleFrameWithNlist,
 )
@@ -23,7 +26,7 @@ def setUp(self):
     def test_self_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         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 +105,7 @@ def test_mask(self):
     def test_self_exception(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         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 +163,7 @@ def test_get_set(self):
             3,
             1,
         )
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         foo = rng.normal([3, 4])
         for ii in [
             "bias_atom_e",

source/tests/common/dpmodel/test_neighbor_stat.py

@@ -9,18 +9,23 @@
     neighbor_stat,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
+
 
 def gen_sys(nframes):
+    rng = np.random.default_rng(GLOBAL_SEED)
     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

@@ -8,6 +8,10 @@
     to_face_distance,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
+
 
 class TestRegion(unittest.TestCase):
     def setUp(self):
@@ -19,7 +23,7 @@ def setUp(self):
         self.prec = 1e-8
 
     def test_inter_to_phys(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         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

@@ -12,6 +12,10 @@
     UpdateSel,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
+
 
 def update_sel(jdata):
     type_map = jdata["model"].get("type_map")
@@ -23,7 +27,7 @@ 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(GLOBAL_SEED).random()
         return super().setUp()
 
     @patch("deepmd.dpmodel.utils.update_sel.UpdateSel.get_nbor_stat")

source/tests/consistent/test_activation.py

@@ -8,6 +8,9 @@
 )
 from deepmd.dpmodel.utils.network import get_activation_fn as get_activation_fn_dp
 
+from ..seed import (
+    GLOBAL_SEED,
+)
 from .common import (
     INSTALLED_PT,
     INSTALLED_TF,
@@ -33,7 +36,9 @@
 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(GLOBAL_SEED).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

@@ -15,6 +15,10 @@
     to_numpy_array,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
+
 dtype = torch.float64
 
 from .test_permutation import (
@@ -58,11 +62,12 @@ 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(GLOBAL_SEED)
+        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 +134,10 @@ 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(GLOBAL_SEED)
+        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 +56,6 @@ def test_jit(
     def test_get_parameters(
         self,
     ):
-        rng = np.random.default_rng()
         nf, nloc, nnei = self.nlist.shape
         ddsub0 = DescrptSeA(
             rcut=self.rcut,

source/tests/pt/model/test_descriptor_se_r.py

@@ -19,6 +19,9 @@
     EnvMatStatSe,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .test_env_mat import (
     TestCaseSingleFrameWithNlist,
 )
@@ -37,7 +40,7 @@ def setUp(self):
     def test_consistency(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         _, _, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 1))
         dstd = rng.normal(size=(self.nt, nnei, 1))
@@ -107,7 +110,7 @@ def test_consistency(
                 )
 
     def test_load_stat(self):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         _, _, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 1))
         dstd = rng.normal(size=(self.nt, nnei, 1))
@@ -157,7 +160,7 @@ def test_load_stat(self):
     def test_jit(
         self,
     ):
-        rng = np.random.default_rng()
+        rng = np.random.default_rng(GLOBAL_SEED)
         _, _, nnei = self.nlist.shape
         davg = rng.normal(size=(self.nt, nnei, 1))
         dstd = rng.normal(size=(self.nt, nnei, 1))