fix(pt ut): make separated uts deterministic (#4162)

Fix failed uts in #4145 .

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

## Summary by CodeRabbit

- **New Features**
- Added a `"seed"` property to multiple JSON configuration files,
enhancing control over randomness in model training and evaluation.
- Introduced a global seed parameter in various test functions to
improve reproducibility across test runs.

- **Bug Fixes**
- Ensured consistent random number generation in tests by integrating a
global seed parameter.

- **Documentation**
- Updated configuration files and test methods to reflect the addition
of the seed parameter for clarity and consistency.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

source/tests/pt/model/models/dpa1.json

@@ -21,7 +21,8 @@
     "activation_function": "tanh",
     "scaling_factor": 1.0,
     "normalize": true,
-    "temperature": 1.0
+    "temperature": 1.0,
+    "seed": 1
   },
   "fitting_net": {
     "neuron": [

source/tests/pt/model/models/dpa2.json

@@ -42,6 +42,7 @@
       "g1_out_conv": false,
       "g1_out_mlp": false
     },
+    "seed": 1,
     "add_tebd_to_repinit_out": false
   },
   "fitting_net": {

source/tests/pt/model/test_descriptor_se_r.py

@@ -63,6 +63,7 @@ def test_consistency(
                 resnet_dt=idt,
                 old_impl=False,
                 exclude_mask=em,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             dd0.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE)
             dd0.dstd = torch.tensor(dstd, dtype=dtype, device=env.DEVICE)
@@ -130,6 +131,7 @@ def test_load_stat(self):
                 precision=prec,
                 resnet_dt=idt,
                 old_impl=False,
+                seed=GLOBAL_SEED,
             )
             dd0.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE)
             dd0.dstd = torch.tensor(dstd, dtype=dtype, device=env.DEVICE)
@@ -180,6 +182,7 @@ def test_jit(
                 precision=prec,
                 resnet_dt=idt,
                 old_impl=False,
+                seed=GLOBAL_SEED,
             )
             dd0.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE)
             dd0.dstd = torch.tensor(dstd, dtype=dtype, device=env.DEVICE)

source/tests/pt/model/test_dipole_fitting.py

@@ -87,6 +87,7 @@ def test_consistency(
                 numb_fparam=nfp,
                 numb_aparam=nap,
                 mixed_types=self.dd0.mixed_types(),
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             ft1 = DPDipoleFitting.deserialize(ft0.serialize())
             ft2 = DipoleFittingNet.deserialize(ft1.serialize())
@@ -139,6 +140,7 @@ def test_jit(
                 numb_fparam=nfp,
                 numb_aparam=nap,
                 mixed_types=mixed_types,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             torch.jit.script(ft0)
 
@@ -180,6 +182,7 @@ def test_rot(self):
                 numb_fparam=nfp,
                 numb_aparam=nap,
                 mixed_types=self.dd0.mixed_types(),
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             if nfp > 0:
                 ifp = torch.tensor(
@@ -234,6 +237,7 @@ def test_permu(self):
             numb_fparam=0,
             numb_aparam=0,
             mixed_types=self.dd0.mixed_types(),
+            seed=GLOBAL_SEED,
         ).to(env.DEVICE)
         res = []
         for idx_perm in [[0, 1, 2, 3, 4], [1, 0, 4, 3, 2]]:
@@ -280,6 +284,7 @@ def test_trans(self):
             numb_fparam=0,
             numb_aparam=0,
             mixed_types=self.dd0.mixed_types(),
+            seed=GLOBAL_SEED,
         ).to(env.DEVICE)
         res = []
         for xyz in [self.coord, coord_s]:
@@ -327,6 +332,7 @@ def setUp(self):
             numb_fparam=0,
             numb_aparam=0,
             mixed_types=self.dd0.mixed_types(),
+            seed=GLOBAL_SEED,
         ).to(env.DEVICE)
         self.type_mapping = ["O", "H", "B"]
         self.model = DipoleModel(self.dd0, self.ft0, self.type_mapping)

source/tests/pt/model/test_dpa1.py

@@ -71,6 +71,7 @@ def test_consistency(
                 use_econf_tebd=ect,
                 type_map=["O", "H"] if ect else None,
                 old_impl=False,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             dd0.se_atten.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE)
             dd0.se_atten.stddev = torch.tensor(dstd, dtype=dtype, device=env.DEVICE)
@@ -125,6 +126,7 @@ def test_consistency(
                     resnet_dt=idt,
                     smooth_type_embedding=sm,
                     old_impl=True,
+                    seed=GLOBAL_SEED,
                 ).to(env.DEVICE)
                 dd0_state_dict = dd0.se_atten.state_dict()
                 dd3_state_dict = dd3.se_atten.state_dict()
@@ -210,6 +212,7 @@ def test_jit(
                 use_econf_tebd=ect,
                 type_map=["O", "H"] if ect else None,
                 old_impl=False,
+                seed=GLOBAL_SEED,
             )
             dd0.se_atten.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE)
             dd0.se_atten.dstd = torch.tensor(dstd, dtype=dtype, device=env.DEVICE)

source/tests/pt/model/test_dpa2.py

@@ -20,6 +20,9 @@
     PRECISION_DICT,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .test_env_mat import (
     TestCaseSingleFrameWithNlist,
 )
@@ -152,6 +155,7 @@ def test_consistency(
                 use_econf_tebd=ect,
                 type_map=["O", "H"] if ect else None,
                 old_impl=False,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
 
             dd0.repinit.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE)
@@ -201,6 +205,7 @@ def test_consistency(
                     add_tebd_to_repinit_out=False,
                     precision=prec,
                     old_impl=True,
+                    seed=GLOBAL_SEED,
                 ).to(env.DEVICE)
                 dd0_state_dict = dd0.state_dict()
                 dd3_state_dict = dd3.state_dict()
@@ -346,6 +351,7 @@ def test_jit(
                 use_econf_tebd=ect,
                 type_map=["O", "H"] if ect else None,
                 old_impl=False,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
 
             dd0.repinit.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE)

source/tests/pt/model/test_embedding_net.py

@@ -39,6 +39,9 @@
 )
 from deepmd.tf.descriptor import DescrptSeA as DescrptSeA_tf
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from ..test_finetune import (
     energy_data_requirement,
 )
@@ -153,7 +156,7 @@ def test_consistency(self):
             sel=self.sel,
             neuron=self.filter_neuron,
             axis_neuron=self.axis_neuron,
-            seed=1,
+            seed=GLOBAL_SEED,
         )
         dp_embedding, dp_force, dp_vars = base_se_a(
             descriptor=dp_d,

source/tests/pt/model/test_ener_fitting.py

@@ -65,6 +65,7 @@ def test_consistency(
                 mixed_types=mixed_types,
                 exclude_types=et,
                 neuron=nn,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             ft1 = DPInvarFitting.deserialize(ft0.serialize())
             ft2 = InvarFitting.deserialize(ft0.serialize())
@@ -168,6 +169,7 @@ def test_jit(
                 numb_aparam=nap,
                 mixed_types=mixed_types,
                 exclude_types=et,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             torch.jit.script(ft0)
 
@@ -177,6 +179,7 @@ def test_get_set(self):
             self.nt,
             3,
             1,
+            seed=GLOBAL_SEED,
         )
         rng = np.random.default_rng(GLOBAL_SEED)
         foo = rng.normal([3, 4])

source/tests/pt/model/test_permutation.py

@@ -88,6 +88,7 @@
         "temperature": 1.0,
         "set_davg_zero": True,
         "type_one_side": True,
+        "seed": 1,
     },
     "fitting_net": {
         "neuron": [24, 24, 24],
@@ -155,6 +156,7 @@
             "update_g2_has_attn": True,
             "attn2_has_gate": True,
         },
+        "seed": 1,
         "add_tebd_to_repinit_out": False,
     },
     "fitting_net": {
@@ -207,6 +209,7 @@
             "g1_out_conv": True,
             "g1_out_mlp": True,
         },
+        "seed": 1,
         "add_tebd_to_repinit_out": False,
     },
     "fitting_net": {
@@ -235,6 +238,7 @@
         "temperature": 1.0,
         "set_davg_zero": True,
         "type_one_side": True,
+        "seed": 1,
     },
     "fitting_net": {
         "neuron": [24, 24, 24],
@@ -264,6 +268,7 @@
                 "scaling_factor": 1.0,
                 "normalize": True,
                 "temperature": 1.0,
+                "seed": 1,
             },
             {
                 "type": "dpa2",
@@ -296,6 +301,7 @@
                     "update_g2_has_attn": True,
                     "attn2_has_gate": True,
                 },
+                "seed": 1,
                 "add_tebd_to_repinit_out": False,
             },
         ],

source/tests/pt/model/test_polarizability_fitting.py

@@ -77,6 +77,7 @@ def test_consistency(
                 mixed_types=self.dd0.mixed_types(),
                 fit_diag=fit_diag,
                 scale=scale,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             ft1 = DPPolarFitting.deserialize(ft0.serialize())
             ft2 = PolarFittingNet.deserialize(ft0.serialize())
@@ -143,6 +144,7 @@ def test_jit(
                 numb_aparam=nap,
                 mixed_types=mixed_types,
                 fit_diag=fit_diag,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             torch.jit.script(ft0)
 
@@ -186,6 +188,7 @@ def test_rot(self):
                 mixed_types=self.dd0.mixed_types(),
                 fit_diag=fit_diag,
                 scale=scale,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             if nfp > 0:
                 ifp = torch.tensor(
@@ -248,6 +251,7 @@ def test_permu(self):
                 mixed_types=self.dd0.mixed_types(),
                 fit_diag=fit_diag,
                 scale=scale,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             res = []
             for idx_perm in [[0, 1, 2, 3, 4], [1, 0, 4, 3, 2]]:
@@ -298,6 +302,7 @@ def test_trans(self):
                 mixed_types=self.dd0.mixed_types(),
                 fit_diag=fit_diag,
                 scale=scale,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             res = []
             for xyz in [self.coord, coord_s]:
@@ -347,6 +352,7 @@ def setUp(self):
             numb_fparam=0,
             numb_aparam=0,
             mixed_types=self.dd0.mixed_types(),
+            seed=GLOBAL_SEED,
         ).to(env.DEVICE)
         self.type_mapping = ["O", "H", "B"]
         self.model = PolarModel(self.dd0, self.ft0, self.type_mapping)

source/tests/pt/model/test_property_fitting.py

@@ -32,6 +32,9 @@
     to_numpy_array,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .test_env_mat import (
     TestCaseSingleFrameWithNlist,
 )
@@ -78,6 +81,7 @@ def test_consistency(
                 bias_atom_p=bias_atom_p,
                 intensive=intensive,
                 bias_method=bias_method,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
 
             ft1 = DPProperFittingNet.deserialize(ft0.serialize())
@@ -146,6 +150,7 @@ def test_jit(
                 mixed_types=self.dd0.mixed_types(),
                 intensive=intensive,
                 bias_method=bias_method,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             torch.jit.script(ft0)
 
@@ -199,6 +204,7 @@ def test_trans(self):
             numb_fparam=0,
             numb_aparam=0,
             mixed_types=self.dd0.mixed_types(),
+            seed=GLOBAL_SEED,
         ).to(env.DEVICE)
         res = []
         for xyz in [self.coord, coord_s]:
@@ -266,6 +272,7 @@ def test_rot(self):
                 mixed_types=self.dd0.mixed_types(),
                 intensive=intensive,
                 bias_method=bias_method,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             if nfp > 0:
                 ifp = torch.tensor(
@@ -320,6 +327,7 @@ def test_permu(self):
             numb_fparam=0,
             numb_aparam=0,
             mixed_types=self.dd0.mixed_types(),
+            seed=GLOBAL_SEED,
         ).to(env.DEVICE)
         res = []
         for idx_perm in [[0, 1, 2, 3, 4], [1, 0, 4, 3, 2]]:
@@ -367,6 +375,7 @@ def test_trans(self):
             numb_fparam=0,
             numb_aparam=0,
             mixed_types=self.dd0.mixed_types(),
+            seed=GLOBAL_SEED,
         ).to(env.DEVICE)
         res = []
         for xyz in [self.coord, coord_s]:
@@ -417,6 +426,7 @@ def setUp(self):
             numb_aparam=0,
             mixed_types=self.dd0.mixed_types(),
             intensive=True,
+            seed=GLOBAL_SEED,
         ).to(env.DEVICE)
         self.type_mapping = ["O", "H", "B"]
         self.model = PropertyModel(self.dd0, self.ft0, self.type_mapping)

source/tests/pt/model/test_se_atten_v2.py

@@ -16,6 +16,9 @@
     PRECISION_DICT,
 )
 
+from ...seed import (
+    GLOBAL_SEED,
+)
 from .test_env_mat import (
     TestCaseSingleFrameWithNlist,
 )
@@ -64,6 +67,7 @@ def test_consistency(
                 use_econf_tebd=ect,
                 type_map=["O", "H"] if ect else None,
                 old_impl=False,
+                seed=GLOBAL_SEED,
             ).to(env.DEVICE)
             dd0.se_atten.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE)
             dd0.se_atten.stddev = torch.tensor(dstd, dtype=dtype, device=env.DEVICE)
@@ -135,6 +139,7 @@ def test_jit(
                 use_econf_tebd=ect,
                 type_map=["O", "H"] if ect else None,
                 old_impl=False,
+                seed=GLOBAL_SEED,
             )
             dd0.se_atten.mean = torch.tensor(davg, dtype=dtype, device=env.DEVICE)
             dd0.se_atten.dstd = torch.tensor(dstd, dtype=dtype, device=env.DEVICE)