Fix: raise error for non mixed type model (deepmodeling#3759)

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

- **Bug Fixes**
- Enhanced error handling in model initialization to ensure
compatibility, now requiring all sub-models to be of mixed type.
- **Refactor**
- Modified code in `se_atten.py` to create a copy of `nlist` before
modifying it, preserving the original `nlist` during subsequent
operations.
- **Tests**
- Updated import statement and instantiation parameters in
`test_linear_atomic_model.py` for `DescrptDPA1` class.
- Altered parameters in the `descriptor` section of
`test_permutation.py` for improved testing.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Anyang Peng <[email protected]>
Co-authored-by: anyangml <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Han Wang <[email protected]>

deepmd/dpmodel/atomic_model/linear_atomic_model.py

@@ -54,6 +54,17 @@ def __init__(
     ):
         super().__init__(type_map, **kwargs)
         super().init_out_stat()
+
+        # check all sub models are of mixed type.
+        model_mixed_type = []
+        for m in models:
+            if not m.mixed_types():
+                model_mixed_type.append(m)
+        if len(model_mixed_type) > 0:
+            raise ValueError(
+                f"LinearAtomicModel only supports AtomicModel of mixed type, the following models are not mixed type: {model_mixed_type}."
+            )
+
         self.models = models
         sub_model_type_maps = [md.get_type_map() for md in models]
         err_msg = []

deepmd/pt/model/atomic_model/linear_atomic_model.py

@@ -61,6 +61,17 @@ def __init__(
     ):
         super().__init__(type_map, **kwargs)
         super().init_out_stat()
+
+        # check all sub models are of mixed type.
+        model_mixed_type = []
+        for m in models:
+            if not m.mixed_types():
+                model_mixed_type.append(m)
+        if len(model_mixed_type) > 0:
+            raise ValueError(
+                f"LinearAtomicModel only supports AtomicModel of mixed type, the following models are not mixed type: {model_mixed_type}."
+            )
+
         self.models = torch.nn.ModuleList(models)
         sub_model_type_maps = [md.get_type_map() for md in models]
         err_msg = []

deepmd/pt/model/descriptor/se_atten.py

@@ -464,7 +464,7 @@ def forward(
             protection=self.env_protection,
         )
         nlist_mask = nlist != -1
-        nlist[nlist == -1] = 0
+        nlist = torch.where(nlist == -1, 0, nlist)
         sw = torch.squeeze(sw, -1)
         # beyond the cutoff sw should be 0.0
         sw = sw.masked_fill(~nlist_mask, 0.0)

source/tests/common/dpmodel/test_linear_atomic_model.py

@@ -15,8 +15,8 @@
 from deepmd.dpmodel.atomic_model.pairtab_atomic_model import (
     PairTabAtomicModel,
 )
-from deepmd.dpmodel.descriptor.se_e2_a import (
-    DescrptSeA,
+from deepmd.dpmodel.descriptor import (
+    DescrptDPA1,
 )
 from deepmd.dpmodel.fitting.invar_fitting import (
     InvarFitting,
@@ -39,10 +39,11 @@ def test_pairwise(self, mock_loadtxt):
         extended_atype = np.array([[0, 0]])
         nlist = np.array([[[1], [-1]]])
 
-        ds = DescrptSeA(
+        ds = DescrptDPA1(
             rcut_smth=0.3,
             rcut=0.4,
             sel=[3],
+            ntypes=2,
         )
         ft = InvarFitting(
             "energy",
@@ -134,10 +135,11 @@ def setUp(self, mock_loadtxt):
                 [0.02, 0.25, 0.4, 0.75],
             ]
         )
-        ds = DescrptSeA(
+        ds = DescrptDPA1(
             self.rcut,
             self.rcut_smth,
-            self.sel,
+            sum(self.sel),
+            self.nt,
         )
         ft = InvarFitting(
             "energy",

source/tests/pt/model/test_linear_atomic_model.py

@@ -15,8 +15,8 @@
     DPZBLLinearEnergyAtomicModel,
     PairTabAtomicModel,
 )
-from deepmd.pt.model.descriptor.se_a import (
-    DescrptSeA,
+from deepmd.pt.model.descriptor import (
+    DescrptDPA1,
 )
 from deepmd.pt.model.model import (
     DPZBLModel,
@@ -55,10 +55,11 @@ def test_pairwise(self, mock_loadtxt):
         extended_atype = torch.tensor([[0, 0]], device=env.DEVICE)
         nlist = torch.tensor([[[1], [-1]]], device=env.DEVICE)
 
-        ds = DescrptSeA(
+        ds = DescrptDPA1(
             rcut_smth=0.3,
             rcut=0.4,
             sel=[3],
+            ntypes=2,
         ).to(env.DEVICE)
         ft = InvarFitting(
             "energy",
@@ -128,10 +129,11 @@ def setUp(self, mock_loadtxt):
                 [0.02, 0.25, 0.4, 0.75],
             ]
         )
-        ds = DescrptSeA(
+        ds = DescrptDPA1(
             self.rcut,
             self.rcut_smth,
-            self.sel,
+            sum(self.sel),
+            self.nt,
         ).to(env.DEVICE)
         ft = InvarFitting(
             "energy",

source/tests/pt/model/test_permutation.py

@@ -68,14 +68,22 @@
     "sw_rmin": 0.2,
     "sw_rmax": 1.0,
     "descriptor": {
-        "type": "se_e2_a",
-        "sel": [46, 92, 4],
-        "rcut_smth": 0.50,
-        "rcut": 6.00,
+        "type": "se_atten",
+        "sel": 40,
+        "rcut_smth": 0.5,
+        "rcut": 4.0,
         "neuron": [25, 50, 100],
-        "resnet_dt": False,
         "axis_neuron": 16,
-        "seed": 1,
+        "attn": 64,
+        "attn_layer": 2,
+        "attn_dotr": True,
+        "attn_mask": False,
+        "activation_function": "tanh",
+        "scaling_factor": 1.0,
+        "normalize": False,
+        "temperature": 1.0,
+        "set_davg_zero": True,
+        "type_one_side": True,
     },
     "fitting_net": {
         "neuron": [24, 24, 24],