add mask to atomic model output when an atomic type exclusion presents.

deepmd/dpmodel/atomic_model/base_atomic_model.py

@@ -8,6 +8,10 @@
 
 import numpy as np
 
+from deepmd.dpmodel.output_def import (
+    FittingOutputDef,
+    OutputVariableDef,
+)
 from deepmd.dpmodel.utils import (
     AtomExcludeMask,
     PairExcludeMask,
@@ -50,6 +54,24 @@
         else:
             self.pair_excl = PairExcludeMask(self.get_ntypes(), self.pair_exclude_types)
 
+    def atomic_output_def(self) -> FittingOutputDef:
+        old_def = self.fitting_output_def()
+        if self.atom_excl is None:
+            return old_def
+        else:
+            old_list = list(old_def.get_data().values())
+            return FittingOutputDef(
+                old_list.append(
+                    OutputVariableDef(
+                        name="mask",
+                        shape=[1],
+                        reduciable=False,
+                        r_differentiable=False,
+                        c_differentiable=False,
+                    )
+                )
+            )
+
     def forward_common_atomic(
         self,
         extended_coord: np.ndarray,
@@ -79,6 +101,7 @@
             atom_mask = self.atom_excl.build_type_exclude_mask(atype)
             for kk in ret_dict.keys():
                 ret_dict[kk] = ret_dict[kk] * atom_mask[:, :, None]
+            ret_dict["mask"] = atom_mask
 
         return ret_dict
 

deepmd/dpmodel/atomic_model/make_base_atomic_model.py

@@ -36,9 +36,18 @@
 
         @abstractmethod
         def fitting_output_def(self) -> FittingOutputDef:
-            """Get the fitting output def."""
+            """Get the output def of developer implemented atomic models."""
             pass
 
+        def atomic_output_def(self) -> FittingOutputDef:
+            """Get the output def of the atomic model.
+
+            By default it is the same as FittingOutputDef, but it
+            allows model level wrapper of the output defined by the developer.
+
+            """
+            return self.fitting_output_def()
+
         @abstractmethod
         def get_rcut(self) -> float:
             """Get the cut-off radius."""

deepmd/dpmodel/descriptor/se_e2_a.py

@@ -26,6 +26,7 @@
     Any,
     List,
     Optional,
+    Tuple,
 )
 
 from deepmd.dpmodel import (
@@ -168,12 +169,12 @@
         self.resnet_dt = resnet_dt
         self.trainable = trainable
         self.type_one_side = type_one_side
-        self.exclude_types = exclude_types
         self.set_davg_zero = set_davg_zero
         self.activation_function = activation_function
         self.precision = precision
         self.spin = spin
-        self.emask = PairExcludeMask(self.ntypes, self.exclude_types)
+        # order matters, placed after the assignment of self.ntypes
+        self.reinit_exclude(exclude_types)
 
         in_dim = 1  # not considiering type embedding
         self.embeddings = NetworkCollection(
@@ -271,6 +272,13 @@
         gg = self.embeddings[embedding_idx].call(ss)
         return gg
 
+    def reinit_exclude(
+        self,
+        exclude_types: List[Tuple[int, int]] = [],
+    ):
+        self.exclude_types = exclude_types
+        self.emask = PairExcludeMask(self.ntypes, exclude_types=exclude_types)
+
     def call(
         self,
         coord_ext,

deepmd/dpmodel/fitting/general_fitting.py

@@ -120,13 +120,12 @@
         self.use_aparam_as_mask = use_aparam_as_mask
         self.spin = spin
         self.mixed_types = mixed_types
-        self.exclude_types = exclude_types
+        # order matters, should be place after the assignment of ntypes
+        self.reinit_exclude(exclude_types)
         if self.spin is not None:
             raise NotImplementedError("spin is not supported")
         self.remove_vaccum_contribution = remove_vaccum_contribution
 
-        self.emask = AtomExcludeMask(self.ntypes, self.exclude_types)
-
         net_dim_out = self._net_out_dim()
         # init constants
         self.bias_atom_e = np.zeros([self.ntypes, net_dim_out])
@@ -214,6 +213,13 @@
         else:
             raise KeyError(key)
 
+    def reinit_exclude(
+        self,
+        exclude_types: List[int] = [],
+    ):
+        self.exclude_types = exclude_types
+        self.emask = AtomExcludeMask(self.ntypes, self.exclude_types)
+
     def serialize(self) -> dict:
         """Serialize the fitting to dict."""
         return {

deepmd/dpmodel/model/make_model.py

@@ -74,7 +74,7 @@
 
         def model_output_def(self):
             """Get the output def for the model."""
-            return ModelOutputDef(self.fitting_output_def())
+            return ModelOutputDef(self.atomic_output_def())
 
         def model_output_type(self) -> str:
             """Get the output type for the model."""
@@ -223,7 +223,7 @@
             )
             model_predict = fit_output_to_model_output(
                 atomic_ret,
-                self.fitting_output_def(),
+                self.atomic_output_def(),
                 cc_ext,
                 do_atomic_virial=do_atomic_virial,
             )

deepmd/pt/model/atomic_model/base_atomic_model.py

@@ -13,6 +13,10 @@
 from deepmd.dpmodel.atomic_model import (
     make_base_atomic_model,
 )
+from deepmd.dpmodel.output_def import (
+    FittingOutputDef,
+    OutputVariableDef,
+)
 from deepmd.pt.utils import (
     AtomExcludeMask,
     PairExcludeMask,
@@ -60,6 +64,24 @@
     def get_model_def_script(self) -> str:
         return self.model_def_script
 
+    def atomic_output_def(self) -> FittingOutputDef:
+        old_def = self.fitting_output_def()
+        if self.atom_excl is None:
+            return old_def
+        else:
+            old_list = list(old_def.get_data().values())
+            return FittingOutputDef(
+                old_list.append(
+                    OutputVariableDef(
+                        name="mask",
+                        shape=[1],
+                        reduciable=False,
+                        r_differentiable=False,
+                        c_differentiable=False,
+                    )
+                )
+            )
+
     def forward_common_atomic(
         self,
         extended_coord: torch.Tensor,
@@ -90,6 +112,7 @@
             atom_mask = self.atom_excl(atype)
             for kk in ret_dict.keys():
                 ret_dict[kk] = ret_dict[kk] * atom_mask[:, :, None]
+            ret_dict["mask"] = atom_mask
 
         return ret_dict
 

deepmd/pt/model/model/make_hessian_model.py

@@ -25,7 +25,7 @@
     Parameters
     ----------
     T_Model
-        The model. Should provide the `forward_common` and `fitting_output_def` methods
+        The model. Should provide the `forward_common` and `atomic_output_def` methods
 
     Returns
     -------
@@ -43,7 +43,7 @@
                 *args,
                 **kwargs,
             )
-            self.hess_fitting_def = copy.deepcopy(super().fitting_output_def())
+            self.hess_fitting_def = copy.deepcopy(super().atomic_output_def())
 
         def requires_hessian(
             self,
@@ -56,7 +56,7 @@
                 if kk in keys:
                     self.hess_fitting_def[kk].r_hessian = True
 
-        def fitting_output_def(self):
+        def atomic_output_def(self):
             """Get the fitting output def."""
             return self.hess_fitting_def
 
@@ -102,7 +102,7 @@
                 aparam=aparam,
                 do_atomic_virial=do_atomic_virial,
             )
-            vdef = self.fitting_output_def()
+            vdef = self.atomic_output_def()
             hess_yes = [vdef[kk].r_hessian for kk in vdef.keys()]
             if any(hess_yes):
                 hess = self._cal_hessian_all(
@@ -128,7 +128,7 @@
             box = box.view([nf, 9]) if box is not None else None
             fparam = fparam.view([nf, -1]) if fparam is not None else None
             aparam = aparam.view([nf, nloc, -1]) if aparam is not None else None
-            fdef = self.fitting_output_def()
+            fdef = self.atomic_output_def()
             # keys of values that require hessian
             hess_keys: List[str] = []
             for kk in fdef.keys():

deepmd/pt/model/model/make_model.py

@@ -72,7 +72,7 @@
 
         def model_output_def(self):
             """Get the output def for the model."""
-            return ModelOutputDef(self.fitting_output_def())
+            return ModelOutputDef(self.atomic_output_def())
 
         @torch.jit.export
         def model_output_type(self) -> str:
@@ -218,7 +218,7 @@
             )
             model_predict = fit_output_to_model_output(
                 atomic_ret,
-                self.fitting_output_def(),
+                self.atomic_output_def(),
                 cc_ext,
                 do_atomic_virial=do_atomic_virial,
             )

source/tests/common/dpmodel/test_dp_atomic_model.py

@@ -50,3 +50,68 @@ def test_self_consistency(
             ret1 = md1.forward_common_atomic(self.coord_ext, self.atype_ext, self.nlist)
 
             np.testing.assert_allclose(ret0["energy"], ret1["energy"])
+
+    def test_excl_consistency(self):
+        type_map = ["foo", "bar"]
+
+        # test the case of exclusion
+        for atom_excl, pair_excl in itertools.product([[], [1]], [[], [[0, 1]]]):
+            ds = DescrptSeA(
+                self.rcut,
+                self.rcut_smth,
+                self.sel,
+            )
+            ft = InvarFitting(
+                "energy",
+                self.nt,
+                ds.get_dim_out(),
+                1,
+                mixed_types=ds.mixed_types(),
+            )
+            md0 = DPAtomicModel(
+                ds,
+                ft,
+                type_map=type_map,
+            )
+            md1 = DPAtomicModel.deserialize(md0.serialize())
+
+            md0.reinit_atom_exclude(atom_excl)
+            md0.reinit_pair_exclude(pair_excl)
+            # hacking!
+            md1.descriptor.reinit_exclude(pair_excl)
+            md1.fitting.reinit_exclude(atom_excl)
+
+            # check energy consistency
+            args = [self.coord_ext, self.atype_ext, self.nlist]
+            ret0 = md0.forward_common_atomic(*args)
+            ret1 = md1.forward_common_atomic(*args)
+            np.testing.assert_allclose(
+                ret0["energy"],
+                ret1["energy"],
+            )
+
+            # check output def
+            out_names = [vv.name for vv in md0.atomic_output_def().get_data().values()]
+            if atom_excl == []:
+                self.assertEqual(out_names, ["energy"])
+            else:
+                self.assertEqual(out_names, ["energy", "mask"])
+                for ii in md0.atomic_output_def().get_data().values():
+                    if ii.name == "mask":
+                        self.assertEqual(ii.shape, [1])
+                        self.assertFalse(ii.reduciable)
+                        self.assertFalse(ii.r_differentiable)
+                        self.assertFalse(ii.c_differentiable)
+
+            # check mask
+            if atom_excl == []:
+                pass
+            elif atom_excl == [1]:
+                self.assertIn("mask", ret0.keys())
+                expected = np.array([1, 1, 0], dtype=int)
+                expected = np.concatenate(
+                    [expected, expected[self.perm[: self.nloc]]]
+                ).reshape(2, 3)
+                np.testing.assert_array_equal(ret0["mask"], expected)
+            else:
+                raise ValueError(f"not expected atom_excl {atom_excl}")

source/tests/pt/model/test_dp_atomic_model.py

@@ -152,6 +152,7 @@ def test_excl_consistency(self):
             md1.descriptor.reinit_exclude(pair_excl)
             md1.fitting_net.reinit_exclude(atom_excl)
 
+            # check energy consistency
             args = [
                 to_torch_tensor(ii)
                 for ii in [self.coord_ext, self.atype_ext, self.nlist]
@@ -162,3 +163,29 @@ def test_excl_consistency(self):
                 to_numpy_array(ret0["energy"]),
                 to_numpy_array(ret1["energy"]),
             )
+
+            # check output def
+            out_names = [vv.name for vv in md0.atomic_output_def().get_data().values()]
+            if atom_excl == []:
+                self.assertEqual(out_names, ["energy"])
+            else:
+                self.assertEqual(out_names, ["energy", "mask"])
+                for ii in md0.atomic_output_def().get_data().values():
+                    if ii.name == "mask":
+                        self.assertEqual(ii.shape, [1])
+                        self.assertFalse(ii.reduciable)
+                        self.assertFalse(ii.r_differentiable)
+                        self.assertFalse(ii.c_differentiable)
+
+            # check mask
+            if atom_excl == []:
+                pass
+            elif atom_excl == [1]:
+                self.assertIn("mask", ret0.keys())
+                expected = np.array([1, 1, 0], dtype=int)
+                expected = np.concatenate(
+                    [expected, expected[self.perm[: self.nloc]]]
+                ).reshape(2, 3)
+                np.testing.assert_array_equal(to_numpy_array(ret0["mask"]), expected)
+            else:
+                raise ValueError(f"not expected atom_excl {atom_excl}")

source/tests/pt/model/test_make_hessian_model.py

@@ -166,8 +166,8 @@ def setUp(self):
         self.model_hess.requires_hessian("energy")
 
     def test_output_def(self):
-        self.assertTrue(self.model_hess.fitting_output_def()["energy"].r_hessian)
-        self.assertFalse(self.model_valu.fitting_output_def()["energy"].r_hessian)
+        self.assertTrue(self.model_hess.atomic_output_def()["energy"].r_hessian)
+        self.assertFalse(self.model_valu.atomic_output_def()["energy"].r_hessian)
         self.assertTrue(self.model_hess.model_output_def()["energy"].r_hessian)
         self.assertEqual(
             self.model_hess.model_output_def()["energy_derv_r_derv_r"].category,