Merge branch 'devel' into 3831

deepmd/dpmodel/descriptor/__init__.py

@@ -18,10 +18,14 @@
 from .se_r import (
     DescrptSeR,
 )
+from .se_t import (
+    DescrptSeT,
+)
 
 __all__ = [
     "DescrptSeA",
     "DescrptSeR",
+    "DescrptSeT",
     "DescrptDPA1",
     "DescrptSeAttenV2",
     "DescrptDPA2",

deepmd/dpmodel/descriptor/dpa1.py

@@ -14,6 +14,9 @@
 from deepmd.env import (
     GLOBAL_NP_FLOAT_PRECISION,
 )
+from deepmd.utils.data_system import (
+    DeepmdDataSystem,
+)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -524,18 +527,36 @@ def deserialize(cls, data: dict) -> "DescrptDPA1":
         return obj
 
     @classmethod
-    def update_sel(cls, global_jdata: dict, local_jdata: dict):
+    def update_sel(
+        cls,
+        train_data: DeepmdDataSystem,
+        type_map: Optional[List[str]],
+        local_jdata: dict,
+    ) -> Tuple[dict, Optional[float]]:
         """Update the selection and perform neighbor statistics.
 
         Parameters
         ----------
-        global_jdata : dict
-            The global data, containing the training section
+        train_data : DeepmdDataSystem
+            data used to do neighbor statictics
+        type_map : list[str], optional
+            The name of each type of atoms
         local_jdata : dict
             The local data refer to the current class
+
+        Returns
+        -------
+        dict
+            The updated local data
+        float
+            The minimum distance between two atoms
         """
         local_jdata_cpy = local_jdata.copy()
-        return UpdateSel().update_one_sel(global_jdata, local_jdata_cpy, True)
+        min_nbor_dist, sel = UpdateSel().update_one_sel(
+            train_data, type_map, local_jdata_cpy["rcut"], local_jdata_cpy["sel"], True
+        )
+        local_jdata_cpy["sel"] = sel[0]
+        return local_jdata_cpy, min_nbor_dist
 
 
 @DescriptorBlock.register("se_atten")

deepmd/dpmodel/descriptor/dpa2.py

@@ -15,6 +15,9 @@
 from deepmd.dpmodel.utils.update_sel import (
     UpdateSel,
 )
+from deepmd.utils.data_system import (
+    DeepmdDataSystem,
+)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -743,30 +746,46 @@ def deserialize(cls, data: dict) -> "DescrptDPA2":
         return obj
 
     @classmethod
-    def update_sel(cls, global_jdata: dict, local_jdata: dict):
+    def update_sel(
+        cls,
+        train_data: DeepmdDataSystem,
+        type_map: Optional[List[str]],
+        local_jdata: dict,
+    ) -> Tuple[dict, Optional[float]]:
         """Update the selection and perform neighbor statistics.
 
         Parameters
         ----------
-        global_jdata : dict
-            The global data, containing the training section
+        train_data : DeepmdDataSystem
+            data used to do neighbor statictics
+        type_map : list[str], optional
+            The name of each type of atoms
         local_jdata : dict
             The local data refer to the current class
+
+        Returns
+        -------
+        dict
+            The updated local data
+        float
+            The minimum distance between two atoms
         """
         local_jdata_cpy = local_jdata.copy()
         update_sel = UpdateSel()
-        local_jdata_cpy["repinit"] = update_sel.update_one_sel(
-            global_jdata,
-            local_jdata_cpy["repinit"],
+        min_nbor_dist, repinit_sel = update_sel.update_one_sel(
+            train_data,
+            type_map,
+            local_jdata_cpy["repinit"]["rcut"],
+            local_jdata_cpy["repinit"]["nsel"],
             True,
-            rcut_key="rcut",
-            sel_key="nsel",
         )
-        local_jdata_cpy["repformer"] = update_sel.update_one_sel(
-            global_jdata,
-            local_jdata_cpy["repformer"],
+        local_jdata_cpy["repinit"]["nsel"] = repinit_sel[0]
+        min_nbor_dist, repformer_sel = update_sel.update_one_sel(
+            train_data,
+            type_map,
+            local_jdata_cpy["repformer"]["rcut"],
+            local_jdata_cpy["repformer"]["nsel"],
             True,
-            rcut_key="rcut",
-            sel_key="nsel",
         )
-        return local_jdata_cpy
+        local_jdata_cpy["repformer"]["nsel"] = repformer_sel[0]
+        return local_jdata_cpy, min_nbor_dist

deepmd/dpmodel/descriptor/hybrid.py

@@ -5,6 +5,7 @@
     Dict,
     List,
     Optional,
+    Tuple,
     Union,
 )
 
@@ -19,6 +20,9 @@
 from deepmd.dpmodel.utils.nlist import (
     nlist_distinguish_types,
 )
+from deepmd.utils.data_system import (
+    DeepmdDataSystem,
+)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -222,22 +226,42 @@ def call(
         return out_descriptor, out_gr, out_g2, out_h2, out_sw
 
     @classmethod
-    def update_sel(cls, global_jdata: dict, local_jdata: dict) -> dict:
+    def update_sel(
+        cls,
+        train_data: DeepmdDataSystem,
+        type_map: Optional[List[str]],
+        local_jdata: dict,
+    ) -> Tuple[dict, Optional[float]]:
         """Update the selection and perform neighbor statistics.
 
         Parameters
         ----------
-        global_jdata : dict
-            The global data, containing the training section
+        train_data : DeepmdDataSystem
+            data used to do neighbor statictics
+        type_map : list[str], optional
+            The name of each type of atoms
         local_jdata : dict
             The local data refer to the current class
+
+        Returns
+        -------
+        dict
+            The updated local data
+        float
+            The minimum distance between two atoms
         """
         local_jdata_cpy = local_jdata.copy()
-        local_jdata_cpy["list"] = [
-            BaseDescriptor.update_sel(global_jdata, sub_jdata)
-            for sub_jdata in local_jdata["list"]
-        ]
-        return local_jdata_cpy
+        new_list = []
+        min_nbor_dist = None
+        for sub_jdata in local_jdata["list"]:
+            new_sub_jdata, min_nbor_dist_ = BaseDescriptor.update_sel(
+                train_data, type_map, sub_jdata
+            )
+            if min_nbor_dist_ is not None:
+                min_nbor_dist = min_nbor_dist_
+            new_list.append(new_sub_jdata)
+        local_jdata_cpy["list"] = new_list
+        return local_jdata_cpy, min_nbor_dist
 
     def serialize(self) -> dict:
         return {

deepmd/dpmodel/descriptor/make_base_descriptor.py

@@ -7,12 +7,16 @@
     Callable,
     List,
     Optional,
+    Tuple,
     Union,
 )
 
 from deepmd.common import (
     j_get_type,
 )
+from deepmd.utils.data_system import (
+    DeepmdDataSystem,
+)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -149,19 +153,33 @@ def deserialize(cls, data: dict) -> "BD":
 
         @classmethod
         @abstractmethod
-        def update_sel(cls, global_jdata: dict, local_jdata: dict):
+        def update_sel(
+            cls,
+            train_data: DeepmdDataSystem,
+            type_map: Optional[List[str]],
+            local_jdata: dict,
+        ) -> Tuple[dict, Optional[float]]:
             """Update the selection and perform neighbor statistics.
 
             Parameters
             ----------
-            global_jdata : dict
-                The global data, containing the training section
+            train_data : DeepmdDataSystem
+                data used to do neighbor statictics
+            type_map : list[str], optional
+                The name of each type of atoms
             local_jdata : dict
                 The local data refer to the current class
+
+            Returns
+            -------
+            dict
+                The updated local data
+            float
+                The minimum distance between two atoms
             """
             # call subprocess
             cls = cls.get_class_by_type(j_get_type(local_jdata, cls.__name__))
-            return cls.update_sel(global_jdata, local_jdata)
+            return cls.update_sel(train_data, type_map, local_jdata)
 
     setattr(BD, fwd_method_name, BD.fwd)
     delattr(BD, "fwd")

deepmd/dpmodel/descriptor/se_e2_a.py

@@ -9,6 +9,9 @@
 from deepmd.env import (
     GLOBAL_NP_FLOAT_PRECISION,
 )
+from deepmd.utils.data_system import (
+    DeepmdDataSystem,
+)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -422,15 +425,32 @@ def deserialize(cls, data: dict) -> "DescrptSeA":
         return obj
 
     @classmethod
-    def update_sel(cls, global_jdata: dict, local_jdata: dict):
+    def update_sel(
+        cls,
+        train_data: DeepmdDataSystem,
+        type_map: Optional[List[str]],
+        local_jdata: dict,
+    ) -> Tuple[dict, Optional[float]]:
         """Update the selection and perform neighbor statistics.
 
         Parameters
         ----------
-        global_jdata : dict
-            The global data, containing the training section
+        train_data : DeepmdDataSystem
+            data used to do neighbor statictics
+        type_map : list[str], optional
+            The name of each type of atoms
         local_jdata : dict
             The local data refer to the current class
+
+        Returns
+        -------
+        dict
+            The updated local data
+        float
+            The minimum distance between two atoms
         """
         local_jdata_cpy = local_jdata.copy()
-        return UpdateSel().update_one_sel(global_jdata, local_jdata_cpy, False)
+        min_nbor_dist, local_jdata_cpy["sel"] = UpdateSel().update_one_sel(
+            train_data, type_map, local_jdata_cpy["rcut"], local_jdata_cpy["sel"], False
+        )
+        return local_jdata_cpy, min_nbor_dist

deepmd/dpmodel/descriptor/se_r.py

@@ -4,6 +4,9 @@
 from deepmd.dpmodel.utils.update_sel import (
     UpdateSel,
 )
+from deepmd.utils.data_system import (
+    DeepmdDataSystem,
+)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -21,6 +24,7 @@
     Any,
     List,
     Optional,
+    Tuple,
 )
 
 from deepmd.dpmodel import (
@@ -345,15 +349,32 @@ def deserialize(cls, data: dict) -> "DescrptSeR":
         return obj
 
     @classmethod
-    def update_sel(cls, global_jdata: dict, local_jdata: dict):
+    def update_sel(
+        cls,
+        train_data: DeepmdDataSystem,
+        type_map: Optional[List[str]],
+        local_jdata: dict,
+    ) -> Tuple[dict, Optional[float]]:
         """Update the selection and perform neighbor statistics.
 
         Parameters
         ----------
-        global_jdata : dict
-            The global data, containing the training section
+        train_data : DeepmdDataSystem
+            data used to do neighbor statictics
+        type_map : list[str], optional
+            The name of each type of atoms
         local_jdata : dict
             The local data refer to the current class
+
+        Returns
+        -------
+        dict
+            The updated local data
+        float
+            The minimum distance between two atoms
         """
         local_jdata_cpy = local_jdata.copy()
-        return UpdateSel().update_one_sel(global_jdata, local_jdata_cpy, False)
+        min_nbor_dist, local_jdata_cpy["sel"] = UpdateSel().update_one_sel(
+            train_data, type_map, local_jdata_cpy["rcut"], local_jdata_cpy["sel"], False
+        )
+        return local_jdata_cpy, min_nbor_dist