Merge branch 'devel' into fix/zbl_freeze

deepmd/backend/pytorch.py

@@ -29,8 +29,8 @@
 
 @Backend.register("pt")
 @Backend.register("pytorch")
-class TensorFlowBackend(Backend):
-    """TensorFlow backend."""
+class PyTorchBackend(Backend):
+    """PyTorch backend."""
 
     name = "PyTorch"
     """The formal name of the backend."""

deepmd/dpmodel/descriptor/se_e2_a.py

@@ -111,6 +111,8 @@ class DescrptSeA(NativeOP, BaseDescriptor):
     exclude_types : List[List[int]]
             The excluded pairs of types which have no interaction with each other.
             For example, `[[0, 1]]` means no interaction between type 0 and type 1.
+    env_protection: float
+            Protection parameter to prevent division by zero errors during environment matrix calculations.
     set_davg_zero
             Set the shift of embedding net input to zero.
     activation_function
@@ -149,6 +151,7 @@ def __init__(
         trainable: bool = True,
         type_one_side: bool = True,
         exclude_types: List[List[int]] = [],
+        env_protection: float = 0.0,
         set_davg_zero: bool = False,
         activation_function: str = "tanh",
         precision: str = DEFAULT_PRECISION,
@@ -169,6 +172,7 @@ def __init__(
         self.resnet_dt = resnet_dt
         self.trainable = trainable
         self.type_one_side = type_one_side
+        self.env_protection = env_protection
         self.set_davg_zero = set_davg_zero
         self.activation_function = activation_function
         self.precision = precision
@@ -192,7 +196,7 @@ def __init__(
                 self.resnet_dt,
                 self.precision,
             )
-        self.env_mat = EnvMat(self.rcut, self.rcut_smth)
+        self.env_mat = EnvMat(self.rcut, self.rcut_smth, protection=self.env_protection)
         self.nnei = np.sum(self.sel)
         self.davg = np.zeros(
             [self.ntypes, self.nnei, 4], dtype=PRECISION_DICT[self.precision]
@@ -378,6 +382,7 @@ def serialize(self) -> dict:
             "trainable": self.trainable,
             "type_one_side": self.type_one_side,
             "exclude_types": self.exclude_types,
+            "env_protection": self.env_protection,
             "set_davg_zero": self.set_davg_zero,
             "activation_function": self.activation_function,
             # make deterministic
@@ -406,7 +411,6 @@ def deserialize(cls, data: dict) -> "DescrptSeA":
         obj["davg"] = variables["davg"]
         obj["dstd"] = variables["dstd"]
         obj.embeddings = NetworkCollection.deserialize(embeddings)
-        obj.env_mat = EnvMat.deserialize(env_mat)
         return obj
 
     @classmethod

deepmd/dpmodel/descriptor/se_r.py

@@ -106,6 +106,7 @@ def __init__(
         trainable: bool = True,
         type_one_side: bool = True,
         exclude_types: List[List[int]] = [],
+        env_protection: float = 0.0,
         set_davg_zero: bool = False,
         activation_function: str = "tanh",
         precision: str = DEFAULT_PRECISION,
@@ -133,6 +134,7 @@ def __init__(
         self.precision = precision
         self.spin = spin
         self.emask = PairExcludeMask(self.ntypes, self.exclude_types)
+        self.env_protection = env_protection
 
         in_dim = 1  # not considiering type embedding
         self.embeddings = NetworkCollection(
@@ -150,7 +152,7 @@ def __init__(
                 self.resnet_dt,
                 self.precision,
             )
-        self.env_mat = EnvMat(self.rcut, self.rcut_smth)
+        self.env_mat = EnvMat(self.rcut, self.rcut_smth, protection=self.env_protection)
         self.nnei = np.sum(self.sel)
         self.davg = np.zeros(
             [self.ntypes, self.nnei, 1], dtype=PRECISION_DICT[self.precision]
@@ -305,6 +307,7 @@ def serialize(self) -> dict:
             "trainable": self.trainable,
             "type_one_side": self.type_one_side,
             "exclude_types": self.exclude_types,
+            "env_protection": self.env_protection,
             "set_davg_zero": self.set_davg_zero,
             "activation_function": self.activation_function,
             # make deterministic
@@ -333,7 +336,6 @@ def deserialize(cls, data: dict) -> "DescrptSeR":
         obj["davg"] = variables["davg"]
         obj["dstd"] = variables["dstd"]
         obj.embeddings = NetworkCollection.deserialize(embeddings)
-        obj.env_mat = EnvMat.deserialize(env_mat)
         return obj
 
     @classmethod

deepmd/dpmodel/fitting/ener_fitting.py

@@ -63,6 +63,7 @@ def __init__(
             use_aparam_as_mask=use_aparam_as_mask,
             spin=spin,
             mixed_types=mixed_types,
+            exclude_types=exclude_types,
         )
 
     @classmethod

deepmd/dpmodel/model/__init__.py

@@ -16,8 +16,12 @@
 from .make_model import (
     make_model,
 )
+from .spin_model import (
+    SpinModel,
+)
 
 __all__ = [
     "DPModel",
+    "SpinModel",
     "make_model",
 ]

deepmd/dpmodel/model/model.py

@@ -8,10 +8,16 @@
 from deepmd.dpmodel.model.dp_model import (
     DPModel,
 )
+from deepmd.dpmodel.model.spin_model import (
+    SpinModel,
+)
+from deepmd.utils.spin import (
+    Spin,
+)
 
 
-def get_model(data: dict) -> DPModel:
-    """Get a DPModel from a dictionary.
+def get_standard_model(data: dict) -> DPModel:
+    """Get a standard DPModel from a dictionary.
 
     Parameters
     ----------
@@ -30,6 +36,7 @@ def get_model(data: dict) -> DPModel:
         fitting = EnergyFittingNet(
             ntypes=descriptor.get_ntypes(),
             dim_descrpt=descriptor.get_dim_out(),
+            mixed_types=descriptor.mixed_types(),
             **data["fitting_net"],
         )
     else:
@@ -41,3 +48,50 @@ def get_model(data: dict) -> DPModel:
         atom_exclude_types=data.get("atom_exclude_types", []),
         pair_exclude_types=data.get("pair_exclude_types", []),
     )
+
+
+def get_spin_model(data: dict) -> SpinModel:
+    """Get a spin model from a dictionary.
+
+    Parameters
+    ----------
+    data : dict
+        The data to construct the model.
+    """
+    # include virtual spin and placeholder types
+    data["type_map"] += [item + "_spin" for item in data["type_map"]]
+    spin = Spin(
+        use_spin=data["spin"]["use_spin"],
+        virtual_scale=data["spin"]["virtual_scale"],
+    )
+    pair_exclude_types = spin.get_pair_exclude_types(
+        exclude_types=data.get("pair_exclude_types", None)
+    )
+    data["pair_exclude_types"] = pair_exclude_types
+    # for descriptor data stat
+    data["descriptor"]["exclude_types"] = pair_exclude_types
+    atom_exclude_types = spin.get_atom_exclude_types(
+        exclude_types=data.get("atom_exclude_types", None)
+    )
+    data["atom_exclude_types"] = atom_exclude_types
+    if "env_protection" not in data["descriptor"]:
+        data["descriptor"]["env_protection"] = 1e-6
+    if data["descriptor"]["type"] in ["se_e2_a"]:
+        # only expand sel for se_e2_a
+        data["descriptor"]["sel"] += data["descriptor"]["sel"]
+    backbone_model = get_standard_model(data)
+    return SpinModel(backbone_model=backbone_model, spin=spin)
+
+
+def get_model(data: dict):
+    """Get a model from a dictionary.
+
+    Parameters
+    ----------
+    data : dict
+        The data to construct the model.
+    """
+    if "spin" in data:
+        return get_spin_model(data)
+    else:
+        return get_standard_model(data)