fix(tf): fix model out_bias deserialize

deepmd/tf/model/model.py

@@ -16,6 +16,9 @@
 from deepmd.common import (
     j_get_type,
 )
+from deepmd.env import (
+    GLOBAL_NP_FLOAT_PRECISION,
+)
 from deepmd.tf.descriptor.descriptor import (
     Descriptor,
 )
@@ -802,10 +805,34 @@ def deserialize(cls, data: dict, suffix: str = "") -> "Descriptor":
         -------
         Descriptor
             The deserialized descriptor
+
+        Raises
+        ------
+        ValueError
+            If both fitting/@variables/bias_atom_e and @variables/out_bias are non-zero
         """
         data = data.copy()
         check_version_compatibility(data.pop("@version", 2), 2, 1)
         descriptor = Descriptor.deserialize(data.pop("descriptor"), suffix=suffix)
+        if data["fitting"].get("@variables", {}).get("bias_atom_e") is not None:
+            # careful: copy each level and don't modify the input array,
+            # otherwise it will affect the original data
+            # deepcopy is not used for performance reasons
+            data["fitting"] = data["fitting"].copy()
+            data["fitting"]["@variables"] = data["fitting"]["@variables"].copy()
+            if (
+                int(np.any(data["fitting"]["@variables"]["bias_atom_e"]))
+                + int(np.any(data["@variables"]["out_bias"]))
+                > 1
+            ):
+                raise ValueError(
+                    "fitting/@variables/bias_atom_e and @variables/out_bias should not be both non-zero"
+                )
+            data["fitting"]["@variables"]["bias_atom_e"] = data["fitting"][
+                "@variables"
+            ]["bias_atom_e"] + data["@variables"]["out_bias"].reshape(
+                data["fitting"]["@variables"]["bias_atom_e"].shape
+            )
         fitting = Fitting.deserialize(data.pop("fitting"), suffix=suffix)
         # pass descriptor type embedding to model
         if descriptor.explicit_ntypes:
@@ -814,8 +841,10 @@ def deserialize(cls, data: dict, suffix: str = "") -> "Descriptor":
         else:
             type_embedding = None
         # BEGINE not supported keys
-        data.pop("atom_exclude_types")
-        data.pop("pair_exclude_types")
+        if len(data.pop("atom_exclude_types")) > 0:
+            raise NotImplementedError("atom_exclude_types is not supported")
+        if len(data.pop("pair_exclude_types")) > 0:
+            raise NotImplementedError("pair_exclude_types is not supported")
         data.pop("rcond", None)
         data.pop("preset_out_bias", None)
         data.pop("@variables", None)
@@ -848,6 +877,17 @@ def serialize(self, suffix: str = "") -> dict:
 
         ntypes = len(self.get_type_map())
         dict_fit = self.fitting.serialize(suffix=suffix)
+        if dict_fit.get("@variables", {}).get("bias_atom_e") is not None:
+            out_bias = dict_fit["@variables"]["bias_atom_e"].reshape(
+                [1, ntypes, dict_fit["dim_out"]]
+            )
+            dict_fit["@variables"]["bias_atom_e"] = np.zeros_like(
+                dict_fit["@variables"]["bias_atom_e"]
+            )
+        else:
+            out_bias = np.zeros(
+                [1, ntypes, dict_fit["dim_out"]], dtype=GLOBAL_NP_FLOAT_PRECISION
+            )
         return {
             "@class": "Model",
             "type": "standard",
@@ -861,7 +901,7 @@ def serialize(self, suffix: str = "") -> dict:
             "rcond": None,
             "preset_out_bias": None,
             "@variables": {
-                "out_bias": np.zeros([1, ntypes, dict_fit["dim_out"]]),  # pylint: disable=no-explicit-dtype
+                "out_bias": out_bias,
                 "out_std": np.ones([1, ntypes, dict_fit["dim_out"]]),  # pylint: disable=no-explicit-dtype
             },
         }

source/tests/consistent/model/test_ener.py

@@ -141,7 +141,9 @@ def pass_data_to_cls(self, cls, data) -> Any:
         if cls is EnergyModelDP:
             return get_model_dp(data)
         elif cls is EnergyModelPT:
-            return get_model_pt(data)
+            model = get_model_pt(data)
+            model.atomic_model.out_bias.uniform_()
+            return model
         elif cls is EnergyModelJAX:
             return get_model_jax(data)
         return cls(**data, **self.additional_data)