Chore: refactor get standard model (deepmodeling#4205)

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## Summary by CodeRabbit


- **Refactor**
- Simplified model component creation by introducing a new function for
better code clarity and reusability.
- Updated model-building functions to utilize the new component creation
logic, enhancing maintainability.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

deepmd/pt/model/model/__init__.py

@@ -72,6 +72,29 @@
 )
 
 
+def _get_standard_model_components(model_params, ntypes):
+    # descriptor
+    model_params["descriptor"]["ntypes"] = ntypes
+    model_params["descriptor"]["type_map"] = copy.deepcopy(model_params["type_map"])
+    descriptor = BaseDescriptor(**model_params["descriptor"])
+    # fitting
+    fitting_net = model_params.get("fitting_net", {})
+    fitting_net["type"] = fitting_net.get("type", "ener")
+    fitting_net["ntypes"] = descriptor.get_ntypes()
+    fitting_net["type_map"] = copy.deepcopy(model_params["type_map"])
+    fitting_net["mixed_types"] = descriptor.mixed_types()
+    if fitting_net["type"] in ["dipole", "polar"]:
+        fitting_net["embedding_width"] = descriptor.get_dim_emb()
+    fitting_net["dim_descrpt"] = descriptor.get_dim_out()
+    grad_force = "direct" not in fitting_net["type"]
+    if not grad_force:
+        fitting_net["out_dim"] = descriptor.get_dim_emb()
+        if "ener" in fitting_net["type"]:
+            fitting_net["return_energy"] = True
+    fitting = BaseFitting(**fitting_net)
+    return descriptor, fitting, fitting_net["type"]
+
+
 def get_spin_model(model_params):
     model_params = copy.deepcopy(model_params)
     if not model_params["spin"]["use_spin"] or isinstance(
@@ -117,25 +140,9 @@ def get_linear_model(model_params):
         if "descriptor" in sub_model_params:
             # descriptor
             sub_model_params["descriptor"]["ntypes"] = ntypes
-            sub_model_params["descriptor"]["type_map"] = copy.deepcopy(
-                model_params["type_map"]
+            descriptor, fitting, _ = _get_standard_model_components(
+                sub_model_params, ntypes
             )
-            descriptor = BaseDescriptor(**sub_model_params["descriptor"])
-            # fitting
-            fitting_net = sub_model_params.get("fitting_net", {})
-            fitting_net["type"] = fitting_net.get("type", "ener")
-            fitting_net["ntypes"] = descriptor.get_ntypes()
-            fitting_net["type_map"] = copy.deepcopy(model_params["type_map"])
-            fitting_net["mixed_types"] = descriptor.mixed_types()
-            if fitting_net["type"] in ["dipole", "polar"]:
-                fitting_net["embedding_width"] = descriptor.get_dim_emb()
-            fitting_net["dim_descrpt"] = descriptor.get_dim_out()
-            grad_force = "direct" not in fitting_net["type"]
-            if not grad_force:
-                fitting_net["out_dim"] = descriptor.get_dim_emb()
-                if "ener" in fitting_net["type"]:
-                    fitting_net["return_energy"] = True
-            fitting = BaseFitting(**fitting_net)
             list_of_models.append(
                 DPAtomicModel(descriptor, fitting, type_map=model_params["type_map"])
             )
@@ -167,24 +174,7 @@ def get_linear_model(model_params):
 def get_zbl_model(model_params):
     model_params = copy.deepcopy(model_params)
     ntypes = len(model_params["type_map"])
-    # descriptor
-    model_params["descriptor"]["ntypes"] = ntypes
-    model_params["descriptor"]["type_map"] = copy.deepcopy(model_params["type_map"])
-    descriptor = BaseDescriptor(**model_params["descriptor"])
-    # fitting
-    fitting_net = model_params.get("fitting_net", None)
-    fitting_net["type"] = fitting_net.get("type", "ener")
-    fitting_net["ntypes"] = descriptor.get_ntypes()
-    fitting_net["type_map"] = copy.deepcopy(model_params["type_map"])
-    fitting_net["mixed_types"] = descriptor.mixed_types()
-    fitting_net["embedding_width"] = descriptor.get_dim_out()
-    fitting_net["dim_descrpt"] = descriptor.get_dim_out()
-    grad_force = "direct" not in fitting_net["type"]
-    if not grad_force:
-        fitting_net["out_dim"] = descriptor.get_dim_emb()
-        if "ener" in fitting_net["type"]:
-            fitting_net["return_energy"] = True
-    fitting = BaseFitting(**fitting_net)
+    descriptor, fitting, _ = _get_standard_model_components(model_params, ntypes)
     dp_model = DPAtomicModel(descriptor, fitting, type_map=model_params["type_map"])
     # pairtab
     filepath = model_params["use_srtab"]
@@ -246,44 +236,28 @@ def get_standard_model(model_params):
     model_params_old = model_params
     model_params = copy.deepcopy(model_params)
     ntypes = len(model_params["type_map"])
-    # descriptor
-    model_params["descriptor"]["ntypes"] = ntypes
-    model_params["descriptor"]["type_map"] = copy.deepcopy(model_params["type_map"])
-    descriptor = BaseDescriptor(**model_params["descriptor"])
-    # fitting
-    fitting_net = model_params.get("fitting_net", {})
-    fitting_net["type"] = fitting_net.get("type", "ener")
-    fitting_net["ntypes"] = descriptor.get_ntypes()
-    fitting_net["type_map"] = copy.deepcopy(model_params["type_map"])
-    fitting_net["mixed_types"] = descriptor.mixed_types()
-    if fitting_net["type"] in ["dipole", "polar"]:
-        fitting_net["embedding_width"] = descriptor.get_dim_emb()
-    fitting_net["dim_descrpt"] = descriptor.get_dim_out()
-    grad_force = "direct" not in fitting_net["type"]
-    if not grad_force:
-        fitting_net["out_dim"] = descriptor.get_dim_emb()
-        if "ener" in fitting_net["type"]:
-            fitting_net["return_energy"] = True
-    fitting = BaseFitting(**fitting_net)
+    descriptor, fitting, fitting_net_type = _get_standard_model_components(
+        model_params, ntypes
+    )
     atom_exclude_types = model_params.get("atom_exclude_types", [])
     pair_exclude_types = model_params.get("pair_exclude_types", [])
     preset_out_bias = model_params.get("preset_out_bias")
     preset_out_bias = _convert_preset_out_bias_to_array(
         preset_out_bias, model_params["type_map"]
     )
 
-    if fitting_net["type"] == "dipole":
+    if fitting_net_type == "dipole":
         modelcls = DipoleModel
-    elif fitting_net["type"] == "polar":
+    elif fitting_net_type == "polar":
         modelcls = PolarModel
-    elif fitting_net["type"] == "dos":
+    elif fitting_net_type == "dos":
         modelcls = DOSModel
-    elif fitting_net["type"] in ["ener", "direct_force_ener"]:
+    elif fitting_net_type in ["ener", "direct_force_ener"]:
         modelcls = EnergyModel
-    elif fitting_net["type"] == "property":
+    elif fitting_net_type == "property":
         modelcls = PropertyModel
     else:
-        raise RuntimeError(f"Unknown fitting type: {fitting_net['type']}")
+        raise RuntimeError(f"Unknown fitting type: {fitting_net_type}")
 
     model = modelcls(
         descriptor=descriptor,