deepmodeling
diff --git a/‎deepmd/entrypoints/show.py‎
Lines changed: 25 additions & 2 deletions b/‎deepmd/entrypoints/show.py‎
Lines changed: 25 additions & 2 deletions
diff --git a/‎deepmd/pt/infer/deep_eval.py‎
Lines changed: 41 additions & 3 deletions b/‎deepmd/pt/infer/deep_eval.py‎
Lines changed: 41 additions & 3 deletions
diff --git a/‎deepmd/pt/utils/finetune.py‎
Lines changed: 8 additions & 2 deletions b/‎deepmd/pt/utils/finetune.py‎
Lines changed: 8 additions & 2 deletions
diff --git a/‎deepmd/utils/argcheck.py‎
Lines changed: 24 additions & 0 deletions b/‎deepmd/utils/argcheck.py‎
Lines changed: 24 additions & 0 deletions
@@ -7,6 +7,10 @@
 from deepmd.utils.econf_embd import (
     sort_element_type,
 )
+from deepmd.utils.model_branch_dict import (
+    OrderedDictTableWrapper,
+    get_model_dict,
+)
 
 log = logging.getLogger(__name__)
 
@@ -33,10 +37,15 @@ def show(
             )
         model_branches = list(model_params["model_dict"].keys())
         model_branches += ["RANDOM"]
+        _, model_branch_dict = get_model_dict(model_params["model_dict"])
         log.info(
             f"Available model branches are {model_branches}, "
             f"where 'RANDOM' means using a randomly initialized fitting net."
         )
+        log.info(
+            "Detailed information: \n"
+            + OrderedDictTableWrapper(model_branch_dict).as_table()
+        )
     if "type-map" in ATTRIBUTES:
         if model_is_multi_task:
             model_branches = list(model_params["model_dict"].keys())
@@ -79,8 +88,22 @@ def show(
             total_observed_types_list = []
             model_branches = list(model_params["model_dict"].keys())
             for branch in model_branches:
-                tmp_model = DeepEval(INPUT, head=branch, no_jit=True)
-                observed_types = tmp_model.get_observed_types()
+                if (
+                    model_params["model_dict"][branch]
+                    .get("info", {})
+                    .get("observed_type", None)
+                    is not None
+                ):
+                    observed_type_list = model_params["model_dict"][branch]["info"][
+                        "observed_type"
+                    ]
+                    observed_types = {
+                        "type_num": len(observed_type_list),
+                        "observed_type": observed_type_list,
+                    }
+                else:
+                    tmp_model = DeepEval(INPUT, head=branch, no_jit=True)
+                    observed_types = tmp_model.get_observed_types()
                 log.info(
                     f"{branch}: Number of observed types: {observed_types['type_num']} "
                 )
 
@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import json
+import logging
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -67,10 +68,15 @@
 from deepmd.utils.econf_embd import (
     sort_element_type,
 )
+from deepmd.utils.model_branch_dict import (
+    get_model_dict,
+)
 
 if TYPE_CHECKING:
     import ase.neighborlist
 
+log = logging.getLogger(__name__)
+
 
 class DeepEval(DeepEvalBackend):
     """PyTorch backend implementation of DeepEval.
@@ -116,15 +122,36 @@ def __init__(
             self.model_def_script = self.input_param
             self.multi_task = "model_dict" in self.input_param
             if self.multi_task:
+                model_alias_dict, model_branch_dict = get_model_dict(
+                    self.input_param["model_dict"]
+                )
                 model_keys = list(self.input_param["model_dict"].keys())
+                if head is None and "Default" in model_alias_dict:
+                    head = "Default"
+                    log.info(
+                        f"Using default head {model_alias_dict[head]} for multitask model."
+                    )
                 if isinstance(head, int):
                     head = model_keys[0]
                 assert head is not None, (
-                    f"Head must be set for multitask model! Available heads are: {model_keys}"
+                    f"Head must be set for multitask model! Available heads are: {model_keys}, "
+                    f"use `dp --pt show your_model.pt model-branch` to show detail information."
                 )
-                assert head in model_keys, (
-                    f"No head named {head} in model! Available heads are: {model_keys}"
+                if head not in model_alias_dict:
+                    # preprocess with potentially case-insensitive input
+                    head_lower = head.lower()
+                    for mk in model_alias_dict:
+                        if mk.lower() == head_lower:
+                            # mapped the first matched head
+                            head = mk
+                            break
+                # replace with alias
+                assert head in model_alias_dict, (
+                    f"No head or alias named {head} in model! Available heads are: {model_keys},"
+                    f"use `dp --pt show your_model.pt model-branch` to show detail information."
                 )
+                head = model_alias_dict[head]
+
                 self.input_param = self.input_param["model_dict"][head]
                 state_dict_head = {"_extra_state": state_dict["_extra_state"]}
                 for item in state_dict:
@@ -253,6 +280,17 @@ def get_has_hessian(self):
         """Check if the model has hessian."""
         return self._has_hessian
 
+    def get_model_branch(self):
+        """Get the model branch information."""
+        if "model_dict" in self.model_def_script:
+            model_alias_dict, model_branch_dict = get_model_dict(
+                self.model_def_script["model_dict"]
+            )
+            return model_alias_dict, model_branch_dict
+        else:
+            # single-task model
+            return {"Default": "Default"}, {"Default": {"alias": [], "info": {}}}
+
     def eval(
         self,
         coords: np.ndarray,
 
@@ -12,6 +12,9 @@
 from deepmd.utils.finetune import (
     FinetuneRuleItem,
 )
+from deepmd.utils.model_branch_dict import (
+    get_model_dict,
+)
 
 log = logging.getLogger(__name__)
 
@@ -44,10 +47,13 @@ def get_finetune_rule_single(
             )
         else:
             model_branch_chosen = model_branch_from
-        assert model_branch_chosen in model_dict_params, (
-            f"No model branch named '{model_branch_chosen}'! "
+        model_alias_dict, model_branch_dict = get_model_dict(model_dict_params)
+        assert model_branch_chosen in model_alias_dict, (
+            f"No model branch or alias named '{model_branch_chosen}'! "
             f"Available ones are {list(model_dict_params.keys())}."
+            f"Use `dp --pt show your_model.pt model-branch` to show detail information."
         )
+        model_branch_chosen = model_alias_dict[model_branch_chosen]
         single_config_chosen = deepcopy(model_dict_params[model_branch_chosen])
     old_type_map, new_type_map = (
         single_config_chosen["type_map"],
 
@@ -2306,6 +2306,16 @@ def model_args(exclude_hybrid=False):
 def standard_model_args() -> Argument:
     doc_descrpt = "The descriptor of atomic environment."
     doc_fitting = "The fitting of physical properties."
+    doc_model_branch_alias = (
+        "List of aliases for this model branch. "
+        "Multiple aliases can be defined, and any alias can reference this branch throughout the model usage. "
+        "Used only in multitask models."
+    )
+    doc_info = (
+        "Dictionary of metadata for this model branch. "
+        "Store arbitrary key-value pairs with branch-specific information. "
+        "Used only in multitask models."
+    )
 
     ca = Argument(
         "standard",
@@ -2321,6 +2331,20 @@ def standard_model_args() -> Argument:
                 [fitting_variant_type_args()],
                 doc=doc_fitting,
             ),
+            Argument(
+                "model_branch_alias",
+                list[str],
+                optional=True,
+                default=[],
+                doc=doc_only_pt_supported + doc_model_branch_alias,
+            ),
+            Argument(
+                "info",
+                dict,
+                optional=True,
+                default={},
+                doc=doc_only_pt_supported + doc_info,
+            ),
         ],
         doc="Standard model, which contains a descriptor and a fitting.",
     )