feat(tf): optimize data modifier calling in deepeval (#5120)

# feat(tf): optimize data modifier calling in deepeval

This PR includes refactoring changes to improve the modifier
architecture in the DeepMD TensorFlow implementation.

## Modified Files

### 1. deepmd/tf/infer/deep_eval.py
- Refactored the modifier initialization logic to use a more generic
approach
- Replaced hardcoded DipoleChargeModifier initialization with a dynamic
class resolution using BaseModifier.get_class_by_type()
- Simplified the modifier parameter extraction by moving it to the
modifier class itself
- Fixed atom_types reshaping issue in the _get_natoms_and_nframes method

### 2. deepmd/tf/modifier/dipole_charge.py
- Added a new static method get_params() to handle parameter extraction
from the model
- This method encapsulates the logic for retrieving modifier-specific
parameters from the model's tensors
- Improved charge map parsing with proper type conversion

## Summary

These changes make the modifier system more extensible by:
- Introducing a plugin-like architecture for modifiers
- Moving modifier-specific initialization logic to the respective
modifier classes
- Making it easier to add new modifier types in the future without
modifying the core DeepEval class
- Fixing a bug in atom type handling

The refactoring follows the DRY principle and improves code
maintainability while preserving existing functionality.

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **Refactor**
  * Switched to a unified, dynamic modifier system for modular handling.
* Normalized atom-count derivation to support multi-frame and varied
input shapes.
* Added module-level logging and stricter error handling that prevents
silent state mutation on modifier load failure.
* **New Features**
* Added a standard API for extracting modifier parameters from a frozen
model for consistent parameter retrieval across modifiers.

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>
<!-- 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>

Author: ChiahsinChu

deepmd/tf/infer/deep_eval.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import json
+import logging
 from collections.abc import (
     Callable,
 )
@@ -51,6 +52,8 @@
     run_sess,
 )
 
+log = logging.getLogger(__name__)
+
 if TYPE_CHECKING:
     from pathlib import (
         Path,
@@ -137,37 +140,25 @@ def __init__(
         self.has_aparam = self.tensors["aparam"] is not None
         self.has_spin = self.ntypes_spin > 0
 
-        # looks ugly...
-        if self.modifier_type == "dipole_charge":
-            from deepmd.tf.modifier import (
-                DipoleChargeModifier,
-            )
+        if kwargs.get("skip_modifier", False):
+            self.modifier_type = None
 
-            t_mdl_name = self._get_tensor("modifier_attr/mdl_name:0")
-            t_mdl_charge_map = self._get_tensor("modifier_attr/mdl_charge_map:0")
-            t_sys_charge_map = self._get_tensor("modifier_attr/sys_charge_map:0")
-            t_ewald_h = self._get_tensor("modifier_attr/ewald_h:0")
-            t_ewald_beta = self._get_tensor("modifier_attr/ewald_beta:0")
-            [mdl_name, mdl_charge_map, sys_charge_map, ewald_h, ewald_beta] = run_sess(
-                self.sess,
-                [
-                    t_mdl_name,
-                    t_mdl_charge_map,
-                    t_sys_charge_map,
-                    t_ewald_h,
-                    t_ewald_beta,
-                ],
-            )
-            mdl_name = mdl_name.decode("UTF-8")
-            mdl_charge_map = [int(ii) for ii in mdl_charge_map.decode("UTF-8").split()]
-            sys_charge_map = [int(ii) for ii in sys_charge_map.decode("UTF-8").split()]
-            self.dm = DipoleChargeModifier(
-                mdl_name,
-                mdl_charge_map,
-                sys_charge_map,
-                ewald_h=ewald_h,
-                ewald_beta=ewald_beta,
-            )
+        from deepmd.tf.modifier import (
+            BaseModifier,
+        )
+
+        self.dm = None
+        if self.modifier_type is not None:
+            try:
+                modifier = BaseModifier.get_class_by_type(self.modifier_type)
+                modifier_params = modifier.get_params_from_frozen_model(self)
+                self.dm = modifier.get_modifier(modifier_params)
+            except Exception as exc:
+                raise RuntimeError(
+                    f"Failed to load data modifier '{self.modifier_type}'. "
+                    f"Use skip_modifier=True to load the model without the modifier. "
+                    f"Error: {exc}"
+                ) from exc
 
     def _init_tensors(self) -> None:
         tensor_names = {
@@ -684,7 +675,8 @@ def _get_natoms_and_nframes(
         coords: np.ndarray,
         atom_types: list[int] | np.ndarray,
     ) -> tuple[int, int]:
-        natoms = len(atom_types[0])
+        # (natoms,) or (nframes, natoms,)
+        natoms = np.shape(atom_types)[-1]
         if natoms == 0:
             assert coords.size == 0
         else:

deepmd/tf/modifier/base_modifier.py

@@ -1,4 +1,8 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
+from abc import (
+    abstractmethod,
+)
+
 from deepmd.dpmodel.modifier.base_modifier import (
     make_base_modifier,
 )
@@ -11,3 +15,23 @@ class BaseModifier(DeepPot, make_base_modifier()):
     def __init__(self, *args, **kwargs) -> None:
         """Construct a basic model for different tasks."""
         DeepPot.__init__(self, *args, **kwargs)
+
+    @staticmethod
+    @abstractmethod
+    def get_params_from_frozen_model(model) -> dict:
+        """Extract the modifier parameters from a model.
+
+        This method should extract the necessary parameters from a model
+        to create an instance of this modifier.
+
+        Parameters
+        ----------
+        model
+            The model from which to extract parameters
+
+        Returns
+        -------
+        dict
+            The modifier parameters
+        """
+        pass

deepmd/tf/modifier/dipole_charge.py

@@ -1,5 +1,8 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import os
+from typing import (
+    TYPE_CHECKING,
+)
 
 import numpy as np
 
@@ -27,6 +30,11 @@
     run_sess,
 )
 
+if TYPE_CHECKING:
+    from deepmd.tf.infer import (
+        DeepEval,
+    )
+
 
 @BaseModifier.register("dipole_charge")
 class DipoleChargeModifier(DeepDipole, BaseModifier):
@@ -487,3 +495,52 @@ def modify_data(self, data: dict, data_sys: DeepmdData) -> None:
             data["force"] -= tot_f.reshape(data["force"].shape)
         if "find_virial" in data and data["find_virial"] == 1.0:
             data["virial"] -= tot_v.reshape(data["virial"].shape)
+
+    @staticmethod
+    def get_params_from_frozen_model(model: "DeepEval") -> dict:
+        """Extract modifier parameters from a DeepEval model.
+
+        Parameters
+        ----------
+        model : DeepEval
+            The DeepEval model instance containing the modifier tensors.
+
+        Returns
+        -------
+        dict
+            Dictionary containing modifier parameters:
+            - model_name : str
+            - model_charge_map : list[int]
+            - sys_charge_map : list[int]
+            - ewald_h : float
+            - ewald_beta : float
+        """
+        t_mdl_name = model._get_tensor("modifier_attr/mdl_name:0")
+        t_mdl_charge_map = model._get_tensor("modifier_attr/mdl_charge_map:0")
+        t_sys_charge_map = model._get_tensor("modifier_attr/sys_charge_map:0")
+        t_ewald_h = model._get_tensor("modifier_attr/ewald_h:0")
+        t_ewald_beta = model._get_tensor("modifier_attr/ewald_beta:0")
+        [mdl_name, mdl_charge_map, sys_charge_map, ewald_h, ewald_beta] = run_sess(
+            model.sess,
+            [
+                t_mdl_name,
+                t_mdl_charge_map,
+                t_sys_charge_map,
+                t_ewald_h,
+                t_ewald_beta,
+            ],
+        )
+        model_charge_map = [
+            int(float(ii)) for ii in mdl_charge_map.decode("UTF-8").split()
+        ]
+        sys_charge_map = [
+            int(float(ii)) for ii in sys_charge_map.decode("UTF-8").split()
+        ]
+        modifier_params = {
+            "model_name": mdl_name.decode("UTF-8"),
+            "model_charge_map": model_charge_map,
+            "sys_charge_map": sys_charge_map,
+            "ewald_h": ewald_h,
+            "ewald_beta": ewald_beta,
+        }
+        return modifier_params