fix: type of the preset out bias (#4135)

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

## Summary by CodeRabbit

- **New Features**
- Enhanced error handling for the `preset_out_bias` parameter, allowing
for better validation of input types.
- Expanded documentation for `preset_out_bias`, providing clearer
guidelines and examples for users.

- **Bug Fixes**
- Improved robustness by ensuring unsupported types for energy values
raise appropriate errors.

- **Tests**
- Added new tests to validate the handling of various input types for
the `preset_out_bias` parameter, ensuring correct processing and error
reporting.

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

Co-authored-by: Han Wang <[email protected]>

deepmd/pt/model/model/__init__.py

@@ -151,6 +151,15 @@ def get_zbl_model(model_params):
     )
 
 
+def _can_be_converted_to_float(value):
+    try:
+        float(value)
+        return True
+    except (TypeError, ValueError):
+        # return false for any failure...
+        return False
+
+
 def _convert_preset_out_bias_to_array(preset_out_bias, type_map):
     if preset_out_bias is not None:
         for kk in preset_out_bias:
@@ -160,7 +169,17 @@ def _convert_preset_out_bias_to_array(preset_out_bias, type_map):
                 )
             for jj in range(len(preset_out_bias[kk])):
                 if preset_out_bias[kk][jj] is not None:
-                    preset_out_bias[kk][jj] = np.array(preset_out_bias[kk][jj])
+                    if isinstance(preset_out_bias[kk][jj], list):
+                        bb = preset_out_bias[kk][jj]
+                    elif _can_be_converted_to_float(preset_out_bias[kk][jj]):
+                        bb = [float(preset_out_bias[kk][jj])]
+                    else:
+                        raise ValueError(
+                            f"unsupported type/value of the {jj}th element of "
+                            f"preset_out_bias['{kk}'] "
+                            f"{type(preset_out_bias[kk][jj])}"
+                        )
+                    preset_out_bias[kk][jj] = np.array(bb)
     return preset_out_bias
 
 

deepmd/utils/argcheck.py

@@ -1772,7 +1772,7 @@ def model_args(exclude_hybrid=False):
     doc_spin = "The settings for systems with spin."
     doc_atom_exclude_types = "Exclude the atomic contribution of the listed atom types"
     doc_pair_exclude_types = "The atom pairs of the listed types are not treated to be neighbors, i.e. they do not see each other."
-    doc_preset_out_bias = "The preset bias of the atomic output. Is provided as a dict. Taking the energy model that has three atom types for example, the preset_out_bias may be given as `{ 'energy': [null, 0., 1.] }`. In this case the bias of type 1 and 2 are set to 0. and 1., respectively.The set_davg_zero should be set to true."
+    doc_preset_out_bias = "The preset bias of the atomic output. Note that the set_davg_zero should be set to true. The bias is provided as a dict. Taking the energy model that has three atom types for example, the `preset_out_bias` may be given as `{ 'energy': [null, 0., 1.] }`. In this case the energy bias of type 1 and 2 are set to 0. and 1., respectively. A dipole model with two atom types may set `preset_out_bias` as `{ 'dipole': [null, [0., 1., 2.]] }`"
     doc_finetune_head = (
         "The chosen fitting net to fine-tune on, when doing multi-task fine-tuning. "
         "If not set or set to 'RANDOM', the fitting net will be randomly initialized."
@@ -1837,7 +1837,7 @@ def model_args(exclude_hybrid=False):
             ),
             Argument(
                 "preset_out_bias",
-                Dict[str, Optional[float]],
+                Dict[str, List[Optional[Union[float, List[float]]]]],
                 optional=True,
                 default=None,
                 doc=doc_only_pt_supported + doc_preset_out_bias,

source/tests/pt/model/test_get_model.py

@@ -63,6 +63,37 @@ def test_model_attr(self):
         self.assertEqual(atomic_model.atom_exclude_types, [1])
         self.assertEqual(atomic_model.pair_exclude_types, [[1, 2]])
 
+    def test_model_attr_energy_float(self):
+        model_params = copy.deepcopy(model_se_e2_a)
+        model_params["preset_out_bias"] = {"energy": ["1.", 3, None]}
+        self.model = get_model(model_params).to(env.DEVICE)
+        atomic_model = self.model.atomic_model
+        self.assertEqual(atomic_model.type_map, ["O", "H", "B"])
+        self.assertEqual(
+            atomic_model.preset_out_bias,
+            {
+                "energy": [
+                    np.array([1.0]),
+                    np.array([3.0]),
+                    None,
+                ]
+            },
+        )
+        self.assertEqual(atomic_model.atom_exclude_types, [1])
+        self.assertEqual(atomic_model.pair_exclude_types, [[1, 2]])
+
+    def test_model_attr_energy_unsupported_type(self):
+        model_params = copy.deepcopy(model_se_e2_a)
+        model_params["preset_out_bias"] = {"energy": [1.0 + 2.0j, 3, None]}
+        with self.assertRaises(ValueError):
+            self.model = get_model(model_params).to(env.DEVICE)
+
+    def test_model_attr_energy_unsupported_value(self):
+        model_params = copy.deepcopy(model_se_e2_a)
+        model_params["preset_out_bias"] = {"energy": ["1.0 + 2.0j", 3, None]}
+        with self.assertRaises(ValueError):
+            self.model = get_model(model_params).to(env.DEVICE)
+
     def test_notset_model_attr(self):
         model_params = copy.deepcopy(model_se_e2_a)
         model_params.pop("atom_exclude_types")