fix(pt): finetuning property/dipole/polar/dos fitting with multi-dime…

…nsional data causes error (#4145)

Fix issue #4108 

If a pretrained model is labeled with energy and the `out_bias` is one
dimension. If we want to finetune a dos/polar/dipole/property model
using this pretrained model, the `out_bias` of finetuning model is
multi-dimension(example: numb_dos = 250). An error occurs:
`RuntimeError: Error(s) in loading state_dict for ModelWrapper:`
` size mismatch for model.Default.atomic_model.out_bias: copying a param
with shape torch.Size([1, 118, 1]) from checkpoint, the shape in current
model is torch.Size([1, 118, 250]).`
` size mismatch for model.Default.atomic_model.out_std: copying a param
with shape torch.Size([1, 118, 1]) from checkpoint, the shape in current
model is torch.Size([1, 118, 250]).`

When using new fitting, old out_bias is useless because we will
recompute the new bias in later code. So we do not need to load old
out_bias when using new fitting finetune.

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

- **New Features**
- Enhanced parameter collection for fine-tuning, refining criteria for
parameter retention.
- Introduced a model checkpoint file for saving and resuming training
states, facilitating iterative development.

- **Tests**
- Added a new test class to validate training and fine-tuning processes,
ensuring model performance consistency across configurations.
<!-- 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/train/training.py

@@ -484,7 +484,7 @@ def collect_single_finetune_params(
                             if i != "_extra_state" and f".{_model_key}." in i
                         ]
                         for item_key in target_keys:
-                            if _new_fitting and ".fitting_net." in item_key:
+                            if _new_fitting and (".descriptor." not in item_key):
                                 # print(f'Keep {item_key} in old model!')
                                 _new_state_dict[item_key] = (
                                     _random_state_dict[item_key].clone().detach()

source/tests/pt/test_training.py

@@ -448,5 +448,73 @@ def tearDown(self) -> None:
         DPTrainTest.tearDown(self)
 
 
+class TestPropFintuFromEnerModel(unittest.TestCase):
+    def setUp(self):
+        input_json = str(Path(__file__).parent / "water/se_atten.json")
+        with open(input_json) as f:
+            self.config = json.load(f)
+        data_file = [str(Path(__file__).parent / "water/data/data_0")]
+        self.config["training"]["training_data"]["systems"] = data_file
+        self.config["training"]["validation_data"]["systems"] = data_file
+        self.config["model"] = deepcopy(model_dpa1)
+        self.config["model"]["type_map"] = ["H", "C", "N", "O"]
+        self.config["training"]["numb_steps"] = 1
+        self.config["training"]["save_freq"] = 1
+
+        property_input = str(Path(__file__).parent / "property/input.json")
+        with open(property_input) as f:
+            self.config_property = json.load(f)
+        prop_data_file = [str(Path(__file__).parent / "property/single")]
+        self.config_property["training"]["training_data"]["systems"] = prop_data_file
+        self.config_property["training"]["validation_data"]["systems"] = prop_data_file
+        self.config_property["model"]["descriptor"] = deepcopy(model_dpa1["descriptor"])
+        self.config_property["training"]["numb_steps"] = 1
+        self.config_property["training"]["save_freq"] = 1
+
+    def test_dp_train(self):
+        # test training from scratch
+        trainer = get_trainer(deepcopy(self.config))
+        trainer.run()
+        state_dict_trained = trainer.wrapper.model.state_dict()
+
+        # test fine-tuning using diffferent fitting_net, here using property fitting
+        finetune_model = self.config["training"].get("save_ckpt", "model.ckpt") + ".pt"
+        self.config_property["model"], finetune_links = get_finetune_rules(
+            finetune_model,
+            self.config_property["model"],
+            model_branch="RANDOM",
+        )
+        trainer_finetune = get_trainer(
+            deepcopy(self.config_property),
+            finetune_model=finetune_model,
+            finetune_links=finetune_links,
+        )
+
+        # check parameters
+        state_dict_finetuned = trainer_finetune.wrapper.model.state_dict()
+        for state_key in state_dict_finetuned:
+            if (
+                "out_bias" not in state_key
+                and "out_std" not in state_key
+                and "fitting" not in state_key
+            ):
+                torch.testing.assert_close(
+                    state_dict_trained[state_key],
+                    state_dict_finetuned[state_key],
+                )
+
+        # check running
+        trainer_finetune.run()
+
+    def tearDown(self):
+        for f in os.listdir("."):
+            if f.startswith("model") and f.endswith(".pt"):
+                os.remove(f)
+            if f in ["lcurve.out"]:
+                os.remove(f)
+            if f in ["stat_files"]:
+                shutil.rmtree(f)
+
+
 if __name__ == "__main__":
     unittest.main()