feat(pt): support zbl finetune

deepmd/pt/model/atomic_model/base_atomic_model.py

@@ -363,21 +363,25 @@ def compute_or_load_stat(
         self,
         merged: Union[Callable[[], list[dict]], list[dict]],
         stat_file_path: Optional[DPPath] = None,
+        compute_or_load_out_stat: bool = True,
     ) -> NoReturn:
         """
-        Compute the output statistics (e.g. energy bias) for the fitting net from packed data.
+        Compute or load the statistics parameters of the model,
+        such as mean and standard deviation of descriptors or the energy bias of the fitting net.
+        When `sampled` is provided, all the statistics parameters will be calculated (or re-calculated for update),
+        and saved in the `stat_file_path`(s).
+        When `sampled` is not provided, it will check the existence of `stat_file_path`(s)
+        and load the calculated statistics parameters.
 
         Parameters
         ----------
-        merged : Union[Callable[[], list[dict]], list[dict]]
-            - list[dict]: A list of data samples from various data systems.
-                Each element, `merged[i]`, is a data dictionary containing `keys`: `torch.Tensor`
-                originating from the `i`-th data system.
-            - Callable[[], list[dict]]: A lazy function that returns data samples in the above format
-                only when needed. Since the sampling process can be slow and memory-intensive,
-                the lazy function helps by only sampling once.
-        stat_file_path : Optional[DPPath]
-            The path to the stat file.
+        merged
+            The lazy sampled function to get data frames from different data systems.
+        stat_file_path
+            The dictionary of paths to the statistics files.
+        compute_or_load_out_stat : bool
+            Whether to compute the output statistics.
+            If False, it will only compute the input statistics (e.g. mean and standard deviation of descriptors).
 
         """
         raise NotImplementedError

deepmd/pt/model/atomic_model/dp_atomic_model.py

@@ -285,6 +285,7 @@ def compute_or_load_stat(
         self,
         sampled_func,
         stat_file_path: Optional[DPPath] = None,
+        compute_or_load_out_stat: bool = True,
     ) -> None:
         """
         Compute or load the statistics parameters of the model,
@@ -300,6 +301,9 @@ def compute_or_load_stat(
             The lazy sampled function to get data frames from different data systems.
         stat_file_path
             The dictionary of paths to the statistics files.
+        compute_or_load_out_stat : bool
+            Whether to compute the output statistics.
+            If False, it will only compute the input statistics (e.g. mean and standard deviation of descriptors).
         """
         if stat_file_path is not None and self.type_map is not None:
             # descriptors and fitting net with different type_map
@@ -323,7 +327,8 @@ def wrapped_sampler():
         self.fitting_net.compute_input_stats(
             wrapped_sampler, protection=self.data_stat_protect
         )
-        self.compute_or_load_out_stat(wrapped_sampler, stat_file_path)
+        if compute_or_load_out_stat:
+            self.compute_or_load_out_stat(wrapped_sampler, stat_file_path)
 
     def get_dim_fparam(self) -> int:
         """Get the number (dimension) of frame parameters of this atomic model."""

deepmd/pt/model/atomic_model/linear_atomic_model.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
+import functools
 from typing import (
-    Callable,
     Optional,
     Union,
 )
@@ -319,6 +319,10 @@ def apply_out_stat(
             The atom types. nf x nloc
 
         """
+        out_bias, out_std = self._fetch_out_stat(self.bias_keys)
+        for kk in self.bias_keys:
+            # nf x nloc x odims, out_bias: ntypes x odims
+            ret[kk] = ret[kk] + out_bias[kk][atype]
         return ret
 
     @staticmethod
@@ -464,34 +468,11 @@ def is_aparam_nall(self) -> bool:
         """
         return False
 
-    def compute_or_load_out_stat(
-        self,
-        merged: Union[Callable[[], list[dict]], list[dict]],
-        stat_file_path: Optional[DPPath] = None,
-    ) -> None:
-        """
-        Compute the output statistics (e.g. energy bias) for the fitting net from packed data.
-
-        Parameters
-        ----------
-        merged : Union[Callable[[], list[dict]], list[dict]]
-            - list[dict]: A list of data samples from various data systems.
-                Each element, `merged[i]`, is a data dictionary containing `keys`: `torch.Tensor`
-                originating from the `i`-th data system.
-            - Callable[[], list[dict]]: A lazy function that returns data samples in the above format
-                only when needed. Since the sampling process can be slow and memory-intensive,
-                the lazy function helps by only sampling once.
-        stat_file_path : Optional[DPPath]
-            The path to the stat file.
-
-        """
-        for md in self.models:
-            md.compute_or_load_out_stat(merged, stat_file_path)
-
     def compute_or_load_stat(
         self,
         sampled_func,
         stat_file_path: Optional[DPPath] = None,
+        compute_or_load_out_stat: bool = True,
     ) -> None:
         """
         Compute or load the statistics parameters of the model,
@@ -507,9 +488,34 @@ def compute_or_load_stat(
             The lazy sampled function to get data frames from different data systems.
         stat_file_path
             The dictionary of paths to the statistics files.
+        compute_or_load_out_stat : bool
+            Whether to compute the output statistics.
+            If False, it will only compute the input statistics (e.g. mean and standard deviation of descriptors).
         """
         for md in self.models:
-            md.compute_or_load_stat(sampled_func, stat_file_path)
+            md.compute_or_load_stat(
+                sampled_func, stat_file_path, compute_or_load_out_stat=False
+            )
+
+        if stat_file_path is not None and self.type_map is not None:
+            # descriptors and fitting net with different type_map
+            # should not share the same parameters
+            stat_file_path /= " ".join(self.type_map)
+
+        @functools.lru_cache
+        def wrapped_sampler():
+            sampled = sampled_func()
+            if self.pair_excl is not None:
+                pair_exclude_types = self.pair_excl.get_exclude_types()
+                for sample in sampled:
+                    sample["pair_exclude_types"] = list(pair_exclude_types)
+            if self.atom_excl is not None:
+                atom_exclude_types = self.atom_excl.get_exclude_types()
+                for sample in sampled:
+                    sample["atom_exclude_types"] = list(atom_exclude_types)
+            return sampled
+
+        self.compute_or_load_out_stat(wrapped_sampler, stat_file_path)
 
 
 class DPZBLLinearEnergyAtomicModel(LinearEnergyAtomicModel):

deepmd/pt/model/atomic_model/pairtab_atomic_model.py

@@ -224,26 +224,31 @@ def deserialize(cls, data) -> "PairTabAtomicModel":
 
     def compute_or_load_stat(
         self,
-        merged: Union[Callable[[], list[dict]], list[dict]],
+        sampled_func: Union[Callable[[], list[dict]], list[dict]],
         stat_file_path: Optional[DPPath] = None,
+        compute_or_load_out_stat: bool = True,
     ) -> None:
         """
-        Compute the output statistics (e.g. energy bias) for the fitting net from packed data.
+        Compute or load the statistics parameters of the model,
+        such as mean and standard deviation of descriptors or the energy bias of the fitting net.
+        When `sampled` is provided, all the statistics parameters will be calculated (or re-calculated for update),
+        and saved in the `stat_file_path`(s).
+        When `sampled` is not provided, it will check the existence of `stat_file_path`(s)
+        and load the calculated statistics parameters.
 
         Parameters
         ----------
-        merged : Union[Callable[[], list[dict]], list[dict]]
-            - list[dict]: A list of data samples from various data systems.
-                Each element, `merged[i]`, is a data dictionary containing `keys`: `torch.Tensor`
-                originating from the `i`-th data system.
-            - Callable[[], list[dict]]: A lazy function that returns data samples in the above format
-                only when needed. Since the sampling process can be slow and memory-intensive,
-                the lazy function helps by only sampling once.
-        stat_file_path : Optional[DPPath]
-            The path to the stat file.
+        sampled_func
+            The lazy sampled function to get data frames from different data systems.
+        stat_file_path
+            The dictionary of paths to the statistics files.
+        compute_or_load_out_stat : bool
+            Whether to compute the output statistics.
+            If False, it will only compute the input statistics (e.g. mean and standard deviation of descriptors).
 
         """
-        self.compute_or_load_out_stat(merged, stat_file_path)
+        if compute_or_load_out_stat:
+            self.compute_or_load_out_stat(sampled_func, stat_file_path)
 
     def forward_atomic(
         self,

deepmd/pt/train/training.py

@@ -510,15 +510,31 @@ 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 (".descriptor." not in item_key):
+                            new_key = item_key.replace(
+                                f".{_model_key}.", f".{_model_key_from}."
+                            )
+                            use_random_initialization = _new_fitting and (
+                                ".descriptor." not in item_key
+                            )
+                            if (
+                                not use_random_initialization
+                                and new_key not in _origin_state_dict
+                            ):
+                                # for ZBL models finetuning from standard models
+                                if ".models.0." in new_key:
+                                    new_key = new_key.replace(".models.0.", ".")
+                                elif ".models.1." in new_key:
+                                    use_random_initialization = True
+                                else:
+                                    raise KeyError(
+                                        f"Key {new_key} not found in pretrained model."
+                                    )
+                            if use_random_initialization:
                                 # print(f'Keep {item_key} in old model!')
                                 _new_state_dict[item_key] = (
                                     _random_state_dict[item_key].clone().detach()
                                 )
                             else:
-                                new_key = item_key.replace(
-                                    f".{_model_key}.", f".{_model_key_from}."
-                                )
                                 # print(f'Replace {item_key} with {new_key} in pretrained_model!')
                                 _new_state_dict[item_key] = (
                                     _origin_state_dict[new_key].clone().detach()

source/tests/pt/model/test_linear_atomic_model_stat.py

@@ -233,16 +233,11 @@ def test_linear_atomic_model_stat_with_bias(self) -> None:
         linear_model.compute_or_load_out_stat(
             self.merged_output_stat, stat_file_path=self.stat_file_path
         )
-        # bias applied to sub atomic models.
         ener_bias = np.array([1.0, 3.0]).reshape(2, 1)
-        linear_ret = []
-        for idx, md in enumerate(linear_model.models):
-            ret = md.forward_common_atomic(*args)
-            ret = to_numpy_array(ret["energy"])
-            linear_ret.append(ret_no_bias[idx] + ener_bias[at])
-            np.testing.assert_almost_equal((ret_no_bias[idx] + ener_bias[at]), ret)
+        ret = to_numpy_array(linear_model.forward_common_atomic(*args)["energy"])
+        np.testing.assert_almost_equal((ret0 + ener_bias[at]), ret)
 
         # linear model not adding bias again
         ret1 = linear_model.forward_common_atomic(*args)
         ret1 = to_numpy_array(ret1["energy"])
-        np.testing.assert_almost_equal(np.mean(np.stack(linear_ret), axis=0), ret1)
+        np.testing.assert_almost_equal(ret, ret1)

source/tests/pt/test_training.py

@@ -30,6 +30,8 @@
 
 
 class DPTrainTest:
+    test_zbl_from_standard: bool = False
+
     def test_dp_train(self) -> None:
         # test training from scratch
         trainer = get_trainer(deepcopy(self.config))
@@ -95,6 +97,34 @@ def test_dp_train(self) -> None:
                         state_dict_finetuned_random[state_key],
                     )
 
+        if self.test_zbl_from_standard:
+            # test fine-tuning using zbl from standard model
+            finetune_model = (
+                self.config["training"].get("save_ckpt", "model.ckpt") + ".pt"
+            )
+            self.config_zbl["model"], finetune_links = get_finetune_rules(
+                finetune_model,
+                self.config_zbl["model"],
+            )
+            trainer_finetune_zbl = get_trainer(
+                deepcopy(self.config_zbl),
+                finetune_model=finetune_model,
+                finetune_links=finetune_links,
+            )
+            state_dict_finetuned_zbl = trainer_finetune_zbl.wrapper.model.state_dict()
+            for state_key in state_dict_finetuned_zbl:
+                if "out_bias" not in state_key and "out_std" not in state_key:
+                    original_key = state_key
+                    if ".models.0." in state_key:
+                        original_key = state_key.replace(".models.0.", ".")
+                    if ".models.1." not in state_key:
+                        torch.testing.assert_close(
+                            state_dict_trained[original_key],
+                            state_dict_finetuned_zbl[state_key],
+                        )
+            # check running
+            trainer_finetune_zbl.run()
+
         # check running
         trainer_finetune.run()
         trainer_finetune_empty.run()
@@ -222,6 +252,18 @@ def setUp(self) -> None:
         self.config["training"]["numb_steps"] = 1
         self.config["training"]["save_freq"] = 1
 
+        self.test_zbl_from_standard = True
+
+        input_json_zbl = str(Path(__file__).parent / "water/zbl.json")
+        with open(input_json_zbl) as f:
+            self.config_zbl = json.load(f)
+        data_file = [str(Path(__file__).parent / "water/data/data_0")]
+        self.config_zbl["training"]["training_data"]["systems"] = data_file
+        self.config_zbl["training"]["validation_data"]["systems"] = data_file
+        self.config_zbl["model"] = deepcopy(model_zbl)
+        self.config_zbl["training"]["numb_steps"] = 1
+        self.config_zbl["training"]["save_freq"] = 1
+
     def tearDown(self) -> None:
         DPTrainTest.tearDown(self)