fix(finetune): calculate fitting stat when using random fitting in finetuning process

deepmd/pd/model/atomic_model/base_atomic_model.py

@@ -515,6 +515,24 @@ def change_out_bias(
         else:
             raise RuntimeError("Unknown bias_adjust_mode mode: " + bias_adjust_mode)
 
+    def compute_fitting_stat(
+        self,
+        sample_merged: Union[Callable[[], list[dict]], list[dict]],
+    ) -> None:
+        """Compute the input statistics (e.g. mean and stddev) for the fittings from packed data..
+
+        Parameters
+        ----------
+        sample_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.
+        """
+        pass
+
     def _get_forward_wrapper_func(self) -> Callable[..., paddle.Tensor]:
         """Get a forward wrapper of the atomic model for output bias calculation."""
 

deepmd/pd/model/atomic_model/dp_atomic_model.py

@@ -403,6 +403,26 @@ def wrapped_sampler():
         if compute_or_load_out_stat:
             self.compute_or_load_out_stat(wrapped_sampler, stat_file_path)
 
+    def compute_fitting_stat(
+        self,
+        sample_merged: Union[Callable[[], list[dict]], list[dict]],
+    ) -> None:
+        """Compute the input statistics (e.g. mean and stddev) for the fittings from packed data..
+
+        Parameters
+        ----------
+        sample_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.
+        """
+        self.fitting_net.compute_input_stats(
+            sample_merged, protection=self.data_stat_protect
+        )
+
     def get_dim_fparam(self) -> int:
         """Get the number (dimension) of frame parameters of this atomic model."""
         return self.fitting_net.get_dim_fparam()

deepmd/pd/model/model/make_model.py

@@ -228,6 +228,8 @@ def change_out_bias(
                 merged,
                 bias_adjust_mode=bias_adjust_mode,
             )
+            if bias_adjust_mode == "set-by-statistic":
+                self.atomic_model.compute_fitting_stat(merged)
 
         def forward_common_lower(
             self,

deepmd/pt/model/atomic_model/base_atomic_model.py

@@ -493,6 +493,24 @@ def change_out_bias(
         else:
             raise RuntimeError("Unknown bias_adjust_mode mode: " + bias_adjust_mode)
 
+    def compute_fitting_stat(
+        self,
+        sample_merged: Union[Callable[[], list[dict]], list[dict]],
+    ) -> None:
+        """Compute the input statistics (e.g. mean and stddev) for the fittings from packed data..
+
+        Parameters
+        ----------
+        sample_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.
+        """
+        pass
+
     def _get_forward_wrapper_func(self) -> Callable[..., torch.Tensor]:
         """Get a forward wrapper of the atomic model for output bias calculation."""
 

deepmd/pt/model/atomic_model/dp_atomic_model.py

@@ -5,6 +5,7 @@
     Any,
     Callable,
     Optional,
+    Union,
 )
 
 import torch
@@ -328,12 +329,30 @@ def wrapped_sampler() -> list[dict]:
             return sampled
 
         self.descriptor.compute_input_stats(wrapped_sampler, stat_file_path)
-        self.fitting_net.compute_input_stats(
-            wrapped_sampler, protection=self.data_stat_protect
-        )
+        self.compute_fitting_stat(wrapped_sampler)
         if compute_or_load_out_stat:
             self.compute_or_load_out_stat(wrapped_sampler, stat_file_path)
 
+    def compute_fitting_stat(
+        self,
+        sample_merged: Union[Callable[[], list[dict]], list[dict]],
+    ) -> None:
+        """Compute the input statistics (e.g. mean and stddev) for the fittings from packed data..
+
+        Parameters
+        ----------
+        sample_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.
+        """
+        self.fitting_net.compute_input_stats(
+            sample_merged, protection=self.data_stat_protect
+        )
+
     def get_dim_fparam(self) -> int:
         """Get the number (dimension) of frame parameters of this atomic model."""
         return self.fitting_net.get_dim_fparam()

deepmd/pt/model/model/make_model.py

@@ -232,6 +232,8 @@ def change_out_bias(
                 merged,
                 bias_adjust_mode=bias_adjust_mode,
             )
+            if bias_adjust_mode == "set-by-statistic":
+                self.atomic_model.compute_fitting_stat(merged)
 
         def forward_common_lower(
             self,

source/tests/pd/test_training.py

@@ -89,7 +89,11 @@ def test_dp_train(self) -> None:
                     state_dict_trained[state_key].numpy(),
                     state_dict_finetuned_empty[state_key].numpy(),
                 )
-                if "fitting_net" not in state_key:
+                if (
+                    ("fitting_net" not in state_key)
+                    or ("fparam" in state_key)
+                    or ("aparam" in state_key)
+                ):
                     np.testing.assert_allclose(
                         state_dict_trained[state_key].numpy(),
                         state_dict_finetuned_random[state_key].numpy(),
@@ -190,6 +194,7 @@ def setUp(self) -> None:
         self.config["training"]["save_freq"] = 1
         self.set_path = Path(__file__).parent / "water/data/data_0" / "set.000"
         shutil.copyfile(self.set_path / "energy.npy", self.set_path / "fparam.npy")
+        self.config["model"]["data_stat_nbatch"] = 100
 
     def tearDown(self) -> None:
         (self.set_path / "fparam.npy").unlink(missing_ok=True)

source/tests/pt/test_training.py

@@ -92,7 +92,11 @@ def test_dp_train(self) -> None:
                     state_dict_trained[state_key],
                     state_dict_finetuned_empty[state_key],
                 )
-                if "fitting_net" not in state_key:
+                if (
+                    ("fitting_net" not in state_key)
+                    or ("fparam" in state_key)
+                    or ("aparam" in state_key)
+                ):
                     torch.testing.assert_close(
                         state_dict_trained[state_key],
                         state_dict_finetuned_random[state_key],
@@ -256,6 +260,7 @@ def setUp(self) -> None:
         self.config["training"]["save_freq"] = 1
         self.set_path = Path(__file__).parent / "water/data/data_0" / "set.000"
         shutil.copyfile(self.set_path / "energy.npy", self.set_path / "fparam.npy")
+        self.config["model"]["data_stat_nbatch"] = 100
 
     def tearDown(self) -> None:
         (self.set_path / "fparam.npy").unlink(missing_ok=True)