refact: compute_output_stats and change_out_bias

deepmd/dpmodel/atomic_model/make_base_atomic_model.py

@@ -51,6 +51,9 @@ def atomic_output_def(self) -> FittingOutputDef:
             """
             return self.fitting_output_def()
 
+        def get_output_keys(self) -> List[str]:
+            return list(self.atomic_output_def().keys())
+
         @abstractmethod
         def get_rcut(self) -> float:
             """Get the cut-off radius."""

deepmd/pt/model/atomic_model/base_atomic_model.py

@@ -8,9 +8,9 @@
     List,
     Optional,
     Tuple,
+    Union,
 )
 
-import numpy as np
 import torch
 
 from deepmd.dpmodel.atomic_model import (
@@ -30,9 +30,6 @@
 from deepmd.pt.utils.stat import (
     compute_output_stats,
 )
-from deepmd.pt.utils.utils import (
-    to_numpy_array,
-)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -190,115 +187,95 @@ def serialize(self) -> dict:
             "pair_exclude_types": self.pair_exclude_types,
         }
 
-    def get_forward_wrapper_func(self) -> Callable[..., torch.Tensor]:
-        """Get a forward wrapper of the atomic model for output bias calculation."""
-
-        def model_forward(coord, atype, box, fparam=None, aparam=None):
-            with torch.no_grad():  # it's essential for pure torch forward function to use auto_batchsize
-                (
-                    extended_coord,
-                    extended_atype,
-                    mapping,
-                    nlist,
-                ) = extend_input_and_build_neighbor_list(
-                    coord,
-                    atype,
-                    self.get_rcut(),
-                    self.get_sel(),
-                    mixed_types=self.mixed_types(),
-                    box=box,
-                )
-                atomic_ret = self.forward_common_atomic(
-                    extended_coord,
-                    extended_atype,
-                    nlist,
-                    mapping=mapping,
-                    fparam=fparam,
-                    aparam=aparam,
-                )
-                return {kk: vv.detach() for kk, vv in atomic_ret.items()}
-
-        return model_forward
-
     def compute_or_load_stat(
         self,
-        sampled_func,
+        merged: Union[Callable[[], List[dict]], List[dict]],
         stat_file_path: Optional[DPPath] = None,
     ):
         """
-        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.
+        Compute the output statistics (e.g. energy bias) for the fitting net from packed data.
 
         Parameters
         ----------
-        sampled_func
-            The sampled data frames from different data systems.
-        stat_file_path
-            The path to the statistics files.
+        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.
+
         """
         raise NotImplementedError
 
     def change_out_bias(
         self,
-        merged,
-        origin_type_map,
-        full_type_map,
+        sample_merged,
         bias_adjust_mode="change-by-statistic",
     ) -> None:
         """Change the output bias according to the input data and the pretrained model.
 
         Parameters
         ----------
-        merged : Union[Callable[[], List[dict]], List[dict]]
+        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.
-        origin_type_map : List[str]
-            The original type_map in dataset, they are targets to change the output bias.
-        full_type_map : List[str]
-            The full type_map in pre-trained model
         bias_adjust_mode : str
             The mode for changing output bias : ['change-by-statistic', 'set-by-statistic']
             'change-by-statistic' : perform predictions on labels of target dataset,
                     and do least square on the errors to obtain the target shift as bias.
             'set-by-statistic' : directly use the statistic output bias in the target dataset.
         """
-        sorter = np.argsort(full_type_map)
-        missing_types = [t for t in origin_type_map if t not in full_type_map]
-        assert (
-            not missing_types
-        ), f"Some types are not in the pre-trained model: {list(missing_types)} !"
-        idx_type_map = sorter[
-            np.searchsorted(full_type_map, origin_type_map, sorter=sorter)
-        ]
-        original_bias = self.get_out_bias()
         if bias_adjust_mode == "change-by-statistic":
             delta_bias = compute_output_stats(
-                merged,
+                sample_merged,
                 self.get_ntypes(),
-                keys=["energy"],
-                model_forward=self.get_forward_wrapper_func(),
+                keys=self.get_output_keys(),
+                model_forward=self._get_forward_wrapper_func(),
             )["energy"]
             self.set_out_bias(delta_bias, add=True)
         elif bias_adjust_mode == "set-by-statistic":
             bias_atom = compute_output_stats(
-                merged,
+                sample_merged,
                 self.get_ntypes(),
-                keys=["energy"],
+                keys=self.get_output_keys(),
             )["energy"]
             self.set_out_bias(bias_atom)
         else:
             raise RuntimeError("Unknown bias_adjust_mode mode: " + bias_adjust_mode)
-        bias_atom = self.get_out_bias()
-        log.info(
-            f"Change output bias of {origin_type_map!s} "
-            f"from {to_numpy_array(original_bias[idx_type_map]).reshape(-1)!s} "
-            f"to {to_numpy_array(bias_atom[idx_type_map]).reshape(-1)!s}."
-        )
+
+    def _get_forward_wrapper_func(self) -> Callable[..., torch.Tensor]:
+        """Get a forward wrapper of the atomic model for output bias calculation."""
+
+        def model_forward(coord, atype, box, fparam=None, aparam=None):
+            with torch.no_grad():  # it's essential for pure torch forward function to use auto_batchsize
+                (
+                    extended_coord,
+                    extended_atype,
+                    mapping,
+                    nlist,
+                ) = extend_input_and_build_neighbor_list(
+                    coord,
+                    atype,
+                    self.get_rcut(),
+                    self.get_sel(),
+                    mixed_types=self.mixed_types(),
+                    box=box,
+                )
+                atomic_ret = self.forward_common_atomic(
+                    extended_coord,
+                    extended_atype,
+                    nlist,
+                    mapping=mapping,
+                    fparam=fparam,
+                    aparam=aparam,
+                )
+                return {kk: vv.detach() for kk, vv in atomic_ret.items()}
+
+        return model_forward

deepmd/pt/model/model/make_model.py

@@ -172,11 +172,12 @@ def forward_common(
             model_predict = self.output_type_cast(model_predict, input_prec)
             return model_predict
 
+        def get_out_bias(self) -> torch.Tensor:
+            return self.atomic_model.get_out_bias()
+
         def change_out_bias(
             self,
             merged,
-            origin_type_map,
-            full_type_map,
             bias_adjust_mode="change-by-statistic",
         ) -> None:
             """Change the output bias of atomic model according to the input data and the pretrained model.
@@ -190,10 +191,6 @@ def change_out_bias(
                 - 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.
-            origin_type_map : List[str]
-                The original type_map in dataset, they are targets to change the output bias.
-            full_type_map : List[str]
-                The full type_map in pre-trained model
             bias_adjust_mode : str
                 The mode for changing output bias : ['change-by-statistic', 'set-by-statistic']
                 'change-by-statistic' : perform predictions on labels of target dataset,
@@ -202,8 +199,6 @@ def change_out_bias(
             """
             self.atomic_model.change_out_bias(
                 merged,
-                origin_type_map,
-                full_type_map,
                 bias_adjust_mode=bias_adjust_mode,
             )
 

deepmd/pt/model/task/invar_fitting.py

@@ -167,11 +167,11 @@ def compute_output_stats(
         bias_atom_e = compute_output_stats(
             merged,
             self.ntypes,
-            keys=["energy"],
+            keys=[self.var_name],
             stat_file_path=stat_file_path,
             rcond=self.rcond,
             atom_ener=self.atom_ener,
-        )["energy"]
+        )[self.var_name]
         self.bias_atom_e.copy_(bias_atom_e.view([self.ntypes, self.dim_out]))
 
     def output_def(self) -> FittingOutputDef:

deepmd/pt/train/training.py

@@ -570,13 +570,8 @@
                             _model_params["new_type_map"],
                         )
                         if isinstance(_model, EnergyModel):
-                            _model.change_out_bias(
-                                _sample_func,
-                                bias_adjust_mode=_model_params.get(
-                                    "bias_adjust_mode", "change-by-statistic"
-                                ),
-                                origin_type_map=new_type_map,
-                                full_type_map=old_type_map,
+                            _model = _model_change_out_bias(
+                                _model, new_type_map, _sample_func, _model_params
                             )
                         else:
                             # need to updated
@@ -1148,3 +1143,31 @@
         print_str += "   %8.1e\n" % cur_lr
         fout.write(print_str)
         fout.flush()
+
+
+def _model_change_out_bias(
+    _model,
+    new_type_map,
+    _sample_func,
+    _model_params,
+):
+    old_bias = _model.get_out_bias()
+    _model.change_out_bias(
+        _sample_func,
+        bias_adjust_mode=_model_params.get("bias_adjust_mode", "change-by-statistic"),
+    )
+    new_bias = _model.get_out_bias()
+
+    model_type_map = _model.get_type_map()
+    sorter = np.argsort(model_type_map)
+    missing_types = [t for t in new_type_map if t not in model_type_map]
+    assert (
+        not missing_types
+    ), f"Some types are not in the pre-trained model: {list(missing_types)} !"
+    idx_type_map = sorter[np.searchsorted(model_type_map, new_type_map, sorter=sorter)]
+    log.info(
+        f"Change output bias of {new_type_map!s} "
+        f"from {to_numpy_array(old_bias[idx_type_map]).reshape(-1)!s} "
+        f"to {to_numpy_array(new_bias[idx_type_map]).reshape(-1)!s}."
+    )
+    return _model