feat(pt): consistent fine-tuning with init-model (deepmodeling#3803)

Fix deepmodeling#3747. Fix deepmodeling#3455. 

- Consistent fine-tuning with init-model, now in pt, fine-tuning include
three steps:
1. Change model params (for multitask fine-tuning, random fitting and
type-related params),
2. Init-model, 
3. Change bias

- By default, input will use user input while fine-tuning, instead of
being overwritten by that in the pre-trained model. When adding
“--use-pretrain-script”, user can use that in the pre-trained model.

- Now `type_map` will use that in the user input instead of overwritten
by that in the pre-trained model.

Note:
1. After discussed with @wanghan-iapcm, **behavior of fine-tuning in TF
is kept as before**. If needed in the future, it can be implemented
then.
2. Fine-tuning using DOSModel in PT need to be fixed. (an issue will be
opened, maybe fixed in another PR, cc @anyangml )

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

- **New Features**
- Added support for using model parameters from a pretrained model
script.
- Introduced new methods to handle type-related parameters and
fine-tuning configurations.

- **Documentation**
- Updated documentation to clarify the model section requirements and
the new `--use-pretrain-script` option for fine-tuning.

- **Refactor**
- Simplified and improved the readability of key functions related to
model training and fine-tuning.

- **Tests**
- Added new test methods and utility functions to ensure consistency of
type mapping and parameter updates.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Duo <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Han Wang <[email protected]>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

deepmd/dpmodel/atomic_model/base_atomic_model.py

@@ -20,6 +20,11 @@
     AtomExcludeMask,
     PairExcludeMask,
 )
+from deepmd.utils.finetune import (
+    get_index_between_two_maps,
+    map_atom_exclude_types,
+    map_pair_exclude_types,
+)
 
 from .make_base_atomic_model import (
     make_base_atomic_model,
@@ -113,6 +118,23 @@ def atomic_output_def(self) -> FittingOutputDef:
             ]
         )
 
+    def change_type_map(
+        self, type_map: List[str], model_with_new_type_stat=None
+    ) -> None:
+        """Change the type related params to new ones, according to `type_map` and the original one in the model.
+        If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
+        """
+        remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
+        self.type_map = type_map
+        self.reinit_atom_exclude(
+            map_atom_exclude_types(self.atom_exclude_types, remap_index)
+        )
+        self.reinit_pair_exclude(
+            map_pair_exclude_types(self.pair_exclude_types, remap_index)
+        )
+        self.out_bias = self.out_bias[:, remap_index, :]
+        self.out_std = self.out_std[:, remap_index, :]
+
     def forward_common_atomic(
         self,
         extended_coord: np.ndarray,

deepmd/dpmodel/atomic_model/dp_atomic_model.py

@@ -135,6 +135,24 @@ def forward_atomic(
         )
         return ret
 
+    def change_type_map(
+        self, type_map: List[str], model_with_new_type_stat=None
+    ) -> None:
+        """Change the type related params to new ones, according to `type_map` and the original one in the model.
+        If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
+        """
+        super().change_type_map(
+            type_map=type_map, model_with_new_type_stat=model_with_new_type_stat
+        )
+        self.type_map = type_map
+        self.descriptor.change_type_map(
+            type_map=type_map,
+            model_with_new_type_stat=model_with_new_type_stat.descriptor
+            if model_with_new_type_stat is not None
+            else None,
+        )
+        self.fitting_net.change_type_map(type_map=type_map)
+
     def serialize(self) -> dict:
         dd = super().serialize()
         dd.update(

deepmd/dpmodel/atomic_model/linear_atomic_model.py

@@ -104,6 +104,23 @@ def get_type_map(self) -> List[str]:
         """Get the type map."""
         return self.type_map
 
+    def change_type_map(
+        self, type_map: List[str], model_with_new_type_stat=None
+    ) -> None:
+        """Change the type related params to new ones, according to `type_map` and the original one in the model.
+        If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
+        """
+        super().change_type_map(
+            type_map=type_map, model_with_new_type_stat=model_with_new_type_stat
+        )
+        for ii, model in enumerate(self.models):
+            model.change_type_map(
+                type_map=type_map,
+                model_with_new_type_stat=model_with_new_type_stat.models[ii]
+                if model_with_new_type_stat is not None
+                else None,
+            )
+
     def get_model_rcuts(self) -> List[float]:
         """Get the cut-off radius for each individual models."""
         return [model.get_rcut() for model in self.models]

deepmd/dpmodel/atomic_model/make_base_atomic_model.py

@@ -140,6 +140,12 @@ def serialize(self) -> dict:
         def deserialize(cls, data: dict):
             pass
 
+        @abstractmethod
+        def change_type_map(
+            self, type_map: List[str], model_with_new_type_stat=None
+        ) -> None:
+            pass
+
         def make_atom_mask(
             self,
             atype: t_tensor,

deepmd/dpmodel/atomic_model/pairtab_atomic_model.py

@@ -135,6 +135,18 @@ def has_message_passing(self) -> bool:
         """Returns whether the atomic model has message passing."""
         return False
 
+    def change_type_map(
+        self, type_map: List[str], model_with_new_type_stat=None
+    ) -> None:
+        """Change the type related params to new ones, according to `type_map` and the original one in the model.
+        If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
+        """
+        assert type_map == self.type_map, (
+            "PairTabAtomicModel does not support changing type map now. "
+            "This feature is currently not implemented because it would require additional work to change the tab file. "
+            "We may consider adding this support in the future if there is a clear demand for it."
+        )
+
     def serialize(self) -> dict:
         dd = BaseAtomicModel.serialize(self)
         dd.update(

deepmd/dpmodel/descriptor/descriptor.py

@@ -129,3 +129,37 @@ def call(
     @abstractmethod
     def has_message_passing(self) -> bool:
         """Returns whether the descriptor block has message passing."""
+
+
+def extend_descrpt_stat(des, type_map, des_with_stat=None):
+    r"""
+    Extend the statistics of a descriptor block with types from newly provided `type_map`.
+
+    After extending, the type related dimension of the extended statistics will have a length of
+    `len(old_type_map) + len(type_map)`, where `old_type_map` represents the type map in `des`.
+    The `get_index_between_two_maps()` function can then be used to correctly select statistics for types
+    from `old_type_map` or `type_map`.
+    Positive indices from 0 to `len(old_type_map) - 1` will select old statistics of types in `old_type_map`,
+    while negative indices from `-len(type_map)` to -1 will select new statistics of types in `type_map`.
+
+    Parameters
+    ----------
+    des : DescriptorBlock
+        The descriptor block to be extended.
+    type_map : List[str]
+        The name of each type of atoms to be extended.
+    des_with_stat : DescriptorBlock, Optional
+        The descriptor block has additional statistics of types from newly provided `type_map`.
+        If None, the default statistics will be used.
+        Otherwise, the statistics provided in this DescriptorBlock will be used.
+
+    """
+    if des_with_stat is not None:
+        extend_davg = des_with_stat["davg"]
+        extend_dstd = des_with_stat["dstd"]
+    else:
+        extend_shape = [len(type_map), *list(des["davg"].shape[1:])]
+        extend_davg = np.zeros(extend_shape, dtype=des["davg"].dtype)
+        extend_dstd = np.ones(extend_shape, dtype=des["dstd"].dtype)
+    des["davg"] = np.concatenate([des["davg"], extend_davg], axis=0)
+    des["dstd"] = np.concatenate([des["dstd"], extend_dstd], axis=0)

deepmd/dpmodel/descriptor/dpa1.py

@@ -37,6 +37,10 @@
 from deepmd.utils.data_system import (
     DeepmdDataSystem,
 )
+from deepmd.utils.finetune import (
+    get_index_between_two_maps,
+    map_pair_exclude_types,
+)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -49,6 +53,7 @@
 )
 from .descriptor import (
     DescriptorBlock,
+    extend_descrpt_stat,
 )
 
 
@@ -194,8 +199,6 @@ class DescrptDPA1(NativeOP, BaseDescriptor):
             Whether to use electronic configuration type embedding.
     type_map: List[str], Optional
             A list of strings. Give the name to each type of atoms.
-            Only used if `use_econf_tebd` is `True` in type embedding net.
-
     spin
             (Only support None to keep consistent with other backend references.)
             (Not used in this version. Not-none option is not implemented.)
@@ -327,6 +330,10 @@ def get_ntypes(self) -> int:
         """Returns the number of element types."""
         return self.se_atten.get_ntypes()
 
+    def get_type_map(self) -> List[str]:
+        """Get the name to each type of atoms."""
+        return self.type_map
+
     def get_dim_out(self) -> int:
         """Returns the output dimension."""
         ret = self.se_atten.get_dim_out()
@@ -382,9 +389,41 @@ def set_stat_mean_and_stddev(
         mean: np.ndarray,
         stddev: np.ndarray,
     ) -> None:
+        """Update mean and stddev for descriptor."""
         self.se_atten.mean = mean
         self.se_atten.stddev = stddev
 
+    def get_stat_mean_and_stddev(self) -> Tuple[np.ndarray, np.ndarray]:
+        """Get mean and stddev for descriptor."""
+        return self.se_atten.mean, self.se_atten.stddev
+
+    def change_type_map(
+        self, type_map: List[str], model_with_new_type_stat=None
+    ) -> None:
+        """Change the type related params to new ones, according to `type_map` and the original one in the model.
+        If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
+        """
+        assert (
+            self.type_map is not None
+        ), "'type_map' must be defined when performing type changing!"
+        remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
+        obj = self.se_atten
+        obj.ntypes = len(type_map)
+        self.type_map = type_map
+        self.type_embedding.change_type_map(type_map=type_map)
+        obj.reinit_exclude(map_pair_exclude_types(obj.exclude_types, remap_index))
+        if has_new_type:
+            # the avg and std of new types need to be updated
+            extend_descrpt_stat(
+                obj,
+                type_map,
+                des_with_stat=model_with_new_type_stat.se_atten
+                if model_with_new_type_stat is not None
+                else None,
+            )
+        obj["davg"] = obj["davg"][remap_index]
+        obj["dstd"] = obj["dstd"][remap_index]
+
     def call(
         self,
         coord_ext,

deepmd/dpmodel/descriptor/dpa2.py

@@ -32,6 +32,10 @@
 from deepmd.utils.data_system import (
     DeepmdDataSystem,
 )
+from deepmd.utils.finetune import (
+    get_index_between_two_maps,
+    map_pair_exclude_types,
+)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -42,6 +46,9 @@
 from .base_descriptor import (
     BaseDescriptor,
 )
+from .descriptor import (
+    extend_descrpt_stat,
+)
 from .dpa1 import (
     DescrptBlockSeAtten,
 )
@@ -353,7 +360,6 @@ def __init__(
             Whether to use electronic configuration type embedding.
         type_map : List[str], Optional
             A list of strings. Give the name to each type of atoms.
-            Only used if `use_econf_tebd` is `True` in type embedding net.
 
         Returns
         -------
@@ -501,6 +507,10 @@ def get_ntypes(self) -> int:
         """Returns the number of element types."""
         return self.ntypes
 
+    def get_type_map(self) -> List[str]:
+        """Get the name to each type of atoms."""
+        return self.type_map
+
     def get_dim_out(self) -> int:
         """Returns the output dimension of this descriptor."""
         ret = self.repformers.dim_out
@@ -542,6 +552,47 @@ def share_params(self, base_class, shared_level, resume=False):
         """
         raise NotImplementedError
 
+    def change_type_map(
+        self, type_map: List[str], model_with_new_type_stat=None
+    ) -> None:
+        """Change the type related params to new ones, according to `type_map` and the original one in the model.
+        If there are new types in `type_map`, statistics will be updated accordingly to `model_with_new_type_stat` for these new types.
+        """
+        assert (
+            self.type_map is not None
+        ), "'type_map' must be defined when performing type changing!"
+        remap_index, has_new_type = get_index_between_two_maps(self.type_map, type_map)
+        self.type_map = type_map
+        self.type_embedding.change_type_map(type_map=type_map)
+        self.exclude_types = map_pair_exclude_types(self.exclude_types, remap_index)
+        self.ntypes = len(type_map)
+        repinit = self.repinit
+        repformers = self.repformers
+        if has_new_type:
+            # the avg and std of new types need to be updated
+            extend_descrpt_stat(
+                repinit,
+                type_map,
+                des_with_stat=model_with_new_type_stat.repinit
+                if model_with_new_type_stat is not None
+                else None,
+            )
+            extend_descrpt_stat(
+                repformers,
+                type_map,
+                des_with_stat=model_with_new_type_stat.repformers
+                if model_with_new_type_stat is not None
+                else None,
+            )
+        repinit.ntypes = self.ntypes
+        repformers.ntypes = self.ntypes
+        repinit.reinit_exclude(self.exclude_types)
+        repformers.reinit_exclude(self.exclude_types)
+        repinit["davg"] = repinit["davg"][remap_index]
+        repinit["dstd"] = repinit["dstd"][remap_index]
+        repformers["davg"] = repformers["davg"][remap_index]
+        repformers["dstd"] = repformers["dstd"][remap_index]
+
     @property
     def dim_out(self):
         return self.get_dim_out()
@@ -555,6 +606,23 @@ def compute_input_stats(self, merged: List[dict], path: Optional[DPPath] = None)
         """Update mean and stddev for descriptor elements."""
         raise NotImplementedError
 
+    def set_stat_mean_and_stddev(
+        self,
+        mean: List[np.ndarray],
+        stddev: List[np.ndarray],
+    ) -> None:
+        """Update mean and stddev for descriptor."""
+        for ii, descrpt in enumerate([self.repinit, self.repformers]):
+            descrpt.mean = mean[ii]
+            descrpt.stddev = stddev[ii]
+
+    def get_stat_mean_and_stddev(self) -> Tuple[List[np.ndarray], List[np.ndarray]]:
+        """Get mean and stddev for descriptor."""
+        return [self.repinit.mean, self.repformers.mean], [
+            self.repinit.stddev,
+            self.repformers.stddev,
+        ]
+
     def call(
         self,
         coord_ext: np.ndarray,