refact: the DPA2 descriptor (#3758)

- Refact the DPA2 descriptor in PyTorch with clearer interface
- Support residual
- Remove bn
- Add numpy implement

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

- **New Features**
- Added a new descriptor class `DescrptDPA2` implementing DPA-2
functionality for computing descriptors and representations based on
input coordinates and atom types.
- Expanded supported backends for DPA-2 descriptor to include DP in
addition to PyTorch.

- **Documentation**
- Updated the supported backends information in the documentation for
the DPA-2 descriptor to reflect the addition of DP backend support.
- Added a reference to the model implementation and a training example
link in the DPA-2 descriptor documentation.

- **Tests**
- Introduced test cases for the `DescrptDPA2` class in different
frameworks like TensorFlow, PyTorch, and DeepMD to cover various
parameters and configurations.
- Validated the functionality of the `DescrptDPA2` descriptor class for
deep learning models in the test case class `TestDescrptDPA2`.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

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

deepmd/dpmodel/descriptor/__init__.py

@@ -2,6 +2,9 @@
 from .dpa1 import (
     DescrptDPA1,
 )
+from .dpa2 import (
+    DescrptDPA2,
+)
 from .hybrid import (
     DescrptHybrid,
 )
@@ -19,6 +22,7 @@
     "DescrptSeA",
     "DescrptSeR",
     "DescrptDPA1",
+    "DescrptDPA2",
     "DescrptHybrid",
     "make_base_descriptor",
 ]

deepmd/dpmodel/descriptor/descriptor.py

@@ -0,0 +1,127 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import logging
+from abc import (
+    ABC,
+    abstractmethod,
+)
+from typing import (
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Union,
+)
+
+import numpy as np
+
+from deepmd.utils.env_mat_stat import (
+    StatItem,
+)
+from deepmd.utils.path import (
+    DPPath,
+)
+from deepmd.utils.plugin import (
+    make_plugin_registry,
+)
+
+log = logging.getLogger(__name__)
+
+
+class DescriptorBlock(ABC, make_plugin_registry("DescriptorBlock")):
+    """The building block of descriptor.
+    Given the input descriptor, provide with the atomic coordinates,
+    atomic types and neighbor list, calculate the new descriptor.
+    """
+
+    local_cluster = False
+
+    def __new__(cls, *args, **kwargs):
+        if cls is DescriptorBlock:
+            try:
+                descrpt_type = kwargs["type"]
+            except KeyError:
+                raise KeyError("the type of DescriptorBlock should be set by `type`")
+            cls = cls.get_class_by_type(descrpt_type)
+        return super().__new__(cls)
+
+    @abstractmethod
+    def get_rcut(self) -> float:
+        """Returns the cut-off radius."""
+        pass
+
+    @abstractmethod
+    def get_nsel(self) -> int:
+        """Returns the number of selected atoms in the cut-off radius."""
+        pass
+
+    @abstractmethod
+    def get_sel(self) -> List[int]:
+        """Returns the number of selected atoms for each type."""
+        pass
+
+    @abstractmethod
+    def get_ntypes(self) -> int:
+        """Returns the number of element types."""
+        pass
+
+    @abstractmethod
+    def get_dim_out(self) -> int:
+        """Returns the output dimension."""
+        pass
+
+    @abstractmethod
+    def get_dim_in(self) -> int:
+        """Returns the input dimension."""
+        pass
+
+    @abstractmethod
+    def get_dim_emb(self) -> int:
+        """Returns the embedding dimension."""
+        pass
+
+    def compute_input_stats(
+        self,
+        merged: Union[Callable[[], List[dict]], List[dict]],
+        path: Optional[DPPath] = None,
+    ):
+        """
+        Compute the input statistics (e.g. mean and stddev) for the descriptors 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.
+        path : Optional[DPPath]
+            The path to the stat file.
+
+        """
+        raise NotImplementedError
+
+    def get_stats(self) -> Dict[str, StatItem]:
+        """Get the statistics of the descriptor."""
+        raise NotImplementedError
+
+    def share_params(self, base_class, shared_level, resume=False):
+        """
+        Share the parameters of self to the base_class with shared_level during multitask training.
+        If not start from checkpoint (resume is False),
+        some seperated parameters (e.g. mean and stddev) will be re-calculated across different classes.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def call(
+        self,
+        nlist: np.ndarray,
+        extended_coord: np.ndarray,
+        extended_atype: np.ndarray,
+        extended_atype_embd: Optional[np.ndarray] = None,
+        mapping: Optional[np.ndarray] = None,
+    ):
+        """Calculate DescriptorBlock."""
+        pass