Merge devel into master

.github/workflows/test_python.yml

@@ -71,4 +71,9 @@ jobs:
         CXX: g++-${{ matrix.gcc }}
         TENSORFLOW_VERSION: ${{ matrix.tf }}
     - run: dp --version
+    - name: Prepare parallel runtime
+      if: ${{ matrix.tf == '' }}
+      run: |
+        sudo apt install libopenmpi-dev openmpi-bin
+        HOROVOD_WITHOUT_GLOO=1 HOROVOD_WITH_TENSORFLOW=1 pip install horovod mpi4py
     - run: pytest --cov=deepmd source/tests && codecov

deepmd/descriptor/descriptor.py

@@ -0,0 +1,344 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Tuple
+
+import numpy as np
+from deepmd.env import tf
+
+
+class Descriptor(ABC):
+    r"""The abstract class for descriptors. All specific descriptors should
+    be based on this class.
+
+    The descriptor :math:`\mathcal{D}` describes the environment of an atom,
+    which should be a function of coordinates and types of its neighbour atoms.
+
+    Notes
+    -----
+    Only methods and attributes defined in this class are generally public,
+    that can be called by other classes.
+    """
+
+    @abstractmethod
+    def get_rcut(self) -> float:
+        """
+        Returns the cut-off radius.
+
+        Returns
+        -------
+        float
+            the cut-off radius
+
+        Notes
+        -----
+        This method must be implemented, as it's called by other classes.
+        """
+
+    @abstractmethod
+    def get_ntypes(self) -> int:
+        """
+        Returns the number of atom types.
+
+        Returns
+        -------
+        int
+            the number of atom types
+
+        Notes
+        -----
+        This method must be implemented, as it's called by other classes.
+        """
+
+    @abstractmethod
+    def get_dim_out(self) -> int:
+        """
+        Returns the output dimension of this descriptor.
+
+        Returns
+        -------
+        int
+            the output dimension of this descriptor
+
+        Notes
+        -----
+        This method must be implemented, as it's called by other classes.
+        """
+
+    def get_dim_rot_mat_1(self) -> int:
+        """
+        Returns the first dimension of the rotation matrix. The rotation is of shape
+        dim_1 x 3
+
+        Returns
+        -------
+        int
+            the first dimension of the rotation matrix
+        """
+        # TODO: I think this method should be implemented as it's called by dipole and
+        # polar fitting network. However, currently not all descriptors have this
+        # method.
+        raise NotImplementedError
+
+    def get_nlist(self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]:
+        """
+        Returns neighbor information.
+
+        Returns
+        -------
+        nlist : tf.Tensor
+            Neighbor list
+        rij : tf.Tensor
+            The relative distance between the neighbor and the center atom.
+        sel_a : list[int]
+            The number of neighbors with full information
+        sel_r : list[int]
+            The number of neighbors with only radial information
+        """
+        # TODO: I think this method should be implemented as it's called by energy
+        # model. However, se_ar and hybrid doesn't have this method.
+        raise NotImplementedError
+
+    @abstractmethod
+    def compute_input_stats(self,
+                            data_coord: List[np.ndarray],
+                            data_box: List[np.ndarray],
+                            data_atype: List[np.ndarray],
+                            natoms_vec: List[np.ndarray],
+                            mesh: List[np.ndarray],
+                            input_dict: Dict[str, List[np.ndarray]]
+                            ) -> None:
+        """
+        Compute the statisitcs (avg and std) of the training data. The input will be
+        normalized by the statistics.
+
+        Parameters
+        ----------
+        data_coord : list[np.ndarray]
+            The coordinates. Can be generated by
+            :meth:`deepmd.model.model_stat.make_stat_input`
+        data_box : list[np.ndarray]
+            The box. Can be generated by
+            :meth:`deepmd.model.model_stat.make_stat_input`
+        data_atype : list[np.ndarray]
+            The atom types. Can be generated by :meth:`deepmd.model.model_stat.make_stat_input`
+        natoms_vec : list[np.ndarray]
+            The vector for the number of atoms of the system and different types of
+            atoms. Can be generated by :meth:`deepmd.model.model_stat.make_stat_input`
+        mesh : list[np.ndarray]
+            The mesh for neighbor searching. Can be generated by
+            :meth:`deepmd.model.model_stat.make_stat_input`
+        input_dict : dict[str, list[np.ndarray]]
+            Dictionary for additional input
+
+        Notes
+        -----
+        This method must be implemented, as it's called by other classes.
+        """
+
+    @abstractmethod
+    def build(self,
+              coord_: tf.Tensor,
+              atype_: tf.Tensor,
+              natoms: tf.Tensor,
+              box_: tf.Tensor,
+              mesh: tf.Tensor,
+              input_dict: Dict[str, Any],
+              reuse: bool = None,
+              suffix: str = '',
+              ) -> tf.Tensor:
+        """
+        Build the computational graph for the descriptor.
+
+        Parameters
+        ----------
+        coord_ : tf.Tensor
+            The coordinate of atoms
+        atype_ : tf.Tensor
+            The type of atoms
+        natoms : tf.Tensor
+            The number of atoms. This tensor has the length of Ntypes + 2
+            natoms[0]: number of local atoms
+            natoms[1]: total number of atoms held by this processor
+            natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
+        box : tf.Tensor
+            The box of frames
+        mesh : tf.Tensor
+            For historical reasons, only the length of the Tensor matters.
+            if size of mesh == 6, pbc is assumed.
+            if size of mesh == 0, no-pbc is assumed.
+        input_dict : dict[str, Any]
+            Dictionary for additional inputs
+        reuse : bool, optional
+            The weights in the networks should be reused when get the variable.
+        suffix : str, optional
+            Name suffix to identify this descriptor
+
+        Returns
+        -------
+        descriptor: tf.Tensor
+            The output descriptor
+
+        Notes
+        -----
+        This method must be implemented, as it's called by other classes.
+        """
+
+    def enable_compression(self,
+                           min_nbor_dist: float,
+                           model_file: str = 'frozon_model.pb',
+                           table_extrapolate: float = 5.,
+                           table_stride_1: float = 0.01,
+                           table_stride_2: float = 0.1,
+                           check_frequency: int = -1,
+                           suffix: str = "",
+                           ) -> None:
+        """
+        Reveive the statisitcs (distance, max_nbor_size and env_mat_range) of the
+        training data.
+
+        Parameters
+        ----------
+        min_nbor_dist : float
+                The nearest distance between atoms
+        model_file : str, default: 'frozon_model.pb'
+                The original frozen model, which will be compressed by the program
+        table_extrapolate : float, default: 5.
+                The scale of model extrapolation
+        table_stride_1 : float, default: 0.01
+                The uniform stride of the first table
+        table_stride_2 : float, default: 0.1
+                The uniform stride of the second table
+        check_frequency : int, default: -1
+                The overflow check frequency
+        suffix : str, optional
+                The suffix of the scope
+
+        Notes
+        -----
+        This method is called by others when the descriptor supported compression.
+        """
+        raise NotImplementedError(
+            "Descriptor %s doesn't support compression!" % type(self).__name__)
+
+    @abstractmethod
+    def prod_force_virial(self,
+                          atom_ener: tf.Tensor,
+                          natoms: tf.Tensor
+                          ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
+        """
+        Compute force and virial.
+
+        Parameters
+        ----------
+        atom_ener : tf.Tensor
+            The atomic energy
+        natoms : tf.Tensor
+            The number of atoms. This tensor has the length of Ntypes + 2
+            natoms[0]: number of local atoms
+            natoms[1]: total number of atoms held by this processor
+            natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
+
+        Returns
+        -------
+        force : tf.Tensor
+            The force on atoms
+        virial : tf.Tensor
+            The total virial
+        atom_virial : tf.Tensor
+            The atomic virial
+        """
+
+    def get_feed_dict(self,
+                      coord_: tf.Tensor,
+                      atype_: tf.Tensor,
+                      natoms: tf.Tensor,
+                      box: tf.Tensor,
+                      mesh: tf.Tensor
+                      ) -> Dict[str, tf.Tensor]:
+        """
+        Generate the feed_dict for current descriptor
+
+        Parameters
+        ----------
+        coord_ : tf.Tensor
+            The coordinate of atoms
+        atype_ : tf.Tensor
+            The type of atoms
+        natoms : tf.Tensor
+            The number of atoms. This tensor has the length of Ntypes + 2
+            natoms[0]: number of local atoms
+            natoms[1]: total number of atoms held by this processor
+            natoms[i]: 2 <= i < Ntypes+2, number of type i atoms
+        box : tf.Tensor
+            The box. Can be generated by deepmd.model.make_stat_input
+        mesh : tf.Tensor
+            For historical reasons, only the length of the Tensor matters.
+            if size of mesh == 6, pbc is assumed. 
+            if size of mesh == 0, no-pbc is assumed. 
+
+        Returns
+        -------
+        feed_dict : dict[str, tf.Tensor]
+            The output feed_dict of current descriptor
+        """
+        feed_dict = {
+            't_coord:0'  :coord_,
+            't_type:0'   :atype_,
+            't_natoms:0' :natoms,
+            't_box:0'    :box,
+            't_mesh:0'   :mesh
+        }
+        return feed_dict
+
+    def init_variables(self,
+                       model_file: str,
+                       suffix : str = "",
+    ) -> None:
+        """
+        Init the embedding net variables with the given dict
+
+        Parameters
+        ----------
+        model_file : str
+            The input model file
+        suffix : str, optional
+            The suffix of the scope
+
+        Notes
+        -----
+        This method is called by others when the descriptor supported initialization from the given variables.
+        """
+        raise NotImplementedError(
+            "Descriptor %s doesn't support initialization from the given variables!" % type(self).__name__)
+
+    def get_tensor_names(self, suffix : str = "") -> Tuple[str]:
+        """Get names of tensors.
+
+        Parameters
+        ----------
+        suffix : str
+            The suffix of the scope
+
+        Returns
+        -------
+        Tuple[str]
+            Names of tensors
+        """
+        raise NotImplementedError("Descriptor %s doesn't support this property!" % type(self).__name__)
+
+    def pass_tensors_from_frz_model(self,
+                                    *tensors : tf.Tensor,
+    ) -> None:
+        """
+        Pass the descrpt_reshape tensor as well as descrpt_deriv tensor from the frz graph_def
+
+        Parameters
+        ----------
+        *tensors : tf.Tensor
+            passed tensors
+
+        Notes
+        -----
+        The number of parameters in the method must be equal to the numbers of returns in
+        :meth:`get_tensor_names`.
+        """
+        raise NotImplementedError("Descriptor %s doesn't support this method!" % type(self).__name__)