Merge branch 'devel' into devel

.github/workflows/build_wheel.yml

@@ -28,7 +28,7 @@ jobs:
       - name: Build wheels
         env:
           CIBW_BUILD: "cp36-* cp37-* cp38-* cp39-* cp310-*"
-          CIBW_MANYLINUX_X86_64_IMAGE: ghcr.io/deepmodeling/manylinux2010_x86_64_tensorflow
+          CIBW_MANYLINUX_X86_64_IMAGE: ghcr.io/deepmodeling/manylinux2014_x86_64_tensorflow
           CIBW_BEFORE_BUILD: pip install tensorflow
           CIBW_SKIP: "*-win32 *-manylinux_i686 *-musllinux*"
         run: |

deepmd/common.py

@@ -448,28 +448,6 @@ def expand_sys_str(root_dir: Union[str, Path]) -> List[str]:
     return matches
 
 
-def docstring_parameter(*sub: Tuple[str, ...]):
-    """Add parameters to object docstring.
-
-    Parameters
-    ----------
-    sub: Tuple[str, ...]
-        list of strings that will be inserted into prepared locations in docstring.
-
-    Notes
-    -----
-    Can be used on both object and classes.
-    """
-
-    @wraps
-    def dec(obj: "_OBJ") -> "_OBJ":
-        if obj.__doc__ is not None:
-            obj.__doc__ = obj.__doc__.format(*sub)
-        return obj
-
-    return dec
-
-
 def get_np_precision(precision: "_PRECISION") -> np.dtype:
     """Get numpy precision constant from string.
 

deepmd/descriptor/se_a.py

@@ -3,8 +3,7 @@
 from typing import Tuple, List, Dict, Any
 
 from deepmd.env import tf
-from deepmd.common import get_activation_func, get_precision, ACTIVATION_FN_DICT, PRECISION_DICT, docstring_parameter, cast_precision
-from deepmd.utils.argcheck import list_to_doc
+from deepmd.common import get_activation_func, get_precision, cast_precision
 from deepmd.env import GLOBAL_TF_FLOAT_PRECISION
 from deepmd.env import GLOBAL_NP_FLOAT_PRECISION
 from deepmd.env import op_module
@@ -91,9 +90,9 @@ class DescrptSeA (DescrptSe):
     set_davg_zero
             Set the shift of embedding net input to zero.
     activation_function
-            The activation function in the embedding net. Supported options are {0}
+            The activation function in the embedding net. Supported options are |ACTIVATION_FN|
     precision
-            The precision of the embedding net parameters. Supported options are {1}
+            The precision of the embedding net parameters. Supported options are |PRECISION|
     uniform_seed
             Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
     
@@ -104,7 +103,6 @@ class DescrptSeA (DescrptSe):
        systems. In Proceedings of the 32nd International Conference on Neural Information Processing
        Systems (NIPS'18). Curran Associates Inc., Red Hook, NY, USA, 4441–4451.
     """
-    @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__ (self, 
                   rcut: float,
                   rcut_smth: float,
@@ -642,7 +640,7 @@ def _compute_dstats_sys_smth (self,
 
     def _compute_std (self,sumv2, sumv, sumn) :
         if sumn == 0:
-            return 1e-2
+            return 1. / self.rcut_r
         val = np.sqrt(sumv2/sumn - np.multiply(sumv/sumn, sumv/sumn))
         if np.abs(val) < 1e-2:
             val = 1e-2

deepmd/descriptor/se_a_ef.py

@@ -2,8 +2,7 @@
 from typing import Tuple, List
 
 from deepmd.env import tf
-from deepmd.common import add_data_requirement,get_activation_func, get_precision, ACTIVATION_FN_DICT, PRECISION_DICT, docstring_parameter
-from deepmd.utils.argcheck import list_to_doc
+from deepmd.common import add_data_requirement
 from deepmd.utils.sess import run_sess
 from deepmd.env import GLOBAL_TF_FLOAT_PRECISION
 from deepmd.env import GLOBAL_NP_FLOAT_PRECISION
@@ -43,13 +42,12 @@ class DescrptSeAEf (Descriptor):
     set_davg_zero
             Set the shift of embedding net input to zero.
     activation_function
-            The activation function in the embedding net. Supported options are {0}
+            The activation function in the embedding net. Supported options are |ACTIVATION_FN|
     precision
-            The precision of the embedding net parameters. Supported options are {1}
+            The precision of the embedding net parameters. Supported options are |PRECISION|
     uniform_seed
             Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
     """
-    @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__(self,
                  rcut: float,
                  rcut_smth: float,

deepmd/descriptor/se_r.py

@@ -2,8 +2,7 @@
 from typing import Tuple, List
 
 from deepmd.env import tf
-from deepmd.common import get_activation_func, get_precision, ACTIVATION_FN_DICT, PRECISION_DICT, docstring_parameter, cast_precision
-from deepmd.utils.argcheck import list_to_doc
+from deepmd.common import get_activation_func, get_precision, cast_precision
 from deepmd.env import GLOBAL_TF_FLOAT_PRECISION
 from deepmd.env import GLOBAL_NP_FLOAT_PRECISION
 from deepmd.env import op_module
@@ -46,13 +45,12 @@ class DescrptSeR (DescrptSe):
             The excluded pairs of types which have no interaction with each other.
             For example, `[[0, 1]]` means no interaction between type 0 and type 1.
     activation_function
-            The activation function in the embedding net. Supported options are {0}
+            The activation function in the embedding net. Supported options are |ACTIVATION_FN|
     precision
-            The precision of the embedding net parameters. Supported options are {1}
+            The precision of the embedding net parameters. Supported options are |PRECISION|
     uniform_seed
             Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
     """
-    @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__ (self, 
                   rcut: float,
                   rcut_smth: float,

deepmd/descriptor/se_t.py

@@ -2,8 +2,7 @@
 from typing import Tuple, List
 
 from deepmd.env import tf
-from deepmd.common import get_activation_func, get_precision, ACTIVATION_FN_DICT, PRECISION_DICT, docstring_parameter, cast_precision
-from deepmd.utils.argcheck import list_to_doc
+from deepmd.common import get_activation_func, get_precision, cast_precision
 from deepmd.env import GLOBAL_TF_FLOAT_PRECISION
 from deepmd.env import GLOBAL_NP_FLOAT_PRECISION
 from deepmd.env import op_module
@@ -44,13 +43,12 @@ class DescrptSeT (DescrptSe):
     set_davg_zero
             Set the shift of embedding net input to zero.
     activation_function
-            The activation function in the embedding net. Supported options are {0}
+            The activation function in the embedding net. Supported options are |ACTIVATION_FN|
     precision
-            The precision of the embedding net parameters. Supported options are {1}
+            The precision of the embedding net parameters. Supported options are |PRECISION|
     uniform_seed
             Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
     """
-    @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__ (self, 
                   rcut: float,
                   rcut_smth: float,

deepmd/fit/dipole.py

@@ -3,8 +3,7 @@
 from typing import Tuple, List
 
 from deepmd.env import tf
-from deepmd.common import add_data_requirement, get_activation_func, get_precision, ACTIVATION_FN_DICT, PRECISION_DICT, docstring_parameter, cast_precision
-from deepmd.utils.argcheck import list_to_doc
+from deepmd.common import add_data_requirement, get_activation_func, get_precision, cast_precision
 from deepmd.utils.network import one_layer, one_layer_rand_seed_shift
 from deepmd.utils.graph import get_fitting_net_variables_from_graph_def
 from deepmd.descriptor import DescrptSeA
@@ -31,13 +30,12 @@ class DipoleFittingSeA (Fitting) :
     seed : int
             Random seed for initializing the network parameters.
     activation_function : str
-            The activation function in the embedding net. Supported options are {0}
+            The activation function in the embedding net. Supported options are |ACTIVATION_FN|
     precision : str
-            The precision of the embedding net parameters. Supported options are {1}        
+            The precision of the embedding net parameters. Supported options are |PRECISION|
     uniform_seed
             Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
     """
-    @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__ (self, 
                   descrpt : tf.Tensor,
                   neuron : List[int] = [120,120,120], 

deepmd/fit/ener.py

@@ -4,8 +4,7 @@
 from packaging.version import Version
 
 from deepmd.env import tf
-from deepmd.common import add_data_requirement, get_activation_func, get_precision, ACTIVATION_FN_DICT, PRECISION_DICT, docstring_parameter, cast_precision
-from deepmd.utils.argcheck import list_to_doc
+from deepmd.common import add_data_requirement, get_activation_func, get_precision, cast_precision
 from deepmd.utils.network import one_layer_rand_seed_shift
 from deepmd.utils.network import one_layer as one_layer_deepmd
 from deepmd.utils.type_embed import embed_atom_type
@@ -76,13 +75,12 @@ class EnerFitting (Fitting):
     atom_ener
             Specifying atomic energy contribution in vacuum. The `set_davg_zero` key in the descrptor should be set.
     activation_function
-            The activation function :math:`\boldsymbol{\phi}` in the embedding net. Supported options are {0}
+            The activation function :math:`\boldsymbol{\phi}` in the embedding net. Supported options are |ACTIVATION_FN|
     precision
-            The precision of the embedding net parameters. Supported options are {1}                
+            The precision of the embedding net parameters. Supported options are |PRECISION|
     uniform_seed
             Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
     """
-    @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__ (self, 
                   descrpt : tf.Tensor,
                   neuron : List[int] = [120,120,120],

deepmd/fit/polar.py

@@ -3,8 +3,7 @@
 from typing import Tuple, List
 
 from deepmd.env import tf
-from deepmd.common import add_data_requirement, cast_precision, get_activation_func, get_precision, ACTIVATION_FN_DICT, PRECISION_DICT, docstring_parameter
-from deepmd.utils.argcheck import list_to_doc
+from deepmd.common import add_data_requirement, cast_precision, get_activation_func, get_precision
 from deepmd.utils.network import one_layer, one_layer_rand_seed_shift
 from deepmd.utils.graph import get_fitting_net_variables_from_graph_def
 from deepmd.descriptor import DescrptLocFrame
@@ -105,8 +104,33 @@ def build (self,
 class PolarFittingSeA (Fitting) :
     """
     Fit the atomic polarizability with descriptor se_a
+
+    Parameters
+    ----------
+    descrpt : tf.Tensor
+            The descrptor
+    neuron : List[int]
+            Number of neurons in each hidden layer of the fitting net
+    resnet_dt : bool
+            Time-step `dt` in the resnet construction:
+            y = x + dt * \phi (Wx + b)
+    sel_type : List[int]
+            The atom types selected to have an atomic polarizability prediction. If is None, all atoms are selected.
+    fit_diag : bool
+            Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.
+    scale : List[float]
+            The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i]
+    diag_shift : List[float]
+            The diagonal part of the polarizability matrix of type i will be shifted by diag_shift[i]. The shift operation is carried out after scale.        
+    seed : int
+            Random seed for initializing the network parameters.
+    activation_function : str
+            The activation function in the embedding net. Supported options are |ACTIVATION_FN|
+    precision : str
+            The precision of the embedding net parameters. Supported options are |PRECISION|
+    uniform_seed
+            Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
     """
-    @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__ (self, 
                   descrpt : tf.Tensor,
                   neuron : List[int] = [120,120,120],
@@ -123,32 +147,6 @@ def __init__ (self,
     ) -> None:
         """
         Constructor
-
-        Parameters
-        ----------
-        descrpt : tf.Tensor
-                The descrptor
-        neuron : List[int]
-                Number of neurons in each hidden layer of the fitting net
-        resnet_dt : bool
-                Time-step `dt` in the resnet construction:
-                y = x + dt * \phi (Wx + b)
-        sel_type : List[int]
-                The atom types selected to have an atomic polarizability prediction. If is None, all atoms are selected.
-        fit_diag : bool
-                Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.
-        scale : List[float]
-                The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i]
-        diag_shift : List[float]
-                The diagonal part of the polarizability matrix of type i will be shifted by diag_shift[i]. The shift operation is carried out after scale.        
-        seed : int
-                Random seed for initializing the network parameters.
-        activation_function : str
-                The activation function in the embedding net. Supported options are {0}
-        precision : str
-                The precision of the embedding net parameters. Supported options are {1}                
-        uniform_seed
-                Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
         """
         if not isinstance(descrpt, DescrptSeA) :
             raise RuntimeError('PolarFittingSeA only supports DescrptSeA')
@@ -431,11 +429,10 @@ class GlobalPolarFittingSeA () :
     seed : int
             Random seed for initializing the network parameters.
     activation_function : str
-            The activation function in the embedding net. Supported options are {0}
+            The activation function in the embedding net. Supported options are |ACTIVATION_FN|
     precision : str
-            The precision of the embedding net parameters. Supported options are {1}    
+            The precision of the embedding net parameters. Supported options are |PRECISION|
     """
-    @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__ (self, 
                   descrpt : tf.Tensor,
                   neuron : List[int] = [120,120,120],

deepmd/fit/wfc.py

@@ -3,8 +3,7 @@
 from typing import Tuple, List
 
 from deepmd.env import tf
-from deepmd.common import ClassArg, add_data_requirement, get_activation_func, get_precision, ACTIVATION_FN_DICT, PRECISION_DICT, docstring_parameter
-from deepmd.utils.argcheck import list_to_doc
+from deepmd.common import ClassArg, add_data_requirement, get_activation_func, get_precision
 from deepmd.utils.network import one_layer, one_layer_rand_seed_shift
 from deepmd.descriptor import DescrptLocFrame
 

deepmd/train/__init__.py

@@ -0,0 +1 @@
+

deepmd/utils/argcheck.py

@@ -75,8 +75,13 @@ def descrpt_some_descrpt_args():
             alias = tuple(alias)
         return self.__plugin.register((name, alias))
 
-    def get_all_argument(self) -> List[Argument]:
+    def get_all_argument(self, exclude_hybrid: bool = False) -> List[Argument]:
         """Get all arguments.
+
+        Parameters
+        ----------
+        exclude_hybrid : bool
+            exclude hybrid descriptor to prevent circular calls
         
         Returns
         -------
@@ -85,6 +90,8 @@ def get_all_argument(self) -> List[Argument]:
         """
         arguments = []
         for (name, alias), metd in self.__plugin.plugins.items():
+            if exclude_hybrid and name == "hybrid":
+                continue
             arguments.append(Argument(name=name, dtype=dict, sub_fields=metd(), alias=alias))
         return arguments
 
@@ -232,7 +239,7 @@ def descrpt_hybrid_args():
     ]
 
 
-def descrpt_variant_type_args():
+def descrpt_variant_type_args(exclude_hybrid: bool = False) -> Variant:
     link_lf = make_link('loc_frame', 'model/descriptor[loc_frame]')
     link_se_e2_a = make_link('se_e2_a', 'model/descriptor[se_e2_a]')
     link_se_e2_r = make_link('se_e2_r', 'model/descriptor[se_e2_r]')

deepmd/utils/type_embed.py

@@ -10,8 +10,7 @@
 from deepmd.utils.network import  embedding_net
 
 from deepmd.utils.graph import get_type_embedding_net_variables_from_graph_def
-from deepmd.common import get_activation_func, get_precision, ACTIVATION_FN_DICT, PRECISION_DICT, docstring_parameter, get_np_precision
-from deepmd.utils.argcheck import list_to_doc
+from deepmd.common import get_activation_func, get_precision
 
 
 def embed_atom_type(
@@ -64,17 +63,16 @@ class TypeEmbedNet():
             Time-step `dt` in the resnet construction:
             y = x + dt * \phi (Wx + b)
     activation_function
-            The activation function in the embedding net. Supported options are {0}
+            The activation function in the embedding net. Supported options are |ACTIVATION_FN|
     precision
-            The precision of the embedding net parameters. Supported options are {1}        
+            The precision of the embedding net parameters. Supported options are |PRECISION| 
     trainable
             If the weights of embedding net are trainable.
     seed
             Random seed for initializing the network parameters.
     uniform_seed
             Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
     """
-    @docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
     def __init__(
             self,
             neuron: List[int]=[],