# SPDX-License-Identifier: LGPL-3.0-or-later
from typing import (
List,
Optional,
Tuple,
)
import numpy as np
from deepmd.common import (
add_data_requirement,
)
from deepmd.env import (
GLOBAL_NP_FLOAT_PRECISION,
GLOBAL_TF_FLOAT_PRECISION,
default_tf_session_config,
op_module,
tf,
)
from deepmd.utils.sess import (
run_sess,
)
from .descriptor import (
Descriptor,
)
from .se import (
DescrptSe,
)
from .se_a import (
DescrptSeA,
)
[docs]@Descriptor.register("se_a_ef")
class DescrptSeAEf(DescrptSe):
r"""Smooth edition descriptor with Ef.
Parameters
----------
rcut
The cut-off radius
rcut_smth
From where the environment matrix should be smoothed
sel : list[str]
sel[i] specifies the maxmum number of type i atoms in the cut-off radius
neuron : list[int]
Number of neurons in each hidden layers of the embedding net
axis_neuron
Number of the axis neuron (number of columns of the sub-matrix of the embedding matrix)
resnet_dt
Time-step `dt` in the resnet construction:
y = x + dt * \phi (Wx + b)
trainable
If the weights of embedding net are trainable.
seed
Random seed for initializing the network parameters.
type_one_side
Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
exclude_types : List[List[int]]
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.
set_davg_zero
Set the shift of embedding net input to zero.
activation_function
The activation function in the embedding net. Supported options are |ACTIVATION_FN|
precision
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
"""
def __init__(
self,
rcut: float,
rcut_smth: float,
sel: List[str],
neuron: List[int] = [24, 48, 96],
axis_neuron: int = 8,
resnet_dt: bool = False,
trainable: bool = True,
seed: Optional[int] = None,
type_one_side: bool = True,
exclude_types: List[List[int]] = [],
set_davg_zero: bool = False,
activation_function: str = "tanh",
precision: str = "default",
uniform_seed=False,
**kwargs,
) -> None:
"""Constructor."""
self.descrpt_para = DescrptSeAEfLower(
op_module.descrpt_se_a_ef_para,
rcut,
rcut_smth,
sel,
neuron,
axis_neuron,
resnet_dt,
trainable,
seed,
type_one_side,
exclude_types,
set_davg_zero,
activation_function,
precision,
uniform_seed,
)
self.descrpt_vert = DescrptSeAEfLower(
op_module.descrpt_se_a_ef_vert,
rcut,
rcut_smth,
sel,
neuron,
axis_neuron,
resnet_dt,
trainable,
seed,
type_one_side,
exclude_types,
set_davg_zero,
activation_function,
precision,
uniform_seed,
)
[docs] def get_rcut(self) -> float:
"""Returns the cut-off radius."""
return self.descrpt_vert.rcut_r
[docs] def get_ntypes(self) -> int:
"""Returns the number of atom types."""
return self.descrpt_vert.ntypes
[docs] def get_dim_out(self) -> int:
"""Returns the output dimension of this descriptor."""
return self.descrpt_vert.get_dim_out() + self.descrpt_para.get_dim_out()
[docs] 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."""
return self.descrpt_vert.filter_neuron[-1]
[docs] def get_rot_mat(self) -> tf.Tensor:
"""Get rotational matrix."""
return self.qmat
[docs] def get_nlist(self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]:
"""Returns neighbor information.
Returns
-------
nlist
Neighbor list
rij
The relative distance between the neighbor and the center atom.
sel_a
The number of neighbors with full information
sel_r
The number of neighbors with only radial information
"""
return (
self.descrpt_vert.nlist,
self.descrpt_vert.rij,
self.descrpt_vert.sel_a,
self.descrpt_vert.sel_r,
)
[docs] def build(
self,
coord_: tf.Tensor,
atype_: tf.Tensor,
natoms: tf.Tensor,
box_: tf.Tensor,
mesh: tf.Tensor,
input_dict: dict,
reuse: Optional[bool] = None,
suffix: str = "",
) -> tf.Tensor:
"""Build the computational graph for the descriptor.
Parameters
----------
coord_
The coordinate of atoms
atype_
The type of atoms
natoms
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 the system
mesh
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
Dictionary for additional inputs. Should have 'efield'.
reuse
The weights in the networks should be reused when get the variable.
suffix
Name suffix to identify this descriptor
Returns
-------
descriptor
The output descriptor
"""
self.dout_vert = self.descrpt_vert.build(
coord_, atype_, natoms, box_, mesh, input_dict
)
self.dout_para = self.descrpt_para.build(
coord_, atype_, natoms, box_, mesh, input_dict, reuse=True
)
coord = tf.reshape(coord_, [-1, natoms[1] * 3])
nframes = tf.shape(coord)[0]
self.dout_vert = tf.reshape(
self.dout_vert, [nframes * natoms[0], self.descrpt_vert.get_dim_out()]
)
self.dout_para = tf.reshape(
self.dout_para, [nframes * natoms[0], self.descrpt_para.get_dim_out()]
)
self.dout = tf.concat([self.dout_vert, self.dout_para], axis=1)
self.dout = tf.reshape(self.dout, [nframes, natoms[0], self.get_dim_out()])
self.qmat = self.descrpt_vert.qmat + self.descrpt_para.qmat
tf.summary.histogram("embedding_net_output", self.dout)
return self.dout
[docs] 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
The atomic energy
natoms
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
The force on atoms
virial
The total virial
atom_virial
The atomic virial
"""
f_vert, v_vert, av_vert = self.descrpt_vert.prod_force_virial(atom_ener, natoms)
f_para, v_para, av_para = self.descrpt_para.prod_force_virial(atom_ener, natoms)
force = f_vert + f_para
virial = v_vert + v_para
atom_vir = av_vert + av_para
return force, virial, atom_vir
[docs]class DescrptSeAEfLower(DescrptSeA):
"""Helper class for implementing DescrptSeAEf."""
def __init__(
self,
op,
rcut: float,
rcut_smth: float,
sel: List[str],
neuron: List[int] = [24, 48, 96],
axis_neuron: int = 8,
resnet_dt: bool = False,
trainable: bool = True,
seed: Optional[int] = None,
type_one_side: bool = True,
exclude_types: List[List[int]] = [],
set_davg_zero: bool = False,
activation_function: str = "tanh",
precision: str = "default",
uniform_seed: bool = False,
) -> None:
DescrptSeA.__init__(
self,
rcut,
rcut_smth,
sel,
neuron,
axis_neuron,
resnet_dt,
trainable,
seed,
type_one_side,
exclude_types,
set_davg_zero,
activation_function,
precision,
uniform_seed,
)
self.sel_a = sel
self.rcut_r = rcut
self.rcut_r_smth = rcut_smth
self.filter_neuron = neuron
self.n_axis_neuron = axis_neuron
self.filter_resnet_dt = resnet_dt
self.seed = seed
self.trainable = trainable
self.op = op
# descrpt config
self.sel_r = [0 for ii in range(len(self.sel_a))]
self.ntypes = len(self.sel_a)
assert self.ntypes == len(self.sel_r)
self.rcut_a = -1
# numb of neighbors and numb of descrptors
self.nnei_a = np.cumsum(self.sel_a)[-1]
self.nnei_r = np.cumsum(self.sel_r)[-1]
self.nnei = self.nnei_a + self.nnei_r
self.ndescrpt_a = self.nnei_a * 4
self.ndescrpt_r = self.nnei_r * 1
self.ndescrpt = self.ndescrpt_a + self.ndescrpt_r
self.useBN = False
self.dstd = None
self.davg = None
add_data_requirement("efield", 3, atomic=True, must=True, high_prec=False)
self.place_holders = {}
avg_zero = np.zeros([self.ntypes, self.ndescrpt]).astype(
GLOBAL_NP_FLOAT_PRECISION
)
std_ones = np.ones([self.ntypes, self.ndescrpt]).astype(
GLOBAL_NP_FLOAT_PRECISION
)
sub_graph = tf.Graph()
with sub_graph.as_default():
name_pfx = "d_sea_ef_"
for ii in ["coord", "box"]:
self.place_holders[ii] = tf.placeholder(
GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + "t_" + ii
)
self.place_holders["type"] = tf.placeholder(
tf.int32, [None, None], name=name_pfx + "t_type"
)
self.place_holders["natoms_vec"] = tf.placeholder(
tf.int32, [self.ntypes + 2], name=name_pfx + "t_natoms"
)
self.place_holders["default_mesh"] = tf.placeholder(
tf.int32, [None], name=name_pfx + "t_mesh"
)
self.place_holders["efield"] = tf.placeholder(
GLOBAL_NP_FLOAT_PRECISION, [None, None], name=name_pfx + "t_efield"
)
self.stat_descrpt, descrpt_deriv, rij, nlist = self.op(
self.place_holders["coord"],
self.place_holders["type"],
self.place_holders["natoms_vec"],
self.place_holders["box"],
self.place_holders["default_mesh"],
self.place_holders["efield"],
tf.constant(avg_zero),
tf.constant(std_ones),
rcut_a=self.rcut_a,
rcut_r=self.rcut_r,
rcut_r_smth=self.rcut_r_smth,
sel_a=self.sel_a,
sel_r=self.sel_r,
)
self.sub_sess = tf.Session(graph=sub_graph, config=default_tf_session_config)
def _normalize_3d(self, a):
na = tf.norm(a, axis=1)
na = tf.tile(tf.reshape(na, [-1, 1]), tf.constant([1, 3]))
return tf.divide(a, na)
[docs] def build(
self, coord_, atype_, natoms, box_, mesh, input_dict, suffix="", reuse=None
):
efield = input_dict["efield"]
davg = self.davg
dstd = self.dstd
with tf.variable_scope("descrpt_attr" + suffix, reuse=reuse):
if davg is None:
davg = np.zeros([self.ntypes, self.ndescrpt])
if dstd is None:
dstd = np.ones([self.ntypes, self.ndescrpt])
t_rcut = tf.constant(
np.max([self.rcut_r, self.rcut_a]),
name="rcut",
dtype=GLOBAL_TF_FLOAT_PRECISION,
)
t_ntypes = tf.constant(self.ntypes, name="ntypes", dtype=tf.int32)
t_ndescrpt = tf.constant(self.ndescrpt, name="ndescrpt", dtype=tf.int32)
t_sel = tf.constant(self.sel_a, name="sel", dtype=tf.int32)
self.t_avg = tf.get_variable(
"t_avg",
davg.shape,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(davg),
)
self.t_std = tf.get_variable(
"t_std",
dstd.shape,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(dstd),
)
coord = tf.reshape(coord_, [-1, natoms[1] * 3])
box = tf.reshape(box_, [-1, 9])
atype = tf.reshape(atype_, [-1, natoms[1]])
efield = tf.reshape(efield, [-1, 3])
efield = self._normalize_3d(efield)
efield = tf.reshape(efield, [-1, natoms[0] * 3])
self.descrpt, self.descrpt_deriv, self.rij, self.nlist = self.op(
coord,
atype,
natoms,
box,
mesh,
efield,
self.t_avg,
self.t_std,
rcut_a=self.rcut_a,
rcut_r=self.rcut_r,
rcut_r_smth=self.rcut_r_smth,
sel_a=self.sel_a,
sel_r=self.sel_r,
)
self.descrpt_reshape = tf.reshape(self.descrpt, [-1, self.ndescrpt])
self.descrpt_reshape = tf.identity(self.descrpt_reshape, name="o_rmat")
self.descrpt_deriv = tf.identity(self.descrpt_deriv, name="o_rmat_deriv")
self.rij = tf.identity(self.rij, name="o_rij")
self.nlist = tf.identity(self.nlist, name="o_nlist")
# only used when tensorboard was set as true
tf.summary.histogram("descrpt", self.descrpt)
tf.summary.histogram("rij", self.rij)
tf.summary.histogram("nlist", self.nlist)
self.dout, self.qmat = self._pass_filter(
self.descrpt_reshape,
atype,
natoms,
input_dict,
suffix=suffix,
reuse=reuse,
trainable=self.trainable,
)
tf.summary.histogram("embedding_net_output", self.dout)
return self.dout
def _compute_dstats_sys_smth(
self, data_coord, data_box, data_atype, natoms_vec, mesh, data_efield
):
dd_all = run_sess(
self.sub_sess,
self.stat_descrpt,
feed_dict={
self.place_holders["coord"]: data_coord,
self.place_holders["type"]: data_atype,
self.place_holders["natoms_vec"]: natoms_vec,
self.place_holders["box"]: data_box,
self.place_holders["default_mesh"]: mesh,
self.place_holders["efield"]: data_efield,
},
)
natoms = natoms_vec
dd_all = np.reshape(dd_all, [-1, self.ndescrpt * natoms[0]])
start_index = 0
sysr = []
sysa = []
sysn = []
sysr2 = []
sysa2 = []
for type_i in range(self.ntypes):
end_index = start_index + self.ndescrpt * natoms[2 + type_i]
dd = dd_all[:, start_index:end_index]
dd = np.reshape(dd, [-1, self.ndescrpt])
start_index = end_index
# compute
dd = np.reshape(dd, [-1, 4])
ddr = dd[:, :1]
dda = dd[:, 1:]
sumr = np.sum(ddr)
suma = np.sum(dda) / 3.0
sumn = dd.shape[0]
sumr2 = np.sum(np.multiply(ddr, ddr))
suma2 = np.sum(np.multiply(dda, dda)) / 3.0
sysr.append(sumr)
sysa.append(suma)
sysn.append(sumn)
sysr2.append(sumr2)
sysa2.append(suma2)
return sysr, sysr2, sysa, sysa2, sysn