# Copyright 2021-2023 @ Shenzhen Bay Laboratory &
# Peking University &
# Huawei Technologies Co., Ltd
#
# This code is a part of MindSPONGE:
# MindSpore Simulation Package tOwards Next Generation molecular modelling.
#
# MindSPONGE is open-source software based on the AI-framework:
# MindSpore (https://www.mindspore.cn/)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
WithEnergyCell
"""
from typing import Union, List, Tuple
import mindspore as ms
import mindspore.numpy as msnp
from mindspore import Tensor
from mindspore import Parameter
from mindspore import ops
from mindspore.ops import functional as F
from mindspore.nn import Cell, CellList
from ...function import Units, get_arguments
from ...partition import NeighbourList
from ...system import Molecule
from ...potential import PotentialCell
from ...potential.bias import Bias
from ...sampling.wrapper import EnergyWrapper
[文档]class WithEnergyCell(Cell):
r"""
Cell that wraps the simulation system with the potential energy function.
This Cell calculates the value of the potential energy of the system at the current coordinates and returns it.
Args:
system(Molecule): Simulation system.
potential(PotentialCell): Potential energy function cell.
bias(Union[Bias, List[Bias]]): Bias potential function cell. Default: ``None``.
cutoff(float): Cut-off distance for neighbour list. If None is given, it will be assigned
as the cutoff value of the of potential energy.
Default: ``None``.
neighbour_list(NeighbourList): Neighbour list. Default: ``None``.
wrapper(EnergyWrapper): Network to wrap and process potential and bias.
Default: ``None``.
kwargs(dict): other args
Inputs:
- **\*inputs** (Tuple(Tensor)) - Tuple of input tensors of 'WithEnergyCell'.
Outputs:
energy, Tensor of shape `(B, 1)`. Data type is float. Total potential energy.
Note:
B: Batchsize, i.e. number of walkers of the simulation.
A: Number of the atoms in the simulation system.
N: Number of the maximum neighbouring atoms.
U: Number of potential energy terms.
V: Number of bias potential terms.
Supported Platforms:
``Ascend`` ``GPU``
"""
def __init__(self,
system: Molecule,
potential: PotentialCell,
bias: Union[Bias, List[Bias]] = None,
cutoff: float = None,
neighbour_list: NeighbourList = None,
wrapper: EnergyWrapper = None,
**kwargs
):
super().__init__(auto_prefix=False)
self._kwargs = get_arguments(locals(), kwargs)
self.system = system
self.potential_function = potential
self.units = Units(self.system.length_unit, self.potential_function.energy_unit)
self.system.units.set_energy_unit(self.energy_unit)
self.bias_function: List[Bias] = None
self._num_biases = 0
self._bias_names = []
if bias is not None:
if isinstance(bias, list):
self._num_biases = len(bias)
self.bias_function = CellList(bias)
elif isinstance(bias, Cell):
self._num_biases = 1
self.bias_function = CellList([bias])
else:
raise TypeError(f'The "bias" must be Cell or list but got: {type(bias)}')
for i in range(self._num_biases):
self._bias_names.append(self.bias_function[i].name)
self.num_walker = self.system.num_walker
self.num_atoms = self.system.num_atoms
self.energy_wrapper = wrapper
if wrapper is None:
self.energy_wrapper = EnergyWrapper(length_unit=self.length_unit, energy_unit=self.energy_unit)
self.wrapper_pace = self.energy_wrapper.update_pace
self.exclude_index = self.potential_function.exclude_index
self.neighbour_list = neighbour_list
if neighbour_list is None:
if cutoff is None and self.potential_function.cutoff is not None:
cutoff = self.potential_function.cutoff
self.neighbour_list = NeighbourList(system, cutoff, exclude_index=self.exclude_index)
else:
self.neighbour_list.set_exclude_index(self.exclude_index)
self.neighbour_index = self.neighbour_list.neighbours
self.neighbour_mask = self.neighbour_list.neighbour_mask
self.num_neighbours = self.neighbour_list.num_neighbours
if self.neighbour_list.cutoff is not None:
if self.potential_function.cutoff is None:
self.potential_function.set_cutoff(self.neighbour_list.cutoff, self.length_unit)
else:
pot_cutoff = self.units.length(self.potential_function.cutoff, self.potential_function.length_unit)
nl_cutoff = self.neighbour_list.cutoff
if self.potential_function.cutoff > self.neighbour_list.cutoff:
raise ValueError(f'The cutoff of the potential function ({pot_cutoff} {self.length_unit}) '
f'cannot be greater than '
f'the cutoff of the neighbour list ({nl_cutoff} {self.length_unit}).')
self.coordinate = self.system.coordinate
self.pbc_box = self.system.pbc_box
self.atom_mass = self.system.atom_mass
self.pbc_box = self.system.pbc_box
self.potential_function.set_pbc(self.pbc_box is not None)
for p in self.potential_function.trainable_params():
p.requires_grad = False
self.potential_function_units = self.potential_function.units
self.length_unit_scale = Tensor(self.units.convert_length_to(
self.potential_function.length_unit), ms.float32)
self.identity = ops.Identity()
self._energies = Parameter(msnp.zeros((self.num_walker, self.num_energies),
dtype=ms.float32), name='energies', requires_grad=False)
bias = msnp.zeros((self.num_walker, 1), dtype=ms.float32)
if self.bias_function is None:
self._biases = None
self._bias = bias
else:
self._biases = Parameter(msnp.zeros((self.num_walker, self._num_biases),
dtype=ms.float32), name='biases', requires_grad=False)
self._bias = Parameter(bias, name='bias', requires_grad=False)
@property
def cutoff(self) -> Tensor:
r"""
Cutoff distance for neighbour list.
Return:
Tensor, cutoff distance.
"""
return self.neighbour_list.cutoff
@property
def neighbour_list_pace(self) -> int:
r"""
Update step for neighbour list.
Return:
int, update steps.
"""
return self.neighbour_list.pace
@property
def length_unit(self) -> str:
r"""
Length unit.
Return:
str, length unit.
"""
return self.units.length_unit
@property
def energy_unit(self) -> str:
r"""
Energy unit.
Return:
str, energy unit.
"""
return self.units.energy_unit
@property
def num_energies(self) -> int:
r"""
Number of energy terms :math:`U`.
Return:
int, number of energy terms.
"""
return self.potential_function.num_energies
@property
def num_biases(self) -> int:
r"""
Number of bias potential energies :math:`V`.
Return:
int, number of bias potential energies.
"""
return self._num_biases
@property
def energy_names(self) -> list:
r"""
Names of energy terms.
Return:
list[str], names of energy terms.
"""
return self.potential_function.energy_names
@property
def bias_names(self) -> list:
r"""
Name of bias potential energies.
Return:
list[str], the bias potential energies.
"""
return self._bias_names
@property
def energies(self) -> Tensor:
r"""
Tensor of potential energy components.
Return:
Tensor, Tensor of shape `(B, U)`. Data type is float.
"""
return self.identity(self._energies)
@property
def biases(self) -> Tensor:
r"""
Tensor of bias potential components.
Return:
Tensor, Tensor of shape `(B, V)`. Data type is float.
"""
if self.bias_function is None:
return None
return self.identity(self._biases)
@property
def bias(self) -> Tensor:
r"""
Tensor of the total bias potential.
Return:
Tensor, Tensor of shape `(B, 1)`. Data type is float.
"""
return self.identity(self._bias)
[文档] def bias_pace(self, index: int = 0) -> int:
"""
Return the update freqenucy for bias potential.
Args:
index(int): Index of bias potential. Default: 0
Returns:
int, update freqenucy.
"""
return self.bias_function[index].update_pace
[文档] def set_pbc_grad(self, grad_box: bool):
r"""
Set whether to calculate the gradient of PBC box.
Args:
grad_box(bool): Whether to calculate the gradient of PBC box.
"""
self.system.set_pbc_grad(grad_box)
return self
[文档] def update_neighbour_list(self) -> Tuple[Tensor, Tensor]:
r"""
Update neighbour list.
Returns:
- neigh_idx, Tensor. Tensor of shape `(B, A, N)`. Data type is int.
Index of neighbouring atoms of each atoms in system.
- neigh_mask, Tensor. Tensor of shape `(B, A, N)`. Data type is bool.
Mask for neighbour list `neigh_idx`.
"""
return self.neighbour_list.update(self.coordinate, self.pbc_box)
[文档] def update_bias(self, step: int):
r"""
Update bias potential.
Args:
step(int): Current simulation step. If it can be divided by update frequency, update the bias potential.
"""
if self.bias_function is not None:
for i in range(self._num_biases):
if self.bias_pace(i) > 0 and step % self.bias_pace(i) == 0:
self.bias_function[i].update(self.coordinate, self.pbc_box)
return self
[文档] def update_wrapper(self, step: int):
r"""
Update energy wrapper.
Args:
step(int): Current simulation step. If it can be divided by update frequency, update the energy wrapper.
"""
if self.wrapper_pace > 0 and step % self.wrapper_pace == 0:
self.energy_wrapper.update()
return self
[文档] def get_neighbour_list(self) -> Tuple[Tensor, Tensor]:
r"""
Get neighbour list.
Returns:
- neigh_idx, Tensor. Tensor of shape `(B, A, N)`. Data type is int.
Index of neighbouring atoms of each atoms in system.
- neigh_mask, Tensor. Tensor of shape `(B, A, N)`. Data type is bool.
Mask for neighbour list `neigh_idx`.
"""
return self.neighbour_list.get_neighbour_list()
[文档] def calc_energies(self) -> Tensor:
"""
Calculate the energy terms of the potential energy.
Return:
Tensor, Tensor of shape `(B, U)`. Data type is float. Energy terms.
"""
neigh_idx, neigh_vec, neigh_dis, neigh_mask = self.neighbour_list(self.coordinate, self.pbc_box)
coordinate = self.coordinate * self.length_unit_scale
pbc_box = self.pbc_box
if pbc_box is not None:
pbc_box *= self.length_unit_scale
neigh_vec *= self.length_unit_scale
neigh_dis *= self.length_unit_scale
energies = self.potential_function(
coordinate=coordinate,
neighbour_index=neigh_idx,
neighbour_mask=neigh_mask,
neighbour_vector=neigh_vec,
neighbour_distance=neigh_dis,
pbc_box=pbc_box
)
return energies
[文档] def calc_biases(self) -> Tensor:
"""
Calculate the bias potential terms.
Return:
Tensor, Tensor of shape `(B, V)`. Data type is float. Bias potential terms.
"""
if self.bias_function is None:
return None
neigh_idx, neigh_vec, neigh_dis, neigh_mask = self.neighbour_list(self.coordinate, self.pbc_box)
coordinate = self.coordinate * self.length_unit_scale
pbc_box = self.pbc_box
if pbc_box is not None:
pbc_box *= self.length_unit_scale
neigh_vec *= self.length_unit_scale
neigh_dis *= self.length_unit_scale
biases = ()
for i in range(self._num_biases):
bias_ = self.bias_function[i](
coordinate=coordinate,
neighbour_index=neigh_idx,
neighbour_mask=neigh_mask,
neighbour_vector=neigh_vec,
neighbour_distance=neigh_dis,
pbc_box=pbc_box
)
biases += (bias_,)
return msnp.concatenate(biases, axis=-1)
def construct(self, *inputs) -> Tensor:
"""calculate the total potential energy (potential energy and bias potential) of the simulation system.
Return:
energy (Tensor): Tensor of shape `(B, 1)`. Data type is float.
Total potential energy.
Note:
B: Batchsize, i.e. number of walkers of the simulation.
"""
#pylint: disable=unused-argument
coordinate, pbc_box = self.system()
neigh_idx, neigh_vec, neigh_dis, neigh_mask = self.neighbour_list(coordinate, pbc_box)
coordinate *= self.length_unit_scale
if pbc_box is not None:
pbc_box *= self.length_unit_scale
if neigh_idx is not None:
neigh_vec *= self.length_unit_scale
neigh_dis *= self.length_unit_scale
energies = self.potential_function(
coordinate=coordinate,
neighbour_index=neigh_idx,
neighbour_mask=neigh_mask,
neighbour_vector=neigh_vec,
neighbour_distance=neigh_dis,
pbc_box=pbc_box
)
energies = F.depend(energies, F.assign(self._energies, energies))
biases = None
if self.bias_function is not None:
biases = ()
for i in range(self._num_biases):
bias_ = self.bias_function[i](
coordinate=coordinate,
neighbour_index=neigh_idx,
neighbour_mask=neigh_mask,
neighbour_vector=neigh_vec,
neighbour_distance=neigh_dis,
pbc_box=pbc_box
)
biases += (bias_,)
biases = msnp.concatenate(biases, axis=-1)
biases = F.depend(biases, F.assign(self._biases, biases))
energy, bias = self.energy_wrapper(energies, biases)
if self.bias_function is not None:
energy = F.depend(energy, F.assign(self._bias, bias))
return energy