337 lines
9.9 KiB
Python
337 lines
9.9 KiB
Python
# ----------------------------------------------------------------------
|
|
# LAMMPS - Large-scale Atomic/Molecular Massively Parallel Simulator
|
|
# https://www.lammps.org/ Sandia National Laboratories
|
|
# LAMMPS Development team: developers@lammps.org
|
|
#
|
|
# Copyright (2003) Sandia Corporation. Under the terms of Contract
|
|
# DE-AC04-94AL85000 with Sandia Corporation, the U.S. Government retains
|
|
# certain rights in this software. This software is distributed under
|
|
# the GNU General Public License.
|
|
#
|
|
# See the README file in the top-level LAMMPS directory.
|
|
# -------------------------------------------------------------------------
|
|
|
|
# ----------------------------------------------------------------------
|
|
# Contributing author: Nicholas Lubbers (LANL)
|
|
# -------------------------------------------------------------------------
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
def calc_n_params(model):
|
|
"""
|
|
Returns the sum of two decimal numbers in binary digits.
|
|
|
|
Parameters:
|
|
model (torch.nn.Module): Network model that maps descriptors to a per atom attribute
|
|
|
|
Returns:
|
|
n_params (int): Number of NN model parameters
|
|
"""
|
|
return sum(p.nelement() for p in model.parameters())
|
|
|
|
class TorchWrapper(torch.nn.Module):
|
|
"""
|
|
A class to wrap Modules to ensure lammps mliap compatability.
|
|
|
|
...
|
|
|
|
Attributes
|
|
----------
|
|
model : torch.nn.Module
|
|
Network model that maps descriptors to a per atom attribute
|
|
|
|
device : torch.nn.Module (None)
|
|
Accelerator device
|
|
|
|
dtype : torch.dtype (torch.float64)
|
|
Dtype to use on device
|
|
|
|
n_params : torch.nn.Module (None)
|
|
Number of NN model parameters
|
|
|
|
n_descriptors : int
|
|
Max number of per atom descriptors
|
|
|
|
n_elements : int
|
|
Max number of elements
|
|
|
|
|
|
Methods
|
|
-------
|
|
forward(descriptors, elems):
|
|
Feeds descriptors to network model to produce per atom energies and forces.
|
|
"""
|
|
|
|
def __init__(self, model, n_descriptors, n_elements, n_params=None, device=None, dtype=torch.float64):
|
|
"""
|
|
Constructs all the necessary attributes for the network module.
|
|
|
|
Parameters
|
|
----------
|
|
model : torch.nn.Module
|
|
Network model that maps descriptors to a per atom attribute
|
|
|
|
n_descriptors : int
|
|
Max number of per atom descriptors
|
|
|
|
n_elements : int
|
|
Max number of elements
|
|
|
|
n_params : torch.nn.Module (None)
|
|
Number of NN model parameters
|
|
|
|
device : torch.nn.Module (None)
|
|
Accelerator device
|
|
|
|
dtype : torch.dtype (torch.float64)
|
|
Dtype to use on device
|
|
"""
|
|
|
|
super().__init__()
|
|
self.model = model
|
|
self.device = device
|
|
self.dtype = dtype
|
|
|
|
# Put model on device and convert to dtype
|
|
self.to(self.dtype)
|
|
self.to(self.device)
|
|
|
|
if n_params is None:
|
|
n_params = calc_n_params(model)
|
|
|
|
self.n_params = n_params
|
|
self.n_descriptors = n_descriptors
|
|
self.n_elements = n_elements
|
|
|
|
def forward(self, elems, descriptors, beta, energy,use_gpu_data=False):
|
|
"""
|
|
Takes element types and descriptors calculated via lammps and
|
|
calculates the per atom energies and forces.
|
|
|
|
Parameters
|
|
----------
|
|
elems : numpy.array
|
|
Per atom element types
|
|
|
|
descriptors : numpy.array
|
|
Per atom descriptors
|
|
|
|
beta : numpy.array
|
|
Expired beta array to be filled with new betas
|
|
|
|
energy : numpy.array
|
|
Expired per atom energy array to be filled with new per atom energy
|
|
(Note: This is a pointer to the lammps per atom energies)
|
|
|
|
|
|
Returns
|
|
-------
|
|
None
|
|
"""
|
|
descriptors = torch.as_tensor(descriptors,dtype=self.dtype, device=self.device).requires_grad_(True)
|
|
elems = torch.as_tensor(elems,dtype=torch.int32, device=self.device)
|
|
elems=elems-1
|
|
device = self.device
|
|
if (use_gpu_data and device == None and str(beta.device).find('CUDA') == 1):
|
|
device = 'cuda' #Override device as it wasn't defined in the model
|
|
with torch.autograd.enable_grad():
|
|
|
|
if (use_gpu_data):
|
|
energy_nn = torch.as_tensor(energy,dtype=self.dtype, device=device)
|
|
energy_nn[:] = self.model(descriptors, elems).flatten()
|
|
else:
|
|
energy_nn = self.model(descriptors, elems).flatten()
|
|
energy[:] = energy_nn.detach().cpu().numpy().astype(np.float64)
|
|
|
|
if (use_gpu_data):
|
|
beta_nn = torch.as_tensor(beta,dtype=self.dtype, device=device)
|
|
beta_nn[:] = torch.autograd.grad(energy_nn.sum(), descriptors)[0]
|
|
else:
|
|
beta_nn = torch.autograd.grad(energy_nn.sum(), descriptors)[0]
|
|
beta[:] = beta_nn.detach().cpu().numpy().astype(np.float64)
|
|
|
|
|
|
class IgnoreElems(torch.nn.Module):
|
|
"""
|
|
A class to represent a NN model agnostic of element typing.
|
|
|
|
...
|
|
|
|
Attributes
|
|
----------
|
|
subnet : torch.nn.Module
|
|
Network model that maps descriptors to a per atom attribute
|
|
|
|
Methods
|
|
-------
|
|
forward(descriptors, elems):
|
|
Feeds descriptors to network model
|
|
"""
|
|
|
|
def __init__(self, subnet):
|
|
"""
|
|
Constructs all the necessary attributes for the network module.
|
|
|
|
Parameters
|
|
----------
|
|
subnet : torch.nn.Module
|
|
Network model that maps descriptors to a per atom attribute
|
|
"""
|
|
|
|
super().__init__()
|
|
self.subnet = subnet
|
|
|
|
def forward(self, descriptors, elems):
|
|
"""
|
|
Feeds descriptors to network model
|
|
|
|
Parameters
|
|
----------
|
|
descriptors : torch.tensor
|
|
Per atom descriptors
|
|
|
|
elems : torch.tensor
|
|
Per atom element types
|
|
|
|
Returns
|
|
-------
|
|
self.subnet(descriptors) : torch.tensor
|
|
Per atom attribute computed by the network model
|
|
"""
|
|
|
|
return self.subnet(descriptors)
|
|
|
|
|
|
class UnpackElems(torch.nn.Module):
|
|
"""
|
|
A class to represent a NN model pseudo-agnostic of element typing for
|
|
systems with multiple element typings.
|
|
|
|
...
|
|
|
|
Attributes
|
|
----------
|
|
subnet : torch.nn.Module
|
|
Network model that maps descriptors to a per atom attribute
|
|
|
|
n_types : int
|
|
Number of atom types used in training the NN model.
|
|
|
|
Methods
|
|
-------
|
|
forward(descriptors, elems):
|
|
Feeds descriptors to network model after adding zeros into
|
|
descriptor columns relating to different atom types
|
|
"""
|
|
|
|
def __init__(self, subnet, n_types):
|
|
"""
|
|
Constructs all the necessary attributes for the network module.
|
|
|
|
Parameters
|
|
----------
|
|
subnet : torch.nn.Module
|
|
Network model that maps descriptors to a per atom attribute.
|
|
|
|
n_types : int
|
|
Number of atom types used in training the NN model.
|
|
"""
|
|
super().__init__()
|
|
self.subnet = subnet
|
|
self.n_types = n_types
|
|
|
|
def forward(self, descriptors, elems):
|
|
"""
|
|
Feeds descriptors to network model after adding zeros into
|
|
descriptor columns relating to different atom types
|
|
|
|
Parameters
|
|
----------
|
|
descriptors : torch.tensor
|
|
Per atom descriptors
|
|
|
|
elems : torch.tensor
|
|
Per atom element types
|
|
|
|
Returns
|
|
-------
|
|
self.subnet(descriptors) : torch.tensor
|
|
Per atom attribute computed by the network model
|
|
"""
|
|
|
|
unpacked_descriptors = torch.zeros(elems.shape[0], self.n_types, descriptors.shape[1], dtype=torch.float64)
|
|
for i, ind in enumerate(elems):
|
|
unpacked_descriptors[i, ind, :] = descriptors[i]
|
|
return self.subnet(torch.reshape(unpacked_descriptors, (elems.shape[0], -1)), elems)
|
|
|
|
|
|
class ElemwiseModels(torch.nn.Module):
|
|
"""
|
|
A class to represent a NN model dependent on element typing.
|
|
|
|
...
|
|
|
|
Attributes
|
|
----------
|
|
subnets : list of torch.nn.Modules
|
|
Per element type network models that maps per element type
|
|
descriptors to a per atom attribute.
|
|
|
|
n_types : int
|
|
Number of atom types used in training the NN model.
|
|
|
|
Methods
|
|
-------
|
|
forward(descriptors, elems):
|
|
Feeds descriptors to network model after adding zeros into
|
|
descriptor columns relating to different atom types
|
|
"""
|
|
|
|
def __init__(self, subnets, n_types):
|
|
"""
|
|
Constructs all the necessary attributes for the network module.
|
|
|
|
Parameters
|
|
----------
|
|
subnets : list of torch.nn.Modules
|
|
Per element type network models that maps per element
|
|
type descriptors to a per atom attribute.
|
|
|
|
n_types : int
|
|
Number of atom types used in training the NN model.
|
|
"""
|
|
|
|
super().__init__()
|
|
self.subnets = subnets
|
|
self.n_types = n_types
|
|
|
|
def forward(self, descriptors, elems, dtype=torch.float64):
|
|
"""
|
|
Feeds descriptors to network model after adding zeros into
|
|
descriptor columns relating to different atom types
|
|
|
|
Parameters
|
|
----------
|
|
descriptors : torch.tensor
|
|
Per atom descriptors
|
|
|
|
elems : torch.tensor
|
|
Per atom element types
|
|
|
|
Returns
|
|
-------
|
|
self.subnets(descriptors) : torch.tensor
|
|
Per atom attribute computed by the network model
|
|
"""
|
|
|
|
self.dtype=dtype
|
|
self.to(self.dtype)
|
|
|
|
per_atom_attributes = torch.zeros(elems.size(dim=0), dtype=self.dtype)
|
|
given_elems, elem_indices = torch.unique(elems, return_inverse=True)
|
|
for i, elem in enumerate(given_elems):
|
|
self.subnets[elem].to(self.dtype)
|
|
per_atom_attributes[elem_indices == i] = self.subnets[elem](descriptors[elem_indices == i]).flatten()
|
|
return per_atom_attributes
|