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lammps/src/ML-IAP/mliap_model_python.cpp

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/* ----------------------------------------------------------------------
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)
------------------------------------------------------------------------- */
#ifdef MLIAP_PYTHON
#include "mliap_model_python.h"
#include "comm.h"
#include "error.h"
#include "lmppython.h"
#include "mliap_data.h"
#include "mliap_model_python_couple.h"
#include "pair_mliap.h"
#include "python_compat.h"
#include <Python.h>
using namespace LAMMPS_NS;
/* ---------------------------------------------------------------------- */
MLIAPModelPython::MLIAPModelPython(LAMMPS *lmp, char *coefffilename, bool is_child) :
MLIAPModel(lmp, coefffilename)
{
model_loaded = 0;
nonlinearflag = 1;
if (is_child)
return;
python->init();
PyGILState_STATE gstate = PyGILState_Ensure();
PyObject *pyMain = PyImport_AddModule("__main__");
if (!pyMain) {
PyGILState_Release(gstate);
error->all(FLERR, "Could not initialize embedded Python");
}
PyObject *coupling_module = PyImport_ImportModule("mliap_model_python_couple");
if (!coupling_module) {
PyErr_Print();
PyErr_Clear();
PyGILState_Release(gstate);
error->all(FLERR, "Loading MLIAPPY coupling module failure.");
}
// Recipe from lammps/src/pair_python.cpp :
// add current directory to PYTHONPATH
PyObject *py_path = PySys_GetObject((char *) "path");
PyList_Append(py_path, PY_STRING_FROM_STRING("."));
// if LAMMPS_POTENTIALS environment variable is set, add it to PYTHONPATH as well
const char *potentials_path = getenv("LAMMPS_POTENTIALS");
if (potentials_path != nullptr) {
PyList_Append(py_path, PY_STRING_FROM_STRING(potentials_path));
}
PyGILState_Release(gstate);
if (coefffilename) read_coeffs(coefffilename);
}
/* ---------------------------------------------------------------------- */
MLIAPModelPython::~MLIAPModelPython()
{
if (model_loaded)
MLIAPPY_unload_model(this);
model_loaded=false;
}
/* ----------------------------------------------------------------------
get number of parameters
---------------------------------------------------------------------- */
int MLIAPModelPython::get_nparams()
{
return nparams;
}
void MLIAPModelPython::read_coeffs(char *fname)
{
PyGILState_STATE gstate = PyGILState_Ensure();
model_loaded = MLIAPPY_load_model(this, fname);
if (PyErr_Occurred()) {
PyErr_Print();
PyErr_Clear();
PyGILState_Release(gstate);
error->all(FLERR, "Loading python model failure.");
}
PyGILState_Release(gstate);
if (model_loaded) {
this->connect_param_counts();
} else {
if (comm->me == 0) utils::logmesg(lmp, "Loading python model deferred.\n");
}
}
// Finalize loading of the model.
void MLIAPModelPython::connect_param_counts()
{
PyGILState_STATE gstate = PyGILState_Ensure();
nelements = MLIAPPY_nelements(this);
nparams = MLIAPPY_nparams(this);
ndescriptors = MLIAPPY_ndescriptors(this);
if (PyErr_Occurred()) {
PyErr_Print();
PyErr_Clear();
PyGILState_Release(gstate);
error->all(FLERR, "Loading python model failure.");
}
PyGILState_Release(gstate);
model_loaded = 1;
if (comm->me == 0) utils::logmesg(lmp, "Loading python model complete.\n");
}
/* ----------------------------------------------------------------------
Calculate model gradients w.r.t descriptors
for each atom beta_i = dE(B_i)/dB_i
---------------------------------------------------------------------- */
void MLIAPModelPython::compute_gradients(MLIAPData *data)
{
if (!model_loaded) { error->all(FLERR, "Model not loaded."); }
PyGILState_STATE gstate = PyGILState_Ensure();
MLIAPPY_compute_gradients(this, data);
if (PyErr_Occurred()) {
PyErr_Print();
PyErr_Clear();
PyGILState_Release(gstate);
error->all(FLERR, "Running python model failure.");
}
PyGILState_Release(gstate);
}
/* ----------------------------------------------------------------------
Calculate model double gradients w.r.t descriptors and parameters
for each atom energy gamma_lk = d2E(B)/dB_k/dsigma_l,
where sigma_l is a parameter, B_k a descriptor,
and atom subscript i is omitted
gamma is in CSR format:
nnz = number of non-zero values
gamma_row_index[inz] = l indices, 0 <= l < nparams
gamma_col_indexiinz] = k indices, 0 <= k < ndescriptors
gamma[i][inz] = non-zero values, 0 <= inz < nnz
egradient is derivative of energy w.r.t. parameters
---------------------------------------------------------------------- */
void MLIAPModelPython::compute_gradgrads(class MLIAPData *)
{
error->all(FLERR, "compute_gradgrads not implemented");
}
/* ----------------------------------------------------------------------
calculate gradients of forces w.r.t. parameters
egradient is derivative of energy w.r.t. parameters
---------------------------------------------------------------------- */
void MLIAPModelPython::compute_force_gradients(class MLIAPData *)
{
error->all(FLERR, "compute_force_gradients not implemented");
}
/* ----------------------------------------------------------------------
count the number of non-zero entries in gamma matrix
---------------------------------------------------------------------- */
int MLIAPModelPython::get_gamma_nnz(class MLIAPData *)
{
// todo: get_gamma_nnz
return 0;
}
double MLIAPModelPython::memory_usage()
{
// todo: get approximate memory usage in coupling code.
return 0;
}
#endif