rename MLIAP package to ML-IAP

src/ML-IAP/mliap_model_python.cpp

@@ -0,0 +1,203 @@
+// clang-format off
+/* ----------------------------------------------------------------------
+   LAMMPS - Large-scale Atomic/Molecular Massively Parallel Simulator
+   https://www.lammps.org/, Sandia National Laboratories
+   Steve Plimpton, sjplimp@sandia.gov
+
+   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 <Python.h>
+#include "mliap_model_python.h"
+#include "mliap_model_python_couple.h"
+#include "pair_mliap.h"
+#include "mliap_data.h"
+#include "error.h"
+#include "utils.h"
+#include "lmppython.h"
+#include "python_compat.h"
+
+
+using namespace LAMMPS_NS;
+
+/* ---------------------------------------------------------------------- */
+
+MLIAPModelPython::MLIAPModelPython(LAMMPS* lmp, char* coefffilename) :
+  MLIAPModel(lmp, coefffilename)
+{
+  model_loaded = 0;
+  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 != NULL) {
+    PyList_Append(py_path, PY_STRING_FROM_STRING(potentials_path));
+  }
+  PyGILState_Release(gstate);
+
+  if (coefffilename) read_coeffs(coefffilename);
+
+  nonlinearflag=1;
+}
+
+/* ---------------------------------------------------------------------- */
+
+MLIAPModelPython::~MLIAPModelPython() {
+  MLIAPPY_unload_model(this);
+}
+
+/* ----------------------------------------------------------------------
+   get number of parameters
+   ---------------------------------------------------------------------- */
+
+
+int MLIAPModelPython::get_nparams()
+{
+  return nparams;
+}
+
+void MLIAPModelPython::read_coeffs(char *fname)
+{
+  PyGILState_STATE gstate = PyGILState_Ensure();
+
+  int 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 (loaded) {
+    this->connect_param_counts();
+  }
+  else {
+    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;
+  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 (not 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