Changes to MLIAP python

- update lammps python package to use setuptools
- refactor MLIAP classes into lammps python package

lammps.mliap package
- change TorchWrapper to use dtype and device as arguments
- turn activation of mliappy into functions (was a class)
- add a check to see if python interpreter is compatible
  with python lib calls internal to lammps

mliap_model_python_couple.pyx:
- load models ending in '.pt' or '.pth' with pytorch rather than pickle

python/lammps/mliap/__init__.py

@@ -0,0 +1,13 @@
+
+# Check compatiblity of this build with the python shared library.
+# If this fails, lammps will segfault because its library will
+# try to improperly start up a new interpreter.
+import sysconfig
+import ctypes
+library = sysconfig.get_config_vars('INSTSONAME')[0]
+pylib = ctypes.CDLL(library)
+if not pylib.Py_IsInitialized():
+    raise RuntimeError("This interpreter is not compatible with python-based mliap for LAMMPS.")
+del sysconfig, ctypes, library, pylib
+
+from .loader import load_model, activate_mliappy

python/lammps/mliap/loader.py

@@ -0,0 +1,52 @@
+# ----------------------------------------------------------------------
+#   LAMMPS - Large-scale Atomic/Molecular Massively Parallel Simulator
+#   http://lammps.sandia.gov, 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)
+# -------------------------------------------------------------------------
+
+
+import sys
+import importlib.util
+import importlib.machinery
+
+def activate_mliappy(lmp):
+    try:
+        # Begin Importlib magic to find the embedded python module
+        # This is needed because the filename for liblammps does not
+        # match the spec for normal python modules, wherein
+        # file names match with PyInit function names.
+        # Also, python normally doesn't look for extensions besides '.so'
+        # We fix both of these problems by providing an explict
+        # path to the extension module 'mliap_model_python_couple' in
+
+        path = lmp.lib._name
+        loader = importlib.machinery.ExtensionFileLoader('mliap_model_python_couple', path)
+        spec = importlib.util.spec_from_loader('mliap_model_python_couple', loader)
+        module = importlib.util.module_from_spec(spec)
+        sys.modules['mliap_model_python_couple'] = module
+        spec.loader.exec_module(module)
+        # End Importlib magic to find the embedded python module
+
+    except Exception as ee:
+        raise ImportError("Could not load MLIAP python coupling module.") from ee
+
+def load_model(model):
+    try:
+        import mliap_model_python_couple
+    except ImportError as ie:
+        raise ImportError("MLIAP python module must be activated before loading\n"
+                          "the pair style. Call lammps.mliap.activate_mliappy(lmp)."
+                          ) from ie
+    mliap_model_python_couple.load_from_python(model)
+

python/lammps/mliap/pytorch.py

@@ -0,0 +1,65 @@
+# ----------------------------------------------------------------------
+#   LAMMPS - Large-scale Atomic/Molecular Massively Parallel Simulator
+#   http://lammps.sandia.gov, 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)
+# -------------------------------------------------------------------------
+
+import numpy as np
+import torch
+
+def calc_n_params(model):
+    return sum(p.nelement() for p in model.parameters())
+
+class TorchWrapper(torch.nn.Module):
+    def __init__(self, model,n_descriptors,n_elements,n_params=None,device=None,dtype=torch.float64):
+        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, bispectrum, beta, energy):
+
+        bispectrum = torch.from_numpy(bispectrum).to(dtype=self.dtype, device=self.device).requires_grad_(True)
+        elems = torch.from_numpy(elems).to(dtype=torch.long, device=self.device) - 1
+
+        with torch.autograd.enable_grad():
+
+            energy_nn = self.model(bispectrum, elems)
+            if energy_nn.ndim > 1:
+                energy_nn = energy_nn.flatten()
+
+            beta_nn = torch.autograd.grad(energy_nn.sum(), bispectrum)[0]
+
+        beta[:] = beta_nn.detach().cpu().numpy().astype(np.float64)
+        energy[:] = energy_nn.detach().cpu().numpy().astype(np.float64)
+
+class IgnoreElems(torch.nn.Module):
+    def __init__(self,subnet):
+        super().__init__()
+        self.subnet = subnet
+
+    def forward(self,bispectrum,elems):
+        return self.subnet(bispectrum)