Merge pull request #258 from lee2tae/develop/omni

ML-IAP pytest code

Author: YutackPark

.pre-commit-config.yaml

@@ -1,5 +1,5 @@
 default_language_version:
-  python: python3.11
+  python: python3.10
 
 ci:
   autoupdate_schedule: quarterly

sevenn/main/sevenn_get_model.py

@@ -93,8 +93,8 @@ def run(args):
     if output_prefix is None:
         output_prefix = 'deployed_parallel' if not get_serial else 'deployed_serial'
 
-    if use_mliap:
-        output_prefix += '_mliap'
+        if use_mliap:
+            output_prefix += '_mliap'
 
     checkpoint_path = None
     if os.path.isfile(checkpoint):

sevenn/mliap.py

@@ -176,26 +176,34 @@ def compute_forces(self, lmp_data):
         self._ensure_model_initialized()  # lazy init
         assert self.model, 'Model must be initialized'
         if lmp_data.nlocal == 0 or lmp_data.npairs <= 1:
-            # waht about a single atom with 0 pairs?
+            # what about a single atom with 0 pairs?
             return
 
         nlocal = lmp_data.nlocal
         ntotal = lmp_data.ntotal
 
         # edge_vectors should be f32 in 7net
-        edge_vectors = torch.as_tensor(lmp_data.rij, torch.float32, self.device)
+        edge_vectors = torch.as_tensor(
+            lmp_data.rij, dtype=torch.float32, device=self.device
+        )
         edge_vectors.requires_grad_(True)
 
         edge_index = torch.vstack(
             [
-                torch.as_tensor(lmp_data.pair_i, torch.int64, self.device),
-                torch.as_tensor(lmp_data.pair_j, torch.int64, self.device),
+                torch.as_tensor(
+                    lmp_data.pair_i, dtype=torch.int64, device=self.device
+                ),
+                torch.as_tensor(
+                    lmp_data.pair_j, dtype=torch.int64, device=self.device
+                ),
             ]
         )
-        elems = torch.as_tensor(lmp_data.elems, torch.int64, self.device)
-        num_atoms = torch.as_tensor(nlocal, torch.int64, self.device)
+        elems = torch.as_tensor(
+            lmp_data.elems, dtype=torch.int64, device=self.device
+        )
+        num_atoms = torch.as_tensor(nlocal, dtype=torch.int64, device=self.device)
         mliap_num_local_ghost = torch.as_tensor(
-            [nlocal, ntotal - nlocal], torch.int64, self.device
+            [nlocal, ntotal - nlocal], dtype=torch.int64, device=self.device
         )
 
         # data prep

tests/lammps_tests/scripts/mliap_skel.lmp

@@ -0,0 +1,24 @@
+  units            metal
+  boundary         __BOUNDARY__
+  atom_style       atomic
+  atom_modify      map yes
+  newton           on
+
+  read_data        __LMP_STCT__
+
+  mass * 1.0  # do not matter since we don't run MD
+
+  pair_style       mliap unified __PAIR_STYLE__ 0
+  pair_coeff       * * __ELEMENT__
+
+  timestep         0.002
+
+  compute pa all pe/atom
+
+  thermo          1
+  fix             1 all nve
+  thermo_style    custom step tpcpu pe ke vol pxx pyy pzz pxy pxz pyz press temp
+  dump            mydump all custom 1 __FORCE_DUMP_PATH__ id type element c_pa x y z fx fy fz
+  dump_modify     mydump sort id element __ELEMENT__
+
+  run 0