Merge pull request #785 from ACEsuit/develop

Develop

mace/modules/radial.py

@@ -131,7 +131,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
     @staticmethod
     def calculate_envelope(
-        x: torch.Tensor, r_max: torch.Tensor, p: int
+        x: torch.Tensor, r_max: torch.Tensor, p: torch.Tensor
     ) -> torch.Tensor:
         r_over_r_max = x / r_max
         envelope = (

tests/test_foundations.py

@@ -6,6 +6,7 @@
 import torch.nn.functional
 from ase.build import molecule
 from e3nn import o3
+from e3nn.util import jit
 from scipy.spatial.transform import Rotation as R
 
 from mace import data, modules, tools
@@ -176,6 +177,33 @@ def test_multi_reference():
     )
 
 
+@pytest.mark.parametrize(
+    "calc",
+    [
+        mace_mp(device="cpu", default_dtype="float64"),
+        mace_mp(model="small", device="cpu", default_dtype="float64"),
+        mace_mp(model="medium", device="cpu", default_dtype="float64"),
+        mace_mp(model="large", device="cpu", default_dtype="float64"),
+        mace_mp(model=MODEL_PATH, device="cpu", default_dtype="float64"),
+        mace_off(model="small", device="cpu", default_dtype="float64"),
+        mace_off(model="medium", device="cpu", default_dtype="float64"),
+        mace_off(model="large", device="cpu", default_dtype="float64"),
+        mace_off(model=MODEL_PATH, device="cpu", default_dtype="float64"),
+    ],
+)
+def test_compile_foundation(calc):
+    model = calc.models[0]
+    atoms = molecule("CH4")
+    atoms.positions += np.random.randn(*atoms.positions.shape) * 0.1
+    batch = calc._atoms_to_batch(atoms)
+    output_1 = model(batch.to_dict())
+    model_compiled = jit.compile(model)
+    output = model_compiled(batch.to_dict())
+    for key in output_1.keys():
+        if isinstance(output_1[key], torch.Tensor):
+            assert torch.allclose(output_1[key], output[key], atol=1e-5)
+
+
 @pytest.mark.parametrize(
     "model",
     [