Test that each model can be converted to TorchScript.

tests/test_models.py

@@ -1,4 +1,5 @@
 import pytest
+import torch
 
 from emle.models import *
 
@@ -29,18 +30,26 @@ def test_emle(alpha_mode):
     """
     Check that we can instantiate the default EMLE model.
     """
+    # Instantiate the default EMLE model.
     model = EMLE(alpha_mode=alpha_mode)
     assert model is not None
 
+    # Make sure the model can be converted to TorchScript.
+    model = torch.jit.script(model)
+
 
 @pytest.mark.parametrize("alpha_mode", ["species", "reference"])
 def test_ani2x(alpha_mode):
     """
     Check that we can instantiate the default ANI2xEMLE model.
     """
+    # Instantiate the ANI2xEMLE model.
     model = ANI2xEMLE(alpha_mode=alpha_mode)
     assert model is not None
 
+    # Make sure the model can be converted to TorchScript.
+    model = torch.jit.script(model)
+
     from torchani.models import ANI2x
 
     # Try using an existing ANI2x model.
@@ -49,19 +58,24 @@ def test_ani2x(alpha_mode):
     # Create a new ANI2xEMLE model with the existing ANI2x model.
     model = ANI2xEMLE(alpha_mode=alpha_mode, ani2x_model=ani2x)
 
+    # Make sure the model can be converted to TorchScript.
+    model = torch.jit.script(model)
+
 
 @pytest.mark.skipif(not has_nnpops, reason="NNPOps not installed")
 @pytest.mark.parametrize("alpha_mode", ["species", "reference"])
 def test_ani2x_nnpops(alpha_mode):
     """
     Check that we can instantiate the default ANI2xEMLE model with NNPOps.
     """
-    import torch
-
+    # Instantiate the ANI2xEMLE model using NNPOps.
     atomic_numbers = torch.tensor([1, 6, 7, 8])
     model = ANI2xEMLE(alpha_mode=alpha_mode, atomic_numbers=atomic_numbers)
     assert model is not None
 
+    # Make sure the model can be converted to TorchScript.
+    model = torch.jit.script(model)
+
 
 @pytest.mark.skipif(not has_mace, reason="mace-torch not installed")
 @pytest.mark.skipif(not has_e3nn, reason="e3nn not installed")
@@ -73,5 +87,9 @@ def test_mace(alpha_mode, mace_model):
     """
     Check that we can instantiate the default MACE model.
     """
+    # Instantiate the MACEEMLE model.
     model = MACEEMLE(alpha_mode=alpha_mode)
     assert model is not None
+
+    # Make sure the model can be converted to TorchScript.
+    model = torch.jit.script(model)