Merge branch 'main' into ref-add-ani2x-test

modelforge/dataset/__init__.py

@@ -7,7 +7,7 @@
 from .spice2 import SPICE2Dataset
 from .spice1openff import SPICE1OpenFFDataset
 from .phalkethoh import PhAlkEthOHDataset
-from .dataset import DatasetFactory, DataModule, NNPInput
+from .dataset import DatasetFactory, DataModule
 from enum import Enum
 
 

modelforge/jax.py

@@ -1,4 +1,4 @@
-from modelforge.dataset import NNPInput
+from modelforge.utils.prop import NNPInput
 
 
 def nnpinput_flatten(nnpinput: NNPInput):
@@ -42,12 +42,9 @@ def convert_NNPInput_to_jax(nnp_input: NNPInput):
     nnp_input.box_vectors = convert_to_jax(nnp_input.box_vectors)
     nnp_input.is_periodic = convert_to_jax(nnp_input.is_periodic)
 
-    if nnp_input.pair_list is not None:
-        nnp_input.pair_list = convert_to_jax(nnp_input.pair_list)
-
-    if nnp_input.per_atom_partial_charge is not None:
-        nnp_input.per_atom_partial_charge = convert_to_jax(
-            nnp_input.per_atom_partial_charge
-        )
+    nnp_input.pair_list = convert_to_jax(nnp_input.pair_list)
+    nnp_input.per_atom_partial_charge = convert_to_jax(
+        nnp_input.per_atom_partial_charge
+    )
 
     return nnp_input

modelforge/potential/ani.py

@@ -12,9 +12,8 @@
 from loguru import logger as log
 from torch import nn
 
-from modelforge.utils.prop import SpeciesAEV
+from modelforge.utils.prop import SpeciesAEV, NNPInput
 
-from modelforge.dataset.dataset import NNPInput
 from modelforge.potential.neighbors import PairlistData
 
 

modelforge/potential/featurization.py

@@ -3,7 +3,7 @@
 from typing import Dict, List, Union
 
 from modelforge.potential.utils import DenseWithCustomDist
-from modelforge.dataset import NNPInput
+from modelforge.utils.prop import NNPInput
 
 
 class AddPerMoleculeValue(nn.Module):

modelforge/potential/neighbors.py

@@ -790,20 +790,15 @@ def _calculate_interacting_pairs(
             Atom positions. Shape: [nr_systems, nr_atoms, 3].
         atomic_subsystem_indices : torch.Tensor
             Indices identifying atoms in subsystems. Shape: [nr_atoms].
-        pair_indices : Optional[torch.Tensor]
-            Precomputed pair indices. If None, will compute pair indices.
+        pair_indices : torch.Tensor
+            Precomputed pair indices.
 
         Returns
         -------
         PairListOutputs
             A dataclass containing 'pair_indices', 'd_ij' (distances), and 'r_ij' (displacement vectors).
         """
 
-        if pair_indices is None:
-            pair_indices = self.enumerate_all_pairs(
-                atomic_subsystem_indices,
-            )
-
         r_ij = self.calculate_r_ij(pair_indices, positions)
         d_ij = self.calculate_d_ij(r_ij)
 
@@ -838,8 +833,9 @@ def forward(self, data: Union[NNPInput, NamedTuple]) -> PairlistData:
         # general input manipulation
         positions = data.positions
         atomic_subsystem_indices = data.atomic_subsystem_indices
-        # calculate pairlist if none is provided
-        if data.pair_list is None:
+        # calculate pairlist if it is not provided
+
+        if data.pair_list is None or data.pair_list.shape[0] == 0:
             # note, we set the flag for unique pairs when instantiated in the constructor
             # and thus this call will return unique pairs if requested.
             pair_list = self.pairlist.enumerate_all_pairs(atomic_subsystem_indices)

modelforge/potential/painn.py

@@ -9,7 +9,7 @@
 from loguru import logger as log
 from openff.units import unit
 
-from modelforge.dataset.dataset import NNPInput
+from modelforge.utils.prop import NNPInput
 from modelforge.potential.neighbors import PairlistData
 from .utils import DenseWithCustomDist
 

modelforge/potential/physnet.py

@@ -8,7 +8,7 @@
 from loguru import logger as log
 from torch import nn
 
-from modelforge.dataset.dataset import NNPInput
+from modelforge.utils.prop import NNPInput
 from modelforge.potential.neighbors import PairlistData
 from .utils import Dense
 

modelforge/potential/potential.py

@@ -626,7 +626,6 @@ def generate_potential(
 
                 jax = import_("jax")
                 from modelforge.jax import nnpinput_flatten, nnpinput_unflatten
-                from modelforge.dataset import NNPInput
 
                 # registering NNPInput multiple times will result in a
                 # ValueError

modelforge/potential/sake.py

@@ -8,7 +8,7 @@
 import torch.nn as nn
 from loguru import logger as log
 
-from modelforge.dataset.dataset import NNPInput
+from modelforge.utils.prop import NNPInput
 from modelforge.potential.neighbors import PairlistData
 
 from .utils import DenseWithCustomDist, scatter_softmax

modelforge/potential/schnet.py

@@ -8,7 +8,7 @@
 import torch.nn as nn
 from loguru import logger as log
 
-from modelforge.dataset.dataset import NNPInput
+from modelforge.utils.prop import NNPInput
 from modelforge.potential.neighbors import PairlistData
 
 

modelforge/potential/tensornet.py

@@ -9,7 +9,7 @@
 
 from modelforge.potential import CosineAttenuationFunction, TensorNetRadialBasisFunction
 
-from modelforge.dataset.dataset import NNPInput
+from modelforge.utils.prop import NNPInput
 from modelforge.potential.neighbors import PairlistData
 
 

modelforge/potential/utils.py

@@ -10,7 +10,7 @@
 import torch.nn as nn
 from openff.units import unit
 
-from modelforge.dataset.dataset import NNPInput
+from modelforge.utils.prop import NNPInput
 
 
 @dataclass(frozen=False)

modelforge/tests/conftest.py

@@ -288,7 +288,7 @@ def generate_uniform_quaternion(u=None):
 def rotation_matrix_from_quaternion(quaternion):
     """Compute a 3x3 rotation matrix from a given quaternion (4-vector).
 
-    Adapted from the numpy implementation in openmm-tools
+    Adapted from the numpy implementation in modelforgeopenmm-tools
 
     https://github.com/choderalab/openmmtools/blob/main/openmmtools/mcmc.py
 

modelforge/tests/precalculated_values.py

@@ -106,7 +106,7 @@ def setup_single_methane_input():
     )
     E = torch.tensor([0.0], requires_grad=True)
     atomic_subsystem_indices = torch.tensor([0, 0, 0, 0, 0], dtype=torch.int32)
-    from modelforge.dataset.dataset import NNPInput
+    from modelforge.utils.prop import NNPInput
 
     modelforge_methane = NNPInput(
         atomic_numbers=atomic_numbers,

modelforge/tests/test_ani.py

@@ -35,7 +35,7 @@ def setup_methane():
         [0, 0, 0, 0, 0], dtype=torch.int32, device=device
     )
 
-    from modelforge.dataset.dataset import NNPInput
+    from modelforge.utils.prop import NNPInput
 
     nnp_input = NNPInput(
         atomic_numbers=torch.tensor([6, 1, 1, 1, 1], device=device),
@@ -80,7 +80,7 @@ def setup_two_methanes():
     )
 
     atomic_numbers = mf_species
-    from modelforge.dataset.dataset import NNPInput
+    from modelforge.utils.prop import NNPInput
 
     nnp_input = NNPInput(
         atomic_numbers=atomic_numbers,

modelforge/tests/test_dataset.py

@@ -13,11 +13,7 @@
 
 @pytest.fixture(scope="session")
 def prep_temp_dir(tmp_path_factory):
-    import uuid
-
-    filename = str(uuid.uuid4())
-
-    fn = tmp_path_factory.mktemp(f"test_dataset_temp")
+    fn = tmp_path_factory.mktemp("test_dataset_temp")
     return fn
 
 
@@ -258,7 +254,7 @@ def test_file_existence_after_initialization(
 def test_caching(prep_temp_dir):
     import contextlib
 
-    local_cache_dir = str(prep_temp_dir) + "/data_test"
+    local_cache_dir = str(prep_temp_dir)
     from modelforge.dataset.qm9 import QM9Dataset
 
     data = QM9Dataset(version_select="nc_1000_v0", local_cache_dir=local_cache_dir)
@@ -334,7 +330,7 @@ def test_metadata_validation(prep_temp_dir):
     which is used to validate if we can use .npz file, or we need to
     regenerate it."""
 
-    local_cache_dir = str(prep_temp_dir) + "/data_test"
+    local_cache_dir = str(prep_temp_dir)
 
     from modelforge.dataset.qm9 import QM9Dataset
 
@@ -471,7 +467,7 @@ def test_data_item_format_of_datamodule(
     """Test the format of individual data items in the dataset."""
     from typing import Dict
 
-    local_cache_dir = str(prep_temp_dir) + "/data_test"
+    local_cache_dir = str(prep_temp_dir)
 
     dm = datamodule_factory(
         dataset_name=dataset_name,
@@ -761,7 +757,7 @@ def test_dataset_downloader(dataset_name, dataset_factory, prep_temp_dir):
     """
     Test the DatasetDownloader functionality.
     """
-    local_cache_dir = str(prep_temp_dir) + "/data_test"
+    local_cache_dir = str(prep_temp_dir)
 
     dataset = dataset_factory(
         dataset_name=dataset_name, local_cache_dir=local_cache_dir
@@ -773,15 +769,15 @@ def test_dataset_downloader(dataset_name, dataset_factory, prep_temp_dir):
 
 
 @pytest.mark.parametrize("dataset_name", _ImplementedDatasets.get_all_dataset_names())
-def test_numpy_dataset_assignment(dataset_name):
+def test_numpy_dataset_assignment(dataset_name, prep_temp_dir):
     """
     Test if the numpy_dataset attribute is correctly assigned after processing or loading.
     """
     from modelforge.dataset import _ImplementedDatasets
 
     factory = DatasetFactory()
     data = _ImplementedDatasets.get_dataset_class(dataset_name)(
-        version_select="nc_1000_v0"
+        version_select="nc_1000_v0", local_cache_dir=str(prep_temp_dir)
     )
     factory._load_or_process_data(data)
 
@@ -1025,7 +1021,7 @@ def test_function_of_self_energy(dataset_name, datamodule_factory, prep_temp_dir
 
 
 def test_shifting_center_of_mass_to_origin(prep_temp_dir):
-    local_cache_dir = str(prep_temp_dir) + "/data_test"
+    local_cache_dir = str(prep_temp_dir)
 
     from modelforge.dataset.dataset import initialize_datamodule
     from openff.units.elements import MASSES

modelforge/tests/test_potentials.py

@@ -1107,3 +1107,37 @@ def test_loading_from_checkpoint_file():
 
     model = load_inference_model_from_checkpoint(chkp_file)
     assert model is not None
+
+
+@pytest.mark.parametrize(
+    "potential_name", _Implemented_NNPs.get_all_neural_network_names()
+)
+def test_saving_torchscript(potential_name, single_batch_with_batchsize, prep_temp_dir):
+    batch = single_batch_with_batchsize(
+        batch_size=1, dataset_name="QM9", local_cache_dir=str(prep_temp_dir)
+    )
+    from modelforge.potential.potential import NeuralNetworkPotentialFactory
+    from modelforge.utils.misc import load_configs_into_pydantic_models
+
+    config = load_configs_into_pydantic_models(potential_name.lower(), "qm9")
+
+    # read default parameters
+    potential = NeuralNetworkPotentialFactory.generate_potential(
+        use="inference",
+        potential_parameter=config["potential"],
+        potential_seed=42,
+    )
+    potential_jit = torch.jit.script(potential)
+    filename = f"{str(prep_temp_dir)}/{potential_name.lower()}_qm9_jit.pt"
+
+    potential_jit.save(filename)
+
+    output = potential(batch.nnp_input)
+    output_jit1 = potential_jit(batch.nnp_input)
+
+    # load the model
+    jit_model = torch.jit.load(filename)
+    output_jit2 = jit_model(batch.nnp_input)
+
+    assert torch.allclose(output["per_system_energy"], output_jit1["per_system_energy"])
+    assert torch.allclose(output["per_system_energy"], output_jit2["per_system_energy"])

modelforge/utils/prop.py

@@ -48,8 +48,8 @@ def __init__(
         per_system_total_charge: torch.Tensor,
         box_vectors: torch.Tensor = torch.zeros(3, 3),
         is_periodic: torch.Tensor = torch.tensor([False]),
-        pair_list: torch.Tensor = None,
-        per_atom_partial_charge: torch.Tensor = None,
+        pair_list: torch.Tensor = torch.tensor([]),
+        per_atom_partial_charge: torch.Tensor = torch.tensor([]),
     ):
         self.atomic_numbers = atomic_numbers
         self.positions = positions
@@ -103,12 +103,8 @@ def to_device(self, device: torch.device):
         self.per_system_total_charge = self.per_system_total_charge.to(device)
         self.box_vectors = self.box_vectors.to(device)
         self.is_periodic = self.is_periodic.to(device)
-
-        if self.pair_list is not None:
-            self.pair_list = self.pair_list.to(device)
-
-        if self.per_atom_partial_charge is not None:
-            self.per_atom_partial_charge = self.per_atom_partial_charge.to(device)
+        self.pair_list = self.pair_list.to(device)
+        self.per_atom_partial_charge = self.per_atom_partial_charge.to(device)
 
         return self