Remove rascaline as a dependency of the main package

.readthedocs.yml

@@ -26,5 +26,5 @@ sphinx:
 python:
    install:
    - method: pip
-     path: .
+     path: .[soap-bpnn]
    - requirements: docs/requirements.txt

pyproject.toml

@@ -12,7 +12,6 @@ dependencies = [
     "ase",
     "torch",
     "hydra-core",
-    "rascaline-torch @ git+https://github.com/luthaf/rascaline#subdirectory=python/rascaline-torch",
     "metatensor-operations==0.2.1",
     "metatensor-torch==0.3.0",
     "metatensor-learn==0.2.1",
@@ -55,7 +54,9 @@ requires = [
 build-backend = "setuptools.build_meta"
 
 [project.optional-dependencies]
-soap-bpnn = []
+soap-bpnn = [
+    "rascaline-torch @ git+https://github.com/luthaf/rascaline@ae05064#subdirectory=python/rascaline-torch",
+]
 alchemical-model = [
   "torch_alchemical @ git+https://github.com/abmazitov/torch_alchemical.git@357a01f",
 ]

src/metatensor/models/experimental/alchemical_model/tests/test_functionality.py

@@ -1,12 +1,12 @@
 import ase
-import rascaline.torch
 import torch
 from metatensor.torch.atomistic import (
     MetatensorAtomisticModel,
     ModelCapabilities,
     ModelEvaluationOptions,
     ModelMetadata,
     ModelOutput,
+    systems_to_torch,
 )
 
 from metatensor.models.experimental.alchemical_model import DEFAULT_HYPERS, Model
@@ -31,7 +31,7 @@ def test_prediction_subset():
 
     alchemical_model = Model(capabilities, DEFAULT_HYPERS["model"])
     system = ase.Atoms("O2", positions=[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
-    system = rascaline.torch.systems_to_torch(system).to(torch.get_default_dtype())
+    system = systems_to_torch(system).to(torch.get_default_dtype())
     system = get_system_with_neighbors_lists(
         system, alchemical_model.requested_neighbors_lists()
     )

src/metatensor/models/experimental/alchemical_model/tests/test_invariance.py

@@ -1,14 +1,14 @@
 import copy
 
 import ase.io
-import rascaline.torch
 import torch
 from metatensor.torch.atomistic import (
     MetatensorAtomisticModel,
     ModelCapabilities,
     ModelEvaluationOptions,
     ModelMetadata,
     ModelOutput,
+    systems_to_torch,
 )
 
 from metatensor.models.experimental.alchemical_model import DEFAULT_HYPERS, Model
@@ -35,13 +35,11 @@ def test_rotational_invariance():
     system = ase.io.read(DATASET_PATH)
     original_system = copy.deepcopy(system)
     system.rotate(48, "y")
-    original_system = rascaline.torch.systems_to_torch(original_system).to(
-        torch.get_default_dtype()
-    )
+    original_system = systems_to_torch(original_system).to(torch.get_default_dtype())
     original_system = get_system_with_neighbors_lists(
         original_system, alchemical_model.requested_neighbors_lists()
     )
-    system = rascaline.torch.systems_to_torch(system).to(torch.get_default_dtype())
+    system = systems_to_torch(system).to(torch.get_default_dtype())
     system = get_system_with_neighbors_lists(
         system, alchemical_model.requested_neighbors_lists()
     )

src/metatensor/models/experimental/alchemical_model/tests/test_regression.py

@@ -2,7 +2,6 @@
 
 import ase.io
 import numpy as np
-import rascaline.torch
 import torch
 from metatensor.learn.data import Dataset
 from metatensor.torch.atomistic import (
@@ -11,6 +10,7 @@
     ModelEvaluationOptions,
     ModelMetadata,
     ModelOutput,
+    systems_to_torch,
 )
 from omegaconf import OmegaConf
 
@@ -46,8 +46,7 @@ def test_regression_init():
     # Predict on the first five systems
     systems = ase.io.read(DATASET_PATH, ":5")
     systems = [
-        rascaline.torch.systems_to_torch(system).to(torch.get_default_dtype())
-        for system in systems
+        systems_to_torch(system).to(torch.get_default_dtype()) for system in systems
     ]
     systems = [
         get_system_with_neighbors_lists(

src/metatensor/models/experimental/alchemical_model/tests/test_torchscript.py

@@ -20,7 +20,7 @@ def test_torchscript():
             )
         },
     )
-    alchemical_model = Model(capabilities, DEFAULT_HYPERS["model"]).to(torch.float64)
+    alchemical_model = Model(capabilities, DEFAULT_HYPERS["model"])
     torch.jit.script(
         alchemical_model, {"energy": alchemical_model.capabilities.outputs["energy"]}
     )
@@ -39,7 +39,7 @@ def test_torchscript_save():
             )
         },
     )
-    alchemical_model = Model(capabilities, DEFAULT_HYPERS["model"]).to(torch.float64)
+    alchemical_model = Model(capabilities, DEFAULT_HYPERS["model"])
     torch.jit.save(
         torch.jit.script(
             alchemical_model,

src/metatensor/models/experimental/pet/tests/test_functionality.py

@@ -1,12 +1,11 @@
 import ase
-import rascaline.torch
-import torch
 from metatensor.torch.atomistic import (
     MetatensorAtomisticModel,
     ModelCapabilities,
     ModelEvaluationOptions,
     ModelMetadata,
     ModelOutput,
+    systems_to_torch,
 )
 
 from metatensor.models.experimental.pet import DEFAULT_HYPERS, Model
@@ -29,11 +28,9 @@ def test_prediction_subset():
         supported_devices=["cpu"],
     )
 
-    model = Model(capabilities, DEFAULT_HYPERS["ARCHITECTURAL_HYPERS"]).to(
-        torch.float64
-    )
+    model = Model(capabilities, DEFAULT_HYPERS["ARCHITECTURAL_HYPERS"])
     structure = ase.Atoms("O2", positions=[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
-    system = rascaline.torch.systems_to_torch(structure)
+    system = systems_to_torch(structure)
     system = get_system_with_neighbors_lists(system, model.requested_neighbors_lists())
 
     evaluation_options = ModelEvaluationOptions(

src/metatensor/models/experimental/soap_bpnn/tests/test_functionality.py

@@ -1,8 +1,7 @@
 import ase
 import metatensor.torch
-import rascaline.torch
 import torch
-from metatensor.torch.atomistic import ModelCapabilities, ModelOutput
+from metatensor.torch.atomistic import ModelCapabilities, ModelOutput, systems_to_torch
 
 from metatensor.models.experimental.soap_bpnn import DEFAULT_HYPERS, Model
 
@@ -26,7 +25,7 @@ def test_prediction_subset_elements():
 
     system = ase.Atoms("O2", positions=[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
     soap_bpnn(
-        [rascaline.torch.systems_to_torch(system).to(torch.get_default_dtype())],
+        [systems_to_torch(system).to(torch.get_default_dtype())],
         {"energy": soap_bpnn.capabilities.outputs["energy"]},
     )
 
@@ -56,11 +55,7 @@ def test_prediction_subset_atoms():
     )
 
     energy_monomer = soap_bpnn(
-        [
-            rascaline.torch.systems_to_torch(system_monomer).to(
-                torch.get_default_dtype()
-            )
-        ],
+        [systems_to_torch(system_monomer).to(torch.get_default_dtype())],
         {"energy": soap_bpnn.capabilities.outputs["energy"]},
     )
 
@@ -82,20 +77,12 @@ def test_prediction_subset_atoms():
     )
 
     energy_dimer = soap_bpnn(
-        [
-            rascaline.torch.systems_to_torch(system_far_away_dimer).to(
-                torch.get_default_dtype()
-            )
-        ],
+        [systems_to_torch(system_far_away_dimer).to(torch.get_default_dtype())],
         {"energy": soap_bpnn.capabilities.outputs["energy"]},
     )
 
     energy_monomer_in_dimer = soap_bpnn(
-        [
-            rascaline.torch.systems_to_torch(system_far_away_dimer).to(
-                torch.get_default_dtype()
-            )
-        ],
+        [systems_to_torch(system_far_away_dimer).to(torch.get_default_dtype())],
         {"energy": soap_bpnn.capabilities.outputs["energy"]},
         selected_atoms=selection_labels,
     )

src/metatensor/models/experimental/soap_bpnn/tests/test_invariance.py

@@ -1,9 +1,8 @@
 import copy
 
 import ase.io
-import rascaline.torch
 import torch
-from metatensor.torch.atomistic import ModelCapabilities, ModelOutput
+from metatensor.torch.atomistic import ModelCapabilities, ModelOutput, systems_to_torch
 
 from metatensor.models.experimental.soap_bpnn import DEFAULT_HYPERS, Model
 
@@ -23,18 +22,18 @@ def test_rotational_invariance():
             )
         },
     )
-    soap_bpnn = Model(capabilities, DEFAULT_HYPERS["model"]).to(torch.float64)
+    soap_bpnn = Model(capabilities, DEFAULT_HYPERS["model"])
 
     system = ase.io.read(DATASET_PATH)
     original_system = copy.deepcopy(system)
     system.rotate(48, "y")
 
     original_output = soap_bpnn(
-        [rascaline.torch.systems_to_torch(original_system)],
+        [systems_to_torch(original_system)],
         {"energy": soap_bpnn.capabilities.outputs["energy"]},
     )
     rotated_output = soap_bpnn(
-        [rascaline.torch.systems_to_torch(system)],
+        [systems_to_torch(system)],
         {"energy": soap_bpnn.capabilities.outputs["energy"]},
     )
 

src/metatensor/models/experimental/soap_bpnn/tests/test_regression.py

@@ -2,10 +2,9 @@
 
 import ase.io
 import numpy as np
-import rascaline.torch
 import torch
 from metatensor.learn.data import Dataset
-from metatensor.torch.atomistic import ModelCapabilities, ModelOutput
+from metatensor.torch.atomistic import ModelCapabilities, ModelOutput, systems_to_torch
 from omegaconf import OmegaConf
 
 from metatensor.models.experimental.soap_bpnn import DEFAULT_HYPERS, Model, train
@@ -40,10 +39,7 @@ def test_regression_init():
     systems = ase.io.read(DATASET_PATH, ":5")
 
     output = soap_bpnn(
-        [
-            rascaline.torch.systems_to_torch(system).to(torch.get_default_dtype())
-            for system in systems
-        ],
+        [systems_to_torch(system).to(torch.get_default_dtype()) for system in systems],
         {"U0": soap_bpnn.capabilities.outputs["U0"]},
     )
     expected_output = torch.tensor([[0.0739], [0.0758], [0.1782], [-0.3517], [-0.3251]])

src/metatensor/models/experimental/soap_bpnn/tests/test_torchscript.py

@@ -17,7 +17,7 @@ def test_torchscript():
             )
         },
     )
-    soap_bpnn = Model(capabilities, DEFAULT_HYPERS["model"]).to(torch.float64)
+    soap_bpnn = Model(capabilities, DEFAULT_HYPERS["model"])
     torch.jit.script(soap_bpnn, {"energy": soap_bpnn.capabilities.outputs["energy"]})
 
 
@@ -34,7 +34,7 @@ def test_torchscript_save():
             )
         },
     )
-    soap_bpnn = Model(capabilities, DEFAULT_HYPERS["model"]).to(torch.float64)
+    soap_bpnn = Model(capabilities, DEFAULT_HYPERS["model"])
     torch.jit.save(
         torch.jit.script(
             soap_bpnn, {"energy": soap_bpnn.capabilities.outputs["energy"]}

src/metatensor/models/utils/composition.py

@@ -1,6 +1,5 @@
-from typing import List, Tuple
+from typing import List, Tuple, Union
 
-import rascaline.torch
 import torch
 from metatensor.learn.data.dataset import _BaseDataset
 from metatensor.torch import Labels, TensorBlock, TensorMap
@@ -9,7 +8,7 @@
 
 
 def calculate_composition_weights(
-    datasets: _BaseDataset, property: str
+    datasets: Union[_BaseDataset, List[_BaseDataset]], property: str
 ) -> Tuple[torch.Tensor, List[int]]:
     """Calculate the composition weights for a dataset.
 
@@ -19,34 +18,33 @@ def calculate_composition_weights(
     :returns: Composition weights for the dataset, as well as the
         list of species that the weights correspond to.
     """
+    if not isinstance(datasets, list):
+        datasets = [datasets]
+
+    species = get_all_species(datasets)
+    # note that this is sorted, and the composition weights are sorted
+    # as well, because the species are sorted in the composition features
 
-    # Get the target for each system in the dataset
-    # TODO: the dataset will be iterable once metatensor PR #500 merged.
     targets = torch.stack(
         [
-            dataset[sample_id]._asdict()[property].block().values
+            sample._asdict()[property].block().values
             for dataset in datasets
-            for sample_id in range(len(dataset))
+            for sample in dataset
         ]
     )
+    targets = targets.squeeze(dim=(1, 2))  # remove component and property dimensions
 
-    # Get the composition for each system in the dataset
-    composition_calculator = rascaline.torch.AtomicComposition(per_system=True)
-    # TODO: the dataset will be iterable once metatensor PR #500 merged.
-    composition_features = composition_calculator.compute(
-        [
-            dataset[sample_id]._asdict()["system"]
-            for dataset in datasets
-            for sample_id in range(len(dataset))
-        ]
-    )
-    composition_features = composition_features.keys_to_properties("center_type")
-    composition_features = composition_features.block().values
+    structure_list = [
+        sample._asdict()["system"] for dataset in datasets for sample in dataset
+    ]
 
-    targets = targets.squeeze(dim=(1, 2))  # remove component and property dimensions
+    dtype = structure_list[0].positions.dtype
+    composition_features = torch.empty((len(structure_list), len(species)), dtype=dtype)
+    for i, structure in enumerate(structure_list):
+        for j, s in enumerate(species):
+            composition_features[i, j] = torch.sum(structure.types == s)
 
     regularizer = 1e-20
-
     while regularizer:
         if regularizer > 1e5:
             raise RuntimeError(
@@ -69,10 +67,6 @@ def calculate_composition_weights(
         except torch._C._LinAlgError:
             regularizer *= 10.0
 
-    species = get_all_species(datasets)
-    # note that this is sorted, and the composition weights are sorted
-    # as well, because the species are sorted in the composition features
-
     return solution, species
 
 

src/metatensor/models/utils/neighbors_lists.py

@@ -1,6 +1,6 @@
 from typing import List
 
-import ase
+import ase.neighborlist
 import torch
 from metatensor.torch import Labels, TensorBlock
 from metatensor.torch.atomistic import (