enable macOS and Windows testsuite (#114)

* enable macOS and Windows testsuite
* split tests and min-tests

.github/pull_request_template.md

@@ -0,0 +1,13 @@
+<!-- What does this implement/fix? Explain your changes here. -->
+
+
+
+# Contributor (creator of pull-request) checklist
+
+ - [ ] Tests updated (for new features and bugfixes)?
+ - [ ] Documentation updated (for new features)?
+ - [ ] Issue referenced (for PRs that solve an issue)?
+
+# Reviewer checklist
+
+ - [ ] CHANGELOG updated with public API or any other important changes?

.github/workflows/tests.yml

@@ -7,19 +7,43 @@ on:
     # Check all PR
 
 jobs:
-  tests:
+  min:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
         include:
-          # - os: ubuntu-22.04
-          #  python-version: "3.9"
           - os: ubuntu-22.04
+            python-version: "3.9"
+          - os: ubuntu-22.04
+            python-version: "3.12"
+          - os: macos-14
+            python-version: "3.12"
+          - os: windows-2022
+            python-version: "3.12"
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+    - run: pip install tox coverage[toml]
+    - name: run Python minimal tests
+      run: tox -e tests-min
+
+  all:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        include:
+          - os: ubuntu-22.04
+            python-version: "3.9"
+          - os: ubuntu-22.04
+            python-version: "3.12"
+          - os: macos-14
+            python-version: "3.12"
+          - os: windows-2022
             python-version: "3.12"
-          # - os: macos-14
-          #  python-version: "3.12"
-          # - os: windows-2022
-          #  python-version: "3.12"
 
     steps:
     - uses: actions/checkout@v4
@@ -30,7 +54,6 @@ jobs:
     - run: pip install tox coverage[toml]
     - name: run Python tests
       run: |
-        tox -e tests-min
         tox -e tests
         coverage xml
     - name: upload to codecov.io

src/torchpme/utils/tuning/ewald.py

@@ -71,7 +71,7 @@ def tune_ewald(
     Example
     -------
     >>> import torch
-    >>> from vesin.torch import NeighborList
+    >>> from vesin import NeighborList
     >>> positions = torch.tensor(
     ...     [[0.0, 0.0, 0.0], [0.5, 0.5, 0.5]], dtype=torch.float64
     ... )
@@ -96,19 +96,23 @@ def tune_ewald(
     example, fixing the cutoff to 0.1
 
     >>> smearing, parameter, cutoff = tune_ewald(
-    ...     torch.sum(charges**2, dim=0), cell, positions, cutoff=0.1, accuracy=1e-1
+    ...     torch.sum(charges**2, dim=0), cell, positions, cutoff=0.4, accuracy=1e-1
     ... )
 
     You can check the values of the parameters, now the cutoff is fixed
 
-    >>> print(smearing)
-    0.03338293212285428
+    >>> print(round(smearing, 4))
+    0.1402
 
-    >>> print(parameter)
-    {'lr_wavelength': 0.009495381717988971}
+    We can also check the value of the other parameter like the ``lr_wavelength``
+
+    >>> print(round(parameter["lr_wavelength"], 3))
+    0.255
+
+    and finally as requested the value of the cutoff is fixed
 
     >>> print(cutoff)
-    0.1
+    0.4
 
     """
     _validate_parameters(sum_squared_charges, cell, positions, exponent, accuracy)

src/torchpme/utils/tuning/p3m.py

@@ -121,7 +121,7 @@ def tune_p3m(
     Example
     -------
     >>> import torch
-    >>> from vesin.torch import NeighborList
+    >>> from vesin import NeighborList
     >>> _ = torch.manual_seed(0)
     >>> positions = torch.tensor(
     ...     [[0.0, 0.0, 0.0], [0.5, 0.5, 0.5]], dtype=torch.float64

src/torchpme/utils/tuning/pme.py

@@ -70,7 +70,7 @@ def tune_pme(
     Example
     -------
     >>> import torch
-    >>> from vesin.torch import NeighborList
+    >>> from vesin import NeighborList
     >>> _ = torch.manual_seed(0)
     >>> positions = torch.tensor(
     ...     [[0.0, 0.0, 0.0], [0.5, 0.5, 0.5]], dtype=torch.float64

tests/calculators/test_values_direct.py

@@ -8,7 +8,7 @@
 from torchpme import Calculator, CoulombPotential
 
 sys.path.append(str(Path(__file__).parents[1]))
-from helpers import neighbor_list_torch
+from helpers import neighbor_list
 
 DTYPE = torch.float64
 
@@ -184,7 +184,7 @@ def test_coulomb_exact(
     positions = scaling_factor * (positions @ orthogonal_transformation)
 
     # Choose a large cutoff that covers all atoms
-    neighbor_indices, neighbor_distances = neighbor_list_torch(
+    neighbor_indices, neighbor_distances = neighbor_list(
         positions=positions,
         periodic=False,
         cutoff=scaling_factor * 10,

tests/calculators/test_values_ewald.py

@@ -21,7 +21,7 @@
     COULOMB_TEST_FRAMES,
     compute_distances,
     define_crystal,
-    neighbor_list_torch,
+    neighbor_list,
 )
 
 DTYPE = torch.float64
@@ -127,7 +127,7 @@ def test_madelung(crystal_name, scaling_factor, calc_name):
         rtol = 9e-4
 
     # Compute neighbor list
-    neighbor_indices, neighbor_distances = neighbor_list_torch(
+    neighbor_indices, neighbor_distances = neighbor_list(
         positions=pos, periodic=True, box=cell, cutoff=sr_cutoff
     )
 
@@ -178,7 +178,7 @@ def test_wigner(crystal_name, scaling_factor):
     madelung_ref /= scaling_factor
 
     # Compute neighbor list
-    neighbor_indices, neighbor_distances = neighbor_list_torch(
+    neighbor_indices, neighbor_distances = neighbor_list(
         positions=positions, periodic=True, box=cell
     )
 
@@ -272,14 +272,24 @@ def test_random_structure(
             prefactor=torchpme.utils.prefactors.eV_A,
         )
 
-    positions.requires_grad = True
-
-    neighbor_indices, neighbor_distances = neighbor_list_torch(
+    neighbor_indices, neighbor_shifts = neighbor_list(
         positions=positions,
         periodic=True,
         box=cell,
         cutoff=cutoff,
         full_neighbor_list=full_neighbor_list,
+        neighbor_shifts=True,
+    )
+
+    # Use manual distance calculation to avoiding issues with the torchscript version of
+    # vesin on Windows. See https://github.com/Luthaf/vesin/issues/21 for more details.
+    positions.requires_grad = True
+
+    neighbor_distances = compute_distances(
+        positions=positions,
+        neighbor_indices=neighbor_indices,
+        cell=cell,
+        neighbor_shifts=neighbor_shifts,
     )
 
     calc.to(dtype=DTYPE)
@@ -303,21 +313,12 @@ def test_random_structure(
     forces_target = torch.tensor(frame.get_forces(), dtype=DTYPE) / scaling_factor**2
     torch.testing.assert_close(forces, forces_target @ ortho, atol=0.0, rtol=5e-3)
 
-    neighbor_indices, neighbor_shifts = neighbor_list_torch(
-        positions=positions,
-        periodic=True,
-        box=cell,
-        cutoff=cutoff,
-        full_neighbor_list=full_neighbor_list,
-        neighbor_shifts=True,
-    )
-
     # Compute stress
     def energy_wrt_strain(strain):
         strained_R = positions + torch.einsum("ab,ib->ia", strain, positions)
         strained_cell = cell + torch.einsum("ab,Ab->Aa", strain, cell)
 
-        d = compute_distances(
+        neighbor_distances = compute_distances(
             strained_R,
             neighbor_indices,
             cell=strained_cell,
@@ -330,7 +331,7 @@ def energy_wrt_strain(strain):
                 cell=strained_cell,
                 positions=strained_R,
                 neighbor_indices=neighbor_indices,
-                neighbor_distances=d,
+                neighbor_distances=neighbor_distances,
             )
         ).sum()
 

tests/helpers.py

@@ -5,7 +5,7 @@
 from typing import Optional
 
 import torch
-from vesin.torch import NeighborList
+from vesin import NeighborList
 
 DTYPE = torch.float64
 SQRT3 = math.sqrt(3)
@@ -239,7 +239,7 @@ def define_crystal(crystal_name="CsCl"):
     return positions, charges, cell, madelung_ref, num_formula_units
 
 
-def neighbor_list_torch(
+def neighbor_list(
     positions: torch.tensor,
     periodic: bool = True,
     box: Optional[torch.tensor] = None,
@@ -260,6 +260,11 @@ def neighbor_list_torch(
         points=positions, box=box, periodic=periodic, quantities="ijdS"
     )
 
+    i = torch.from_numpy(i.astype(int)).to(device=positions.device)
+    j = torch.from_numpy(j.astype(int))
+    d = torch.from_numpy(d).to(dtype=positions.dtype, device=positions.device)
+    S = torch.from_numpy(S).to(dtype=positions.dtype, device=positions.device)
+
     neighbor_indices = torch.stack([i, j], dim=1)
 
     if not neighbor_shifts:

tests/requirements.txt

@@ -3,4 +3,4 @@ coverage[toml]
 pytest
 pytest-cov
 scipy
-vesin-torch >= 0.2.0
+vesin >= 0.2.0

tests/utils/test_tuning.py

@@ -14,7 +14,7 @@
 from torchpme.utils import tune_ewald, tune_p3m, tune_pme
 
 sys.path.append(str(Path(__file__).parents[1]))
-from helpers import define_crystal, neighbor_list_torch
+from helpers import define_crystal, neighbor_list
 
 DTYPE = torch.float32
 DEVICE = "cpu"
@@ -51,7 +51,7 @@ def test_parameter_choose(calculator, tune, param_length, accuracy):
     assert len(params) == param_length
 
     # Compute neighbor list
-    neighbor_indices, neighbor_distances = neighbor_list_torch(
+    neighbor_indices, neighbor_distances = neighbor_list(
         positions=pos, periodic=True, box=cell, cutoff=sr_cutoff
     )
 
@@ -84,7 +84,7 @@ def test_odd_interpolation_nodes():
         learning_rate=0.75,
     )
 
-    neighbor_indices, neighbor_distances = neighbor_list_torch(
+    neighbor_indices, neighbor_distances = neighbor_list(
         positions=pos, periodic=True, box=cell, cutoff=sr_cutoff
     )