More tests

src/dxtb/_src/calculators/types/energy.py

@@ -325,15 +325,31 @@ def singlepoint(
 
         if kwargs.get("store_hcore", copts.hcore):
             self.cache["hcore"] = self.integrals.hcore
+        else:
+            if self.integrals.hcore is not None:
+                if self.integrals.hcore.requires_grad is False:
+                    self.integrals.hcore.clear()
 
         if kwargs.get("store_overlap", copts.overlap):
             self.cache["overlap"] = self.integrals.overlap
+        else:
+            if self.integrals.overlap is not None:
+                if self.integrals.overlap.requires_grad is False:
+                    self.integrals.overlap.clear()
 
         if kwargs.get("store_dipole", copts.dipole):
             self.cache["dipint"] = self.integrals.dipole
+        else:
+            if self.integrals.dipole is not None:
+                if self.integrals.dipole.requires_grad is False:
+                    self.integrals.dipole.clear()
 
         if kwargs.get("store_quadrupole", copts.quadrupole):
             self.cache["quadint"] = self.integrals.quadrupole
+        else:
+            if self.integrals.quadrupole is not None:
+                if self.integrals.quadrupole.requires_grad is False:
+                    self.integrals.quadrupole.clear()
 
         self._ncalcs += 1
         return result

src/dxtb/_src/components/interactions/coulomb/secondorder.py

@@ -32,11 +32,11 @@
     # Define atomic numbers, positions, and charges
     numbers = torch.tensor([14, 1, 1, 1, 1])
     positions = torch.tensor([
-        [0.00000000000000, -0.00000000000000, 0.00000000000000],
-        [1.61768389755830, 1.61768389755830, -1.61768389755830],
+        [+0.00000000000000, -0.00000000000000, +0.00000000000000],
+        [+1.61768389755830, +1.61768389755830, -1.61768389755830],
         [-1.61768389755830, -1.61768389755830, -1.61768389755830],
-        [1.61768389755830, -1.61768389755830, 1.61768389755830],
-        [-1.61768389755830, 1.61768389755830, 1.61768389755830],
+        [+1.61768389755830, -1.61768389755830, +1.61768389755830],
+        [-1.61768389755830, +1.61768389755830, +1.61768389755830],
     ])
 
     q = torch.tensor([

src/dxtb/_src/components/interactions/coulomb/thirdorder.py

@@ -33,11 +33,11 @@
     # Define atomic numbers and their positions
     numbers = torch.tensor([14, 1, 1, 1, 1])
     positions = torch.tensor([
-        [0.00000000000000, -0.00000000000000, 0.00000000000000],
-        [1.61768389755830, 1.61768389755830, -1.61768389755830],
+        [+0.00000000000000, -0.00000000000000, +0.00000000000000],
+        [+1.61768389755830, +1.61768389755830, -1.61768389755830],
         [-1.61768389755830, -1.61768389755830, -1.61768389755830],
-        [1.61768389755830, -1.61768389755830, 1.61768389755830],
-        [-1.61768389755830, 1.61768389755830, 1.61768389755830],
+        [+1.61768389755830, -1.61768389755830, +1.61768389755830],
+        [-1.61768389755830, +1.61768389755830, +1.61768389755830],
     ])
 
     # Atomic charges

src/dxtb/_src/components/interactions/solvation/alpb.py

@@ -29,11 +29,11 @@
 
     numbers = torch.tensor([14, 1, 1, 1, 1])
     positions = torch.tensor([
-        [0.00000000000000, -0.00000000000000, 0.00000000000000],
-        [1.61768389755830, 1.61768389755830, -1.61768389755830],
+        [+0.00000000000000, -0.00000000000000, +0.00000000000000],
+        [+1.61768389755830, +1.61768389755830, -1.61768389755830],
         [-1.61768389755830, -1.61768389755830, -1.61768389755830],
-        [1.61768389755830, -1.61768389755830, 1.61768389755830],
-        [-1.61768389755830, 1.61768389755830, 1.61768389755830],
+        [+1.61768389755830, -1.61768389755830, +1.61768389755830],
+        [-1.61768389755830, +1.61768389755830, +1.61768389755830],
     ])
     charges = torch.tensor([
         -8.41282505804719e-2,

src/dxtb/_src/components/interactions/solvation/born.py

@@ -31,11 +31,11 @@
     # Define atomic numbers and positions of the atoms
     numbers = torch.tensor([14, 1, 1, 1, 1])
     positions = torch.tensor([
-        [0.00000000000000, -0.00000000000000, 0.00000000000000],
-        [1.61768389755830, 1.61768389755830, -1.61768389755830],
+        [+0.00000000000000, -0.00000000000000, +0.00000000000000],
+        [+1.61768389755830, +1.61768389755830, -1.61768389755830],
         [-1.61768389755830, -1.61768389755830, -1.61768389755830],
-        [1.61768389755830, -1.61768389755830, 1.61768389755830],
-        [-1.61768389755830, 1.61768389755830, 1.61768389755830],
+        [+1.61768389755830, -1.61768389755830, +1.61768389755830],
+        [-1.61768389755830, +1.61768389755830, +1.61768389755830],
     ])
 
     # Calculate the Born radii for the given atomic configuration

src/dxtb/_src/integral/base.py

@@ -187,15 +187,12 @@ def setup(self, positions: Tensor, **kwargs) -> None:
         """
 
     def __str__(self) -> str:
-        dict_repr = []
-        for key, value in self.__dict__.items():
-            if isinstance(value, Tensor):
-                value_repr = f"{value.shape}"
-            else:
-                value_repr = repr(value)
-            dict_repr.append(f"    {key}: {value_repr}")
-        dict_str = "{\n" + ",\n".join(dict_repr) + "\n}"
-        return f"{self.__class__.__name__}({dict_str})"
+        return (
+            f"{self.__class__.__name__}("
+            f"Family: {self.family}, "
+            f"Number of Atoms: {self.numbers.shape[-1]}, "
+            f"Setup?: {self.is_setup()})"
+        )
 
     def __repr__(self) -> str:
         return str(self)
@@ -328,6 +325,22 @@ def clear(self) -> None:
         self._norm = None
         self._gradient = None
 
+    @property
+    def requires_grad(self) -> bool:
+        """
+        Check if any field of the integral class is requires gradient.
+
+        Returns
+        -------
+        bool
+            Flag for gradient requirement.
+        """
+        for field in (self._matrix, self._gradient, self._norm):
+            if field is not None and field.requires_grad:
+                return True
+
+        return False
+
     def normalize(self, norm: Tensor | None = None) -> None:
         """
         Normalize the integral (changes ``self.matrix``).
@@ -401,6 +414,8 @@ def __str__(self) -> str:
             d["_matrix"] = self._matrix.shape
         if self._gradient is not None:
             d["_gradient"] = self._gradient.shape
+        if self._norm is not None:
+            d["_norm"] = self._norm.shape
 
         return f"{self.__class__.__name__}({d})"
 

src/dxtb/_src/integral/driver/factory.py

@@ -57,7 +57,9 @@ def new_driver(
         return new_driver_pytorch(numbers, par, device=device, dtype=dtype)
 
     if name == labels.INTDRIVER_AUTOGRAD:
-        return new_driver_pytorch2(numbers, par, device=device, dtype=dtype)
+        return new_driver_pytorch_no_analytical(
+            numbers, par, device=device, dtype=dtype
+        )
 
     if name == labels.INTDRIVER_LEGACY:
         return new_driver_legacy(numbers, par, device=device, dtype=dtype)
@@ -97,7 +99,7 @@ def new_driver_pytorch(
     return _IntDriver(numbers, par, ihelp, device=device, dtype=dtype)
 
 
-def new_driver_pytorch2(
+def new_driver_pytorch_no_analytical(
     numbers: Tensor,
     par: Param,
     device: torch.device | None = None,

src/dxtb/_src/integral/types/quadrupole.py

@@ -68,7 +68,7 @@ def traceless(self) -> Tensor:
         zx zy zz       6 7 8      6 7 8
         """
 
-        if self.matrix.shape[-3] != 9:
+        if self.matrix.ndim not in (3, 4) or self.matrix.shape[-3] != 9:
             raise RuntimeError(
                 "Quadrupole integral must be a tensor tensor of shape "
                 f"'(9, nao, nao)' but is {self.matrix.shape}."

src/dxtb/_src/integral/utils.py

@@ -30,4 +30,5 @@
 
 def snorm(ovlp: Tensor) -> Tensor:
     d = ovlp.diagonal(dim1=-1, dim2=-2)
-    return torch.where(d == 0.0, 0.0, torch.pow(d, -0.5))
+    zero = torch.tensor(0.0, dtype=d.dtype, device=d.device)
+    return torch.where(d == 0.0, zero, torch.pow(d, -0.5))

src/dxtb/_src/integral/wrappers.py

@@ -72,10 +72,8 @@
 from dxtb._src.xtb.gfn1 import GFN1Hamiltonian
 from dxtb._src.xtb.gfn2 import GFN2Hamiltonian
 
-from .driver.factory import new_driver
 from .driver.manager import DriverManager
 from .factory import new_dipint, new_overlap, new_quadint
-from .types import DipoleIntegral, OverlapIntegral, QuadrupoleIntegral
 
 __all__ = ["hcore", "overlap", "dipint", "quadint"]
 
@@ -100,20 +98,20 @@ def hcore(numbers: Tensor, positions: Tensor, par: Param, **kwargs: Any) -> Tens
 
     Raises
     ------
-    TypeError
+    ValueError
         If the Hamiltonian parametrization does not contain meta data or if the
         meta data does not contain a name to select the correct Hamiltonian.
     ValueError
         If the Hamiltonian name is unknown.
     """
     if par.meta is None:
-        raise TypeError(
+        raise ValueError(
             "Meta data of Hamiltonian parametrization must contain a name. "
             "Otherwise, the correct Hamiltonian cannot be selected internally."
         )
 
     if par.meta.name is None:
-        raise TypeError(
+        raise ValueError(
             "The name field of the meta data of the Hamiltonian "
             "parametrization must contain a name. Otherwise, the correct "
             "Hamiltonian cannot be selected internally."

src/dxtb/_src/xtb/base.py

@@ -131,6 +131,13 @@ def clear(self) -> None:
         """
         self._matrix = None
 
+    @property
+    def requires_grad(self) -> bool:
+        if self._matrix is None:
+            return False
+
+        return self._matrix.requires_grad
+
     def get_occupation(self) -> Tensor:
         """
         Obtain the reference occupation numbers for each orbital.

test/test_calculator/test_cache/test_integrals.py

@@ -0,0 +1,88 @@
+# This file is part of dxtb.
+#
+# SPDX-Identifier: Apache-2.0
+# Copyright (C) 2024 Grimme Group
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Test caching integrals.
+"""
+
+from __future__ import annotations
+
+import pytest
+import torch
+
+from dxtb._src.typing import DD, Tensor
+from dxtb.calculators import GFN1Calculator
+
+from ...conftest import DEVICE
+
+opts = {"cache_enabled": True, "verbosity": 0}
+
+
+@pytest.mark.parametrize("dtype", [torch.float, torch.double])
+def test_overlap_deleted(dtype: torch.dtype) -> None:
+    dd: DD = {"device": DEVICE, "dtype": dtype}
+
+    numbers = torch.tensor([3, 1], device=DEVICE)
+    positions = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], **dd)
+
+    calc = GFN1Calculator(numbers, opts={"verbosity": 0}, **dd)
+    assert calc._ncalcs == 0
+
+    # overlap should not be cached
+    assert calc.opts.cache.store.overlap == False
+
+    # one successful calculation
+    energy = calc.get_energy(positions)
+    assert calc._ncalcs == 1
+    assert isinstance(energy, Tensor)
+
+    # cache should be empty
+    assert calc.cache.overlap is None
+
+    # ... but also the tensors in the calculator should be deleted
+    assert calc.integrals.overlap is not None
+    assert calc.integrals.overlap._matrix is None
+    assert calc.integrals.overlap._norm is None
+    assert calc.integrals.overlap._gradient is None
+
+
+@pytest.mark.parametrize("dtype", [torch.float, torch.double])
+def test_overlap_retained_for_grad(dtype: torch.dtype) -> None:
+    dd: DD = {"device": DEVICE, "dtype": dtype}
+
+    numbers = torch.tensor([3, 1], device=DEVICE)
+    positions = torch.tensor(
+        [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], **dd, requires_grad=True
+    )
+
+    calc = GFN1Calculator(numbers, opts={"verbosity": 0}, **dd)
+    assert calc._ncalcs == 0
+
+    # overlap should not be cached
+    assert calc.opts.cache.store.overlap == False
+
+    # one successful calculation
+    energy = calc.get_energy(positions)
+    assert calc._ncalcs == 1
+    assert isinstance(energy, Tensor)
+
+    # cache should still be empty ...
+    assert calc.cache.overlap is None
+
+    # ... but the tensors in the calculator should still be there
+    assert calc.integrals.overlap is not None
+    assert calc.integrals.overlap._matrix is not None
+    assert calc.integrals.overlap._norm is not None

test/test_indexhelper/test_extra.py

@@ -168,7 +168,8 @@ def test_spread_unique_batch() -> None:
     x = torch.randn((nbatch, nat_u, 3), device=DEVICE)
 
     # pollutes CUDA memory
-    assert False
+    if DEVICE is not None:
+        assert False
 
     out = ihelp.spread_uspecies_to_atom(x, dim=-2, extra=True)
     assert out.shape == torch.Size((nbatch, nat, 3))

test/test_integrals/test_driver/__init__.py

@@ -0,0 +1,16 @@
+# This file is part of dxtb.
+#
+# SPDX-Identifier: Apache-2.0
+# Copyright (C) 2024 Grimme Group
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.