Reduce tolerance

test/test_grad/test_hessian.py

@@ -38,13 +38,12 @@
 from tad_dftd4 import dftd4
 from tad_dftd4.typing import DD, Tensor
 
+from ..conftest import DEVICE
 from .samples_hessian import samples
 
 sample_list = ["LiH", "SiH4", "PbH4-BiH3", "MB16_43_01"]
 
-tol = 1e-8
-
-device = None
+tol = 1e-7
 
 
 def test_fail() -> None:
@@ -72,10 +71,10 @@ def dummy(x: Tensor) -> Tensor:
 @pytest.mark.parametrize("dtype", [torch.double])
 @pytest.mark.parametrize("name", sample_list)
 def test_single(dtype: torch.dtype, name: str) -> None:
-    dd: DD = {"device": device, "dtype": dtype}
+    dd: DD = {"device": DEVICE, "dtype": dtype}
 
     sample = samples[name]
-    numbers = sample["numbers"].to(device)
+    numbers = sample["numbers"].to(DEVICE)
     positions = sample["positions"].to(**dd)
     charge = torch.tensor(0.0, **dd)
 
@@ -107,13 +106,13 @@ def test_single(dtype: torch.dtype, name: str) -> None:
 @pytest.mark.parametrize("name1", ["LiH"])
 @pytest.mark.parametrize("name2", sample_list)
 def skip_test_batch(dtype: torch.dtype, name1: str, name2: str) -> None:
-    dd: DD = {"device": device, "dtype": dtype}
+    dd: DD = {"device": DEVICE, "dtype": dtype}
 
     sample1, sample2 = samples[name1], samples[name2]
     numbers = pack(
         [
-            sample1["numbers"].to(device),
-            sample2["numbers"].to(device),
+            sample1["numbers"].to(DEVICE),
+            sample2["numbers"].to(DEVICE),
         ]
     )
     positions = pack(

test/test_grad/test_nan.py

@@ -27,9 +27,10 @@
 from tad_dftd4 import dftd4
 from tad_dftd4.typing import DD
 
-tol = 1e-8
+from ..conftest import DEVICE
+
+tol = 1e-7
 
-device = None
 
 # sample, which previously failed with NaN's in tad-dftd3
 numbers = torch.tensor([6, 6, 6, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1, 7, 8, 8, 8])
@@ -68,9 +69,9 @@
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.float, torch.double])
 def test_single(dtype: torch.dtype) -> None:
-    dd: DD = {"device": device, "dtype": dtype}
+    dd: DD = {"device": DEVICE, "dtype": dtype}
 
-    nums = numbers.to(device=device)
+    nums = numbers.to(device=DEVICE)
     pos = positions.to(**dd)
     chrg = charge.to(**dd)
     par = {k: v.to(**dd) for k, v in param.items()}
@@ -96,12 +97,12 @@ def test_single(dtype: torch.dtype) -> None:
 @pytest.mark.parametrize("dtype", [torch.float, torch.double])
 @pytest.mark.parametrize("name", ["LiH", "SiH4"])
 def test_batch(dtype: torch.dtype, name: str) -> None:
-    dd: DD = {"device": device, "dtype": dtype}
+    dd: DD = {"device": DEVICE, "dtype": dtype}
 
     nums = pack(
         (
-            numbers.to(device=device),
-            samples[name]["numbers"].to(device=device),
+            numbers.to(device=DEVICE),
+            samples[name]["numbers"].to(device=DEVICE),
         )
     )
     pos = pack(

test/test_grad/test_param.py

@@ -28,21 +28,21 @@
 from tad_dftd4 import dftd4
 from tad_dftd4.typing import DD, Callable, Tensor
 
-sample_list = ["LiH", "AmF3", "SiH4"]
+from ..conftest import DEVICE
 
-tol = 1e-8
+sample_list = ["LiH", "AmF3", "SiH4"]
 
-device = None
+tol = 1e-7
 
 
 def gradchecker(dtype: torch.dtype, name: str) -> tuple[
     Callable[[Tensor, Tensor, Tensor, Tensor], Tensor],  # autograd function
     tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor],
 ]:
-    dd: DD = {"device": device, "dtype": dtype}
+    dd: DD = {"device": DEVICE, "dtype": dtype}
 
     sample = samples[name]
-    numbers = sample["numbers"].to(device=device)
+    numbers = sample["numbers"].to(device=DEVICE)
     positions = sample["positions"].to(**dd)
     charge = torch.tensor(0.0, **dd)
 
@@ -104,13 +104,13 @@ def gradchecker_batch(dtype: torch.dtype, name1: str, name2: str) -> tuple[
     Callable[[Tensor, Tensor, Tensor, Tensor], Tensor],  # autograd function
     tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor],
 ]:
-    dd: DD = {"device": device, "dtype": dtype}
+    dd: DD = {"device": DEVICE, "dtype": dtype}
 
     sample1, sample2 = samples[name1], samples[name2]
     numbers = pack(
         [
-            sample1["numbers"].to(device=device),
-            sample2["numbers"].to(device=device),
+            sample1["numbers"].to(device=DEVICE),
+            sample2["numbers"].to(device=DEVICE),
         ]
     )
     positions = pack(

test/test_grad/test_pos.py

@@ -32,7 +32,7 @@
 
 sample_list = ["LiH", "SiH4", "PbH4-BiH3", "MB16_43_01"]
 
-tol = 1e-8
+tol = 1e-7
 
 
 def gradchecker(dtype: torch.dtype, name: str) -> tuple[