Reduce number of tests

grimme-lab · Jun 29, 2024 · 3f93593 · 3f93593
1 parent 6c5bf86
commit 3f93593
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Showing 25 changed files with 496 additions and 83 deletions.
diff --git a/test/test_a_memory_leak/test_higher_deriv.py b/test/test_a_memory_leak/test_higher_deriv.py
@@ -37,14 +37,11 @@
 from ..utils import nth_derivative
 from .util import garbage_collect, has_memleak_tensor
 
-sample_list = ["H2O", "SiH4", "MB16_43_01"]
+slist = ["H2O", "SiH4"]
+slist_large = ["MB16_43_01"]
 
 
-@pytest.mark.filterwarnings("ignore")
-@pytest.mark.parametrize("dtype", [torch.float, torch.double])
-@pytest.mark.parametrize("name", sample_list)
-@pytest.mark.parametrize("n", [1, 2, 3, 4])
-def test_single(dtype: torch.dtype, name: str, n: int) -> None:
+def execute(name: str, dtype: torch.dtype, n: int) -> None:
     dd: DD = {"dtype": dtype, "device": DEVICE}
 
     def fcn():
@@ -80,3 +77,20 @@ def fcn():
     garbage_collect()
 
     assert not leak, "Memory leak detected"
+
+
+@pytest.mark.filterwarnings("ignore::UserWarning")
+@pytest.mark.parametrize("dtype", [torch.float, torch.double])
+@pytest.mark.parametrize("name", slist)
+@pytest.mark.parametrize("n", [1, 2, 3, 4])
+def test_single(dtype: torch.dtype, name: str, n: int) -> None:
+    execute(name, dtype, n)
+
+
+@pytest.mark.large
+@pytest.mark.filterwarnings("ignore::UserWarning")
+@pytest.mark.parametrize("dtype", [torch.float, torch.double])
+@pytest.mark.parametrize("name", slist_large)
+@pytest.mark.parametrize("n", [1, 2, 3, 4])
+def test_large(dtype: torch.dtype, name: str, n: int) -> None:
+    execute(name, dtype, n)
diff --git a/test/test_a_memory_leak/test_repulsion.py b/test/test_a_memory_leak/test_repulsion.py
@@ -37,13 +37,11 @@
 from ..conftest import DEVICE
 from .util import garbage_collect, has_memleak_tensor
 
-sample_list = ["H2O", "SiH4", "MB16_43_01"]
+slist = ["H2O", "SiH4"]
+slist_large = ["MB16_43_01"]
 
 
-@pytest.mark.filterwarnings("ignore")
-@pytest.mark.parametrize("dtype", [torch.float, torch.double])
-@pytest.mark.parametrize("name", sample_list)
-def test_single(dtype: torch.dtype, name: str) -> None:
+def execute(name: str, dtype: torch.dtype) -> None:
     dd: DD = {"dtype": dtype, "device": DEVICE}
 
     def fcn():
@@ -106,3 +104,18 @@ def fcn():
     garbage_collect()
 
     assert not leak, "Memory leak detected"
+
+
+@pytest.mark.filterwarnings("ignore::UserWarning")
+@pytest.mark.parametrize("dtype", [torch.float, torch.double])
+@pytest.mark.parametrize("name", slist)
+def test_single(dtype: torch.dtype, name: str) -> None:
+    execute(name, dtype)
+
+
+@pytest.mark.large
+@pytest.mark.filterwarnings("ignore::UserWarning")
+@pytest.mark.parametrize("dtype", [torch.float, torch.double])
+@pytest.mark.parametrize("name", slist_large)
+def test_large(dtype: torch.dtype, name: str) -> None:
+    execute(name, dtype)
diff --git a/test/test_a_memory_leak/test_scf.py b/test/test_a_memory_leak/test_scf.py
@@ -49,7 +49,7 @@ def test_xitorch(dtype: torch.dtype, run_gc: bool, create_graph: bool) -> None:
     dd: DD = {"dtype": dtype, "device": DEVICE}
 
     def fcn():
-        sample = samples["SiH4"]
+        sample = samples["LiH"]
         numbers = sample["numbers"].to(DEVICE)
         positions = sample["positions"].clone().to(**dd)
         charges = torch.tensor(0.0, **dd)

diff --git a/test/test_a_memory_leak/util.py b/test/test_a_memory_leak/util.py
@@ -39,18 +39,12 @@ def garbage_collect() -> None:
         torch.cuda.empty_cache()
 
 
-def _tensors_from_gc(gpu_only=False) -> Generator[Tensor, None, None]:
+def _tensors_from_gc() -> Generator[Tensor, None, None]:
+    # return [obj for obj in gc.get_objects() if isinstance(obj, Tensor)]
     for obj in gc.get_objects():
         try:
-            if torch.is_tensor(obj):
-                tensor = obj
-            elif hasattr(obj, "data") and torch.is_tensor(obj.data):
-                tensor = obj.data
-            else:
-                continue
-
-            if tensor.is_cuda or not gpu_only:
-                yield tensor
+            if isinstance(obj, Tensor):
+                yield obj
         except Exception:  # nosec B112 pylint: disable=broad-exception-caught
             continue
 

diff --git a/test/test_dispersion/test_grad_param.py b/test/test_dispersion/test_grad_param.py
@@ -31,7 +31,8 @@
 from ..conftest import DEVICE
 from .samples import samples
 
-sample_list = ["LiH", "SiH4", "MB16_43_01", "PbH4-BiH3"]
+slist = ["LiH", "SiH4"]
+slist_large = ["MB16_43_01", "PbH4-BiH3"]
 
 tol = 1e-8
 
@@ -66,7 +67,7 @@ def func(*inputs: Tensor) -> Tensor:
 
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
-@pytest.mark.parametrize("name", sample_list)
+@pytest.mark.parametrize("name", slist)
 def test_gradcheck(dtype: torch.dtype, name: str) -> None:
     """
     Check a single analytical gradient of parameters against numerical
@@ -77,8 +78,21 @@ def test_gradcheck(dtype: torch.dtype, name: str) -> None:
 
 
 @pytest.mark.grad
+@pytest.mark.large
 @pytest.mark.parametrize("dtype", [torch.double])
-@pytest.mark.parametrize("name", sample_list)
+@pytest.mark.parametrize("name", slist_large)
+def test_gradcheck_large(dtype: torch.dtype, name: str) -> None:
+    """
+    Check a single analytical gradient of parameters against numerical
+    gradient from `torch.autograd.gradcheck`.
+    """
+    func, diffvars = gradchecker(dtype, name)
+    assert dgradcheck(func, diffvars, atol=tol)
+
+
+@pytest.mark.grad
+@pytest.mark.parametrize("dtype", [torch.double])
+@pytest.mark.parametrize("name", slist)
 def test_gradgradcheck(dtype: torch.dtype, name: str) -> None:
     """
     Check a single analytical gradient of parameters against numerical
@@ -88,6 +102,19 @@ def test_gradgradcheck(dtype: torch.dtype, name: str) -> None:
     assert dgradgradcheck(func, diffvars, atol=tol)
 
 
+@pytest.mark.grad
+@pytest.mark.large
+@pytest.mark.parametrize("dtype", [torch.double])
+@pytest.mark.parametrize("name", slist_large)
+def test_gradgradcheck_large(dtype: torch.dtype, name: str) -> None:
+    """
+    Check a single analytical gradient of parameters against numerical
+    gradient from `torch.autograd.gradgradcheck`.
+    """
+    func, diffvars = gradchecker(dtype, name)
+    assert dgradgradcheck(func, diffvars, atol=tol)
+
+
 def gradchecker_batch(dtype: torch.dtype, name1: str, name2: str) -> tuple[
     Callable[[Tensor, Tensor, Tensor, Tensor], Tensor],  # autograd function
     tuple[Tensor, Tensor, Tensor, Tensor],  # differentiable variables
@@ -129,7 +156,7 @@ def func(*inputs: Tensor) -> Tensor:
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
 @pytest.mark.parametrize("name1", ["LiH"])
-@pytest.mark.parametrize("name2", sample_list)
+@pytest.mark.parametrize("name2", slist)
 def test_gradcheck_batch(dtype: torch.dtype, name1: str, name2: str) -> None:
     """
     Check a single analytical gradient of parameters against numerical
@@ -139,14 +166,42 @@ def test_gradcheck_batch(dtype: torch.dtype, name1: str, name2: str) -> None:
     assert dgradcheck(func, diffvars, atol=tol)
 
 
+@pytest.mark.grad
+@pytest.mark.large
+@pytest.mark.parametrize("dtype", [torch.double])
+@pytest.mark.parametrize("name1", ["LiH"])
+@pytest.mark.parametrize("name2", slist_large)
+def test_gradcheck_batch_large(dtype: torch.dtype, name1: str, name2: str) -> None:
+    """
+    Check a single analytical gradient of parameters against numerical
+    gradient from `torch.autograd.gradcheck`.
+    """
+    func, diffvars = gradchecker_batch(dtype, name1, name2)
+    assert dgradcheck(func, diffvars, atol=tol)
+
+
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
 @pytest.mark.parametrize("name1", ["LiH"])
-@pytest.mark.parametrize("name2", sample_list)
+@pytest.mark.parametrize("name2", slist)
 def test_gradgradcheck_batch(dtype: torch.dtype, name1: str, name2: str) -> None:
     """
     Check a single analytical gradient of parameters against numerical
     gradient from `torch.autograd.gradgradcheck`.
     """
     func, diffvars = gradchecker_batch(dtype, name1, name2)
     assert dgradgradcheck(func, diffvars, atol=tol)
+
+
+@pytest.mark.grad
+@pytest.mark.large
+@pytest.mark.parametrize("dtype", [torch.double])
+@pytest.mark.parametrize("name1", ["LiH"])
+@pytest.mark.parametrize("name2", slist_large)
+def test_gradgradcheck_batch_large(dtype: torch.dtype, name1: str, name2: str) -> None:
+    """
+    Check a single analytical gradient of parameters against numerical
+    gradient from `torch.autograd.gradgradcheck`.
+    """
+    func, diffvars = gradchecker_batch(dtype, name1, name2)
+    assert dgradgradcheck(func, diffvars, atol=tol)
diff --git a/test/test_dispersion/test_grad_pos.py b/test/test_dispersion/test_grad_pos.py
@@ -32,7 +32,8 @@
 from ..conftest import DEVICE
 from .samples import samples
 
-sample_list = ["LiH", "SiH4", "MB16_43_01", "PbH4-BiH3"]
+slist = ["LiH", "SiH4"]
+slist_large = ["MB16_43_01", "PbH4-BiH3"]
 
 tol = 1e-8
 
@@ -63,7 +64,7 @@ def func(positions: Tensor) -> Tensor:
 
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
-@pytest.mark.parametrize("name", sample_list)
+@pytest.mark.parametrize("name", slist)
 def test_gradcheck(dtype: torch.dtype, name: str) -> None:
     """
     Check a single analytical gradient of parameters against numerical
@@ -74,8 +75,21 @@ def test_gradcheck(dtype: torch.dtype, name: str) -> None:
 
 
 @pytest.mark.grad
+@pytest.mark.large
 @pytest.mark.parametrize("dtype", [torch.double])
-@pytest.mark.parametrize("name", sample_list)
+@pytest.mark.parametrize("name", slist_large)
+def test_gradcheck_large(dtype: torch.dtype, name: str) -> None:
+    """
+    Check a single analytical gradient of parameters against numerical
+    gradient from `torch.autograd.gradcheck`.
+    """
+    func, diffvars = gradchecker(dtype, name)
+    assert dgradcheck(func, diffvars, atol=tol)
+
+
+@pytest.mark.grad
+@pytest.mark.parametrize("dtype", [torch.double])
+@pytest.mark.parametrize("name", slist)
 def test_gradgradcheck(dtype: torch.dtype, name: str) -> None:
     """
     Check a single analytical gradient of parameters against numerical
@@ -85,6 +99,19 @@ def test_gradgradcheck(dtype: torch.dtype, name: str) -> None:
     assert dgradgradcheck(func, diffvars, atol=tol)
 
 
+@pytest.mark.grad
+@pytest.mark.large
+@pytest.mark.parametrize("dtype", [torch.double])
+@pytest.mark.parametrize("name", slist_large)
+def test_gradgradcheck_large(dtype: torch.dtype, name: str) -> None:
+    """
+    Check a single analytical gradient of parameters against numerical
+    gradient from `torch.autograd.gradgradcheck`.
+    """
+    func, diffvars = gradchecker(dtype, name)
+    assert dgradgradcheck(func, diffvars, atol=tol)
+
+
 def gradchecker_batch(dtype: torch.dtype, name1: str, name2: str) -> tuple[
     Callable[[Tensor], Tensor],  # autograd function
     Tensor,  # differentiable variables
@@ -122,7 +149,7 @@ def func(positions: Tensor) -> Tensor:
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
 @pytest.mark.parametrize("name1", ["LiH"])
-@pytest.mark.parametrize("name2", sample_list)
+@pytest.mark.parametrize("name2", slist)
 def test_gradcheck_batch(dtype: torch.dtype, name1: str, name2: str) -> None:
     """
     Check a single analytical gradient of parameters against numerical
@@ -132,10 +159,24 @@ def test_gradcheck_batch(dtype: torch.dtype, name1: str, name2: str) -> None:
     assert dgradcheck(func, diffvars, atol=tol)
 
 
+@pytest.mark.grad
+@pytest.mark.large
+@pytest.mark.parametrize("dtype", [torch.double])
+@pytest.mark.parametrize("name1", ["LiH"])
+@pytest.mark.parametrize("name2", slist_large)
+def test_gradcheck_batch_large(dtype: torch.dtype, name1: str, name2: str) -> None:
+    """
+    Check a single analytical gradient of parameters against numerical
+    gradient from `torch.autograd.gradcheck`.
+    """
+    func, diffvars = gradchecker_batch(dtype, name1, name2)
+    assert dgradcheck(func, diffvars, atol=tol)
+
+
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
 @pytest.mark.parametrize("name1", ["LiH"])
-@pytest.mark.parametrize("name2", sample_list)
+@pytest.mark.parametrize("name2", slist)
 def test_gradgradcheck_batch(dtype: torch.dtype, name1: str, name2: str) -> None:
     """
     Check a single analytical gradient of parameters against numerical
@@ -147,7 +188,20 @@ def test_gradgradcheck_batch(dtype: torch.dtype, name1: str, name2: str) -> None
 
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
-@pytest.mark.parametrize("name", sample_list)
+@pytest.mark.parametrize("name1", ["LiH"])
+@pytest.mark.parametrize("name2", slist_large)
+def test_gradgradcheck_batch_large(dtype: torch.dtype, name1: str, name2: str) -> None:
+    """
+    Check a single analytical gradient of parameters against numerical
+    gradient from `torch.autograd.gradgradcheck`.
+    """
+    func, diffvars = gradchecker_batch(dtype, name1, name2)
+    assert dgradgradcheck(func, diffvars, atol=tol)
+
+
+@pytest.mark.grad
+@pytest.mark.parametrize("dtype", [torch.double])
+@pytest.mark.parametrize("name", slist)
 def test_autograd(dtype: torch.dtype, name: str) -> None:
     dd: DD = {"dtype": dtype, "device": DEVICE}
 
@@ -173,7 +227,7 @@ def test_autograd(dtype: torch.dtype, name: str) -> None:
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
 @pytest.mark.parametrize("name1", ["LiH"])
-@pytest.mark.parametrize("name2", sample_list)
+@pytest.mark.parametrize("name2", slist)
 def test_autograd_batch(dtype: torch.dtype, name1: str, name2: str) -> None:
     dd: DD = {"dtype": dtype, "device": DEVICE}
 
@@ -215,7 +269,7 @@ def test_autograd_batch(dtype: torch.dtype, name1: str, name2: str) -> None:
 
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
-@pytest.mark.parametrize("name", sample_list)
+@pytest.mark.parametrize("name", slist)
 def test_backward(dtype: torch.dtype, name: str) -> None:
     """Compare with reference values from tblite."""
     dd: DD = {"dtype": dtype, "device": DEVICE}
@@ -251,7 +305,7 @@ def test_backward(dtype: torch.dtype, name: str) -> None:
 @pytest.mark.grad
 @pytest.mark.parametrize("dtype", [torch.double])
 @pytest.mark.parametrize("name1", ["LiH"])
-@pytest.mark.parametrize("name2", sample_list)
+@pytest.mark.parametrize("name2", slist)
 def test_backward_batch(dtype: torch.dtype, name1: str, name2: str) -> None:
     """Compare with reference values from tblite."""
     dd: DD = {"dtype": dtype, "device": DEVICE}

diff --git a/test/test_external/test_field.py b/test/test_external/test_field.py
@@ -112,8 +112,8 @@ def test_batch(dtype: torch.dtype, name1: str, name2: str, scf_mode: str) -> Non
 
 
 @pytest.mark.parametrize("dtype", [torch.float, torch.double])
-@pytest.mark.parametrize("name1", sample_list)
-@pytest.mark.parametrize("name2", sample_list)
+@pytest.mark.parametrize("name1", ["LiH"])
+@pytest.mark.parametrize("name2", ["LiH"])
 @pytest.mark.parametrize("name3", sample_list)
 @pytest.mark.parametrize(
     "scf_mode", [labels.SCF_MODE_IMPLICIT_NON_PURE, labels.SCF_MODE_FULL]