Remove xp_extension() for top-level linalg tests

Author: honno

array_api_tests/test_linalg.py

@@ -35,9 +35,6 @@
 from ._array_module import linalg
 
 
-pytestmark = [pytest.mark.xp_extension('linalg')]
-
-
 # Standin strategy for not yet implemented tests
 todo = none()
 
@@ -79,6 +76,7 @@ def _test_namedtuple(res, fields, func_name):
         assert hasattr(res, field), f"{func_name}() result namedtuple doesn't have the '{field}' field"
         assert res[i] is getattr(res, field), f"{func_name}() result namedtuple '{field}' field is not in position {i}"
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=positive_definite_matrices(),
     kw=kwargs(upper=booleans())
@@ -126,6 +124,7 @@ def cross_args(draw, dtype_objects=numeric_dtype_objects):
     )
     return draw(arrays1), draw(arrays2), kw
 
+@pytest.mark.xp_extension('linalg')
 @given(
     cross_args()
 )
@@ -164,6 +163,7 @@ def test_cross(x1_x2_kw):
             ], dtype=res.dtype)
         assert_exactly_equal(res_stack, exact_cross)
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=xps.floating_dtypes(), shape=square_matrix_shapes),
 )
@@ -177,6 +177,7 @@ def test_det(x):
 
     # TODO: Test that res actually corresponds to the determinant of x
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=dtypes, shape=matrix_shapes),
     # offset may produce an overflow if it is too large. Supporting offsets
@@ -211,6 +212,7 @@ def true_diag(x_stack):
 
     _test_stacks(linalg.diagonal, x, **kw, res=res, dims=1, true_val=true_diag)
 
+@pytest.mark.xp_extension('linalg')
 @given(x=symmetric_matrices(finite=True))
 def test_eigh(x):
     res = linalg.eigh(x)
@@ -234,6 +236,7 @@ def test_eigh(x):
     # TODO: Test that res actually corresponds to the eigenvalues and
     # eigenvectors of x
 
+@pytest.mark.xp_extension('linalg')
 @given(x=symmetric_matrices(finite=True))
 def test_eigvalsh(x):
     res = linalg.eigvalsh(x)
@@ -247,6 +250,7 @@ def test_eigvalsh(x):
 
     # TODO: Test that res actually corresponds to the eigenvalues of x
 
+@pytest.mark.xp_extension('linalg')
 @given(x=invertible_matrices())
 def test_inv(x):
     res = linalg.inv(x)
@@ -291,6 +295,7 @@ def test_matmul(x1, x2):
         assert res.shape == stack_shape + (x1.shape[-2], x2.shape[-1])
         _test_stacks(linalg.matmul, x1, x2, res=res)
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes),
     kw=kwargs(axis=todo, keepdims=todo, ord=todo)
@@ -300,6 +305,7 @@ def test_matrix_norm(x, kw):
     pass
 
 matrix_power_n = shared(integers(-1000, 1000), key='matrix_power n')
+@pytest.mark.xp_extension('linalg')
 @given(
     # Generate any square matrix if n >= 0 but only invertible matrices if n < 0
     x=matrix_power_n.flatmap(lambda n: invertible_matrices() if n < 0 else
@@ -321,6 +327,7 @@ def test_matrix_power(x, n):
     func = lambda x: linalg.matrix_power(x, n)
     _test_stacks(func, x, res=res, true_val=true_val)
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes),
     kw=kwargs(rtol=todo)
@@ -346,6 +353,7 @@ def test_matrix_transpose(x):
 
     _test_stacks(linalg.matrix_transpose, x, res=res, true_val=true_val)
 
+@pytest.mark.xp_extension('linalg')
 @given(
     *two_mutual_arrays(dtype_objects=numeric_dtype_objects,
                        two_shapes=tuples(one_d_shapes, one_d_shapes))
@@ -369,6 +377,7 @@ def test_outer(x1, x2):
 
     assert_exactly_equal(res, true_res)
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes),
     kw=kwargs(rtol=todo)
@@ -377,6 +386,7 @@ def test_pinv(x, kw):
     # res = linalg.pinv(x, **kw)
     pass
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=xps.floating_dtypes(), shape=matrix_shapes),
     kw=kwargs(mode=sampled_from(['reduced', 'complete']))
@@ -412,6 +422,7 @@ def test_qr(x, kw):
     # Check that r is upper-triangular.
     assert_exactly_equal(r, _array_module.triu(r))
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=xps.floating_dtypes(), shape=square_matrix_shapes),
 )
@@ -469,6 +480,7 @@ def x2_shapes(draw):
     x2 = xps.arrays(dtype=xps.floating_dtypes(), shape=x2_shapes())
     return x1, x2
 
+@pytest.mark.xp_extension('linalg')
 @given(*solve_args())
 def test_solve(x1, x2):
     # TODO: solve() is currently ambiguous, in that some inputs can be
@@ -481,6 +493,7 @@ def test_solve(x1, x2):
     # res = linalg.solve(x1, x2)
     pass
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes),
     kw=kwargs(full_matrices=todo)
@@ -489,6 +502,7 @@ def test_svd(x, kw):
     # res = linalg.svd(x, **kw)
     pass
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes),
 )
@@ -505,6 +519,7 @@ def test_tensordot(x1, x2, kw):
     # res = linalg.tensordot(x1, x2, **kw)
     pass
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes),
     kw=kwargs(offset=todo)
@@ -522,6 +537,7 @@ def test_vecdot(x1, x2, kw):
     # res = linalg.vecdot(x1, x2, **kw)
     pass
 
+@pytest.mark.xp_extension('linalg')
 @given(
     x=xps.arrays(dtype=xps.floating_dtypes(), shape=shapes),
     kw=kwargs(axis=todo, keepdims=todo, ord=todo)