Merge pull request data-apis#246 from honno/faster-default-runs

Faster default runs

README.md

@@ -254,7 +254,7 @@ jobs:
 > There are several ways to avoid this problem:
 >
 > - Increase the maximum number of examples, e.g., by adding `--max-examples
->   200` to the test command (the default is `100`, see below). This will
+>   200` to the test command (the default is `20`, see below). This will
 >   make it more likely that the failing case will be found, but it will also
 >   make the tests take longer to run.
 > - Don't use `-o xfail_strict=True`. This will make it so that if an XFAIL
@@ -275,7 +275,7 @@ jobs:
 The tests make heavy use
 [Hypothesis](https://hypothesis.readthedocs.io/en/latest/). You can configure
 how many examples are generated using the `--max-examples` flag, which
-defaults to `100`. Lower values can be useful for quick checks, and larger
+defaults to `20`. Lower values can be useful for quick checks, and larger
 values should result in more rigorous runs. For example, `--max-examples
 10_000` may find bugs where default runs don't but will take much longer to
 run.

array_api_tests/test_linalg.py

@@ -119,6 +119,7 @@ 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.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=positive_definite_matrices(),
@@ -175,6 +176,7 @@ def cross_args(draw, dtype_objects=dh.real_dtypes):
     )
     return draw(arrays1), draw(arrays2), kw
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     cross_args()
@@ -209,6 +211,7 @@ def exact_cross(a, b):
     # vectors.
     _test_stacks(linalg.cross, x1, x2, dims=1, matrix_axes=(axis,), res=res, true_val=exact_cross)
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=arrays(dtype=all_floating_dtypes(), shape=square_matrix_shapes),
@@ -224,6 +227,7 @@ def test_det(x):
 
     # TODO: Test that res actually corresponds to the determinant of x
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=arrays(dtype=xps.scalar_dtypes(), shape=matrix_shapes()),
@@ -261,6 +265,7 @@ def true_diag(x_stack, offset=0):
 
     _test_stacks(linalg.diagonal, x, **kw, res=res, dims=1, true_val=true_diag)
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(x=symmetric_matrices(finite=True))
 def test_eigh(x):
@@ -299,6 +304,7 @@ def test_eigh(x):
     # TODO: Test that res actually corresponds to the eigenvalues and
     # eigenvectors of x
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(x=symmetric_matrices(finite=True))
 def test_eigvalsh(x):
@@ -319,6 +325,7 @@ def test_eigvalsh(x):
 
     # TODO: Test that res actually corresponds to the eigenvalues of x
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(x=invertible_matrices())
 def test_inv(x):
@@ -372,19 +379,22 @@ def _test_matmul(namespace, x1, x2):
                                expected=stack_shape + (x1.shape[-2], x2.shape[-1]))
         _test_stacks(matmul, x1, x2, res=res)
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     *two_mutual_arrays(dh.real_dtypes)
 )
 def test_linalg_matmul(x1, x2):
     return _test_matmul(linalg, x1, x2)
 
+@pytest.mark.unvectorized
 @given(
     *two_mutual_arrays(dh.real_dtypes)
 )
 def test_matmul(x1, x2):
     return _test_matmul(_array_module, x1, x2)
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=finite_matrices(),
@@ -410,6 +420,7 @@ def test_matrix_norm(x, kw):
                  res=res)
 
 matrix_power_n = shared(integers(-100, 100), key='matrix_power n')
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     # Generate any square matrix if n >= 0 but only invertible matrices if n < 0
@@ -433,6 +444,7 @@ 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.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=finite_matrices(shape=rtol_shared_matrix_shapes),
@@ -457,13 +469,15 @@ def _test_matrix_transpose(namespace, x):
 
     _test_stacks(matrix_transpose, x, res=res, true_val=true_val)
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=arrays(dtype=xps.scalar_dtypes(), shape=matrix_shapes()),
 )
 def test_linalg_matrix_transpose(x):
     return _test_matrix_transpose(linalg, x)
 
+@pytest.mark.unvectorized
 @given(
     x=arrays(dtype=xps.scalar_dtypes(), shape=matrix_shapes()),
 )
@@ -503,6 +517,7 @@ def test_outer(x1, x2):
 def test_pinv(x, kw):
     linalg.pinv(x, **kw)
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=arrays(dtype=all_floating_dtypes(), shape=matrix_shapes()),
@@ -545,6 +560,7 @@ def test_qr(x, kw):
     # Check that R is upper-triangular.
     assert_exactly_equal(R, _array_module.triu(R))
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=arrays(dtype=all_floating_dtypes(), shape=square_matrix_shapes),
@@ -617,6 +633,7 @@ def _x2_shapes(draw):
     x2 = arrays(shape=x2_shapes, dtype=mutual_dtypes.map(lambda pair: pair[1]))
     return x1, x2
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(*solve_args())
 def test_solve(x1, x2):
@@ -635,6 +652,7 @@ def test_solve(x1, x2):
     ph.assert_result_shape("solve", in_shapes=[x1.shape, x2.shape],
                            out_shape=res.shape, expected=expected_shape)
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=finite_matrices(),
@@ -685,6 +703,7 @@ def test_svd(x, kw):
     _test_stacks(lambda x: linalg.svd(x, **kw).S, x, dims=1, res=S)
     _test_stacks(lambda x: linalg.svd(x, **kw).Vh, x, res=Vh)
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=finite_matrices(),
@@ -818,6 +837,7 @@ def _test_tensordot(namespace, x1, x2, kw):
                            expected=result_shape)
     _test_tensordot_stacks(x1, x2, kw, res)
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     *two_mutual_arrays(dh.numeric_dtypes, two_shapes=tensordot_shapes()),
@@ -826,13 +846,15 @@ def _test_tensordot(namespace, x1, x2, kw):
 def test_linalg_tensordot(x1, x2, kw):
     _test_tensordot(linalg, x1, x2, kw)
 
+@pytest.mark.unvectorized
 @given(
     *two_mutual_arrays(dh.numeric_dtypes, two_shapes=tensordot_shapes()),
     tensordot_kw,
 )
 def test_tensordot(x1, x2, kw):
     _test_tensordot(_array_module, x1, x2, kw)
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=arrays(dtype=xps.numeric_dtypes(), shape=matrix_shapes()),
@@ -910,6 +932,7 @@ def true_val(x, y, axis=-1):
                  matrix_axes=(axis,), true_val=true_val)
 
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     *two_mutual_arrays(dh.numeric_dtypes, mutually_broadcastable_shapes(2, min_dims=1)),
@@ -918,6 +941,7 @@ def true_val(x, y, axis=-1):
 def test_linalg_vecdot(x1, x2, data):
     _test_vecdot(linalg, x1, x2, data)
 
+@pytest.mark.unvectorized
 @given(
     *two_mutual_arrays(dh.numeric_dtypes, mutually_broadcastable_shapes(2, min_dims=1)),
     data(),
@@ -929,6 +953,7 @@ def test_vecdot(x1, x2, data):
 # spec, so we just limit to reasonable values here.
 max_ord = 100
 
+@pytest.mark.unvectorized
 @pytest.mark.xp_extension('linalg')
 @given(
     x=arrays(dtype=all_floating_dtypes(), shape=shapes(min_side=1)),

array_api_tests/test_manipulation_functions.py

@@ -46,6 +46,7 @@ def assert_array_ndindex(
             assert out[out_idx] == x[x_idx], msg
 
 
+@pytest.mark.unvectorized
 @given(
     dtypes=hh.mutually_promotable_dtypes(None, dtypes=dh.numeric_dtypes),
     base_shape=hh.shapes(),