address comments

Author: vtavana

doc/conf.py

@@ -55,6 +55,7 @@
     "sphinx.ext.autodoc",
     "sphinx.ext.autosummary",
     "sphinxcontrib.googleanalytics",
+    "sphinx.ext.mathjax",
 ]
 
 googleanalytics_id = "G-554F8VNE28"

dpnp/dpnp_iface_linearalgebra.py

@@ -67,9 +67,9 @@ def dot(a, b, out=None):
 
     Parameters
     ----------
-    a : {dpnp_array, usm_ndarray, scalar}
+    a : {dpnp.ndarray, usm_ndarray, scalar}
         First input array. Both inputs `a` and `b` can not be scalars at the same time.
-    b : {dpnp_array, usm_ndarray, scalar}
+    b : {dpnp.ndarray, usm_ndarray, scalar}
         Second input array. Both inputs `a` and `b` can not be scalars at the same time.
     out : {dpnp.ndarray, usm_ndarray}, optional
         Alternative output array in which to place the result. It must have
@@ -413,9 +413,9 @@ def tensordot(a, b, axes=2):
 
     Parameters
     ----------
-    a : {dpnp_array, usm_ndarray, scalar}
+    a : {dpnp.ndarray, usm_ndarray, scalar}
         First input array. Both inputs `a` and `b` can not be scalars at the same time.
-    b : {dpnp_array, usm_ndarray, scalar}
+    b : {dpnp.ndarray, usm_ndarray, scalar}
         Second input array. Both inputs `a` and `b` can not be scalars at the same time.
     axes : int or (2,) array_like
         * integer_like
@@ -502,7 +502,7 @@ def tensordot(a, b, axes=2):
         iter(axes)
     except Exception:
         if not isinstance(axes, int):
-            raise ValueError("Axes must be an integer.")
+            raise TypeError("Axes must be an integer.")
         axes_a = tuple(range(-axes, 0))
         axes_b = tuple(range(0, axes))
     else:
@@ -561,11 +561,11 @@ def vdot(a, b):
 
     Parameters
     ----------
-    a : {dpnp_array, usm_ndarray, scalar}
+    a : {dpnp.ndarray, usm_ndarray, scalar}
         First input array. Both inputs `a` and `b` can not be
         scalars at the same time. If `a` is complex, the complex
         conjugate is taken before the calculation of the dot product.
-    b : {dpnp_array, usm_ndarray, scalar}
+    b : {dpnp.ndarray, usm_ndarray, scalar}
         Second input array. Both inputs `a` and `b` can not be
         scalars at the same time.
 

tests/helper.py

@@ -8,7 +8,11 @@
 
 
 def assert_dtype_allclose(
-    dpnp_arr, numpy_arr, check_type=True, check_only_type_kind=False
+    dpnp_arr,
+    numpy_arr,
+    check_type=True,
+    check_only_type_kind=False,
+    factor=8,
 ):
     """
     Assert DPNP and NumPy array based on maximum dtype resolution of input arrays
@@ -28,6 +32,7 @@ def assert_dtype_allclose(
     The 'check_only_type_kind' parameter (False by default) asserts only equal type kinds
     for all data types supported by DPNP when set to True.
     It is effective only when 'check_type' is also set to True.
+    The parameter `factor` scales the resolution used for comparing the arrays.
 
     """
 
@@ -44,7 +49,7 @@ def assert_dtype_allclose(
             if is_inexact(numpy_arr)
             else -dpnp.inf
         )
-        tol = 8 * max(tol_dpnp, tol_numpy)
+        tol = factor * max(tol_dpnp, tol_numpy)
         assert_allclose(dpnp_arr.asnumpy(), numpy_arr, atol=tol, rtol=tol)
         if check_type:
             numpy_arr_dtype = numpy_arr.dtype

tests/test_dot.py

@@ -383,7 +383,7 @@ def test_tensordot(self, dtype, axes):
 
         result = dpnp.tensordot(ia, ib, axes=axes)
         expected = numpy.tensordot(a, b, axes=axes)
-        assert_dtype_allclose(result, expected)
+        assert_dtype_allclose(result, expected, factor=16)
 
     @pytest.mark.parametrize("dtype", get_complex_dtypes())
     @pytest.mark.parametrize("axes", [-3, -2, -1, 0, 1, 2])
@@ -399,7 +399,7 @@ def test_tensordot_complex(self, dtype, axes):
 
         result = dpnp.tensordot(ia, ib, axes=axes)
         expected = numpy.tensordot(a, b, axes=axes)
-        assert_dtype_allclose(result, expected)
+        assert_dtype_allclose(result, expected, factor=16)
 
     @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True))
     @pytest.mark.parametrize(
@@ -424,7 +424,7 @@ def test_tensordot_axes(self, dtype, axes):
 
         result = dpnp.tensordot(ia, ib, axes=axes)
         expected = numpy.tensordot(a, b, axes=axes)
-        assert_dtype_allclose(result, expected)
+        assert_dtype_allclose(result, expected, factor=16)
 
     @pytest.mark.parametrize("dtype1", get_all_dtypes())
     @pytest.mark.parametrize("dtype2", get_all_dtypes())
@@ -440,7 +440,7 @@ def test_tensordot_input_dtype_matrix(self, dtype1, dtype2):
 
         result = dpnp.tensordot(ia, ib)
         expected = numpy.tensordot(a, b)
-        assert_dtype_allclose(result, expected)
+        assert_dtype_allclose(result, expected, factor=16)
 
     def test_tensordot_strided(self):
         for dim in [1, 2, 3, 4]:
@@ -475,7 +475,7 @@ def test_tensordot_error(self):
         a = dpnp.arange(24).reshape(2, 3, 4)
         b = dpnp.arange(24).reshape(3, 4, 2)
         # axes should be an integer
-        with pytest.raises(ValueError):
+        with pytest.raises(TypeError):
             dpnp.tensordot(a, b, axes=2.0)
 
         # Axes must consist of two sequences

tests/third_party/cupy/linalg_tests/test_product.py

@@ -415,8 +415,6 @@ def test_zerodim_kron(self, xp, dtype):
         }
     )
 )
-@pytest.mark.usefixtures("allow_fall_back_on_numpy")
-@testing.gpu
 class TestProductZeroLength(unittest.TestCase):
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose()