fix cpu issues

Author: vtavana

dpnp/tests/test_arraycreation.py

@@ -703,7 +703,7 @@ def test_dpctl_tensor_input(func, args):
 
 
 @pytest.mark.parametrize("start", [0, -5, 10, -2.5, 9.7])
-@pytest.mark.parametrize("stop", [0, 10, -2, 20.5, 1000])
+@pytest.mark.parametrize("stop", [0, 10, -2, 20.5, 120])
 @pytest.mark.parametrize(
     "num",
     [1, 5, numpy.array(10), dpnp.array(17), dpt.asarray(100)],
@@ -852,7 +852,7 @@ def test_space_num_error():
 @pytest.mark.parametrize("endpoint", [True, False])
 def test_geomspace(sign, dtype, num, endpoint):
     start = 2 * sign
-    stop = 256 * sign
+    stop = 127 * sign
 
     func = lambda xp: xp.geomspace(
         start, stop, num, endpoint=endpoint, dtype=dtype

dpnp/tests/test_indexing.py

@@ -449,7 +449,7 @@ def test_array_method(self, dtype):
 class TestPut:
     @pytest.mark.parametrize("a_dt", get_all_dtypes(no_none=True))
     @pytest.mark.parametrize(
-        "indices", [[0, 2], [-4, 4]], ids=["[0, 2]", "[-4, 4]"]
+        "indices", [[0, 2], [-3, 4]], ids=["[0, 2]", "[-3, 4]"]
     )
     @pytest.mark.parametrize("ind_dt", get_all_dtypes(no_none=True))
     @pytest.mark.parametrize(
@@ -504,8 +504,8 @@ def test_input_1d(self, a_dt, indices, ind_dt, vals, mode):
     @pytest.mark.parametrize("a_dt", get_all_dtypes(no_none=True))
     @pytest.mark.parametrize(
         "indices",
-        [[0, 7], [3, 4], [-8, 8]],
-        ids=["[0, 7]", "[3, 4]", "[-8, 8]"],
+        [[0, 7], [3, 4], [-7, 8]],
+        ids=["[0, 7]", "[3, 4]", "[-7, 8]"],
     )
     @pytest.mark.parametrize("ind_dt", get_integer_dtypes())
     @pytest.mark.parametrize("mode", ["clip", "wrap"])

dpnp/tests/test_mathematical.py

@@ -3751,8 +3751,8 @@ def test_matmul_bool(self, shape1, shape2):
         ],
     )
     def test_matmul_axes_ND_ND(self, dtype, axes):
-        a = generate_random_numpy_array((2, 5, 3, 4), dtype)
-        b = generate_random_numpy_array((4, 2, 5, 3), dtype)
+        a = generate_random_numpy_array((2, 5, 3, 4), dtype, low=-5, high=5)
+        b = generate_random_numpy_array((4, 2, 5, 3), dtype, low=-5, high=5)
         ia = dpnp.array(a)
         ib = dpnp.array(b)
 
@@ -3820,8 +3820,8 @@ def test_matmul_axes_1D_1D(self):
         ],
     )
     def test_matmul_axes_out(self, dtype, axes, out_shape):
-        a = generate_random_numpy_array((2, 5, 3, 4), dtype)
-        b = generate_random_numpy_array((4, 2, 5, 3), dtype)
+        a = generate_random_numpy_array((2, 5, 3, 4), dtype, low=-5, high=5)
+        b = generate_random_numpy_array((4, 2, 5, 3), dtype, low=-5, high=5)
         ia = dpnp.array(a)
         ib = dpnp.array(b)
 
@@ -3872,8 +3872,8 @@ def test_matmul_axes_out_1D(self, axes, b_shape, out_shape):
         ],
     )
     def test_matmul_dtype_matrix_inout(self, in_dt, out_dt, shape1, shape2):
-        a1 = generate_random_numpy_array(shape1, in_dt)
-        a2 = generate_random_numpy_array(shape2, in_dt)
+        a1 = generate_random_numpy_array(shape1, in_dt, low=-5, high=5)
+        a2 = generate_random_numpy_array(shape2, in_dt, low=-5, high=5)
         b1 = dpnp.asarray(a1)
         b2 = dpnp.asarray(a2)
 
@@ -3901,8 +3901,8 @@ def test_matmul_dtype_matrix_inout(self, in_dt, out_dt, shape1, shape2):
         ],
     )
     def test_matmul_dtype_matrix_inputs(self, dtype1, dtype2, shape1, shape2):
-        a1 = generate_random_numpy_array(shape1, dtype1)
-        a2 = generate_random_numpy_array(shape2, dtype2)
+        a1 = generate_random_numpy_array(shape1, dtype1, low=-5, high=5)
+        a2 = generate_random_numpy_array(shape2, dtype2, low=-5, high=5)
         b1 = dpnp.asarray(a1)
         b2 = dpnp.asarray(a2)
 
@@ -4115,9 +4115,10 @@ def test_matmul_strided_vec_mat(self, shape, incx, incy, transpose):
     )
     def test_matmul_out1(self, order1, order2, out_order, dtype):
         # test gemm with out keyword
-        a1 = numpy.arange(20, dtype=dtype).reshape(5, 4, order=order1)
-        a2 = numpy.arange(28, dtype=dtype).reshape(4, 7, order=order2)
-
+        a1 = generate_random_numpy_array((5, 4), dtype, low=-5, high=5)
+        a2 = generate_random_numpy_array((4, 7), dtype, low=-5, high=5)
+        a1 = numpy.array(a1, order=order1)
+        a2 = numpy.array(a2, order=order2)
         b1 = dpnp.asarray(a1)
         b2 = dpnp.asarray(a2)
 
@@ -4139,8 +4140,8 @@ def test_matmul_out2(self, trans, dtype):
         # test gemm_batch with out keyword
         # the base of input arrays is c-contiguous
         # the base of output array is c-contiguous or f-contiguous
-        a1 = numpy.arange(24, dtype=dtype).reshape(2, 3, 4)
-        a2 = numpy.arange(40, dtype=dtype).reshape(2, 4, 5)
+        a1 = generate_random_numpy_array((2, 3, 4), dtype, low=-5, high=5)
+        a2 = generate_random_numpy_array((2, 4, 5), dtype, low=-5, high=5)
         b1 = dpnp.asarray(a1)
         b2 = dpnp.asarray(a2)
 
@@ -4167,8 +4168,8 @@ def test_matmul_out3(self, trans, dtype):
         # test gemm_batch with out keyword
         # the base of input arrays is f-contiguous
         # the base of output array is c-contiguous or f-contiguous
-        a1 = numpy.arange(24, dtype=dtype).reshape(2, 4, 3)
-        a2 = numpy.arange(40, dtype=dtype).reshape(2, 5, 4)
+        a1 = generate_random_numpy_array((2, 4, 3), dtype, low=-5, high=5)
+        a2 = generate_random_numpy_array((2, 5, 4), dtype, low=-5, high=5)
         b1 = dpnp.asarray(a1)
         b2 = dpnp.asarray(a2)
 

dpnp/tests/test_product.py

@@ -257,8 +257,8 @@ def test_scalar(self, dtype):
         ],
     )
     def test_basic(self, dtype, shape1, shape2):
-        a = generate_random_numpy_array(shape1, dtype)
-        b = generate_random_numpy_array(shape2, dtype)
+        a = generate_random_numpy_array(shape1, dtype, low=-5, high=5)
+        b = generate_random_numpy_array(shape2, dtype, low=-5, high=5)
         ia = dpnp.array(a)
         ib = dpnp.array(b)
 
@@ -328,8 +328,8 @@ def test_out_scalar(self, dtype):
         ],
     )
     def test_out(self, dtype, shape1, shape2, out_shape):
-        a = generate_random_numpy_array(shape1, dtype)
-        b = generate_random_numpy_array(shape2, dtype)
+        a = generate_random_numpy_array(shape1, dtype, low=-5, high=5)
+        b = generate_random_numpy_array(shape2, dtype, low=-5, high=5)
         ia = dpnp.array(a)
         ib = dpnp.array(b)
 
@@ -466,8 +466,8 @@ def test_scalar(self, dtype):
         ],
     )
     def test_basic(self, dtype, shape1, shape2):
-        a = generate_random_numpy_array(shape1, dtype)
-        b = generate_random_numpy_array(shape2, dtype)
+        a = generate_random_numpy_array(shape1, dtype, low=-5, high=5)
+        b = generate_random_numpy_array(shape2, dtype, low=-5, high=5)
         ia = dpnp.array(a)
         ib = dpnp.array(b)
 
@@ -617,7 +617,7 @@ def setup_method(self):
             ((6,), (6, 10), (10, 7), (7, 8)),
             ((4, 6), (6, 10), (10, 7), (7,)),
             ((6,), (6, 10), (10, 7), (7,)),
-            ((4, 6), (6, 9), (9, 7), (7, 8), (8, 3)),
+            ((4, 6), (6, 2), (2, 7), (7, 5), (5, 3)),
         ],
         ids=[
             "two_arrays",
@@ -637,7 +637,7 @@ def test_basic(self, shapes, dtype):
         numpy_array_list = []
         dpnp_array_list = []
         for shape in shapes:
-            a = generate_random_numpy_array(shape, dtype, low=-4, high=4)
+            a = generate_random_numpy_array(shape, dtype, low=-2, high=2)
             ia = dpnp.array(a)
 
             numpy_array_list.append(a)
@@ -661,7 +661,7 @@ def test_basic(self, shapes, dtype):
             ((6,), (6, 10), (10, 7), (7, 8), (8,)),
             ((4, 6), (6, 10), (10, 7), (7,), (4,)),
             ((6,), (6, 10), (10, 7), (7,), ()),
-            ((4, 6), (6, 9), (9, 7), (7, 8), (8, 3), (4, 3)),
+            ((4, 6), (6, 2), (2, 7), (7, 5), (5, 3), (4, 3)),
         ],
         ids=[
             "two_arrays",
@@ -681,7 +681,7 @@ def test_out(self, shapes, dtype):
         numpy_array_list = []
         dpnp_array_list = []
         for shape in shapes[:-1]:
-            a = generate_random_numpy_array(shape, dtype, low=-4, high=4)
+            a = generate_random_numpy_array(shape, dtype, low=-2, high=2)
             ia = dpnp.array(a)
 
             numpy_array_list.append(a)
@@ -777,8 +777,8 @@ def test_scalar(self, dtype):
     @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
     @pytest.mark.parametrize("axes", [0, 1, 2])
     def test_basic(self, dtype, axes):
-        a = generate_random_numpy_array((4, 4, 4), dtype)
-        b = generate_random_numpy_array((4, 4, 4), dtype)
+        a = generate_random_numpy_array((4, 4, 4), dtype, low=-5, high=5)
+        b = generate_random_numpy_array((4, 4, 4), dtype, low=-5, high=5)
         ia = dpnp.array(a)
         ib = dpnp.array(b)
 
@@ -798,8 +798,8 @@ def test_basic(self, dtype, axes):
         ],
     )
     def test_axes(self, dtype, axes):
-        a = generate_random_numpy_array((2, 5, 3, 4), dtype)
-        b = generate_random_numpy_array((4, 2, 5, 3), dtype)
+        a = generate_random_numpy_array((2, 5, 3, 4), dtype, low=-5, high=5)
+        b = generate_random_numpy_array((4, 2, 5, 3), dtype, low=-5, high=5)
         ia = dpnp.array(a)
         ib = dpnp.array(b)
 
@@ -810,8 +810,8 @@ def test_axes(self, dtype, axes):
     @pytest.mark.parametrize("dtype1", get_all_dtypes())
     @pytest.mark.parametrize("dtype2", get_all_dtypes())
     def test_input_dtype_matrix(self, dtype1, dtype2):
-        a = generate_random_numpy_array((3, 4, 5), dtype1)
-        b = generate_random_numpy_array((4, 5, 2), dtype2)
+        a = generate_random_numpy_array((3, 4, 5), dtype1, low=-5, high=5)
+        b = generate_random_numpy_array((4, 5, 2), dtype2, low=-5, high=5)
         ia = dpnp.array(a)
         ib = dpnp.array(b)