fix a few issues

Author: Vahid Tavanashad

dpnp/tests/test_mathematical.py

@@ -19,6 +19,7 @@
 
 import dpnp
 from dpnp.dpnp_array import dpnp_array
+from dpnp.dpnp_utils import map_dtype_to_device
 
 from .helper import (
     assert_dtype_allclose,
@@ -2860,7 +2861,7 @@ def test_divide(self, dtype):
         dp_array1 = dpnp.array(np_array1)
         dp_array2 = dpnp.array(np_array2)
         if numpy.issubdtype(dtype, numpy.integer):
-            out_dtype = dpnp.float64
+            out_dtype = map_dtype_to_device(dpnp.float64, dp_array1.sycl_device)
         else:
             out_dtype = _get_output_data_type(dtype)
         dp_out = dpnp.empty(expected.shape, dtype=out_dtype)

dpnp/tests/test_product.py

@@ -289,10 +289,10 @@ def test_out_scalar(self, dtype):
         b = generate_random_numpy_array(10, dtype)
         ib = dpnp.array(b)
 
-        scalar_dtype = map_dtype_to_device(type(a), ib.sycl_device)
-        result_dtype = dpnp.result_type(scalar_dtype, ib)
-        out = numpy.empty(10, dtype=result_dtype)
-        dp_out = dpnp.array(out)
+        np_res_dtype = numpy.result_type(type(a), b)
+        out = numpy.empty(10, dtype=np_res_dtype)
+        dp_res_dtype = map_dtype_to_device(np_res_dtype, ib.sycl_device)
+        dp_out = dpnp.array(out, dtype=dp_res_dtype)
         result = dpnp.dot(a, ib, out=dp_out)
         expected = numpy.dot(a, b, out=out)
 
@@ -382,7 +382,7 @@ def test_out_error_scalar(self, ia):
         out = numpy.empty((10,), dtype=numpy.complex64)
         # For scalar dpnp raises TypeError, and for empty array raises ValueError
         # NumPy raises ValueError for both cases
-        assert_raises(ValueError, dpnp.dot, ia, ib, out=dp_out)
+        assert_raises((TypeError, ValueError), dpnp.dot, ia, ib, out=dp_out)
         assert_raises(ValueError, numpy.dot, a, b, out=out)
 
         # output shape is incorrect

dpnp/tests/test_sum.py

@@ -52,11 +52,11 @@ def test_sum(shape, dtype_in, dtype_out, transpose, keepdims, order):
     axes.append(tuple(axes_range))
 
     for axis in axes:
-        if numpy.issubdtype(dtype_out, numpy.bool):
+        if numpy.issubdtype(dtype_out, numpy.bool_):
             # If summation is zero and dtype=numpy.bool is passed to numpy.sum
             # NumPy returns True which is not correct
             numpy_res = a_np.sum(axis=axis, keepdims=keepdims).astype(
-                numpy.bool
+                numpy.bool_
             )
         else:
             numpy_res = a_np.sum(axis=axis, dtype=dtype_out, keepdims=keepdims)

dpnp/tests/third_party/cupy/core_tests/test_elementwise.py

@@ -94,20 +94,19 @@ class TestElementwiseType(unittest.TestCase):
     @testing.for_int_dtypes(no_bool=True)
     @testing.numpy_cupy_array_equal(accept_error=OverflowError)
     def test_large_int_upper_1(self, xp, dtype):
-        a = xp.array([0], dtype=numpy.int8)
+        a = xp.array([0], dtype=xp.int8)
         b = xp.iinfo(dtype).max
         return a + b
 
     @testing.for_int_dtypes(no_bool=True)
     @testing.numpy_cupy_array_equal(accept_error=OverflowError)
     def test_large_int_upper_2(self, xp, dtype):
-        if (
-            numpy.issubdtype(dtype, numpy.unsignedinteger)
-            and numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0"
-        ):
+        flag = dtype in [numpy.int16, numpy.int32, numpy.int64, numpy.longlong]
+        flag = numpy.issubdtype(dtype, numpy.unsignedinteger) or flag
+        if flag and numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0":
             pytest.skip("numpy promotes dtype differently")
 
-        a = xp.array([1], dtype=numpy.int8)
+        a = xp.array([1], dtype=xp.int8)
         b = xp.iinfo(dtype).max - 1
         return a + b
 
@@ -150,14 +149,26 @@ def test_large_int_upper_4(self, xp, dtype):
     @testing.for_int_dtypes(no_bool=True)
     @testing.numpy_cupy_array_equal(accept_error=OverflowError)
     def test_large_int_lower_1(self, xp, dtype):
-        a = xp.array([0], dtype=numpy.int8)
+        if (
+            dtype in [numpy.int16, numpy.int32, numpy.int64, numpy.longlong]
+            and numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0"
+        ):
+            pytest.skip("numpy promotes dtype differently")
+
+        a = xp.array([0], dtype=xp.int8)
         b = xp.iinfo(dtype).min
         return a + b
 
     @testing.for_int_dtypes(no_bool=True)
     @testing.numpy_cupy_array_equal(accept_error=OverflowError)
     def test_large_int_lower_2(self, xp, dtype):
-        a = xp.array([-1], dtype=numpy.int8)
+        if (
+            dtype in [numpy.int16, numpy.int32, numpy.int64, numpy.longlong]
+            and numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0"
+        ):
+            pytest.skip("numpy promotes dtype differently")
+
+        a = xp.array([-1], dtype=xp.int8)
         b = xp.iinfo(dtype).min + 1
         return a + b