updates for obtaining out dtype

Author: Vahid Tavanashad

dpnp/tests/test_binary_ufuncs.py

@@ -23,7 +23,6 @@
     has_support_aspect16,
     numpy_version,
 )
-from .test_umath import _get_output_data_type
 
 """
 The scope includes tests with only functions which are instances of
@@ -195,10 +194,7 @@ def test_divide(self, dtype):
         expected = numpy.divide(a, b)
 
         ia, ib = dpnp.array(a), dpnp.array(b)
-        if numpy.issubdtype(dtype, numpy.integer) or dtype == dpnp.bool:
-            out_dtype = map_dtype_to_device(dpnp.float64, ia.sycl_device)
-        else:
-            out_dtype = _get_output_data_type(dtype)
+        out_dtype = map_dtype_to_device(expected.dtype, ia.sycl_device)
         iout = dpnp.empty(expected.shape, dtype=out_dtype)
         result = dpnp.divide(ia, ib, out=iout)
 

dpnp/tests/test_mathematical.py

@@ -17,6 +17,7 @@
 
 import dpnp
 from dpnp.dpnp_array import dpnp_array
+from dpnp.dpnp_utils import map_dtype_to_device
 
 from .helper import (
     assert_dtype_allclose,
@@ -32,10 +33,33 @@
     has_support_aspect64,
     numpy_version,
 )
-from .test_umath import _get_output_data_type
 from .third_party.cupy import testing
 
 
+def _get_output_data_type(dtype):
+    """Return a data type specified by input `dtype` and device capabilities."""
+    dtype_float16 = any(
+        dpnp.issubdtype(dtype, t) for t in (dpnp.bool, dpnp.int8, dpnp.uint8)
+    )
+    dtype_float32 = any(
+        dpnp.issubdtype(dtype, t) for t in (dpnp.int16, dpnp.uint16)
+    )
+    if dtype_float16:
+        out_dtype = dpnp.float16 if has_support_aspect16() else dpnp.float32
+    elif dtype_float32:
+        out_dtype = dpnp.float32
+    elif dpnp.issubdtype(dtype, dpnp.complexfloating):
+        out_dtype = dpnp.complex64
+        if has_support_aspect64() and dtype != dpnp.complex64:
+            out_dtype = dpnp.complex128
+    else:
+        out_dtype = dpnp.float32
+        if has_support_aspect64() and dtype != dpnp.float32:
+            out_dtype = dpnp.float64
+
+    return out_dtype
+
+
 @pytest.mark.parametrize("deg", [True, False])
 class TestAngle:
     def test_angle_bool(self, deg):
@@ -2323,7 +2347,7 @@ def test_hypot(self, dtype):
         expected = numpy.hypot(a, b)
 
         ia, ib = dpnp.array(a), dpnp.array(b)
-        out_dt = _get_output_data_type(dtype)
+        out_dt = map_dtype_to_device(expected.dtype, ia.sycl_device)
         iout = dpnp.empty(expected.shape, dtype=out_dt)
         result = dpnp.hypot(ia, ib, out=iout)
 

dpnp/tests/test_umath.py

@@ -9,6 +9,7 @@
 )
 
 import dpnp
+from dpnp.dpnp_utils import map_dtype_to_device
 
 from .helper import (
     assert_dtype_allclose,
@@ -108,30 +109,6 @@ def test_umaths(test_cases):
     assert_allclose(result, expected, rtol=1e-6)
 
 
-def _get_output_data_type(dtype):
-    """Return a data type specified by input `dtype` and device capabilities."""
-    dtype_float16 = any(
-        dpnp.issubdtype(dtype, t) for t in (dpnp.bool, dpnp.int8, dpnp.uint8)
-    )
-    dtype_float32 = any(
-        dpnp.issubdtype(dtype, t) for t in (dpnp.int16, dpnp.uint16)
-    )
-    if dtype_float16:
-        dt_out = dpnp.float16 if has_support_aspect16() else dpnp.float32
-    elif dtype_float32:
-        dt_out = dpnp.float32
-    elif dpnp.issubdtype(dtype, dpnp.complexfloating):
-        dt_out = dpnp.complex64
-        if has_support_aspect64() and dtype != dpnp.complex64:
-            dt_out = dpnp.complex128
-    else:
-        dt_out = dpnp.float32
-        if has_support_aspect64() and dtype != dpnp.float32:
-            dt_out = dpnp.float64
-
-    return dt_out
-
-
 class TestArctan2:
     @pytest.mark.parametrize(
         "dtype", get_all_dtypes(no_none=True, no_complex=True)
@@ -142,10 +119,10 @@ def test_arctan2(self, dtype):
         expected = numpy.arctan2(a, b)
 
         ia, ib = dpnp.array(a), dpnp.array(b)
-        dt_out = _get_output_data_type(dtype)
+        dt_out = map_dtype_to_device(expected.dtype, ia.sycl_device)
         iout = dpnp.empty(expected.shape, dtype=dt_out)
-        result = dpnp.arctan2(ia, ib, out=iout)
 
+        result = dpnp.arctan2(ia, ib, out=iout)
         assert result is iout
         assert_dtype_allclose(result, expected)
 
@@ -188,7 +165,7 @@ def test_copysign(self, dtype):
         expected = numpy.copysign(a, b)
 
         ia, ib = dpnp.array(a), dpnp.array(b)
-        dt_out = _get_output_data_type(dtype)
+        dt_out = map_dtype_to_device(expected.dtype, ia.sycl_device)
         iout = dpnp.empty(expected.shape, dtype=dt_out)
         result = dpnp.copysign(ia, ib, out=iout)
 
@@ -307,7 +284,7 @@ def test_logaddexp(self, dtype):
         expected = numpy.logaddexp(a, b)
 
         ia, ib = dpnp.array(a), dpnp.array(b)
-        dt_out = _get_output_data_type(dtype)
+        dt_out = map_dtype_to_device(expected.dtype, ia.sycl_device)
         iout = dpnp.empty(expected.shape, dtype=dt_out)
         result = dpnp.logaddexp(ia, ib, out=iout)
 
@@ -450,7 +427,7 @@ def test_reciprocal(self, dtype):
         expected = numpy.reciprocal(a)
 
         ia = dpnp.array(a)
-        dt_out = _get_output_data_type(dtype)
+        dt_out = map_dtype_to_device(expected.dtype, ia.sycl_device)
         iout = dpnp.empty(expected.shape, dtype=dt_out)
         result = dpnp.reciprocal(ia, out=iout)
 
@@ -500,7 +477,7 @@ def test_basic(self, func_params, dtype):
             expected = getattr(numpy, func)(a)
 
         ia = dpnp.array(a)
-        dt_out = _get_output_data_type(dtype)
+        dt_out = map_dtype_to_device(expected.dtype, ia.sycl_device)
         iout = dpnp.empty(expected.shape, dtype=dt_out)
         result = getattr(dpnp, func)(ia, out=iout)
         assert result is iout
@@ -591,7 +568,7 @@ def func_params(self, request):
     @pytest.mark.filterwarnings("ignore:overflow encountered:RuntimeWarning")
     @pytest.mark.usefixtures("suppress_divide_invalid_numpy_warnings")
     @pytest.mark.parametrize("dtype", get_all_dtypes(no_none=True))
-    def test_out(self, func_params, dtype):
+    def test_basic(self, func_params, dtype):
         func = func_params["func"]
         values = func_params["values"]
         a = generate_random_numpy_array(
@@ -600,10 +577,7 @@ def test_out(self, func_params, dtype):
         expected = getattr(numpy, func)(a)
 
         ia = dpnp.array(a)
-        if func == "square":
-            dt_out = numpy.int8 if dtype == dpnp.bool else dtype
-        else:
-            dt_out = _get_output_data_type(dtype)
+        dt_out = map_dtype_to_device(expected.dtype, ia.sycl_device)
         iout = dpnp.empty(expected.shape, dtype=dt_out)
         result = getattr(dpnp, func)(ia, out=iout)