Fixed gh-1468

Function _reduce_over_axis promotes input array to requested
result data type and carries out reduction computation in that
data type. This is done in dtype if implementation supports it.

If implementation does not support the requested dtype, we reduce
in the default_dtype, and cast to the request dtype afterwards.

Author: oleksandr-pavlyk

dpctl/tensor/_reduction.py

@@ -118,7 +118,7 @@ def _reduction_over_axis(
                 dpt.full(
                     res_shape,
                     _identity,
-                    dtype=_default_reduction_type_fn(inp_dt, q),
+                    dtype=dtype,
                     usm_type=res_usm_type,
                     sycl_queue=q,
                 ),
@@ -142,21 +142,51 @@ def _reduction_over_axis(
                 "Automatically determined reduction data type does not "
                 "have direct implementation"
             )
-        tmp_dt = _default_reduction_type_fn(inp_dt, q)
-        tmp = dpt.empty(
-            res_shape, dtype=tmp_dt, usm_type=res_usm_type, sycl_queue=q
-        )
-        ht_e_tmp, r_e = _reduction_fn(
-            src=arr2, trailing_dims_to_reduce=red_nd, dst=tmp, sycl_queue=q
-        )
-        host_tasks_list.append(ht_e_tmp)
-        res = dpt.empty(
-            res_shape, dtype=res_dt, usm_type=res_usm_type, sycl_queue=q
-        )
-        ht_e, _ = ti._copy_usm_ndarray_into_usm_ndarray(
-            src=tmp, dst=res, sycl_queue=q, depends=[r_e]
-        )
-        host_tasks_list.append(ht_e)
+        if _dtype_supported(res_dt, res_dt, res_usm_type, q):
+            tmp = dpt.empty(
+                arr2.shape, dtype=res_dt, usm_type=res_usm_type, sycl_queue=q
+            )
+            ht_e_cpy, cpy_e = ti._copy_usm_ndarray_into_usm_ndarray(
+                src=arr2, dst=tmp, sycl_queue=q
+            )
+            host_tasks_list.append(ht_e_cpy)
+            res = dpt.empty(
+                res_shape, dtype=res_dt, usm_type=res_usm_type, sycl_queue=q
+            )
+            ht_e_red, _ = _reduction_fn(
+                src=tmp,
+                trailing_dims_to_reduce=red_nd,
+                dst=res,
+                sycl_queue=q,
+                depends=[cpy_e],
+            )
+            host_tasks_list.append(ht_e_red)
+        else:
+            buf_dt = _default_reduction_type_fn(inp_dt, q)
+            tmp = dpt.empty(
+                arr2.shape, dtype=buf_dt, usm_type=res_usm_type, sycl_queue=q
+            )
+            ht_e_cpy, cpy_e = ti._copy_usm_ndarray_into_usm_ndarray(
+                src=arr2, dst=tmp, sycl_queue=q
+            )
+            tmp_res = dpt.empty(
+                res_shape, dtype=buf_dt, usm_type=res_usm_type, sycl_queue=q
+            )
+            host_tasks_list.append(ht_e_cpy)
+            res = dpt.empty(
+                res_shape, dtype=res_dt, usm_type=res_usm_type, sycl_queue=q
+            )
+            ht_e_red, r_e = _reduction_fn(
+                src=tmp,
+                trailing_dims_to_reduce=red_nd,
+                dst=tmp_res,
+                sycl_queue=q,
+                depends=[cpy_e],
+            )
+            ht_e_cpy2, _ = ti._copy_usm_ndarray_into_usm_ndarray(
+                src=tmp_res, dst=res, sycl_queue=q, depends=[r_e]
+            )
+            host_tasks_list.append(ht_e_cpy2)
 
     if keepdims:
         res_shape = res_shape + (1,) * red_nd