Update searchsorted for array api 2025 spec

CHANGELOG.md

@@ -10,6 +10,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
+* Updated `searchsorted` implementations to align with the 2025.12 array API spec [gh-2902](https://github.com/IntelPython/dpnp/pull/2902)
+
 ### Deprecated
 
 ### Removed

dpnp/dpnp_iface_searching.py

@@ -373,15 +373,13 @@ def searchsorted(a, v, side="left", sorter=None):
 
     """
 
-    usm_a = dpnp.get_usm_ndarray(a)
-    if dpnp.isscalar(v):
-        usm_v = dpt.asarray(v, sycl_queue=a.sycl_queue, usm_type=a.usm_type)
-    else:
-        usm_v = dpnp.get_usm_ndarray(v)
+    a = dpnp.get_usm_ndarray(a)
+    if not dpnp.isscalar(v):
+        v = dpnp.get_usm_ndarray(v)
 
-    usm_sorter = None if sorter is None else dpnp.get_usm_ndarray(sorter)
+    sorter = None if sorter is None else dpnp.get_usm_ndarray(sorter)
     return dpnp_array._create_from_usm_ndarray(
-        dpt.searchsorted(usm_a, usm_v, side=side, sorter=usm_sorter)
+        dpt.searchsorted(a, v, side=side, sorter=sorter)
     )
 
 

dpnp/tensor/_searchsorted.py

@@ -26,36 +26,41 @@
 # THE POSSIBILITY OF SUCH DAMAGE.
 # *****************************************************************************
 
+from typing import Literal
 
-from typing import Literal, Union
-
-import dpctl
 import dpctl.utils as du
 
+import dpnp.tensor as dpt
+
 from ._compute_follows_data import (
     ExecutionPlacementError,
     get_coerced_usm_type,
     get_execution_queue,
 )
 from ._copy_utils import _empty_like_orderK
-from ._ctors import empty
+from ._ctors import empty_like
+from ._scalar_utils import _get_dtype, _get_queue_usm_type, _validate_dtype
 from ._tensor_impl import _copy_usm_ndarray_into_usm_ndarray as ti_copy
 from ._tensor_impl import _take as ti_take
 from ._tensor_impl import (
     default_device_index_type as ti_default_device_index_type,
 )
 from ._tensor_sorting_impl import _searchsorted_left, _searchsorted_right
-from ._type_utils import isdtype, result_type
+from ._type_utils import (
+    _resolve_weak_types_all_py_ints,
+    _to_device_supported_dtype,
+    isdtype,
+)
 from ._usmarray import usm_ndarray
 
 
 def searchsorted(
     x1: usm_ndarray,
-    x2: usm_ndarray,
+    x2: usm_ndarray | int | float | complex | bool,
     /,
     *,
     side: Literal["left", "right"] = "left",
-    sorter: Union[usm_ndarray, None] = None,
+    sorter: usm_ndarray | None = None,
 ) -> usm_ndarray:
     """searchsorted(x1, x2, side='left', sorter=None)
 
@@ -68,8 +73,8 @@ def searchsorted(
             input array. Must be a one-dimensional array. If `sorter` is
             `None`, must be sorted in ascending order; otherwise, `sorter` must
             be an array of indices that sort `x1` in ascending order.
-        x2 (usm_ndarray):
-            array containing search values.
+        x2 (usm_ndarray | int | float | complex | bool):
+            search value or values.
         side (Literal["left", "right]):
             argument controlling which index is returned if a value lands
             exactly on an edge. If `x2` is an array of rank `N` where
@@ -85,13 +90,11 @@ def searchsorted(
             array of indices that sort `x1` in ascending order. The array must
             have the same shape as `x1` and have an integral data type.
             Out of bound index values of `sorter` array are treated using
-            `"wrap"` mode documented in :py:func:`dpctl.tensor.take`.
+            `"wrap"` mode documented in :py:func:`dpnp.tensor.take`.
             Default: `None`.
     """
     if not isinstance(x1, usm_ndarray):
         raise TypeError(f"Expected dpnp.tensor.usm_ndarray, got {type(x1)}")
-    if not isinstance(x2, usm_ndarray):
-        raise TypeError(f"Expected dpnp.tensor.usm_ndarray, got {type(x2)}")
     if sorter is not None and not isinstance(sorter, usm_ndarray):
         raise TypeError(f"Expected dpnp.tensor.usm_ndarray, got {type(sorter)}")
 
@@ -101,27 +104,43 @@ def searchsorted(
             "Expected either 'left' or 'right'"
         )
 
-    if sorter is None:
-        q = get_execution_queue([x1.sycl_queue, x2.sycl_queue])
-    else:
-        q = get_execution_queue(
-            [x1.sycl_queue, x2.sycl_queue, sorter.sycl_queue]
-        )
+    q1, x1_usm_type = x1.sycl_queue, x1.usm_type
+    q2, x2_usm_type = _get_queue_usm_type(x2)
+    q3 = sorter.sycl_queue if sorter is not None else None
+    q = get_execution_queue(tuple(q for q in (q1, q2, q3) if q is not None))
     if q is None:
         raise ExecutionPlacementError(
             "Execution placement can not be unambiguously "
             "inferred from input arguments."
         )
 
+    res_usm_type = get_coerced_usm_type(
+        tuple(
+            ut
+            for ut in (
+                x1_usm_type,
+                x2_usm_type,
+            )
+            if ut is not None
+        )
+    )
+    dpt.validate_usm_type(res_usm_type, allow_none=False)
+    sycl_dev = q.sycl_device
+
     if x1.ndim != 1:
         raise ValueError("First argument array must be one-dimensional")
 
     x1_dt = x1.dtype
-    x2_dt = x2.dtype
+    x2_dt = _get_dtype(x2, sycl_dev)
+    if not _validate_dtype(x2_dt):
+        raise ValueError(
+            "dpt.searchsorted search value argument has "
+            f"unsupported data type {x2_dt}"
+        )
 
     _manager = du.SequentialOrderManager[q]
     dep_evs = _manager.submitted_events
-    ev = dpctl.SyclEvent()
+    x1_deps = dep_evs
     if sorter is not None:
         if not isdtype(sorter.dtype, "integral"):
             raise ValueError(
@@ -132,7 +151,7 @@ def searchsorted(
                 "Sorter array must be one-dimension with the same "
                 "shape as the first argument array"
             )
-        res = empty(x1.shape, dtype=x1_dt, usm_type=x1.usm_type, sycl_queue=q)
+        res = empty_like(x1)
         ind = (sorter,)
         axis = 0
         wrap_out_of_bound_indices_mode = 0
@@ -146,31 +165,30 @@ def searchsorted(
             depends=dep_evs,
         )
         x1 = res
+        x1_deps = [ev]
         _manager.add_event_pair(ht_ev, ev)
 
-    if x1_dt != x2_dt:
-        dt = result_type(x1, x2)
-        if x1_dt != dt:
-            x1_buf = _empty_like_orderK(x1, dt)
-            dep_evs = _manager.submitted_events
-            ht_ev, ev = ti_copy(
-                src=x1, dst=x1_buf, sycl_queue=q, depends=dep_evs
-            )
-            _manager.add_event_pair(ht_ev, ev)
-            x1 = x1_buf
-        if x2_dt != dt:
-            x2_buf = _empty_like_orderK(x2, dt)
-            dep_evs = _manager.submitted_events
-            ht_ev, ev = ti_copy(
-                src=x2, dst=x2_buf, sycl_queue=q, depends=dep_evs
-            )
-            _manager.add_event_pair(ht_ev, ev)
-            x2 = x2_buf
+    dt1, dt2 = _resolve_weak_types_all_py_ints(x1_dt, x2_dt, sycl_dev)
+    dt = _to_device_supported_dtype(dpt.result_type(dt1, dt2), sycl_dev)
+
+    if x1_dt != dt:
+        x1_buf = _empty_like_orderK(x1, dt)
+        # get the submitted events again to ensure the copy waits take call
+        ht_ev, ev = ti_copy(src=x1, dst=x1_buf, sycl_queue=q, depends=x1_deps)
+        _manager.add_event_pair(ht_ev, ev)
+        x1 = x1_buf
+
+    if not isinstance(x2, usm_ndarray):
+        x2 = dpt.asarray(x2, dtype=dt, usm_type=res_usm_type, sycl_queue=q)
+    elif x2_dt != dt:
+        x2_buf = _empty_like_orderK(x2, dt)
+        ht_ev, ev = ti_copy(src=x2, dst=x2_buf, sycl_queue=q, depends=dep_evs)
+        _manager.add_event_pair(ht_ev, ev)
+        x2 = x2_buf
 
-    dst_usm_type = get_coerced_usm_type([x1.usm_type, x2.usm_type])
     index_dt = ti_default_device_index_type(q)
 
-    dst = _empty_like_orderK(x2, index_dt, usm_type=dst_usm_type)
+    dst = _empty_like_orderK(x2, index_dt, usm_type=res_usm_type)
 
     dep_evs = _manager.submitted_events
     if side == "left":

dpnp/tests/tensor/test_usm_ndarray_searchsorted.py

@@ -26,6 +26,8 @@
 # THE POSSIBILITY OF SUCH DAMAGE.
 # *****************************************************************************
 
+import ctypes
+
 import dpctl
 import numpy as np
 import pytest
@@ -37,6 +39,30 @@
     skip_if_dtype_not_supported,
 )
 
+_integer_dtypes = [
+    "i1",
+    "u1",
+    "i2",
+    "u2",
+    "i4",
+    "u4",
+    "i8",
+    "u8",
+]
+
+_floating_dtypes = [
+    "f2",
+    "f4",
+    "f8",
+]
+
+_complex_dtypes = [
+    "c8",
+    "c16",
+]
+
+_all_dtypes = ["?"] + _integer_dtypes + _floating_dtypes + _complex_dtypes
+
 
 def _check(hay_stack, needles, needles_np):
     assert hay_stack.dtype == needles.dtype
@@ -103,19 +129,7 @@ def test_searchsorted_strided_bool():
     )
 
 
-@pytest.mark.parametrize(
-    "idt",
-    [
-        dpt.int8,
-        dpt.uint8,
-        dpt.int16,
-        dpt.uint16,
-        dpt.int32,
-        dpt.uint32,
-        dpt.int64,
-        dpt.uint64,
-    ],
-)
+@pytest.mark.parametrize("idt", _integer_dtypes)
 def test_searchsorted_contig_int(idt):
     q = get_queue_or_skip()
     skip_if_dtype_not_supported(idt, q)
@@ -135,19 +149,7 @@ def test_searchsorted_contig_int(idt):
     )
 
 
-@pytest.mark.parametrize(
-    "idt",
-    [
-        dpt.int8,
-        dpt.uint8,
-        dpt.int16,
-        dpt.uint16,
-        dpt.int32,
-        dpt.uint32,
-        dpt.int64,
-        dpt.uint64,
-    ],
-)
+@pytest.mark.parametrize("idt", _integer_dtypes)
 def test_searchsorted_strided_int(idt):
     q = get_queue_or_skip()
     skip_if_dtype_not_supported(idt, q)
@@ -174,12 +176,12 @@ def _add_extended_fp(array):
     array[-1] = dpt.nan
 
 
-@pytest.mark.parametrize("idt", [dpt.float16, dpt.float32, dpt.float64])
-def test_searchsorted_contig_fp(idt):
+@pytest.mark.parametrize("fdt", _floating_dtypes)
+def test_searchsorted_contig_fp(fdt):
     q = get_queue_or_skip()
-    skip_if_dtype_not_supported(idt, q)
+    skip_if_dtype_not_supported(fdt, q)
 
-    dt = dpt.dtype(idt)
+    dt = dpt.dtype(fdt)
 
     hay_stack = dpt.linspace(0, 1, num=255, dtype=dt, endpoint=True)
     _add_extended_fp(hay_stack)
@@ -195,12 +197,12 @@ def test_searchsorted_contig_fp(idt):
     )
 
 
-@pytest.mark.parametrize("idt", [dpt.float16, dpt.float32, dpt.float64])
-def test_searchsorted_strided_fp(idt):
+@pytest.mark.parametrize("fdt", _floating_dtypes)
+def test_searchsorted_strided_fp(fdt):
     q = get_queue_or_skip()
-    skip_if_dtype_not_supported(idt, q)
+    skip_if_dtype_not_supported(fdt, q)
 
-    dt = dpt.dtype(idt)
+    dt = dpt.dtype(fdt)
 
     hay_stack = dpt.repeat(
         dpt.linspace(0, 1, num=255, dtype=dt, endpoint=True), 4
@@ -243,12 +245,12 @@ def _add_extended_cfp(array):
     return dpt.sort(dpt.concat((ev, array)))
 
 
-@pytest.mark.parametrize("idt", [dpt.complex64, dpt.complex128])
-def test_searchsorted_contig_cfp(idt):
+@pytest.mark.parametrize("cdt", _complex_dtypes)
+def test_searchsorted_contig_cfp(cdt):
     q = get_queue_or_skip()
-    skip_if_dtype_not_supported(idt, q)
+    skip_if_dtype_not_supported(cdt, q)
 
-    dt = dpt.dtype(idt)
+    dt = dpt.dtype(cdt)
 
     hay_stack = dpt.linspace(0, 1, num=255, dtype=dt, endpoint=True)
     hay_stack = _add_extended_cfp(hay_stack)
@@ -263,12 +265,12 @@ def test_searchsorted_contig_cfp(idt):
     )
 
 
-@pytest.mark.parametrize("idt", [dpt.complex64, dpt.complex128])
-def test_searchsorted_strided_cfp(idt):
+@pytest.mark.parametrize("cdt", _complex_dtypes)
+def test_searchsorted_strided_cfp(cdt):
     q = get_queue_or_skip()
-    skip_if_dtype_not_supported(idt, q)
+    skip_if_dtype_not_supported(cdt, q)
 
-    dt = dpt.dtype(idt)
+    dt = dpt.dtype(cdt)
 
     hay_stack = dpt.repeat(
         dpt.linspace(0, 1, num=255, dtype=dt, endpoint=True), 4
@@ -315,7 +317,7 @@ def test_searchsorted_validation():
         x1 = dpt.arange(10, dtype="i4")
     except dpctl.SyclDeviceCreationError:
         pytest.skip("Default device could not be created")
-    with pytest.raises(TypeError):
+    with pytest.raises(ValueError):
         dpt.searchsorted(x1, None)
     with pytest.raises(TypeError):
         dpt.searchsorted(x1, x1, sorter=dict())
@@ -405,3 +407,19 @@ def test_searchsorted_strided_scalar_needle():
     needles = dpt.asarray(needles_np)
 
     _check(hay_stack, needles, needles_np)
+
+
+@pytest.mark.parametrize(
+    "py_zero",
+    [bool(0), int(0), float(0), complex(0), np.float32(0), ctypes.c_int(0)],
+)
+@pytest.mark.parametrize("dt", _all_dtypes)
+def test_searchsorted_py_scalars(py_zero, dt):
+    q = get_queue_or_skip()
+    skip_if_dtype_not_supported(dt, q)
+
+    x = dpt.zeros(10, dtype=dt, sycl_queue=q)
+
+    r1 = dpt.searchsorted(x, py_zero)
+    assert isinstance(r1, dpt.usm_ndarray)
+    assert r1.shape == ()