Backport gh-2483

CHANGELOG.md

@@ -4,7 +4,7 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.18.0] - 05/DD/2025
+## [0.18.0] - 06/04/2025
 
 This release achieves 100% compliance with Python Array API specification (revision [2024.12](https://data-apis.org/array-api/2024.12/)).
 The release provides enhanced compatibility with NumPy 2.2.5. Window and mathematical routines are complemented by a set of new functions.
@@ -18,6 +18,8 @@ Moreover, it adds support to build `dpnp` from the source for AMD GPUs.
 * Added implementation of `dpnp.bartlett` [#2366](https://github.com/IntelPython/dpnp/pull/2366)
 * Added implementation of `dpnp.convolve` [#2205](https://github.com/IntelPython/dpnp/pull/2205)
 * Added implementation of `dpnp.kaiser` [#2387](https://github.com/IntelPython/dpnp/pull/2387)
+* Added implementation of `dpnp.bitwise_count` [#2308](https://github.com/IntelPython/dpnp/pull/2308)
+* Added implementation of `dpnp.common_type` [#2391](https://github.com/IntelPython/dpnp/pull/2391)
 * Added implementation of `dpnp.interp` [#2417](https://github.com/IntelPython/dpnp/pull/2417)
 * Added support to build `dpnp` for specified AMD GPU architecture using [CodePlay oneAPI plug-in](https://developer.codeplay.com/products/oneapi/amd/home/) [#2302](https://github.com/IntelPython/dpnp/pull/2302)
 
@@ -43,7 +45,7 @@ Moreover, it adds support to build `dpnp` from the source for AMD GPUs.
 * Updated FFT module to ensure an input array is Hermitian before calling complex-to-real FFT [#2444](https://github.com/IntelPython/dpnp/pull/2444)
 * Aligned `black` configuration with the list of supported python versions [#2457](https://github.com/IntelPython/dpnp/pull/2457)
 * Use `pyproject.toml` instead of `setup.py` aligning with current packaging best practices [#2462](https://github.com/IntelPython/dpnp/pull/2462)
-* Added a clarification to `dpnp.linalg.cond` docstring about its behavior with singular matrices [#2500] (https://github.com/IntelPython/dpnp/pull/2500)
+* Added a clarification to `dpnp.linalg.cond` docstring about its behavior with singular matrices [#2460](https://github.com/IntelPython/dpnp/pull/2460)
 
 ### Fixed
 
@@ -52,6 +54,7 @@ Moreover, it adds support to build `dpnp` from the source for AMD GPUs.
 * Added handling of empty string passed to a test env variable defining data type scope as a `False` value [#2415](https://github.com/IntelPython/dpnp/pull/2415)
 * Resolved build issues on non-Intel targets in `dpnp.i0` and `dpnp.kaiser` [#2439](https://github.com/IntelPython/dpnp/pull/2439)
 * Ensure consistency in the `dpnp.linalg.LinAlgError` exception raised on singular input matrices for both non-batched and batched cases in `dpnp.linalg.inv` [#2458] (https://github.com/IntelPython/dpnp/pull/2458)
+* Updated test f/w to correct a check of array interface while converting to `numpy.ndarray` for comparison [#2467] (https://github.com/IntelPython/dpnp/pull/2467)
 
 
 ## [0.17.0] - 02/26/2025

conda-recipe/meta.yaml

@@ -1,6 +1,6 @@
 {% set max_compiler_and_mkl_version = environ.get("MAX_BUILD_CMPL_MKL_VERSION", "2026.0a0") %}
 {% set required_compiler_and_mkl_version = "2025.0" %}
-{% set required_dpctl_version = "0.20.0*" %}
+{% set required_dpctl_version = "0.20.0" %}
 
 {% set pyproject = load_file_data('pyproject.toml') %}
 {% set py_build_deps = pyproject.get('build-system', {}).get('requires', []) %}

dpnp/dpnp_utils/dpnp_algo_utils.pyx

@@ -76,9 +76,9 @@ cdef ERROR_PREFIX = "DPNP error:"
 
 
 def convert_item(item):
-    if getattr(item, "__sycl_usm_array_interface__", False):
+    if hasattr(item, "__sycl_usm_array_interface__"):
         item_converted = dpnp.asnumpy(item)
-    elif getattr(item, "__array_interface__", False):  # detect if it is a container (TODO any better way?)
+    elif hasattr(item, "__array_interface__"):  # detect if it is a container (TODO any better way?)
         mod_name = getattr(item, "__module__", 'none')
         if (mod_name != 'numpy'):
             item_converted = dpnp.asnumpy(item)

dpnp/tests/third_party/cupy/math_tests/test_hyperbolic.py

@@ -1,19 +1,24 @@
 import unittest
 
+import numpy
+
 from dpnp.tests.helper import has_support_aspect64
 from dpnp.tests.third_party.cupy import testing
 
 
 class TestHyperbolic(unittest.TestCase):
 
     @testing.for_all_dtypes()
-    @testing.numpy_cupy_allclose(atol=1e-5, type_check=has_support_aspect64())
+    @testing.numpy_cupy_allclose(
+        atol={numpy.float16: 1e-3, "default": 1e-5},
+        type_check=has_support_aspect64(),
+    )
     def check_unary(self, name, xp, dtype):
         a = testing.shaped_arange((2, 3), xp, dtype)
         return getattr(xp, name)(a)
 
     @testing.for_dtypes(["e", "f", "d"])
-    @testing.numpy_cupy_allclose(atol=1e-5)
+    @testing.numpy_cupy_allclose(atol={numpy.float16: 1e-3, "default": 1e-5})
     def check_unary_unit(self, name, xp, dtype):
         a = xp.array([0.2, 0.4, 0.6, 0.8], dtype=dtype)
         return getattr(xp, name)(a)
@@ -31,7 +36,7 @@ def test_arcsinh(self):
         self.check_unary("arcsinh")
 
     @testing.for_dtypes(["e", "f", "d"])
-    @testing.numpy_cupy_allclose(atol=1e-5)
+    @testing.numpy_cupy_allclose(atol={numpy.float16: 1e-3, "default": 1e-5})
     def test_arccosh(self, xp, dtype):
         a = xp.array([1, 2, 3], dtype=dtype)
         return xp.arccosh(a)

dpnp/tests/third_party/cupy/math_tests/test_trigonometric.py

@@ -1,5 +1,6 @@
 import unittest
 
+import numpy
 import pytest
 
 from dpnp.tests.helper import has_support_aspect64
@@ -24,7 +25,7 @@ def check_binary(self, name, xp, dtype):
         return getattr(xp, name)(a, b)
 
     @testing.for_dtypes(["e", "f", "d"])
-    @testing.numpy_cupy_allclose(atol=1e-5)
+    @testing.numpy_cupy_allclose(atol={numpy.float16: 1e-3, "default": 1e-5})
     def check_unary_unit(self, name, xp, dtype):
         a = xp.array([0.2, 0.4, 0.6, 0.8], dtype=dtype)
         return getattr(xp, name)(a)

environments/dpctl_pkg.txt

@@ -1,2 +1,2 @@
 --index-url https://pypi.anaconda.org/dppy/label/dev/simple
-dpctl>=0.20.0dev0
+dpctl>=0.20.0

environments/dpctl_pkg.yml

@@ -2,4 +2,4 @@ name: Install dpctl package
 channels:
   - dppy/label/dev
 dependencies:
-  - dpctl>=0.20.0dev0
+  - dpctl>=0.20.0

pyproject.toml

@@ -5,8 +5,7 @@ requires = [
   "build>=1.2.2",
   "cmake>=3.31.6",
   "cython>=3.0.12",
-  # WARNING: use only dpctl version available on PyPi
-  "dpctl>=0.19.0",
+  "dpctl>=0.20.0",
   "ninja>=1.11.1; platform_system!='Windows'",
   # NOTE: no DPNP restriction on NumPy version, so follow NumPy's drop schedule
   "numpy>=1.25.0",
@@ -48,8 +47,7 @@ dependencies = [
   # TODO: do we have to set sycl runtime dependencies here
   # "dpcpp-cpp-rt>=0.59.0",
   # "intel-cmplr-lib-rt>=0.59.0"
-  # WARNING: use the latest dpctl dev version, otherwise stable w/f will fail
-  "dpctl>=0.20.0dev0",
+  "dpctl>=0.20.0",
   "numpy>=1.25.0"
 ]
 description = "Data Parallel Extension for NumPy"