Add support of python 3.11 for public CI

.github/workflows/conda-package.yml

@@ -37,7 +37,7 @@ jobs:
 
     strategy:
       matrix:
-        python: ['3.8', '3.9', '3.10']
+        python: ['3.8', '3.9', '3.10', '3.11']
         os: [ubuntu-20.04, windows-latest]
 
     runs-on: ${{ matrix.os }}
@@ -114,7 +114,7 @@ jobs:
 
     strategy:
       matrix:
-        python: ['3.8', '3.9', '3.10']
+        python: ['3.8', '3.9', '3.10', '3.11']
         os: [ubuntu-20.04, ubuntu-latest]
 
         experimental: [false]
@@ -217,7 +217,7 @@ jobs:
 
     strategy:
       matrix:
-        python: ['3.8', '3.9', '3.10']
+        python: ['3.8', '3.9', '3.10', '3.11']
         experimental: [false]
 
     continue-on-error: ${{ matrix.experimental }}
@@ -350,7 +350,7 @@ jobs:
 
     strategy:
       matrix:
-        python: ['3.8', '3.9', '3.10']
+        python: ['3.8', '3.9', '3.10', '3.11']
         os: [ubuntu-20.04, windows-latest]
 
     runs-on: ${{ matrix.os }}

.github/workflows/generate_coverage.yaml

@@ -14,7 +14,7 @@ jobs:
         shell: bash -l {0}
 
     env:
-      python-ver: '3.10'
+      python-ver: '3.11'
       CHANNELS: '-c dppy/label/dev -c intel -c conda-forge --override-channels'
 
     steps:

.github/workflows/pre-commit.yml

@@ -19,5 +19,5 @@ jobs:
       - uses: actions/[email protected]
       - uses: actions/[email protected]
         with:
-          python-version: '3.10'
+          python-version: '3.11'
       - uses: pre-commit/[email protected]

conda-recipe/build.sh

@@ -1,5 +1,8 @@
 #!/bin/bash
 
+# This is necessary to help DPC++ find Intel libraries such as SVML, IRNG, etc in build prefix
+export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:${BUILD_PREFIX}/lib"
+
 # Intel LLVM must cooperate with compiler and sysroot from conda
 echo "--gcc-toolchain=${BUILD_PREFIX} --sysroot=${BUILD_PREFIX}/${HOST}/sysroot -target ${HOST}" > icpx_for_conda.cfg
 export ICPXCFG="$(pwd)/icpx_for_conda.cfg"

setup.py

@@ -37,6 +37,7 @@ def _get_cmdclass():
 Programming Language :: Python :: 3.8
 Programming Language :: Python :: 3.9
 Programming Language :: Python :: 3.10
+Programming Language :: Python :: 3.11
 Programming Language :: Python :: Implementation :: CPython
 Topic :: Software Development
 Topic :: Scientific/Engineering

tests/helper.py

@@ -1,10 +1,32 @@
 from sys import platform
 
 import dpctl
+import numpy
+from numpy.testing import assert_allclose, assert_array_equal
 
 import dpnp
 
 
+def assert_dtype_allclose(dpnp_arr, numpy_arr):
+    """
+    Assert DPNP and NumPy array based on maximum dtype resolution of input arrays
+    for floating and complex types.
+    For other dtypes the assertion is based on exact matching of the arrays.
+
+    """
+
+    is_inexact = lambda x: dpnp.issubdtype(x.dtype, dpnp.inexact)
+    if is_inexact(dpnp_arr) or is_inexact(numpy_arr):
+        tol = 8 * max(
+            numpy.finfo(dpnp_arr.dtype).resolution,
+            numpy.finfo(numpy_arr.dtype).resolution,
+        )
+        assert_allclose(dpnp_arr.asnumpy(), numpy_arr, atol=tol, rtol=tol)
+    else:
+        assert_array_equal(dpnp_arr.asnumpy(), numpy_arr)
+        assert dpnp_arr.dtype == numpy_arr.dtype
+
+
 def get_complex_dtypes(device=None):
     """
     Build a list of complex types supported by DPNP based on device capabilities.

tests/test_dot.py

@@ -20,11 +20,11 @@ def test_dot_ones(type):
     assert_array_equal(expected, result)
 
 
-@pytest.mark.parametrize("type", get_all_dtypes(no_bool=True, no_complex=True))
-def test_dot_arange(type):
+@pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True, no_complex=True))
+def test_dot_arange(dtype):
     n = 10**2
-    m = 10**3
-    a = numpy.hstack((numpy.arange(n, dtype=type),) * m)
+    m = 10**3 if dtype is not inp.float32 else 10**2
+    a = numpy.hstack((numpy.arange(n, dtype=dtype),) * m)
     b = numpy.flipud(a)
     ia = inp.array(a)
     ib = inp.array(b)

tests/test_fft.py

@@ -3,7 +3,7 @@
 
 import dpnp
 
-from .helper import has_support_aspect64
+from .helper import assert_dtype_allclose, has_support_aspect64
 
 pytestmark = pytest.mark.skipif(
     not has_support_aspect64(), reason="Aborted on Iris Xe: SAT-6028"
@@ -20,10 +20,9 @@ def test_fft(type, norm):
     dpnp_data = dpnp.array(data)
 
     np_res = numpy.fft.fft(data, norm=norm)
-    dpnp_res = dpnp.asnumpy(dpnp.fft.fft(dpnp_data, norm=norm))
+    dpnp_res = dpnp.fft.fft(dpnp_data, norm=norm)
 
-    numpy.testing.assert_allclose(dpnp_res, np_res, rtol=1e-4, atol=1e-7)
-    assert dpnp_res.dtype == np_res.dtype
+    assert_dtype_allclose(dpnp_res, np_res)
 
 
 @pytest.mark.parametrize(
@@ -38,8 +37,7 @@ def test_fft_ndim(type, shape, norm):
     np_res = numpy.fft.fft(np_data, norm=norm)
     dpnp_res = dpnp.fft.fft(dpnp_data, norm=norm)
 
-    numpy.testing.assert_allclose(dpnp_res, np_res, rtol=1e-4, atol=1e-7)
-    assert dpnp_res.dtype == np_res.dtype
+    assert_dtype_allclose(dpnp_res, np_res)
 
 
 @pytest.mark.parametrize(
@@ -56,8 +54,7 @@ def test_fft_ifft(type, shape, norm):
     np_res = numpy.fft.ifft(np_data, norm=norm)
     dpnp_res = dpnp.fft.ifft(dpnp_data, norm=norm)
 
-    numpy.testing.assert_allclose(dpnp_res, np_res, rtol=1e-4, atol=1e-7)
-    assert dpnp_res.dtype == np_res.dtype
+    assert_dtype_allclose(dpnp_res, np_res)
 
 
 @pytest.mark.parametrize("type", ["float32", "float64", "int32", "int64"])
@@ -71,5 +68,4 @@ def test_fft_rfft(type, shape):
     np_res = numpy.fft.rfft(np_data)
     dpnp_res = dpnp.fft.rfft(dpnp_data)
 
-    numpy.testing.assert_allclose(dpnp_res, np_res, rtol=1e-4, atol=1e-7)
-    assert dpnp_res.dtype == np_res.dtype
+    assert_dtype_allclose(dpnp_res, np_res)

tests/test_sycl_queue.py

@@ -6,7 +6,7 @@
 
 import dpnp
 
-from .helper import get_all_dtypes, is_win_platform
+from .helper import assert_dtype_allclose, get_all_dtypes, is_win_platform
 
 list_of_backend_str = [
     "host",
@@ -670,8 +670,7 @@ def test_fft_rfft(type, shape, device):
     np_res = numpy.fft.rfft(np_data)
     dpnp_res = dpnp.fft.rfft(dpnp_data)
 
-    numpy.testing.assert_allclose(dpnp_res, np_res, rtol=1e-4, atol=1e-7)
-    assert dpnp_res.dtype == np_res.dtype
+    assert_dtype_allclose(dpnp_res, np_res)
 
     expected_queue = dpnp_data.get_array().sycl_queue
     result_queue = dpnp_res.get_array().sycl_queue

tests/test_umath.py

@@ -480,7 +480,7 @@ def test_tan(self):
         np_array = numpy.array(array_data, dtype=numpy.float64)
         expected = numpy.tan(np_array, out=out)
 
-        assert_array_equal(expected, result)
+        assert_allclose(expected, result)
 
     @pytest.mark.parametrize(
         "dtype",

tests/third_party/cupy/fft_tests/test_fft.py

@@ -23,7 +23,11 @@
 class TestFft(unittest.TestCase):
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(
-        rtol=1e-4, atol=1e-7, accept_error=ValueError, contiguous_check=False
+        rtol=1e-4,
+        atol=1e-7,
+        accept_error=ValueError,
+        contiguous_check=False,
+        type_check=False,
     )
     def test_fft(self, xp, dtype):
         a = testing.shaped_random(self.shape, xp, dtype)
@@ -33,7 +37,11 @@ def test_fft(self, xp, dtype):
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_allclose(
-        rtol=1e-4, atol=1e-7, accept_error=ValueError, contiguous_check=False
+        rtol=1e-4,
+        atol=1e-7,
+        accept_error=ValueError,
+        contiguous_check=False,
+        type_check=False,
     )
     def test_ifft(self, xp, dtype):
         a = testing.shaped_random(self.shape, xp, dtype)
@@ -154,7 +162,9 @@ def test_ifftn(self, xp, dtype):
 @testing.gpu
 class TestRfft(unittest.TestCase):
     @testing.for_all_dtypes(no_complex=True)
-    @testing.numpy_cupy_allclose(rtol=1e-4, atol=1e-7, contiguous_check=False)
+    @testing.numpy_cupy_allclose(
+        rtol=1e-4, atol=1e-7, contiguous_check=False, type_check=False
+    )
     def test_rfft(self, xp, dtype):
         a = testing.shaped_random(self.shape, xp, dtype)
         out = xp.fft.rfft(a, n=self.n, norm=self.norm)

tests/third_party/cupy/sorting_tests/test_sort.py

@@ -317,7 +317,8 @@ def test_argsort_zero_dim(self, xp, dtype):
     @testing.numpy_cupy_array_equal()
     def test_argsort_one_dim(self, xp, dtype):
         a = testing.shaped_random((10,), xp, dtype)
-        return self.argsort(a)
+        res = self.argsort(a)
+        return a[res]
 
     @testing.for_all_dtypes()
     @testing.numpy_cupy_array_equal()