Merge branch 'master' into compare-shape

Author: vtavana

.github/workflows/openssf-scorecard.yml

@@ -68,6 +68,6 @@ jobs:
 
       # Upload the results to GitHub's code scanning dashboard.
       - name: "Upload to code-scanning"
-        uses: github/codeql-action/upload-sarif@1b549b9259bda1cb5ddde3b41741a82a2d15a841 # v3.28.13
+        uses: github/codeql-action/upload-sarif@45775bd8235c68ba998cffa5171334d58593da47 # v3.28.15
         with:
           sarif_file: results.sarif

CHANGELOG.md

@@ -26,11 +26,13 @@ This release achieves 100% compliance with Python Array API specification (revis
 * Updated `dpnp.fix` to return output with the same data-type of input [#2392](https://github.com/IntelPython/dpnp/pull/2392)
 * Updated `dpnp.einsum` to add support for `order=None` [#2411](https://github.com/IntelPython/dpnp/pull/2411)
 * Updated Python Array API specification version supported to `2024.12` [#2416](https://github.com/IntelPython/dpnp/pull/2416)
+* Removed `einsum_call` keyword from `dpnp.einsum_path` signature [#2421](https://github.com/IntelPython/dpnp/pull/2421)
 
 ### Fixed
 
 * Resolved an issue with an incorrect result returned due to missing dependency from the strided kernel on a copy event in `dpnp.erf` [#2378](https://github.com/IntelPython/dpnp/pull/2378)
 * Updated `conda create` commands build and install instructions of `Quick start guide` to avoid a compilation error [#2395](https://github.com/IntelPython/dpnp/pull/2395)
+* 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)
 
 
 ## [0.17.0] - 02/26/2025

dpnp/dpnp_iface_linearalgebra.py

@@ -472,7 +472,7 @@ def einsum(
     )
 
 
-def einsum_path(*operands, optimize="greedy", einsum_call=False):
+def einsum_path(*operands, optimize="greedy"):
     """
     einsum_path(subscripts, *operands, optimize="greedy")
 
@@ -498,7 +498,7 @@ def einsum_path(*operands, optimize="greedy", einsum_call=False):
         * if a list is given that starts with ``einsum_path``, uses this as the
           contraction path
         * if ``False`` or ``None`` no optimization is taken
-        * if ``True`` defaults to the "greedy" algorithm
+        * if ``True`` defaults to the ``"greedy"`` algorithm
         * ``"optimal"`` is an algorithm that combinatorially explores all
           possible ways of contracting the listed tensors and chooses the
           least costly path. Scales exponentially with the number of terms
@@ -601,7 +601,7 @@ def einsum_path(*operands, optimize="greedy", einsum_call=False):
     return numpy.einsum_path(
         *operands,
         optimize=optimize,
-        einsum_call=einsum_call,
+        einsum_call=False,
     )
 
 

dpnp/tests/config.py

@@ -1,6 +1,6 @@
 import os
 
-all_int_types = int(os.getenv("DPNP_TEST_ALL_INT_TYPES", 0))
-float16_types = int(os.getenv("DPNP_TEST_FLOAT_16", 0))
-complex_types = int(os.getenv("DPNP_TEST_COMPLEX_TYPES", 0))
-bool_types = int(os.getenv("DPNP_TEST_BOOL_TYPES", 0))
+all_int_types = bool(os.getenv("DPNP_TEST_ALL_INT_TYPES", 0))
+float16_types = bool(os.getenv("DPNP_TEST_FLOAT_16", 0))
+complex_types = bool(os.getenv("DPNP_TEST_COMPLEX_TYPES", 0))
+bool_types = bool(os.getenv("DPNP_TEST_BOOL_TYPES", 0))

dpnp/tests/test_ndarray.py

@@ -23,7 +23,7 @@
 class TestAsType:
     @pytest.mark.usefixtures("suppress_complex_warning")
     @pytest.mark.parametrize("res_dtype", get_all_dtypes())
-    @pytest.mark.parametrize("arr_dtype", get_all_dtypes())
+    @pytest.mark.parametrize("arr_dtype", get_all_dtypes(no_none=True))
     @pytest.mark.parametrize(
         "arr",
         [[-2, -1, 0, 1, 2], [[-2, -1], [1, 2]], []],
@@ -35,7 +35,7 @@ def test_basic(self, arr, arr_dtype, res_dtype):
 
         expected = a.astype(res_dtype)
         result = ia.astype(res_dtype)
-        assert_allclose(expected, result)
+        assert_allclose(result, expected)
 
     def test_subok_error(self):
         x = dpnp.ones(4)
@@ -88,18 +88,18 @@ def test_create_from_usm_ndarray_error(arr):
         dpnp.ndarray._create_from_usm_ndarray(arr)
 
 
-@pytest.mark.parametrize("arr_dtype", get_all_dtypes())
+@pytest.mark.parametrize("arr_dtype", get_all_dtypes(no_none=True))
 @pytest.mark.parametrize(
     "arr",
     [[-2, -1, 0, 1, 2], [[-2, -1], [1, 2]], []],
     ids=["[-2, -1, 0, 1, 2]", "[[-2, -1], [1, 2]]", "[]"],
 )
 def test_flatten(arr, arr_dtype):
-    numpy_array = get_abs_array(arr, arr_dtype)
-    dpnp_array = dpnp.array(numpy_array)
-    expected = numpy_array.flatten()
-    result = dpnp_array.flatten()
-    assert_array_equal(expected, result)
+    a = get_abs_array(arr, arr_dtype)
+    ia = dpnp.array(a)
+    expected = a.flatten()
+    result = ia.flatten()
+    assert_array_equal(result, expected)
 
 
 @pytest.mark.parametrize(
@@ -110,17 +110,16 @@ def test_flatten(arr, arr_dtype):
 @pytest.mark.parametrize("order", ["C", "F"])
 def test_flags(shape, order):
     usm_array = dpt.usm_ndarray(shape, order=order)
-    numpy_array = numpy.ndarray(shape, order=order)
-    dpnp_array = dpnp.ndarray(shape, order=order)
-    assert usm_array.flags == dpnp_array.flags
-    assert numpy_array.flags.c_contiguous == dpnp_array.flags.c_contiguous
-    assert numpy_array.flags.f_contiguous == dpnp_array.flags.f_contiguous
+    a = numpy.ndarray(shape, order=order)
+    ia = dpnp.ndarray(shape, order=order)
+    assert usm_array.flags == ia.flags
+    assert a.flags.c_contiguous == ia.flags.c_contiguous
+    assert a.flags.f_contiguous == ia.flags.f_contiguous
 
 
 @pytest.mark.parametrize(
     "dtype",
     [numpy.complex64, numpy.float32, numpy.int64, numpy.int32, numpy.bool_],
-    ids=["complex64", "float32", "int64", "int32", "bool"],
 )
 @pytest.mark.parametrize("strides", [(1, 4), (4, 1)], ids=["(1, 4)", "(4, 1)"])
 @pytest.mark.parametrize("order", ["C", "F"])
@@ -130,13 +129,11 @@ def test_flags_strides(dtype, order, strides):
     usm_array = dpt.usm_ndarray(
         (4, 4), dtype=dtype, order=order, strides=strides
     )
-    numpy_array = numpy.ndarray(
-        (4, 4), dtype=dtype, order=order, strides=numpy_strides
-    )
-    dpnp_array = dpnp.ndarray((4, 4), dtype=dtype, order=order, strides=strides)
-    assert usm_array.flags == dpnp_array.flags
-    assert numpy_array.flags.c_contiguous == dpnp_array.flags.c_contiguous
-    assert numpy_array.flags.f_contiguous == dpnp_array.flags.f_contiguous
+    a = numpy.ndarray((4, 4), dtype=dtype, order=order, strides=numpy_strides)
+    ia = dpnp.ndarray((4, 4), dtype=dtype, order=order, strides=strides)
+    assert usm_array.flags == ia.flags
+    assert a.flags.c_contiguous == ia.flags.c_contiguous
+    assert a.flags.f_contiguous == ia.flags.f_contiguous
 
 
 def test_flags_writable():
@@ -383,9 +380,9 @@ def test_print_dpnp_zero_shape():
     "dtype", get_all_dtypes(no_float16=False, no_complex=True)
 )
 def test_scalar_type_casting(func, shape, dtype):
-    numpy_array = numpy.full(shape, 5, dtype=dtype)
-    dpnp_array = dpnp.full(shape, 5, dtype=dtype)
-    assert func(numpy_array) == func(dpnp_array)
+    a = numpy.full(shape, 5, dtype=dtype)
+    ia = dpnp.full(shape, 5, dtype=dtype)
+    assert func(a) == func(ia)
 
 
 # Numpy will raise an error when converting a.ndim > 0 to a scalar
@@ -396,12 +393,12 @@ def test_scalar_type_casting(func, shape, dtype):
 )
 @pytest.mark.parametrize("shape", [tuple(), (1,), (1, 1), (1, 1, 1)])
 @pytest.mark.parametrize(
-    "dtype", get_all_dtypes(no_float16=False, no_complex=True, no_none=True)
+    "dtype", get_all_dtypes(no_float16=False, no_complex=True)
 )
 def test_scalar_type_casting_by_method(method, shape, dtype):
-    numpy_array = numpy.full(shape, 4.7, dtype=dtype)
-    dpnp_array = dpnp.full(shape, 4.7, dtype=dtype)
-    assert getattr(numpy_array, method)() == getattr(dpnp_array, method)()
+    a = numpy.full(shape, 4.7, dtype=dtype)
+    ia = dpnp.full(shape, 4.7, dtype=dtype)
+    assert_allclose(getattr(a, method)(), getattr(ia, method)(), rtol=1e-06)
 
 
 @pytest.mark.parametrize("shape", [(1,), (1, 1), (1, 1, 1)])
@@ -452,18 +449,18 @@ def test_ravel():
 
 
 def test_repeat():
-    numpy_array = numpy.arange(4).repeat(3)
-    dpnp_array = dpnp.arange(4).repeat(3)
-    assert_array_equal(numpy_array, dpnp_array)
+    a = numpy.arange(4).repeat(3)
+    ia = dpnp.arange(4).repeat(3)
+    assert_array_equal(a, ia)
 
 
 def test_clip():
-    numpy_array = numpy.arange(10)
-    dpnp_array = dpnp.arange(10)
-    result = dpnp.clip(dpnp_array, 3, 7)
-    expected = numpy.clip(numpy_array, 3, 7)
+    a = numpy.arange(10)
+    ia = dpnp.arange(10)
+    result = dpnp.clip(ia, 3, 7)
+    expected = numpy.clip(a, 3, 7)
 
-    assert_array_equal(expected, result)
+    assert_array_equal(result, expected)
 
 
 def test_rmatmul_dpnp_array():