Merge fafce08 into ebe4868

.github/workflows/conda-package.yml

@@ -529,7 +529,7 @@ jobs:
         env:
           DPNP_TEST_ALL_INT_TYPES: 1
         run: |
-          pytest -n auto -ra --pyargs ${{ env.PACKAGE_NAME }}.tests
+          pytest -ra --pyargs ${{ env.PACKAGE_NAME }}.tests
 
   upload:
     name: Upload ['${{ matrix.os }}', python='${{ matrix.python }}']

dpnp/dpnp_algo/dpnp_elementwise_common.py

@@ -594,7 +594,10 @@ def __call__(self, x, decimals=0, out=None, dtype=None):
                 dtype = x_usm.dtype
 
             out_usm = None if out is None else dpnp.get_usm_ndarray(out)
-            x_usm = dpt.round(x_usm * 10**decimals, out=out_usm)
+            # the output of x_usm multiplied by 10^decimals should be
+            # float to avoid overflow for integer dtypes
+            x_usm = dpt.multiply(x_usm, float(10**decimals))
+            x_usm = dpt.round(x_usm, out=out_usm)
             res_usm = dpt.divide(x_usm, 10**decimals, out=out_usm)
 
             if dtype is not None:

dpnp/dpnp_iface_manipulation.py

@@ -1469,7 +1469,20 @@ def copyto(dst, src, casting="same_kind", where=True):
             f"but got {type(dst)}"
         )
     if not dpnp.is_supported_array_type(src):
+        src_orig = src
         src = dpnp.array(src, sycl_queue=dst.sycl_queue)
+        if not hasattr(src_orig, "dtype"):
+            # This case (scalar, list, etc) needs special handling to
+            # behave similar to NumPy
+            if dpnp.issubdtype(src, dpnp.integer) and dpnp.issubdtype(
+                dst, dpnp.unsignedinteger
+            ):
+                if dpnp.any(src < 0):
+                    raise OverflowError(
+                        "Cannot copy negative values to an unsigned int array"
+                    )
+
+                src = src.astype(dst.dtype)
 
     if not dpnp.can_cast(src.dtype, dst.dtype, casting=casting):
         raise TypeError(

dpnp/tests/test_sycl_queue.py

@@ -810,6 +810,7 @@ def test_reduce_hypot(device):
             [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
             [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0],
         ),
+        pytest.param("round", [1.234, 2.567], 2),
         pytest.param("searchsorted", [11, 12, 13, 14, 15], [-10, 20, 12, 13]),
         pytest.param(
             "subtract",

dpnp/tests/test_usm_type.py

@@ -740,6 +740,7 @@ def test_1in_1out(func, data, usm_type):
         pytest.param("maximum", [0.0, 1.0, 2.0], [3.0, 4.0, 5.0]),
         pytest.param("minimum", [0.0, 1.0, 2.0], [3.0, 4.0, 5.0]),
         pytest.param("nextafter", [1, 2], [2, 1]),
+        pytest.param("round", [1.234, 2.567], 2),
         pytest.param("searchsorted", [11, 12, 13, 14, 15], [-10, 20, 12, 13]),
         pytest.param(
             "tensordot",

dpnp/tests/third_party/cupy/core_tests/test_elementwise.py

@@ -4,7 +4,11 @@
 import pytest
 
 import dpnp as cupy
-from dpnp.tests.helper import has_support_aspect64
+from dpnp.tests.helper import (
+    has_support_aspect64,
+    is_win_platform,
+    numpy_version,
+)
 from dpnp.tests.third_party.cupy import testing
 
 
@@ -94,20 +98,22 @@ class TestElementwiseType(unittest.TestCase):
     @testing.for_int_dtypes(no_bool=True)
     @testing.numpy_cupy_array_equal(accept_error=OverflowError)
     def test_large_int_upper_1(self, xp, dtype):
-        a = xp.array([0], dtype=numpy.int8)
+        a = xp.array([0], dtype=xp.int8)
         b = xp.iinfo(dtype).max
         return a + b
 
     @testing.for_int_dtypes(no_bool=True)
     @testing.numpy_cupy_array_equal(accept_error=OverflowError)
     def test_large_int_upper_2(self, xp, dtype):
-        if (
-            numpy.issubdtype(dtype, numpy.unsignedinteger)
-            and numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0"
-        ):
-            pytest.skip("numpy promotes dtype differently")
+        if numpy_version() < "2.0.0":
+            flag = dtype in [xp.int16, xp.int32, xp.int64, xp.longlong]
+            if xp.issubdtype(dtype, xp.unsignedinteger) or flag:
+                pytest.skip("numpy doesn't raise OverflowError")
+
+            if dtype in [xp.int8, xp.intc] and is_win_platform():
+                pytest.skip("numpy promotes dtype differently")
 
-        a = xp.array([1], dtype=numpy.int8)
+        a = xp.array([1], dtype=xp.int8)
         b = xp.iinfo(dtype).max - 1
         return a + b
 
@@ -116,7 +122,7 @@ def test_large_int_upper_2(self, xp, dtype):
     def test_large_int_upper_3(self, xp, dtype):
         if (
             numpy.issubdtype(dtype, numpy.unsignedinteger)
-            and numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0"
+            and numpy_version() < "2.0.0"
         ):
             pytest.skip("numpy promotes dtype differently")
         elif (
@@ -134,7 +140,7 @@ def test_large_int_upper_3(self, xp, dtype):
     def test_large_int_upper_4(self, xp, dtype):
         if (
             numpy.issubdtype(dtype, numpy.unsignedinteger)
-            and numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0"
+            and numpy_version() < "2.0.0"
         ):
             pytest.skip("numpy promotes dtype differently")
         elif (
@@ -150,14 +156,28 @@ def test_large_int_upper_4(self, xp, dtype):
     @testing.for_int_dtypes(no_bool=True)
     @testing.numpy_cupy_array_equal(accept_error=OverflowError)
     def test_large_int_lower_1(self, xp, dtype):
-        a = xp.array([0], dtype=numpy.int8)
+        if numpy_version() < "2.0.0":
+            if dtype in [xp.int16, xp.int32, xp.int64, xp.longlong]:
+                pytest.skip("numpy doesn't raise OverflowError")
+
+            if dtype in [xp.int8, xp.intc] and is_win_platform():
+                pytest.skip("numpy promotes dtype differently")
+
+        a = xp.array([0], dtype=xp.int8)
         b = xp.iinfo(dtype).min
         return a + b
 
     @testing.for_int_dtypes(no_bool=True)
     @testing.numpy_cupy_array_equal(accept_error=OverflowError)
     def test_large_int_lower_2(self, xp, dtype):
-        a = xp.array([-1], dtype=numpy.int8)
+        if numpy_version() < "2.0.0":
+            if dtype in [xp.int16, xp.int32, xp.int64, xp.longlong]:
+                pytest.skip("numpy doesn't raise OverflowError")
+
+            if dtype in [xp.int8, xp.intc] and is_win_platform():
+                pytest.skip("numpy promotes dtype differently")
+
+        a = xp.array([-1], dtype=xp.int8)
         b = xp.iinfo(dtype).min + 1
         return a + b
 
@@ -166,7 +186,7 @@ def test_large_int_lower_2(self, xp, dtype):
     def test_large_int_lower_3(self, xp, dtype):
         if (
             numpy.issubdtype(dtype, numpy.unsignedinteger)
-            and numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0"
+            and numpy_version() < "2.0.0"
         ):
             pytest.skip("numpy promotes dtype differently")
         elif (

dpnp/tests/third_party/cupy/core_tests/test_ndarray_complex_ops.py

@@ -4,7 +4,6 @@
 import pytest
 
 import dpnp as cupy
-from dpnp.tests.helper import has_support_aspect64
 from dpnp.tests.third_party.cupy import testing
 
 
@@ -41,8 +40,10 @@ def test_conjugate_pass(self, xp, dtype):
 
 class TestAngle(unittest.TestCase):
 
+    # For dtype=int8, uint8, NumPy returns float16, but dpnp returns float32
+    # so type_check=False
     @testing.for_all_dtypes()
-    @testing.numpy_cupy_array_almost_equal(type_check=has_support_aspect64())
+    @testing.numpy_cupy_array_almost_equal(type_check=False)
     def test_angle(self, xp, dtype):
         x = testing.shaped_arange((2, 3), xp, dtype)
         return xp.angle(x)

dpnp/tests/third_party/cupy/core_tests/test_ndarray_copy_and_view.py

@@ -338,7 +338,7 @@ def test_astype_type(self, src_dtype, dst_dtype, order):
         b = astype_without_warning(a, dst_dtype, order=order)
         a_cpu = testing.shaped_arange((2, 3, 4), numpy, src_dtype)
         b_cpu = astype_without_warning(a_cpu, dst_dtype, order=order)
-        assert b.dtype.type == b_cpu.dtype.type
+        assert b.dtype == b_cpu.dtype
 
     @testing.for_orders("CAK")
     @testing.for_all_dtypes()

dpnp/tests/third_party/cupy/creation_tests/test_ranges.py

@@ -227,7 +227,13 @@ def test_linspace_mixed_start_stop2(self, xp, dtype_range, dtype_out):
         # TODO (ev-br): np 2.0: had to bump the default rtol on Windows
         #               and numpy 1.26+weak promotion from 0 to 5e-6
         if xp.dtype(dtype_range).kind in "u":
-            start = xp.array([160, 120], dtype=dtype_range)
+            # to avoid overflow, limit `val` to be smaller
+            # than xp.iinfo(dtype).max
+            if dtype_range == xp.uint8 or dtype_out == xp.uint8:
+                val = 125
+            else:
+                val = 160
+            start = xp.array([val, 120], dtype=dtype_range)
         else:
             start = xp.array([-120, 120], dtype=dtype_range)
         stop = 0

dpnp/tests/third_party/cupy/indexing_tests/test_indexing.py

@@ -204,6 +204,10 @@ class TestChoose(unittest.TestCase):
     @testing.for_all_dtypes()
     @testing.numpy_cupy_array_equal()
     def test_choose(self, xp, dtype):
+        # TODO: include additional dtype when dpnp#2201 is merged
+        dtype_list = [xp.int8, xp.int16]
+        if dtype in dtype_list or xp.issubdtype(dtype, xp.unsignedinteger):
+            pytest.skip("dpnp.choose() does not support new integer dtypes.")
         a = xp.array([0, 2, 1, 2])
         c = testing.shaped_arange((3, 4), xp, dtype)
         return a.choose(c)

dpnp/tests/third_party/cupy/linalg_tests/test_einsum.py

@@ -464,9 +464,7 @@ def test_scalar_float(self, xp, dtype):
     )
 )
 class TestEinSumBinaryOperation:
-    @testing.for_all_dtypes_combination(
-        ["dtype_a", "dtype_b"], no_bool=False, no_float16=False
-    )
+    @testing.for_all_dtypes_combination(["dtype_a", "dtype_b"], no_int8=True)
     @testing.numpy_cupy_allclose(
         type_check=has_support_aspect64(), contiguous_check=False
     )
@@ -555,13 +553,18 @@ def test_scalar_2(self, xp, dtype):
     )
 )
 class TestEinSumTernaryOperation:
-    @testing.for_all_dtypes_combination(
-        ["dtype_a", "dtype_b", "dtype_c"], no_bool=False, no_float16=False
-    )
+
+    @testing.for_all_dtypes_combination(["dtype_a", "dtype_b", "dtype_c"])
     @testing.numpy_cupy_allclose(
         type_check=has_support_aspect64(), contiguous_check=False
     )
     def test_einsum_ternary(self, xp, dtype_a, dtype_b, dtype_c):
+        flag = all(
+            dtype in [xp.int8, xp.uint8]
+            for dtype in [dtype_a, dtype_b, dtype_c]
+        )
+        if self.subscripts == "ij,jk,kl" and flag:
+            pytest.skip("avoid overflow")
         a = testing.shaped_arange(self.shape_a, xp, dtype_a)
         b = testing.shaped_arange(self.shape_b, xp, dtype_b)
         c = testing.shaped_arange(self.shape_c, xp, dtype_c)

dpnp/tests/third_party/cupy/linalg_tests/test_product.py

@@ -40,7 +40,7 @@
 )
 class TestDot(unittest.TestCase):
 
-    @testing.for_all_dtypes_combination(["dtype_a", "dtype_b"])
+    @testing.for_all_dtypes_combination(["dtype_a", "dtype_b"], no_int8=True)
     @testing.numpy_cupy_allclose(type_check=has_support_aspect64())
     def test_dot(self, xp, dtype_a, dtype_b):
         shape_a, shape_b = self.shape
@@ -238,14 +238,14 @@ def test_dot_vec3(self, xp, dtype):
         b = testing.shaped_arange((2,), xp, dtype)
         return xp.dot(a, b)
 
-    @testing.for_all_dtypes()
+    @testing.for_all_dtypes(no_int8=True)
     @testing.numpy_cupy_allclose()
     def test_transposed_dot(self, xp, dtype):
         a = testing.shaped_arange((2, 3, 4), xp, dtype).transpose(1, 0, 2)
         b = testing.shaped_arange((2, 3, 4), xp, dtype).transpose(0, 2, 1)
         return xp.dot(a, b)
 
-    @testing.for_all_dtypes()
+    @testing.for_all_dtypes(no_int8=True)
     @testing.numpy_cupy_allclose()
     def test_transposed_dot_with_out(self, xp, dtype):
         a = testing.shaped_arange((2, 3, 4), xp, dtype).transpose(1, 0, 2)
@@ -320,14 +320,14 @@ def test_reversed_inner(self, xp, dtype):
         b = testing.shaped_reverse_arange((5,), xp, dtype)[::-1]
         return xp.inner(a, b)
 
-    @testing.for_all_dtypes()
+    @testing.for_all_dtypes(no_int8=True)
     @testing.numpy_cupy_allclose()
     def test_multidim_inner(self, xp, dtype):
         a = testing.shaped_arange((2, 3, 4), xp, dtype)
         b = testing.shaped_arange((3, 2, 4), xp, dtype)
         return xp.inner(a, b)
 
-    @testing.for_all_dtypes()
+    @testing.for_all_dtypes(no_int8=True)
     @testing.numpy_cupy_allclose()
     def test_transposed_higher_order_inner(self, xp, dtype):
         a = testing.shaped_arange((2, 4, 3), xp, dtype).transpose(2, 0, 1)
@@ -355,14 +355,14 @@ def test_multidim_outer(self, xp, dtype):
         b = testing.shaped_arange((4, 5), xp, dtype)
         return xp.outer(a, b)
 
-    @testing.for_all_dtypes()
+    @testing.for_all_dtypes(no_int8=True)
     @testing.numpy_cupy_allclose()
     def test_tensordot(self, xp, dtype):
         a = testing.shaped_arange((2, 3, 4), xp, dtype)
         b = testing.shaped_arange((3, 4, 5), xp, dtype)
         return xp.tensordot(a, b)
 
-    @testing.for_all_dtypes()
+    @testing.for_all_dtypes(no_int8=True)
     @testing.numpy_cupy_allclose()
     def test_transposed_tensordot(self, xp, dtype):
         a = testing.shaped_arange((2, 3, 4), xp, dtype).transpose(1, 0, 2)
@@ -516,13 +516,13 @@ def test_matrix_power_1(self, xp, dtype):
         a = testing.shaped_arange((3, 3), xp, dtype)
         return xp.linalg.matrix_power(a, 1)
 
-    @testing.for_all_dtypes()
+    @testing.for_all_dtypes(no_int8=True)
     @testing.numpy_cupy_allclose()
     def test_matrix_power_2(self, xp, dtype):
         a = testing.shaped_arange((3, 3), xp, dtype)
         return xp.linalg.matrix_power(a, 2)
 
-    @testing.for_all_dtypes()
+    @testing.for_all_dtypes(no_int8=True)
     @testing.numpy_cupy_allclose()
     def test_matrix_power_3(self, xp, dtype):
         a = testing.shaped_arange((3, 3), xp, dtype)

dpnp/tests/third_party/cupy/logic_tests/test_comparison.py

@@ -242,7 +242,7 @@ def test_allclose_array_scalar(self, xp, dtype):
 
 class TestIsclose(unittest.TestCase):
 
-    @testing.for_all_dtypes(no_complex=True)
+    @testing.for_all_dtypes(no_complex=True, no_int8=True)
     @testing.numpy_cupy_array_equal()
     def test_is_close_finite(self, xp, dtype):
         # In numpy<1.10 this test fails when dtype is bool

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

@@ -60,8 +60,8 @@
 )
 class TestMatmul(unittest.TestCase):
 
-    @testing.for_all_dtypes(name="dtype1")
-    @testing.for_all_dtypes(name="dtype2")
+    @testing.for_all_dtypes(name="dtype1", no_int8=True)
+    @testing.for_all_dtypes(name="dtype2", no_int8=True)
     @testing.numpy_cupy_allclose(
         rtol=1e-3, atol=1e-3, type_check=has_support_aspect64()
     )  # required for uint8
@@ -70,8 +70,8 @@ def test_operator_matmul(self, xp, dtype1, dtype2):
         x2 = testing.shaped_arange(self.shape_pair[1], xp, dtype2)
         return operator.matmul(x1, x2)
 
-    @testing.for_all_dtypes(name="dtype1")
-    @testing.for_all_dtypes(name="dtype2")
+    @testing.for_all_dtypes(name="dtype1", no_int8=True)
+    @testing.for_all_dtypes(name="dtype2", no_int8=True)
     @testing.numpy_cupy_allclose(
         rtol=1e-3, atol=1e-3, type_check=has_support_aspect64()
     )  # required for uint8
@@ -97,8 +97,8 @@ def test_cupy_matmul(self, xp, dtype1, dtype2):
 )
 class TestMatmulOut(unittest.TestCase):
 
-    @testing.for_all_dtypes(name="dtype1")
-    @testing.for_all_dtypes(name="dtype2")
+    @testing.for_all_dtypes(name="dtype1", no_int8=True)
+    @testing.for_all_dtypes(name="dtype2", no_int8=True)
     @testing.numpy_cupy_allclose(
         rtol=1e-3, atol=1e-3, accept_error=TypeError  # required for uint8
     )
@@ -140,7 +140,7 @@ def test_overlap_both(self, xp, dtype, shape):
 
 class TestMatmulStrides:
 
-    @testing.for_all_dtypes()
+    @testing.for_all_dtypes(no_int8=True)
     @testing.numpy_cupy_allclose(rtol=1e-3, atol=1e-3)  # required for uint8
     def test_relaxed_c_contiguous_input(self, xp, dtype):
         x1 = testing.shaped_arange((2, 2, 3), xp, dtype)[:, None, :, :]
@@ -171,6 +171,7 @@ class TestMatmulLarge(unittest.TestCase):
 
     # Avoid overflow
     skip_dtypes = {
+        (numpy.int8, numpy.int8),
         (numpy.int8, numpy.uint8),
         (numpy.int8, numpy.int16),
         (numpy.int8, numpy.float16),

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

@@ -176,7 +176,8 @@ def test_sqrt(self):
         self.check_unary("sqrt")
 
     @testing.for_all_dtypes(no_complex=True)
-    @testing.numpy_cupy_allclose(atol=1e-5, type_check=has_support_aspect64())
+    # atol=1e-3 is needed for int8
+    @testing.numpy_cupy_allclose(atol=1e-3, type_check=has_support_aspect64())
     def test_cbrt(self, xp, dtype):
         a = testing.shaped_arange((2, 3, 4), xp, dtype)
         return xp.cbrt(a)