Merge branch 'master' into nan-to-num-broadcasting

Author: antonwolfy

.github/workflows/openssf-scorecard.yml

@@ -72,6 +72,6 @@ jobs:
 
       # Upload the results to GitHub's code scanning dashboard.
       - name: "Upload to code-scanning"
-        uses: github/codeql-action/upload-sarif@b20883b0cd1f46c72ae0ba6d1090936928f9fa30 # v4.32.0
+        uses: github/codeql-action/upload-sarif@45cbd0c69e560cd9e7cd7f8c32362050c9b7ded2 # v4.32.2
         with:
           sarif_file: results.sarif

CHANGELOG.md

@@ -47,7 +47,11 @@ Also, that release drops support for Python 3.9, making Python 3.10 the minimum
 * Clarified behavior on repeated `axes` in `dpnp.tensordot` and `dpnp.linalg.tensordot` functions [#2733](https://github.com/IntelPython/dpnp/pull/2733)
 * Improved documentation of `file` argument in `dpnp.fromfile` [#2745](https://github.com/IntelPython/dpnp/pull/2745)
 * Aligned `dpnp.trim_zeros` with NumPy 2.4 to support a tuple of integers passed with `axis` keyword [#2746](https://github.com/IntelPython/dpnp/pull/2746)
+<<<<<<< nan-to-num-broadcasting
 * Extended `dpnp.nan_to_num` to support broadcasting of `nan`, `posinf`, and `neginf` keywords [#2754](https://github.com/IntelPython/dpnp/pull/2754)
+=======
+* Aligned `strides` property of `dpnp.ndarray` with NumPy and CuPy implementations [#2747](https://github.com/IntelPython/dpnp/pull/2747)
+>>>>>>> master
 
 ### Deprecated
 

dpnp/dpnp_array.py

@@ -105,6 +105,16 @@ def __init__(
         else:
             buffer = usm_type
 
+        if strides is not None:
+            # dpctl expects strides as elements displacement in memory,
+            # while dpnp (and numpy as well) relies on bytes displacement
+            if dtype is None:
+                dtype = dpnp.default_float_type(
+                    device=device, sycl_queue=sycl_queue
+                )
+            it_sz = dpnp.dtype(dtype).itemsize
+            strides = tuple(el // it_sz for el in strides)
+
         sycl_queue_normalized = dpnp.get_normalized_queue_device(
             device=device, sycl_queue=sycl_queue
         )
@@ -1855,16 +1865,53 @@ def std(
     @property
     def strides(self):
         """
-        Return memory displacement in array elements, upon unit
-        change of respective index.
+        Tuple of bytes to step in each dimension when traversing an array.
 
-        For example, for strides ``(s1, s2, s3)`` and multi-index
-        ``(i1, i2, i3)`` position of the respective element relative
-        to zero multi-index element is ``s1*s1 + s2*i2 + s3*i3``.
+        The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`
+        is::
 
-        """
+            offset = sum(dpnp.array(i) * a.strides)
 
-        return self._array_obj.strides
+        For full documentation refer to :obj:`numpy.ndarray.strides`.
+
+        See Also
+        --------
+        :obj:`dpnp.lib.stride_tricks.as_strided` : Return a view into the array
+            with given shape and strides.
+
+        Examples
+        --------
+        >>> import dpnp as np
+        >>> y = np.reshape(np.arange(2 * 3 * 4, dtype=np.int32), (2, 3, 4))
+        >>> y
+        array([[[ 0,  1,  2,  3],
+                [ 4,  5,  6,  7],
+                [ 8,  9, 10, 11]],
+               [[12, 13, 14, 15],
+                [16, 17, 18, 19],
+                [20, 21, 22, 23]]], dtype=np.int32)
+        >>> y.strides
+        (48, 16, 4)
+        >>> y[1, 1, 1]
+        array(17, dtype=int32)
+        >>> offset = sum(i * s for i, s in zip((1, 1, 1), y.strides))
+        >>> offset // y.itemsize
+        17
+
+        >>> x = np.reshape(np.arange(5*6*7*8, dtype=np.int32), (5, 6, 7, 8))
+        >>> x = x.transpose(2, 3, 1, 0)
+        >>> x.strides
+        (32, 4, 224, 1344)
+        >>> offset = sum(i * s for i, s in zip((3, 5, 2, 2), x.strides))
+        >>> x[3, 5, 2, 2]
+        array(813, dtype=int32)
+        >>> offset // x.itemsize
+        813
+
+        """
+
+        it_sz = self.itemsize
+        return tuple(el * it_sz for el in self._array_obj.strides)
 
     def sum(
         self,
@@ -2335,23 +2382,20 @@ def view(self, /, dtype=None, *, type=None):
 
         # resize on last axis only
         axis = ndim - 1
-        if old_sh[axis] != 1 and self.size != 0 and old_strides[axis] != 1:
+        if (
+            old_sh[axis] != 1
+            and self.size != 0
+            and old_strides[axis] != old_itemsz
+        ):
             raise ValueError(
                 "To change to a dtype of a different size, "
                 "the last axis must be contiguous"
             )
 
         # normalize strides whenever itemsize changes
-        if old_itemsz > new_itemsz:
-            new_strides = list(
-                el * (old_itemsz // new_itemsz) for el in old_strides
-            )
-        else:
-            new_strides = list(
-                el // (new_itemsz // old_itemsz) for el in old_strides
-            )
-        new_strides[axis] = 1
-        new_strides = tuple(new_strides)
+        new_strides = tuple(
+            old_strides[i] if i != axis else new_itemsz for i in range(ndim)
+        )
 
         new_dim = old_sh[axis] * old_itemsz
         if new_dim % new_itemsz != 0:
@@ -2361,9 +2405,10 @@ def view(self, /, dtype=None, *, type=None):
             )
 
         # normalize shape whenever itemsize changes
-        new_sh = list(old_sh)
-        new_sh[axis] = new_dim // new_itemsz
-        new_sh = tuple(new_sh)
+        new_sh = tuple(
+            old_sh[i] if i != axis else new_dim // new_itemsz
+            for i in range(ndim)
+        )
 
         return dpnp_array(
             new_sh,

dpnp/dpnp_iface_arraycreation.py

@@ -105,7 +105,7 @@ def _get_empty_array(
         elif a.flags.c_contiguous:
             order = "C"
         else:
-            strides = _get_strides_for_order_k(a, _shape)
+            strides = _get_strides_for_order_k(a, _dtype, shape=_shape)
             order = "C"
     elif order not in "cfCF":
         raise ValueError(
@@ -122,15 +122,15 @@ def _get_empty_array(
     )
 
 
-def _get_strides_for_order_k(x, shape=None):
+def _get_strides_for_order_k(x, dtype, shape=None):
     """
     Calculate strides when order='K' for empty_like, ones_like, zeros_like,
     and full_like where `shape` is ``None`` or len(shape) == x.ndim.
 
     """
     stride_and_index = sorted([(abs(s), -i) for i, s in enumerate(x.strides)])
     strides = [0] * x.ndim
-    stride = 1
+    stride = dpnp.dtype(dtype).itemsize
     for _, i in stride_and_index:
         strides[-i] = stride
         stride *= shape[-i] if shape else x.shape[-i]

dpnp/dpnp_iface_indexing.py

@@ -731,10 +731,10 @@ def diagonal(a, offset=0, axis1=0, axis2=1):
     elif 0 < offset < m:
         out_shape = a_shape[:-2] + (min(n, m - offset),)
         out_strides = a_straides[:-2] + (st_n + st_m,)
-        out_offset = st_m * offset
+        out_offset = st_m // a.itemsize * offset
     else:
         out_shape = a_shape[:-2] + (0,)
-        out_strides = a_straides[:-2] + (1,)
+        out_strides = a_straides[:-2] + (a.itemsize,)
         out_offset = 0
 
     return dpnp_array(

dpnp/dpnp_utils/dpnp_utils_linearalgebra.py

@@ -185,7 +185,7 @@ def _define_contig_flag(x):
     """
 
     flag = False
-    x_strides = x.strides
+    x_strides = dpnp.get_usm_ndarray(x).strides
     x_shape = x.shape
     if x.ndim < 2:
         return True, True, True

dpnp/fft/dpnp_utils_fft.py

@@ -193,12 +193,13 @@ def _compute_result(dsc, a, out, forward, c2c, out_strides):
         )
         result = a
     else:
+        out_usm = None if out is None else dpnp.get_usm_ndarray(out)
         if (
             out is not None
-            and out.strides == tuple(out_strides)
-            and not ti._array_overlap(a_usm, dpnp.get_usm_ndarray(out))
+            and out_usm.strides == tuple(out_strides)
+            and not ti._array_overlap(a_usm, out_usm)
         ):
-            res_usm = dpnp.get_usm_ndarray(out)
+            res_usm = out_usm
             result = out
         else:
             # Result array that is used in oneMKL must have the exact same
@@ -223,6 +224,10 @@ def _compute_result(dsc, a, out, forward, c2c, out_strides):
                         if a.dtype == dpnp.complex64
                         else dpnp.float64
                     )
+            # cast to expected strides format
+            out_strides = tuple(
+                el * dpnp.dtype(out_dtype).itemsize for el in out_strides
+            )
             result = dpnp_array(
                 out_shape,
                 dtype=out_dtype,
@@ -419,7 +424,8 @@ def _fft(a, norm, out, forward, in_place, c2c, axes, batch_fft=True):
     if cufft_wa:  # pragma: no cover
         a = dpnp.moveaxis(a, -1, -2)
 
-    a_strides = _standardize_strides_to_nonzero(a.strides, a.shape)
+    strides = dpnp.get_usm_ndarray(a).strides
+    a_strides = _standardize_strides_to_nonzero(strides, a.shape)
     dsc, out_strides = _commit_descriptor(
         a, forward, in_place, c2c, a_strides, index, batch_fft
     )

dpnp/linalg/dpnp_utils_linalg.py

@@ -215,7 +215,7 @@ def _batched_inv(a, res_type):
     _manager.add_event_pair(ht_ev, copy_ev)
 
     ipiv_stride = n
-    a_stride = a_h.strides[0]
+    a_stride = a_h.strides[0] // a_h.itemsize
 
     # Call the LAPACK extension function _getrf_batch
     # to perform LU decomposition of a batch of general matrices
@@ -298,7 +298,7 @@ def _batched_lu_factor(a, res_type):
         dev_info_h = [0] * batch_size
 
         ipiv_stride = n
-        a_stride = a_h.strides[0]
+        a_stride = a_h.strides[0] // a_h.itemsize
 
         # Call the LAPACK extension function _getrf_batch
         # to perform LU decomposition of a batch of general matrices
@@ -471,8 +471,8 @@ def _batched_qr(a, mode="reduced"):
         dtype=res_type,
     )
 
-    a_stride = a_t.strides[0]
-    tau_stride = tau_h.strides[0]
+    a_stride = a_t.strides[0] // a_t.itemsize
+    tau_stride = tau_h.strides[0] // tau_h.itemsize
 
     # Call the LAPACK extension function _geqrf_batch to compute
     # the QR factorization of a general m x n matrix.
@@ -535,8 +535,8 @@ def _batched_qr(a, mode="reduced"):
     )
     _manager.add_event_pair(ht_ev, copy_ev)
 
-    q_stride = q.strides[0]
-    tau_stride = tau_h.strides[0]
+    q_stride = q.strides[0] // q.itemsize
+    tau_stride = tau_h.strides[0] // tau_h.itemsize
 
     # Get LAPACK function (_orgqr_batch for real or _ungqf_batch for complex
     # data types) for QR factorization
@@ -1818,7 +1818,7 @@ def dpnp_cholesky_batch(a, upper_lower, res_type):
     )
     _manager.add_event_pair(ht_ev, copy_ev)
 
-    a_stride = a_h.strides[0]
+    a_stride = a_h.strides[0] // a_h.itemsize
 
     # Call the LAPACK extension function _potrf_batch
     # to computes the Cholesky decomposition of a batch of

dpnp/scipy/linalg/_utils.py

@@ -37,6 +37,7 @@
 
 """
 
+# pylint: disable=duplicate-code
 # pylint: disable=no-name-in-module
 # pylint: disable=protected-access
 
@@ -144,7 +145,7 @@ def _batched_lu_factor_scipy(a, res_type):  # pylint: disable=too-many-locals
         dev_info_h = [0] * batch_size
 
         ipiv_stride = k
-        a_stride = a_h.strides[-1]
+        a_stride = a_h.strides[-1] // a_h.itemsize
 
         # Call the LAPACK extension function _getrf_batch
         # to perform LU decomposition of a batch of general matrices

dpnp/tests/test_arraycreation.py

@@ -861,12 +861,12 @@ def test_full_order(order1, order2):
 def test_full_strides():
     a = numpy.full((3, 3), numpy.arange(3, dtype="i4"))
     ia = dpnp.full((3, 3), dpnp.arange(3, dtype="i4"))
-    assert ia.strides == tuple(el // a.itemsize for el in a.strides)
+    assert ia.strides == a.strides
     assert_array_equal(ia, a)
 
     a = numpy.full((3, 3), numpy.arange(6, dtype="i4")[::2])
     ia = dpnp.full((3, 3), dpnp.arange(6, dtype="i4")[::2])
-    assert ia.strides == tuple(el // a.itemsize for el in a.strides)
+    assert ia.strides == a.strides
     assert_array_equal(ia, a)