Add implementation of dpnp.ndarray.view method (#2520)

This PR adds implementation of `dpnp.ndarray.view` method.
All places in the code with connected TODO comments were updated
properly.

Author: antonwolfy

CHANGELOG.md

@@ -10,6 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 * Added `--target-cuda[=ARCH]` option to replace the deprecated `--target=cuda`, allowing users to build for CUDA devices with optional architecture selection using [CodePlay oneAPI plug-in](https://developer.codeplay.com/products/oneapi/nvidia/home/) [#2478](https://github.com/IntelPython/dpnp/pull/2478)
 * Added several new `pre-commit` rules, including protection against direct commits to master/maintenance branches [#2500](https://github.com/IntelPython/dpnp/pull/2500)
+* Added implementation of `dpnp.ndarray.view` method [#2520](https://github.com/IntelPython/dpnp/pull/2520)
 
 ### Changed
 

dpnp/dpnp_array.py

@@ -25,6 +25,7 @@
 # *****************************************************************************
 
 import dpctl.tensor as dpt
+import dpctl.tensor._type_utils as dtu
 from dpctl.tensor._numpy_helper import AxisError
 
 import dpnp
@@ -1979,5 +1980,126 @@ def var(
             correction=correction,
         )
 
+    def view(self, dtype=None, *, type=None):
+        """
+        New view of array with the same data.
+
+        For full documentation refer to :obj:`numpy.ndarray.view`.
+
+        Parameters
+        ----------
+        dtype : {None, str, dtype object}, optional
+            The desired data type of the returned view, e.g. :obj:`dpnp.float32`
+            or :obj:`dpnp.int16`. By default, it results in the view having the
+            same data type.
+
+            Default: ``None``.
+
+        Notes
+        -----
+        Passing ``None`` for `dtype` is the same as omitting the parameter,
+        opposite to NumPy where they have different meaning.
+
+        ``view(some_dtype)`` or ``view(dtype=some_dtype)`` constructs a view of
+        the array's memory with a different data type. This can cause a
+        reinterpretation of the bytes of memory.
+
+        Only the last axis has to be contiguous.
 
-# 'view'
+        Limitations
+        -----------
+        Parameter `type` is supported only with default value ``None``.
+        Otherwise, the function raises ``NotImplementedError`` exception.
+
+        Examples
+        --------
+        >>> import dpnp as np
+        >>> x = np.ones((4,), dtype=np.float32)
+        >>> xv = x.view(dtype=np.int32)
+        >>> xv[:] = 0
+        >>> xv
+        array([0, 0, 0, 0], dtype=int32)
+
+        However, views that change dtype are totally fine for arrays with a
+        contiguous last axis, even if the rest of the axes are not C-contiguous:
+
+        >>> x = np.arange(2 * 3 * 4, dtype=np.int8).reshape(2, 3, 4)
+        >>> x.transpose(1, 0, 2).view(np.int16)
+        array([[[ 256,  770],
+                [3340, 3854]],
+        <BLANKLINE>
+            [[1284, 1798],
+                [4368, 4882]],
+        <BLANKLINE>
+            [[2312, 2826],
+                [5396, 5910]]], dtype=int16)
+
+        """
+
+        if type is not None:
+            raise NotImplementedError(
+                "Keyword argument `type` is supported only with "
+                f"default value ``None``, but got {type}."
+            )
+
+        old_sh = self.shape
+        old_strides = self.strides
+
+        if dtype is None:
+            return dpnp_array(old_sh, buffer=self, strides=old_strides)
+
+        new_dt = dpnp.dtype(dtype)
+        new_dt = dtu._to_device_supported_dtype(new_dt, self.sycl_device)
+
+        new_itemsz = new_dt.itemsize
+        old_itemsz = self.dtype.itemsize
+        if new_itemsz == old_itemsz:
+            return dpnp_array(
+                old_sh, dtype=new_dt, buffer=self, strides=old_strides
+            )
+
+        ndim = self.ndim
+        if ndim == 0:
+            raise ValueError(
+                "Changing the dtype of a 0d array is only supported "
+                "if the itemsize is unchanged"
+            )
+
+        # resize on last axis only
+        axis = ndim - 1
+        if old_sh[axis] != 1 and self.size != 0 and old_strides[axis] != 1:
+            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_dim = old_sh[axis] * old_itemsz
+        if new_dim % new_itemsz != 0:
+            raise ValueError(
+                "When changing to a larger dtype, its size must be a divisor "
+                "of the total size in bytes of the last axis of the array"
+            )
+
+        # normalize shape whenever itemsize changes
+        new_sh = list(old_sh)
+        new_sh[axis] = new_dim // new_itemsz
+        new_sh = tuple(new_sh)
+
+        return dpnp_array(
+            new_sh,
+            dtype=new_dt,
+            buffer=self,
+            strides=new_strides,
+        )

dpnp/dpnp_utils/dpnp_utils_einsum.py

@@ -945,7 +945,6 @@ def _transpose_ex(a, axeses):
         stride = sum(a.strides[axis] for axis in axes)
         strides.append(stride)
 
-    # TODO: replace with a.view() when it is implemented in dpnp
     return dpnp_array(
         shape,
         dtype=a.dtype,
@@ -1151,8 +1150,7 @@ def dpnp_einsum(
             operands[idx] = operands[idx].sum(axis=sum_axes, dtype=result_dtype)
 
     if returns_view:
-        # TODO: replace with a.view() when it is implemented in dpnp
-        operands = [a for a in operands]
+        operands = [a.view() for a in operands]
     else:
         operands = [
             dpnp.astype(a, result_dtype, copy=False, casting=casting)

dpnp/linalg/dpnp_utils_linalg.py

@@ -1290,15 +1290,8 @@ def _nrm2_last_axis(x):
     """
 
     real_dtype = _real_type(x.dtype)
-    # TODO: use dpnp.sum(dpnp.square(dpnp.view(x)), axis=-1, dtype=real_dtype)
-    # w/a since dpnp.view() in not implemented yet
-    # Сalculate and sum the squares of both real and imaginary parts for
-    # compelex array.
-    if dpnp.issubdtype(x.dtype, dpnp.complexfloating):
-        y = dpnp.abs(x) ** 2
-    else:
-        y = dpnp.square(x)
-    return dpnp.sum(y, axis=-1, dtype=real_dtype)
+    x = dpnp.ascontiguousarray(x)
+    return dpnp.sum(dpnp.square(x.view(real_dtype)), axis=-1)
 
 
 def _real_type(dtype, device=None):

dpnp/tests/test_ndarray.py

@@ -74,6 +74,36 @@ def test_attributes(self):
         assert_equal(self.two.itemsize, self.two.dtype.itemsize)
 
 
+class TestView:
+    def test_none_dtype(self):
+        a = numpy.ones((1, 2, 4), dtype=numpy.int32)
+        ia = dpnp.array(a)
+
+        expected = a.view()
+        result = ia.view()
+        assert_allclose(result, expected)
+
+        expected = a.view()  # numpy returns dtype(None) otherwise
+        result = ia.view(None)
+        assert_allclose(result, expected)
+
+    @pytest.mark.parametrize("dt", [bool, int, float, complex])
+    def test_python_types(self, dt):
+        a = numpy.ones((8, 4), dtype=numpy.complex64)
+        ia = dpnp.array(a)
+
+        result = ia.view(dt)
+        if not has_support_aspect64() and dt in [float, complex]:
+            dt = result.dtype
+        expected = a.view(dt)
+        assert_allclose(result, expected)
+
+    def test_type_error(self):
+        x = dpnp.ones(4, dtype="i4")
+        with pytest.raises(NotImplementedError):
+            x.view("i2", type=dpnp.ndarray)
+
+
 @pytest.mark.parametrize(
     "arr",
     [

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

@@ -25,7 +25,6 @@ def get_strides(xp, a):
     return a.strides
 
 
-@pytest.mark.skip("'dpnp_array' object has no attribute 'view' yet")
 class TestView:
 
     @testing.numpy_cupy_array_equal()
@@ -98,9 +97,9 @@ def test_view_relaxed_contiguous(self, xp, dtype):
     )
     @testing.numpy_cupy_equal()
     def test_view_flags_smaller(self, xp, order, shape):
-        a = xp.zeros(shape, numpy.int32, order)
+        a = xp.zeros(shape, dtype=numpy.int32, order=order)
         b = a.view(numpy.int16)
-        return b.flags.c_contiguous, b.flags.f_contiguous, b.flags.owndata
+        return b.flags.c_contiguous, b.flags.f_contiguous  # , b.flags.owndata
 
     @pytest.mark.parametrize(
         ("order", "shape"),
@@ -112,7 +111,7 @@ def test_view_flags_smaller(self, xp, order, shape):
     @testing.with_requires("numpy>=1.23")
     def test_view_flags_smaller_invalid(self, order, shape):
         for xp in (numpy, cupy):
-            a = xp.zeros(shape, numpy.int32, order)
+            a = xp.zeros(shape, dtype=numpy.int32, order=order)
             with pytest.raises(ValueError):
                 a.view(numpy.int16)
 
@@ -121,17 +120,17 @@ def test_view_flags_smaller_invalid(self, order, shape):
         [
             ("C", (6,)),
             ("C", (3, 10)),
-            ("C", (0,)),
+            # ("C", (0,)), # dpctl-2119
             ("C", (1, 6)),
             ("C", (3, 2)),
         ],
         ids=str,
     )
     @testing.numpy_cupy_equal()
     def test_view_flags_larger(self, xp, order, shape):
-        a = xp.zeros(shape, numpy.int16, order)
+        a = xp.zeros(shape, dtype=numpy.int16, order=order)
         b = a.view(numpy.int32)
-        return b.flags.c_contiguous, b.flags.f_contiguous, b.flags.owndata
+        return b.flags.c_contiguous, b.flags.f_contiguous  # , b.flags.owndata
 
     @pytest.mark.parametrize(
         ("order", "shape"),
@@ -144,7 +143,7 @@ def test_view_flags_larger(self, xp, order, shape):
     @testing.with_requires("numpy>=1.23")
     def test_view_flags_larger_invalid(self, order, shape):
         for xp in (numpy, cupy):
-            a = xp.zeros(shape, numpy.int16, order)
+            a = xp.zeros(shape, dtype=numpy.int16, order=order)
             with pytest.raises(ValueError):
                 a.view(numpy.int32)
 
@@ -161,7 +160,7 @@ def test_view_smaller_dtype_multiple(self, xp):
     @testing.numpy_cupy_array_equal()
     def test_view_smaller_dtype_multiple2(self, xp):
         # x is non-contiguous, and stride[-1] != 0
-        x = xp.ones((3, 4), xp.int32)[:, :1:2]
+        x = xp.ones((3, 4), dtype=xp.int32)[:, :1:2]
         return x.view(xp.int16)
 
     @testing.with_requires("numpy>=1.23")
@@ -184,7 +183,7 @@ def test_view_non_c_contiguous(self, xp):
 
     @testing.numpy_cupy_array_equal()
     def test_view_larger_dtype_zero_sized(self, xp):
-        x = xp.ones((3, 20), xp.int16)[:0, ::2]
+        x = xp.ones((3, 20), dtype=xp.int16)[:0, ::2]
         return x.view(xp.int32)
 
 
@@ -387,7 +386,7 @@ def test_astype_strides_broadcast(self, xp, src_dtype, dst_dtype):
         dst = astype_without_warning(src, dst_dtype, order="K")
         return get_strides(xp, dst)
 
-    @pytest.mark.skip("'dpnp_array' object has no attribute 'view' yet")
+    @pytest.mark.skip("dpctl-2121")
     @testing.numpy_cupy_array_equal()
     def test_astype_boolean_view(self, xp):
         # See #4354
@@ -454,7 +453,7 @@ def __array_finalize__(self, obj):
         self.info = getattr(obj, "info", None)
 
 
-@pytest.mark.skip("'dpnp_array' object has no attribute 'view' yet")
+@pytest.mark.skip("subclass array is not supported")
 class TestSubclassArrayView:
 
     def test_view_casting(self):

dpnp/tests/third_party/cupy/testing/_array.py

@@ -171,13 +171,14 @@ def assert_array_equal(
         )
 
     if strides_check:
-        if actual.strides != desired.strides:
+        strides = tuple(el // desired.itemsize for el in desired.strides)
+        if actual.strides != strides:
             msg = ["Strides are not equal:"]
             if err_msg:
                 msg = [msg[0] + " " + err_msg]
             if verbose:
                 msg.append(" x: {}".format(actual.strides))
-                msg.append(" y: {}".format(desired.strides))
+                msg.append(" y: {}".format(strides))
             raise AssertionError("\n".join(msg))