Merge pull request #1110 from IntelPython/array-api-cleanup

Improvements to array API conformity

dpctl/tensor/_ctors.py

@@ -472,7 +472,12 @@ def asarray(
 
 
 def empty(
-    sh, dtype=None, order="C", device=None, usm_type="device", sycl_queue=None
+    shape,
+    dtype=None,
+    order="C",
+    device=None,
+    usm_type="device",
+    sycl_queue=None,
 ):
     """
     Creates `usm_ndarray` from uninitialized USM allocation.
@@ -509,7 +514,7 @@ def empty(
     dtype = _get_dtype(dtype, sycl_queue)
     _ensure_native_dtype_device_support(dtype, sycl_queue.sycl_device)
     res = dpt.usm_ndarray(
-        sh,
+        shape,
         dtype=dtype,
         buffer=usm_type,
         order=order,
@@ -650,7 +655,12 @@ def arange(
 
 
 def zeros(
-    sh, dtype=None, order="C", device=None, usm_type="device", sycl_queue=None
+    shape,
+    dtype=None,
+    order="C",
+    device=None,
+    usm_type="device",
+    sycl_queue=None,
 ):
     """
     Creates `usm_ndarray` with zero elements.
@@ -687,7 +697,7 @@ def zeros(
     dtype = _get_dtype(dtype, sycl_queue)
     _ensure_native_dtype_device_support(dtype, sycl_queue.sycl_device)
     res = dpt.usm_ndarray(
-        sh,
+        shape,
         dtype=dtype,
         buffer=usm_type,
         order=order,
@@ -698,7 +708,12 @@ def zeros(
 
 
 def ones(
-    sh, dtype=None, order="C", device=None, usm_type="device", sycl_queue=None
+    shape,
+    dtype=None,
+    order="C",
+    device=None,
+    usm_type="device",
+    sycl_queue=None,
 ):
     """
     Creates `usm_ndarray` with elements of one.
@@ -734,7 +749,7 @@ def ones(
     sycl_queue = normalize_queue_device(sycl_queue=sycl_queue, device=device)
     dtype = _get_dtype(dtype, sycl_queue)
     res = dpt.usm_ndarray(
-        sh,
+        shape,
         dtype=dtype,
         buffer=usm_type,
         order=order,
@@ -746,7 +761,7 @@ def ones(
 
 
 def full(
-    sh,
+    shape,
     fill_value,
     dtype=None,
     order="C",
@@ -805,14 +820,14 @@ def full(
             usm_type=usm_type,
             sycl_queue=sycl_queue,
         )
-        return dpt.copy(dpt.broadcast_to(X, sh), order=order)
+        return dpt.copy(dpt.broadcast_to(X, shape), order=order)
 
     sycl_queue = normalize_queue_device(sycl_queue=sycl_queue, device=device)
     usm_type = usm_type if usm_type is not None else "device"
     fill_value_type = type(fill_value)
     dtype = _get_dtype(dtype, sycl_queue, ref_type=fill_value_type)
     res = dpt.usm_ndarray(
-        sh,
+        shape,
         dtype=dtype,
         buffer=usm_type,
         order=order,
@@ -872,11 +887,11 @@ def empty_like(
     if device is None and sycl_queue is None:
         device = x.device
     sycl_queue = normalize_queue_device(sycl_queue=sycl_queue, device=device)
-    sh = x.shape
+    shape = x.shape
     dtype = dpt.dtype(dtype)
     _ensure_native_dtype_device_support(dtype, sycl_queue.sycl_device)
     res = dpt.usm_ndarray(
-        sh,
+        shape,
         dtype=dtype,
         buffer=usm_type,
         order=order,

dpctl/tensor/_manipulation_functions.py

@@ -31,6 +31,29 @@
 )
 
 
+class finfo_object(np.finfo):
+    """
+    numpy.finfo subclass which returns Python floating-point scalars for
+    eps, max, min, and smallest_normal.
+    """
+
+    def __init__(self, dtype):
+        _supported_dtype([dpt.dtype(dtype)])
+        super().__init__()
+
+        self.eps = float(self.eps)
+        self.max = float(self.max)
+        self.min = float(self.min)
+
+    @property
+    def smallest_normal(self):
+        return float(super().smallest_normal)
+
+    @property
+    def tiny(self):
+        return float(super().tiny)
+
+
 def _broadcast_strides(X_shape, X_strides, res_ndim):
     """
     Broadcasts strides to match the given dimensions;
@@ -122,46 +145,46 @@ def permute_dims(X, axes):
     )
 
 
-def expand_dims(X, axes):
+def expand_dims(X, axis):
     """
-    expand_dims(X: usm_ndarray, axes: int or tuple or list) -> usm_ndarray
+    expand_dims(X: usm_ndarray, axis: int or tuple or list) -> usm_ndarray
 
     Expands the shape of an array by inserting a new axis (dimension)
-    of size one at the position specified by axes; returns a view, if possible,
+    of size one at the position specified by axis; returns a view, if possible,
     a copy otherwise with the number of dimensions increased.
     """
     if not isinstance(X, dpt.usm_ndarray):
         raise TypeError(f"Expected usm_ndarray type, got {type(X)}.")
-    if not isinstance(axes, (tuple, list)):
-        axes = (axes,)
+    if not isinstance(axis, (tuple, list)):
+        axis = (axis,)
 
-    out_ndim = len(axes) + X.ndim
-    axes = normalize_axis_tuple(axes, out_ndim)
+    out_ndim = len(axis) + X.ndim
+    axis = normalize_axis_tuple(axis, out_ndim)
 
     shape_it = iter(X.shape)
-    shape = tuple(1 if ax in axes else next(shape_it) for ax in range(out_ndim))
+    shape = tuple(1 if ax in axis else next(shape_it) for ax in range(out_ndim))
 
     return dpt.reshape(X, shape)
 
 
-def squeeze(X, axes=None):
+def squeeze(X, axis=None):
     """
-    squeeze(X: usm_ndarray, axes: int or tuple or list) -> usm_ndarray
+    squeeze(X: usm_ndarray, axis: int or tuple or list) -> usm_ndarray
 
-    Removes singleton dimensions (axes) from X; returns a view, if possible,
+    Removes singleton dimensions (axis) from X; returns a view, if possible,
     a copy otherwise, but with all or a subset of the dimensions
     of length 1 removed.
     """
     if not isinstance(X, dpt.usm_ndarray):
         raise TypeError(f"Expected usm_ndarray type, got {type(X)}.")
     X_shape = X.shape
-    if axes is not None:
-        if not isinstance(axes, (tuple, list)):
-            axes = (axes,)
-        axes = normalize_axis_tuple(axes, X.ndim if X.ndim != 0 else X.ndim + 1)
+    if axis is not None:
+        if not isinstance(axis, (tuple, list)):
+            axis = (axis,)
+        axis = normalize_axis_tuple(axis, X.ndim if X.ndim != 0 else X.ndim + 1)
         new_shape = []
         for i, x in enumerate(X_shape):
-            if i not in axes:
+            if i not in axis:
                 new_shape.append(x)
             else:
                 if x != 1:
@@ -222,9 +245,9 @@ def broadcast_arrays(*args):
     return [broadcast_to(X, shape) for X in args]
 
 
-def flip(X, axes=None):
+def flip(X, axis=None):
     """
-    flip(X: usm_ndarray, axes: int or tuple or list) -> usm_ndarray
+    flip(X: usm_ndarray, axis: int or tuple or list) -> usm_ndarray
 
     Reverses the order of elements in an array along the given axis.
     The shape of the array is preserved, but the elements are reordered;
@@ -233,20 +256,20 @@ def flip(X, axes=None):
     if not isinstance(X, dpt.usm_ndarray):
         raise TypeError(f"Expected usm_ndarray type, got {type(X)}.")
     X_ndim = X.ndim
-    if axes is None:
+    if axis is None:
         indexer = (np.s_[::-1],) * X_ndim
     else:
-        axes = normalize_axis_tuple(axes, X_ndim)
+        axis = normalize_axis_tuple(axis, X_ndim)
         indexer = tuple(
-            np.s_[::-1] if i in axes else np.s_[:] for i in range(X.ndim)
+            np.s_[::-1] if i in axis else np.s_[:] for i in range(X.ndim)
         )
     return X[indexer]
 
 
-def roll(X, shift, axes=None):
+def roll(X, shift, axis=None):
     """
     roll(X: usm_ndarray, shift: int or tuple or list,\
-         axes: int or tuple or list) -> usm_ndarray
+         axis: int or tuple or list) -> usm_ndarray
 
     Rolls array elements along a specified axis.
     Array elements that roll beyond the last position are re-introduced
@@ -257,7 +280,7 @@ def roll(X, shift, axes=None):
     """
     if not isinstance(X, dpt.usm_ndarray):
         raise TypeError(f"Expected usm_ndarray type, got {type(X)}.")
-    if axes is None:
+    if axis is None:
         res = dpt.empty(
             X.shape, dtype=X.dtype, usm_type=X.usm_type, sycl_queue=X.sycl_queue
         )
@@ -266,8 +289,8 @@ def roll(X, shift, axes=None):
         )
         hev.wait()
         return res
-    axes = normalize_axis_tuple(axes, X.ndim, allow_duplicate=True)
-    broadcasted = np.broadcast(shift, axes)
+    axis = normalize_axis_tuple(axis, X.ndim, allow_duplicate=True)
+    broadcasted = np.broadcast(shift, axis)
     if broadcasted.ndim > 1:
         raise ValueError("'shift' and 'axis' should be scalars or 1D sequences")
     shifts = {ax: 0 for ax in range(X.ndim)}
@@ -495,8 +518,7 @@ def finfo(dtype):
     """
     if isinstance(dtype, dpt.usm_ndarray):
         raise TypeError("Expected dtype type, got {to}.")
-    _supported_dtype([dpt.dtype(dtype)])
-    return np.finfo(dtype)
+    return finfo_object(dtype)
 
 
 def _supported_dtype(dtypes):

dpctl/tensor/_reshape.py

@@ -75,17 +75,17 @@ def reshaped_strides(old_sh, old_sts, new_sh, order="C"):
     return new_sts if valid else None
 
 
-def reshape(X, newshape, order="C", copy=None):
+def reshape(X, shape, order="C", copy=None):
     """
-    reshape(X: usm_ndarray, newshape: tuple, order="C") -> usm_ndarray
+    reshape(X: usm_ndarray, shape: tuple, order="C") -> usm_ndarray
 
     Reshapes given usm_ndarray into new shape. Returns a view, if possible,
     a copy otherwise. Memory layout of the copy is controlled by order keyword.
     """
     if not isinstance(X, dpt.usm_ndarray):
         raise TypeError
-    if not isinstance(newshape, (list, tuple)):
-        newshape = (newshape,)
+    if not isinstance(shape, (list, tuple)):
+        shape = (shape,)
     if order in "cfCF":
         order = order.upper()
     else:
@@ -97,9 +97,9 @@ def reshape(X, newshape, order="C", copy=None):
             f"Keyword 'copy' not recognized. Expecting True, False, "
             f"or None, got {copy}"
         )
-    newshape = [operator.index(d) for d in newshape]
+    shape = [operator.index(d) for d in shape]
     negative_ones_count = 0
-    for nshi in newshape:
+    for nshi in shape:
         if nshi == -1:
             negative_ones_count = negative_ones_count + 1
         if (nshi < -1) or negative_ones_count > 1:
@@ -108,14 +108,14 @@ def reshape(X, newshape, order="C", copy=None):
                 "value which can only be -1"
             )
     if negative_ones_count:
-        v = X.size // (-np.prod(newshape))
-        newshape = [v if d == -1 else d for d in newshape]
-    if X.size != np.prod(newshape):
-        raise ValueError(f"Can not reshape into {newshape}")
+        v = X.size // (-np.prod(shape))
+        shape = [v if d == -1 else d for d in shape]
+    if X.size != np.prod(shape):
+        raise ValueError(f"Can not reshape into {shape}")
     if X.size:
-        newsts = reshaped_strides(X.shape, X.strides, newshape, order=order)
+        newsts = reshaped_strides(X.shape, X.strides, shape, order=order)
     else:
-        newsts = (1,) * len(newshape)
+        newsts = (1,) * len(shape)
     copy_required = newsts is None
     if copy_required and (copy is False):
         raise ValueError(
@@ -141,11 +141,11 @@ def reshape(X, newshape, order="C", copy=None):
                     flat_res[i], X[np.unravel_index(i, X.shape, order=order)]
                 )
         return dpt.usm_ndarray(
-            tuple(newshape), dtype=X.dtype, buffer=flat_res, order=order
+            tuple(shape), dtype=X.dtype, buffer=flat_res, order=order
         )
     # can form a view
     return dpt.usm_ndarray(
-        newshape,
+        shape,
         dtype=X.dtype,
         buffer=X,
         strides=tuple(newsts),

dpctl/tests/test_usm_ndarray_manipulation.py

@@ -483,7 +483,7 @@ def test_incompatible_shapes_raise_valueerror(shapes):
         assert_broadcast_arrays_raise(input_shapes[::-1])
 
 
-def test_flip_axes_incorrect():
+def test_flip_axis_incorrect():
     try:
         q = dpctl.SyclQueue()
     except dpctl.SyclQueueCreationError:
@@ -492,10 +492,10 @@ def test_flip_axes_incorrect():
     X_np = np.ones((4, 4))
     X = dpt.asarray(X_np, sycl_queue=q)
 
-    pytest.raises(np.AxisError, dpt.flip, dpt.asarray(np.ones(4)), axes=1)
-    pytest.raises(np.AxisError, dpt.flip, X, axes=2)
-    pytest.raises(np.AxisError, dpt.flip, X, axes=-3)
-    pytest.raises(np.AxisError, dpt.flip, X, axes=(0, 3))
+    pytest.raises(np.AxisError, dpt.flip, dpt.asarray(np.ones(4)), axis=1)
+    pytest.raises(np.AxisError, dpt.flip, X, axis=2)
+    pytest.raises(np.AxisError, dpt.flip, X, axis=-3)
+    pytest.raises(np.AxisError, dpt.flip, X, axis=(0, 3))
 
 
 def test_flip_0d():