Merging developmental snapshot tagged 0.14.6dev2 to gold/2021

.github/workflows/conda-package.yml

@@ -486,7 +486,7 @@ jobs:
           done
 
   array-api-conformity:
-    needs: test_linux
+    needs: build_linux
     runs-on:  ${{ matrix.runner }}
 
     strategy:

dpctl/_backend.pxd

@@ -299,6 +299,7 @@ cdef extern from "syclinterface/dpctl_sycl_platform_manager.h":
 
 
 cdef extern from "syclinterface/dpctl_sycl_platform_interface.h":
+    cdef bool DPCTLPlatform_AreEq(const DPCTLSyclPlatformRef, const DPCTLSyclPlatformRef)
     cdef DPCTLSyclPlatformRef DPCTLPlatform_Copy(const DPCTLSyclPlatformRef)
     cdef DPCTLSyclPlatformRef DPCTLPlatform_Create()
     cdef DPCTLSyclPlatformRef DPCTLPlatform_CreateFromSelector(
@@ -308,6 +309,7 @@ cdef extern from "syclinterface/dpctl_sycl_platform_interface.h":
     cdef const char *DPCTLPlatform_GetName(const DPCTLSyclPlatformRef)
     cdef const char *DPCTLPlatform_GetVendor(const DPCTLSyclPlatformRef)
     cdef const char *DPCTLPlatform_GetVersion(const DPCTLSyclPlatformRef)
+    cdef size_t DPCTLPlatform_Hash(const DPCTLSyclPlatformRef)
     cdef DPCTLPlatformVectorRef DPCTLPlatform_GetPlatforms()
     cdef DPCTLSyclContextRef DPCTLPlatform_GetDefaultContext(
         const DPCTLSyclPlatformRef)

dpctl/_sycl_platform.pxd

@@ -21,6 +21,8 @@
     SYCL platform-related helper functions.
 """
 
+from libcpp cimport bool
+
 from ._backend cimport DPCTLSyclDeviceSelectorRef, DPCTLSyclPlatformRef
 
 
@@ -40,6 +42,7 @@ cdef class SyclPlatform(_SyclPlatform):
     cdef int _init_from_selector(self, DPCTLSyclDeviceSelectorRef DSRef)
     cdef int _init_from__SyclPlatform(self, _SyclPlatform other)
     cdef DPCTLSyclPlatformRef get_platform_ref(self)
+    cdef bool equals(self, SyclPlatform)
 
 
 cpdef list get_platforms()

dpctl/_sycl_platform.pyx

@@ -21,10 +21,13 @@
 """ Implements SyclPlatform Cython extension type.
 """
 
+from libcpp cimport bool
+
 from ._backend cimport (  # noqa: E211
     DPCTLCString_Delete,
     DPCTLDeviceSelector_Delete,
     DPCTLFilterSelector_Create,
+    DPCTLPlatform_AreEq,
     DPCTLPlatform_Copy,
     DPCTLPlatform_Create,
     DPCTLPlatform_CreateFromSelector,
@@ -35,6 +38,7 @@ from ._backend cimport (  # noqa: E211
     DPCTLPlatform_GetPlatforms,
     DPCTLPlatform_GetVendor,
     DPCTLPlatform_GetVersion,
+    DPCTLPlatform_Hash,
     DPCTLPlatformMgr_GetInfo,
     DPCTLPlatformMgr_PrintInfo,
     DPCTLPlatformVector_Delete,
@@ -274,6 +278,42 @@ cdef class SyclPlatform(_SyclPlatform):
         else:
             return SyclContext._create(CRef)
 
+    cdef bool equals(self, SyclPlatform other):
+        """
+        Returns true if the :class:`dpctl.SyclPlatform` argument has the
+        same underlying ``DPCTLSyclPlatformRef`` object as this
+        :class:`dpctl.SyclPlatform` instance.
+
+        Returns:
+            :obj:`bool`: ``True`` if the two :class:`dpctl.SyclPlatform` objects
+            point to the same ``DPCTLSyclPlatformRef`` object, otherwise
+            ``False``.
+        """
+        return DPCTLPlatform_AreEq(self._platform_ref, other.get_platform_ref())
+
+    def __eq__(self, other):
+        """
+        Returns True if the :class:`dpctl.SyclPlatform` argument has the
+        same underlying ``DPCTLSyclPlatformRef`` object as this
+        :class:`dpctl.SyclPlatform` instance.
+
+        Returns:
+            :obj:`bool`: ``True`` if the two :class:`dpctl.SyclPlatform` objects
+            point to the same ``DPCTLSyclPlatformRef`` object, otherwise
+            ``False``.
+        """
+        if isinstance(other, SyclPlatform):
+            return self.equals(<SyclPlatform> other)
+        else:
+            return False
+
+    def __hash__(self):
+        """
+        Returns a hash value by hashing the underlying ``sycl::platform`` object.
+
+        """
+        return DPCTLPlatform_Hash(self._platform_ref)
+
 
 def lsplatform(verbosity=0):
     """

dpctl/_sycl_queue.pxd

@@ -29,7 +29,7 @@ from ._sycl_event cimport SyclEvent
 from .program._program cimport SyclKernel
 
 
-cdef void default_async_error_handler(int) nogil except *
+cdef void default_async_error_handler(int) except * nogil
 
 cdef public api class _SyclQueue [
     object Py_SyclQueueObject, type Py_SyclQueueType

dpctl/tensor/__init__.py

@@ -135,6 +135,8 @@
     logical_not,
     logical_or,
     logical_xor,
+    maximum,
+    minimum,
     multiply,
     negative,
     not_equal,
@@ -274,6 +276,8 @@
     "log1p",
     "log2",
     "log10",
+    "maximum",
+    "minimum",
     "multiply",
     "negative",
     "not_equal",

dpctl/tensor/_copy_utils.py

@@ -13,6 +13,7 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
+import builtins
 import operator
 
 import numpy as np
@@ -245,6 +246,23 @@ def _broadcast_shapes(sh1, sh2):
         ).shape
 
 
+def _broadcast_strides(X_shape, X_strides, res_ndim):
+    """
+    Broadcasts strides to match the given dimensions;
+    returns tuple type strides.
+    """
+    out_strides = [0] * res_ndim
+    X_shape_len = len(X_shape)
+    str_dim = -X_shape_len
+    for i in range(X_shape_len):
+        shape_value = X_shape[i]
+        if not shape_value == 1:
+            out_strides[str_dim] = X_strides[i]
+        str_dim += 1
+
+    return tuple(out_strides)
+
+
 def _copy_from_usm_ndarray_to_usm_ndarray(dst, src):
     if any(
         not isinstance(arg, dpt.usm_ndarray)
@@ -267,7 +285,7 @@ def _copy_from_usm_ndarray_to_usm_ndarray(dst, src):
     except ValueError as exc:
         raise ValueError("Shapes of two arrays are not compatible") from exc
 
-    if dst.size < src.size:
+    if dst.size < src.size and dst.size < np.prod(common_shape):
         raise ValueError("Destination is smaller ")
 
     if len(common_shape) > dst.ndim:
@@ -278,17 +296,127 @@ def _copy_from_usm_ndarray_to_usm_ndarray(dst, src):
         common_shape = common_shape[ones_count:]
 
     if src.ndim < len(common_shape):
-        new_src_strides = (0,) * (len(common_shape) - src.ndim) + src.strides
+        new_src_strides = _broadcast_strides(
+            src.shape, src.strides, len(common_shape)
+        )
+        src_same_shape = dpt.usm_ndarray(
+            common_shape, dtype=src.dtype, buffer=src, strides=new_src_strides
+        )
+    elif src.ndim == len(common_shape):
+        new_src_strides = _broadcast_strides(
+            src.shape, src.strides, len(common_shape)
+        )
         src_same_shape = dpt.usm_ndarray(
             common_shape, dtype=src.dtype, buffer=src, strides=new_src_strides
         )
     else:
-        src_same_shape = src
-        src_same_shape.shape = common_shape
+        # since broadcasting succeeded, src.ndim is greater because of
+        # leading sequence of ones, so we trim it
+        n = len(common_shape)
+        new_src_strides = _broadcast_strides(
+            src.shape[-n:], src.strides[-n:], n
+        )
+        src_same_shape = dpt.usm_ndarray(
+            common_shape,
+            dtype=src.dtype,
+            buffer=src.usm_data,
+            strides=new_src_strides,
+            offset=src._element_offset,
+        )
 
     _copy_same_shape(dst, src_same_shape)
 
 
+def _empty_like_orderK(X, dt, usm_type=None, dev=None):
+    """Returns empty array like `x`, using order='K'
+
+    For an array `x` that was obtained by permutation of a contiguous
+    array the returned array will have the same shape and the same
+    strides as `x`.
+    """
+    if not isinstance(X, dpt.usm_ndarray):
+        raise TypeError(f"Expected usm_ndarray, got {type(X)}")
+    if usm_type is None:
+        usm_type = X.usm_type
+    if dev is None:
+        dev = X.device
+    fl = X.flags
+    if fl["C"] or X.size <= 1:
+        return dpt.empty_like(
+            X, dtype=dt, usm_type=usm_type, device=dev, order="C"
+        )
+    elif fl["F"]:
+        return dpt.empty_like(
+            X, dtype=dt, usm_type=usm_type, device=dev, order="F"
+        )
+    st = list(X.strides)
+    perm = sorted(
+        range(X.ndim), key=lambda d: builtins.abs(st[d]), reverse=True
+    )
+    inv_perm = sorted(range(X.ndim), key=lambda i: perm[i])
+    st_sorted = [st[i] for i in perm]
+    sh = X.shape
+    sh_sorted = tuple(sh[i] for i in perm)
+    R = dpt.empty(sh_sorted, dtype=dt, usm_type=usm_type, device=dev, order="C")
+    if min(st_sorted) < 0:
+        sl = tuple(
+            slice(None, None, -1)
+            if st_sorted[i] < 0
+            else slice(None, None, None)
+            for i in range(X.ndim)
+        )
+        R = R[sl]
+    return dpt.permute_dims(R, inv_perm)
+
+
+def _empty_like_pair_orderK(X1, X2, dt, res_shape, usm_type, dev):
+    if not isinstance(X1, dpt.usm_ndarray):
+        raise TypeError(f"Expected usm_ndarray, got {type(X1)}")
+    if not isinstance(X2, dpt.usm_ndarray):
+        raise TypeError(f"Expected usm_ndarray, got {type(X2)}")
+    nd1 = X1.ndim
+    nd2 = X2.ndim
+    if nd1 > nd2 and X1.shape == res_shape:
+        return _empty_like_orderK(X1, dt, usm_type, dev)
+    elif nd1 < nd2 and X2.shape == res_shape:
+        return _empty_like_orderK(X2, dt, usm_type, dev)
+    fl1 = X1.flags
+    fl2 = X2.flags
+    if fl1["C"] or fl2["C"]:
+        return dpt.empty(
+            res_shape, dtype=dt, usm_type=usm_type, device=dev, order="C"
+        )
+    if fl1["F"] and fl2["F"]:
+        return dpt.empty(
+            res_shape, dtype=dt, usm_type=usm_type, device=dev, order="F"
+        )
+    st1 = list(X1.strides)
+    st2 = list(X2.strides)
+    max_ndim = max(nd1, nd2)
+    st1 += [0] * (max_ndim - len(st1))
+    st2 += [0] * (max_ndim - len(st2))
+    perm = sorted(
+        range(max_ndim),
+        key=lambda d: (builtins.abs(st1[d]), builtins.abs(st2[d])),
+        reverse=True,
+    )
+    inv_perm = sorted(range(max_ndim), key=lambda i: perm[i])
+    st1_sorted = [st1[i] for i in perm]
+    st2_sorted = [st2[i] for i in perm]
+    sh = res_shape
+    sh_sorted = tuple(sh[i] for i in perm)
+    R = dpt.empty(sh_sorted, dtype=dt, usm_type=usm_type, device=dev, order="C")
+    if max(min(st1_sorted), min(st2_sorted)) < 0:
+        sl = tuple(
+            slice(None, None, -1)
+            if (st1_sorted[i] < 0 and st2_sorted[i] < 0)
+            else slice(None, None, None)
+            for i in range(nd1)
+        )
+        R = R[sl]
+    return dpt.permute_dims(R, inv_perm)
+
+
 def copy(usm_ary, order="K"):
     """copy(ary, order="K")
 
@@ -334,28 +462,15 @@ def copy(usm_ary, order="K"):
             "Unrecognized value of the order keyword. "
             "Recognized values are 'A', 'C', 'F', or 'K'"
         )
-    c_contig = usm_ary.flags.c_contiguous
-    f_contig = usm_ary.flags.f_contiguous
-    R = dpt.usm_ndarray(
-        usm_ary.shape,
-        dtype=usm_ary.dtype,
-        buffer=usm_ary.usm_type,
-        order=copy_order,
-        buffer_ctor_kwargs={"queue": usm_ary.sycl_queue},
-    )
-    if order == "K" and (not c_contig and not f_contig):
-        original_strides = usm_ary.strides
-        ind = sorted(
-            range(usm_ary.ndim),
-            key=lambda i: abs(original_strides[i]),
-            reverse=True,
-        )
-        new_strides = tuple(R.strides[ind[i]] for i in ind)
+    if order == "K":
+        R = _empty_like_orderK(usm_ary, usm_ary.dtype)
+    else:
         R = dpt.usm_ndarray(
             usm_ary.shape,
             dtype=usm_ary.dtype,
-            buffer=R.usm_data,
-            strides=new_strides,
+            buffer=usm_ary.usm_type,
+            order=copy_order,
+            buffer_ctor_kwargs={"queue": usm_ary.sycl_queue},
         )
     _copy_same_shape(R, usm_ary)
     return R
@@ -432,26 +547,15 @@ def astype(usm_ary, newdtype, order="K", casting="unsafe", copy=True):
             "Unrecognized value of the order keyword. "
             "Recognized values are 'A', 'C', 'F', or 'K'"
         )
-    R = dpt.usm_ndarray(
-        usm_ary.shape,
-        dtype=target_dtype,
-        buffer=usm_ary.usm_type,
-        order=copy_order,
-        buffer_ctor_kwargs={"queue": usm_ary.sycl_queue},
-    )
-    if order == "K" and (not c_contig and not f_contig):
-        original_strides = usm_ary.strides
-        ind = sorted(
-            range(usm_ary.ndim),
-            key=lambda i: abs(original_strides[i]),
-            reverse=True,
-        )
-        new_strides = tuple(R.strides[ind[i]] for i in ind)
+    if order == "K":
+        R = _empty_like_orderK(usm_ary, target_dtype)
+    else:
         R = dpt.usm_ndarray(
             usm_ary.shape,
             dtype=target_dtype,
-            buffer=R.usm_data,
-            strides=new_strides,
+            buffer=usm_ary.usm_type,
+            order=copy_order,
+            buffer_ctor_kwargs={"queue": usm_ary.sycl_queue},
         )
     _copy_from_usm_ndarray_to_usm_ndarray(R, usm_ary)
     return R
@@ -492,6 +596,8 @@ def _extract_impl(ary, ary_mask, axis=0):
     dst = dpt.empty(
         dst_shape, dtype=ary.dtype, usm_type=ary.usm_type, device=ary.device
     )
+    if dst.size == 0:
+        return dst
     hev, _ = ti._extract(
         src=ary,
         cumsum=cumsum,
@@ -517,7 +623,7 @@ def _nonzero_impl(ary):
         mask_nelems, dtype=cumsum_dt, sycl_queue=exec_q, order="C"
     )
     mask_count = ti.mask_positions(ary, cumsum, sycl_queue=exec_q)
-    indexes_dt = ti.default_device_int_type(exec_q.sycl_device)
+    indexes_dt = ti.default_device_index_type(exec_q.sycl_device)
     indexes = dpt.empty(
         (ary.ndim, mask_count),
         dtype=indexes_dt,