Merge pull request #1210 from IntelPython/feature/sum-reduction

Implement `dpctl.tensor.sum` reduction operation

dpctl/tensor/CMakeLists.txt

@@ -47,6 +47,7 @@ pybind11_add_module(${python_module_name} MODULE
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/boolean_reductions.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/device_support_queries.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/elementwise_functions.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/sum_reductions.cpp
 )
 set(_clang_prefix "")
 if (WIN32)

dpctl/tensor/__init__.py

@@ -104,6 +104,7 @@
     sqrt,
     subtract,
 )
+from ._reduction import sum
 
 __all__ = [
     "Device",
@@ -191,4 +192,5 @@
     "multiply",
     "subtract",
     "equal",
+    "sum",
 ]

dpctl/tensor/_reduction.py

@@ -0,0 +1,167 @@
+#                       Data Parallel Control (dpctl)
+#
+#  Copyright 2020-2023 Intel Corporation
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  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.
+
+from numpy.core.numeric import normalize_axis_tuple
+
+import dpctl
+import dpctl.tensor as dpt
+import dpctl.tensor._tensor_impl as ti
+
+from ._type_utils import _to_device_supported_dtype
+
+
+def _default_reduction_dtype(inp_dt, q):
+    """Gives default output data type for given input data
+    type `inp_dt` when reduction is performed on queue `q`
+    """
+    inp_kind = inp_dt.kind
+    if inp_kind in "bi":
+        res_dt = dpt.dtype(ti.default_device_int_type(q))
+        if inp_dt.itemsize > res_dt.itemsize:
+            res_dt = inp_dt
+    elif inp_kind in "u":
+        res_dt = dpt.dtype(ti.default_device_int_type(q).upper())
+        res_ii = dpt.iinfo(res_dt)
+        inp_ii = dpt.iinfo(inp_dt)
+        if inp_ii.min >= res_ii.min and inp_ii.max <= res_ii.max:
+            pass
+        else:
+            res_dt = inp_dt
+    elif inp_kind in "f":
+        res_dt = dpt.dtype(ti.default_device_fp_type(q))
+        if res_dt.itemsize < inp_dt.itemsize:
+            res_dt = inp_dt
+    elif inp_kind in "c":
+        res_dt = dpt.dtype(ti.default_device_complex_type(q))
+        if res_dt.itemsize < inp_dt.itemsize:
+            res_dt = inp_dt
+
+    return res_dt
+
+
+def sum(arr, axis=None, dtype=None, keepdims=False):
+    """sum(x, axis=None, dtype=None, keepdims=False)
+
+    Calculates the sum of the input array `x`.
+
+    Args:
+        x (usm_ndarray):
+            input array.
+        axis (Optional[int, Tuple[int,...]]):
+            axis or axes along which sums must be computed. If a tuple
+            of unique integers, sums are computed over multiple axes.
+            If `None`, the sum if computed over the entire array.
+            Default: `None`.
+        dtype (Optional[dtype]):
+            data type of the returned array. If `None`, the default data
+            type is inferred from the "kind" of the input array data type.
+                * If `x` has a real-valued floating-point data type,
+                  the returned array will have the default real-valued
+                  floating-point data type for the device where input
+                  array `x` is allocated.
+                * If x` has signed integral data type, the returned array
+                  will have the default signed integral type for the device
+                  where input array `x` is allocated.
+                * If `x` has unsigned integral data type, the returned array
+                  will have the default unsigned integral type for the device
+                  where input array `x` is allocated.
+                * If `x` has a complex-valued floating-point data typee,
+                  the returned array will have the default complex-valued
+                  floating-pointer data type for the device where input
+                  array `x` is allocated.
+                * If `x` has a boolean data type, the returned array will
+                  have the default signed integral type for the device
+                  where input array `x` is allocated.
+            If the data type (either specified or resolved) differs from the
+            data type of `x`, the input array elements are cast to the
+            specified data type before computing the sum. Default: `None`.
+        keepdims (Optional[bool]):
+            if `True`, the reduced axes (dimensions) are included in the result
+            as singleton dimensions, so that the returned array remains
+            compatible with the input arrays according to Array Broadcasting
+            rules. Otherwise, if `False`, the reduced axes are not included in
+            the returned array. Default: `False`.
+    Returns:
+        usm_ndarray:
+            an array containing the sums. If the sum was computed over the
+            entire array, a zero-dimensional array is returned. The returned
+            array has the data type as described in the `dtype` parameter
+            description above.
+    """
+    if not isinstance(arr, dpt.usm_ndarray):
+        raise TypeError(f"Expected dpctl.tensor.usm_ndarray, got {type(arr)}")
+    nd = arr.ndim
+    if axis is None:
+        axis = tuple(range(nd))
+    if not isinstance(axis, (tuple, list)):
+        axis = (axis,)
+    axis = normalize_axis_tuple(axis, nd, "axis")
+    red_nd = len(axis)
+    perm = [i for i in range(nd) if i not in axis] + list(axis)
+    arr2 = dpt.permute_dims(arr, perm)
+    res_shape = arr2.shape[: nd - red_nd]
+    q = arr.sycl_queue
+    inp_dt = arr.dtype
+    if dtype is None:
+        res_dt = _default_reduction_dtype(inp_dt, q)
+    else:
+        res_dt = dpt.dtype(dtype)
+        res_dt = _to_device_supported_dtype(res_dt, q.sycl_device)
+
+    res_usm_type = arr.usm_type
+    if red_nd == 0:
+        return dpt.zeros(
+            res_shape, dtype=res_dt, usm_type=res_usm_type, sycl_queue=q
+        )
+
+    host_tasks_list = []
+    if ti._sum_over_axis_dtype_supported(inp_dt, res_dt, res_usm_type, q):
+        res = dpt.empty(
+            res_shape, dtype=res_dt, usm_type=res_usm_type, sycl_queue=q
+        )
+        ht_e, _ = ti._sum_over_axis(
+            src=arr2, trailing_dims_to_reduce=red_nd, dst=res, sycl_queue=q
+        )
+        host_tasks_list.append(ht_e)
+    else:
+        if dtype is None:
+            raise RuntimeError(
+                "Automatically determined reduction data type does not "
+                "have direct implementation"
+            )
+        tmp_dt = _default_reduction_dtype(inp_dt, q)
+        tmp = dpt.empty(
+            res_shape, dtype=tmp_dt, usm_type=res_usm_type, sycl_queue=q
+        )
+        ht_e_tmp, r_e = ti._sum_over_axis(
+            src=arr2, trailing_dims_to_reduce=red_nd, dst=tmp, sycl_queue=q
+        )
+        host_tasks_list.append(ht_e_tmp)
+        res = dpt.empty(
+            res_shape, dtype=res_dt, usm_type=res_usm_type, sycl_queue=q
+        )
+        ht_e, _ = ti._copy_usm_ndarray_into_usm_ndarray(
+            src=tmp, dst=res, sycl_queue=q, depends=[r_e]
+        )
+        host_tasks_list.append(ht_e)
+
+    if keepdims:
+        res_shape = res_shape + (1,) * red_nd
+        inv_perm = sorted(range(nd), key=lambda d: perm[d])
+        res = dpt.permute_dims(dpt.reshape(res, res_shape), inv_perm)
+    dpctl.SyclEvent.wait_for(host_tasks_list)
+
+    return res

dpctl/tensor/libtensor/include/kernels/boolean_reductions.hpp

@@ -183,7 +183,7 @@ struct SequentialBooleanReduction
     void operator()(sycl::id<1> id) const
     {
 
-        auto inp_out_iter_offsets_ = inp_out_iter_indexer_(id[0]);
+        auto const &inp_out_iter_offsets_ = inp_out_iter_indexer_(id[0]);
         const py::ssize_t &inp_iter_offset =
             inp_out_iter_offsets_.get_first_offset();
         const py::ssize_t &out_iter_offset =