Add roll kernels

dpctl/tensor/CMakeLists.txt

@@ -37,6 +37,7 @@ pybind11_add_module(${python_module_name} MODULE
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/copy_and_cast_usm_to_usm.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/copy_numpy_ndarray_into_usm_ndarray.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/copy_for_reshape.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/copy_for_roll.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/linear_sequences.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/integer_advanced_indexing.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/libtensor/source/boolean_advanced_indexing.cpp

dpctl/tensor/_manipulation_functions.py

@@ -15,7 +15,8 @@
 #  limitations under the License.
 
 
-from itertools import chain, product, repeat
+import operator
+from itertools import chain, repeat
 
 import numpy as np
 from numpy.core.numeric import normalize_axis_index, normalize_axis_tuple
@@ -426,10 +427,11 @@ def roll(X, shift, axis=None):
     if not isinstance(X, dpt.usm_ndarray):
         raise TypeError(f"Expected usm_ndarray type, got {type(X)}.")
     if axis is None:
+        shift = operator.index(shift)
         res = dpt.empty(
             X.shape, dtype=X.dtype, usm_type=X.usm_type, sycl_queue=X.sycl_queue
         )
-        hev, _ = ti._copy_usm_ndarray_for_reshape(
+        hev, _ = ti._copy_usm_ndarray_for_roll_1d(
             src=X, dst=res, shift=shift, sycl_queue=X.sycl_queue
         )
         hev.wait()
@@ -438,31 +440,20 @@ def roll(X, shift, axis=None):
     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)}
+    shifts = [
+        0,
+    ] * X.ndim
     for sh, ax in broadcasted:
         shifts[ax] += sh
-    rolls = [((np.s_[:], np.s_[:]),)] * X.ndim
-    for ax, offset in shifts.items():
-        offset %= X.shape[ax] or 1
-        if offset:
-            # (original, result), (original, result)
-            rolls[ax] = (
-                (np.s_[:-offset], np.s_[offset:]),
-                (np.s_[-offset:], np.s_[:offset]),
-            )
 
+    exec_q = X.sycl_queue
     res = dpt.empty(
-        X.shape, dtype=X.dtype, usm_type=X.usm_type, sycl_queue=X.sycl_queue
+        X.shape, dtype=X.dtype, usm_type=X.usm_type, sycl_queue=exec_q
     )
-    hev_list = []
-    for indices in product(*rolls):
-        arr_index, res_index = zip(*indices)
-        hev, _ = ti._copy_usm_ndarray_into_usm_ndarray(
-            src=X[arr_index], dst=res[res_index], sycl_queue=X.sycl_queue
-        )
-        hev_list.append(hev)
-
-    dpctl.SyclEvent.wait_for(hev_list)
+    ht_e, _ = ti._copy_usm_ndarray_for_roll_nd(
+        src=X, dst=res, shifts=shifts, sycl_queue=exec_q
+    )
+    ht_e.wait()
     return res
 
 
@@ -550,7 +541,6 @@ def _concat_axis_None(arrays):
             hev, _ = ti._copy_usm_ndarray_for_reshape(
                 src=src_,
                 dst=res[fill_start:fill_end],
-                shift=0,
                 sycl_queue=exec_q,
             )
         fill_start = fill_end

dpctl/tensor/_reshape.py

@@ -165,7 +165,7 @@ def reshape(X, shape, order="C", copy=None):
         )
         if order == "C":
             hev, _ = _copy_usm_ndarray_for_reshape(
-                src=X, dst=flat_res, shift=0, sycl_queue=X.sycl_queue
+                src=X, dst=flat_res, sycl_queue=X.sycl_queue
             )
             hev.wait()
         else: