Roll must reduce shift steps by size along axis

dpctl/tensor/_manipulation_functions.py

@@ -311,7 +311,7 @@ def flip(X, /, *, axis=None):
     return X[indexer]
 
 
-def roll(X, /, shift, *, axis=None):
+def roll(x, /, shift, *, axis=None):
     """
     roll(x, shift, axis)
 
@@ -343,41 +343,45 @@ def roll(X, /, shift, *, axis=None):
             `device` attributes as `x` and whose elements are shifted relative
             to `x`.
     """
-    if not isinstance(X, dpt.usm_ndarray):
-        raise TypeError(f"Expected usm_ndarray type, got {type(X)}.")
-    exec_q = X.sycl_queue
+    if not isinstance(x, dpt.usm_ndarray):
+        raise TypeError(f"Expected usm_ndarray type, got {type(x)}.")
+    exec_q = x.sycl_queue
     _manager = dputils.SequentialOrderManager[exec_q]
     if axis is None:
         shift = operator.index(shift)
-        dep_evs = _manager.submitted_events
         res = dpt.empty(
-            X.shape, dtype=X.dtype, usm_type=X.usm_type, sycl_queue=exec_q
+            x.shape, dtype=x.dtype, usm_type=x.usm_type, sycl_queue=exec_q
         )
+        sz = operator.index(x.size)
+        shift = (shift % sz) if sz > 0 else 0
+        dep_evs = _manager.submitted_events
         hev, roll_ev = ti._copy_usm_ndarray_for_roll_1d(
-            src=X,
+            src=x,
             dst=res,
             shift=shift,
             sycl_queue=exec_q,
             depends=dep_evs,
         )
         _manager.add_event_pair(hev, roll_ev)
         return res
-    axis = normalize_axis_tuple(axis, X.ndim, allow_duplicate=True)
+    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 = [
         0,
-    ] * X.ndim
+    ] * x.ndim
+    shape = x.shape
     for sh, ax in broadcasted:
-        shifts[ax] += sh
-
+        n_i = operator.index(shape[ax])
+        shifted = shifts[ax] + operator.index(sh)
+        shifts[ax] = (shifted % n_i) if n_i > 0 else 0
     res = dpt.empty(
-        X.shape, dtype=X.dtype, usm_type=X.usm_type, sycl_queue=exec_q
+        x.shape, dtype=x.dtype, usm_type=x.usm_type, sycl_queue=exec_q
     )
     dep_evs = _manager.submitted_events
     ht_e, roll_ev = ti._copy_usm_ndarray_for_roll_nd(
-        src=X, dst=res, shifts=shifts, sycl_queue=exec_q, depends=dep_evs
+        src=x, dst=res, shifts=shifts, sycl_queue=exec_q, depends=dep_evs
     )
     _manager.add_event_pair(ht_e, roll_ev)
     return res

dpctl/tensor/libtensor/source/copy_for_roll.cpp

@@ -326,7 +326,7 @@ copy_usm_ndarray_for_roll_nd(const dpctl::tensor::usm_ndarray &src,
         // normalize shift parameter to be 0 <= offset < dim
         py::ssize_t dim = src_shape_ptr[i];
         size_t offset =
-            (shifts[i] > 0) ? (shifts[i] % dim) : dim + (shifts[i] % dim);
+            (shifts[i] >= 0) ? (shifts[i] % dim) : dim + (shifts[i] % dim);
 
         normalized_shifts.push_back(offset);
     }

dpctl/tests/test_usm_ndarray_manipulation.py

@@ -657,6 +657,30 @@ def test_roll_2d(data):
     assert_array_equal(Ynp, dpt.asnumpy(Y))
 
 
+def test_roll_out_bounds_shifts():
+    "See gh-1857"
+    get_queue_or_skip()
+
+    x = dpt.arange(4)
+    y = dpt.roll(x, np.uint64(2**63 + 2))
+    expected = dpt.roll(x, 2)
+    assert dpt.all(y == expected)
+
+    x_empty = x[1:1]
+    y = dpt.roll(x_empty, 11)
+    assert y.size == 0
+
+    x_2d = dpt.reshape(x, (2, 2))
+    y = dpt.roll(x_2d, np.uint64(2**63 + 1), axis=1)
+    expected = dpt.roll(x_2d, 1, axis=1)
+    assert dpt.all(y == expected)
+
+    x_2d_empty = x_2d[:, 1:1]
+    y = dpt.roll(x_2d_empty, 3, axis=1)
+    expected = dpt.empty_like(x_2d_empty)
+    assert dpt.all(y == expected)
+
+
 def test_roll_validation():
     get_queue_or_skip()