diff --git a/imperative/python/megengine/functional/tensor.py b/imperative/python/megengine/functional/tensor.py
index 3c71e82c..4cd1daba 100644
--- a/imperative/python/megengine/functional/tensor.py
+++ b/imperative/python/megengine/functional/tensor.py
@@ -45,6 +45,7 @@ __all__ = [
     "ones_like",
     "repeat",
     "reshape",
+    "roll",
     "split",
     "squeeze",
     "stack",
@@ -1172,3 +1173,75 @@ def copy(inp, device=None):
     if device is None:
         return apply(Identity(), inp)[0]
     return apply(Copy(comp_node=as_device(device).to_c()), inp)[0]
+
+
+def roll(
+    inp: Tensor,
+    shift: Union[int, Iterable[int]],
+    axis: Optional[Union[int, Iterable[int]]] = None,
+):
+    """
+    Roll the tensor along the given axis(or axes). Elements that are shifted
+    beyond the last position are re-introduced at the first position.
+
+    :param inp: input tensor.
+    :param shift: the number of places by which the elements of the tensor are
+        shifted. If shift is a tuple, axis must be a tuple of the same size,
+        and each axis will be rolled by the corresponding shift value.
+    :param axis: axis along which to roll. If axis is not specified, the tensor
+        will be flattened before rolling and then restored to the original shape.
+        Duplicate axes is allowed if it is a tuple. Default: None.
+
+    Examples:
+
+    .. testcode::
+
+        import numpy as np
+        from megengine import tensor
+        import megengine.functional as F
+
+        x = tensor([[1,2],[3,4],[5,6]], np.int32)
+        y = F.roll(x, 1, 0)
+        print(y.numpy())
+
+    Outputs:
+
+    .. testoutput::
+
+        [[5 6]
+         [1 2]
+         [3 4]]
+
+    """
+    shp_bak = None
+    if axis is None:
+        shp_bak = inp.shape
+        inp = inp.flatten()
+        axis = 0
+    shp = inp.shape
+    dim = len(shp)
+    if isinstance(shift, int):
+        assert isinstance(axis, int)
+        shift, axis = [shift,], [axis,]
+    assert len(shift) == len(axis)
+    out = inp
+    for i in range(len(shift)):
+        axis_ = axis[i]
+        shift_ = shift[i]
+        axis_normalized_ = axis_ + dim if axis_ < 0 else axis_
+        assert (
+            dim > axis_normalized_ >= 0
+        ), "axis out of range (expected to be in range of [{}, {}], but got {})".format(
+            -dim, dim - 1, axis_
+        )
+        if shift_ == 0:
+            continue
+        size = shp[axis_normalized_]
+        if shift_ > 0:
+            a, b = split(out, [size - shift_,], axis=axis_normalized_)
+        else:
+            a, b = split(out, [-shift_,], axis=axis_normalized_)
+        out = concat((b, a), axis=axis_normalized_)
+    if shp_bak is not None:
+        out = out.reshape(shp_bak)
+    return out
diff --git a/imperative/python/test/unit/functional/test_tensor.py b/imperative/python/test/unit/functional/test_tensor.py
index a0ed6d1f..144a5e28 100644
--- a/imperative/python/test/unit/functional/test_tensor.py
+++ b/imperative/python/test/unit/functional/test_tensor.py
@@ -636,3 +636,33 @@ def test_tile(shape, reps, is_varnode):
     cases = [{"input": np.random.randn(*shape).astype("float32")}]
 
     opr_test(cases, tile_func, ref_fn=lambda inp: np.tile(inp, reps), network=network)
+
+
+@pytest.mark.parametrize(
+    "shape, shifts, axis",
+    [
+        ((2, 3), 0, None),
+        ((2, 3), 1, 0),
+        ((2, 3, 4, 5), (-1, 1), (0, 1)),
+        ((2, 3, 4, 5), (-2, 1, 2), (1, 2, 3)),
+    ],
+)
+@pytest.mark.parametrize("is_varnode", [True, False])
+def test_roll(shape, shifts, axis, is_varnode):
+    if is_varnode:
+        network = Network()
+    else:
+        network = None
+
+    inp = np.random.randn(*shape).astype("float32")
+
+    def func(inp):
+        return F.roll(inp, shifts, axis)
+
+    cases = [
+        {"input": inp},
+    ]
+
+    opr_test(
+        cases, func, ref_fn=lambda inp: np.roll(inp, shifts, axis), network=network
+    )