diff --git a/mindspore/ops/operations/__init__.py b/mindspore/ops/operations/__init__.py
index 22f4c77c2f..9b4b5cbaf1 100644
--- a/mindspore/ops/operations/__init__.py
+++ b/mindspore/ops/operations/__init__.py
@@ -22,7 +22,7 @@ A collection of operators to build neural networks or to compute functions.
 from .image_ops import (CropAndResize)
 from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         Diag, DiagPart, DType, ExpandDims, Eye,
-                        Fill, Ones, Zeros, GatherNd, GatherV2, SparseGatherV2, InvertPermutation,
+                        Fill, Ones, Zeros, SequenceMask, GatherNd, GatherV2, SparseGatherV2, InvertPermutation,
                         IsInstance, IsSubClass, ArgMaxWithValue, OnesLike, ZerosLike,
                         Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue, Meshgrid,
                         SameTypeShape, ScatterAdd, ScatterSub, ScatterMul, ScatterDiv, ScatterMax, ScatterMin,
@@ -182,6 +182,7 @@ __all__ = [
     'Fill',
     'Ones',
     'Zeros',
+    'SequenceMask',
     'OnesLike',
     'ZerosLike',
     'Select',
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index bb7a591915..0efd56f401 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1146,7 +1146,7 @@ class Ones(PrimitiveWithInfer):
 
     @prim_attr_register
     def __init__(self):
-        """Initialize Fill"""
+        """Initialize Ones"""
 
     def __infer__(self, dims, dtype):
         if isinstance(dims['value'], int):
@@ -1197,7 +1197,7 @@ class Zeros(PrimitiveWithInfer):
 
     @prim_attr_register
     def __init__(self):
-        """Initialize Fill"""
+        """Initialize Zeros"""
 
     def __infer__(self, dims, dtype):
         if isinstance(dims['value'], int):
@@ -1221,6 +1221,65 @@ class Zeros(PrimitiveWithInfer):
         return out
 
 
+class SequenceMask(PrimitiveWithInfer):
+    r"""
+    Generates sequence mask according to input lengths.
+
+    Creates a mask tensor which retains the first N elements in tensor by setting the values
+    to be True or one. The rest values in mask are set to False or zero.
+
+    Args:
+        max_length (int): Nonnegative integer, size of the last dimension in mask. Default: None.
+
+    Inputs:
+        - **lengths** (Union[tuple[int], list[int]]) - Defines the first N elements that are retained.
+        Only constant value is allowed.
+        - **dtype** (mindspore.dtype) - The specified type of output tensor. Only constant value is allowed.
+
+    Outputs:
+        Tensor.
+        If max_length is set, the shape of the output is (lengths.shape, max_length).
+        If max_length is not set and the biggest value in lengths is x. Then, the shape of
+        the output is (lengths.shape, x).
+
+    Examples:
+        >>> from mindspore.ops import operations as P
+        >>> sequence_mask = P.SequenceMask()
+        >>> mask = sequence_mask([2, 2, 4], mindspore.int32)
+        >>> print(mask)
+        [[1, 1, 0, 0],
+         [1, 1, 0, 0],
+         [1, 1, 1, 1]]
+
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """Initialize SequenceMask"""
+
+    def __infer__(self, lengths, dtype, max_length=None):
+        validator.check_value_type("shape", lengths['value'], [tuple, list], self.name)
+        valid_types = [mstype.bool_, mstype.int8, mstype.int16, mstype.int32, mstype.int64,
+                       mstype.uint8, mstype.uint32, mstype.uint64,
+                       mstype.float16, mstype.float32, mstype.float64]
+        validator.check_subclass("dtype", dtype['value'], valid_types, self.name)
+        nptype = mstype.dtype_to_nptype(dtype['value'])
+        if max_length is None:
+            max_length = np.max(lengths['value'])
+        else:
+            validator.check_non_negative_int(max_length['value'])
+            max_length = max_length['value']
+        row_vector = np.arange(0, max_length)
+        col_matrix = np.expand_dims(lengths['value'], -1)
+        result = (row_vector < col_matrix).astype(nptype)
+        out = {
+            'value': Tensor(result),
+            'shape': result.shape,
+            'dtype': dtype['value']
+        }
+        return out
+
+
 class OnesLike(PrimitiveWithInfer):
     """
     Creates a new tensor. The values of all elements are 1.
diff --git a/tests/ut/python/ops/test_array_ops.py b/tests/ut/python/ops/test_array_ops.py
index 3992508265..b0808828af 100644
--- a/tests/ut/python/ops/test_array_ops.py
+++ b/tests/ut/python/ops/test_array_ops.py
@@ -42,6 +42,28 @@ def test_expand_dims():
     assert output.asnumpy().shape == (1, 2, 2)
 
 
+def test_sequence_mask():
+    list_ = [2, 2, 4]
+    sequence_mask = P.SequenceMask()
+    mask1 = sequence_mask(list_, mstype.int32)
+    mask2 = sequence_mask(list_, mstype.int32, 5)
+    assert mask1.shape == (3, 4)
+    assert mask1.dtype == mstype.int32
+    assert mask2.shape == (3, 5)
+    assert mask2.dtype == mstype.int32
+
+
+def test_sequence_mask_1():
+    list_ = [[2, 2, 4], [3, 4, 4]]
+    sequence_mask = P.SequenceMask()
+    mask1 = sequence_mask(list_, mstype.bool_)
+    mask2 = sequence_mask(list_, mstype.bool_, 5)
+    assert mask1.shape == (2, 3, 4)
+    assert mask1.dtype == mstype.bool_
+    assert mask2.shape == (2, 3, 5)
+    assert mask2.dtype == mstype.bool_
+
+
 def test_cast():
     input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
     input_x = Tensor(input_np)