diff --git a/mindspore/python/mindspore/ops/function/math_func.py b/mindspore/python/mindspore/ops/function/math_func.py
index a0df517b6d4..c3b1a2de813 100644
--- a/mindspore/python/mindspore/ops/function/math_func.py
+++ b/mindspore/python/mindspore/ops/function/math_func.py
@@ -5777,20 +5777,18 @@ def remainder(x, y):
     return out
 
 
-def accumulate_n(*x):
+def accumulate_n(x):
     r"""
     Computes accumulation of all input tensors element-wise.
 
-    AccumulateNV2 is similar to AddN, but there is a significant difference
-    among them: AccumulateNV2 will not wait for all of its inputs to be ready
-    before summing. That is to say, AccumulateNV2 is able to save
-    memory when inputs are ready at different time since the minimum temporary
-    storage is proportional to the output size rather than the input size.
+    AccumulateNV2 is similar to AddN, but there is a significant difference among them: AccumulateNV2 will not wait
+    for all of its inputs to be ready before summing. That is to say, AccumulateNV2 is able to save memory when inputs
+    are ready at different time since the minimum temporary storage is proportional to the output size rather than the
+    input size.
 
     Args:
-        - **x** (Union(tuple[Tensor], list[Tensor])) - The input tuple or list
-          is made up of multiple tensors whose dtype is number to be added together.
-          Each element of tuple or list should have the same shape.
+        x (Union(tuple[Tensor], list[Tensor])): The input tuple or list is made up of multiple tensors whose dtype is
+            number to be added together. Each element of tuple or list should have the same shape.
 
     Returns:
         Tensor, has the same shape and dtype as each entry of the `x`.
@@ -5805,7 +5803,7 @@ def accumulate_n(*x):
     Examples:
         >>> x = Tensor(np.array([1, 2, 3]), mindspore.float32)
         >>> y = Tensor(np.array([4, 5, 6]), mindspore.float32)
-        >>> output = ops.accumulate_n(x, y, x, y)
+        >>> output = ops.accumulate_n([x, y, x, y])
         >>> print(output)
         [10. 14. 18.]
     """
diff --git a/tests/st/ops/ascend/test_accumulate_nv2.py b/tests/st/ops/ascend/test_accumulate_nv2.py
index a446cb8f6e9..bcd47c1f967 100644
--- a/tests/st/ops/ascend/test_accumulate_nv2.py
+++ b/tests/st/ops/ascend/test_accumulate_nv2.py
@@ -26,7 +26,7 @@ def accumulate_n_forward_functional(nptype):
     input_x = Tensor(np.array([1, 2, 3]).astype(nptype))
     input_y = Tensor(np.array([4, 5, 6]).astype(nptype))
 
-    output = F.accumulate_n(input_x, input_y, input_x, input_y)
+    output = F.accumulate_n([input_x, input_y, input_x, input_y])
     expected = np.array([10., 14., 18.])
     np.testing.assert_array_almost_equal(output.asnumpy(), expected)