diff --git a/mindspore/ops/operations/_grad_ops.py b/mindspore/ops/operations/_grad_ops.py
index 8bfd8012c5..5fa34591ef 100644
--- a/mindspore/ops/operations/_grad_ops.py
+++ b/mindspore/ops/operations/_grad_ops.py
@@ -976,12 +976,12 @@ class MaxPoolGradGrad(_PoolGrad):
         super(MaxPoolGradGrad, self).__init__(kernel_size, strides, pad_mode)
 
     def infer_shape(self, x1_shape, x2_shape, grad_shape):
-        return x1_shape
+        return x2_shape
 
     def infer_dtype(self, x1_dtype, x2_dtype, grad_dtype):
         args = {'x1_dtype': x1_dtype, 'x2_dtype': x2_dtype, 'grad_dtype': grad_dtype}
         validator.check_tensors_dtypes_same_and_valid(args, [mstype.float16], self.name)
-        return x1_dtype
+        return x2_dtype
 
 
 def _get_max_pool3d_grad_pads_by_pad_mode(input_shape, kernel_size, strides, pad_mode):
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 9ec7d9ef32..b511a3a1e3 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -3376,13 +3376,13 @@ class TensorScatterUpdate(PrimitiveWithInfer):
     `indices`, with values from `update`. This operation is almost equivalent to using
     ScatterNd, except that the updates are applied on `input_x` instead of a zero tensor.
 
-    `indices` must have rank atleast 2, the last axis is the depth of each index
+    `indices` must have rank at least 2, the last axis is the depth of each index
     vectors. For each index vector, there must be a corresponding value in `update`. If
     the depth of each index tensor matches the rank of `input_x`, then each index
     vector corresponds to a scalar in `input_x` and each update updates a scalar. If
     the depth of each index tensor is less than the rnak of `input_x`, then each index
     vector corresponds to a slice in `input_x`, and each update updates a slice.
-    
+
     The order in which updates are applied is nondeterministic, meaning that if there
     are multiple index vectors in `indices` that correspond to the same position, the
     value of that position in the output will be nondeterministic.
@@ -3390,7 +3390,7 @@ class TensorScatterUpdate(PrimitiveWithInfer):
     Inputs:
         - **input_x** (Tensor) - The target tensor. The dimension of input_x must be no less than indices.shape[-1].
         - **indices** (Tensor) - The index of input tensor whose data type is int32 or int64.
-          The rank must be atleast 2.
+          The rank must be at least 2.
         - **update** (Tensor) - The tensor to update the input tensor, has the same type as input,
           and update.shape = indices.shape[:-1] + input_x.shape[indices.shape[-1]:].
 
@@ -3520,7 +3520,7 @@ class ScatterNdUpdate(_ScatterNdOp):
         - **indices** (Tensor) - The index of input tensor, with int32 data type.
           The rank of indices must be at least 2 and `indices_shape[-1] <= len(shape)`.
         - **updates** (Tensor) - The tensor to be updated to the input tensor, has the same type as input.
-          the shape is `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
+          The shape is `indices_shape[:-1] + x_shape[indices_shape[-1]:]`.
 
     Outputs:
         Tensor, has the same shape and type as `input_x`.